Proceedings of the 3rd International Conference on BioGeoSciences: Modeling Natural Environments (Springer Proceedings in Earth and Environmental Sciences) 3030889181, 9783030889180

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Springer Proceedings in Earth and Environmental Sciences

Rolando Cardenas Vladimir Mochalov Oscar Parra Osmel Martin   Editors

Proceedings of the 3rd International Conference on BioGeoSciences Modeling Natural Environments

Springer Proceedings in Earth and Environmental Sciences Series Editors Natalia S. Bezaeva, The Moscow Area, Russia Heloisa Helena Gomes Coe, Niterói RJ Brazil, Brazil Muhammad Farrakh Nawaz, Department of Forestry and Range Management, University of Agriculture, Faisalabad, Pakistan

The series Springer Proceedings in Earth and Environmental Sciences publishes proceedings from scholarly meetings and workshops on all topics related to Environmental and Earth Sciences and related sciences. This series constitutes a comprehensive up-to-date source of reference on a field or subfield of relevance in Earth and Environmental Sciences. In addition to an overall evaluation of the interest, scientific quality, and timeliness of each proposal at the hands of the publisher, individual contributions are all refereed to the high quality standards of leading journals in the field. Thus, this series provides the research community with well-edited, authoritative reports on developments in the most exciting areas of environmental sciences, earth sciences and related fields.

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Rolando Cardenas · Vladimir Mochalov · Oscar Parra · Osmel Martin Editors

Proceedings of the 3rd International Conference on BioGeoSciences Modeling Natural Environments

Editors Rolando Cardenas Planetary Science Laboratory Universidad Central “Marta Abreu” de Las Villas Santa Clara, Villa Clara, Cuba Oscar Parra Centre for Environmental Studies EULA University of Concepcion Concepcion, Chile

Vladimir Mochalov Petrozavodsk State University Petrozavodsk, Russia Osmel Martin Planetary Science Laboratory Universidad Central “Marta Abreu” de Las Villas Santa Clara, Villa Clara, Cuba

ISSN 2524-342X ISSN 2524-3438 (electronic) Springer Proceedings in Earth and Environmental Sciences ISBN 978-3-030-88918-0 ISBN 978-3-030-88919-7 (eBook) https://doi.org/10.1007/978-3-030-88919-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

The 3rd International Conference on BioGeoSciences was held during June 24th– 28th, 2019, at Universidad Central “Marta Abreu” de Las Villas and nearby venues in Villa Clara, Cuba. Several national and international institutions sponsored it, such as the Ministry of Higher Education of Cuba, the International Centre for Theoretical Physics (Italy), and the University of Concepcion (Chile). There were participants from Russia, Chile, Brazil, Nicaragua, and the local country. As in the two previous versions, the main fields treated were Mathematical Modeling of Natural Environments and Earth and Planetary Sciences. There were sessions for contributed papers, for posters, and for discussions on future collaborations between the participants. Scientific results in the modeling of natural environments, from the small molecular-cellular scales to the enormous astrobiological-cosmological ones, were discussed. The dissemination of this knowledge is intended to contribute to the UNESCO Sustainable Development Goals 2030, especially those related with Environment, such as 6 (Water) and 13–15 (Climate, Ocean, and Land). This activity is also part of the program of the Cuban Network of UNESCO Chairs. In this Proceedings the best selected papers of the Conference are presented, organized according to parts (“spheres”) that our Earth and in general rocky planetary bodies can contain: lithosphere, hydrosphere, atmosphere, magnetosphere, biosphere. It is also included an interesting part on Information Technologies in BioGeoSciences. As with the Proceedings of the 2nd International Conference on BioGeoSciences, also published by Springer Nature, this is an interdisciplinary book which shall be useful to students and researchers engaged in Natural and Exact Sciences. Santa Clara, Cuba Petrozavodsk, Russia Concepcion, Chile Santa Clara, Cuba

Rolando Cardenas Vladimir Mochalov Oscar Parra Osmel Martin

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Contents

Opening Talk: What Pervades the Milky Way: The Snotty or the Stringy? Photoautotrophy or Chemoautotrophy? . . . . . . . . . . . . . . . Rolando Cardenas, Noel Perez, Osmel Martin, and Jorge Horvath

1

The Lithosphere and the Hydrosphere Diffusivity of Some Ions in Natural Bentonite . . . . . . . . . . . . . . . . . . . . . . . . Julio Omar Prieto García, Noor Gehan Geulamussein, Yailet Albernas Carvajal, Alfredo Curbelo Sánchez, Mixary Enríquez García, and Ángel Mollineda Trujillo Cassava Husk Powder as an Eco-Friendly Adsorbent for the Removal of Nickel (II) Ions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lisdelys González-Rodríguez, Julio Omar Prieto García, Lien Rodríguez-López, Yoan Hidalgo-Rosa, Manuel A. Treto-Suaréz, Mixary Garcia Enriquez, and Ángel Mollineda Trujillo Study of the Hydrodynamic Transport of Nitrate as a Pollutant of Rivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Roxana Pérez García, Lorgio Félix Batard Martínez, Yanelis Estrada Hernández, and Jorge Alberto Cárdenas Pestana Diagnosis of the Land Cover/Use Effect on Nutrient Discharge from Three Biobio River Sub-Basins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rebeca Martínez-Retureta, Mauricio Aguayo, Lien Rodríguez-López, Iongel Duran-Llacer, and Norberto José Abreu An Improvement Method to Study the Spatio—Temporal Dynamics of Rancho Luna Beach´ Shoreline Applying Remote Sensing Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Laura Castellanos Torres, Alain Muñoz Caravaca, Iván Figueroa Reyes, Eugenio Olalde Chang, Minerva Sánchez Llull, and Lester Caravaca Colina

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Contents

Sea Surface Temperature Trends in the Southern Cuban Shelves for the Period 1982–2018 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alain Muñoz Caravaca, Laura Castellanos Torres, and Liesvy Valladares Alfonso Hydrodynamic Characteristics of the Nuevitas Bay, Camagüey, from the Numerical Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Liesvy Valladares Alfonso, Alain Muñoz Caravaca, Felivalentín Lamas Torres, and Laura Castellanos Torres

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The Atmosphere and the Magnetosphere Hurricane Related Coastal Flooding in the Province of Ciego de Avila, Cuba: Hazard, Vulnerability and Risk Study . . . . . . . . . . . . . . . . . . . 105 Felipe Matos Pupo, Osvaldo E. Pérez López, and Alexey Valero Jorge Natural Emissions to Atmosphere: Biogenic Emissions in the Citrus Plantations of Western Cuba . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Ricardo Manso, Yosdany González, Javier Bolufé, Rosemary López, Israel Borrajero, Juan Carlos Peláez, and Miguel Aranguren Equipment for Studying the Earth’s Magnetic Field . . . . . . . . . . . . . . . . . . 137 Sergey Y. Khomutov, Nikolay N. Semakov, and Vladimir Mochalov The Biosphere Thermodynamic Analysis of Some Reactions of Selenium, Telurium, Arsenic, Antimony and Derivatives in Chemosynthesis . . . . . . 149 Julio Omar Prieto García, Noor Gehan Geulamussein, Yailet Albernas Carvajal, Noel Pérez Díaz, and Daimel Castillo Díaz Impact of the Chicxulub Asteroid: Potential Implications on Phyotoplankton and Anammox Bacteria . . . . . . . . . . . . . . . . . . . . . . . . . . 163 Noel Perez, Osmel Martin, Rolando Cardenas Ortiz, and Yoel Sanchez Alvarez Darwinian Evolution from a Generational Point of View . . . . . . . . . . . . . . 185 Osmel Martin, José Suarez-Lezcano, and Yoelsy Leyva Technical and Economic Viability of Agricultural Residue-Based Power Generation in Southern Chile Through Discrete Location Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 Jorge Jimenez, Cristian Rivas, and Rodrigo De La Fuente Comparative Study Between a Deterministic and Stochastic model’s for the Hematopoietic Reconstitution . . . . . . . . . . . . . . . . . . . . . . . . 211 Dennis Lumpuy Obregón and Miguel Ángel Martínez Hernández

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Information Technologies in BioGeoSciences Image Modification to Reduce Eye Strain . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 Vladimir Mochalov Problems of the Commutative and Grouping Properties of the Addition of Floating Point Numbers in Modern Programming Languages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 Vladimir Mochalov and Anastasia Mochalova

Opening Talk: What Pervades the Milky Way: The Snotty or the Stringy? Photoautotrophy or Chemoautotrophy? Rolando Cardenas, Noel Perez, Osmel Martin, and Jorge Horvath

Abstract It is briefly reviewed the interdisciplinary area of the Quantification of Habitability, with emphasis in the energy sources needed to fuel metabolisms. Keywords Habitability · Photosynthesis · Chemosynthesis

1 A General Conceptual Model for Abiogenesis—Biogenesis Rather than on the very definition of Life, we emphasize in this work the definition of Habitability and the several forms to quantify it. It should be noted that a habitable environment should not necessarily be inhabited. A natural question arises: can we devise a general conceptual model for Abiogenesis (origin of Life)—Biogenesis (evolution of Life), in principle valid for the entire observed Universe? In this sense, it is not uncommon to hear that research related to extraterrestrial life will inevitably have a bias towards life as we know it on Earth. However, accepting that the most basic laws of Nature, formulated by Physics and Chemistry, are valid in all the observed Universe (something strongly supported by an abundant set of astrophysical observations) leads to a general conceptual model for Abiogenesis-Biogenesis, in principle applicable to any part of the observed Universe. Actually, this model can be inferred from the following four premises for life to arise and evolve (Hoehler 2007; Cockell et al. 2016): (1)

The presence of biogenic chemical elements in adequate concentrations (on Earth all known species contain at least CHON, P and S)

R. Cardenas (B) · N. Perez · O. Martin Planetary Science Laboratory, Universidad Central “Marta Abreu” de Las Villas, Santa Clara, Cuba e-mail: [email protected] J. Horvath Institute for Astronomy, Geophysics and Atmospheric Sciences, University of Sao Paulo, Sao Paulo, Brazil © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Cardenas et al. (eds.), Proceedings of the 3rd International Conference on BioGeoSciences, Springer Proceedings in Earth and Environmental Sciences, https://doi.org/10.1007/978-3-030-88919-7_1

1

2

(2) (3)

(4)

R. Cardenas et al.

A solvent in which the above mentioned elements can react to form the complex biological molecules (water plays this role in life on Earth). An external energy source to overcome the activation barriers of biochemical reactions, to maintain the high degree of organization of living things (low entropy), and to do work. In life known on Earth these sources are luminous energy for photosynthetic species, and energy of redox chemical reactions for chemosynthetic ones (there are also mixotrophs, capable of using both kinds of energy sources, according to their availability). The existence of a favorable physical-chemical environment allowing the viability of living entities (background radiation, temperature range, pH, salinity, etc.).

Research taking into account modern models of nucleosynthesis and stellar evolution suggests that the necessary minimum quantities of biogenic chemical elements could have been available in the Universe around 1800 million years after the Big Bang. Actually, there is some evidence on the existence of rocky planets a couple of billion years after the Big Bang, around 12 billion years ago. Since rocky planetary bodies (planets and satellites) are the most suitable places to meet the premises of the above mentioned model of abiogenesis-biogenesis, it means that habitable environments could have arisen in the Universe a long time ago.

2 The Quantification of Habitability Quantification of habitability is an interdisciplinary and emerging area of Natural and Exact Sciences. There are three (complementary) approaches to address it (Shock and Holland 2007). The astrobiological one focuses on investigating the most basic conditions (premises) for the existence of primary producers anywhere in the Universe, the biogeochemical focuses on the organism-environment interaction, while the ecological pays special attention to the interactions between organisms within the context of the ecosystem. Despite their complementarity, much remains to be done to better understand the complex phenomenon of life in the Universe. On another hand, the existence of rocky planetary bodies in the Solar System, and the frequent discovery of exoplanets of this type in the last two decades, have propelled the development of quantitative criteria for habitability, encouraging interactions between astrobiologists, planetary scientists and environmentalists. The main objective of this talk is to briefly review the development of habitability metrics, especially the energetic aspect of them.

Opening Talk: What Pervades the Milky Way: The Snotty …

3

2.1 Quantitative Habitability Theory Within the astrobiological school of Quantitative Habitability, of special interest is the emerging and interdisciplinary Quantitative Habitability Theory (QHT), which traces a bridge between Ecology and Astrobiology, and whose main objective is to explain the distribution, abundance and productivity of life. It is scalable in time and space, so that it can be applied both at planetary and ecosystem scales, and to any life at any stage of evolution of the Universe (Mendez 2010, Cardenas et al. 2014). Its main postulate suggests that, in principle, a habitability index HI may be written as a product of functions of environmental variables f i {x j } influencing life: HI =

n 

fi

  xj

(1)

i=1

A crucial aspect of habitability indexes is that with them net primary productivity NPP can be estimated (Mendez 2010): N P P = H I.N P P max

(2)

where NPPmax is the maximum possible NPP. Combining the conceptual model for abiogenesis-biogenesis with QHT (Eq. 1), a generic habitability index can be formulated as: H I = f M f K f E f PC

(3)

where f M , f K , f E and f PC are functions representing the chemical (mineral), kinetic, energetic and physicochemical (environmental) premises for abiogenesis and biogenesis, respectively.

2.2 Quantification of the Energetic Aspect of Life In this talk we will focus in the quantification of the energetic aspect of Life. As said in Sect. 1, on Earth we know photosynthetic organisms, which use light as source of energy, and chemosynthetic ones, which use the energy released in redox chemical reactions. The so called primary producers obtain carbon from inorganic sources (like CO2 ), so they are called autotrophs, not depending on other living beings to get carbon, (unlike heterotrophs, who obtain carbon from organic matter synthesized by other beings). So photoautotrophy and chemoautotrophy are the basis of the biosphere, while photoheterotrophy and chemoheterotrophy are at a higher trophic level. Because CO2 is closely related to the evolution of the inorganic world (carbonate rocks, karst), autotrophs are also often called photolithotrophs or

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R. Cardenas et al.

chemolithotrophs, depending on whether they use light or chemical energy as primary energy source. They form the basis of the trophic or food assembly, of which depend all other organisms, that’s why they are called primary producers. Therefore, to estimate the habitability of natural environments, these two groups of organisms are very important and emphasis on them is put in this talk. While photoautotrophs can be unicellular (e.g., phytoplankton) or multicellular (e.g., higher plants), all chemoautotrophs discovered so far are unicellular organisms (prokaryotes). In most cases in the literature, like in this talk, for simplicity autotrophs are called photosynthetic or chemosynthetic organisms. It is noteworthy that until the 1970s, photosynthesis was by far considered the dominant mechanism of primary production in our planet. However, after the detection in 1977 of ecosystems based on chemosynthesis in the Galapagos Fault in the eastern Pacific, frequent discoveries in oceanic and continental depths have shown diverse ecosystems very dependent on chemosynthesis as primary production mechanism (Schulze-Makuch and Irwin 2008; Pohlman 2011; Sarbu et al. 1996; Por 2008). Furthermore, it has been suggested that in principle any redox chemical process releasing at least 10 kJ/mole of free energy may sustain microbial metabolism (Pohlman 2011). This greatly expands the possibilities for chemosynthesis on our planet and other rocky planetary bodies. It is noted that so far sulfur compounds appear to be the more used, particularly the oxidation of hydrogen sulfide by oxygen in the aquatic environment: H2 S + 2O2 → S O42− + 2H +

(4)

The oxidation reactions of methane and ammonia are also used by chemoautotrophs on Earth (and may sustain microbial life in other planetary bodies). For an excellent review on energy sources and their availability in the Solar System we recommend (Cockell et al. 2016). The ubiquity of chemosynthesis at planetary depths has motivated to propose a new model for the biosphere on Earth (Por 2008). It consists of a surface biosphere (eubiosphere), which basically depends on photosynthesis and has an oxidant redox state; a bacteriosphere in the deep crust where only live prokaryotes that perform chemosynthesis from compounds originating from the mantle (especially sulfur compounds), and has a reducing redox state; and an intermediate deuterobiosphere in the oxidizing-reducing interface dependent on both mechanisms of primary production, although probably more on chemosynthesis. The main biomes (major subclasses of ecosystems) that make up the deuterobiosphere are caves, anchialine caves (connected underground with the sea), cold seeps into the deep ocean, and hydrothermal vents at the junction of tectonic plates on the ocean floor or near underwater volcanoes (Por 2008). The deuterobiosphere is largely an avegetal aquatic world, mainly populated by chemosynthetic prokaryotes and invertebrate animals. These relatively recent discoveries, which go in crescendo, have led some authors to suggest that in our planet subsurface life is comparable in mass and volume to the surface one, and even that life on Earth may have emerged deep and not on the sea surface (Schulze-Makuch and Irwin 2008; Gold 1992). This makes evident

Opening Talk: What Pervades the Milky Way: The Snotty …

5

the relevance of chemosynthesis when quantitatively estimating the habitability of a rocky planetary body. This inspires the title of this talk: What pervades the Milky Way, the Snotty or the Stringy? Photoautotrophy or Chemoautotrophy? It is rather poetic and because photoautotrophy tend to form mucous organic materials (snotty), while chemoautotrophy tends to form fibrous inorganic materials (stringy).

2.3 Merging the Astrobiological and Ecological Schools In (Rodríguez-López et al. 2019) some of us showed a way to link the astrobiological and ecological schools of Quantitative Habitability, modifying the phytoplanktonzooplankton dynamics presented in (Ferrero et al. 2006). First we introduced the average net primary production in the photic zone, estimating it using an averaged version of Eq. (2): N P P = H I .N P P max

(5)

Then the dynamics phytoplankton–zooplankton was described by:   N P P dA =A − qH dt As

(6)

dH = H [eT q A − μ] dt

(7)

where A and H are (volumetric) biomass densities of phytoplankton and zooplankton, respectively; μ is the mortality rate of zooplankton, q is predation efficiency, while eT is the transformation efficiency, i.e., conversion efficiency of predated (phytoplankton) matter to zooplankton biomass. For the sake of dimensional homogeneity, it was introduced the (surface) density of phytoplankton carbon biomass AS .

2.4 A Case Study Many modeling efforts have been done to describe photosynthetic life, dominant at planetary surface, and thus in places readily reachable by researchers in many cases. However, because most chemosynthesis-based ecosystems are in places of difficult access to humans, and even to machines in many cases, the current level of knowledge on them presents many gaps, which limits the possibilities for mathematical modeling of their potential for habitability. However, in our Planetary Science Laboratory, apart for photosynthesisdominated ecosystems, we also work with chemoautotrophy-based ecosystems (Cardenas et al. 2019). For instance, currently we are working with sulfurous caves,

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starting with the paradigmatic example of Movile Cave in Rumania, the first discovered continental ecosystem totally dependent on chemoautotrophy (Sarbu et al. 1996). In caves usually energy and nutrients are the limiting factors affecting biological productivity, so in this preliminary modeling we are assuming it is energy release through the oxidation of sulfur compounds (although other chemoautotrophs, such as methanotrophic bacteria, are present). We are considering a prey-predator model in which sulfur oxidizing bacteria are the prey and sulfur reducing bacteria are predators. It is worth noting that in Movile Cave sulfur oxidation to sulfate in general passes through intermediate- oxidation chemical species, such as thiosulfate, tetrationate and elemental sufur, and eventually to sulfate. However, in this first model we assume the oxidation is straight until sulfate (Eq. 4). The inverse process is done by sulfur reducing bacteria, which are predators of the sulfur oxidizing bacteria. The equations of the model are then: f 1 [Sob][Sr b] r1 [H2 S][Sob] d[Sob] − − d1 [Sob] + re [Sr b] = dt k1 + [H2 S] k2 + [Sob]

(8)

η f 1 [Sob][Sr b] d[Sr b] = − d2 [Sr b] dt k2 + [Sob]

(9)

where [Sob] and [Srb] are biomass concentrations of sulfur oxidizing bacteria and sulfur reducing bacteria, respectively, t is time, [H 2 S] is the concentration of hydrogen sulfide, k 1 and k 2 are semi-saturation constants, f 1 is the feeding rate of predators and η their assimilation rate, while d 1 and d 2 are mortality rates of prey and predator, respectively. In Eq. (8) the last term is the respiratory model for sulfur reducing bacteria, appearing in the prey equation because H 2 S is released in this process (sulfurous respiration), so being indirectly beneficial for the prey (sulfur oxidation bacteria). The critical or singular points ([Sob], [Srb]) of the system (8) and (9) are the trivial one (0, 0) and: d2 k2 η f 1 − d2

(10)

ηd 2 k2 (d1 (k1 + [H2 S]) − r1 [H2 S]) (k1 + [H2 S])(d2− η f 1 )(d2 − ηre )

(11)

[Sob] = [Sr b] =

All variables and parameters of this model should be finite and equal or greater than zero, thus from Eq. (10) it is implied: η f 1 > d2 which leads to two possibilities in Eq. (11): (1)

Numerator and denominator are both negative.

(12)

Opening Talk: What Pervades the Milky Way: The Snotty …

(2)

7

Numerator and denominator are both positive.

The first option implies for the denominator d2 > ηre , which in turn implies for the numerator: [H2 S] >

d1 k1 r1 − d1

(13)

We have not found numerical values for chemoautotrophic species for the parameters in Eq. (13), thus we used those for the predator-prey part of the model appearing in (Amemiya et al. 2007). This gives: [H2 S] >

d1 k1 = 0.0025 mg/L r1 − d1

(14)

This threshold concentration for H 2 S is far smaller than its typical concentration in hydrothermal fluids (the main source of this substance in Movile Cave), which is around 1800 mg/L. Therefore, even using data for other metabolisms (Amemiya et al. 2007), we hypothesize that item number 1 holds for the system (10) and (11), and not number 2, which would imply the inverse situation: [H2 S]
Hg > Cu > Cd > Zn > Sn And the techonphility index, which establishes the ratio of the annual global metal production related to the average concentration of the ion in the earth’s crust. The relationship of some elements is established as shown: Cd > Pb = Hg > Cu > Zn > Cr > Ni The adsorption by living organisms is considerably high in ionic form when compared with the elemental form of the elements. It is important to note that the oxidation states play an important role in these effects. To this is added the ease forming bonds with organic compounds, which facilitates the aforementioned effects. The contamination of water by the dumping of metallic waste is a way of damaging the environment. The diffusivity of these ions in minerals is a source of environmental pollution. The net flow J of ions of a species in solution may be related to the concentration gradient, according to Fick’s law: J = −D grad N where D the proportionality constant is called the diffusion or diffusivity constant. For a linear sample    J = − D N x While this expression of the diffusion law is adequate, it should be taken into account that the fundamental cause of diffusion is the chemical potential gradient and not just the concentration gradient. Diffusivity strongly depends on the concentration, so in many cases it can only be estimated for very low concentrations, that is, at infinite dilution (indicated by a zero superscript). From a practical point of view it is assumed that diffusivity at infinite dilution is applied for higher concentrations, sometimes as high as 5 or 10% mol of one species in another. In heterogeneous systems it is possible to apply the diffusional model that allows to obtain the effective coefficient and from this, the theoretical diffusivity. It is important to note that the hydrated radius of the ions as well as their polarizing character and

Diffusivity of Some Ions in Natural Bentonite

13

polarizability play a prominent role in the diffusion process of the ions in solution through an adsorbent matrix where the active surface, surface area, dimensions of micropores, micropore volume play an important role.

2 Materials and Methods Bentonite and pure salts are used as adsorbents for analysis containing ions Ni (II), Zn (II), Cu (II), Pb (II), Cd (II) and Cr (III). The mathematical expression that allows to obtain the effective diffusivity is used from the diffusional model: qt = k p t 0.5 where: qt kp t

adsorption capacity over time (mg of adsorbed/g of adsorbent) diffusional constant (mg/g min0.5 ) time (min) From this expression it is possible to obtain the effective diffusivity through: qe kp = r



D 

where: qe kp D T

adsorption capacity in equilibrium (mg of adsorbed/g of adsorbent) diffusional constant (mg/g min0,5 ) effective diffusivity (cm2 /min) time (min) Through the effective diffusivity the theoretical diffusivity is obtained by: D=

DT da 

where: D DT da 

effective diffusivity (cm2 /min) theoretical diffusivity (cm2 /min) apparent density (g/cm3 ) tortuosity

Therefore, the values of bulk density (0.34 g/cm3 ) and tortuosity (1.66) of the adsorbent used are required. (Myroslav Spynskyy 2000).

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3 Analysis of the Results The most significant physical properties of the bentonite material used are show on the Table 1. As can be seen in Table 1, the studied bentonite has a relatively high porosity value, above 50%, a manifestation of its high roughness and capillarity. Compressibility demonstrates the high degree of compaction, as it reduces its original volume to almost a quarter. Tortuosity values reflect a high degree of disorder of the channels and surface of the solid. The value of the obtained flow rate corroborates the relative high porosity value. The specific surface area determined (S equal to 87 m2 /g) is in the range reported and considered relatively low, which makes it possible to predict that it is a calcium bentonite (Heller-Kallai 2006).

3.1 Chemical Analysis The previous results are consistent with those obtained by other authors, in similar works, regarding the presence of high percentage values of silica, followed by alumina and iron oxide, the latter being influential in the color of the sample, the Magnesium and calcium oxides are higher than potassium and sodium oxides. There are also lower percentages of titanium oxide, often found in clays (Table 2). The Si/Al ratios obtained is 4.25 and is in the range for clays of the bentonite type. The highest percentages correspond to SiO2 and Al2 O3 and Fe2 O3 . Table 1 Physical properties of bentonite

Parameters

Values

Pycnometric density

2.09 g/cm3

Apparent density

0.82 g/cm3

Apparent density by imprisonment

1.15 g/cm3

Flow rate

0

Tortuosity

1.19

Compressibility

29.8%

Porosity

61%

Specífic surface

87.1 m3 /g

Table 2 Chemical composition of bentonite Al2 O3

SiO2

TiO2

MgO

CaO

Fe2 O3

Na2 O

K2 O

12.7

54.10

0.94

1.67

3.05

8.7

1.06

0.20

Diffusivity of Some Ions in Natural Bentonite

15

Fig. 1 Bentonite X-ray diffractogram

3.2 X- Ray Diffraction The analysis by XRD (Fig. 1) allows to verify the presence of smectite (spaced d: 14.65 Å, 4.39 Å, 4.13 Å and 2.53 Å) as a major component, and feldspar (d: 3.08 Å,) found in the isomorphic series Albita-Anortite: Alb-30% and An-70%. It can be said that the analyzed bentonite is a smectite of acceptable purity. The d060 diffraction signal makes it possible to distinguish between dioctahedral and trioctahedral smectites, because the size of the cell in the b-axis is sensitive to the size of the cations and the occupation of the sites in the octahedral layer. The value of d060 = 1.50 Å is typical and confirms the dioctahedral character, deduced from the structural formula, and from the information obtained by chemical analysis, where the Si/Al ratio indicated a characteristic value of this type of smectite (Brown 1961).

3.3 Thermal Analysis With the thermogravimetric analysis (TGA) the presence of the clay mineral in the sample under study is verified. In Fig. 2, it is possible to observe the first endothermic peak, characteristic of montmorillonite, at 48.11 °C and other less pronounced (80.81; 94.01; 119.81 °C) characteristic of calcium montmorillonite, which correspond to water loss, and can be extended up to 250 °C (Ramachandran et al. 2002; Todor 1976). The small peak at 406.71 °C represents the loss of water from the montmorillonite hydroxyl, specifically the calcium. Thus at 579.72 °C, a small endothermic reaction can be observed, which may indicate the initial stage of montmorillonite transformation, decomposition or phase change of an impurity. The weight loss was around 9.5%, which represent the percentage of water in the sample and corresponds to what is reported in the literature.

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Fig. 2 TDA and TG of bentonite

3.4 Qualitative Analysis by FTIR Spectroscopy The structural analysis was performed with FTIR spectroscopy. The spectrum obtained (Fig. 3) has the characteristic appearance of clay minerals and in particular aluminous smectites. The bands between 1120 and 461 cm−1 correspond to phyllosilicate structures. (Madejova 2003). There are three distinct regions. In the area with the highest wave numbers (3600– 3400 cm−1 ), stretching vibrations of the octahedral layer are identified, punctually at 3424 cm−1 , a slightly wide band is observed that denotes the presence of absorbed water, which is confirmed in the central zone (1641 cm−1 ), where a band is identified that denotes the deformation vibration of the interlaminar water. The zone with the lowest wave numbers (518–461 cm−1 ) indicates the characteristic vibrations of Si-O tension (518 cm−1 ) and Si-O-Si deformation (461 cm−1 ). The small peak at 406.71 °C represents the loss of water from the calcium montmorillonite hydroxyl. At 579.72 °C The aluminum character of the octahedral layer is manifested by the intensity of the absorption band centered at 1031 cm−1 that is assigned to the deformation of the Al-Al-OH bonds. (Madejová 2003; Andke and Mozgawa 1993; Sagar Naya and Singh 2007).

Diffusivity of Some Ions in Natural Bentonite

17

Fig. 3 IR spectra of bentonite

Fig. 4 Graphical representation of the zero charge point

3.5 Zero Load Point The pH at the zero charge point, pHPZC obtained has a value of 8.1 (Fig. 4) that allows the montmorillonite to be classified as basic, this indicates the possibility of adsorbing metal ions and confirms the classification as calcium by being in the range reported by Bellini.

3.6 EDS (Energy Dispersive System) Microanalysis Figure 5 shows the percentages of the elements present in the starting ore. These results were obtained by the EDS elemental microanalysis of bentonite. It is observed that it has high oxygen (63.94%) and silicon (19.91%), characteristic of bentonite.

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Fig. 5 EDS Analysis of bentonite

They also reveal that the clay analyzed is calcium (0.51%), and that it has other typical elements such as iron (2.96%) and magnesium (1.39%) in low proportions. The presence of 4.03% of C is in the normal values of the composition of this type of smectite, indicates presence of organic matter. The percentage of aluminum (6.13%) confirms that it is an aluminous smectite (Heller-Kallai 2006).

3.7 Scanning Electron Microscopy (SEM) Scanning electron microscopy analysis allowed to determine the morphology of the starting mineral. Figure 6 shows the micrograph of bentonite under study, where dispersed particles of different sizes, not larger than 10 µm, can be observed at both ×1000 and ×2000. Coalescence bonded laminar aggregates are formed in detail forming a flake-shaped structure. The presence of rolled tubular laminar

Fig. 6 Scanning electron microscopy analysis

Diffusivity of Some Ions in Natural Bentonite Table 3 Effective and theoretical diffusivities obtained

19

Ions and molecules Effective diffusivity Theoretical (m2 /seg) diffusivity (m2 /seg) Ni (II)

4.8 × 10–12

2.3 × 10–11

Cu (II)

8.1 ×

10–11

2.3 × 10–10

Zn (II)

4.8 ×

10–12

2.3 × 10–11

Cd (II)

7.2 × 10–12

3.5 × 10–11

Pb (II)

6.6 ×

10–14

3.2 × 10–13

Cr (III)

4.8 ×

10–11

2.3 × 10–10

forms confirms the dioctahedral character of the bentonite under study. (Heller-Kallai 2006). From the results obtained by the different methods and the analysis of the calculated structural formula it can be concluded that the bentonite under study is a calcium montmorillonite, with a low specific surface area and low porosity (Table 3). The diameter of the aqueous complex corresponding to Cr (III) of 138 pm, the smallest of all the aqueous ions present in the study, justifies the greater effective diffusivity of the Cr (III) ion in aqueous solution in the bentonite made of the high load of the water complex that facilitates the passage through the montmorillonite plates. The low diffusivity of the Pb (II) ion is linked to its diameter, corresponding to 294 pm, the highest in the series worked. Another aspect to consider in the low diffusivity of this ion is its structure in water [Pb4 (OH)4 ]4+ . The high diffusivity of the Cu (II) ion is interesting, which is due to the fact that its complex ion [Cu(H2 O)6 ]2+ has a Jahn-Teller effect, which facilitates diffusion between the plates.

4 Conclusions 1. 2.

The substrate where the diffusive process occurs is a calcium montmorillonite, with low specific surface area and lower porosity. The diffusive process is favored by the high charge and low radius of the ion complex present in the heterogeneous system of bentonite mass-aqueous solution.

References Andke, M. and W. Mozgawa, Vibrational spectroscopy of the amorphous silicates. Vibrational Spectroscopy, 1993. 5(1): p. 75-84. Brown, G., ed. The X-ray Identification and Clay Structures of Clay Minerals 2nd edition ed., ed. C.M.G. Mineralogical Society. 1961, Jarrold&Sons Ltd.: London. Heller-Kallai, L., Hanbook of clay science. 2006: Elsevier Ltd.

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Madejova, J., FTIR techniques in clay mineral studies. Vibrational Spectroscopy, 2003. 31: p. 10. Myroslav Spynskyy, B. Study of selection mechanism of heavy metal (Pb II, Cu II, Ni II and Cd II ) adsorption in clinoptilolite. Colloids and interface. 2000. 5, 183–194. Ramachandran, V.S., et al., eds. Handbook of Thermal Analysis of Construction Materials. First ed. Construction Materials Science and Technology Series, ed. V.S. Ramachandran. 2002, William Andrews Publishing/Noyes Publications: New York, U.S.A. Sagar Naya, P. and B.K. Singh, Instrumental characterization of clay by XRF, XRD and FTIR. Bulletin of Materials. Science2007. 30(3): p. 235–238. Todor, D.N., Thermal Analysis of Minerals. 1976, Kent: Abacuss Press. 256.

Cassava Husk Powder as an Eco-Friendly Adsorbent for the Removal of Nickel (II) Ions Lisdelys González-Rodríguez, Julio Omar Prieto García, Lien Rodríguez-López, Yoan Hidalgo-Rosa, Manuel A. Treto-Suaréz, Mixary Garcia Enriquez, and Ángel Mollineda Trujillo Abstract Here, the potential of cassava husk powder as a ecofriendly bio-sorbent of nickel (Ni) ions in aqueous media was presented. This bio-material was characterized as sorbent using several physical properties and analytical techniques which displayed a product with adequate properties as bio-sorbent toward Ni (II) ions. The sorption process was studied via six kinetic models and seven thermodynamic models. The kinetic Elovich model and the liquid film diffusion model both provided a high degree of correlation with the experimental data at 45 °C which suggests a chemisorption process. The thermodynamic studied displayed an excellent correlation with the Temkin model which suggests a uniform distribution of binding energies. From these analyses, the activation energy (Ea), the change of the standard Gibbs free energy (G°), the standard enthalpy (H°) and the standard entropy (S°) of the sorption process were estimated using the thermodynamic equilibrium coefficients. This study revealed that Ni (II) ions adsorption process in cassava husk powder was reversible, exothermic, and with strong electrostatic interaction between L. González-Rodríguez (B) Facultad de Ciencias Ambientales, Universidad de Concepción, Concepción, Chile e-mail: [email protected] L. González-Rodríguez · Y. Hidalgo-Rosa · M. A. Treto-Suaréz ANID-Millennium Science Initiative Program-Millennium Nuclei On Catalytic Process Towards Sustainable Chemistry (CSC), Santiago de Chile, Chile J. O. P. García · M. G. Enriquez Facultad de Química y Farmacia, Universidad Central “Marta Abreu de Las Villas”, Carretera de Camajuani km 5, Villa Clara 50100 Santa Clara, Cuba L. Rodríguez-López Facultad de Ingeniería y Tecnología, Universidad San Sebastián, Lientur 1457, Concepción, Chile Y. Hidalgo-Rosa · M. A. Treto-Suaréz Departamento de Química Inorgánica, Centro de Energía UC, Centro de Investigación en Nanotecnología Y Materiales Avanzados CIEN-UC, Pontificia Universidad Católica de Chile, Avenida Vicuna Mackenna, 4860 Santiago, Chile Á. M. Trujillo Centro de Investigaciones Agropecuarias, Universidad Central “Marta Abreu de Las Villas”, Villa Clara 50100 Santa Clara, Cuba © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Cardenas et al. (eds.), Proceedings of the 3rd International Conference on BioGeoSciences, Springer Proceedings in Earth and Environmental Sciences, https://doi.org/10.1007/978-3-030-88919-7_3

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metal and sorbent surface. The cassava husk powder showed an adsorption capacity of qe (25 °C) = 1.388 mg/g and qe (45 °C) = 1.265 mg/g. The bio-adsorbent is a low-cost alternative and promising green technology for efficient large-scale Ni (II) ions removal from industrial wastewater. It is an affordable technology that could also help reduce pollution in the environment. Keywords Bio-adsortion · Nickel · Heavy metal · Cassava husk powder

1 Introduction Contamination of water bodies by heavy metals is one of today’s major environmental problems (Gisi et al. 2016). Metals such as cadmium, copper, zinc, chromium, lead, chromium, mercury, and nickel are commonly found in industrial effluents (Samad et al. 2019; Chen et al. 2015; Martínez and Bassas 2001). These metal ions are considered to be potentially devastating to ecosystems and human health. Nickel (Ni) (II) is a highly toxic heavy metal known for high neurotoxicity, besides being associated with serious health effects such as dermatitis, nausea, disorders in the respiratory and central nervous system, lung disorders, genetic mutations, and cancer (World Health Organization 2008). It can also lead to memory impairment, prolonged reaction times, and reduced ability to comprehend (Järup 2003). Exposure routes are mainly ingestion of contaminated soil and dust with inhalation being a significant route (Bose-O’Reilly et al. 2018). Given the negative impacts on human health, there is a constant need to reduce their concentration in air, soil, and water bodies. In this framework, several alternatives have been used to reduce the presence of Ni (II) in several mediums, (Renu and Singh 2017) such as chemical precipitation, filtration, coagulation, oxidation-reduction, osmosis, solvent extraction, electrochemical treatments, sorption into several materials, among others. In the sorbent material area, several inorganic and bio-sorbent have been reported for the removal metal ions, (Vakili et al. 2020) like of Ni (II), including silicates (González-Rodríguez 2009; Treto-Suárez et al. 2020; El-Naggar and Abou-Mesalam 2007) activated carbon (Abdulrazak et al. 2017; Bibaj et al. 2019), sugar cane bagasse (Prieto García et al. 2009; Rodriguez et al. 2013), and agricultural by-products (Garcia et al. 2020; Georgieva et al. 2020), among others (Anbia et al. 2015). The agricultural by-products are one efficient wastewater treatment alternatives due to are easy recovery, environmentally eco-friendly, lowest implementation and maintenance costs concerning traditional heavy metal recovery treatments in aqueous effluents (Martínez-Hernández and Brito-Castillo 2019). Despite these advantages, the agricultural by-products have been little studied as metal ion sorbent. For example, Hossain et al. (2012) and Martínez-Hernández and Brito-Castillo (2019) reported the palm oil fruit shells and the cassava root husks powder, respectively, as biosorbent for copper removal from water. These materials displayed a equilibrium sorption capacity of copper between 28–60 mg/g and 0.14 mmol/g, respectively.

Cassava Husk Powder as an Eco-Friendly Adsorbent …

23

The cassava is an agricultural product very use, abundant and cheap in all territories Cuban and many countries of the region, while the Ni (II) is very present in several national-industrial activities. In this framework, herein, the prepared cassava husk powder adsorbent was studied as an environmentally eco-friendly and low-cost material for Ni (II) removal from aqueous solutions. The obtain ion of the an bio-adsorbent from cassava peel waste can benefit the agricultural sector, taking advantage of this waste as a raw material for Ni (II) adsorption and increasing the environmental benefits. Also, the nickel industries located in Moa and Nicaro generate industrial effluents containing heavy metals. Among them is the waste liquor from the sulfide precipitation plant of the “Pedro Sotto Alba” nickel company in Moa, characterized by its high acidity (Martínez and Bassas 2001) and the presence of several metals among them nickel. Therefore, this research proposes the use of a waste material highly available in the Caribbean region as an adsorbent medium in the treatment of aqueous solutions contaminated with Ni (II). For this goal, the cassava husk powder was prepared and characterized by sorbent. Furthermore, the sorption process of Ni (II) was studied in water medium using the kinetic models of Pseudo-second-order, Pseudo-first-order, Elovich, Diffusion film liquid, Intraparticle diffusion and Bangham’s. The thermodynamics study was performed evaluated of the Langmuir, Freundlich, DubininRadushkevich, Redlich, Toth, Temkin and Flory-Huggins models. From these analysis were estimated several thermodynamics parameters of the sorption process, i.e. the activation energy (Ea), the change of the standard Gibbs free energy (G°), the standard enthalpy (H°) and the standard entropy (S°).This study demostred the potencial of these environmentally eco-friendly material which can be a promising adsorbent for the treatment of aqueous solutions contaminated with Ni (II).

2 Methodology 2.1 Study Area and Bio-Adsorbent Preparation The cassava peel was collected from a farm located in the “Valle del Yabú” in the municipality of Santa Clara, province of Villa Clara, Cuba (22.43 °N; 78.98 °W). In Cuba, more than 100 thousand hectares are allocated for planting this crop. In recent years, efforts have been made to achieve the planting of 13.42 hectares per thousand inhabitants. The cassava peel was washed with a solution of HCl at pH 5.5 to remove the dirt from the surface of the cassava peel. Previous research has shown that HCl treatment increases porosity and low density in the adsorbents (Prieto García et al. 2009). Drying was carried out in two stages: (1) dried out in the sun for a period of 24 h and, (2) them, at 80 °C for 4 h in the oven. Subsequently, it was crushed and adjusted to a particle size of 0.125 mm (Fig. 1).

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L. González-Rodríguez et al.

Fig. 1 Study area from the cassava peel is collected

2.2 Physical Characterization of the Cassava Husk Powder The adsorbent physical characterization included the parameters showed in Table 1. Determining parameters such as the apparent density using Eq. (1), and apparent density by imprisonment from pycnometric method. Then, both parameters are used for determining the compressibility (C) in Eq. (2), property that powders have to decrease the volume they occupy by the external force action (e.g. vibration, pressure, or agitation). Mohsenin and Martin equations (Mohsenin 1970; Cerón et al. 2015; Lowell et al. 2004) were used for the calculation of porosity and flow rate parameters. Equations 3 and 4 were used for the calculation of flow rate (vf ) and tortuosity () respectively. Ten replicates were made in all cases. The adsorbent materials are characterized by a large specific surface capable of interacting with the adsorbates of interest. For this reason, the estimation of this propierty is very important in the characterization of the adsorbent materials. The specific surface area (S) of cassava husk powder was estimated using the organic dye methylene blue (MB: C16 H18 N3 SCl) as reference (Treto-Suárez et al. 2020; Santamarina et al. 2002). In this method, a kinetic study of sorption process was carried out from MB samples. A sample of known concentration (5 mg/l) of MB was placed in contact with 0.1 g of the cassava husk powder for 1 h to determine Table 1 Some parameters calculated in the chemical and physical analysis No. Equations da =

m

Parameters

4

d a and d g are apparent and granular density (g/cm3 ), n is the number of replications, V is the occupied volume by powder (ml), mp is C = (1 − ddag ) ∗ 100 powder mass (g); d is the diameter (cm), t is time, : Tortuosity mp (g/cm3 ); q: milligrams adsorbed per gram of adsorbent (mg/g); Am : v f = 0.785d 2t MB surface area (Å); S: Surface area of adsorbent (m2 /g), N:  = 2 − da Avogadro number, t: time (s)

5

S = qt N Am 10−20

1 2 3

n v

Cassava Husk Powder as an Eco-Friendly Adsorbent …

25

the time of maximum sorption and perform the thermodynamic study at different concentrations. The thermodynamic study was carried out from MB samples of known concentration (0.6, 2.8, 3.0, 4.5, 6.0, 7.5. 10.5, and 15.0 mg/l) and placed in contact with 0.1 g of material to evaluate by the Langmuir model. If this data display correlation with this model, it can be considered that the MB adsorption is homogeneous over the entire surface and the monolayer mass can be estimated, and therefore, the specific surface (S) can be calculated using the Eq. (5) (see Table 1). The surface area of MB (Am ) was theoretically determined by molecular descriptors, using the Dragon program (Mauri et al. 2006), where the area corresponding to the complete molecule (C16 H18 N3 SCl) is A1 = 507.2 Ao . Fourier Transform Infrared (FTIR) analysis was performed using a Phillips FTIR model PV-9512 spectrophotometer equipment. The adsorbent was encapsulated in KBr pellets, to be analyzed in the range 4000–500 cm−1 . The point of zero charge (pHPZC ) of a adsorbent indicates the most suitable range of the pH value to achieve efficient removal. These properties was obtained following the study by Davranche et al. (2003). Briefly, the mixture was stirred at 300 rpm at room temperature, and the pH value of the several solutions was measured by a pH meter. For the determination of the acidic and basic sites on the surface of the bio-sorbent, the Boehm titration method was used (Schönherr et al. 2018).

2.3 Adsorption Test The adsorption tests were carried out following the methodology proposed by Maldonado and Urquizo (2012). The concentration of Ni (II) were determined by atomic absorption spectroscopytechnique from the accredited laboratory of the Centro de Investigaciones Agropecuarias (CIAP, http://ciap.uclv.edu.cu/) at Universidad Central “Marta Abreu de las Villas”, Cuba. The adjustment of the kinetic models and thermodinamic models to the experimental data was done with the models shown in Tables 2 and 3. The coefficient (R2 ) is used to check the validity of these models. R2 close to 1 indicates that the graphs obtained are better adjusted to the described isotherms. Also, the sum of the squares of errors (SSE), was used for the establishment of the best-fitting kinetic model. SSE would then be equal to 0. The statistical analysis was performed by Linear regressions with marginal distribution v1.0 app belonging to the statistical package OriginLab 9.8.0.200 (Academic) software.

2.4 Thermodynamic Parameters Estimation To determine the nature of the adsorption process it is important to estimate the change in thermodynamic parameters in the adsorption process, i.e. the change of the standard Gibbs free energy (G°), the standard enthalpy (H°) and the standard entropy (S°). The measured parameters were assessed to determine the spontaneity

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L. González-Rodríguez et al.

Table 2 Kinetic models of heavy metal absorption used in this study Models

Equations

Parameters

Pseudo-first order

ln(qe − qt ) = lnqe − k1 t

qe : adsorbate adsorbed per gram of the adsorbent at equilibrium (mg/g); qt : the amount of metal adsorbed at any time (mg/g); t: time (min); t 1/2 : half lifetime (min); k 1 : first-order pseudo speed constant (g/mg min)

t1 = 2

Pseudo-second order

ln2 k1

=

1 k2 qe2

t1 =

1 qe k 2

t qt 2

+

  1 qe

t

k 2 : is the rate constant for the pseudo-second-order rate (g/mg min); h2 : initial adsorption rate (mol/g min)

h2 = k2 qe 2 Elovich

qt = α + β s *ln t

Diffusivity liquid film

ln(1 −

Intraparticle diffusion

qt =

Bangham’s

C0 log( C0 −q ) = log tm

α: initial sorption rate (mg/g min); β s : desorption constant (mg/g min); t: time (min)

qt qe ) = −kfq t ki *t 1/2 + C

k fq : speed constant (s−1 )

+c



k0 m 2.303V



k i : intraparticle diffusion rate constant (mg/g min), C: solute concentration in equilibrium (mg/L) +∝

logt

α and k 0 : Bangham system constant, m: adsorbent mass (g),

and reversibility of Ni (II) ions adsorption on bio-sorbent at 25 and 45 °C temperature. The G° was estimated using the Eq. (6). In this equation the K° value is dimensionless, which is related to density calculated in Eq. (3) and distribution coefficient (KD ) (Thakur et al. 2017). G ◦ = −RT ln K ◦

(6)

Parameters H° and S° were derived from the Van’t Hoff Eq. (7), where, R is the gas constant (8.314 J mol–1 K–1 ), and T is the absolute temperature (K). lnk ◦ = −

S ◦ H + RT R

(7)

The values of H° and S° can be respectively calculated from the slope and intercept of the plot of lnK versus 1/T. Thus, H° corresponds to an isosteric heat of adsorption with zero surface coverage (Sahu et al. 2008). Last, the activated energy (Ea, kJ/mol) was determined by the Arrhenius Eq. (8).   B2 T1 ∗ T2 ln Ea = −R ∗ T2 − T1 B1

(8)

Cassava Husk Powder as an Eco-Friendly Adsorbent …

27

Table 3 Thermodynamic models of heavy metal absorption used in this study Models

Equations

Langmuir

qe =

Parameters

Freundlich

qe = k F ∗ Cen

k F : an indicator of absorptive capacity, n: adsorption intensity

Dubinin-Radushkevich

lnqe = lnqm − βe E 2

E: Polanyi potential, β e : coefficient related to adsorption energy, qm : maximum absorption (mg/g)

Redlich

qe =

Toth

qe =

Q 0 K L Ce 1+K L Ce

qe : adsorbate adsorbed per gram of the adsorbent at equilibrium (mg/g); Q0 represents the monolayer coverage capacity (mg/g), K L : Langmuir isotherm constant, Ce : equilibrium concentration of adsorbate (mg/L)

1

aCe

(1+bCtn ) 

qm n C 0  1 1 +Ce n h K T

b: Constante de Redlich (L/mg), a: Constante de Redlich (L/g) n: constant whose value varies (1.5; 1.6; 1.7; 1.8), K T : Toth equilibrium constant, C 0 : adsorbate initial concentration

Temkin

qt = BT ln A T + BT lnCt

AT : Temkin isotherm equilibrium binding constant (L/mg); BT : Temkin isotherm constant, C t : solute concentration in solution over time (mg/L)

Flory–Huggins

θ C0

θ indicates the degree of surface coverage of the adsorbent surface, K FH and nFH are the Flory–Huggins equilibrium constant (L/mg)

= K F H (1 − θ)n F H

θ =1−

Ce C0

Previously determining the value of the rate constant of the internal diffusion process (B, h−1 ) at 25 °C and 45 °C. If the H° change value of adsorbent is higher than 40 kJ mol–1 , the process is chemisorption which includes strong electrostatic bond between metal ions and cassava husk powder surface (El-Araby et al. 2017). Similarly, low activation energy (Ea between 5 and 40 kJmol–1 ) are characteristics for physisorption, while higher activation energies (40–800 kJmol–1 ) suggest a chemisorption process (Gorzin et al. 2018). The negative value of H° implies that the adsorption phenomenon is exothermic. The sign of S° indicates whether the adsorption reaction is an associative or dissociative mechanism. The negative values of G° indicate that the adsorption process is spontaneous.

3 Results and Discussion This section displays the main results obtained from the characterization of the cassava husk powder as a sorbent and its possible use in Ni (II) remotion in an aqueous medium. To this goal, physical characterization of this bio-material was

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L. González-Rodríguez et al.

done together with a kinetic and thermodynamic study of the Ni(II)-sorption process. Finally, the preliminary cost analysis was briefly discussed.

3.1 Physical Characterization of Cassava Husk Powder Moisture content in the cassava husk was close to 66.57%. The shell had a starch frac of tion corresponding to 45.36% as an inherent result of the selection and cleaning process. This moisture content decreased to 47.19% due to the drying process. The values of both parameters are in correspondence with those reported by Martínez-Hernández and Brito-Castillo in 2019 for other similar materials (MartínezHernández and Brito-Castillo 2019). Table 4 shows the results determined in the physical characterization of the bio-adsorbent cassava husk powder. This material display a grain porosity of 0.52%, a tortuosity of 1.34 value and Zero flow rate which reflect a moderate degree disorder of the channels and a strong adhesion. These propierties agree with materials with moderate sorption capacity (Rodríguez-Díaz et al. 2015). The estimated specific surface area is smaller than 1 m2 g−1 which implies that the material is not very porous which agree with the surface area reported by other similar bio-sorbent (Jorgetto et al. 2014). On the other hand, the determination of the basic and acid sites in the material show that the concentration of the surface acid sites is slightly higher than the concentration of basic sites while the pHPZC value is 3.1. This result can be attributed to the acid wash to which it is subjected in the pretreatment and indicates that the adsorbent acquires a positive charge below pH Table 4 Physcical propierties determined to the bio-adsorbent cassava husk powder

Parameters Apparent density

Value (g/mL1 )

0.42

Apparent density by entrapment (g/cm3 )

0.66

Flow rate (m/s)

0

Porosity (%)

52

Compressibility (%)

36

Tortuosity Surface Area by MB method

1.34 (m2 g−1 )

0.72

Shape factor

0.52

pHPZC

3.1

Humidity (%)

47.19

Volatiles (%)

42.02

Ash (%)

3.11

Carbon fixed (%)

7.08

Basic sities

0

Acid sites

0.686

Cassava Husk Powder as an Eco-Friendly Adsorbent …

29

Fig. 2 The FTIR spectrum of cassava husk powder

3.1 due to the protonation of the basic groups like carboxylic, hydroxyl, and amine groups. This result also suggests that above pH 3.1 material surface is negatively charged and the adsorption may be favored. Different measurements of the pH of the solutions were carried out and yielded an average of 3.1, identifying the powder used as an acid bio-sorbent. These physical propierties agree with reported by Castro et al. in 2014 to the cassava root husk powder (Vakili et al. 2020; Martínez-Hernández and Brito-Castillo 2019). Figure 2 show the powder infrared spectra of the casava husk powder. This spectra indicate the presence of a large OH stretch band in the wavenumber of 3400 cm−1 alongside with intense CH stretch band found in 2929 cm−1 . FTIR spectrum demonstrate the existence of alcohol in bands found between 1000 and 1200 cm−1 , amine and carboxylic groups absorption band in the region of 1738 cm−1 . The strong band at 1640 cm−1 may be attributed to primary and secondary O–H vibration from glucoside units. Other amino acids that may be present at low amounts are present from 1368 to 1244 cm−1 may be assigned to C-N stretch. This result also agree with the reported by other similar bio-materials (Jorgetto et al. 2014).

3.2 Adsorption Test The adsorption of Ni (II) was studied in water medium using the kinetic models of Pseudo-second-order, Pseudo-first-order, Elovich, Diffusion film liquid, Intraparticle diffusion and Bangham’s models, while the thermodynamics studied was performed

30

L. González-Rodríguez et al.

evaluated the Langmuir, Freundlich, Dubinin-Radushkevich, Redlich, Toth, Temkin and Flory–Huggins models. According to coefficient (R2 ), the Ni2+ responds to a process of chemisorption adjusting to Elovich’s model at 45 °C (Table 5), where the process display an initial sorption rate at 0.297 g mg−1 min−1 , an equilibrium sorption capacity of qe = 1.265 mg/g, and a desorption constant of 0.298 mg/g. From the temperature (25 °C), the kinetic adjusting better to diffusion models with an increase of qe = 0.123 mg/g (qe (25 °C) = 1.388 mg/g) which suggest that the diffusion is an aspect to be taken into account. This sorption capacity is an promising results and agrees with the reported to other bio-adsorbent; such as: Aloe barbadensis Miller leaves (ABL) biomass (10 mg Ni(II)/g) (Gupta and Kumar 2009), Moringa oleifera seeds (29.6 mg/g) (Marques et al. 2012), ficus Religiosa (Peepal) leaves (6.35 mg/g) (Aslam et al. 2010), fly ash (0.03 mg/g) (Rao et al. 2002), bagasse fly ash (6.48 mg/g) (Srivastava et al. 2006), coal fly ash (0.87 mg/g) (Dash et al. 2018) orange lees (10 mg/g) (Feng et al. 2011), Jute fibres (4.23 mg/g) (Shukla and Pai 2005), banana peel (6.8 mg/g) (Annadural and Juang 2003) and rice husk (8.86 mg/g) (Mishra and Patel 2009). From the determination coefficients (R2 ) obtained from the fits to the equilibrium experimental data (Table 6), the best fitting thermodynamic models are those described by Temkin and Flory–Huggins for the temperature of 25 °C and 45 °C, respectively. Temkin model takes into account adsorbent–adsorbate interactions ignoring the extreme values of concentrations. This model follows the assumption that there is an indirect relation between adsorption energy and adsorbate-adsorbent interactions and that the heat of adsorption of all molecules in the layer decreases linearly as a result of increased surface coverage (Ringot et al. 2007). For this reason, the R2 change with increased temperature suggests that heat of adsorption, which function of temperature, of all molecules in the layer does not decrease linearly with the increase of temperature (Aharoni and Ungarish 1976). Therefore, the adsorption of Ni (II) is characterized by a uniform distribution of binding energies with a chemical interaction. This result agree with other reports, for example, Hutson and Yang (Hutson and Yang 1997) confirmed that the adsorption of cadmium ion onto nano zero-valent iron particles follows a chemisorption process used the Temkin model. The results of the experimental model at 45 °C are best described by Flory–Huggins model where R2 are higher than other models. Therefore, the retention of Ni (II) ions at 45 °C is as a monolayer and the process would have only involved functional groups on the surface of the adsorbent (Nechifor et al. 2015). The low R2 values for the Dubinin-Radushkevich model allow ruling out adsorption in micropores. The Redlich isotherm, which corresponds to a hybrid of the Langmuir and Freundlich models, can also be ruled out.

3.3 Thermodynamic Magnitudes Several thermodynamics parameters of the sorption process were determinated to investigate the thermodynamic behavior of the Ni (II) adsorption onto prepared

0.949 0.463 0.912 0.969 0.957 0.903

Fit

y = −0.078 + 0.763

y = −0.010x + 0.285

y = 0.744x − 0.477

y = −0.095x + 0.061

y = 0.409x − 0.209

y = 0.701x − 2.125

Pseudo-first-order

Pseudo-second-order

Elovich

Diffusion film liquid

Intraparticle diffusion

Bangham’s

25 °C R2

Model

0.046

0.043

0.030

0.396

45.79

0.340

SSE

Table 5 Results of kinetic adsortion models. The bold represent the best adjusment

y = 0.020x − 1.904

y = 0.063x − 0.436

y = −0.079x + 0.274

y = 0.297x − 0.298

y = −0.362x + 10.776

y = −0.076 + 0.642

Fit

45 °C

0.941

0.912

0.952

0.980

0.839

0.942

R2

0.033

0.380

0.311

0.029

24.82

0.313

SSE

Cassava Husk Powder as an Eco-Friendly Adsorbent … 31

Flory–Huggins

Temkin

Toth

y = −0.013x − 0.052 0.643

0.993

0.310

y = −0.805x + 1.814

0.164

0.697

y = 34008x + 821

Dubinin–Raduskevich

y = 0.001x + 0.100

0.858

y = −0.545x + 0.888

Freundlich

y = −0.001x + 0.152

0.536

y = −0.027x + 0.413

Langmuir

Redlich

R2

25 °C

Fit

Models

0.056

0.010

0.342

1.456

0.378

0.056

0.389

SSE

Table 6 Results of thermodynamic adsortion models. The bold represent the best adjustment

y = −0.003x + 0.197

y = −0.096x − 0.246

y = −1.408x + 2.135

y = −0.001x + 0.152

y = 0.001x + 0.157

y = 947x + 354

y = −228x + 1.409

Fit

45 °C

0.984

0.736

0.122

0.084

0.573

0.451

0.219

R2

0.470

0.081

0.001

0.004

1.778

0.168

0.008

SSE

32 L. González-Rodríguez et al.

Cassava Husk Powder as an Eco-Friendly Adsorbent …

33

Table 7 Thermodynamic magnitudes at different temperature for the adsorption using Ci = 22 mg L−1 of Ni (II) using m = 0.01 g of cassava husk powder at pH = 6.0 Temperature (K)

K

G° (kJmol−1 )

S° (kJmol−1 K)

H° (kJmol−1 )

298 (25 °C)

8.965

−5.434

−0.242

−77.55

318 (45 °C)

1.252

−0.594

E a is the activation energy, G° is the change of the standard Gibbs free energy, H° is the standard enthalpy and S° is the standard entropy in the process

* Where

cassava husk powder. Table 7 shows the thermodynamic magnitudes calculated considering the thermodynamic equilibrium constants for both temperatures. In this study, the negative values of standard Gibbs free energy change (G°) confirm that the process is thermodinamicaly spontaneous at both temperatures. In the process, G° value increases slightly at the temperature of 25 °C. This result indicates that the process is more spontaneous at 25 °C than 45 °C and agree with the more Ni (II) adsorption at a lower temperature qe (25 °C) = 1.388 mg/g and qe (45 °C) = 1.265 mg/g de Ni (II). Acoording to H° (H° < 0), the adsorption process is exothermic with a strong electrostatic chemical bonding between Ni (II) and adsorbent surface. The activation energy value (Ea = 40.94 kJ/mol) suggest that the adsorption of Ni (II) corresponds to chemisorption which agree with the kinetic result (adjusting to Elovich’ s model). In physical adsorption, the activation energy is small due to the existence of weak forces between metal ions and the adsorbent surface. In our case, the Ea is considerable and this event illustrates that chemisorption reaction has occurred (Gorzin et al. 2018). The negative entropy (S° = −0.242 Jmol−1 K−1 ) suggested that Ni (II) adsorption is an reversible process which also agree with the kinetic results. This is a limitation since the bio-adsorbent made from cassava husk powder cannot be reused. However, the abundance and low cost of the adsorbent compensates this limitation.

3.4 Preliminary Cost Analysis Activated carbons are the most widely used adsorbents for the treatment of wastewaters (Gisi et al. 2016). To find alternatives to commercial activated carbon, we visited the Merck Laboratory (https://www.sigmaaldrich.com/). One commercially available adsorbent (activated carbon) is purchased for 395.00 USD/kg or 117.00 USD per 250 g. It becomes more problematic for developing countries to afford the cost and demand of activated carbon. However, following the procedure reported in Gupta and Babu (2008) and showed in Table 8, the total cost for the preparation of 1 kg of cassava husk powder without activated was 6.80 USD. According to the literature reviewed, the novel low-cost cassava husk powder adsorbent has minor price than other bio-adsorbent reported (Gupta and Babu 2008). The Cuban cassava

34 Table 8 Total cost for preparing 1 kg of adsorbent from cassava peel

L. González-Rodríguez et al. Material

Unit cost (USD.)

Cassava Amount used

Net price (USD.)

0.50 per liter

1L

0.50

Cost of drying 1 0

–

–

Cost of drying 2 1.50 per 1–100 kWh

80 °C for 4 h

6.00

HCl

Net cost (USD.)

6.50

Other overhead costs (10% of the net cost)

0.30

Total cost (USD.)

6.80

peel is available in all territory, abundant and cheap. Therefore, is a promising green technology for efficient large-scale Ni (II) ions removal from industrial wastewater.

4 Conclusions An low-cost eco-friendly bio-sorbent of Ni (II) ions based in cassava peel was prepared and characterized using several physical parameters and adsorption models. This bio-material displays adequate physical properties as bio-sorbent. The kinetic adsorption process show an equilibrium sorption capacity of qe (25 °C) = 1.388 mg/g and qe (45 °C) = 1.265 mg/g. The adsorption process is adjusted to Elovich (at 45 °C) and Diffusion (at 25 °C) models, which suggest a chemical sorption, where the diffusion is an aspect to be taken into account. The thermodynamic study revealed that best-fitting thermodynamic models are those described by Temkin and Flory-Huggins for 25 °C and 45 °C, respectively. These results also suggest an chemisorption process, where the retention of Ni (II) ions only involves functional groups on the surface of the adsorbent forming a monolayer at 45 °C. The thermodynamic parameters such as H◦, G◦, and S◦ suggest that the adsorption process is exothermic, spontaneous and irreversible. The cassava husk powder is a low-cost alternative and promising green technology for efficient large-scale Ni (II) ions removal from industrial wastewater. It is an affordable technology that could also help reduce pollution in the environment. Acknowledgements L. G-R wish to thank the Chilean Development and Innovation National Agency (ANID) (Chile) for Doctoral Grant (21170226) and Proyect FONDEF IT19I0004. Y. H-R thank the PhD. Programme in Molecular Physical Chemistry from Universidad Andrés Bello and ANID/FONDAP/15110019. M. T-S thank the ANID-Postdoctoral Grant 3210271. The authors

Cassava Husk Powder as an Eco-Friendly Adsorbent …

35

acknowledged the assistance from the Chemistry Department of University “Martha Abreu de las Villas”, Cuba. The authors thank the Editors and reviewers for their valuable comments and suggestions which improved the quality of the paper.

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Nechifor G, Pascu DE, Neagu MP, Traistaru GA, Albu PC. Comparative study of temkin and floryhuggins isotherms for adsorption of phosphate anion on membranes. UPB Sci Bull Ser B Chem Mater Sci 2015;77:63–72. J. O. Prieto García;, Aguilar; PJV, Alberteris; EP, Wasserman; BB, Ibañez SM. ESTUDIO DE LA SORCIÓN E INTERCAMBIO DE IONES NÍQUEL (II) MEDIANTE IMPULSOS ULTRASÓNICOS EN UN PRODUCTO ZEOLITOIDE. Av En Energías Renov y Medio Ambient 2002;6:1–4. https://doi.org/10.1017/CBO9781107415324.004. J. O. Prieto García. ESTUDIO CINÉTICO Y TERMODINÁMICO DE LA ADSORCIÓN DE IONES Cu (II) EN UN ADSORBENTE DE BASE SILÍCICA. Rev Cuba Química 2012;XXIV:40–7. J. O. Prieto García, Wasserman BB, Gonzalez DC, Vicente FEM, Trujillo ÁM. LA ADSORCIÓN Y EL INTERCAMBIO DE IONES NÍQUEL (II) UTILIZANDO CENIZAS DE BAGAZO DE CAÑA DE AZUCAR. ASADES 2009;13:47–54. Rao M, Parwate A V., Bhole AG. Removal of Cr6+ and Ni2+ from aqueous solution using bagasse and fly ash. Waste Manag 2002;22:821–30. https://doi.org/https://doi.org/10.1016/S0956-053 X(02)00011-9. Renu, Agarwal M, Singh K. Heavy metal removal from wastewater using various adsorbents: A review. J Water Reuse Desalin 2017;7:387–419. https://doi.org/10.2166/wrd.2016.104. Ringot D, Lerzy B, Chaplain K, Bonhoure JP, Auclair E, Larondelle Y. In vitro biosorption of ochratoxin A on the yeast industry by-products: Comparison of isotherm models. Bioresour Technol 2007;98:1812–21. https://doi.org/https://doi.org/10.1016/j.biortech.2006.06.015. Rodriguez J, Bravo L, Prieto J, Becerra G, Trujillo A, Carlos M, et al. Empleo de ceniza de bagazo de caña como un material adsorbente de bajo de costo en la eliminación de iones Ni (II). Rev Cent Azucar 2013;50:9. Rodríguez-Díaz JM, García JOP, Sánchez LRB, da Silva MGC, da Silva VL, Arteaga-Pérez LE. Comprehensive Characterization of Sugarcane Bagasse Ash for Its Use as an Adsorbent. Bioenergy Res 2015;8:1885–95. https://doi.org/https://doi.org/10.1007/s12155-015-9646-6. Sahu AK, Srivastava VC, Mall ID, Lataye DH. Adsorption of furfural from aqueous solution onto activated carbon: Kinetic, equilibrium and thermodynamic study. Sep Sci Technol 2008;43:1239– 59. https://doi.org/https://doi.org/10.1080/01496390701885711. Samad KA, Salleh ISM, Zahari MAKM, Yussof HW. Batch study on the removal of mercury (II) ion from industrial wastewater using activated palm oil fuel ash. Mater Today Proc 2019;17:1126–32. https://doi.org/https://doi.org/10.1016/j.matpr.2019.06.536. Santamarina, J. C., Klein, K. A., Wang, Y. H. & Prencke E. Specifc surface: Determination and relevance. Can Geotech J 2002;39:233–241. https://doi.org/10.1139/t01-077. Schönherr J, Buchheim JR, And PS, Adelhelm P. Boehm Titration Revisited (Part I): Practical Aspects for Achieving a High Precision in Quantifying Oxygen-Containing Surface Groups on Carbon Materials. J Carbon Res 2018;4:21. https://doi.org/https://doi.org/10.3390/c4020021. Shukla SR, Pai RS. Adsorption of Cu(II), Ni(II) and Zn(II) on modified jute fibres. Bioresour Technol 2005;96:1430–8. https://doi.org/https://doi.org/10.1016/j.biortech.2004.12.010. Srivastava VC, Mall ID, Mishra IM. Equilibrium modelling of single and binary adsorption of cadmium and nickel onto bagasse fly ash. Chem Eng J 2006;117:79–91. https://doi.org/https:// doi.org/10.1016/j.cej.2005.11.021. Taamneh Y, Sharadqah S. The removal of heavy metals from aqueous solution using natural Jordanian zeolite. Appl Water Sci 2017;7:2021–8. https://doi.org/https://doi.org/10.1007/s13201-0160382-7. Thakur AK, Nisola GM, Limjuco LA, Parohinog KJ, Torrejos REC, Shahi VK, et al. Polyethylenimine-modified mesoporous silica adsorbent for simultaneous removal of Cd(II) and Ni(II) from aqueous solution. J Ind Eng Chem 2017;49:133–44. https://doi.org/https://doi.org/ 10.1016/j.jiec.2017.01.019. Treto-Suárez MA, Prieto-García JO, Mollineda-Trujillo Á, Lamazares E, Hidalgo-Rosa Y, MenaUlecia K. Kinetic study of removal heavy metal from aqueous solution using the synthetic

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Study of the Hydrodynamic Transport of Nitrate as a Pollutant of Rivers Roxana Pérez García, Lorgio Félix Batard Martínez, Yanelis Estrada Hernández, and Jorge Alberto Cárdenas Pestana

Abstract The problems of pollution are more and more debated in the international environment due to the dysfunctions that can cause to the ecology and the biodiversity. This work aims to solve the case of non-stationary one-dimensional equation of dispersion-advection that includes terms of chemical-biological reactance and loads that may be spilled. For the attainment of such purpose, we offered the theoretical basis on which this study is based. It is the solution in quadrature for problems of the parabolic type that model the concentration of the nitrate like pollutant in a river, valuing a single source of pollutants in the right semi-axis. The analytic solution for the cases of Zero index cases of the coefficient of the Riemann problem is obtained using the results of (Estrada Hernández, Y. (2015) Solución de problemas para ecuaciones en derivadas parciales de tipo parabólico e hiperbólico con condiciones de contorno dadas por semiejes., Universidad Central “Marta Abreu” de Las Villas (UCLV) Cuba). Keywords Riemann problem · Analytic solution · Initial boundary value problem

1 Introduction At present days, nitrates have become one of the main sources of diffuse pollution due to the increase in nitrogen fertilizers and the man’s own action to throw waste into the rivers. Its excessive increase can be detrimental to both the river itself and the species that habit it as well as man if these waters are used in irrigation or communicate with drinking supply systems. Therefore, as a tool of control, mathematical modeling has been taking into account. This work aims to solve the case of non-stationary one-dimensional equation of dispersion-advection that includes terms of chemical-biological reactance and loads that may be spilled. For the achievement of such purpose, we offered the theoretical R. P. García (B) · L. F. B. Martínez · Y. E. Hernández · J. A. C. Pestana Universidad Central “Marta Abreu” de Las Villas, Camajuaní Road km. 5.5, Santa Clara, Villa Clara, Cuba e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Cardenas et al. (eds.), Proceedings of the 3rd International Conference on BioGeoSciences, Springer Proceedings in Earth and Environmental Sciences, https://doi.org/10.1007/978-3-030-88919-7_4

39

40

R. P. García et al.

basis on which this study is based. It is the solution in quadrature for parabolic type problems that predict the concentration of the nitrate as a pollutant in a river, taking into account a single source of pollutants in the right semi-axis. We use a model, proposed in Pérez Garcia (2017) and solve this model for general initial conditions by reducing the problem to a Riemann—Hilbert problem with zero index, having unique solution, and also more practical application (Estrada Hernández 2015). This Riemann problem have been used by the Group of Differential Equations of the Faculty of Mathematics, Physics and Computation, from the Universidad Central “Marta Abreu” de Las Villas, to solve open Partial Differential Equations problems by reducing it to boundary value problems of analytical function theory. For this it was used Fourier Transform, when the coefficient and the independent term of the Riemann boundary problem belong to the related class of functions of the Lp spaces and when the boundary conditions are given by semi axis (Cárdenas Pestana 2016).

2 Necessary Definitions It is used a new analytical technique for general initial conditions that usually does not have an analytical solution and require the use of numerical methods. The methods used here represent a contribution to the theory of Partial differential equations. To introduce this method, we are going to use some definitions given by Estrada Hernández (2015) that allow us to understand better the problem we try to solve here. That is why we want to define the Fourier Integral and Index concepts that specifically we are going to use here,

2.1 Fourier Integral Be f : R → C, ∀x ∈ R, if exists the integral 

+∞ −∞

f (τ )e−i xτ dτ

then it is called the Fourier Transform of the function f(x) and it is given by the expression 1 F(x) = V f (τ ) = √ 2π



+∞ −∞

f (τ )e−i xτ dτ.

The inverse transform of F(x) is defined in Kolmogorov (1975) by

Study of the Hydrodynamic Transport of Nitrate …

1 f (τ ) = V −1 F(x) = √ 2π

41



+∞

F(x)ei xτ d x

−∞

2.2 Index If m(τ ) is the boundary value of an analytical function at the superior (inferior) semi plane, with exception may be, of a finite number of poles in this semi plane, then the following equality comes true: I nd m(τ ) = N − P(I nd m(τ ) = P − N ) where I ndm(τ ) denotes the index of m(τ ). N and P represent the numbers of zeros and poles at the superior and inferior semi plane respectively. We consider each zero and pole so many times as their multiplicity order (Estrada Hernández 2015).

2.3 Class of Functions Be f a function f : R → K (K is R o C), it says that f belongs to a class of Hölder, if exist A and λ constants such that, A > 0 and λ ∈ (0, 1] for which it is fulfill that: 1. 2.

| f (x2 ) − f (x1 )| ≤ A|x2 − x1 |λ ∀x1 , x2 ∈ R; ∃N > 0 : si|x1 | > N , |x2 | > N  λ   | f (x2 ) − f (x1 )| ≤ A x12 − x11  ∀x1 , x2 ∈ R

We named A and λ, coefficient and index of Hölder respectively. The class of functions that satisfy the Hölder’s condition for some index λ is denoted by Hλ (R). We say that f : R → K is an element of L 2 (R) if +∞ | f (x)|2 d x < +∞. −∞

One of the interesting properties of the class L 2 (R), is that the product of an L 2 (R) function for a boundary function is also of L 2 (R), and it has an evident + demonstration. The space L + 2 (R) is the space of functions F of L 2 (R) which are analytically extended to the superior semi plane y > 0 and, before that, it has to fulfill: +∞   +  F (x + i y)2 d x < cte(same ∀y > 0) −∞

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− Of the same way, L − of L 2 (R) which are 2 (R) is the space of functions F analytically extendable to the inferior semi plane y < 0 and it has to fulfill:

+∞   −  F (x + i y)2 d x < cte(same ∀y < 0) −∞

The class of functions f ∈ L 2 (R) such that f ≡ 0 if x < 0(x > 0) it denotes by L 2+ (R)(L 2− (R)). Carry on with the analysis of this type of class of functions we can say that the following theorem come true (Zhen-Gang 2008): in order that the function f + (x) be an element of L 2+ (R) is necessary and sufficient that his transform F + (t) =  + − V f (x) be an element of L + 2 (R) too. In order that the function f (x)  be an − element of L 2− R) is necessary and sufficient that his transform F (t) = V f − (x) be an element of L − 2 (R) too. The class of functions L λ2 (R) it defines by L λ2 (R) = L 2 (R) ∩ Hλ (R), and the class of L 2 (R) functions that belongs  to one of the Hölder’s functions are denoted by the symbol {{0}}, that is, {{0}} = λ(0,1] Hλ (R) ∩ L 2 (R). The spaces {0}λ are those of the functions who have their transform in L λ2 (R), that is V { f (x)} is an element of L λ2 (R), if f ∈ {0}λ . The class of functions f ∈ {0}λ that fulfill f ≡ 0 if x < 0(x > 0) it denotes by λ− L λ+ 2 (R)(L 2 (R)). λ The class of functions F ± ∈ L 2 (R) who are analytically extendable to the superior (inferior) semi plane and satisfies: ⎛+∞ ⎞   +  − 2 2  F (x + i y) d x < M, i f y > 0⎝  F (x + i y) d x < M, i f y < 0⎠,

+∞ 

−∞

−∞

λ− where M is independent of y and it denotes by L λ+ 2 (R)(L 2 (R)). The class of functions f who does not annul over R such that f (±∞) = 1 and λ− λ+ λ− ( f − 1) is an element of L λ+ 2 (R)(L 2 (R)) it denotes by L 2 (R + 1)(L 2 (R + 1)). From the previous theorem and definitions is proved the following theorem: A λ− necessary and sufficient condition so that the function f belongs to L λ+ 2 (R)(L 2 (R)) λ+ λ− is that his Fourier transform F belongs to L 2 (R)(L 2 (R)) (Estrada Hernández 2015).

  2.4 Riemann Problem in the Class of Functions L λ± R 2 The Riemann boundary problem consists to find the F + (x) and F − (x) analytically extendable functions to the superior and inferior semi plane respectively, who satisfy

Study of the Hydrodynamic Transport of Nitrate …

43

the condition: F + (x) = D(x)F − (x) + H (x) over the real axis. Where D(x) and H (x) are the coefficient and the independent term of the Riemann problem  problem, respectively. To obtain the solution of the Riemann  ± λ R (L (R)) class, it requires that D(x) belongs to the L R + 1 class in the L λ± 2 2 2 and the independent term belongs to L λ2 (R)(L 2 (R)); been L λ2 (R + 1) the class of functions f who satisfies the following conditions: 1. 2. 3.

f does not have zeros, either poles over R. lim f (x) = 1

x→+∞

( f − 1) ∈ L λ2 (R)

which represent class already defined previously in Pérez Garcia (2017). As we intend to work with the zero index cases, we used four theorems defined (Estrada Hernández 2015) which guarantee the specific use of this cases and also, involved the coefficient and the independent term of the Riemann problem. Theorem 1 The numerator (denominator) of D(x) does not have zeros (nor poles) for x ∈ R, if and only if one of the following conditions is fulfilled: 1. 2.

β00 β10 = 0 (γ00 γ10 = 0) β10 = 0, b2 β00 β01 > 0 (γ10 = 0, b2 γ00 γ01 > 0) From the following limit lim D(x) =

|x|→+∞

lim

β00 − i xβ10 +

|x|→+∞ γ

00

− i xγ10 +

x2 β b2 01 x2 γ b2 01

It is possible to obtain the following theorem. Theorem 2 The limit of the second member of the last expression exists and it is different to zero if and only if, one of the following conditions is fulfilled: β01 . γ01

(a)

β01 γ01 = 0, in this case, the indicated limit is l =

(b) (c)

β01 = γ01 = 0, β10 γ10 = 0, in this case, the indicated limit is l = βγ1010 β01 = γ01 = β10 = γ10 = 0, β00 γ00 = 0, in this case, the indicated limit is l = βγ0000

Where: β01 , β10 , γ01 , γ10 , β00 , γ00 , are numerical values (constants) used at the initial conditions. Theorem 3 If it is fulfilled, at the same time, one of the conditions of the first theorem and one of the conditions of the second theorem, then, (D(x) − 1) ∈ L λ2x (R).

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Theorem 4 If it is fulfilled, at the same time, one of the conditions of the first  theorem,

 and one of the conditions of the second theorem, and before that V x, 0+ , x V x, 0+ and Vt (x, 0+ ) belong to L 2x (R), then, the independent term H(x) of the Riemann problem belongs to L 2x (R). The demonstration and better treatment of these theorems appear in Estrada Hernández (2015).

3 Results 3.1 General One-Dimensional Equation According to Zhen-Gang (2008) based on the law of conservation of the mass, the change of concentration of a reactant can be calculated using an equation of balance of mass. Its one-dimensional form responds to the equation:   ∂C(x, t) ∂ ∂C(x, t) ∂C(x, t) = −U + D(x) +S+R+Q ∂t ∂x ∂x ∂x

(1)

where: C t x U D S R Q

is the concentration of pollutant is the time is the distance is the advection speed in the x-axis direction is the dispersion coefficient sources and sinks of the water body due to settlement and resuspension is the kinetic reaction terms of chemical and biological processes are the external loads of the aquatic system

In statistical physics D(x) is defined as the mean square deviation of x per unit of time, where is fulfilled that ( x)2 . t→+∞ t

D(x) = lim

3.2 Fluids Characteristics To apply the theory proposed by Estrada Hernández (2015) it is considered to put the pollutant source or sources in the right semi axis and in the left semi axis there

Study of the Hydrodynamic Transport of Nitrate …

45

will be no value at all, see Fig. 1. This mean that the mathematical representation at any equation of the pollutant source is going to be zero on the left semi axis. This consideration does not refute what really happens when we have to model the river water quality in which exists a spill of any substance. This study must be in agreement with the conservation law of mass, dividing into segments, guarantee that there will be one pollutant point source in each study length. Then, when is said that there is not any pollutant source on the left semi axis, it is not ignored the physical reality. It is assumed that the pollutant concentration which spills on the river from a previous source is already included inside of the concentration determined at the observation or measurement point after their position. The analysis is directed, in general, to a steady regimen, that is not rotational, nor compressible, and is a slightly viscous flux, in a way the pollutants it could transport with laminar flux. The model is more applicable to rivers which are sinuous, constituting a good approximation to start to analyze this problem and the difficulties of the solution. Any other analysis for any other surface waterbody must be quite similar to what we did before. We always must differentiate the characteristics of each one at the initial conditions and attending to the physical, biological, chemical and morphological processes. Conceiving that the medium is homogeneous so neither D nor U depend on x, where in a first order kinetics. R(x, t) = −kC(x, t) We assume that only phytoplankton will be taken as a nitrate consumer, so according to Michaelis-Menten equations k(x, t) =

Fig. 1 Boundary conditions

Vm ax ´ C(x, t) k 21 + C(x, t)

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where k 21 it’s called Michaelis-Menten constant or saturation constant. For a first approximation, k 21 = C(x, t) is taken so that the speed of the contaminant processing would be of the form: k(x, t) =

Vm ax ´ 2

Then: R(x, t) = −

Vm ax ´ C(x, t) 2

(2)

Nitrate interaction with the bottom is null, so S=0

(3)

Then substituting (2) and (3) and Q = constant in (1), dividing the expression by the factor D and passing everything to the right member we have. ∂ 2 C(x, t) U ∂C(x, t) 1 ∂C(x, t) Vm ax Q ´ − − − C(x, t) + =0 ∂x2 D ∂x D ∂t 2D D

(4)

The Eq. (4) is turned into the canonical form using the method explain in Tijonov and Samarsky (1972) and we get: vx x (x, t) −

Vm ax 1 U U2 ´ vt (x, t) + Me−( 2D x−( 4D + 2 )t) = 0 D

where U2

C(x, t) = e( 2D x−( 4D + U

Vm ax ´ 2

)t)

· v(x, t)

3.3 Boundary Conditions The analysis focuses on a stable, irrotational, incomprehensible and slightly viscous regime flow, so that it can carry contaminants whose current is laminar. The boundary conditions that are worked are the following 





β00 C x, 0+ + β10 C x x, 0+ + β01 Ct x, 0+ = 0, −a < x < 0 





γ00 C x, 0+ + γ10 C x x, 0+ + γ01 Ct x, 0+ = g12 (x), x > 0

Study of the Hydrodynamic Transport of Nitrate …

47

where βi j yγi j ; i = 0, 1; j = 0, 1 are real numbers and f (x, t) ∈ L 2x (R), g11 (x) ∈ L 2x (−∞, 0) and g12 (x) ∈ L 2x (0, +∞), 

C x, 0+  C x x, 0+

 Ct x, 0+

concentration of contaminant for t = 0. variation instant of the concentration of contaminant in the direction of the x-axis the initial time. how quickly the pollutant concentration changes at the initial time.

Now, we proceed to write the initial value problem of parabolic type with general initial conditions divided by semi axis as follows: 

1 − vx x (x, t) − vt (x, t) + Me D 

C(x, t) = e

U 2D

U 2D

 2 V ´ t x− U4D + m2ax

2

x−( U4D +

Vm ax ´ 2

)t

= 0 − a < x < +∞



· v(x, t)







β00 v x, 0+ + β10 vx x, 0+ + β01 vt x, 0+ = 0, −a < x < +∞ 





γ00 v x, 0+ + γ10 vx x, 0+ + γ01 vt x, 0+ = g12 (x), x > 0 where: g12 (x) =

g12 (x) U

e 2D x  2  U Vm ax U ´ + β01 + , β00 = β00 + β10 2D 4D 2  2  U Vm ax U ´ + γ01 + , γ00 = γ00 + γ10 2D 4D 2   At the region D = (x, y) ∈ R2 : 0 < t < +∞ and βi j y γi j ; i = 0, 1; j = 0, 1 are real numbers. U U That express the weight of the function, where Me− 2D x ∈ f or x > 0 as Me− 2D x it U is bounded for this region with constantM. It also has to be fulfilled that g12 (x)e− 2D x and it is fulfilled because we are assuming that g12 (x) L 2x and one function of L 2x multiplied by one of L 2x belongs to L 2x this is necessary to apply Fourier transform and Inverse Fourier Transform.

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3.4 Zero Index Cases When the Riemann problem is already obtained and the solubility conditions have been analyzed, it is possible to determine the zero index cases. (a) (b) (c) (d) (e) (f) (g) (h)

β10 β10 β01 β01 β10 β10 β10 β01

= 0, D1 β00 β01 > 0, γ 10 = 0, D1 γ00 γ01 > 0 = 0, D1 β00 β01 < 0, γ 10 = 0, D1 γ00 γ01 < 0, β10 β01 γ10 γ01 < 0 = γ01 = 0, β00 β10 < 0, γ00 γ10 < 0 = γ01 = 0, β00 β10 > 0, γ00 γ10 > 0 = 0, D1 β00 β01 > 0, γ 10 = 0, D1 γ00 γ01 > 0 = 0, D1 β00 β01 > 0, γ 10 = 0, D1 γ00 γ01 > 0 = γ10 = 0, D1 β00 β01 > 0, D1 γ00 γ01 > 0 = β10 = γ01 = γ10 = 0, β00 γ00 = 0

These zero index cases guarantee the uniqueness of the solution, and each case is representative of some practical study cases. It can be analyzed, from the point of view of physical conditions, as variations with time and space; and the pollutant in the river under study.

4 Solution to the Model To solve the model we used the specific case a) to give an example of the solution method we propose here. In this case the solution of the problem takes the form: ⎤ ⎡ +∞  +∞ F + (y) + H1 (y) −(y 2 Dt+i x y) 1 ⎣ e dy + V (y, t)e−i x yt dy ⎦ v(x, t) = √ (y − ai)(y − bi) 2π −a

−a

where F + (y) =

y − bi 1 √ y − di 2π

+∞ h 3 (τ )ei yτ dτ, 0

with h 3 = V −1 [H3 ], H3 (y) = and:

β01 (y − ai) y − di H2 (y) − H1 (y), H1 (y) = −(β00 − i xβ10 )V (x, o) γ01 (y − ci) y − bi

Study of the Hydrodynamic Transport of Nitrate …

49

 β01 d V

+ (x, 0+ ) − H1 (x) = G − 11 (x) − (β00 − i xβ10 )V x, 0 dy  γ01 d V

+ (x, 0+ ) H2 (x) = G + − 12 (x) − (γ00 − i xγ10 )V x, 0 dy where G − 11 (y) = 0, as   −1 − G− g11 (x) = 11 (y) = V =

√1 2π

g11 (x) = 0

√1 2π

+∞ 

+∞  −∞

− g11 (x)ei xt dt

0ei xt dt = 0

−∞ − ⇒ g11 (x)

= 0; ∀x ∈ R

well a, b, c, d, are the roots of the polynomials D(x), a, b numerator roots and c, d denominator roots, where it is fulfilled that a > 0, b < 0, c > 0yd < 0. How U2

C(x, t) = e( 2D x−( 4D + U

Vm ax ´ 2

)t)

.v(x, t)

we have 

C(x, t) = e

U 2D

 2 V ´ x− U4D + m2ax t

+

√1 2π +∞ 

−a

 +∞  F + (y)+H1 (y) −a

e−( y 

(y−ai)(y−bi)

2

Dt+i x y )

dy

V (y, t)e−i x yt dy

Which is the concentration of the pollutant at any moment of time. case (b) we have two possibilities. case (b1 ) all roots of P1 (x) and P2 (x) are in the upper semiplane (this happens when D1 > 0, and, β01 β10 < 0 y D1 γ 01 γ10 < 0). In this case the solution of the problem takes the form: ⎤ ⎡ +∞  +∞ F + (y) + H1 (y) −(y 2 Dt+i x y) 1 ⎣ e dy + V (y, t)e−i x yt dy ⎦ v(x, t) = √ (y − ai)(y − bi) 2π −a

−a

where 1 F (y) = √ 2π +

+∞ h 4 (τ )ei yτ dτ, with h 4 = V −1 [H4 ], 0

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H4 (y) =

β01 (y − ai)(y − bi) (y − ai)(y − di) H2 (y) − H1 (y) γ01 (y − ci)(y − di) (y − ci)(y − bi)

with  a, b,  c y d 2complex  numbers  with 2imaginary part greater than zero if 4 D1 β 01 β00  > D12 β10 and 4 D1 γ 01 γ00  > D12 γ10 , or imaginary number on the positive     2 2 y 4 D1 γ 01 γ00  < D12 γ10 . (can also get a mixed imaginary axis 4 D1 β 01 β00  < D12 β10 case). Where 

C(x, t) = e

U 2D

 2 V ´ t x− U4D + m2ax

+

√1 2π +∞ 

−a

 +∞  F + (y)+H1 (y) −a

e−( y 

2

(y−ai)(y−bi)

Dt+i x y )

dy

V (y, t)e−i x yt dy

case (b2 ) all roots of P1 (x) and P2 (x) are in the lower semiplane (this happens when 1 > 0, and, β01 β10 > 0 y γ01 γ10 > 0. D In this case the solution of the problem takes the form: 

C(x, t) = e

U 2D

 2 V ´ t x− U4D + m2ax

+

√1 2π +∞ 

−a

 +∞  F + (y)+H1 (y) −a

e−( y 

(y−ai)(y−bi)

2

Dt+i x y )

dy

V (y, t)e−i x yt dy

where F + (y) =

(y − ai)(y − bi) 1 √ (y − ci)(y − di) 2π



+∞

h 5 (τ )ei yτ dτ ; h 5 = V −1 [H5 ]

0

and H4 (y) =

β01 (y − ci)(y − di) H1 (y), H2 (y) − γ01 (y − ai)(y − bi)

but numbers with imaginary part less than zero if    now a, b, c y d2 are complex 2 and 4 D1 γ 01 γ00  > D12 γ10 , or imaginary number on the negative 4 D1 β 01 β00  > D12 β10 1   1 1 2 2    . (can also get a mixed imaginary axis 4 D β 01 β00 < D2 β10 and 4 D γ 01 γ00  < D12 γ10 case). c-h case solutions can be obtained by performing the same analysis.

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5 Conclusions It was found the analytical solution of the parabolic problem that model the nitrate concentration as a pollutant in a river with general initial conditions given by semi axis, reducing the model to a Riemann problem of two specific zero index cases. These results constitute an important contribution to the theory of initial boundary value problems of partial differential equations, since there are not analytical techniques that face this kind of problems, where the initial conditions differ in different parts of the axis. This analytical method should allow professionals of different research areas to work with relative easiness when they face this kind of problems in the field. It will be interesting, as part of futures works, to implement the numerical simulation of the algorithm used here to solve this types of problems we often find around us and have to be resolved.

References Cárdenas Pestana, J.A., 2016. Propuesta y análisis de un modelo matemático para la determinación de la calidad del agua de aguas superficiales (Bachelor Thesis, Universidad Central” Marta Abreu” de Las Villas). Estrada Hernández, Y. (2015) Solución de problemas para ecuaciones en derivadas parciales de tipo parabólico e hiperbólico con condiciones de contorno dadas por semiejes., Universidad Central “Marta Abreu” de Las Villas (UCLV) Cuba. Kolmogorov, A.N.y.F., S. V (1975) Elementos de la teoría de funciones y del análisis funcional. Pérez Garcia, R. (2017). Estudio del transporte hidrodinámico del nitrato como contaminante de los ríos. tesis presentada en opción al título de Licenciatura en matemáticas. Tutores: Batard Martínez, Lorgio Félix; Estrada Herández, Yanelis.. Tijonov, A.N. and Samarsky, A.A. (eds) (1972) Equations of Mathematical Physics, MIR, Moscu. Zhen-Gang, J. (2008). Hydrodynamics and water quality. Modeling rivers, lakes and estuaries. New Jersey: Jhon Wiley and Sons.

Diagnosis of the Land Cover/Use Effect on Nutrient Discharge from Three Biobio River Sub-Basins Rebeca Martínez-Retureta, Mauricio Aguayo, Lien Rodríguez-López, Iongel Duran-Llacer, and Norberto José Abreu

Abstract Water is an essential resource for developing several human activities. The increase in nutrients transport to surface waters is one of the leading factors promoting water quality loss; mainly caused due to land-use changes. In this study, the contribution of nitrogen and total phosphorus to the surface water bodies in three sub-basins of the Biobio River dominated by native forests, forestry, and agriculture are compared. Nutrient sampling points were taken at each sub-basin during the summer of 2013 and 2014; obtained results were compared using the MannWhitney statistical analysis. Noticeable differences in total nitrogen concentration were observed between the sub-basins with agricultural and forestry dominance in summer 2013. Additionally, significant differences in the total nitrogen concentration are reported in the sub-basin dominated by agricultural coverage when compared to those dominated by forest lands and native forests in 2014 summer. No significant differences were found between the sub-basins of the Biobio River for the study period in the total phosphorus records. The study depicts that different soil covers

R. Martínez-Retureta (B) · M. Aguayo (B) · I. Duran-Llacer Environmental Sciences Center EULA-Chile, University of Concepcion, 4070386 Concepcion, Chile e-mail: [email protected] M. Aguayo e-mail: [email protected] M. Aguayo Territorial Planning Department, University of Concepcion, 4070386 Concepcion, Chile L. Rodríguez-López Facultad de Ingeniería y Tecnología, Universidad San Sebastián, Lientur 1457, Concepción, Chile N. J. Abreu (B) Center of Waste Management and Bioenergy, Scientific and Technological Bioresources Nucleus, Universidad de La Frontera, Casilla 54-D, 4780000 Temuco, Chile e-mail: [email protected] Department of Chemical Engineering, Universidad de La Frontera, Casilla 54-D, 4780000 Temuco, Chile © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Cardenas et al. (eds.), Proceedings of the 3rd International Conference on BioGeoSciences, Springer Proceedings in Earth and Environmental Sciences, https://doi.org/10.1007/978-3-030-88919-7_5

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play an important role in the total nitrogen retention, however, in the case of total phosphorus, results suggest that concentration is given by geological factors. Keywords Land use change · Nutrient transport · Total nitrogen · Total phosporous

1 Introduction In recent decades, water quality degradation has become a global concern (Hashemi et al. 2016). Water bodies hosting important ecosystems are subject to anthropogenic and climatic stressors that often act in synergy (Diamantini et al. 2018). Around the world, inefficient management policies of water resources have been conducted characterized by massive deforestation and by the contamination of freshwater bodies with several sources such as wastewater, industrial discharges, and agricultural fertilizers in the runoff, among others. In basins with prevailing wood soil cover, nutrient transport depends on its structure, density and some other factors including the rainfall amount, surface runoff, geological substrate, type of vegetation and the proximity to anthropic sources (Likens 1995; Souza et al. 2013). The forest coverage percentage in a basin has a positive correlation with the concentration of nutrients in the runoff since they are effectively retained. Huang and Klemas (2012) suggest that forests and grasslands can be considered as pollutants sinks. The nutrients transport in the water flow through forests is considered essential for the maintenance of natural ecosystems (Likens 1995). Such phenomena play a key role in maintaining the balance of nutrients draining to water bodies (Oyarzún et al. 1997). Forests in Chile cover approximately 24% of the total area (FAO 2014). A large forest plantation program has been created since 1974, through Decree-Law 701, subsidized wood plantations to promote economic growth and face the severe erosion resulting from land degradation due to agriculture and excessive grazing since 1990, according to FAO (2014), more than 1 million plantation hectares have been established. According to several researches, deforestation, forest degradation and native forest substitution by exotic plantations remain a constant trend in Chile (Aguayo et al. 2009; Altamirano and Lara 2010; Schulz et al. 2010; Nahuelhual et al. 2012; Miranda et al. 2015; Zamorano-Elgueta et al. 2015; Martínez-Retureta et al. 2020). Results obtained by Aguayo et al. (2009), in a study conducted from 1979 to 2000 in the south-central zone of Chile, a loss of 28% of the native forests took place; 71% of the native forest lost area was destined for fast-growing forest plantations. Different studies show that forestry operations have had consequences on hydrological, erosive, nutrient processes and sediment transport (Oyarzún et al. 1997; Aguayo et al. 2009, 2016; Martínez-Retureta et al. 2020; Iroumé et al. 2006, 2010; Huber et al. 2008; Lara et al. 2009; Iroumé and Hardin 2013). According to Huber et al. (2008) and Lara et al. (2009), water consumption is higher in forest plantations, compared to the consumption of native bushes and forests. On the other hand,

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55

Iroumé et al. (2006, 2010) reported a significant increase in water production after the final harvest. The studies conducted by Pizarro et al. (Martínez-Retureta et al. 2020; Iroumé and Palacios 2013; Aguayo et al. 2016; Pizarro et al. 2006; Little et al. 2009), show decreasing trends in flows, due to the increase in forest plantation coverage. However, most of these studies have focused more on water quantity than quality. There is a lack of investigations concerning the effect of replacing native forests with forest plantations on water quality. However, watersheds covered by forest plantations could have different concentrations of nitrogen and phosphorus, due to higher water consumption and less dilution of these nutrients, compared to basins dominated by native forests. Such behavior suggests that there are also temporal differences in the retention of such nutrients when fast-growing species are cut. In this study, the contribution of nitrogen and total phosphorus to surface water bodies during 2013 and 2014 in three sub-basins of the Biobio River, dominated by forestry, agriculture, and native forest activity is studied.

2 Materials and Methods 2.1

Description of the Study Area

In this study, three areas defined by hydrographic sub-basins were defined: a subbasin dominated by agricultural land, another sub-basin dominated by forest plantations and the third one by native forests. The sub-basins take part in the Biobio River basin, located between 37° 73  S and 71° 95 W, covering an area of 4,340 km2 . The study basins have a rainfall regime, with a warm temperate climate and elevations that fluctuate from 117 to 1793 m above sea level (Fig. 1).

2.2 Meteorological Data Most influential meteorological stations within sub-basins were determined using the Thiessen polygons method. Thus, the sub-basin dominated by an agricultural use belongs to the stations of (i) Ercilla (Vida Nueva), (ii) Encimar Malleco, and (iii) Pilguen. Meanwhile, the weather stations of Encimar Malleco and Laguna Malleco were used for the sub-basin dominated by forest cover. Finally, for the sub-basin dominated by native forest cover, Laguna Malleco meteorological station was the most influential one. From each weather station, rainfall data for the summer from 2013 to 2014 was selected, according to the database provided by the General Directorate of Water (DGA) of the Chilean government´s Public Works Ministry. Summer rainfall values in the agricultural sub-basin range from 97.5 to 180 mm, with the highest rainfall in 2014. The average value of rainfall within the sub-basin

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Fig. 1 Physical-geographical location of sub-basins dominated by agricultural land, forest plantations, and native forests

during this period was 138.8 mm. Meanwhile, for the sub-basin dominated by a native forest cover, values range from 303.3 to 571 mm. On the other hand, in the sub-basin covered by forest plantations, rainfall fluctuates between 237.15 mm and 461.4 mm for 2013 and 2014, respectively. Higher precipitation values are observed during the 2014 period if compared to 2013 (Fig. 2).

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700 600 500 400 300 200 100 0

Native Forest

Plantation

Agriculture

Fig. 2 Average monthly rainfall distribution for 2013 and 2014 within the native forests, plantations, and agriculture sub-basins

2.3 Land Cover/Use For the land cover/use analysis, studies carried out by the National Forestry Corporation (CONAF) for the Biobio Region (2008) and the Araucanía Region (2014) were considered. It was determined that the first sub-basin was dominated by agriculture with 22,032 ha (52%), followed by forest plantations with 17,512 ha (47%) and native forest with 2,916 ha (7%). The second sub-basin is dominated by forest plantations with 10,744 ha (83.6%), followed by native forests with 1,440 ha (11.2%) and with 596.6 ha (4.6%) of agricultural cover/use. Meanwhile, the third sub-basin is occupied by 15,675 ha (67%) of native forest and, to a lesser extent, land cover/uses such as thickets with 4,853 ha (20.8%), forest plantations with 1,703 ha (7.3%) and agriculture with 913.3 ha (3.9%) (Fig. 3).

2.4 Soil Types The analysis of the different soil types was carried out using the Geographic Information System, ArcGIS version 10.1. Data from the agrological study of the VIII and IX Chilean regions for the years 1999 and 2002 (CIREN 1999; EstudioAgrológico and Región 2002) was used. In the sub-basin dominated by native forest cover, three different types of soil prevail: Misceláneo Quebrada (MQ) that occupies 9.3% of the total sub-basin area, Terreno Rocoso (R) soils with 35.3% and Asociación Caburga, francolimosa (CBG) with 31.6% of the total area. The sub-basin dominated by a forest cover, presents five different types of soils, with different variations. Predominant soils correspond to Mayulermo, francolimosa (MYO) series, whose most prominent variations are MYO-2 with 51.5% of the area. This soil type has a silty, deep, slightly wavy loam surface texture with 2–5% slope and well-drained. The MYO-10 stands out with a 25.7%, which corresponds to the phase of silty loam, deep, moderately wavy surface texture with an 8–15% slope and well-drained. Meanwhile, in the sub-basin

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Fig. 3 Land cover/uses in the native forests, plantations, and agriculture sub-basins

dominated by agricultural land use, the types of soils belonging to the MYO series stand out, occupying an area of 58.7% and the Collipulli, francoarcillolimosa (CPL) series, with an area of 28.9%, with different variations.

2.5 Monitoring Point Selection Six monitoring points were taken within the study watersheds, except for the subbasin dominated by agricultural land cover/use, where seven sampling points were determined. The selection considered representative places distributed homogeneously throughout the sub-basins, including the upper, middle, and lower parts within the study area. The points´ location was stored in a Global Positioning System (GPS) (Fig. 4).

2.6 Sampling Procedure A water extraction method was applied using a plastic bucket for sampling. Before sample collection, the bucket was washed three times with the river water. Physicalchemical properties of the sample were measured in situ. In this way pH, conductivity, and dissolved oxygen (DO) were measured using a pH meter (Hanna, model: HI 9126), a conductivity meter (Hanna, model: HI 9835) and a dissolved oxygen detector

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Fig. 4 Influence areas of the sampling stations

(Hanna, model: HI 9146) respectively. All the equipment was calibrated previously and the respective sets were inserted inside the bucket, verifying they get correctly covered with water.

2.7 Laboratory Analysis Once collected, samples were stored and sent to the EULA-Chile Environmental Science Center, where they were analyzed using the 4500 PB Standard Methods 22th Edition analytical method for total phosphorus measurement and the 4500–NC Standard Methods 22th Edition method for total nitrogen.

2.8 Statistical Methods Data distribution was determined using the Statgraphics Centurion XVI software. Afterward, Mann-Whitney statistical analysis was applied for a significance level of 0.5, using the SPSS Statistics 17.0 software, to prove the existence of significant differences.

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3 Results 3.1 Analysis of Physical-Chemical Parameters The minimum temperature was recorded in the sub-basin dominated by forest cover with 11.5 °C, while the maximum was found in the sub-basin dominated by an agricultural use with 17.7 °C. Average temperatures recorded were 14.62 °C, 13.77 °C, and 15.97 °C for sub-basins dominated by land with native forest, forest, and agricultural plantations respectively with medians of 14.9, 13.95 and 15.8 °C, respectively. According to conductivity, the higher value was recorded in the native forest covered sub-basin (52.8 µs). Meanwhile, the minimum value was recorded in the subbasin dominated by forest plantations cover/use (22.64 µs). Averages were 39.18, 26.1 and 33.88 µs in the sub-basins dominated by native, forestry and agricultural forest land cover/uses with medians of 38.1, 25.45 and 32.0 µs, respectively. The maximum pH value (8.2) was recorded in the sub-basin dominated by a native forest cover. On the other hand, the lesser pH value (6.32) was obtained in the sub-basin dominated by agricultural land use. The average was 7.54, 7.31 and 6.88 for sub-basins dominated by native, forest and agricultural forests cover/uses, with medians of 7.92, 7.26 and 6.99, respectively. Regarding D.O. values, maximum (8.76 ppm) and minimum concentration value (2.24 ppm) were recorded in the sub-basin dominated by agricultural land use. Averages were 7.11, 7.76 and 5.19 ppm for the watersheds dominated by native forest, forest, and agricultural land cover/uses, with medians of 7.22, 7.67 and 4.09 ppm, respectively.

3.2 Total Nitrogen Figure 5a depicts the behavior of total nitrogen values measured in the study watersheds. Results indicate that, in the summer of 2013, the maximum concentration of total nitrogen was obtained in the sub-basin dominated by agricultural use (1.3 mg/L), while the minimum concentration was recorded in the sub-basin dominated by native forests (0.04 mg/L). On the other hand, in the total nitrogen values measured during summer 2014 (Fig. 5b), the maximum concentration (0.77 mg/L) was obtained in the sub-basin dominated by agricultural use, while the minimum concentration (0.07 mg/L) of total nitrogen was obtained in the native coverage watershed. According to the results displayed in Fig. 5a, regarding the total nitrogen concentrations measured during 2013 summer, the highest average and median values (0.41 mg/L and 0.17 mg/L respectively) occurred within the sub-basin dominated by agricultural land use, while lowest average and median values (0.10 mg/L

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Fig. 5 Total nitrogen concentration behavior in the study watersheds during summer of 2013 (a) and 2014 (b)

and 0.095 mg/L respectively) were observed in the forest plantations dominated sub-basin. Likewise, for summer 2014, Fig. 5b shows that the highest average (0.55 mg/L) and median (0.48 mg/L) total nitrogen concentration values occurred within the watershed dominated by agricultural land use. Meanwhile, the lowest average (0.20 mg/L) was observed in the sub-basin dominated by a forest cover and the lowest median 0.18 (mg/L) in the sub-basin dominated by native cover/use. Mann-Whitney´s statistical analysis allowed determining that the total nitrogen concentration during summer 2013 in the agricultural watershed is significantly higher than the concentrations obtained for the sub-basin with forest dominance, according to the p-value of 0.026 (p < 0.05). However, the concentration of total nitrogen in the agricultural watershed was not significantly higher than the concentration registered in the sub-basin dominated by a native forest cover, since a p-value of 0.233 was obtained (p > 0.05).

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3.3 Total Phosphorus As can be seen in Fig. 6a, the maximum concentration of total phosphorus (0.06 mg/L) was obtained in the sub-basin dominated by the agricultural land cover/use for summer 2013. On the other hand, the minimum concentration ( 0.05). Finally, there was also no significant difference in the concentration of total phosphorus between the sub-basins dominated by an agricultural and forest land cover, whose test showed a p-value of 0.597 (p > 0.05). Significant differences were not found either by summer 2014 between both sub-basins; the p-value was 0.550 (p > 0.05).

4 Discussions 4.1 Total Nitrogen It is widely known that, generally, lands covered by agricultural activities are much more susceptible to erosion due to runoff generated by rainfall than soils dominated by forest and native cover/uses, given the scarce vegetation. Tong et al. (Tong and Chen 2002) found that total nitrogen concentration has a strong correlation with the coverage with agricultural land and not so much with the use of forest lands. This study also showed that farmland had much more nitrogen, especially after rainfall. On the other hand, Liú et al. (Liu and Jun 2013), modeled the behavior of the export coefficients for different land uses, concluding that soils with urban and agricultural covered soils have a greater concentration of total nitrogen and that wooded areas depicted fewer concentration levels. The sub-basin dominated by forest plantations, despite being three times smaller, requires a greater amount of water for tree growth and intercepts much more water than the sub-basin dominated by agricultural use. Similarly, it must be considered that the sub-basin dominated by an agricultural use has a greater volume available for the dilution of pollutants due to the difference in coverage; nevertheless, in this sub-basin, a greater concentration of total nitrogen was found. It must be also analyzed the different soil types present in the study area. In the sub-basin dominated by agricultural use, there is an important area dominated by soils

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of the CPL series highly susceptible to erosion, which have a smoothly undulating to broken topography favoring nutrient exports. However, this sub-basin is dominated by soils of the MYO series, whose slopes fluctuate between 2° and 15° being almost flat soils. Similarly, the sub-basin dominated by forest plantations is dominated only by one type of soil, which belongs to the MYO series. Although both sub-basins have similar soil types, from undulating to almost flat terrain, a significant difference in the concentration of total nitrogen was found. Such foundings indicate that the different soil cover/uses play an important role in total nitrogen retention. In this way, nitrogen is exported mainly by surface runoff, diluted by the available flows, and their transport is influenced by the soil type characteristics. On the other hand, native forests are closer to the pre-mountain range, with soil types classified as R, CBG, and MQ. Such soils present high slopes, mainly hills susceptible to erosion and consequently subject to greater nutrient transport. However, these soils are covered by native forests generating the retention of the surface runoff caused by rainfall. Therefore, the native forest cover/use plays a fundamental role in poor nutrients and organic matter transport. On the other hand, in the sub-basin dominated by agricultural activity, there is a great dynamic, between intensive agriculture, fertilizer supply, livestock activity, and household activities, which are mainly close to water bodies playing an important role in the supply of nitrogen to the channels, compared to the other two sub-basins. Although the existence of significant differences in the concentration of total nitrogen could only be demonstrated between these two sub-basins during 2014, the general trend of the data indicates that, in the agricultural sub-basin, the concentration of total nitrogen was higher than that dominated by native forests. According to Pérez et al. (Pérez et al. 1998), in the forests belonging to the coastal mountain range of southern Chile, nitrogen is strongly retained within the forest, immobilized either in the plant or in the microbial biomass. The aforementioned would explain the behavior observed in the present study where the sub-basin with native dominance possesses a lower concentration of total nitrogen in the water, compared with the sub-basin of agricultural dominance, thus playing an important role the different land cover/uses. Similar results were obtained by Oyarzún et al. (Oyarzún and Huber 2003) in a comparative study between micro-basins with land cover/use dominated by agriculture and native forest. Nitrate is the dominant form of nitrogen found in surface waters due to its high water solubility, it is considered as the main pollutant of aquatic ecosystems caused by agricultural activities, as a result of the intensive use of chemical fertilizers and the increasing application of animal manure (Herpe et al. 2000). Therefore, fertilization also plays an important role in the measured nitrogen concentration when assessing nutrient transport. Regarding the concentration of total nitrogen between the sub-basins dominated by a native and forest cover, no significant differences were found in the analyzed period. Such results disagree with the study conducted by Oyarzun et al. (2007). According to their results, the average total nitrogen retention is significantly higher in catchment areas covered by native forest than in the other basins analyzed covered by exotic tree plantations. Oyarzun et al. (2007) suggest that the lower nitrogen

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retention in basins with exotic plantations could be explained by the predominance of a single tree species, the minimum canopy coverage if compared to native forests that have multiple strata and high coverage, and the diversity of lowland species.

4.2 Total Phosphorus Unlike nitrogen, phosphorus moves to surface waters primarily through its association with organic matter. Therefore, surface phosphorus flows are generally mediated by erosive processes. In contrast to the results obtained, Tong et al. (2002) found that total phosphorus had a strong correlation with the use of agricultural land, but not with the use of forest land. The author further emphasizes that phosphorus production in agricultural land was 20 times higher than in forested basins; obtaining significant differences between the studied sub-basins. Agricultural soils are generally prone to pollutants transport, also affected by the rains through the generated runoff. According to Sharpley et al. (1992), phosphorus is lost mainly from particulate soils and in dissolved forms through runoff and erosion. Water erosion is proportional to the intensity of rainfall (Bonilla and Vidal 2011; Bonilla and Johnson 2012); such behavior causes the phosphorus loss in southern Chile due to the rainfall predominance in the area. On the other hand, Borie and Rubio (2003) found lower amounts of phosphorus in the forest ecosystems of southern Chile than those covered by agricultural land. According to Likens and Bormann (1995), transport of nutrients such as total phosphorus, from unaltered forest ecosystems, generally depends not only on the soil cover/use but also on the magnitude of rainfall, the surface runoff, geological substrate, and the proximity to anthropic sources. Undoubtedly, rainfall plays an important role, since phosphorus adheres strongly to the soil through chemical reactions, which makes its transport more difficult (Ávila et al. 2007). Additionally, Cárdenas (2007) compared the nutrients export in basins dominated by native forest versus exotic plantations. It was observed that total phosphorus retention was greater in the sub-basin dominated by a native cover. Similarly, Lovett et al. (2000) found that the average phosphorus retention was significantly different between native forest basins and forest plantations. On the contrary, no relationship was found between native forest cover and phosphorus retention. However, Oyarzún et al. (2007), analyzed six basins with different percentages of native forest cover and plantations; any significant differences in the total phosphorus concentration in running water was found. In the aforementioned study, maximum concentrations were found in the forested sub-basins, suggesting that soil erosion due to logging practices could affect contributing to the export of phosphorus. The results obtained in the present investigation could be attributed to the fact that land cover/use does not play a remarkable role in phosphorus retention. This behavior is supposed to be related to the fact that the three sub-basins under study have similar soil types originated by volcanic ash. Therefore, the phosphorus supply

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in this study would not be determined by the different types of land cover/uses but rather by geology.

5 Conclusions According to the results of this study, land cover/use is an important factor to analyze in terms of nitrogen supply even when the contribution of total nitrogen and total phosphorus within a hydrographic sub-basin is not only determined by the soil cover type they present but also by different factors and its interaction generating several processes, whose dynamics are decisive in the nutrients transport. The land covers based on forest plantations and native forests, significantly influence nutrients retention and can be considered green sinks of pollutants. The vegetation retains several particles where nitrogen and phosphorus adhere, preventing them from reaching surface water bodies. Precipitation and soil types have an important contribution to nitrogen and total phosphorus transport. However, the greater nutrients transport is caused due to the fertilizer supplies applied to the land, that can be easily carried through erosion and boosted by rainfall or irrigation of intensive crops. Although results showed significant differences only for total nitrogen concentration between the sub-basins dominated by agriculture and forest plantations in summer 2013; there is a general trend to a higher nitrogen concentration in the subbasin dominated by an agricultural use compared to the other sub-basins studied, which is evidenced in the results obtained for summer 2014. To prove this difference, the study could be continued during the following drainage periods. Therefore, the land cover/uses characteristics of these sub-basins strongly influence the total nitrogen molecules retention. Conversely, the existence of significant differences in the total phosphorous concentration between the sub-basins due to land cover/use could not be demonstrated. As observed in other researches, total phosphorus transport could be determined by geology. It is worth noting that the implementation of strips on the surface waters riversides, as a management technique, could play an important role in nutrient retention, especially in the case of nitrogen in terms of agricultural activity. Acknowledgements The authors appreciate to National Agency for Research and Development of Chilean Government ANID, National Doctorate Scholarship 2016 (Grant No. 21160323), project VRID-Enlace No. 218.191.002-1, and CHRIAM Water Center (Project ANID/FONDAP/15130015) for the financial founding provided. Sincere gratitude to the Chilean General Water Directorate DGA; the Chilean Meteorological Directorate (DMC) and the National Forestry Corporation (CONAF) for the information provided.

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An Improvement Method to Study the Spatio—Temporal Dynamics of Rancho Luna Beach´ Shoreline Applying Remote Sensing Tools Laura Castellanos Torres , Alain Muñoz Caravaca , Iván Figueroa Reyes , Eugenio Olalde Chang , Minerva Sánchez Llull , and Lester Caravaca Colina Abstract The beaches constitute a very fragile and limited resource representing a huge weight on the economies of many countries and regions of the world. Constant monitoring of them is necessary because they are very fragile ecosystems but they are very expensive so the remote sensing techniques has proven to be an efficient alternative to assess the accretion-erosion processes. This research evaluates the spatial dynamics of the Rancho Luna beach shoreline by analyzing five the SPOT satellite images every two years, in the same seasonal period. A supervised classification was performed by the Maximum Likelihood method and 70 transects were defined every 10 m for the 700 m length of shoreline. An improvement of the traditional method to create the perpendicular transects is proposed and the methodology for this is presented. An erosive process is observed in 86% of defined transects and a loss of 8007.81 m2 of the beach area. The results of the study are in correspondence with previous studies based on direct measurements of beach profiles. Keywords Beach · Erosion · Geographic information systems · Remote sensing

1 Introduction The beaches constitute a very fragile and limited resource representing a huge weight on the economies of many countries and regions of the world. Also called the most dynamic and interesting element in the coastal zone given by the tourist use, the risk of erosion and human activity itself (Ojeda et al. 2013). These conditions have channeled governments, researchers and society in general, in the search for an adequate balance between the necessary exploitation of these spaces and the problems L. C. Torres (B) · A. M. Caravaca · E. O. Chang · M. S. Llull · L. C. Colina Centro de Estudios Ambientales de Cienfuegos, Calle 17 Esq. 45 Reina, Apartado Postal 5, CP 59350, Cienfuegos, Cuba e-mail: [email protected] I. F. Reyes Organización Regulatoria de Seguridad Ambiental, CITMA, Cienfuegos, Cuba © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Cardenas et al. (eds.), Proceedings of the 3rd International Conference on BioGeoSciences, Springer Proceedings in Earth and Environmental Sciences, https://doi.org/10.1007/978-3-030-88919-7_6

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Fig. 1 Study area, Rancho Luna beach

of their conservation. An example of this is the Rancho Luna beach, in the province of Cienfuegos, Cuba, located in the center-south of the island with Latitude: 22.037004° and Longitude: 80.421871° (Fig. 1). This beach represents one of the most important resources such as bathing area and tourism potential in the region. Its proximity to the Rancho Luna community, the Faro Luna hotel less than 200 m away, the Cienfuegos dolphinarium, as well as the city of Cienfuegos and the province of Villa Clara, facilitates the arrival of a large number of bathers in summer. According to studies conducted by the Center for Environmental Research of Cienfuegos, in summer almost 5000 people visit it daily since the geographical location of the beach is an excellent destination for the development of sun and beach tourism and nautical activities. According to Yu et al. (2011), changes in the shoreline can be the result of numerous causes, mainly associated with waves, tides, winds, periodic storms, the change in sea level, as well as the geomorphological process of erosion and accretion and human activity. The shoreline is also the reflection of recent changes along the shore. The waves change the morphology of the coast and form a distinctive coastal shape. Monitoring changes in the shoreline helps identify the nature of the processes that cause these changes in any specific area, assessing human impact and allowing planning management strategies (Salghuna and Bharathvaj 2015). Currently, the Rancho Luna beach does not have a good state of conservation, does not respond to the necessary requirements to offer a quality tourism product

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that is sustainable over time and from the environmental perspective has suffered significant losses in the beach profile. The direct influence of the anthropic activity on the beach strip, the increase in the level of use together with an inadequate management of the resource for decades, and the existence of constructions and vehicular roads on the dune have caused the appearance and intensification of beach degradation processes. Processes that are evidenced in the compaction of sand, deforestation and presence of exotic plant species, the formation of active erosion grooves and an evident retreat of the shoreline (Caravaca-Colina et al. 2015; Gómez-Castro and Bastida 2004). The application of remote sensing can be used effectively to monitor changes along the coastal area including the shoreline with a reasonable accuracy (Salghuna and Bharathvaj 2015). This technology associated with geographic information systems makes it possible to carry out complex studies with a high analysis capacity that allows evaluating the behavior of natural processes and ecosystems, as well as their seasonality and evolution over time (Del-Río and Prieto 2008; Fenwick and Alexander 2008; Ojeda et al. 2013; Jackson et al. 2012). Given the elements above is arising the need to assess the dynamics of the beach Rancho Luna, focusing the efforts in the study of the shoreline dynamics that permit to analyze the erosion - accretion process observed at the beach in the studied period.

1.1 Materials and Methods The study use five SPOT´s satellite images (Earth Observation Satellite) corresponding to the years 2004, 2006, 2010, 2012 and 2014, all of them in a same period, of the two established for Cuba (Artola et al. 2006; Batista 2016; Llacer 2016), the rainy period (May–October) with the aim of achieving seasonal stability in the study. The methodology followed in this research take in account many steps such as: selection of the study area, preparation of images, supervised classification, extraction of coast lines and areas to be analyzed and change detection, see Table 1. The variation in the behavior of the shoreline of the Rancho Luna beach is evaluated in an equal extension to 700 m length. Was used the software SNAP by ESA (European Space Agency) to perform radiometric correction of images and supervised classification by the method of maximum correlation (Maximum likelihood) thereby identified the regions of interest (ROIs): wet sand and dry sand (Fig. 2). The QGis 2.18.15 software was used on, the areas obtained were converted to a polygon and the simplify geometry tool (Vector Panel / geometric tool / simplify geometries) was also used to soften the contours. For the analysis of the variation of the shoreline, the following criteria were taken based on the Cuban Beach Standard NC 1331 (2020), NC 1332 (2020) and (Del-Río and Prieto 2008; Ojeda et al. 2013) (Fig. 3).

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Table 1 Methodology followed in this investigation

Selection of the study area • Identification of correct images for the study (spatial and temporal resolution) • Search of free databases to obtain images Preprocessing • Definition of interest regions. ROIs • Supervised classification. Maximum likelihood method • Convert the raster resulting from the supervised classification to a polygon layer • Apply filters to remove polygons that are not of interest • Filter Smooth Polygons Area analysis • Calculate areas of the polygons • Compare the years analyzed • Perform statistical behavior analysis Analysis of the dynamics of shorelines • • • • • • •

Convert polygons to lines Identify lines of interest to compare Interception line sea—wet sand Interception line wet sand-dry sand Dry sand interception line-boundary line of the study area Baseline for transect construction Merge of the “sea—wet sand” interception lines of all years, leaving the outermost limit to the coastline

Creation of transects • • • • •

1. 2. 3.

Creation of a parallel line to the baseline 10 m apart Divide both lines into equal number of nodes Match a secant line between two parallel nodes Cut the transects for each period studied Statistical analysis of the variability of the transects for each period studied

Last mark of instantaneous tide on the beach profile (intersection of the tide on the wet sand). Defined by the last wet tide mark on the beach profile (boundary between wet sand and dry sand, outer boundary of the dry beach (backshore). A third line was identified by a perennial path which is in contact with the limit of the coastal dune in most of the length of the beach.

For transects construction baseline was necessary identify, many studies consider the use of a line parallel to the coastline as shown in Fig. 4a, but in our case, not cut perpendicular the shoreline in all sections, and a better result was observed using a similar line to the contour of the coastline shown in Fig. 4b. The baseline was created applying the number one criteria in all the years analyzed. A merger of all the lines was carried out and the most separated position of the coast

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Fig. 2 Example of processed classified image

was chosen, of all the lines identified. The line was smoothing and used for the elaboration of transects that allowed analyzing the behavior of the coast by sections.

1.2 Construction of Transects According to studies (Guerreiro et al. 2013; Jackson et al. 2012), for measurements on beach profiles is use the method classic of transects perpendicular to the shoreline. This method allows to determine the distance closest to reality, between a shoreline and its displacement in time, although it is effective when the geographical characteristics of the area to be measured are uniform, when geographical accidents are identified (very sharp curves), is not effective because these features can cause transects to be cast in unfavorable orientations, that are highly oblique. In the study we present a modification to the method to create transects. A parallel line to the baseline is created at a distance of 10 m allowing to maintain a geographical similarity between the two (the distance will depend on the amplitude of the baseline curves, less amplitude = less distance between parallel lines and inversely. Both lines are divided into 70 nodes every 10 m, taking into account the length of the shoreline and criteria taken from previous studies (El-Hallaqa and Odwan 2018;

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Fig. 3 Lines according to criteria for analysis

Fig. 4 a Perpendicular transect to parallel line. b Transects from similar line to shoreline

Fenwick and Alexander 2008; Hashmi and Ahmad 2018). Secant lines between parallel nodes of the two lines with an extension of 40 m were created. These secant lines are transects used to evaluate the changes of the coastline in the period studied (Fig. 5).

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Fig. 5 Transects elaborated for the analysis of the dynamics of the shoreline. a Classic method of perpendicular transects to the shoreline. b Proposed method: secant lines between parallel points

1.3 Dynamics of the Beach Strip Area in Time The line defined by criterion three was used to evaluate the variation of the beach strip area. The space between the second and third lines was used to calculate the area of the beach strip (dry sand) and is represented in supervised classification analysis by the maximum correlation method.

2 Results and Discussion An erosive behavior is observed in 97% of the shoreline, except for the eastern of the beach where periods of accretion and erosion interspersed (Fig. 6). West Beach area (transects 1 to 20) shows an erosive behavior with the exception of the period from 2006 to 2010, although the latter is not being significant compared to the values presented by the receding shoreline, Fig. 7. The central region (transects from 21 to 40) in the first period studied shows an erosive behavior similar to the western region, decreasing its intensity in the subsequent periods and reaching accretion in 99% of cases in the last one. The third coastal section (transects from 41 to 70), towards the eastern region, must be divided for its analyze. The first half (west part), shows accretion process for the two initial periods and erosive for the next two periods. The eastern part presents a well-marked erosive process in the first stage of the study that is not observed in the remaining years analyzed, when and stable behavior it is observed, neither erosion nor accretion. In Fig. 8 can be quantitatively processes identify of loss observed in the study whit values from −20 to 5 m as extreme variation, being positive only 10 of the 70 analyzed transects.

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Fig. 6 Variability map in the 2004–2014 period

The third section of the beach, the extreme eastern presents a morph-dynamic behavior swinging as seen in Fig. 9, this behavior may be influenced by the morphology of the coast and the marine climate according to previous studies (Thieler and Danforth 1994). Analysis of area variation was made to beach strip the period studied, resulting from the supervised classification for each image. The beach strip area has a total lost −8006.25 m2 for 36.5% from the initial area of study and was identified a direct relationship with shoreline erosive behavior. The Fig. 10 shows a correlation of 0.94 between the area m2 and the years studied, this being a significant result of the trend of loss behavior presented by the beach area. Table 2 shows the values of the area each year, as well as the variation that occurs from one period to another. The range of variation of values is around −3000 to − 2000 m2 of loss, except for the year 2010, which has a very low value compared to the rest of the study. It may be conditioned because an image was not obtained to evaluate the year 2008 with the necessary conditions and a temporary jump is generated in the four-year study in the period 2006 to 2010, although this value does not minimize the erosive behavior that is present in the investigation. As can be seen in Table 2, there is a tendency of loss of the area, which corresponds to the tendency identified by Caravaca-Colina et al. (2015) measurements in situ in the same period, as shown Table 3.

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Fig. 7 Comparison of the studied periods, identification of the 70 elaborated transects Variation in period 2004 – 2014 Transect 10

Longitud (m)

5 0 -5 -10 -15 -20 -25

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70

Fig. 8 General variation of the values for each transect in 2004–2014 period

3 Conclusions The spatial dynamics of the Rancho Luna beach shoreline are determined and the variation of the beach strip area is quantified allowing a detailed analysis of the beach’s behavior over time.

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Fig. 9 Swinging morph-dynamic behavior observed in the study. Variation of the beach strip area Area variation Tendence

Linear (Tendence)

24000

Area m2

22000

y = -1909.6x + 23837 R² = 0.9439

20000 18000 16000 14000 12000

2004

2006

2010

2012

2014

Years

Fig. 10 Variation of dry sand area in 2004–2014 period Table 2 Areas for years studied Years Areas

(m2 )

Variation (m2 )

2004

2006

2010

2012

2014

21,966.42

1 9071.02

19,169.77

15,888.54

13,958.61

−2895.41

98.75

−3281.23

−1929.92

An Improvement Method to Study the Spatio … Table 3 Comparison between study values and published in 2015

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Years

Current study

Caravaca-Colina (2015)

Loss average

−18,011

−21.43

Total loss (m 2 )

−8006.25

−10,027

A methodology for the dynamics studies of shoreline is presented and improvements are proposed to the traditional method of elaboration of transects perpendicular to the shoreline to calculate seasonal and spatial variability, eliminating errors of overlap or non-perpendicular to the study line. The dynamics of the Rancho Luna beach shoreline in the 2004–2014 period show an erosive behavior. In the eastern zone, cumulative processes predominate, although the gains are not significant for the recovery of the beach. As a consequence, the beach area shows a gain and loss behavior in the periods analyzed, the final result being an erosive process equal to −8006.25 m2 for 36.5% of the initial study area. The use of geographic information systems together with remote sensing techniques allows complex, high-value studies that facilitate the acquisition of information for integrated management and decision-making in ecosystems.

References Artola, A. C., Pedroso, B. L., Ojeda, O. S., Montenegro, R. V., Rivera, C. F., Cancino, V. C., . . . García, L. D. (2006). La sequía meteorológica y agrícola en la República de Cuba y la República Dominicana (pp. 172). Batista, D. R. P.-D. (2016). Reseña bibliográfica. Algunas consideraciones sobre el comportamiento de la sequía agrícola en la agricultura de cuba y el uso de imágenes por satélites en su evaluación. Cultivos Tropicales, 37(3), 22–41. https://doi.org/10.13140/RG.2.1.4591.3843 Caravaca-Colina, L., Caravaca, A. M., Carrio, J. A., Gómez-Castro, & Osorioa-Arias. (2015). Comportamiento erosivo de la playa de Rancho Luna, Cienfuegos, Cuba. Revista Investigaciones Marina, 35, 68-79. Del-Río, L., & Prieto, F. J. G. (2008). Fotointerpretación aplicada al análisis dinámico de la línea de costa https://www.researchgate.net/publication/259979567 El-Hallaqa, M. A., & Odwan, M. S. (2018). Spatio-Temporal Analysis of Gaza Strip Shoreline Using GIS and Remote Sensing The Egyptian International Journal of Engineering Sciences and Technology. https://www.researchgate.net/publication/327665832 Fenwick, C., & Alexander, C. (2008). Rates and Processes of Shoreline Change at Ft. Pulaski National Monument. Gómez-Castro, E., & Bastida, E. L. (2004). Estudio de la estructura y dinámica de la playa Rancho Luna. (Master en Manejo Integrado de Zonas Costeras), Universidad de Cienfuegos, Cienfuegos. Guerreiro, J. S., Ranieri, L. A., El-Robrini, M., & Vila-Concejo, A. (2013). Seasonal changes of a dynamic macrotidal beach: Case Study of Marieta Beach (Amazon Coast/Brazil). Journal of Coastal Research, 65. Hashmi, S. G. M. D., & Ahmad, S. R. (2018). GIS-Based Analysis and Modeling of Coastline Erosion and Accretion along the Coast of Sindh Pakistan Journal of Coastal Zone Management, 21(1), 7. doi: https://doi.org/10.4172/2473-3350.1000455

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Jackson, C. W., Alexander, C. R., & Bush, M. D. (2012). Application of the AMBURR package for spatio-temporal analysis of shoreline change:Jekyll Island, Georgia, USA. Computers &Geosciences, 41. Llacer, I. D. (2016). Amount of rainy days and its distribution by intervals in normal conditions and severe drought in the western part of Cuba. Revista Cubana de Meteorología, 22. https://www. researchgate.net/publication/322234748 Merlotto, A., Bértola, G. R., & Piccolo, M. C. (2013). Seasonal morphodynamic classification of beaches in Necochea municipality, Buenos Aires Province, Argentina. Ciencias Marinas, 39, 331–347. Ojeda, Z., Díaz, C., Prieto, C., & Álvarez, F. (2013). Línea de Costa y Sistemas de Información Geográfica: Modelo de datos para la caracterización y cálculo de indicadores en la costa Andaluza. Investigaciones Geográficas, Instituto Interuniversitario de Geografía, Universidad de Alicante, No60, 37–52. https://doi.org/10.14198/INGEO2013.60.02 Oficina Nacional de Normalización (ONN). PAISAJE, PLAYAS, REGLAS GENERALES DE EXPLOTACIÓN Y CONSERVACIÓN, Normas Cubanas Online, NC 1331 (2020). Oficina Nacional de Normalización (ONN). PAISAJE, ÁREAS DE PLAYA, REQUISITOS GENERALES DE PROYECTO PARA EL ORDENAMIENTO, Normas Cubanas Online, NC 1332 (2020). Salghuna, N. N., & Bharathvaj, S. A. (2015). Shoreline change analysis for northern part of the coroman del coast abstract Aquatic Procedia, 4, 317–324. www.sciencedirect.com. https://doi. org/10.1016/j.aqpro.2015.02.043 Thieler, E. R., & Danforth, W. W. (1994). Historical Shoreline Mapping (II): Application of the Digital Shoreline Mapping and Analysis Systems (DSMS/DSAS) to Shoreline Change Mapping in Puerto Rico Journal of Coastal Research, 10, 600–620. http://www.jstor.org/stable/4298256 Yu, K., Hu, C., Muller-Karger, F. E., Lu, D., & Soto, I. (2011). Shoreline changes in westcentral Florida between 1987 and 2008 from Landsat observations. International Journal of Remote Sensing, 32(23), 8299-8313. https://doi.org/https://doi.org/10.1080/01431161.2010. 535045 doi:01431161.2010.535045

Sea Surface Temperature Trends in the Southern Cuban Shelves for the Period 1982–2018 Alain Muñoz Caravaca , Laura Castellanos Torres , and Liesvy Valladares Alfonso

Abstract This work has the objective to investigate the recent Sea Surface Temperature (SST) trends on southern Cuban shelves over the years 1982–2018 using monthly AVHRR SST NOAA product. This paper extends and updates the previous studies about SST on the Southern Cuban shelves with the aim of improving understanding of how global-scale climate changes translate into them and it could potentially help to better understand the influence of Sea Surface Temperature on mangroves deaths, coral bleaching, fisheries behavior and species displacement among others. The SST annual average has a value of 27.8784 °C and a range of 22.1774–2.4022 °C, for the western shelf, while for the eastern one it is 28.3395 °C with a range of 23.4504– 32.0313 °C. The SST trend is 0.0168 °C yr-1 and 0.0156 °C yr-1 , for western and eastern shelves respectively. During the last 36 years, the SST in the southwestern shelf has increased by 0.725 °C; while in the southeast it increases by 0.644 °C. If the current conditions that force the behavior of the climate in the Caribbean region are sustained, by 2050 a SST of up to 1,348 °C and 1,199 °C could be reached in the southwestern and southeastern Cuban shelves. Keywords Sea surface temperature · Caribbean · Cuba

1 Introduction The acquisition of synoptic conventional oceanographic data over large areas and for long periods of time is still extremely difficult and expensive, compared to satellite data. The data obtained by orbital sensors with conventional synoptic vision and with large spatial resolutions can provide time series of high frequency data for long periods of time (Good et al. 2006). The spatial and temporal patterns of SST variation play a fundamental role in determining the conditions for the survival of organisms that inhabit shallow waters, as they reflect the ranges of occurrence of the processes that take place there (Lough A. M. Caravaca (B) · L. C. Torres · L. V. Alfonso Centro de Estudios Ambientales de Cienfuegos. AP 5, Ciudad Nuclear, 59350 Cienfuegos, Cuba e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Cardenas et al. (eds.), Proceedings of the 3rd International Conference on BioGeoSciences, Springer Proceedings in Earth and Environmental Sciences, https://doi.org/10.1007/978-3-030-88919-7_7

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et al. 2018). The surface water column functions as an interface in the exchange of heat between the atmosphere and the ocean, it is an important physical characteristic that influences the transfer of water vapor and gases between the ocean and the atmosphere (Belkin 2016; Defforge and Merlis 2017). As Deser et al. (2010) have explained, SSTs are governed by both atmospheric and oceanic processes. On the atmospheric side, wind speed, air temperature, cloudiness, and humidity are the dominant factors regulating the exchange of energy at the sea surface. On the oceanic side, heat transport by currents, vertical mixing, and boundary layer depth influence the SST. Under normal conditions, the sea surface is hotter, the more heat and humidity are concentrated in the lower levels of the atmosphere, which creates unstable conditions, cold and dry air at medium levels and hot and humid air at low levels. In the tropics, thunderstorms and rainfall occurs most frequently in warmer regions. For example, in the Caribbean Sea, variations in the SST have an effect on rainfall in both local and remote areas (Palacios-Hernández et al. 2017; AntuñaMarrero et al. 2015). The annual and also inter annual variations of the SST in this region are generally within the temperature range of 26–29.5 °C, which makes even small temperature anomalies in the Caribbean Sea important for variations in rain and hurricane path (Taylor et al. 2002; Gamble and Curtis 2008; Glenn et al. 2015; Taylor et al. 2018). SST has been considered the environmental variable with the greatest impact on global bleaching and in combination with other stressors coming from land that has been studied in the Caribbean region as a significant contributor to the health of the Regional Marine Ecological Systems (Lewandowska et al. 2014; Romero-Rodríguez et al. 2014; Belkin 2016; Dunstan et al. 2018). In Cuba the SST has been addressed as a common factor for the study of shrimp and lobster’s fisheries or assess its influence on the weather and climate dynamic (Hernández 2002) (Somoza et al. 2006). In this context, this work has the objective to investigate the recent SST trends on Cuban southern shelves over the years 1982– 2018 using monthly AVHRR SST NOAA product. This paper extends and updates the previous studies about SST on mentioned Cuban shelves with the aim of improving understanding of how the climate change effects are expressed in the studied areas.

2 Materials and Methods 2.1 Study Area The present study takes place in the two southern - Cuban shelves “Gulf of Batabanó” and the “Gulf of Ana María and Guacanayabo” as shown in Fig. 1. The southwestern shelf corresponds to Batabano Gulf, this area it is located in the tropical climatic zone of the Caribbean Region, Fig. 1; between 18–25°N and 87– 73°W. It receives an annual sunshine of more than 25,000 h of light in the year, the atmospheric pressure is 1017 hpa. The average air temperature in winter is between 20° and 22 °C, while in summer it is 26 °C and more. The rain regime (May–October)

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Fig. 1 Cuban southern shelves where study area correspond to the Gulfs of Batabano, Ana María and Guacanayabo where SST were studied

is more than 1200 mm and in the dry period (November–April) it ranges is between 200 and 300 mm, (Somoza et al. 2006). The Cuban southeastern shelf covers the gulfs of Ana María and Guacanayabo. The gulf of Ana María is of high value for fisheries and tourism. It is limited by the coasts of the Ciego de Ávila, Camagüey and Sancti Spíritus provinces and the keys of the edge of the Island´ shelf of the Canarreos archipelago, which extend from the María Aguilar tip to the Labyrinth of the Twelve Leagues, separated from the Gulf of Guacanayabo to the east, by a group of low keys and reefs.

2.2 Sea Surface Temperature Data The SST images, obtained by the AVHRR / NOAA sensor, available at http://pod aac.jpl.nasa.gov/ monthly products for the period 1982–2018 were used. The product used is: “AVHRR Oceans Pathfinder Global 4 km equal-angle all SST v5 (NOAA, NASA)”. All data were kept in NETCDF format and processed with the SeaDAS 7.4 program. The spatial resolution of this product is 4 × 4 km, (Franch 2017). The warming of the SST on the Cuban small shelves presented by this work use monthly data, because daily or weekly data contain many missing information due to the cloud cover in the images (García 2015). The temperature analysis is performed at 308 and 349 sampling points for the southwestern and southeastern shelves, respectively, Fig. 1. SST monthly and annual area-averaged means were calculated. Existing work refers to various statistical

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methods to reduce the observable autocorrelation in the study of trends in oceanographic variables or variables related to environmental sciences, while identifying this autocorrelation as an indicator of other processes such as the magnitude of the changes and the resilient capacity of ecosystems, for example: empirical orthogonal function method, analysis of harmonics, principal components or others that minimize the effect of autocorrelation (Ming-An et al. 2003; Hannachi et al. 2007; Michelle et al. 2013; Lenton et al. 2017; Palacios-Hernández et al. 2017; Miftahuddin and Ilhamsyah 2018). In this work it was applied a seasonal decomposition procedure to both series. By this way the series were divide into three components: an estimate of the trendcycle component; seasonal indices and the irregular components. The trend-cycle component is estimated by smoothing the time series data using a simple moving average with an amplitude k equal to the amplitude of seasonality s. A 12-months moving average has been applied to monthly data to smooth out the inter-annual variability and remove seasonal fluctuations that is estimated by the second component, allowing the trend due to other natural processes and anthropogenic influences to be determined. Autocorrelation is tested over the trend-cycle derived subseries, using Durbin-Watson method, and it is not observed. The slope of the linear regression over this subseries were used for indicating the magnitude of change during the analyzed period for both series (García 2015). Once the trend-cycle has been estimated, it can be removed from the data. This is done by dividing the original data by the estimated component (called a “moving average radius”), leaving: St Rt =

Yt T Ct

where: St : Cyclical variations with a set frequency. The effects seasonal are repeated on a regular and predictable basis. Ct : Cyclical variations around the trend line. T : General long-term pattern observed over the whole complete data. Rt : The residual component that remains after the other three components. Yt : SST time series. The resulting estimates of the seasonality-irregularity component are averaged using all the observations within each month to remove the irregular component, resulting in an estimator of the seasonal component. The seasonal components are then adjusted so that a seasonal average has a value of 100. All the significance tests in the following are at the 95% significant level. The results are discussed in the light of the most up-to-date international references for the region.

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3 Results and Discussion The comparison of the means of the montlhy data series corresponding to both coastal shelves indicates that there is a statistically significant difference among them. The histogram of SST values corroborates the existence of two thermal periods that are related to summer and winter in the northern hemisphere, Fig. 2. In this way, six months of winter (November–April) and the rest of summer (May–October) can be grouped. It is noted that for the winter period the lowest temperatures are reached on the western shelf. This behavior of the SST in the western sector of the Island corresponds to the dynamics of the cold fronts, whose impact is degraded as it advances to the East, (Mitrani-Arenal and Onoe Díaz-Rodríguez 2004). For the summer, however, a quite similar behavior is observed in both shelves. The seasonal decompositional procedure applied for shows that during the whole studied period, the average SST for the southwestern shelf was 27.8784 ± 0.246231 °C with a range among 22.1774–32.4022 °C. For the southeastern shelf it was 28.3395 ± 0.204544 °C with range among 23.4504–32.0313 °C. Monthly seasonal index and mean SST subseries are quite similar. The maximum and minimum temperature is arised by the month of August and January for the southwestern shelf respectively, while for the southeastern one is arised by September and February, Fig. 3. According to Palacios, the highest temperature values in the Caribbean Sea are reached near the southern coasts of Cuba and decrease radially towards the Yucatan Channel, Jamaica and Isla Española, behavior that follows the results obtained in this work, (Palacios-Hernández et al. 2017). The obtained result modifies the known behavior of the SST in the southern Cuban shelves. Cerdeira identified the SST of southwestern shelf as the warmest of the all waters around Cuba even for winter or summer seasons, (Cerdeira Estrada et al. 2005). Mitrani and Díaz-Rodríguez, have described the relationship between the thermal vertical structure of Cuban waters and tropical cyclone activity, and they fundament the possible connection between the thermal characteristics of the sea surface layer

Winter

Summer

Fig. 2 Histogram of SST values for southwestern (up) and southeastern shelves (bottom)

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Fig. 3 Monthly seasonal index and mean SST subseries of southwestern (up) and southeastern shelves (bottom), period 1982–2018

and tropical cyclone activity, based on data derived from 37 oceanographic campaigns during the period from 1966 to 1993 on offshore observation stations. They also conclude that western shelf is warmer than the eastern one, (Mitrani-Arenal and Onoe Díaz-Rodríguez 2004) contrary to what was obtained in this work because the highest SST values were determined at the southeastern shelf. The linear regression of the monthly area-average data series shows an increasing trend with values of 0.0155 and 0.0179 °C yr−1 , for eastern and western shelves, respectively. It can be seen from the behavior of both time series that since 2012 there is an increase in the SST, in most cases greater than the average temperature for the whole period, Fig. 4. While the SST increase 0.036 and 0.1440 °C for eastern

Temperature °C

29.5 29 28.5 28 27.5 27 26.5

1986

1993

2000

2007

2014

Fig. 4 The trend-cycle derived subseries of SST southeastern (black line) and southwestern (gray line) Cuban shelves in the period 1982 – 2018. SST linear trend are represented by the correspondent dashed lines

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Fig. 5 SST annual average for the western (up) and eastern (bottom) Cuban´s shelves in the studied period 1982–2018

and western shelves among 1982 – 2012, the rest of the period, only six years, it increases by 0.0936 and 0.1008 °C respectively. The annual average of the SST also increases in the last period, making the trend of the series more positive, 0.0179 °C yr−1 and 0.0155 °C yr−1 or 1.79 °C and 1.55 °C per century for southwestern and southeastern shelves respectively, Fig. 5, that are agreed with the trend studies reported by (Antuña-Marrero et al. 2015), for Caribbean region for the period 1972–2005, 1.41 ± 0.68 °C per century. If the current conditions that force the behavior of the climate in the Caribbean region are sustained, by 2050 a SST of up to 1,348 °C and 1,199 °C could be reached in the southwestern and southeastern of Cuban shelves. Accordingly, with Taylor the future SST trends in the Antilles may range between 0.39 and 2.21 °C per century for scenarios representing low CO2 emissions through business-as-usual. For the wider Caribbean the range is between 0.43 and 2.15 °C per century, which it is also consistent with the obtained results on our report (Taylor et al. 2018). Belkin has listed the SST changes from 1957 to 2012 for 66 large marine ecosystems (Belkin 2016). Based on that list Caribbean Sea net SST changes was 0.15 °C and it is classified with Slow Warming, that means temperature changes among 0.0– 0.4 °C. From the trend calculated on this report for the whole period 1982–2018, the increase of the SST is 0.725 °C and 0.644 °C, for southwestern and southeastern Cuban´s shelves respectively, which places these Cuban coastal marine platforms at the top of the Moderate Warming classification, in the Belkin’s report, noting that its study period extends only to 2012.

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This increase in SST has an impact on marine life, which has been reported by many authors (Spence et al. 2004; MacDonald et al. 2005; Klotzbach 2011; Lough et al. 2018) . In Cuba, however, there are few works that relate this cause directly to coral bleaching, acidification or any other effect of SST increment. Rather, bleaching and mangroves deaths are directly related and as the first cause to the impact of hurricanes, which in turn finds a direct and very intense relationship with SST. Given the depth of the Cuban mesophotic reefs, these ecosystems should not suffer as much the impact of the increase in sea temperature and acidification of the waters, risk factors that enhance coral bleaching or death, however it is necessary to verify these relationships and to quantify them, (Alcolado and Valderrama 2016).

4 Conclusions The SST annual average has a value of 27,887 °C and a range of 22,177–32,402 °C, for the southwestern shelf, while for the southeastern one it is 28,339 °C with a range of 23,450–32,031 °C. In both platforms, the SST shows positive trends with values of 0.0179 °C yr−1 and 0.0155 °C yr−1 , for western and eastern shelves respectively. The behavior of the SST in the coastal marine platforms in the south of Cuba is similar to those described for the neighboring areas in the Caribbean Sea, and shows an increase in the trend in the period 2012–2018 that places it in the Moderate category, higher than that reported in framed studies until 2012. During the last 36 years, the Sea Surface Temperature in the southwestern sector of the Cuban shelf has increased by 0.725 °C, while in the southeastern it increases by 0.644 °C. If the current conditions that force the behavior of the climate in the Caribbean region are sustained, by 2050 a SST of up to 1,348 °C and 1,199 °C could be reached in the southwestern and southeastern Cuban shelfs.

References Alcolado, P., & Valderrama, S. (2016). Reporte de blanqueamiento de corales del año 2016 en Cuba. Technical Report, Instituto de Oceanología de Cuba. Obtenido de https://www.researchgate.net/ publication/313604077_Reporte_de_blanqueamiento_de_corales_del_ano_2016_en_Cuba Antuña-Marrero, J., Helge Otterå, O., Robock, A., & d. S. Mesquita, M. (2015). Modelled and observed sea surface temperature trends for the Caribbean and Antilles. INTERNATIONAL JOURNAL OF CLIMATOLOGY. https://doi.org/10.1002/joc.4466 Belkin, I. (2016). Chapter 5.2: Sea surface temperature trends in large marine ecosystems. En I. U. UNEP, Large Marine Ecosystems: Status and Trends (págs. 101–109). Nairobi: United Nations Environment Programme. Cerdeira Estrada, S., Müller Karger, F., & Gallegos García, A. (2005). Variability of the sea surface temperature around Cuba. Gulf of Mexico Science, Dauphin Island, 161–171. Defforge, C., & Merlis, T. (2017). Observed warming trend in sea surface temperature at tropical cyclone genesis. Geophys. Res. Lett(44), 1034–1040. https://doi.org/10.1002/2016GL071045

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Deser, C., Alexander, M. A., Xie, S.-P., & Phillips, A. S. (2010). Sea surface temperature variability: patterns and mechanisms. Annual Review of Marine Science, 2, 115–143. Dunstan , P., Foster, S., King, E., Risbey, J., O’Kane, T., Monselesa, D., . . . Thompson, P. (2018). Global patterns of change and variation in sea surface temperature and chlorophyll a. Springer Nature, 8:14624. https://doi.org/10.1038/s41598-018-33057-y Franch, B. V.-R. (2017). A 30+ year AVHRR land surface reflectance climate data record and its applica-tion to wheat yield monitoring. Remote Sensing, 9(3), 226. Gamble, D., & Curtis, S. (2008). Caribbean precipitation: review, model and prospect. Progress in Physical Geography (32), 265. https://doi.org/10.1177/0309133308096027 García, M. J. (2015). Recent warming in the Balearic Sea and Spanish Mediterranean coast. Towards an earlier and longer summer . Atmósfera, 28(3), 149–160. Glenn, E., Comarazamy, D., & Smith, T. (2015). Detection of recent regional surface temperature warming in the Caribbean and surrounding region. Geophys. Res. Lett., 42, 6785-6792. Good, S., Corlett, G., Remedios, J. J., Noyes, E., & Llewellyn-Jones, D. (2006). The Global Trend in Sea Surface Temperature from 20 Years of Advanced Very High Resolution Radiometer Data. American Meteorological Society. https://doi.org/10.1175/JCLI4049.1 Hannachi, A., Jolliffe , I., & Stephenson , D. (2007). Empirical orthogonal functions and related techniques in atmospheric science: A review. Int. J. Climatol.(27), 1119–1152. Hernández, B. (2002). Variabilidad interanual de las anomalías de la temperatura superficial del mar en aguas cubnas y su relación con eventos El Niño-Oscilación del Sur (ENOS). Revista de Investigaciones Marinas, 30(2), 21-31. Klotzbach, P. J. (2011). The Influence of El Niño–Southern Oscillation and the Atlantic Multidecadal Oscillation on Caribbean Tropical Cyclone Activity. American Meteorological Society, 24, 721731. https://doi.org/10.1175/2010JCLI3705.1 Lenton, T., Dakos, V., Bathiany, S., & Scheffer, M. (2017). Observed trends in the magnitude and persistence of monthly temperature variability. Nature Scientific Report, 7: 5940 . https://doi.org/ 10.1038/s41598-017-06382-x Lewandowska, A., Boyce, D., Hofmann, M., Matthiessen, B., Sommer, U., & Worm, B. (2014). Effects of sea surface warming on marine plankton. Ecology Letters. https://doi.org/10.1111/ele. 12265 Lough, J., Anderson, K., & Hughes, T. (2018). Increasing thermal stress for tropical coral reefs: 1871–2017. Springer Nature, 8:6079. https://doi.org/10.1038/s41598-018-24530-9 MacDonald, M., Ruebens, M., Wang, L., & Franz, B. (2005). The SeaDAS Processing and Analysis System: SeaWIFS, MODIS, and Beyond. AGU Fall Meeting Abstracts. Michelle L., L., Collins , D., & Zeng-Zhen, H. (2013). Linear trends in sea surface temperature of the tropical Pacific Ocean and implications for the El Niño-Southern Oscillation. Clim Dyn, 40, 1223-1236. Miftahuddin , M., & Ilhamsyah , Y. (2018). Modeling of sea surface temperature using linear models with autocorrelation Indian Ocean. IOP Conference Series: Earth Environmental Science, 176(012038). Ming-An, L., Ching-Dong, Y., Chao-Hsiung, C., Jui-Wen, C., & Kuo-Tien , L. (2003). Empirical orthogonal function analysis of AVHRR sea surface temperature patterns in Taiwan strait. Journal of Marine Science and Technology, 11(1), 1-7. Mitrani-Arenal, I., & Onoe Díaz-Rodríguez, O. (2004). Relación entre la estructura térmica vertical de las aguas cubanas y la actividad de los ciclones tropicales. Ciencia Marinas, 30(2), 335–341. Obtenido de. https://doi.org/10.7773/cm.v30i2.182 Palacios-Hernández, E., Carrillo, L., Meza-Romero, S., & Ávalos-Cueva, D. (2017). Variabilidad espacio temporal de la temperatura superficial del mar en el Mar Caribe. RA XIMHAI, 13(3), 243-265. Romero-Rodríguez, D., Bernal, G., & Zea, S. (octubre-diciembre de 2014). Variables ambientales durante blanqueamiento coralino en el Caribe colombiano. Rev. Acad. Colomb. Cienc., 38(149), 345-55.

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Hydrodynamic Characteristics of the Nuevitas Bay, Camagüey, from the Numerical Simulation Liesvy Valladares Alfonso , Alain Muñoz Caravaca , Felivalentín Lamas Torres , and Laura Castellanos Torres

Abstract The hydrodynamic model D-Flow FM is used to perform the numerical simulation of the tide and the ocean currents. The model is forced with six principal tidal constituents, as atmospheric forcing: a constant wind with southwest direction and speed equal to 3.5 m/s is used. The tidal results obtained are validated by comparison comparing them with data obtained from the WXTIDE tidal prediction program. It is developed the calculation of the residence time of the bay based on the injection of a passive tracer as an instantaneous pulse in the domain. An unstructured triangular grid is obtained for the entire study area. The correlation coefficients resulting from the tide validation are greater than 0.90 and the residence time of the bay is equal to 32 days. Keywords D-Flow FM · Nuevitas bay · Numerical simulation · Ocean currents · Residence time

1 Introduction The relationship between the hydrodynamic, biological and chemical characteristics of ecosystems is a recurring theme in specialized literature (Deleersnijder et al. 1998; Guo et al. 2000; Monse 2002; Wen-Cheng et al. 2008). Numerical modeling (sometimes called numerical simulation) is a technique based on numerical calculation, used in many fields of study since the 1960s. One of the applications that can be given to this technique is hydrodynamic simulation. This work presents the hydrodynamic characterization of the Nuevitas bay. It was developed the simulation of the ocean currents, the tide and the trajectory of a passive tracer in the field. The hydrodynamic model is conservative in its results because it does not consider river contributions. So therefore, the exchange regimes and currents analyzed in this work are favorable to renewal times greater than those

L. V. Alfonso (B) · A. M. Caravaca · F. L. Torres · L. C. Torres Research Environment Center of Cienfuegos, 17th St & 46 Avenue, Cienfuegos, Cuba © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Cardenas et al. (eds.), Proceedings of the 3rd International Conference on BioGeoSciences, Springer Proceedings in Earth and Environmental Sciences, https://doi.org/10.1007/978-3-030-88919-7_8

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that consider river contributions, rainfall or other factors that reduce the density of water and favors the exchange between the bay and the ocean.

2 Materials and Methods 2.1 Study Site The Nuevitas bay, located on the northeastern Cuban shelf, in the municipality of the same name, belonging to Camagüey province, occupies an area of 201 km2 with a maximum depth of 49.5 m and an average of 5 m, (Fig. 1) and its exchange with the ocean is partially limited by the Sabana—Camagüey Archipelago and a narrow and winding channel that links it to the Old Bahama Channel. The bay is 22 km from northeast to southwest, while from southeast to northwest it is 25 km. It has two large lobes and a short artificial canal, whose name is Zanja del Gobierno, which communicates towards the Ensenada de Sabinal. This system has a truly impressive flow of water that rises at each high tide to about 86 million cubic meters (m3 ), equivalent to a current of 3980 m3 per second, and at low tide it can increase due to the contribution made by the waters of the five rivers that flow into it.

Fig. 1 Location map and bathymetric characteristics of the Nuevitas bay, Camagüey, Cuba

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2.2 Numerical Model Currents and tides were modeled in the adjacent marine area, in an enough large domain to encompass the processes that occur outside of the bay. The hydrodynamic model D-Flow FM (D-Flow Flexible Mesh) was used, it’s included in the Delft3D Flexible Mesh Suite program. This package can simulate storm surges, hurricanes, tsunamis, detailed flows and water levels, waves, the transportation of sediments and morphology, water quality and ecology, and it is capable of managing the interactions between these processes.

2.3 Computational Model To solve the equations of motion and thereby simulate the circulation pattern, a numerical domain consisting of a matrix of 15683 elements and 8249 nodes was constructed, and a minimum distance between nodes of 50 m; with what it is guaranteed to obtain a model that is representative to the characteristics of the coastline, (Fig. 2). This domain does not consider the flows that could exist through the “Zanja del Gobierno” channel, which communicates towards the Ensenada de

Fig. 2 Computational model or calculation mesh of the Nuevitas bay, Camagüey, made up of 15683 elements and 8249 nodes

94 Table 1 Characteristics of the tidal constituents

L. V. Alfonso et al. Constituents

Amplitude (meters)

Phase (grades)

Frequency (grades/hour)

S2

0.0407

239.15

30.00

P1

0.0069

183.43

14.96

O1

0.0288

238.2

13.94

N2

0.0345

257.83

28.44

M2

0.18

261.01

28.98

K1

0.0283

191.98

15.04

Sabinal. Neither are considered in this model the flows from intermittent rivers that in the rainy season, contribute with surface water by runoff to the bay.

2.4 Forcing As forcing of the model, the tide was used at the oceanic boundary of the computational domain, using the harmonic constituents that are presented in Table 1. The wind was considered as an atmospheric forcing. Because there was no real wind data; a constant wind was established with a southwest direction and a speed equal to 3.5 m.sec−1 . The model was calibrated taking as a reference the tide signal recorded at the Punta de Sotavento and Punta Santo Domingo points, by the WXTIDE tide program. Both registers with a total of 4320 values: one value every 5 min, for 15 days.

2.5 Observation Points To verify the results, several observation points were defined in the domain (Fig. 3), (Table 2). These observation points made possible to evaluate the variation of the tide and the currents inside and outside the bay. They also made possible to identify the evolution of the concentration of a passive tracer launched inside the bay and thereby estimate the speed of the transport processes of a pollutant.

2.6 Calculation of the Water Renewal Time The initial mixing process of rivers and other coastal waters produces a zone of low salinity that gradually mixes with ocean water. The speed with what this process

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Fig. 3 Observation points established in the computational model for the calibration of the model and the analysis of the pollutant transport processes

Table 2 Positioning of the observation points. Geographic Coordinate System, WGS 1984

Station

Longitude

Latitude

Oeste Termoeléctrica

−77.326512

21.594087

Frente Termoeléctrica

−77.275701

21.579104

Este Termoeléctrica

−77.215119

21.564122

Lobulo Sur

−77.233359

21.505494

Salida Canal

−77.152257

21.535785

Dentro Canal S

−77.135190

21.566337

Dentro Canal N

−77.112488

21.592752

Entrada Canal

−77.103987

21.08907

occurs in the estuary is a measure of its ability to purify itself. For a given domain, the simplest exchange time scale, is the ratio of volume (V ) to daily flow (Q) entering or leaving it. A formulation of this concept is as follow: θ=

Vt Qt

(1)

where t is the time and the parameter θ is frequently called as: “residence time” (Gómez-Gesteira et al. 2003; Muñoz Caravaca et al. 2012; Rasmussen and Josefson 2001; Shen and Haas 2004) “average residence time” (Pagés and Andrefouet 2001) “water exchange rate” (Kraines et al. 2001), “flushing time” by (Geyer et al. 2000;

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Monse 2002), “e-folding flushing time” by (Delhez et al. 2004) or “turnover time” by (Arneborg 2004).

2.7 Residence Time Distribution Function In the context of this work, it was considered appropriate to study the behavior of the residence time of the waters of the Nuevitas bay. To estimate the residence time distribution function, the injection of a passive tracer can be considered, either as an instantaneous pulse or continuously, described as a passing method (Levenspiel 1972). If it is assumed that a known amount of a tracer is injected into a system at time t = 0, resulting in an initial concentration C0 , no more tracer is injected after t = 0, and the flow and volume of the domain remain constant in time, then the consecutive measurement of the concentration at the outlet of the system makes possible to define a function of the renewal time of the waters, in a semi-closed system as it is the case of the Nuevitas bay. This model also assumes that all the water that leaves the estuary is completely mixed, which imposes a condition of linearity, which can be described according to the following expression: M(t) = M(0)e−(Q/V )t = C0 e−(t)T f

(2)

where t is the time, M is the total mass of the tracer that remains in the estuary, c is the concentration of the tracer and T f is the renewal time. When t = T f , it means that 37% of the initial mass still remains in the system or, what is the same, that the initial mass has decreased by a factor of 1/e, deriving a time scale known as residence time. In this work the renewal time is determined at the observation point called: Dentro Canal S and at the point denoted Oeste termoeléctrica. From the launch of a conservative tracer in the form of an instantaneous pulse, with an initial concentration of 100%, within the domain, it is shown in Fig. 4.

3 Results The typical tide in the Bay of Nuevitas is semi-diurnal with an average amplitude of 0.45 m, which corresponds to that obtained by Iturralde-Vinent and Serrano (2015). The results of the implemented model also reach a similar amplitude and the reduction of the amplitude and the modification of the tidal phase in the bay are noted in relation to the same signal outside (Fig. 5). Calibration of the model gives excellent results (Fig. 6). Two points were selected: Entrada Canal (Punta de Sotavento) and Frente Termoeléctrica (Punta Santo Domingo), the Pearson correlation coefficients obtained were (correlation, r = 0.92,

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Fig. 4 Moment at which the passive tracer is injected, t = 0

Fig. 5 Variation of the tide in the entrance channel of the bay (red) and the Frente Termoeléctrica interior observation point (blue)

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Fig. 6 Tidal signal calculated by the Delft-3D model, at two observation points Santo Domingo (left) and Sotavento (right), compared with the signal obtained from the global WxTide model

n = 4 320) and (correlation, r = 0.90, n = 4 320) respectively, with mean square error equal to 0.003, in both cases. This result reveals the goodness of the calibrated model for use in subsequent analyzes. Unfortunately, at the time of this report there are no current measurements, with what we could have another validation criterion of the resulting model.

3.1 Currents The bathymetric characteristics of the Nuevitas bay determine the pattern of water circulation in it. The narrow and sinuous inlet channel marks a northeast turn, forcing the greatest flow (and ebb) to develop in the northwestern sector with a northwestsoutheast orientation (Fig. 7). The currents are mainly the result of the tide and reach their highest values in the inlet channel with 1.63 m.sec−1 , in the simulated period. These results correspond to previous studies carried out by Camagüey researchers that report currents of up to 1.80 m.sec−1 . Note that inside the bay the velocity vectors are small, representing values less than 0.17 m.sec−1 . In the area where the Frente Termoeléctrica point is located, the speeds reach a maximum of 0.17 m.sec−1 , which is given for being in the main axis of circulation of the water and the shape of the coastline that, when forming tips, these get in the way and favor the increase of speed.

3.2 Residence Times of the Water The following shows the behavior of the concentration of a passive tracer, at different moments (Fig. 8). This tracer was included, as an instantaneous pulse, inside the entire bay; and from that moment on, the process of exporting it to the outside begins. The displacement

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Fig. 7 Circulation pattern during filling (left) and emptying (right) in the Nuevitas bay, from the numerical simulation

Fig. 8 Displacement pattern of a conservative tracer at four moments. At the top: 30 days on the left and 45 days on the right. At the bottom: 60 days on the left and 90 days on the right

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of the tracer follows the course of the currents in the bay, but there is an area of concentration towards the northwest, which appears to be a space of less exchange with oceanic waters. The local renewal time, calculated from the modeling carried out in this work and at the selected points, aims to values that are in the range between 33 and 54 days. While the Center for Environmental Engineering and Management of Bays and Coasts (CIMAB) of Camagüey, Cuba, refers that the renewal time for the Bay of Nuevitas ranges between 22 and 24 days. The values calculated by the Delft-3D model are conservative, take into consideration, that fluvial contributions or other discharges of fresh water to the bay, which accelerate the exchange, are not included.

4 Conclusions • The hydrodynamic model of the bay calculates the maximum speeds of the currents in the inlet channel, with values equal to 1.63 m.sec−1 , in the simulated period. While in the northwestern lobe of the bay, speeds lower than 0.17 m.sec−1 are determined. • It is shown that the Nuevitas bay is an ecosystem of little exchange due to the slow period of self-purification. Its renewal time to the northwest lobule is long, presenting a value equal to 54 days, while in the south of the entrance channel only 33 days are required.

References Arneborg, L. (2004). Turnover times for water above sill level in Gullmar Fjord. Continental Shelf Research, 24, 443–460. Deleersnijder, E., Wang, J., & Moers, N. K. (1998). A two compartment model for understanding the simulated three-dimensional circulation in Prince William Sound, Alaska. Continental Shelf Research, 18, 279–287. Delhez, E. J. M., Heemink, A. W., & Deleersnijder, E. (2004). Residence time in a semi-enclosed domain from the solution of an adjoint problem. Estuarine, Coastal and Shelf Science, 61, 691– 702. Geyer, W. R., Morris, J. T., Pahl, F. G., & Jay, D. A. (2000). Interaction between physical processes and ecosystem structure:a comparative approach. In J. E. (Ed. . Hobbie (Ed.), Estuarine Science: a Synthetic Approach to Research and Practice. Island Press, Washington, DC. Gómez-Gesteira, M., DeCastro, M., & Prego, R. (2003). Dependence of the water residence time in Ria of Pontevedra (NW Spain) on the seawater inflow and the river discharge. Estuarine Coastal and Shelf Science, 58, 567–573. Guo, L., Santschi, P. H., & Warnken, K. W. (2000). Trace metal composition of colloidal organic material in marine environments. Marine Chemistry, 70, 257–275. Iturralde-Vinent, M.A. y H. Serrano. (2015). Peligros y vulnerabilidades de la zona marino-costera de Cuba: estado actual y perspectivas ante el cambio climático hasta el 2100. Editorial Academia. La Habana. 74 pp.

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Kraines, S. B., Isobe, M., & Komiyama, H. (2001). Seasonnal variations in the exchange of water and water-borne particles at Majuro Atoll, the Republic of the Marshall Island. Coral Reefs, 20, 330–340. Levenspiel, O. (1972). Chemical reaction engineering. John Wiley and Sons, New York. Monse, N. (2002). A comment on the use of flushing time, residence time, and age as transport time scales. Limnol. Oceanogr., 47(5), 1545–1553. Muñoz Caravaca, A., Douillet, P., Díaz García, O., Fichez, R., Herrera, R., Alcantara Carrio, J., & Rodríguez, A. (2012). Flushing time in the Cienfuegos Bay, Cuba. Natural Resource Modelling. https://doi.org/10.1111/j.1939-7445.2012.00126.x Pagés, J., & Andrefouet, S. (2001). A reconnaissance approach for hydrology of atoll lagoons. Coral Reefs, 20, 409–414. Rasmussen, B., & Josefson, A. B. (2001). Consistent estimates for the residence time of micro-tidal estuaries. Estuarine Coastal and Shelf Science, 54, 65–73. Shen, J., & Haas, L. (2004). Calculating age and residence time in the tidal York River using three-dimensional model experiments. Estuarine, Coastal and Shelf Science, 61, 449–461. Wen-Cheng, L., Wei-Bo, C., Jan-Tai, K., & Chin, W. (2008). Numerical determination of residence time and age in a partially mixed estuary using three-dimensional hydrodynamic model. Estuarine, Coastal and Shelf Science, 28, 1068–1088.

The Atmosphere and the Magnetosphere

Hurricane Related Coastal Flooding in the Province of Ciego de Avila, Cuba: Hazard, Vulnerability and Risk Study Felipe Matos Pupo , Osvaldo E. Pérez López , and Alexey Valero Jorge

Abstract The province of Ciego de Avila has been impacted by hurricanes of different intensity since the end of the 20th Century. The most severe impacts have taken place during the last forty years. Hurricanes Kate (1985), George (1998), Ike (2008) and Irma (2017) are just four remarkable examples; the latter was a cat 5 in the Saffir-Simpson Hurricane Wind Scale. The aforementioned hurricanes and others have caused coastal flooding in both, the northern and southern coasts of the said province. Modeling coastal flooding as a natural phenomenon implies a complex research process, where risk areas, with emphasis in the most sensitive ones, have been spatially determined (Júcaro and Jagüeyal on the southern coast and Punta Alegre on the northern coast). The methods used to assess hazard, and the methodology to estimate risk are addressed in this work. Vulnerability values are also included, as they encompass critical factors upon which human beings may influence to minimize hurricane related risks; thus making possible the improvement of Disaster Contingency and Recovery Plans, aimed at preventing or mitigating social and economic damages. This is precisely the goal of this scientific output. Our results may be easily replicated in other places of Cuba, or even extrapolated to the island states of the Caribbean region. Keywords Coastal flooding · Hurricanes · Hazard · Vulnerability · Risk

1 Introduction Coastal flooding resulting from extreme hydrometeorological events is a major problem in Cuba. The severe impacts from this phenomenom have increased the concern of the population regarding ways to reduce social and economic losses [1, 2]. F. M. Pupo (B) · A. V. Jorge Centro Meteorológico Provincial de Ciego de Ávila, Avda. de los Deportes, Ciego de Ávila, Cuba O. E. P. López Instituto de Meteorología, Loma de Casablanca, La Habana, Cuba © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Cardenas et al. (eds.), Proceedings of the 3rd International Conference on BioGeoSciences, Springer Proceedings in Earth and Environmental Sciences, https://doi.org/10.1007/978-3-030-88919-7_9

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This phenomenon has not been evenly studied in Cuban coastal areas. The Havana seafront, known as The Malecon, has been the most studied area [3–12]. Since 2002, studies have focused on other areas like the coast of the provinces of Guantanamo [13–16]; Holguin (Gibara and Banes) [17–20] and Cienfuegos [21]. Hidalgo et al. [22] performed a broader spatial analysis covering the coast from Gibara (Holguín) to Punta de Maisí (Guantánamo). Other studies have focused on social impact [1], methodological analysis [23], consequences on protected areas [24], and classification of a unique methodology used for this purpose [25]. Hazard, Vulnerability and Risk studies (PVR by the Spanish acronym) got started in all the Cuban provinces in 2005. During the first phase, coastal flooding and flooding related to heavy rain and strong winds were prioritized. These studies were carried out in the ten municipalities of Ciego de Ávila (CA) province during the 2007–2011 period, and followed Guideline 1 (2005) and Guideline 1 (2010) from the National Defense Council to tackle natural disasters. A report about every phenomenon studied was generated [26]. During the development of these studies, CA received the impact of hurricane Ike (2008), and the event was used to analyze the limits of hurricane-related flooding. An Environmental Impact Assessment (EIA) related to this meteor was developed [27]. During the 2017 hurricane season, Cuba was hit by the fourth cat 5 hurricane since 1791. The storm had a direct and severe impact on the province of Ciego de Avila, so an EIA was performed [28]. The study included a detailed analysis of the coastal areas inundated by this system. Before Hurricane Irma’s EIA, [29] had obtained a new chronology of coastal flooding for a bigger number of provinces, including CA, which were updated by Córdova et al. [30]. However, it does not constitute a chronology by itself, because it only dealt with seven coastal flooding cases, which took place in or after 1964. The need to foster research on the reconstruction of past extreme surge events was highlighted, because it is limited in the study area [31]. This could enhance existing chronologies and increase risk perception of these phenomena. Research to enhance knowledge on coastal floodings as extreme phenomena is very important for Cuba because of the impacts they can generate, and due to the large number of human settlements located in coastal areas. Besides, the conditions of the Cuban archipelago make these settlements more vulnerable. The objective of this research is to present the spatial behavior of coastal flooding in Ciego de Ávila province (Cuba), using hazard, vulnerability and risk components.

2 Materials and Methods To study the risks associated to extreme hydrometeorological events, the physical and geographical characteristics of the study area should be considered and the methods to estimate hazard, vulnerability and risk should be defined.

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2.1 General Characteristics of the Province. Physical and Geographical Characterization Cuba is the largest island of the Caribbean region. Ciego de Avila is a one of the Cuban provinces. It is located in Central Cuba (Fig. 1). Its limits are the Old Bahama Channel to the north, the Gulf of Ana María to the south, the province of Sancti Spíritus to the west and the province of Camagüey to the east. The location of CA, according to the geographical coordinates is as follows: Lat: 20°50 00 S, and 22°27 00 N. Long: 79°07 42 W and 78°08 42 E. For this kind of study, it is important to keep in mind the different factors that influence risk value (percent of flooded area in relation to the study area, amount of houses affected typology and technical condition; number of people affected (gender and age), impact on roads; impact on the most fragile coastal ecosystems). The efficacy of risk management and vulnerability indicators will largely depend on the behavior of these factors. Coping with extreme disaster will get more or less complex depending on the above-mentioned characteristics. The study included the 88% of the province area (Table 1). The keys on both coasts were excluded. The study covered 79% of the province coasts. From the administrative point of view, Cuba is divided into provinces, which at the same time are divided into municipalities. The municipalities are divided into constituencies. The province of Ciego de Avila has 10 municipalities and 62 constituencies. The relief of CA province is mostly flat and the Júcaro-Morón plain is a very significant element of that geography (Fig. 2). Punta Alegre, Turiguanó and Cunagua heights rise from this plain, and also a portion of the Bamburanao-Jatibonico mountain range, where the highest elevations of the province are located, including the Merino Peak (highest elevation), with a height of 396.6 m above mean sea level (MSL).

Fig. 1 Location of study area

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Table 1 Main characteristics of the province Ciego de Ávila, Cuba Indicator

Estate

Total area of the province (km2 )

6,971.63 Including 654 keys of the northern coast and 122 keys of the southern coast

Comments

Area of the province (km2 ) considered for 6,124.69 Keys are not included this study Coast length (km)

338.00

265 in the northern coast and 73 in the southern coast

Coast length (km) considered for this study

268.70

Keys not included

Municipalities (u)

10

Constituencies

62

Fig. 2 Altimetry map of the province Ciego de Ávila, Cuba

From the geological point of view, the region is a saddle or structural depression with a predominance of carbonate rokc from the Miocene linking two older and more complex structures consisting of igneous, metamorphic and sedimentary rocks towards the west and east ends of the province. This situation is reflected on the spatial design of the water system because the oldest blocks of the central-west and

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central east regions act as dispersion centers of water resources, while in the karstic plain, that cover the central part, currents are ephemeral and disappear in the karst.

2.2 Hazard, Vulnerability and Risk Assessment: General Aspects The study of coastal flooding risks consists of different steps such as numeric modeling of wave setup and surge, cartography of coastal flooding maximum limit (hazard scenarios or areas that can be affected) using the Geographic Information System (GIS); analysis of vulnerability based on the indicators used during the Hazard, Vulnerability and Risk assessment [32, 33] and risk assessment based on hazard and vulnerability. A list of maps and images used for this study is shown below: • Digital Elevation Model (DEM) in a scale of 1:25,000 and in digital format (GRID), to map inland flooding and the analysis of areas that can be affected by flooding. • Contour map to make the correction to the DEM if necessary. • Map or bathymetric data of the study area to make the frame grid for the calculation of the wave setup model. • Geomorphologic map. • Coastline map to determine limit of the emerged terrain. • Soil map to determine drainage capacity of inundated areas. • Map of villages and settlements in order to know affected populations (scale 1:5,000). This was included in the thematic layers of the Planimeter made by GEOCUBA Company. • Road Map to identify blocked access routes. This is a very useful information for evacuation plans. • Map with the municipalities’ constituency limits, which was updated according to the New Administrative Division. • Map with the administrative limits of the municipalities and the province (scale 1:25,000), with corrections included. • Satellite images as supplementary information in order to achieve results. Hazard assessment Hazard scenarios were developed by the National Group for Risk Assessment from the Environment Agency (AMA by its Spanish acronym) of the Ministry of Science, Technology and Environment (CITMA by its Spanish acronym), with wave and surge data gathered by the Institute of Meteorology (INSMET by its Spanish acronym). Data on coastal bathymetry and topography were provided by the Institute of Ocean Sciences (ICIMAR by its Spanish acronym). Three impact intensities related to flooding hazards associated to cat 1, 3 and 5 hurricanes were considered (Table 2)

110 Table 2 Categories of the hurricanes considered in the study on coastal flooding risks according to the Saffir-Simpson Hurricane Wind scale

F. M. Pupo et al. Category

Central pressure (hPa)

Maximum sustained winds (km/h)

1

980

118–153

3

945–964

178–209

5

< 920

> 250

[34]. The main hurricane-related variables used for the work were the return periods, wave maximum estimated height, storm surge and mean sea level rise and a hazard value for every intensity. INSMET tropical cyclone databases were used, as well as those of the Tropical Forecast Center of the National Hurricane Center of the United States, corresponding to the 1851–2005 period [35]. Data were processed using the “Eye of the Storm” and “HURREVAC” softwares. The storm surge mathematic modeling was done using the High Resolution Numeric Model MONSAC 3.1 [36] and its bathymetric database. This database consists of a rectangular grid of 241 rows and 561 columns. It covers the area between 18° and 24° N and 73° and 87° W, for a total of 135 201 points. The Spatial Representation of Pitch Hight is of 2,775 km. The estimation of extreme regimes was done using the method of peak frequencies [37]. Data were arranged in a descending order according to the Saffir-Simpson Hurricane Wind Scale. Risk assessment was done using the methodology proposed by Salas et al. [38]. Storm surge and wave setup modelings were carried out in nine sites, covering both coasts of the province (Fig. 3), taking into consideration the geographical location of Cuba. An incidence angle according to the hurricane path was considered at every site, which is critical in the modeling process (Table 3). Besides the direction of hurricane incidence angle, the MONSAC takes into consideration the system translation speed, the area of influence of maximum winds, atmospheric pressure and hurricane eye diameter, among other things. The outputs focused on the determination of the return periods of the hurricanes studied (cat 1, 3 and 5) at every site and on surge values and wave hight (Table 4). These aspects were used to map expected flooding limits after the storm. The elaboration, checking, interpolation and processing of bathymetrical data was carried out using the GIS (MapInfo version 9.0). The Vertical Mapper 3.1.1 tool was used to prepare the bathymetry data in the form of a regular grid with a spatial resolution of 50 m among points as basic information in SWAN (Simulating Waves Nearshore Model), to develop wave modeling based on the grids and the inundation maps. Matlab 7 tools were used to speed up data processing for modeling. Wave–induced mean sea level rise was estimated using the balance equation, which includes balance between wave energy (Radiation Tensor Gradient) and dynamic pressure (Dingemans et al. 1987). The SWAN model is used to solve this equation.

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Fig. 3 Map showing sites where storm surge and wave setup were estimated in the province of Ciego de Avila

Table 3 Sites where storm surge and wave setup were estimated in the province of Ciego de Avila, regarding hurricane incidence angle (Imp_Dir) Site/ Modelling

Place

Municipality

Location

CoordX

CoordY

Site 1

Guillermo Key

Morón

Northern Coast

−78,6860

22,6147

90

Site 2

Coco Key

Morón

Northern Coast

−78,4026

22,5542

90

Site 3

Paredón Morón Grande Key

Northern Coast

−78,1628

22,4818

90

Site 4

Máximo Gómez

Northern Coast

−78,8010

22,3883

45

Site 5

Punta de La Morón Virgen

Northern Coast

−78,6179

22,3320

45

Site 6

Cunagua Beach

Bolivia

Northern Coast

−78,3054

22,1735

45

Site 7

Cayo Coco causeway

Morón

Northern Coast

−78,4946

22,3355

90

Site 8

Júcaro

Venezuela

Southern Coast

−78,8576

21,6176

180

Site 9

Arbitrary Site

Baraguá

Southern Coast

−78,6722

21,5491

225

Chambas

Imp_Dir

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Table 4 Hurricanes return periods at the studied sites and hurricane-related mean sea level rise values (H1, H3 and H5: Hurricanes of Category 1, 3 and 5, respectively) Site/ Modelling

Return Period for Hurricanes (years)

Total Elevation (meters)

H1

H3

H5

H1

H3

H5

Site 1

6.3

17.3

130.2

1.46

2.97

5.42

Site 2

6.8

18.7

140.8

1.47

3.02

5.50

Site 3

6.6

18.2

137.1

1.43

2.92

5.31

Site 4

6.0

16.5

124.0

1.72

3.71

6.94

Site 5

6.6

18.2

137.1

1.73

3.73

6.97

Site 6

6.9

19.2

144.7

1.74

3.75

6.98

Site 7

6.6

18.2

137.1

1.55

3.24

5.94

Site 8

6.9

19.2

144.7

1.84

4.05

7.57

Site 9

6.8

18.7

140.8

1.83

4.04

7.57

Vulnerability assessment Information regarding vulnerability types (structural, non-structural, functional, social, ecological and economic) was recorded in Excel spreadsheets. Every type of vulnerability was determined by applying the methodological guidelines proposed by Cuba’s Environment Agency [33]. Authors made a few adjustments to represent zones with no hazard, and consequently with no vulnerability and no risk. Total vulnerability (the sum of every vulnerability previously estimated) of the constituencies of the 10 municipalities was estimated depending on the values recorded (Table 5). Risk assessment The assessment of total risk was determined by multiplying H x V, taking into account the degree of vulnerability (V) and the coastal flooding hazard (H) previously estimated. The cost of assets under potential damage was not available, so the result only includes the specific risk, which was classified according to the risk interval values (Table 6). The values of the three components used to assess risk (H, V and R) were spatially represented in each municipality, although they were estimated at a constituency scale. Table 5 Classification of vulnerability based on the calculated values

Assessed vulnerability interval values

Vulnerability classification

0,00

Without vulnerability

0,10 a 0,33

Low vulnerability

0,34 a 0,66

Medium vulnerability

0,67 a 1,00

High vulnerability

Hurricane Related Coastal Flooding in the Province of Ciego … Table 6 Risk classification based on the range of calculated values

Assessed risk interval values

113 Risk classification

0,00

Without risk

0,10 a 0,33

Low risk

0,34 a 0,67

Medium risk

0,68 a 1,00

High risk

3 Results and Discussion 3.1 Hazard Spatial Behavior The coastal flooding hazard is a combination of physical and geographical factors that make up the Hazard Scenario, and meteorological factors regarded as the triggering factor [40], in this case, hurricanes. The Hazard Scenario was spatially represented (Fig. 4), which contributes to determine the hazard-prone municipalities and constituencies. Figure 4 shows the occurrence of coastal flooding related to cat 1, 3 and 5 hurricanes and the fact that inundation prone area is larger on the northern coast for every scenario.

Fig. 4 Coastal flooding hazard scenarios related to cat 1, 3 and 5 hurricanes in Ciego de Avila province

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Fig. 5 Constituencies of Ciego de Ávila province under coastal flooding hazard, taking as reference maximum potential damage (a cat 5 hurricane)

For every scenario, all the coastal municipalities of the province can be affected. Besides, the non-coastal municipality of Ciro Redondo has a small flooding prone area to the north, in the constituency of Peonia. A constituency (Las Veinte) in the coastal municipality of Baragua has a similar situation. This area, located in the southern coast of the province, is covered by large healthy mangrove stands that act as a natural barrier against wave energy. The results of this work are a well-documented approach to what really happens in the flooding-prone areas of Ciego de Avila. This is the first time that flooding is modeled in the northern and southern coasts of the province. Hurricane-related coastal flooding risk has been identified in 19 constituencies from six municipalities (Fig. 5). However, only certain portions -and not the entire area- of these municipalities are under inundation hazard. The flooding magnitude is closely related not only to hurricane intensity, but also to the predominance of flat and low areas like the ones of the province of Ciego de Avila. On the northern coast (Chambas, Morón and Bolivia municipalities), biogenic cumulative coasts (mangrove) with small abrasive-cumulative sectors prevail. Taking into consideration the characteristics of their sediments, both coasts are muddy and protected by ecosystems like mangrove swamps and coral reefs.

3.2 Vulnerability Behavior Total vulnerability is the sum of independent vulnerabilities. The behavior of vulnerabilities for every hurricane category (Fig. 6A–C), shows the highest vulnerability

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Fig. 6 Coastal flooding vulnerability related to cat 1 (A), 3 (B) and 5 (C) hurricanes in the province of Ciego de Avila

values on the southern coast. However, vulnerable areas are larger in the northern coast, which means that coastal flooding related hazard is also greater. Maps show the degree of vulnerability at every constituency affected by coastal flooding. It is high in the constituencies of Jucaro and Jagueyal, but low and medium in the rest. Tabulated information regarding vulnerability values has been included to avoid bias among constituencies with similar category (high, medium, low) (Table 7). The municipalities affected are partially vulnerable regarding area and 18 constituencies are not vulnerable under any scenario. Hazard is present in 19

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Table 7 Vulnerability values of coastal flooding for cat 1, 3 and 5 hurricanes (V-H1, V-H3 and V-H5) in the province of Ciego de Avila Municipality

Constituency

V-H1

V-H3

V-H5

Chambas

1. Punta Alegre

0,32

0,47

0,64

Chambas

2. El Asiento

0,00

0,00

0,21

Chambas

3. Las Palmas

0,23

0,37

0,56

Chambas

4. Falla

0,13

0,22

0,32

Chambas

5. Ranchuelo

0,15

0,20

0,31

Chambas

6. Los Perros

0,21

0,32

0,41

Morón

7. Turiguanó

0,15

0,19

0,24

Morón

8. Este

0,17

0,26

0,34

Morón

9. Oeste

0,31

0,38

0,42

Morón

10. Patria

0,06

0,13

0,21

Morón

11. Vaquerito

0,00

0,00

0,52

Bolivia

12. La Loma

0,12

0,20

0,28

Bolivia

13. Bolivia

0,26

0,34

0,41

Bolivia

14. Yarual

0,17

0,26

0,39

Ciro Redondo

15. Peonía

0,05

0,06

0,08

Venezuela

16. Jagüeyal

0,26

0,49

0,70

Venezuela

17. Júcaro

0,53

0,64

0,89

Baraguá

18. Las 20

0,26

0,36

0,53

Baraguá

19. Baraguá

0,28

0,37

0,55

constituencies, where the degree of vulnerability is proportional to it. Both significantly depend on the category of the hurricanes that impact the area. Júcaro and Jagüeyal were the most vulnerable constituencies on the southern coast, and the former the most vulnerable of the province. On the north coast, Punta Alegre represents another critical case in the province. Although its degree of vulnerability is medium, under the impact of a cat 5 hurricane, values are very close to high (Fig. 5, Table 7).

3.3 Risk Spatial Distribution and Behavior Spatial distribution of hazard is similar to that of vulnerability, with the highest values on the southern coast and the largest area on the northern coast. On a first phase, in the analysis of the coastal flooding risk associated to cat 1 hurricanes, the spatial distribution (Fig. 7A) and a bar chart of the behavior of every constituency affected (Fig. 7B) are shown.

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Fig. 7 Risks of coastal flooding related to cat 1 hurricanes in Ciego de Avila province: spatial distribution (A) and a graph including values per constituencies (B)

Two constituencies did not show flooding risks related to cat 1 hurricanes as they were not impacted by such storms. Jucaro is the only one showing high risk, while eight constituencies show medium risk; the rest show low risk. Among the low risk constituencies, Yarual (Municipality of Bolivia) shows the highest risk (0.32). The highest risk values of those with Medium risk were recorded fort the constituency of Punta Alegre (Municipality of Chambas) and in the municipality of Baragua. The situation changes for coastal flooding associated to cat 3 hurricanes because risk values increase on both, the northern and southern coasts (Fig. 8A, B). Under the effects of a cat 3 hurricane, three constituencies show High risk, but, Júcaro remains as the constituency with the highest risk (0.96). Among the nine constituencies with Medium risk (seven on the northern coast and two on the southern one), the highest risk values are present in Punta Alegre, Las Palmas, Oeste, Baraguá

Fig. 8 Coastal flooding risk related to cat 3 hurricanes in the province of Ciego de Avila: Spatial distribution (A), graph of values per constituencies (B)

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Fig. 9 Coastal flooding risk related to cat 5 hurricanes in the province of Ciego de avila: Spatial distribution (A), graph of values per constituencies (B)

and Las 20. Among the six constituencies with Low risk, Peonía (Ciro Redondo municipality) shows the lowest risk. Under the effect of cat 5 hurricanes (Fig. 9A, B), the scenario changes significantly on the northern coast of the province. Two new constituencies under risk appear: El Asiento (Chambas), with Low risk and Vaquerito (Morón municipality) with Medium risk and two others show high risk: Falla and Punta Alegre (Chambas municipality). Summing up, 19 constituencies are under hurricane related coastal flooding risk, which has a close relation to hazard and vulnerabilities in six municipalities; one of them with very low risk (Constituency of Peonia, Ciro Redondo municipality). When doing this kind of research, ranking the affected constituencies according to risk value is recommended. In our work, constituencies are listed from higher to lower risk (descending) as follows: Júcaro, Jagüeyal, Punta Alegre, Oeste, Las Palmas, Baraguá, Las 20, Bolivia, Los Perros, Vaquerito, Yarual, Este, Falla, Ranchuelo, La Loma, Turiguanó, Patria, El Asiento and Peonía. This order makes possible the improvement of risk management, prioritizing the most vulnerable sites. Emphasis is put on the indicators that influence total vulnerability, based on the assessment of each vulnerability. As part of the risk management process, the indicators used to assess vulnerabilities must be examined and updated because they undergo changes overtime, mainly demographic ones, quite variable at the spatial and temporal scales. The updating of frequency and intensity of the natural phenomena that brings about these risks and the environmental conditions related to them-particularly climate change and its relation to extreme meteorological phenomena-must not be overlooked. We should also focus on the changes of the climate of Cuba [41], which are related to the changes at a global scale [42]. Extreme meteorological events, particularly hurricanes should be prioritized because of their relation to coastal flooding, so looking into historical data of hurricanes in the Atlantic with emphasis in the study area is critical in this type of research [43–47]; Cuba [48–53]. In the context of a changing climate, mean sea level rise is also extremely important [54, 55]. The

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magnitude of coastal flooding is highly dependent on the behavior of sea level when hurricanes impact a certain area. Since 1999, several studies have focused on the trends of coastal flooding based on the variations of mean sea level (MSL) [56–59].

4 Conclusions Chambas, Morón, Bolivia, Venezuela, Baragua, and to a lesser degree Ciro Redondo have been regarded as hurricane related coastal flooding prone municipalities. In the case of Ciro Redondo, it will only be affected by this phenomenon under extreme conditions like the impact of a cat 5 hurricane. Of the 19 constituencies under these circumstances, 4 are in the southern coast and 15 in the northern coast, where inundation-prone area is evidently larger. In Ciego de Ávila province, the areas with medium vulnerabilities to coastal flooding prevail. The most vulnerable municipalities are Venezuela and Chambas, on the southern and northern regions, respectively. The northern coast is spatially more vulnerable, although the highest vulnerability value was recorded for the constituency of Jucaro, located on the southern coast of the municipality of Venezuela. According to what was previously said, Venezuela and Chambas are the municipalities with the highest coastal flooding risk. However, 19 (31%) out of the 62 constituencies of Ciego de Ávila province are under the risk of coastal flooding related to hurricanes: Júcaro, Jagüeyal, Punta Alegre, Oeste, Las Palmas, Baraguá, Las 20, Bolivia, Los Perros, Vaquerito, Yarual, Este, Falla, Ranchuelo, La Loma, Turiguanó, Patria, El Asiento and Peonía. The first three show the highest risk values. Acknowledgements Our special thanks to the Institute of Meteorology, the Institute of Ocean Sciences and the National Group for Risk Assessment from the Environment Agency, for the information provided to undertake this research. Also, the support of Lic. Vicente Osmel Rodríguez Cárdenas is greatly appreciated. We greatly appreciate the financial support of the Territorial Program of Science and Technology (Medio Ambiente y Desarrollo Sostenible), which made possible this research.

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26. Moreno-Suárez, O., Rodríguez-Ríos, Y., Matos-Pupo, F., López-Mayea, A., González-Abreu, R., Rodríguez-Cueto, Y., González-Ramírez, I., Rodríguez-Zayas, M., Hernández-Hernández, S., Vidal-Olivera, V., Sorí-Gómez, R.: Estudio de peligro, vulnerabilidad y riesgos de desastres de inundaciones por penetraciones del mar, inundaciones por intensas lluvias y afectaciones por fuertes vientos en la provincia Ciego de Ávila. CITMA Ciego de Ávila. 118 p (2011). 27. Delgado-Gómez Y., Pazos-Alberdi, C., Cruz-Álvarez, J., Olivera-Isern, M.C., MorenoSuárez, O., Benedico-Rodríguez, A.O., López-Mayea, A., Navarro-Díaz, J., CórdovaGarcía, O., Galardy-Zamora, R., Valentín-Pérez, M., Matos-Pupo, F., Zúñiga-Ríos, A., Guimarais-Bermejo, M.; Acevedo-Rodríguez, C.H.; Gómez-Fernández, R.; Pina-Amargós, F., Salvat-Torres, H., Hernández-Fernández, L., Cardoso-Gómez, P.E., Parada-Isada, A., PérezCarmenate, R., Valdés-González, L.: Evaluación de los impactos ambientales provocados por el paso de los huracanes Gustav y Ike en la provincia de Ciego de Ávila. Unidad de Medio Ambiente, CITMA Ciego de Ávila. 38 p (2008). 28. UMA: Informe de Evaluación del Impacto Ambiental de Desastre provocado por Irma (Huracán de Gran Intensidad) en la provincia Ciego de Ávila. Unidad de Medio Ambiente, CITMA Ciego de Ávila. 33 p (2017). 29. Hidalgo, A., Mitrani, I., Pérez, G., Hernández, N., Córdova, O.L., Regueira, V., Ramírez, W., González, C.M., Machado, A., Perigó, E., Lorenzo, L., Agüero, K., Vinajera, V., Jiménez, J., Boligán, M., Sánchez, L., Victorero, A., Mursulí, A., García, L., Ortega, F.E., Castellanos, M. J., Araujo, L.A., Báez, E., Laborde, N., Casals, R.: Cronología de las inundaciones costeras por penetración del mar para el archipiélago cubano. Informe Científico-Técnico. Instituto de Meteorología, 40 p (2016). 30. Córdova, O.L., García, M., López, A.: Cronología de las inundaciones costeras en la provincia de Ciego de Ávila. Período 1960–2017. Revista Cubana de Meteorología, 24(2):136–145 (2018). 31. Matos-Pupo, F., Peros, M., Rodríguez, Y., Hernández-Fernández, L., Guash, F.: Depósitos de huracanolitos en Jardines de la Reina (Cuba) provocados por oleajes extremos: paleohuracanes o paleotsunamis?. XI Congreso de Ciencias del Mar, MarCuba 2018. Palacio de Convenciones, Cuba (2018). 32. AMA: Lineamientos Metodológicos para la Realización de los Estudios de Peligro, Vulnerabilidad y Riesgos de Desastre de Inundación por Penetración del Mar, Inundaciones por Intensas lluvias y Afectaciones por Fuertes Vientos. Agencia de Medio Ambiente, CITMA (2008). 33. AMA: Cuba. Metodologías para la determinación de riesgos de desastres a nivel territorial. Parte 1. Grupo de Evaluación de Riesgo de la Agencia de Medio Ambiente (AMA) del Ministerio de Ciencia, Tecnología y Medio Ambiente (CITMA). PNUD Cuba. ISBN: 978–959–300–033–8. 110 p (2014). 34. Simpson, R. H.: The hurricane disaster potential scale. Weatherwise. USA 27, pp. 169-186 (1974). 35. NHC: Hurricane Best Track Files (HURDAT). Atlantic Hurricane Database Re-analysis Project. Available: http://www.nhc.noaa.gov/data/hurdat/hurdat_atlantic_1851-2017.txt, last accessed 2020/7/20. 36. Pérez-Parrado, R., Salas, I., Samper, S., Dole, J., Pérez, A., Rodríguez, C., Pantaleón, B.: Mapas de Peligro por surgencia para el archipiélago cubano. Escenario actual y previsto por Cambio Climático, Capítulo 3. Informe de resultado del Proyecto de Investigación No. 01309168 (Informe Final) “Impacto de la surgencia en el archipiélago cubano, considerando los Cambios Climáticos”, Programa Nacional de Cambio Climático en Cuba. INSMET. La Habana, Cuba. 207 p (2006). 37. Martín, M.J.: Análisis Medio y Extremal del Oleaje. Cuadernos de Investigación. C 22. CEDEX. Madrid (1990). 38. Salas, I., Pérez-Parrado, R., Samper, S., Dole, J.: Surgencia provocada por los ciclones tropicales. Archipiélago Cubano. Informe Técnico para la Directiva 1. Agencia de Medio Ambiente e INSMET. 92 p (2008). 39. Dingemans, M.W., Radder, A.C., de Vriend, H.J.: Computation of the driving forces of waveinduced currents. Coastal Engineering 11, pp 539-563 (1987).

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40. Vulnerabilidad y Evaluación del Riesgo. http://www.insmet.cu/sometcuba/v04_n01, last accessed 2020/7/20. 41. Planos, E., Vega, R., Guevara, A.: Impacto del Cambio Climático y Medidas de Adaptación en Cuba. Instituto de Meteorología, Agencia de Medio Ambiente, Ministerio de Ciencia, Tecnología y Medio Ambiente. La Habana, Cuba, 430 p (2013). 42. IPCC: Cambio climático 2013. Bases físicas. Contribución del Grupo de trabajo I al Quinto Informe de Evaluación del Grupo Intergubernamental de Expertos sobre el Cambio Climático. Cambridge y Nueva York: Cambridge University Press (2013). 43. Landsea, C.W.: A climatology of intense (or major) Atlantic hurricanes. Mon. Wea. Rev., 121, 1703–1713 (1993). 44. Ballester, M., González, C.: Variaciones y tendencias de la ciclogénesis en el Atlántico Norte. Boletín de la Sociedad Meteorológica de Cuba. Vol.3, No.1 (1997). 45. Landsea, C.W.: Counting Atlantic tropical cyclones back to 1900. Eos, Trans. Amer. Geophys. Union, 88, 197 (2007). 46. Landsea, C.W., Glenn, D.A., Bredemeyer, W., Chenoweth, M., Ellis, R., Gamache, J., Hufstetler, L., Mock, C., Pérez, R., Prieto, R., Sánchez-Sesma, J., Thomas, D., Woolcock, L.: A Reanalysis of the 1911–20 Atlantic Hurricane Database. Journal of Climate (21): 2138-2168 (2008). 47. Landsea, C.W., Franklin, J., Beven, J.: The revised Atlantic hurricane database (HURDAT2). April 2014. The National Hurricane Center (NHC), Miami, Florida, USA (2014). 48. Pérez, R., Vega, R., Limia, M.: Los ciclones tropicales que han afectado a las provincias de Ciudad de La Habana y La Habana. Boletín de la Sociedad Meteorológica de Cuba. Vol.6, No.1 (2000). 49. Pérez, R., Vega, R., Limia, M.: Los huracanes más intensos y desastrosos de Cuba en los últimos dos siglos. Boletín de la Sociedad Meteorológica de Cuba. Vol.7, No.2 (2001). 50. Limia, M., Vega, R., Pérez R.: Climatología de los ciclones tropicales que han afectado a Cuba y sus provincias. Memorias del Congreso Iberoamericano de Meteorología “La Meteorología y el Desarrollo Sostenible”, Ciudad de La Habana, Cuba (2003). 51. Pérez, R.: Cronología de los Huracanes de Cuba. Instituto de Meteorología, Agencia de Medio Ambiente, Ministerio de Ciencias, Tecnología y Medio Ambiente. La Habana, Cuba. 7 p (2013). 52. Córdova, O.L., García, M., Machado, A., Borrego, R.: Huracanes que afectaron a Ciego de Ávila y sus periodos de retorno. Periodo 1851 a 2017. Revista Cubana de Meteorología, 24 (3): 245–255 (2018). 53. Roura, P., Sistachs, V., Vega, R., Alpizar, M.: Caracterización estadística climatológica de huracanes en Cuba durante el período 1791-2016. Revista Cubana de Meteorología, 24 (3): 304-312 (2018). 54. Hernández, M., Marzo, O., Acanda, A.: Tendencia lineal del nivel del mar en algunas localidades del Archipiélago Cubano. Serie Oceanológica, No. 7, ISSN 2072–800x (2010). 55. Pérez-Parrado, R.: Ascenso del nivel del mar en Cuba por Cambio Climático. Revista Cubana de Meteorología, 25(1): 76-83 (2019). 56. Mitrani, I., Vega, R., Pérez-Parrado, R., Salas, I.: Determinación de las características extremas del viento, el oleaje y la sobreelevación del nivel del mar en las costas de Cuba. Revista Cubana de Meteorología, 6(1): 38-42 (1999). 57. Mitrani, I., Pérez-Parrado, R., García, O., Salas, I., Juantorena, J., Ballester, M., Beauballet, P., Pérez, A.L., Rodríguez, C.: Las zonas más expuestas a las inundaciones costeras en el territorio cubano y su sensibilidad al posible incremento del nivel medio del mar por cambio climático. Revista Cubana de Meteorología, 7(1): 45-50 (2000). 58. Mitrani, I., Díaz, O.O., Vichot, A., Hernández, I., Hidalgo, A., García, E., Rodríguez, J.A.: Tendencias climáticas de las inundaciones costeras severas en áreas de Cuba. Revista Ciencias de la Tierra y del Espacio, 13(2): 68-84 (2012). 59. Mitrani, I.: Las inundaciones costeras en Cuba y sus tendencias. Agencia de Medio Ambiente (Ed): 160p, ISBN: 978–3000–061–1 (2014).

Natural Emissions to Atmosphere: Biogenic Emissions in the Citrus Plantations of Western Cuba Ricardo Manso , Yosdany González, Javier Bolufé, Rosemary López, Israel Borrajero, Juan Carlos Peláez, and Miguel Aranguren

Abstract Biogenic volatile organic compounds emitted by terrestrial ecosystems play an important role in determining atmospheric constituents that control air quality and climate. These emissions include volatile organic compounds and nitrogen oxides from soil microorganisms. Considering that the extensive land used for citrus plantations may play an important role in the chemistry of the atmosphere, the objective of this study was to estimate biogenic emissions in the citrus plantations of Jagüey Grande and Ceiba. The GloBEIS model has the appropriate algorithms to make various estimates of emissions of various components, taking as input data meteorological, physiological and land use variables, among others. The main source data are from Institute of Meteorology of Cuba and The Citric Enterprise Jagüey. Approximately 66.25% of the biogenic emissions correspond to Jagüey Grande due to its greater cultivation area. The influence of the leaf area index on the emissions is evidenced. The highest emissions of total monoterpenes and other volatile organic compounds corresponded to the periods of highest temperature in the months of July and August and at 13 h due to the direct influence of this variable on the two emission processes of these species. We found that biogenic emissions for the year 2015 were almost entirely due to the emissions of Organic Volatile Biogenic Compounds (99.1%). The total monoterpenos emissions was (40.45%) and other Biogenic Volatile Organic Compounds (59.53%). Nitrogen monoxide emissions are influenced by temperature, in this case of the soil, reaching its maximum emission between 12 and 14 h. Keywords Biogenic emissions · Volatile organic compounds · Atmospheric chemistry R. Manso (B) · Y. González · J. Bolufé · R. López Pollution and Atmospheric Chemistry Center, Institute of Meteorology, La Habana, Cuba e-mail: [email protected] I. Borrajero · J. C. Peláez Center for Atmosphere Physics, Institute of Meteorology, La Habana, Cuba M. Aranguren The Citric Enterprise Jagüey, Tropical Fruit Research Institute, La Habana, Cuba © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Cardenas et al. (eds.), Proceedings of the 3rd International Conference on BioGeoSciences, Springer Proceedings in Earth and Environmental Sciences, https://doi.org/10.1007/978-3-030-88919-7_10

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1 Introduction The Earth is a single and partially self-regulating system that consists of interlinked physical, chemical and biological components. Biogenic volatile organic compounds (BVOCs) emitted by terrestrial ecosystems into the atmosphere play an important role in determining atmospheric constituents that control air quality and climate. These BVOCs are produced by a variety of sources in terrestrial ecosystems, but most of the global total emission is from foliage. Terrestrial vegetation emits large amounts of non methane organic vapors known as biogenic volatile organic compounds (BVOCs) (700–1000 TgC yr−1 ) (Laothawornkitkul et al. 2009). Terpenes Cn5 H n8 (e.g., isoprene, monoterpenes, and sesquiterpenes) are of particular importance because they are abundant in the atmosphere and are chemically very reactive. Furthermore, it is estimated that vegetation alone contributes to approximately 90% of the global volatile organic compound emissions (Guenther et al. 2006; Piccot et al. 1992). Biogenic volatile organic compounds (BVOCs) are known to have effects on regional air quality (Churkina et al. 2017; Shimada et al. 2017) and climate (Jiang et al. 2010). The tree composition can markedly influence the concentration of specific BVOCs in the forest air, which also exhibits cyclic diurnal variations (Antonelli et al. 2020). Ormeño et al. (2009) demonstrates that terpene concentration increases the flammability of leaf litter. The complexity of these factors, their interactions and the different responses of different BVOCs (Peñuelas et al. 2003) produces the large qualitative and quantitative, spatial and temporally variability of emission s and frequent deviations from current standard emissions models (Loreto and Schnitzler 2010). It is hypothesized that forest BVOCs are not only involved in some plant-related physiological functions, but they also have a central role in forest ecosystems (Šimpraga et al. 2019). The nitrogen cycle in nature occurs through nitrification and denitrification processes carried out by microorganisms, it contributes significantly to the concentration of nitrogen oxides in the atmosphere. Soil microorganisms emit NO and other gases such as N2 , N2 O and CO, but they are less relevant in emissions, although the term NOX is used as a reference due to the high reactivity of NO in the atmospheric environment to produce NO2 (Williams et al. 1992). Citrus plantations occupy considerable surfaces in some regions of Cuba, mainly in Jagüey Grande in the Matanzas province and in Ceiba in the Artemisa province, and their biogenic emissions could interact with other components and play an important role in the chemistry of the atmosphere in those regions. Citrus production in Cuba had its golden age in the 1980s, when one million tons of oranges and grapefruit were produced, mainly, as well as some lemon or small quotas of mandarin, lime and sour orange. The main market in this decade was Eastern Europe which changed its economic relations, causing an economic crisis in Cuba. Other subsequent factors such as the impact on the western region of various hurricanes between 2001 and 2005 and the favorable conditions created by natural

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phenomena, for example, facilitated the entry of various pests or the exacerbation of others.

2 Materials and Methods An alternative to making measurements in situ is the application of mathematical models, based on meteorological and physiological information from the plants. The citrus plantations of Jagüey Grande, in the Jagüey municipality of the Matanzas province, and that of Ceiba in the Caimito municipality, of the Artemisa province were selected for their extension and quality of their agro-cultural exploitation. Regarding the characteristics of the soils, in the municipality of Caimito, specifically in the southern sector, to which the Ceiba citrus area belongs, it is characterized by a predominantly carbonate substrate, typified by krastic processes and a coverage of ferralitic soils. In the municipality, a great variety of soils are located, ferralitic, fersialitic, calcimorphic humic, red Rendzina and little evolved soils are located (Bayón 2004). In Jagüey Grande, according to Aranguren et al. (2015), the soils are of the typical red ferralitic type, according to the new genetic classification of Cuban soils and cataloged as ferralsol rhodic in correlation with the “World Reference Base” (Hernández and Bayón 2005). Locally there is a dominance of calcareous materials that give the soil a moderate permeability and good drainage. The citrus growing area of Ceiba has soils with a mainly ferralitic soil cover (Bayón 2004). In Jagüey Grande, according to Aranguren et al. (2015), the soils are of the typical red ferralitic type. Both plantations are around 150 km apart from each other and belong to the same physical-geographical region. The study of variations in the average temperature per month and per hour was made, and the differences were very slight, of barely 0.5 degrees Celsius. The meteorological data (used, temperature, cloud cover (cloud cover), humidity, and wind speed) for the model runs were from 2015 from the Jagüey Grande and Güira de Melena stations for Ceiba (Climate Center of Institute of Meteorology). The leaf area index (LAI) is a useful variable to characterize the dynamics and productivity of crops; it has a direct relationship between the leaf area and the capacity of the plant to carry out photosynthesis (RIAC 2006). It is defined as the area of the leaves per unit of soil surface area and is one of the most useful parameters to characterize the vegetation, being a very valuable measure that helps to evaluate the density and biomass of the vegetation cover (Almenares 2013). The calculation of the leaf area index (LAI) follows the methodology set forth by Pozo et al. (1994). Studies conducted by Aranguren et al. (2002), in Persian lime plants in Jagüey Grande, determined the effect of different viroid which affects the leaf area of the plants in comparison with healthy plants as a control.

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PAR radiation is determined from solar radiation for the entire spectrum, based on relationships found in previous works carried out at the Center for Atmospheric Physics of the Institute of Meteorology, which estimated the ratio between the Global radiation on the entire spectrum and the Global PAR depending on the height of the sun for the conditions of Cuba. In this work, the estimation of solar irradiance uses the Heliosat II method (Rigollier et al. 2004) based on the archive of available satellite images and the validation of the method with better measurements existing on land from recent studies (Borrajero et al. 2016). In addition, the GloBEIS method was applied, which relates cloud cover with PAR radiation for the entire region and any day (Guenther et al. 2003; EPA 2007). GloBEIS was used in this study because it uses vegetation species-specific emissions factors, it requires minimal inputs, and it can be run on a desktop computer. The emission factors used in GloBEIS (Global Biosphere Emission and Interaction System) are the same as those reported by Guenther et al. (1995) and the MEGAN (Model of Emission of Gas and Aerosols from Nature) model, but expressed at the leaf level and not the canopy level. GloBEIS calculates emissions similar to Guenther et al. (2000) as well as the more recent MEGAN model (Guenther et al. 2006, 2012). This model calculates biogenic emissions of isoprene, total monoterpenes, other volatile organic compounds (OVOC), as well as nitrogen monoxide (NO) emissions from soils for any scale and domain, and has the ability to model prolonged periods of drought and high temperatures Guenther (2017). The model requires at least 3 input files defined by the user, meteorology with hourly meteorological variables (temperature and cloud cover or PAR solar radiation), land use, definition and geographic location of the domain or study area. The values obtained for PAR by processing the GOES 13 visible images have a spatial resolution of 1 km x pixel. The emission factors are defined within the model, but there is the possibility of introducing new factors that adjust to the vegetation present in the defined domain.

3 Analysis and Discussion of Results Emissions of volatile organic compounds and nitrogen oxides are highly dependent on local characteristics. PAR radiation absorption is regulated by chloroplast pigments, the absorption spectrum maxima coincide with the absorption maxima of chlorophyll and carotenoids (Larcher 1977). PAR radiation values follow a homogeneous spatial distribution, although sometimes small changes occur due to local peculiarities or by the effect for a given time of other meteorological elements (Lecha et al. 1994). The processing of the visible images of the GOES satellites to determine solar radiation allows extending the evaluation of PAR radiation for the entire territory of Cuba, based on previously determined regularities between PAR and solar radiation with measurements on the ground.

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Figures 1 and 2 show the behavior of PAR radiation at the Jagüey Grande meteorological station in the same location as the plantation and the Caimito meteorological station a few kilometers from the Ceiba plantation. The maxims occur in April when cloudiness decreases while in June in the middle of the rainy season (Lecha et al. 1994). The maximum values obtained at 12 noon in June reach values between 1000 and 1100 W / m2 , while in December they barely reach between 700 and 800 W /

Fig. 1 Daily PAR radiation at the Jagüey Grande meteorological station in 2015

Fig. 2 Daily PAR radiations at the Guira de Melena meteorological station in 2015

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m2 . During the month of June the sun reaches a higher zenith height and the sun’s rays strike perpendicularly, although the maximum radiation does not occur due to greater cloudiness. In citrus PAR is a good indicator of vegetative growth and photosynthetic activity according the Inter-American Citrus Network. LAI measurements were made in the Jagüey Grande plantations in the 90 s applying the methodology of Pozo et al. (1994). For Cuba, citrus fruits, especially the Valencia orange tree as an evaluated species, present mean LAI values close to 6.4 (Pérez et al. 2003). Some citrus species showed greater annual variations in LAI and production in Ceiba del Agua (Cuba) than in Florida (United States), where LAI was more stable and therefore production (Aranguren 2015). These results correspond to local conditions typical of the largest study area (Jagüey Grande). This value is slightly higher than the one provided by default in the GLOBEIS of 5.0 (Figs. 3, 4, 5 and 6). The figures show similar monthly and daily behaviors in the two stations. The results for isoprene are not shown because its values were insignificant compared to the other BVOCs. Below we show two tables that reflect the execution of the GloBEIS model for two LAI values in the two areas studied. Tables 1 and 2 shows the result of the model execution with the two LAIs (GLOBEIS default value and determined value). Tables 1 and 2 show the result of the model execution with the two LAIs (GLOBEIS default value and determined value). These results demonstrate the marked influence of the LAI on the emissions of COVBs. The total biogenic emissions (EBT) of the citrus growing areas of study, for the year 2015, taking the LAI = 6.4 as reference were in the order of 2654.1 tons

Fig. 3 Monthly daily emissions of total monoterpenes (TMT), in Ceiba and Jagüey Grande in dependence of LAI and hours

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Fig. 4 Monthly emissions of total monoterpenes (TMT), in Ceiba and Jagüey Grande in dependence of LAI

Fig. 5 Monthly emissions of total other volatile compounds (OVC), in Ceiba and Jagüey Grande in dependence of LAI

per year, corresponding almost entirely to the emissions of COVBs (99.1%). This annual emission value corresponds to around 7.27 tons per day on average, without considering the effect of pests or environmental factors (Figs. 7 and 8). Regarding the study areas, approximately 66.25% of the EBT correspond to Jagüey Grande due to the much larger area dedicated to citrus compared to Ceiba, to which the remaining 33.75% correspond. This proportion is maintained when analyzing the different COVBs and NO.

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Fig. 6 Daily emissions of other volatile compounds (OVC), in Ceiba and Jagüey Grande in dependence of LAI and hours Table 1 Emissions LAI = 6.4 (determined value) LAI = 6.4 Citrus area

Compound NO

Ceiba

ISOP

TMT

OVC

BOCVs

TBE

8.3

0.2

359.0

528.4

887.7

895.9

Jagüey Grande

16.2

0.4

704.6

1037.0

1742.0

1758.2

Total

24.5

0.6

1063.7

1565.4

2629.7

2654.1

NO: Nitrogen monoxide, ISOP: Isoprene, TMT: total monoterpenes, OCV: others volatile organic compounds, BVOCs biogenic volatile organic compounds, TBE: Total biogenic emissions. Table 2 Emissions LAI = 5 (GLOBEIS default value) LAI = 5 Citrus area

Compound NO

ISOP

TMT

8.3

0.1

294.6

Jagüey Grande

16.2

0.2

Total

24.5

0.4

Ceiba

OVC

BOCVs

TBE

433.6

728.3

736.5

578.1

850.9

1429.3

1445.5

872.7

1284.4

2157.5

2182.0

NO: Nitrogen monoxide, ISOP: isoprene, TMT: Total monoterpenes, OCV: Others volatile organic compounds, BVOCs: Biogenic volatile organic compounds, TBE: Total biogenic emissions.

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Fig. 7 Monthly TMT emissions depending on the LAI in Ceiba

Fig. 8 Monthly OVC emissions depending on the LAI in Ceiba

The highest emissions of COVBs belong to Other Volatile Compounds (OVC) with 59.53%, due to the considerable number of species included; followed by monoterpenes (TMT) with 40.45%, while isoprene (ISOP) are practically negligible, with 0.02% of the total emissions of COVBs, in correspondence to studies carried out in various citrus species previously (Fares et al. 2011). TMT emissions also occur at night, unlike isoprene, which does not accumulate in leaves (Camargo et al. 2010), although at a lower rate than it occurs during the day.

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This behavior of TMT emissions shows the influence that temperature has on both emission processes of these COVBs, since as the temperature of the leaves of the plants increases the emissions of these species increase, as explained previously in analysis of monthly emissions. The highest emissions occur at 17 h. There is not direct relationship between solar radiation an obviously PAR radiation with emissions, and they also depend on the temperature regime to which the leaves of the trees were exposed in previous hours. Fares et al. (2011), demonstrate direct biogenic emission of acetaldehyde and acetone from Citrus. They wrote that interestingly, past field studies of citrus attributed emissions of acetaldehyde and acetone to atmospheric oxidation processes (e.g. photo oxidation of linalool) because no detectable emission was observed from branch enclosures and for all species Isoprene emissions were negligible in comparison with monoterpenes, suggesting that Citrus species are not significant isoprene emitters and that the methyl-erythritol-phosphate biosynthetic pathway in the leaves (Hampel et al. 2005) produces mainly monoterpenes rather than isoprene in Citrus sp. The emissions generated by soils as a cause of nitrification and denitrification processes produced by bacteria, it is observed that, as with the emissions of BVOCs, NO emissions are influenced by temperature, in this case of the soil, reaching its maximum emission between 12 and 14 h due to the direct relationship between bacteriological action and temperature (Velasco and Bernabe 2004). In the case of NO, emissions show a more homogeneous behavior throughout the year, with a sustained increase from April reaching the maximum values in July and August, coinciding with the months with the highest temperature of both air and soil. The soils in both plantations are not the same, but for the GloBEIS model they respond as similar. Together with little significant difference in air temperature (soil temperature data are not available), it determines that the extension of the planted area is the determining variable in emissions. Guenther et al. (2000) (Figs. 9 and 10). In the case of NO, emissions show a more homogeneous behavior throughout the year, with a sustained increase from April reaching the maximum values in July and August, coinciding with the months with the highest temperature of both air and soil. Our results will be useful in atmospheric chemistry models to estimate whether BVOC emitted from these crop species play a significant role in regional air quality. The future composition of forests and crops may have an unexpected effect on atmospheric chemistry and air quality.

4 Conclusions This study is the first to calculate biogenic emissions in Cuba, particularly in citrus areas of the western region of Cuba. Although further research is needed. Approximately 66.25% of the biogenic emissions correspond to Jagüey Grande due to its greater cultivation area.

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Fig. 9 Monthly emissions of nitrogen oxide (NO) in Ceiba and Jagüey

Fig. 10 Daily emissions of nitrogen oxide (NO) in Ceiba and Jagüey

The use of the foliar area index for citrus fruits used as an input parameter of the GLOBEIS model, previously calculated in Jagüey Grande (6.4), is slightly higher than the default value of the model (5). The estimate of biogenic emissions varies in 21.9%. The methods used to determine photosynthetically active radiation (PAR radiation), based on incident solar radiation and cloud cover, allow the calculation of this variable to be extended to the entire territory of Cuba.

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The emissions of total monoterpenes (40.45%) and of other Biogenic Volatile Organic Compounds (59.53%) were much higher than the emissions of isoprene, practically negligible (0.02%). In the case of NO, emissions show a more homogeneous behavior throughout the year, with a sustained increase from April reaching the maximum values in July and August, coinciding with the months with the highest temperature of both air and soil.

5 Recommendations Perform in situ measurements of meteorological variables and physiological factors, to improve the GLoBEIS input data in analyzed areas. It is important to include crop emissions in models of BVOCs emissions at regional and global scales, and the predictive capabilities of the model depend on the correct parameterization of the species-specific emission potential.

References Almenares, G. (2013) Caracterización del desarrollo vegetativo, floración y cuajado en naranjos [Citrus sinensis (L.) Osbeck] y su manejo en la región oriental de Cuba. Tesis presentada en opción al grado científico de Doctor en Ciencias Agrícolas. La Habana: IIFT, 2013. 110 h Antonelli M., Donelli D., Barbieri G., Valussi M., Maggini V. and F, Firenzuoli (2020). Forest Volatile Organic Compounds and Their Efects on Human Health: A State-of-the-Art Review. International Journal of Environmental Research and Public Health Review. Int. J. Environ. Res. Public Health 2020, 17, 6506; https://doi.org/10.3390/ijerph17186506 www.mdpi.com/journal/ ijerph Aranguren, M., Pérez, R. y Rodríguez, D. (2002). Viroid induced changes in the yield components of Persian limes trees (Citrus latifolia Tan). In: ISHS Acta Horticulturae 632: XXVI International Horticultural Congress. Toronto, Canada. (Horticultura, Art and Science for Life) Aranguren, M. Pérez J. y Pérez Y. (2015) Determinación de los índices bioclimáticos y tipo de clima para la vid en las condiciones de Jagüey Grande, Matanzas, Cuba. ISSN papel: 0253–5785 ISSN on line: 2072–2001. Disponible en: http://cagricola.uclv.edu.cu Bayón P. (2004) Atlas ambiental territorial del municipio Caimito, La Habana, Cuba: apuntes del diagnóstico geoecológico de sus paisajes Borrajero I. et al. (2016) Informes de resultados: Uso del WRF-Solar y el post-procesamiento MOS para el pronóstico de la radiación solar con fines energéticos en Cuba. Estimación de la radiación solar a partir de imágenes de satélite. Proyecto: Pronóstico de la radiación solar y potencia a generar en las plantas fotovoltaicas conectadas a la red eléctrica nacional. Centro de Física de la Atmósfera. INSMET. Camargo Y., Bolaño T., Álvarez A. (2010). Emisiones de compuestos orgánicos volátiles de origen biogénico y su contribución a la dinámica atmosférica Rev. Intropica ISSN 1794–161X 5 77 - 86 Santa Marta, Colombia, mayo de 2010 Churkina G., Kuik F., Bonn B., Lauer A., Grote R. , Tomiak K and Tim M. Butler. (2017) Effect of VOC Emissions from Vegetation on Air Quality in Berlin during a Heatwave. Environ. Sci. Technol. 2017, 51, 6120−6130. Downloaded via 200.55.149.5 on August 9, 2018 at 19:34:05 (UTC). https://doi.org/10.1021/acs.est.6b06514

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Equipment for Studying the Earth’s Magnetic Field Sergey Y. Khomutov , Nikolay N. Semakov, and Vladimir Mochalov

Abstract The paper considers the possibilities of study of the Earth’s magnetic field using data not only from the magnetic observatory, but also from repeat stations and sensor networks. The first section describes the equipment of the magnetic observatory. The second section describes the possibility of using various instruments for absolute magnetic measurements at the observatory and at Repeat stations, as well as the specification of the fluxgate sensors developed at the Novosibirsk magnetic observatory. The third section discusses the possibility of building low-cost sensor networks as the basis for cost-effective measurements of the Earth’s magnetic field. Keywords Earth’s magnetic field · Magnetic observatory · Observations · Variometers · Sensor networks

1 Introduction Many fundamental, scientific and applied tasks require information about the behavior of the Earth’s magnetic field in wide time and spatial domains. For example, the main task of the magnetic observatories is to obtain information about long-term Supported by IKIR FEB RAS, projects AAAA-A17-117080110043-4 and AAAA-A21121011290003-0. S. Y. Khomutov (B) Institute of Cosmophysical Research and Radio Wave Propagation FEB RAS, Elizovskiy district, Mirnaya str., 7, Paratunka 684034, Kamchatka region, Russia e-mail: [email protected] N. N. Semakov Trofimuk Institute of Petroleum Geology and Geophysics of Siberian Branch Russian Academy of Sciences, Koptug ave. 3, Novosibirsk 630090, Russia e-mail: [email protected] V. Mochalov Petrozavodsk State University, Lenin Str., 33, Petrozavodsk 185910, Republic of Karelia, Russia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Cardenas et al. (eds.), Proceedings of the 3rd International Conference on BioGeoSciences, Springer Proceedings in Earth and Environmental Sciences, https://doi.org/10.1007/978-3-030-88919-7_11

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changes of the Earth’s magnetic field at some point on the surface. In contrast to the observatory, the magnetic station is focused on recording only magnetic variations. The tasks of spatial study of the magnetic field are solved using distributed measurements on a network of magnetic observatories, magnetic Repeat station survey (RS), magnetic surveys of various levels (at surface, by aircraft and satellites), magnetotelluric explorations, magnetovariation sounding, etc. The organization of stationary magnetic measurements and magnetic surveys is a complex, multi-level and diverse task, including work on choosing a place, creating infrastructure, calibration and certification of magnetometers, training of personnel, etc. These issues are discussed in detail in monographs and manuals (Jankowski and Sucksdorff 1996; Nechaev 2006; INTERMAGNET 2012). A detailed description of the work at the Repeat stations is presented in (Newitt et al. 1996). It is assumed that these magnetic measurements are made with the highest possible accuracy. But there are some tasks, which can not require high accuracy, for example, educational goals, magnetic measurements in place with very strong spatial magnetic gradients, preliminary estimations of magnetic environment, etc. Moreover, some errors or specific requirements (speed of observations, low cost of magnetometers) can restrict the accuracy of magnetic measurements at observatories and Repeat stations.

2 Magnetometers for Magnetic Observatory The kind of magnetic measurements defines the configuration of the required equipment. At present, two types of measurements are performed at magnetic observatories: variational and absolute. The requirements for these measurements are determined by the standards of the modern magnetic observatory network INTERMAGNET (INTERMAGNET 2012). Variation measurements are performed automatically, with a high frequency and sensitivity, however, they often have low long-term stability and depend on various factors, for example, temperature effects or tilt of sensors. More important is fact that only variations relative to some hardware level are measured. Long-term stability is provided by absolute observations that provide the total field vector. But they have relatively low accuracy, are performed manually, rarely enough, and require high qualification of the observers. Combining these two types of measurements allows us to obtain the total field components at times from minutes to decades. Variometers are used to record the variations (dH, dD, dZ), (dX, dY, dZ), (F, dD, dI) or others, depending on the orientation of the magnetometer axes, and have the following specifications: measurement frequency 1 Hz, resolution is 0.1 nT, at least, dynamic range up to 8000 nT (depending on latitude), temperature dependence no more than 0.25 nT/◦ C, long-term stability is 5 nT/year (INTERMAGNET 2012). The non-orthogonality of the sensor axes, the accuracy of time synchronization, the digital filters used, etc. are also specified.

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Fig. 1 Fluxgate magnetometers FGE (a) and LEMI-018 (b) and quartz variometer “Quartz-3” (c). Only sensors are presented at photos

The most well-known modern variometers (Fig. 1) are fluxgate magnetometers FGE-DTU (DTU Space, National Space Institute, Denmark, DTU Space Homepage 2021) and LEMI-018 and LEMI-025 (Lviv Center of Institute for Space Research, Ukraine, Lviv Center 2021), dIdD GSM-19FD vector magnetometer (GEM Systems, Canada, (GEM Systems Homepage, 2021)) based on Overhauser scalar sensors and coil systems, as well as already obsolete digital quartz variation station “Quartz” (IZMIRAN, Russia). The cost of such magnetometers ranges from 20 000 to 60 000 USD, depending on the model and configuration. For absolute measurements, two types of magnetometers are used in general. The DIfluxes (declinometer-inclinometers) measure the absolute values of magnetic declination D and inclination I. The scalar magnetometers record the total field intensity F. DIflux magnetometers are a non-magnetic theodolite with a fluxgate sensor mounted at the telescope, and electronics with display. A series of angular measurements at various orientations of the sensor relative to the magnetic meridian and the vertical allows to calculate the D and I in the absolute sense (to obtain D the remote target must be observed, astronomical or geodetic azimuth of target must be known). Absolute measurements are performed manually by a qualified observer. One full set of the D, I measurements takes about 10–20 minutes. The measurement accuracy of D, I is determined by the parameters of the theodolite used and usually lies in the range of 0.1–0.3’. The accuracy of the scalar magnetometer specified by the INTERMAGNET standards is 1 nT, a resolution is 0.1 nT and a measurement frequency of at least 0.033 Hz (30 s) (INTERMAGNET, 2012). The most famous observatory class DIfluxes (Fig. 2) are models based on theodolites Theo 010B, Theo 020B (Zeiss), 3T2KP (UOMZ, Russia) and Wild T1 (Switzerland) with fluxgate sensors and electronics of DMI (Danish Meterological Institute, Denmark, DTU Space Homepage 2021), Mag-01 (Bartington Ltd., UK, Bartington Instruments Limited Homepage 2021) and LEMI-203 and LEMI-205 (Lviv Center of Institute for Space Research, Ukraine, Lviv Center 2021). It should be noted that almost all non-magnetic theodolites used are standard instruments in which part of

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Fig. 2 Magnetometers for absolute magnetic measurements: DIfluxes LEMI-203 with theodolite 3T2KP (a) and Mag-01H with theodolite Wild T1 (b) and scalar Overhauser magnetometer POS-1 (c). The electronic units are not shown

the important magnetic elements are replaced by non-magnetic analogs, with the expected loss of accuracy. The cost of such magnetometers can reach 30 000 USD or more. The most common scalar instruments in observational practice are the Overhauser magnetometers GEM-19 and GEM-90 from GEM Systems (Canada) and the Russian POS-1 (Laboratory of Quantum Magnetometry UrFU, Yekaterinburg, Research Laboratory of Quantum Magnetometry 2021). The cost of these magnetometers can reach 10 000 USD.

3 Possibilities of Using Instruments of Different Sensitivity for Absolute Geomagnetic Measurements at the Observatory and at Repeat Stations The scalar magnetometers, such as GSM-19 or POS-1, described in the previous section are used both in magnetic observatories (MO) and at Repeat stations (RS). They found wide application in magnetic exploration, to search the weak magnetic anomalies in archaeological and other prospecting works. This is due not only to the high accuracy of modern technologies of total field intensity measurements, but also because such measurements can be made very quickly, as well as the simplicity of their organization and implementation. Observations of declination and inclination have some differences at observatories and at Repeat stations. Observations at RS are difficult to organize and usually very limited in duration, but require the large set of independent measurements to produce

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results with acceptable accuracy. Objectively, there are many factors that reduce the accuracy of measurements at RS. These are, for example, bad weather conditions, unstable tripod of theodolite, inaccuracy of remote mark azimuth, etc. The source of significant errors of observations at RS is diurnal and seasonal variations and induction effects. To reduce these effects the data from nearest MOs must be used, but long distance between RS and MO also is source of reduction problems. Some possible solution is the using of not very accurate variometers directly at RS during observations. Summary effect decrease the final accuracy of declination up to 1’ and of inclination up to 0.5’ and more (see Table 2.1 in Newitt et al. 1996). As a result, DIfluxes with less accurate theodolites can be used for observations at RS. Moreover, this reduces the duration of single measurement, since it is not necessary to perform a full cycle of readings at different positions of the theodolite telescope, which is necessary to remove a number of errors such as the misalignment of the telescope and fluxgate sensor, remained magnetization of the theodolite, the index (vertical collimation) error, etc., to achieve the declare accuracy of precise theodolite. Low-precision theodolites are usually significantly cheaper than theodolites needed for magnetic observatories. In addition, their demagnetization is much easier, which reduces the cost of the DIflux with such theodolite. The accuracy of the fluxgate sensor as a null indicator must match the accuracy of the theodolite, so the requirements for the sensor can be reduced. Simple fluxgate sensor is developed at Novosibirsk magnetic observatory (IAGA code is NVS) by A. F. Pavlov. It has small dimensions and low power consumption, while maintaining high measurement accuracy. The device has been developed and improved for 30 years and tested under various conditions. Main technical specifications: – – – –

Supply voltage—4–5 V; Consumption current: 10 mA; Dimensions of the electronic unit: 160 × 95 × 65 mm; Sensor sensitivity: 0.1–0.2 nT.

For the manufacture of a rod-type differential fluxgate sensor (included in the second harmonic scheme), amorphous permalloy grade 82K3XSR (0.025 × 6) was used. Annealing was carried out in a specially manufactured thermostat according to specifications. The sensitivity of the sensors was determined in a double permalloy screen. The instrument kit includes an electronic unit (zero-position indicator) connected by a shielded cable to a fluxgate sensor, which is mounted on a non-magnetic theodolite telescope. Theodolites of the 4T30P and 4T15P types are quite suitable as theodolites for work at RS. Successful experience of “demagnetization” of such theodolites is available in Novosibirsk. Example of such DIflux is presented at Fig. 3. The using of low-precise theodolite without vertical automatic index or vertical circle level requirements to set and check the levelling of device more carefully during observations. Some alternative of DIflux for measurements at RS can be a new POS-4 vector magnetometer, Fig. 3 (UrFU, Yekaterinburg, see, for example, Khomutov et al.

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Fig. 3 Absolute magnetometers DIflux with theodolite 4T30 (a) and vector Overhauser magnetometer POS-4 (b), which can be used for observations at Repeat Stations

2016; Khomutov 2017). The magnetometer uses an Overhauser sensor POS to measure the total field intensity F, a solenoid for measuring the vertical component Z and a coil system for measuring the horizontal component by the additional field method. Accordingly, the elements F and Z are defined in the absolute sense. The third component depends on the uncertain orientation of the coil system and will be resolved in future models. The FZ-models of magnetometer (POS-3) has been actively used during several years at the Arti Observatory (Institute of geophysics UrB RAS, Yekaterinburg) to study interannual changes of the spatial distribution of the magnetic field in the Urals. Magnetometer POS-4 is easy to use, but has relative high cost, which is compared with observatory class variometer. Of course, user also get the continuous records of measures components with rate up to 5–10 s.

4 Wireless Sensor Networks as the Basis for Cost-Effective Measurements of the Earth’s Magnetic Field For educational and research purposes, inexpensive magnetometers can be used as sensors for measuring the Earth’s magnetic field. So, in (Shahsavani 2019), a comparison of an inexpensive magneto-inductive magnetometer with a proton magnetometer is given. Low-cost magnetometers include HMC5883L-based modules (3-Axis 2021). This is a three-axis magnetometer with a 12-bit ADC with an accuracy of determining the direction of 1–2◦ . The HMC5883L is a multi-chip module with a digital interface for measuring weak magnetic fields in applications such as magnetometers, compasses, etc. The HMC5883L includes an integrated high-resolution magnetoresistive sensor of the HMC118x series plus a special purpose integrated

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circuit (ASIC) containing an amplifier, an automatic driver demagnetization, bias reset, and 12-bit ADC, which ensures the accuracy of determining the direction within 1-2◦ . The I2C serial bus provides a simple interface. Chip HMC5883L in a 16-pin LCC case for surface mounting has dimensions 3.0 × 3.0 × 0.9 mm and is used in such applications as mobile phones, tablets, user electronics, navigation systems, etc. The cost of modules based on the HMC5883L is approximately 2–3 USD. The Xtrinsic MAG3110 magnetometer (Xtrinsic 2021) includes three magnetic field sensors oriented along the x, y, and z axes and an integrated circuit for organizing signal processing, exchanging via the I2C interface, and implementing other functionalities. The cost of modules based on the MAG3110 is approximately 10 USD. Main technical characteristics of the magnetometer MAG3110: – – – – – – – – –

wide dynamic range ±1000 µT; sensitivity 0.10 µT; noise down to 0.25 µT rms; the maximum sampling frequency 80 Hz; I2C interface with a frequency of 400 kHz; supply voltage 1.95–3.6 V; interrupt mechanism for data synchronization; ultra small-sized 2 × 2 × 0.8 mm 10-pin DFN housing; range of working temperatures from −45 to +85◦ C.

9-axis sensor MPU-9250 9DOF (MPU 2021)—an InvenSense module for determining the position in space, which includes a 3-axis gyroscope, 3-axis accelerometer and 3-axis magnetometer. The working range of the magnetometer is 4800. The module has a 16-bit ADC. Claimed accuracy is 0.16 µT. The cost of modules based on MPU-9250 9DOF is approximately 3–4 USD. Wireless sensor network (WSN) is a distributed network of maintenance-free miniature electronic devices (network nodes) that collect data on environmental parameters and transmit them to the base station via relay from node to node using wireless communication (Faludi 2010, Fig. 4). A network node, called a sensor, contains a sensor that receives data from the external environment (the sensor itself), a microcontroller, memory, a radio transmitter, an autonomous power source, and sometimes actuators. It is also possible to transfer control actions from network nodes to the external environment (Mahalik 2007). Distinctive features of wireless sensor networks (WSNs) include the self-organization property, the wireless data transmission environment, hardware and software limitations and stand-alone power supply of the network nodes, fixed allocation of the network nodes, focus on the problems of monitoring and remote management of distributed objects in a limited area, small amount of data transmitted by the network (Mochalov 2015; Mochalov and Pshenichnikov 2014). Also related to the features of the WSN (Mochalov and Pshenichnikov 2014): the ability to transmit information over significant distances with low transmitter power (by relaying); low cost of nodes and their small size; ease of installation, no cabling

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Fig. 4 Wireless sensor network

required (thanks to fully wireless technology and battery power); the ability to install such networks on an existing and operating facility without additional work; low cost of maintenance. The above features of wireless sensor networks have determined the appropriateness of their use in solving important problems in the following areas: – monitoring the telecommunications infrastructure of networks; – monitoring of transportation routes (railways, subway, etc.), oil and gas pipelines, engineering networks of energy and heat supply; – environmental, biological and medical monitoring; – automation of life support systems and “Smart Home” class systems; – identification and prevention of emergency situations (monitoring of seismic activity and volcanic activity, analysis of the atmosphere and weather forecast for timely warning of natural disasters); – other applications. In general, wireless sensor networks can be created on the basis of various standards, protocols and technologies, for example: Bluetooth, ZigBee; 6loWPAN; Wi-Fi LoRa; DigiMesh; IEEE 802.15.4 standard; WiMedia/MBOA UWB (Ultra Wideband) standard ECMA368 (based on IEEE 802.15.3a standard) and DS-UWB Forum standard IEEE 802.15.4a and others. At the functional level, a sensor network will be understood as a distributed network of terminal functional nodes (F-nodes) that collect environmental data and transmit the collected information to one or more information collection centers. In general, information can be transmitted both via wired and wireless communication networks. In the case of using wireless networks, due to the self-organization

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property and the use of special network protocols, it becomes possible to use the transit nodes (T-nodes) to transmit information by relaying from one network node to another (Fig. 4) (Mochalov 2015). Thus, inexpensive magnetometers are proposed to be placed as sensors on the functional nodes of the sensor network. Transit nodes will transmit the collected information about the distributed Earth’s magnetic field measurements to the information collection centers. Such distributed monitoring of the Earth’s magnetic field may be relevant for educational, research and experimental purposes. For time synchronization of measurements of the Earth’s magnetic field can be used inexpensive GPS/Glonass receivers. The work (Mochalov and Mochalova 2017) describes the use of the “Sensor signal analysis network” (SSAN) complex for distributed time-synchronized analysis of electromagnetic radiation. The SSAN complex, based on the use of low-cost mini-computers, can be adapted for measuring the Earth’s magnetic field both as a information collection center and as a functional node of the WSN when a magnetometer is connected to it.

5 Conclusions A brief analysis of systems for observing the Earth’s magnetic field allows us to distinguish three conditional classes of magnetometers. The instruments of the observatory class have high accuracy and stability, are used in accordance with special technologies and methods regulated by the international standards IAGA and INTERMAGNET. At the same time, they are expected to have a high cost, are unique and require special conditions that can be provided only at observatories. The second class of devices is magnetometers for field measurements and measurements at remote stationary points (for example, at Repeat stations). In terms of their specification, they are close to the magnetometers of the observatory class, although they have a lower accuracy. However, at the same time, field magnetometers are significantly less demanding to the conditions of use, they are mobile and have a relatively low cost. The third class, conventionally referred to “public magnetometry”, consists of magnetic sensors that are embedded or can be embedded in user devices like smartphones. They have very low accuracy, but they are quite cheap. The tasks solved by magnetometers of the field and observatory classes are well known, they have long been set by the fundamental and applied sciences, as well as by various organizations with practical interests. Those devices that can be called “home magnetometers”, of course, can not be directly used for these tasks. But due to their availability and the possibility of combining them in a network of different ranks, they can be used, for example, for educational purposes, for various control functions, or for recording unique magnetic events of significant strength.

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References Jankowski, J., Sucksdorff, C.: Guide for magnetic measurements and observatory practice. Warsaw (1996) Nechaev, S.A.: Guide for stationary geomagnetic observations. Publishing House of the Institute of Geography. B. B. Sochava SB RAS, Irkutsk (2006) (in Russian) INTERMAGNET Technical Reference Manual. Version 4.6, St-L. Benoit (Eds.), Edinburgh (2012) Newitt, L.R., Barton, C.E. , Bitterly, J.: Guide for Magnetic Repeat Station Surveys. IAGA (1996) DTU Space Homepage, https://www.space.dtu.dk/english/research/instruments_systems_ methods. Last accessed 20 May 2021 Lviv Center of Institute for Space Research Homepage, https://www.isr.lviv.ua. Last accessed 20 May 2021 GEM Systems Homepage, https://www.gemsys.ca. Last accessed 20 May 2021 Bartington Instruments Limited Homepage, https://www.bartington.com/product/mag-01h-di/. Last accessed 20 May 2021 Research Laboratory of Quantum Magnetometry, UrFU Homepage, https://science.urfu.ru/en/. Last accessed 20 May 2021 Khomutov, S., Sapunov, V., Denisov, A., Savelyev, D., Babakhanov, I.: Overhauser vector magnetometer POS-4: Results of continuous measurements during 2015-2016 at geophysical observatory “Paratunka” of IKIR FEB RAS, Kamchatka, Russia. E3S Web Conf. 11(00007), 1–5 (2016). https://doi.org/10.1051/e3sconf/20161100007 Khomutov, S.Y.: Measurements using a POS-4 vector magnetometer over 2.5 years: the possibilities and results of integration into the magnetic field monitoring system at the Paratunka Geophysical Observatory, IKIR FEB RAS (Kamchatka). In: Deep structure, geodynamics, thermal field of the Earth, interpretation of geophysical fields. Ninth scientific readings YP Bulashevich 2017, pp. 448–452. IGF UB RAS, Ekaterinburg (2017) Shahsavani, H.: Comparison of a low-cost magneto-inductive magnetometer with a proton magnetometer: a case study on the Galali iron ore deposit in western Iran. Near Surface Geophysics 17(1), 69–84 (2019) 3-Axis Digital Compass IC HMC5883L, https://cdn-shop.adafruit.com/datasheets/HMC5883L_3Axis_Digital_Compass_IC.pdf. Last accessed 20 May 2021 Xtrinsic MAG3110 Three-Axis, Digital Magnetometer, https://www.nxp.com/docs/en/data-sheet/ MAG3110.pdf. Last accessed 20 May 2021 MPU-9250 Nine-Axis (Gyro + Accelerometer + Compass) MEMS MotionTrackingT M Device. https://www.invensense.com/products/motion-tracking/9-axis/mpu-9250/. Last accessed 20 May 2021 Faludi, R.: Building Wireless Sensor Networks. O’Reilly Media (2010) Mahalik, N.P. (Ed.): Sensor networks and configuration. Fundamentals, Standards, Platforms, and Applications. Springer (2007). https://doi.org/10.1007/3-540-37366-7 Mochalov, V.A.: Multi-agent Bio-inspired Algorithms for Wireless Sensor Network Design. In: The IEEE 17th International Conference on Advanced Communication Technology, ICACT 2015, pp.33–42. Phoenix Park, Korea (2015) Mochalov, V.A., Pshenichnikov, A.P.: The principles of the construction and functioning of sensor networks. Study guide. 2nd edn. MTUSI, Moscow (2014) Mochalov, V.A., Mochalova, A.V.: Application of “Sensor signal analysis network” complex for distributed, time synchronized analysis of electromagnetic radiation. Solar-Terrestrial Relations and Physics of Earthquake Precursors, E3S Web Conf. 20(02010), 1–6 (2017). https://doi.org/ 10.1051/e3sconf/20172002010

The Biosphere

Thermodynamic Analysis of Some Reactions of Selenium, Telurium, Arsenic, Antimony and Derivatives in Chemosynthesis Julio Omar Prieto García, Noor Gehan Geulamussein, Yailet Albernas Carvajal, Noel Pérez Díaz, and Daimel Castillo Díaz Abstract In the present research it is stipulated when taking Ho , Go and So an approximation of the occurrence of reactions under the scheme: CO2 + O2 + X → CH2 O + Y, where X can be As, Sb, Te, Se and derivatives and Y X oxidation products, verifiable from redox potentials under standard conditions in chemosynthesis processes. In addition, these values of state functions allow us to predict interfering reactions that can affect the process of obtaining energy for nitrogen, sulfur, iron and hydrogen bacteria. Finally, we propose a series of reactions that are justified from the thermodynamic point of view allowing the process of chemosynthesis without the presence of dioxygen. Keywords Thermodinamic reactions · Chemosynthesis · Redox potentials

J. O. P. García (B) Department of Chemistry Chemistry and Pharmacy Faculty, Central University “Marta Abreu” of Las Villas, Road to Camajuaní Km 5 ½Villa Clara, Santa Clara, Cuba 54830, USA e-mail: [email protected] N. G. Geulamussein Department of Chemistry, Science Faculty, Eduardo Mondlane University, Maputo, Mozambique, France Y. A. Carvajal Chemical Engineering Department. Chemistry and Pharmacy Faculty, Central University “Marta Abreu” of Las Villas, Road to Camajuaní Km 5 ½Villa Clara, 54830 Santa Clara, Cuba, USA e-mail: [email protected] N. P. Díaz Department of Physics. Mathematical, Physics and Computation Faculty, Central University “Marta Abreu” of Las Villas, Road to Camajuaní Km 5 ½Villa Clara, 54830 Santa Clara, Cuba, USA e-mail: [email protected] D. C. Díaz Hiesaren Aurkako T4 Elkartea, Bilbao, Bizkaia, France © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Cardenas et al. (eds.), Proceedings of the 3rd International Conference on BioGeoSciences, Springer Proceedings in Earth and Environmental Sciences, https://doi.org/10.1007/978-3-030-88919-7_12

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1 Introduction Chemotrophic or chemosynthetic organisms are those capable of using reduced inorganic compounds as substrates to obtain energy and use it in respiratory metabolism. It is a faculty known as chemosynthesis. These can be chemoautotrophs or chemoheterotrophs. Like photoautotrophs (such as algae and plants), chemoautotrophs use CO2 as the main source of carbon, but unlike them, they do not use light as a source of energy and instead obtain it by oxidation of reduced inorganic compounds, such as NH3 , NO2 − , H2 , reduced forms of sulfur (H2 S, S, S2 O3 − ) or Fe2 + . Its cellular carbon is derived from CO2 and is assimilated through reactions of the Calvin cycle, analogously to plants. As a result of their distinctive ability to grow in strictly mineral media, in the absence of light, these organisms are often referred to as chemolithotrophs (lithos, rock). In contrast, chemoheterotrophic (or simply heterotrophic) organisms, such as animals and fungi, oxidize reduced organic molecules, such as glucose (via glycolysis), triglycerides (via beta oxidation) or amino acids (via oxidative deamination) to obtain metabolic energy (ATP) and reducing power; In addition, they are unable to use CO2 as a carbon source. They are found in habitats such as deep sediments or around underwater reliefs or ocean ridges where the earth’s crust is thin and there are hydrothermal vents or even magma outlet. These bacteria transform the chemical products of the vents, toxic to many living beings, into food and energy, playing the role of producing organisms in the ecosystem of the afotic zone of the ocean. From these bacteria, small trophic chains may arise based on chemosynthesis, rather than photosynthesis.

2 Types of Chemosynthetic Bacteria 2.1 Colorless Sulfur Bacteria Colorless sulfur bacteria oxidize sulfur or reduced sulfur compounds. They are obligate aerobic bacteria since they need oxygen for oxidation. They are responsible for transformation of hydrogen sulfide (H2 S), from the decomposition of organic matter, into sulfate (SO4 2− ) assimilable by plants, thereby closing the sulfur cycle (Jimeno and Ballesteros 2009). The reactions are as follows: H2 S +

1 O2 → S + H2 O + 209k J/mol 2

2S + 3O2 + 2H2 O → 2S O4−2 + 4H + + 498k J/mol

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Sulfate production causes extremely acidic conditions, with pH lower than 2; Acidithiobacillus thiooxidans is exceptionally resistant to these conditions and is found in nature in very acidic environments. The ability of sulfur oxidizing bacteria to produce sulfuric acid is sometimes used in agriculture to correct alkaline soils; With the plow, sulfur powder is introduced into the soil that sulfobacteria naturally present in the soil oxidize, lowering the pH of the soil to more suitable values for cultivation.

2.2 Nitrogen Bacteria Nitrogen bacteria oxidize reduced nitrogen compounds. They are widely spread in the soil and are responsible for ammonia oxidation (NH3 ), usually from the decomposition of organic matter (dead bodies, excretion), and transforming it into nitrates (NO3 − ) assimilable by plants; thus closing the nitrogen cycle. Nitrosifying bacteria and nitrifying bacteria can be distinguished. • Nitrosifying bacteria. They transform ammonia into nitrites (for example, Nitrosomonas), according to the following reaction: 2N H3 + 3O2 → 2N O2− + 2H + + 2H2 O + 272k J/mol • Nitrifying bacteria. They act after the previous ones, transforming the nitrites into nitrates (for example Nitrobacter), according to the following reaction: N O2− +

1 O2 → N O3− + 75k J/mol 2

2.3 Iron Bacteria Iron bacteria oxidize ferrous iron compounds (Fe2+ ) to ferric ones (Fe3+ ), transforming iron carbonate deposits into iron oxide deposits. The reaction is as follows: 2FeC O3 + 3H2 O +

1 O2 → 2Fe(O H )3 + 2C O2 + 167k J/mol 2

Ferrous iron oxidizes spontaneously at neutral pH, but not at acidic pH, so these bacteria must live in acidic environments for survival; Acidithiobacillus ferrooxidans lives in the waters that drain through the galleries of coal mines, which are often acidic and contain ferrous iron.

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2.4 Hydrogen Bacteria Hydrogen bacteria are facultative chemoautotrophs capable of using molecular hydrogen as an energy sourc: H2 +

1 O2 → H2 O + 239k J/mol 2

Hydrogen is first activated by hydrogenase enzyme and then transferred to the NAD which is oxidized in the respiratory chain and ATP is synthesized by oxidative phosphorylation (Brock 1978).

3 Material and Methods The following reaction is taken as pattern of chemosynthesis, where X can be atoms, ions or molecules such as H2 S, H2 , S8 , S2 O3 2− , NH3 , NO2 − y Fe2+ : C O 2 + O2 + X → C H 2 O + Y + H2 O where Y is the product of oxidation. Possible elements of analysis are Se, Te, Sb and As. From the thermodynamic point of view, the incidence of the enthalpy, entropy and free energy variation state functions is evaluated to direct the development of the reactions (Table 1). The analysis of Se and derivatives leads, based on the possible reactions chosen, from the thermodynamic point of view, to: The values of H, G are expressed in kJ / mol, while S is in kJ/(mol. K). (a) Table 1 Direction of the development of reactions for H, S and G H

S

G

–

+

–

At full temperature

+

–

+

Impossible

–

–

±

Possible at lower temperatures

+

+

±

Possible at high temperatures

Possibility of reaction development

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C O2(g) + O2(g) + 4H2 Se(g) → C H2 O(g) + 4Se(g) + 3H2 O H = −880.6 S = −0.5 G = −819.9 Possible at low temperature. When considering the normal electrode potentials in aqueous solutions at 25° C, one can have an idea of the occurrence of the process. In this case the potential value of the total reaction gives 1.63 V, indicating that the reaction proceeds spontaneously. (b) C O2(g) + O2(g) + Se(g) + 2H2(g) → C H2 O(ac) + H2 SeO3(ac) H = −1206.1 S = −0.5 G = −774.8 Possible at low temperature. When considering the normal electrode potentials in aqueous solutions at 25° C, one can have an idea of the occurrence of the process. In this case the potential value of the total reaction gives 0.36 V, which indicates that the reaction proceeds spontaneously. (c) C O2(g) + 2H2(g) +

3 O2(g) + Se(g) → C H2 O(ac) + H2 SeO2(ac) 2

H = −330.8 S = −0.6 G = 63.5 Possible at low temperature. When considering the normal electrode potentials in aqueous solutions at 25° C, one can have an idea of the occurrence of the process. In this case the potential value of the total reaction gives 0.42 V, which indicates that the reaction proceeds spontaneously. The analysis of Te and derivatives leads, based on the possible reactions chosen, from the thermodynamic point of view: (a) C O 2(g) + 25 O2(g) + T e(g) + 4H 2(g) → C H 2 O(g) + T e(O H )6(g) + H2 O H = −330.8 S = −0.6 G = −228.9

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Possible at low temperature. When considering the normal electrode potentials in aqueous solutions at 25° C, one can have an idea of the occurrence of the process. In this case the potential value of the total reaction gives 0.53 V, which indicates that the reaction proceeds spontaneously. (b) C O 2(g) + O2(g) + T e(g) + 2H 2(g) → C H 2 O(ac) + T eO 2(ac) + 3H 2 O H = 314.4 S = −0.4 G = −318 Very favorable reaction. In this case the potential value of the total reaction gives 0.70 V, which indicates that the reaction proceeds spontaneously. (c) C O 2(g) + O2(g) + 2T e(g) + H 2 O(g) → C H 2 O(ac) + T eO 2(ac) H = 144.5 S = −429.2 G = 201.1 Its execution is not possible from the thermodynamic point of view. The analysis of As and derivatives leads, based on the possible reactions chosen, from the thermodynamic point of view: (a) C O 2(g) + 2O 2(g) + As (s) + 27 H 2(g) → C H 2 O(g) + H3 As O 4(g) + H2 O H = −906.9 S = −1.5 G = 1510.9 Possible at low temperature. When considering the normal electrode potentials in aqueous solutions at 25° C, one can have an idea of the occurrence of the process. In this case the potential value of the total reaction gives 0.86 V, indicating that the reaction proceeds spontaneously. (b) C O 2(g) + O2(g) + As H 3(g) + H 2 O → C H 2 O(ac) + H3 As O 4(ac) H = −468.3

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S = 0.09 G = 109.5 Possible at low temperature. When considering the normal electrode potentials in aqueous solutions at 25° C, one can have an idea of the occurrence of the process. In this case the potential value of the total reaction gives 1.22 V, which indicates that the reaction proceeds spontaneously. (c) C O 2(g) + O2(g) + As (g) +

5 H → C H 2 O(ac) + H2 O+H As O 2(ac) 2 2(g) H = −464.2 S = −212.8 G = −135.1

Possible at low temperature. When considering the normal electrode potentials in aqueous solutions at 25° C, one can have an idea of the occurrence of the process. In this case the potential value of the total reaction gives 1.48 V, indicating that the reaction proceeds spontaneously. (d) C O 2(g) + 21 O2(g) + As (g) + 2H2 O → C H 2 O(ac) +H As O 2(ac) H = −636 S = −1214.1 G = −300.21 Possible at low temperature. When considering the normal electrode potentials in aqueous solutions at 25° C, one can have an idea of the occurrence of the process. In this case the potential value of the total reaction gives 1.48 V, indicating that the reaction proceeds spontaneously. (e) C O 2(g) + 23 O2(g) + H2 O + 2 As (g) → C H 2 O(ac) +As 2 O 5(ac) H = −362.1 S = −336, 2

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G = −40.9 Possible at low temperature. When considering the normal electrode potentials in aqueous solutions at 25° C, one can have an idea of the occurrence of the process. In this case the potential value of the total reaction gives 1.23 V, which indicates that the reaction proceeds spontaneously. (f) C O 2(g) + 3O2(g) + 2 As H 3(g) → C H 2 O(ac) + 2H 2 O+As 2 O 5(ac) H = −1351.4 S = −433.4 G = −653.3 Possible at low temperature. When considering the normal electrode potentials in aqueous solutions at 25° C, one can have an idea of the occurrence of the process. In this case the potential value of the total reaction gives 1.46 V, indicating that the reaction proceeds spontaneously. All these reactions are possible at low temperatures given the values of the analyzed state functions, whose negative values justify their occurrence. There are a number of interfering reactions in the chemosynthesis process, for example, in ferrobacteria. SeO 2 + 4FeC O 3 + 2X H 2 O → 2Fe2 O 3 .X H 2 O + 4C O 2(g) + Se H = 448.14 S = 98.41 G = 214.8 In this reaction the values of H, S and G are positive and therefore, the occurrence of the reaction at high temperature is possible. In hydrogen bacteria, two important interfering reactions are possible: Se + H N O 3 → H2 SeO 4 + 2N O H = −445.5 S = 49.3

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G = −77.45 Whose values of H and G are negative and S positive, confirming the occurrence at any temperature. Now, the reaction of H2 with H2 SeO4 , is outlined below; H2 + H 2 SeO 4 → Se + 4H2 O H = −570.45 S = 65.193 G = −531.37 The values of H and G are negative and S positive, indicating that the reaction occurs at any temperature. Another interfering reactions are linked to sulfobacteria 8H 2 Se + 64S → 8H 2 S 8 + Se8 H = −570.45 S = 65.193 G = −531.37 In which the signs of the analyzed state functions are repeated and with it the possibility of occurring at any temperature. H2 T e + S → H2 S + T e H = −175.4 S = −172.3 G = −172.9 As can be seen, it can occur at low temperatures given the negative sign of the three state functions.

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2 As H 3 + 3S → 3H2 S + 2 As This reaction can occur at any temperature given the negative values of H and G and positive of S. H = −326.8 S = 0.15 G = −377 Analyzing the following possibility 2H 3 As O 3 + 3H2 S → As 2 S3 + 6H2 O H = −337.2 S = 1246.5 G = −2087.1 This reaction occurs at any temperature. Another reaction that may be interfering is the following: 2H 3 As O 3 + 3H2 S → As 2 S3 + 6H2 O H = −337.2 S = 1246.5 G = −2087.1 This reaction can occur at any temperature. The following interesting reaction, which occurs at low temperatures, is: As 4 O6 + 6H2 S → 2 As 2 S3 + 6H2 O H = −612.9 S = −700.6

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G = −13405.9 The following interfering reaction is found in nitrogen bacteria as an example: 3SeO2 + N H3 → 3Se8 + 2N2 + C H2 O H = 606.5 S = 334.7 G = 422.1 The reaction occurs at high temperatures, which is corroborated by the 2.61 V value of the reaction from its potentials. The previous reaction causes a decrease in ammonia and therefore in the formation of ammonium ion. − + 2N H + 4 + 3O2 → 2N O 2 + 4H + 2H2 O − 2N O − 2 + O2 → 2N O 3

Now the possibility that reactions occur without the presence of dioxigen can be proposed starting from self-oxidation–reduction processes: C O2 + 3H As O2 + H2 O → C H2 O + H3 As O4 + 2 As + O2 H = 1889.3 S = 560.8 G = −1758.4 Possible at high temperature. When considering the normal electrode potentials in aqueous solutions at 25° C, one can have an idea of the occurrence of the process. In this case the potential value of the total reaction gives 0.31 V, which indicates that the reaction proceeds spontaneously. Another interesting possibilty is the following: C O2 + 3Sb2 O4 + H2 O → C H2 O + 2Sb2 O5 + 2Sb + 2O2

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H = −234 S = 667.6 G = −1420.8 Possible at any temperature. When considering the normal electrode potentials in aqueous solutions at 25° C, one can have an idea of the occurrence of the process. In this case the potential value of the total reaction gives 0.43 V, which indicates that the reaction proceeds spontaneously. The following proposed possibilities may not be fulfilled in all cases: C O2 + 2H2 SeO3 + H2 O → C H2 O + H2 SeO4 + Se H = 602.8 S = −97.7 G = 63.4 Impossible at any temperature. When considering the normal electrode potentials in aqueous solutions at 25° C, one can have an idea of the non-occurrence of the process. In this case the potential value of the total reaction is -0.41 V, which indicates that the reaction does not proceed.

4 Conclusions 1.

2.

3.

It can be achieved by taking Ho , Go and So an approximation of the occurrence of reactions under the scheme: CO2 + O2 + X → CH2 O + Y, where X can be As, Sb, Te, Se and derivatives and Y products of oxidation of X, verifiable from redox potentials under standard conditions. The use of the values of Ho , Go and So allows to predict interfering reactions that can affect the process of obtaining energy for bacteria of nitrogen, sulfur, iron and hydrogen. The dismutation process allows, from thermodynamic point of view, to justify obtaining energy for the enzymes of bacteria studied without the presence of O2 .

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References Jimeno, A. & Ballesteros, M. 2009. Biology 2. Grupo Promotor Santillana. ISBN 974–84–7918– 349–3. Brock, T.D. 1978. Biology of microorganis, 2ª edition. Omega Editions, Barcelona,774 pp. ISBN 84–282–0328–8.

Impact of the Chicxulub Asteroid: Potential Implications on Phyotoplankton and Anammox Bacteria Noel Perez, Osmel Martin, Rolando Cardenas Ortiz, and Yoel Sanchez Alvarez

Abstract In this paper, from the photobiological point of view, the emergence and evolution of the photosynthesis of the phytoplankton primary producers is approached after the impact of a large asteroid on our planet. Coupled, the influence of temperature drop and ocean acidification on primary chemosynthetic producers, specifically, anammox bacteria, was studied. As a massive impact prototype, the asteroid of Chicxulub was considered, an event that occurred about 65 million years ago and of which there are traces in several places of the national geography including the City of Santa Clara. Corresponding to our results, although the occurrence of catastrophic events can drastically affect the habitability of ecosystems and the planet in general, life as a complex phenomenon shows enormous resilience to disturbances of this nature. An appreciable and relatively rapid recovery of phytoplankton photosynthetic activity after an event like Chicxulub is evidence of this. Keywords Natural catastrophes · Asteroid impact · Chicxulub impact · Phytoplankton · Anammox

N. Perez (B) · O. Martin · R. C. Ortiz Central University Marta Abreu From Las Villas, Santa Clara, VC, Cuba e-mail: [email protected] O. Martin e-mail: [email protected] R. C. Ortiz e-mail: [email protected] Y. Sanchez Alvarez Universidad Médica Serafín Ruiz de Zarate Ruiz, Santa Clara, VC, Cuba e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Cardenas et al. (eds.), Proceedings of the 3rd International Conference on BioGeoSciences, Springer Proceedings in Earth and Environmental Sciences, https://doi.org/10.1007/978-3-030-88919-7_13

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1 Introduction Studying and predicting the behavior of biological systems in the face of major environmental disturbances is an extremely challenging task. Let us take into account that both climate and living systems show enormous intrinsic complexity, nuanced by a high degree of non-linearity, multiple interactions and feedback mechanisms, which makes it difficult, even for relatively small time scales, to make predictions with a high degree of reliability of its evolution. The situation is even more complex when it is intended to study a phenomenon such as the biological evolution of our planet that involves, not only much longer time scales, but where the coupling with the climate constitutes an essential component in the analysis. Hence, many studies conducted in this highly interdisciplinary area are limited to giving, rather than exhaustive results, projections or general trends in the behavior of these systems. However, despite these limitations, studies that focus on the complex interaction between climate, the biosphere and the cosmic environment are essential to understand the dynamics of our planet and by extrapolation, of other potentially inhabited exoplanets. Our planet has been affected in the course of its evolution by a large number of catastrophic events among which can be cited: impacts of asteroids and comets, magmatism and intense volcanism, solar and stellar explosions. Such catastrophes can bring about a drastic climate change that affects life, and have been responsible, not infrequently for mass extinctions that occurred in Earth’s past. A welldocumented example in the scientific literature is the impact of the Chicxulub asteroid, which focuses on this work. The impacts of large asteroids or comets constitute a latent danger for the development of life in the planetary context (Chapman 2004, Mathias et al. 2017). The frequency with which these events occur varies significantly on the geological scale; so for example, there is clear evidence that during the Hadeic the Earth was subject to a massive bombardment of asteroids, this fact being able to have some influence on the genesis of the first forms of life on our planet (Cockell 2006). The probability of an impact decreases significantly with the increase in the mass of the bolide (Mathias et al. 2017) thus the frequency of the impact of an asteroid of a few tens of meters is extremely high, while the probability of a body collision of about 10 km is very low, approximately one event every 100 million years (Pierazzo and Artemieva 2012; Pierazzo et al. 2010). Although there is a low probability, the impact with a large asteroid would have catastrophic planetary consequences affecting both the development of life and its own existence. In general, the potential of these events to significantly disturb the biosphere is conditioned by the ability to induce global climate change. The size of the impactor, its chemical composition, kinetic energy and impact angle are the main elements to consider (Artemieva and Morgan 2017). It is also necessary to take into account the characteristic of the target and geological of the planet to estimate the magnitude of the disturbance (Artemieva and Morgan 2017; Kaiho and Oshima 2017; Pierazzo et al. 2003b). In fact, an impact such as Chicxulub had a low probability (∼ 13%) of inducing global climate change on Earth (Kaiho and Oshima 2017). The majority of the great alterations in the terrestrial climate and

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that marked the end of the reign of the great dinosaurs were conditioned, at least to a large extent, by the characteristics of the target (a shallow sea on sedimentary rock rich in carbonate and anhydrite) that it would become a potent source of dust, soot, sulfur compounds and chlorine. The sulfur compounds, mainly in the form of S O 2 and S O 3 , by interacting with the water vapor of the atmosphere generated a large amount of sulfate aerosols which, together with the chlorine released and the nitrogen oxides formed, reduced dramatically stratospheric ozone (Artemieva and Morgan 2017; Ishida et al. 2007; Perez et al. 2014). To clarify the potential influence of a massive impact on habitability, it is very convenient to analyze an extreme case as a prototype to make estimates. In our particular case, we consider as a reference the impact of the Chicxulub asteroid (Bardeen et al. 2017; Kaiho and Oshima 2017; Kring 2007; Schulte et al. 2010). This catastrophic event occurred 65.5 million years ago in the Yucatan Peninsula in a region very close to where part of today’s Cuba was. It is estimated that the event occurred when an object collided about 10 km in diameter against the surface, a fact that would have important consequences on the global climate (Pierazzo et al. 2003b, Pope et al. 1994; Vellekoop et al. 2014) and that would significantly alter the course of biological evolution on our planet (Alvarez et al. 1980; Perez et al. 2013; Schulte et al. 2010). We must also bear in mind that the impact of Chicxulub is not only the last biggest event in the history of the Earth, but one of the most studied both from the climatic point of view and its influence on the biosphere. This particular fact makes it possible to make more precise estimates of its influence on habitability that in principle could be extrapolated to other similar scenarios. On the other hand, due to its proximity, Cuba has many places where evidence of this catastrophe has been found, such as the Moncada, Peñalver, Amaro and Santa Clara formations (specifically in Loma del Capiro). In this last place, the idea of protecting the existing geosite, a natural environment that could be transformed into a not too distant future into a geopark, is managed (Rojas Consuegra et al. 2018). In general, it is estimated that the most persistent affectation associated with a great impact is the substantial increase in the opacity of the atmosphere due to the accumulation of large amounts of ejected material during the collision. As a consequence, radiative transport through the atmosphere is very limited and the planetary surface cooled, this being known as the winter impact phase, a period characterized by low temperatures and darkness (Pope et al. 1994; Vellekoop et al. 2014). In the case of Chicxulub, due to the characteristics of the target, the ejection of material into the atmosphere reached extraordinary levels, which has led to several important studies (Artemieva and Morgan 2017; Ishida et al. 2007; Vellekoop et al. 2014). As a result, solar radiation was reduced to practically zero for several months and the earth’s surface temperature decreased between 10◦ C and 20◦ C (Kaiho and Oshima 2017). In addition, the impact affected the composition of the atmosphere with an ozone reduction (Pierazzo et al. 2003b), a persistent process for several years after the suspended material settled. This brought additional stress to the biota directly exposed to the influence of UV radiation when the light on the planetary surface had been appreciably restored (Perez et al. 2013, 2014, Pierazzo et al. 2003b). It should

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be specified that the effects associated with variations in the thickness of the ozone layer after an impact only occur on habitable planets whose conditions are similar to those on Earth, and where the oxygen content in the atmosphere is sufficiently high, generally greater than 10−2 P AL (Kastling and Catling 2003; Segura et al. 2003). Planets with levels below that value (such as Earth during the Archaic eon), the ozone layer does not exist or is too thin to effectively block UV radiation. In these cases, it is presumable to assume a biota formed by unicellular organisms refugees in the marine environment, protected from UV radiation by the water column (Cockell, 2000). A significant increase in water turbidity occurs following an impact due to the continuous deposition of material from the atmosphere. As a consequence of this process there is a considerable reduction of the photic zone (where photosynthesis is possible) and therefore, a decrease in the photosynthetic potential of the planet for several years (Perez et al. 2014; Robertson et al. 2013). Massive deposition of sediments, mainly sulfuric acid, may have contributed to the acidification of aquatic surfaces (Tyrrell et al. 2015) negatively impacting both marine and aquatic ecosystems. It is very likely that the combination of the factors mentioned above (temperature, p H , light, sediments), affected virtually all life forms on the planet. Several studies suggest that the impact of Chicxulub caused the mass extinction of about 50% of the number of species living on the surface, where dinosaurs are included. Although the superficial biosphere; mostly dependent on photosynthesis, it was the most affected, the possibility that the underground biosphere was also to a lesser or greater extent, due to the impact, is not excluded. Also, keep in mind that the temperature decreased significantly along the water column to reach hundreds of meters (Kaiho and Oshima 2017). Additionally, the impact and deposition could have affected or favored different forms of chemosynthetic life, which merits a more detailed study.

2 Photosynthesis Models Ultraviolet (UV) radiation has a harmful potential over marine photosynthesis (Steemann-Nielsen 1964). However, large-scale research on the subject has begun after the reduction of stratospheric ozone and increased exposure to UV radiation, specifically due to the biological damage caused by UV-B (280−320nm). Special attention has been given to the effects of UV on photosynthesis of phytoplankton in the Southern Ocean, a region that is regularly exposed to the Antarctic “ozone hole” (Weiler and Penhale 1994). Several experimental works have shown that photoinhibition of photosynthesis in Antarctica and other environments are mainly due to natural UV rays (Furgal and Smith 1997; Herbert 1992; Maske 1984, Villafañe et al. 1995). This has motivated the development of models that define how inhibition can increase with ozone depletion and how it can be affected by changes in UV transparency of natural waters (Cullen and Neale 1997; Neale 2000). Since the level of damage to organisms associated with UV is usually a function of wavelength, it is

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necessary to establish appropriate weighting functions to quantify this dependence (Caldwell et al. 1986; Coo Hill 1989). In particular, spectral weighting functions provide an approach to adequately scale different exposure spectra by their “effectiveness” in the inhibition of photosynthesis. In reality, responses to UV rays reflect an interaction between damage and repair processes in living organisms (Vincent and Neale 2000). The term repair has a generic meaning because it refers to all processes that restore or reactivate photosynthetic function. The activity of several mechanisms that influence phytoplankton repair, such as photosynthesis and photoenzymatic repair, depend on simultaneous exposure to UV-A and photosynthetically active radiation (PAR). Hiriart-Baer and Smith (2004), introduced the R model to describe damage and kinetic repair in photosynthesis. This model is useful when the level of repair is low but significant.   ⎞ ∗ ∗ 1 − e−( Hinh +r T ) Hinh rT B ⎝ ⎠ + ∗ P B = Ppot  ∗ 2 H Hinh + r T inh + r T ⎛

(1)

B , the rate where, PB , is the average photosynthesis rate in the exposure period, Ppot ∗ of potential photosynthesis in the absence of UV radiation, Hinh , pruned exposure for inhibition, r the rate of repair of the damage caused by UV radiation and T the incubation period. It is usually more convenient in research to express the R model based on radiometric units (Fritz et al. 2008) as follows:

 EU∗ V PB 1 − e−E P A R /E S  ∗ ∗ ∗ = + 1 − e−E P A R /E S e−(( HU V /EU V )+HU V ) ∗ B 1 + EU V 1 + EU∗ V Ppot

(2)

where,EU∗ V , HU∗ V , represents the UV inhibitory radiations and inhibitory fluence, convolved with a biological action spectrum ε (λ) respectively, which weighs the UV wavelengths according to their potential to inhibit photosynthesis (Cullen et al. 1992; Neale 2000), E P A R is the irradiance of photosynthetically active radiation, the E S parameter is the irradiance that gives 63% of the maximum photosynthesis rate if UV radiation is insignificant and is a measure of the species’ efficiency in the use of RFA. The smaller its value, the greater the efficiency (Fritz et al. 2008). In the previous equation, the first term of the right member is the E model, and the second, the H model. Both models are simplified cases of the R model. Model H is the time-dependent term with r = 0 while for model E it is the term steady state. Model H is used when repair is not active to predict the average photosynthesis rate during a specific exposure period (Neale et al. 1998). Model E is used if the repair is active and the exposures are long enough to reach a stable state (Cullen et al. 1992). During the course of this investigation, it was studied how the process of photosynthesis of phytoplankton could emerge and develop after a catastrophic event such

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as the impact of the Chicxulub asteroid. To evaluate the rate of photosynthesis PB , B , model E of photosynthesis was normalized with respect to the maximum rate Ppot used for phytoplankton, which assumes good capacity to repair the damage caused by UV radiation (Fritz et al. 2008): PB 1 − e−E P A R (z)/E S = (z) B 1 + EU∗ V (z) Ppot

(3)

In the previous equation, the left member represents the relative rate of photosynthesis to the z depth. Equations 4 and 5, respectively, are used to calculate the photosynthetic active irradiations and inhibitory UV. E P A R (z) =

λf

E(λ, z)λ

(4)

λi ∗ E inh (z) =

λf

ε(λ).E(λ).λ

(5)

λi

where λi and λ f are the wavelength limits. For photosynthetically active radiation, in the case of marine phytoplankton, visible light was taken (400 − 700nm). For the UV inhibitory range 280nm ≤ λ < 400nm was taken. To make the estimates, the λ increase has been taken equal to 1nm in all cases modeled in correspondence with previous works (Peñate-Alvariño et al. 2010; Perez et al. 2013). With a view to modeling radiative transport in the ocean several approaches can be taken. A very simple one assumes a field of stationary light under water where there is no emission obtaining a Lambert–Beer type solution of the radiative transport equation, which is written as a function of the spectral irradiations E(λ, z) as follows:   E(λ, z) = E λ, 0− e−K (λ).z

(6)

  where,z, represents the depth of the water column. The irradiance E λ, 0− below the ocean surface is obtained by subtracting the light reflected by the atmosphere–ocean interface:     E λ, 0− = (1 − R)E λ, 0+

(7)

  Being, E l, 0+ the irradiance of the incident light above the surface and R is the reflection coefficient calculated by the Fresnel formula of the optics. The set of attenuation coefficients K (λ) can be obtained by linear interpolation of the reference tables of the optical classification of the oceanic and coastal waters of Jerlov (Jerlov,

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1976, Peñate-Alvariño et al. 2010). This method allows obtaining attenuation coefficients in the range of 200 − 700nm nanometers to nanometer (Peñate-Alvariño et al. 2010).

3 Anoxigenic Oxidation Process of Ammonium Anoxigenic oxidation of ammonium (anammox) is one of the fundamental processes   of nitrogen conversion innature and is characterized by the reaction of nitrite N O − 2  with ammonium N H + 4 under anoxic conditions to form nitrogen gas (N2 ). This process was a great surprise due to its discovery more than a hundred years after having identified other basic actors in the nitrogen cycle in nature, such as nitrogen fixation, denitrification and nitrification. In addition, both the biological oxidation of ammonium under oxic conditions and the harsh conditions necessary for chemical oxidation in industrial processes seemed good reasons why the anammox process could not occur (Perez et al. 2019). Today, in a more biological context, the term anammox is used to refer to a wide variety of chemoautotrophic bacteria whose metabolic energy comes from the following chemical reaction (Carvajal-Arroyo et al. 2013; Strous et al. 1998): − − + N H+ 4 + 1.32N O 2 + 0.066H C O 3 + 0, 13H →

→ 1.02N2 + 0.26N O − 3 + 0, 066CH2 O0,5 N0,15 + 2, 03H2 O In the process represented by the previous chemical reaction the elementary composition of biomass is referred to as C H 2 O0.5 N0.15 (Strous et al. 1998). Although there are other reactions to this process proposed in the literature, the biomass growth calculated using this particular stoichiometry and that observed experimentally correlate very well as reported by Strous et al. (1998). The anammox bacteria species release, in the form of molecular nitrogen, an important part of the nitrogen fixed by the ecosystems reaching in some environments 65% of the production of N2 (Trimmer and Nicholls Joana 2009). Due to the importance of nitrogen for living organisms, any alteration in the contribution of anammox could have profound consequences on the biosphere by altering its availability by both photosynthetic and chemosynthetic communities. These bacteria have found important applications in plants for the treatment of wastewater (Zhu et al. 2017) whose studies come from most of the data (Carvajal-Arroyo et al. 2013; Hoekstra et al. 2018; Ibrahim et al. 2016). Lacking a general theoretical model for modeling chemosynthesis, relative activity of anammox (R A A) is considered a measure of habitability, which is an indicator of how active anammox metabolism is when changing environmental conditions.

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RAA =

AA A Ar e f er ence

(8)

In the previous equation A Ar e f er ence is the value of anammox activity to optimal p H and A A is the value of anammox activity at different p H , both are expressed in −1 h −1 . The activity of anammox (A A) is the amount of N2 produced mmol N2 ·L liquid per unit of time, according to the chemical reaction mentioned above (CarvajalArroyo et al. 2013). These bacteria are particularly sensitive to changes in the temperature and p H of the medium (Carvajal-Arroyo et al. 2013; Lotti et al. 2015). In practice, the term relative activity is used in most research on this subject. The anammox metabolism dependence on temperature can be modeled in principle as a simple Arrhenius model (Hoekstra et al. 2018; Lotti et al. 2015; Zhu et al. 2017), in the form: R A A(T ) = R A Amax e−Ea /RT

(9)

where, E a , is the apparent activation energy. For E a , a value of 239k J.mol −1 was assumed, consistent with that estimated by Hoekstra et al. (2018) in its studies on the changes in the RAA associated with long-term temperature fluctuations, a condition that makes it ideal for exploring a scenario like Chicxulub. After a massive impact, such as Chicxulub, there was a rapid decrease in surface temperature that led to a global winter (Bardeen et al. 2017). Although there is a certain level of uncertainty in the estimates, a decrease of approximately 20◦ C (Pierazzo et al. 2003b) is very likely, being able to reach even greater falls in correspondence with the results obtained in more recent studies (Artemieva and Morgan 2017; Bardeen et al. 2017). In correspondence with the latter, winter was not only more drastic but also more persistent, with an estimated duration that varies from several years to decades. Importantly, global cooling also affected the ocean sensitively. It has been estimated that the temperature changes along the water column reached hundreds of meters, gradually decreasing with depth (Bardeen et al. 2017; Kaiho and Oshima 2017). We must also consider that, although the decrease in the estimated temperature is much less than that reported on the surface, its effect is much more persistent due to the enormous heat capacity of the water. Most anammox species show their greatest activity between 30◦ C and 40◦ C (Tomaszewski et al. 2017). The prevailing temperatures after the impact of a large asteroid such as Chicxulub were below those values (they fell between 10◦ C and 20◦ C according to Kaiho and Oshima (2017). For temperatures below the optimum temperature (temperature at which the greatest activity is reached) the Arrhenius type model provides good estimates of the relative activity of the anammox (Hoekstra et al. 2018; Lotti et al. 2015; Tomaszewski et al. 2017). On the other hand, despite the benefits of using an Arrhenius type analytical model, it is also possible to perform a more detailed modeling by conveniently interpolating the experimental data provided by other studies (Daverey et al. 2015;

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Lotti et al. 2015). This is the method used to study the influence of p H on the R A A considering that there is no analytical expression to describe this process. Acidification is recognized as another of the most important side effects associated with the occurrence of a great impact on Earth. The deposition of a large amount of chemical compounds, mainly in the form of sulfuric acid, is responsible for a decrease in the pH values of water, an effect that negatively affects both oceanic and freshwater ecosystems. Other chemical species such as carbon dioxide and nitrates may also have contributed, although to a lesser extent, to the process of acidification in the ocean (Tyrrell et al. 2015). Aaccording to the results of (Carvajal-Arroyo et al. 2013), there is a drastic inhibition of anammox suspended cultures if the pH of the medium is less than 6.5 or greater than 8.0. We must also bear in mind that the impact of the acidification process depends not only on the net amount of deposited material, but also on how quickly it is delivered to the ocean (Ohno et al. 2014; Tyrrell et al. 2015).

4 Evolution of the Process of Photosynthesis of Phytoplankton After the Impact of the Chicxulub Asteroid The impact of the Chicxulub asteroid injected a large amount of sulfur gases into the middle and high stratosphere through the plumes followed by the explosion (Pierazzo et al. 2003a). Most of these gases would be oxidized to sulfates which, when dissolved in atmospheric water, would generate acid rains. It is estimated that the amount of these gases was considerably higher than in the contemporary atmosphere (Ishida et al. 2007; Pierazzo et al. 2003a). They would reduce the solar energy that reaches the earth’s surface causing a drastic decrease of the temperature in the troposphere and inhibiting the process of photosynthesis for several years. In addition, the presence of sulfur compounds can catalyze the conversion of nitrogen oxides formed into nitric acid being able to also activate chlorine, if this halogen is in sufficient quantity (Tie and Brasseaur 1995). For the impact of Chicxulub, an injection of chlorine sufficient to destroy all ozone is estimated (Kring, 1999). The destruction of stratospheric ozone allows the increase of the RUV on the Earth’s surface, mainly of UV-B (280nm − 315nm), although it also increases UV-A levels to some extent. From a biological point of view, UV-B usually has much more pronounced harmful effects than its less energy counterpart. However, the increase in the aerosol column can shield the UV radiation increase to some extent due to the dispersion effect. While aerosols increase the dispersion of UV radiation in the stratosphere, ozone reduction decreases it. These combinations of atmospheric disturbances had a significant influence on the evolution of the process of photosynthesis of primary producers after the impact, mainly based on the dynamics of aerosols and ozone (Perez et al. 2014). The content of both evolves as the atmosphere recovers (Ishida et al. 2007).

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5 Model to Evaluate the Evolution of Photosynthesis of Phytoplankton With a view to conducting a more detailed study of the post-impact atmospheric evolution, a model is introduced that takes into account the temporal evolution of both ozone and aerosol content suspended in the atmosphere, as shown in Table 1. Now, our model atmosphere considers the presence of sulfate aerosols and a reduced ozone layer (Ishida et al. 2007). Sulfate aerosols are formed from S O 2 and S O 3 injected into the atmosphere whose composition is 80% and 20%, respectively (Pierazzo et al. 2003b). According to estimates by Ishida et al. (2007) for aerosol concentrations ≥ 10−3 g/cm2 the atmosphere is practically opaque for the band corresponding to the visible spectrum. If the aerosol column is ≥ 10−4 g/cm2 the optical thickness of the atmosphere is very large for the UV band, which makes the downward flow of radiation extremely small. For these conditions, UV radiation is more sensitive to aerosol content than ozone. For aerosol contents of the order of 10−4 g/cm 2 − 10−5 g/cm 2 the UV is sensitive to both ozone reduction and sulfate aerosols. Finally, for aerosol levels < 10−5 g/cm 2 the surface UV is basically determined by the amount of ozone. The radiative treatment in the atmosphere is carried out with the use of the radiative transport code NCAR / ACD TUV: Tropospheric Ultraviolet & Visisble Radiation Model (https://www2.acom.ucar.edu/modeling/tropospheric-ultraviolet-and-vis ible- tuv-radiation-model), which allows changing various parameters of the atmosphere under study. In our investigation we consider solar zenith angles of 0◦ ,30◦ and 60◦ . The spectral irradiations at depth z in the water column E(λ, z) were calculated using the Lambert–Beer law of optics (Eq. 6). The K (λ) coefficients are the attenuation coefficients that characterize the optical type of ocean water and were those defined by Jerlov (1976) and interpolated by (Peñate-Alvariño et al. 2010). Spectral irradiances below the surface E(λ, 0− ) were found through Eq. 7 and the irradiance Table 1 Evolution of sulfate aerosol and ozone contents for eight years after the impact of Chicxulub according to estimates by Pierazzo et al. (2003a) and Ishida et al. (2007)   Time Aerosols O3 O3cont Time (year) Aerosols O3 O3cont     2 2 g/cm g/cm (year ) 0.5

1.69E-02

0.22

4.5

8.51E-06

0.66

1.0

1.27E-02

0.27

5.0

2.76E-06

0.71

1.5

5.84E-03

0.33

5.5

8.04E-07

0.77

2.0

2.10E-03

0.38

6.0

2.00E-07

0.82

2.5

7.02E-04

0.44

6.5

2.00E-07

0.88

3.0

2.45E-04

0.49

7.0

2.00E-07

0.93

3.5

8.44E-05

0.55

7.5

2.00E-07

0.99

4.0

2.70E-05

0.60

8.0

2.00E-07

1.00

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  of the incident light on the surface E l, 0+ by the code referred to above. The total irradiance at depth z for the PAR, E P A R (z), was estimated by Eq. 4 taking the visible light band between λi = 400nm and λ f = 700nm to solve the sum. In the case of ∗ the UV band (inhibitory), the spectral irradiance E inh (z) was calculated using the expression 5. Finally, to evaluate the normalized photosynthesis rate P (previously B ), the photosynthesis model P B ) with respect to the maximum rate PS (formerly Ppot E (Fritz et al. 2008) was used as follows: P 1 − e−E P A R (z)/E S (z) = PS 1 + EU∗ V (z)

(10)

With respect to the E S efficiency parameter, a rather wide range is considered, from 5W/m 2 to 150W/m 2 , with a view to including most (if not all) known photosynthetic species.

6 Results and Discussion Figure 1 shows the behavior of the relative rate of photosynthesis at various times after the impact of the Chicxulub asteroid, considering a contemporary model atmosphere very similar to what actually existed at the time of the impact. Note that the combined effects of ozone and the aerosol column are included in the radiative treatment, which allows us to infer more realistic estimates. In correspondence with Fig. 1, during the first two years the photosynthetic activity was practically zero due to the low levels of surface illumination. From that moment on, the figure shows how rates recover progressively showing a monotonous character that decreases with depth and where the aerosols that still remains in the column contribute to favor. During the period after 4 years, although the aerosol content has 100 2 year

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Fig. 1 Relative rates of photosynthesis in oceanic waters type III, solar zenith angle of 0◦ and efficiency parameter E S = 25W m 2 (Perez et al. 2014)

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60

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decreased significantly and the ozone is still depressed, the relative rates of photosynthesis in the ocean water column show a similar behavior, shifting the maximum rate to greater depth where the UV is more attenuated by the water column. In general, the depth at which the maximum rate will be depends on the type of ocean water, that is, on the protective capabilities against UV radiation of each of them, which increases as their opacity increases. Correspondingly, the photic zone decreases its thickness depending on the type of water. Thus, in ocean waters type I (lighter) this region reaches 200m, those of type II (intermediate) at 80m and types III reach only 50m (see Figs. 2 and 3). Another important conclusion of this work is that, despite the negative impact of the reduction of the ozone layer, this can have a rather limited effect on the photosynthetic potential integrated throughout the photic zone. This trend reveals the effective blocking of the UV radiation exerted by the water column, an influence that becomes much more marked for the turbid waters typical of a post-impact scenario. In fact, in the temporal evolution of the integrated photosynthesis rates shown in Fig. 4, variations in photosynthetically active radiation are more prevalent than the 100 2 year

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Fig. 2 Relative rates of photosynthesis in oceanic waters type I, solar zenith angle of 0◦ and efficiency parameter E S = 25W m 2 (Perez et al. 2014)

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Fig. 3 Relative rates of photosynthesis in oceanic waters type II, solar zenith angle of 0◦ and efficiency parameter E S = 25W m 2 (Perez et al. 2014)

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Fig. 4 Integrated photosynthesis rate in the photic zone for solar zenith angle of 0◦ and efficiency parameter E S = 25W m 2 (Perez et al. 2014)

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Time; year inhibitory component linked to UV radiation. Similar behavior is observed for solar zenith angles of 30◦ and 60◦ . According to estimates made with the use of the radiative transport code, around 4.5 years after the impact of the asteroid is when the UV radiation on the surface is heavier. This behavior is conditioned both by the low aerosol content in the atmosphere, and by a very limited ozone layer (Ishida et al. 2007). Just then, the biological damage associated with UV reaches its maximum value, a fact that will be more noticeable at low latitudes and that negatively impacts primary photosynthetic productivity. In this sense it is convenient to highlight that, although both factors affect, the biological damage is more controlled by the dynamics of ozone than by the level of aerosol in the atmosphere (Fig. 5). The radiative transport code used estimates a DNA damage function that is nothing more than integration between 256 and 370 nm of UV irradiation at ground level, weighted with a spectrum of DNA damage action. This range of UV wavelengths is the most damaging to DNA (Seckmeyer et al. 1997, Setlow, 1974). According to estimates made, about 4.5 years after the impact of the asteroid is when the DNA 0.5

DNA Damage

Fig.5 DNA damage at ground level after the impact of Chicxulub for solar zenith angle of 0°, 30° and 60° (Perez et al. 2014)

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0.3 0.2 0.1 0 0

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Fig. 6 Relative rates of photosynthesis in oceanic waters type III, solar zenith angle of 0◦ and efficiency parameter E S = 25W m 2 (Perez et al. 2014)

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4,5 year

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Depth; m damage is greatest. This behavior is conditioned both by the low aerosol content in the atmosphere, and by a very limited ozone layer (Ishida et al. 2007). Just then, the surface biological damage associated with UV reaches its maximum value, a fact that will be more noticeable at low latitudes and that negatively impacts primary photosynthetic productivity. On the other hand, if we establish a comparison between the contemporary and archaic scenario, during the time of greatest biological damage, we see that the primary productivity of the former on the surface of the ocean appreciably exceeds the estimates for the latter (Fig. 6). The behavior shown in Fig. 6 is mainly due to the fact that there was no ozone layer in the Archaic and, consequently, a greater flow of UV radiation reached the planetary surface, increasing phytoplankton inhibition. In the ocean waters of type I and II, a behavior similar to that observed in those of type III can be observed. Based on the previous study, it can be seen that during the first years after the impact of the asteroid the greatest influence on photosynthesis is darkness, controlled by the excess of aerosols, being the worst scenario during the first two years when the highest values are reached of concentration of them in the atmosphere. When the earth’s surface reaches light again, biological damage is governed primarily by ozone dynamics. Phytoplankton productivity varied during this process depending on the content of aerosols and the ozone column, this dependence being less sensitive for type II and III waters than for type I. Finally, even at the time of greater DNA damage after of the impact, the atmosphere provides a more favorable radiative environment to house photosynthetic organisms than that provided during the archaic eon.

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6.1 Influence of the Impact of Chicxulub on Primary Chemosynthetic Producers: The Case of Anammox A massive impact such as that of the Chicxulub asteroid is probably capable of affecting to some extent all life forms including chemosynthetics. Among the environmental factors that could most influence are the rapid global cooling associated with the large amount of material (smoke, dust, ash and aerosols) injected into the atmosphere, and the acidification of the ocean, a process conditioned by the progressive transit of compounds of sulfur and nitrogen from the atmosphere to the ocean. The selection of these two environmental alterations responds, first of all, to the existence of plausible estimates of the actual impact of Chicxulub on the Earth’s climate; and secondly, due to the high sensitivity reported in the literature for anammox species under temperature and p H variations (Carvajal-Arroyo et al. 2013; Lotti et al. 2015).

6.2 Model to Evaluate the Effect of Temperature and pH on the Activity of Anammox Bacteria With the objective of quantifying the affectation to the relative activity of the anammox associated to the temperature variations that follows a massive impact such as that of Chicxulub, the temporal evolution of the average sea temperature was considered based on the depth reported by Kaiho and Oshima (2017) for two different impact scenarios. The first, of less intensity, includes the emission of 1500T g of black carbon in the form of soot, while the second, of greater intensity, implies the emission of 2600T g of soot. The average ocean temperature profiles were taken into account for the previous scenarios obtained by Kaiho and Oshima (2017) using a Global Atmosphere–Ocean Climate Climate Model developed by the Japan Meteorological Research Institute (MRI-CGCM3). Depths of 2 m were considered as representative of shallow water (< 100m) and 100m for deep water and an Arrhenius type model set out in Sect. 3. For the study of the behavior of the R A A as a function of p H , the analysis was based on the behavior of this variable due to sulfate additions previously reported by Tyrrell et al. (2015). In the first case, the induced effects were studied considering 15, 30 and 60Pmol of sulfuric acid with a time scale for the deposition of 6 months. In a second case, the same procedure was repeated but assuming a time scale of only 10 h according to the most recent estimates suggested by Ohno et al. (2014). According to Tyrrell et al. (2015), such rapid additions exacerbate the influence on p H values if we compare them with the behavior shown by the first case. Finally, as a prototype to evaluate the influence of p H on the anammox communities, we consider the data published by Carvajal-Arroyo et al. (2013) for suspended bacteria shown in Fig. 7.

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100

Fig. 7 Effect of p H on the relative activity of suspended anammox (Carvajal-Arroyo et al. 2013)

RAA; %

80 60 40 20 0 6.5

7.0

7.5

8.0

8.5

pH

7 Results and Discussion Figures 8 and 9 show the behavior of anammox activity associated with long-term temperature fluctuations for events that inject 1500T g and 2600T g of soot into the atmosphere. As you can see, the activity of the anammox is greatly reduced for several years after the event, depending largely on the amount of soot emitted into the atmosphere and the depth of the ocean considered in the study. It can also be seen, as a general trend, a significant decrease in the range of 2 to 4 years after impact. It is also possible to clearly appreciate that anammox species confined in deeper waters take longer to recover the original activity than those that inhabit shallow water, despite registering a lower fluctuation of the average temperature. On the other hand, the scenario with the highest amount of soot (2600T g) show the highest inhibition rates and the greatest persistence over time, a trend that is shown more clearly in Fig. 10. 100 2m

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Fig. 8 Effect of temperature on relative activity (R A A) for two different depths considering 1500T g of soot (Pérez et al. 2018)

100 m

60 40 20 0 0

2

4

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Fig. 9 Effect of temperature on relative activity (R A A) for two different depths considering 2600T g of soot (Pérez et al. 2018)

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Fig. 10 Comparison between relative activity of anammox at 100m deep in the ocean as a function of the amount of soot (Pérez et al. 2018)

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Time; year Accordingly, the behavior for both cases is very similar during the first two years after the event. However, as of this date, the recovery of the anammox tends to occur in a much more pronounced way when the amount of soot is lower, which implies that the water temperature increases more rapidly. Meanwhile, the second case reflects the persistence of low temperatures for a longer time, a situation that tends to be quite unfavorable for the development of this particular type of species. Figures 11 and 12, shows the influence of p H on the activity of anammox. As can be seen, the anammox communities were seriously affected as a result of the decrease in p H values. The magnitude of the affectation can vary according to the net amount of sulfuric acid produced during the event and the time scale that governs the atmospheric deposition process. The larger and faster the addition of H2 S O 4 , the more marked and abrupt is the change of the p H in the ocean, and consequently, greater devastation and inhibitory effect on anammox bacteria is observed.

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Fig. 11 Effect of p H on the relative activity of anammox (R A A) in suspended cultures with a 6-month time scale for different additions of H2 S O 4 (Pérez et al. 2018)

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Fig. 12 Effect of p H on the relative activity of anammox (R A A) in suspended cultures with a 10h time scale for different additions of H2 S O 4 (Pérez et al. 2018)

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Time; year As can be seen in the Figs. 11 and 12, the anammox communities were seriously affected as a result of the decrease in p H values. The magnitude of the affectation can vary according to the net amount of sulfuric acid produced during the event and the time scale that governs the atmospheric deposition process. The larger and faster the addition of H2 S O 4 , the more marked and abrupt is the change of the p H in the ocean, and consequently, greater devastation and inhibitory effect on anammox bacteria is observed. On the other hand, Fig. 13 shows that if the addition of H2 S O 4 is slow, the adverse effect on the R A A is less drastic but more lasting over time. The foregoing corresponds to a longer acidification period caused by a low deposition rate of sulfuric acid to the ocean. In addition, it can be deduced from Fig. 13 that anammox has good recovery capabilities once sea p H conditions return to normal. The potential influence of nitrates on R A A was also considered taking into account that this chemical species, in particular, has an inhibitory effect on anammox if its concentration is greater than 50m M (Carvajal-Arroyo et al. 2013). However,

Impact of the Chicxulub Asteroid: Potential Implications …

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Fig. 13 Effect of p H on the relative activity of anammox (R A A) in suspended cultures for additions of H2 S O 4 of 60Pmol and different time scales (Pérez et al. 2018)

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Time; year according to our estimates, its influence is negligible due to its low concentration in the ocean after the impact of Chicxulub (Tyrrell et al. 2015). Freshwater ecosystems with a much smaller volume, will suffer more as a result of the drastic decrease in surface temperature, reaching up to 26◦ C in some regions (Bardeen et al. 2017). Due to a similar reason, they would be particularly affected by acidification during the Chicxulub impact. On the other hand, having a fairly limited volume, the effects of dilution in this type of system also tend to be small. In addition, the entry of substance as a result of rain and evaporation could be more harmful to these ecosystems than to their oceanic counterparts. Something similar is expected in the case of groundwater ecosystems. In any case, the behavior will depend critically on the residence times of sulfur compounds in the atmosphere, estimates that vary from several months to a few days. According to the results obtained previously, the anammox communities would be seriously affected during a massive impact by the combined effect of the decrease in temperature and p H . In both cases, the levels of inhibition could be significant considering the high sensitivity of these species to a decrease in both temperature and p H . We must take into account that the majority of the anammox species studied so far show their highest activity in warm conditions (25◦ C − 30◦ C) and neutral or slightly basic media ( p H 7 to 8) (Tomaszewski et al. 2017). On the other hand, we must also consider that anammox activity does not depend independently of both variables. In most of the cases studied (Daverey et al. 2015; Tomaszewski et al. 2017), such a combination (low temperatures and p H ) can reinforce the negative effects on anammox in a non-linear way, which suggests a significant increase in the degree of inhibition in a scenario similar to that of Chicxulub. As can be seen during this study, the process of global cooling and ocean acidification could significantly alter the activity of anammox bacteria, and by extension, other chemosynthetic communities whose metabolism is influenced by changes in temperature and p H . In addition, the models used explain why the recovery of chemosynthetic bacteria such as anammox, was relatively rapid after a great environmental stress caused by an impact event like Chicxulub.

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Darwinian Evolution from a Generational Point of View Osmel Martin , José Suarez-Lezcano , and Yoelsy Leyva

Abstract The potential implications of an extended generational overlap in the context of the biological evolution of our planet are discussed. A special emphasis is made on the replication patterns exhibited by contemporary bacteria considered as a feasible prototype of the first forms of life in our planet. Accordingly, it is hypothesized that the existence of a large number of overlapping generations could act as an important source of individual variability for those ancestral species, impacting the way that well-established processes like natural selection and biological evolution operate in nature, a principle that, probably, could also be applied to the prebiotic era. Keywords Natural selection · Fitness · Overlapping generations · Darwinian evolution

1 Introduction According to the evolutionary theory, life in our planet evolved and diversified from a common ancestor [1, 2]. Geological evidences suggest that this complicated process began relatively early in the Earth´s history, probably about 3.5 billion years ago, during the Archean eon [3, 4]. It is also probable that the first rudimentary ecosystems, mainly integrated by prokaryotes, found a suitable habitat in the surroundings of O. Martin (B) Laboratorio de Ciencia Planetaria, Universidad Central “Marta Abreu” de Las Villas, Santa Clara, Cuba e-mail: [email protected] J. Suarez-Lezcano Escuela de Enfermería, Pontificia Universidad Católica del Ecuador Sede Esmeraldas (PUCESE), Ecuador, Cuba e-mail: [email protected] Y. Leyva Departamento de Física, Facultad de Ciencias, Universidad de Tarapacá, Casilla 7-D, Arica, Chile e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Cardenas et al. (eds.), Proceedings of the 3rd International Conference on BioGeoSciences, Springer Proceedings in Earth and Environmental Sciences, https://doi.org/10.1007/978-3-030-88919-7_14

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hydrothermal vents in the Archean seas [5], a process that preceded the ulterior spread and diversified the different forms of life in our planet. In accordance with the same theory, the underlying mechanism governing the evolution of life in our planet is the principle of natural selection [6], a concept that has also been extended to the context of the inanimate systems [7, 8]. Introduced originally by Darwin, natural selection deals with the ability of living beings’ populations of surviving and evolving under the continuous variations of the environmental conditions. In the contemporary version [6], the principle of natural selection rests on two basic elements. On one hand, the individual variability with a marked emphasis on the genetics, on the other hand, the impact of the environmental conditions acting as a selector of the best fitted individuals and their offspring for a certain condition of the medium. During this work, considering the replication patterns exhibited by contemporary bacteria as prototype of the earliest microorganisms in the Earth, the potential implications of a generalized generational overlap within those ancient populations are discussed from an evolutionary point of view. In this case, the existence of a great number of overlapping generations, a common feature exhibited by many contemporary species [9, 10], are due to the accumulation of stochastic fluctuations of the generation time during a long process of continuous replication within those populations. The same arguments are also extended to the study of the replicator systems during the prebiotic era [11, 12], a central component in most of the theories about the origin of life.

2 A Simple Model of Cellular Fission with Generational Overlap Let us consider an elemental model of the cellular fission during the exponential phase. The structure of the population growth follows a typical tree outline in Fig. 1. The values tij in the edges of the graph represent the specific lifetime (generation time) of each cell j in the generation i.e. During the simulation, these values were computed according to: ti j = T + t random

(1)

where T is the most probable lifetime of the cell and t random represents the possibility of a stochastic fluctuation of this value. In this particular case, it is assumed that the stochastic fluctuations of the cellular lifetime follow a normal (Gaussian) distribution with mean (μ = 0) and standard deviation σ . To estimate the time elapse to reach a particular couple of daughter cells in generation i, the specific time-path is computed adding the precedent computed times in the graph. For instance, the minimal time to reach cells 7 and 8 belonging to the third generation (37 and 38 in the graph) is determined by T34 = t01 + t12 + t24 . The same

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Fig. 1 The structure of the time paths for the first three generations

procedure is repeated for each couple of daughter cells to include all possible generations. Note that the number of different time-paths to reach a certain generation n is given by 2n−1 , just half of the number of cells for this generation. With the aim to estimate how many different generations are cohabiting at a certain time t, the number of times that the following condition holds for each generation for the different time-paths is counted: Ti j ≤ t < Ti+1,k

(2)

where i and i + 1 represent consecutive generations, whereas the indexes j, k are constrained to the interval j = 1..2i−1 and k = 1..2i ,respectively. If the above condition holds for some valid array of indexes (i, j, k), then generation i is present at this time for this specific time-path. During the numerical simulation, the mean value of the cellular lifetime was normalized (T = 1). To compute the stochastic fluctuations t random the standard

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deviation was set to σ = 0.2. With the aim of avoiding unrealistic low values for the cellular lifetimes ti j , during the computations, the values of t random were constrained to be greater than t random ≥ −4σ . Although unlikely, in the cases that the above condition fails, the values of t random were recomputed until completing the previous condition. The main results derived from these procedures are discussed in the next section.

3 Estimating the Degree of Generational Overlap and Its Potential Biological Implications Figure 2 shows how the generational composition behaves at some specific times, considering only one cell at the beginning. According to the figure, the probability of finding overlapping generations increases in a noticeable way with time. Only when t = 2T the population remains pure from the generational point of view. For greater times, the degree of dispersion gradually increases to reach 6 different generations when t = 15T . Note that the relative contribution of the most probable generation is diminishing as the contributions of the nearest generations are continually increasing. A similar tendency is shown in Fig. 3, when the size of the initial sample was increased to include a thousand cells. However, in this case, the dispersive effects in the structure of the population are more remarkable than those exhibited in the previous one.

Fig. 2 The increase of overlapping generations with time from a common cell

Fig. 3 The increase of overlapping generations with time from a sample of a thousand cells

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For instance, opposite to the first case, now when t = 2T , the population includes two additional generations. The same tendency occurred for the other times, reaching a total of ten generations (four generations more than the previous case) when t = 15T . In the same way, the prevalence of the most probable generation is even less prevalent than in the previous case, a behavior that is clearer in Fig. 4. The increases of the number of overlapping generations with time reported here is very similar to the behavior displayed by a classical model of a random walk. However, whereas in the classical model the uncertainty spreads on space, in this particular case the dispersion occurs in time. Supported by this analogy, it is possible to infer the number of possible generations coexisting at some arbitrary time t = nT , where n is an integer. Considering that √ for an arbitrary time-path of lengthn, the standard deviation is given by σn = nσ and assuming t = kσn , the potential number of different generations G is given approximately by: G=

√ 2kσn 2t = = 2 nkσ T T

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where k is a measure of the probability to include generation n at nT . According to the above expression, √ the number of possible generations living at the same time t = nT increases as n. Setting the value of k = 4 in Eq. 3, it is possible to reproduce, approximately, the degree of generational dispersion derived from the simulation considering only one cell at the beginning. However, as the size of the initial sample was increased to include a thousand cells, the value k = 6 is more appropriate to account the emergence of some marginal generations where the number of individuals is extremely small in comparison with the relative contributions of the more favored generations. Now, according to that, and considering the

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parameterization used here, it is possible to extrapolate the behavior of the generational dispersion in the future. For instance, for a time compatible with a 10 000 generation experiment [13], the dispersion may range from 160 to 240 different generations coexisting at the same time, according to the size of the initial sample. Such continuous increases of overlapping generations over time may enhance the possibility of the population surviving under abrupt changes of environmental conditions, probably, a typical condition during the early Earth. For instance, assuming that 95 percent of the offspring per generation share the similar features of their parents, the possibility that, at least, one of them could differ from the common ancestor after n generations can be easily calculated as. P = 1 − 0.95 R(N )

(4)

where R(N ) is the number of successful replication events linking an organism in the generation n with their offspring in the generation n + n. For a population in the phase of exponential growth R(n) is given by R(n) = 2n , whereas for the stationary case, the expression is simply R(n) = n. Evaluating the above equation, we found out that the probability of being different reaches high values, greater than 99 percent, after 7 generations in the phase of exponential growth. For the stationary case, a similar value is reached after 90 generations. However, despite these differences, both values are remarkably lower than the degree of generational dispersion computed above. We must note that, in not few cases, the viability of an entire population could be assured by the presence of only one individual with the right phenotype [14], a behavior that could be crucial to preserve the earliest communities on Earth, subjected to an extreme selective environment. On the other hand, if it is assumed that contemporary bacteria are the offspring of a common ancient ancestor, then setting its time at about 3.5 Gyrs ago [3, 4], it is possible to infer the levels of dispersion for these species at present. To do that, let us assume that the generation time is very long, approximately one year. In correspondence with the parameterization used here, this selection constrains the minimal time for the replication to 2.5 months, also, a relatively long time. Even assuming such conservative considerations, according to Eq. 3, the number of different generations at present may reach an extremely large value, in the order of 105. Furthermore, during the simulation, it is assumed that the cellular lifetime fluctuations are rather symmetric around the mean value T . However, this assumption is not necessarily true in the most general case. In principle, the minimal value of the bacterial lifetime is basically determined by the thermodynamic laws [15],in this particular work, a twenty percent of the most probable T value is assumed. However, the superior limit could be poorly constrained on the nature, spanning for several years [16, 17], even millenniums [18], when the environmental conditions are hostile. In these cases, disperse by natural forces (winds, sea currents and so on), individuals belonging to the same generation begin to desynchronize as a

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consequence of the environmental differences in their new habitats [19]. Furthermore, taking into account the great environmental diversity showed by the known habitats in the Earth, it is obvious that these kind of processes may contribute in a noticeable way to the increase of the generational dispersion at planetary scale and consequently, to a remarkably increment of the diversity and robustness displayed by these systems. The same analyses are also valid for the case of the replicator systems in the context of the prebiotic evolution. The chemical reactions involved in the replication processes also tend to occur in a continuous way for a wide range of environmental conditions. However, there are important differences between both scenarios. For instance, the model of cellular fission discussed above is not necessarily valid for inanimate replicators. In general, before being degraded, a replicator species may produce an important number of copies of itself according to the environmental conditions and the availability of substrate [20]. On the other hand, these prebiotic systems were, probably, more exposed to the impact of environmental variations than the biological ones [1, 21]. Many of the regulation and repair systems present in the living cells probably lack these primitive systems. Without these mechanisms, the prebiotic systems probably evolved very quickly with time, a process that takes place at a much slower rate for the contemporary living beings, independently of the adversity of the medium. In general, among the environmental factors promoting the degree of generational dispersion in these inanimate systems, the temperature seems to play the major role. In fact, conditioned by an Arrhenius-like behavior, many chemical reactions are particularly sensitive to temperature variations. In these cases, the rate of replication may differ in appreciable way between neighboring regions if they are subjected to different temperatures, a situation that could be typical in the surroundings of hydrothermal vents where the temperature differences between the hottest and coldest regions may reach hundreds of degrees [5]. In accordance, it is not discarded that a remarkably generational overlap characterized the populations around these kind of systems, an issue that deserve a more exhaustive study. The same arguments are also extrapolated to a planetary scale. The difference of temperature between polar and equatorial regions may impose different regimens in the population growths of the species inhabiting these respective regions. Even, considering that the first forms of life arose in relatively well determined area, as they spread in the planet their growth patterns began to diverse according to the environmental conditions in their new habitats. While in the equatorial areas the rate of growth is high, in the polar regions the growth is much more limited. In accordance, equatorial populations tend to be generational ahead in comparison with their similar in the polar regions contributing, in a noticeable way, to expand the generational overlap at planetary scale. Furthermore, it is also obvious that this effect was greater in the Earth history when the temperature differences between these extreme latitudes was larger.

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4 Conclusions and Further Directions Overlapping generations seem to be an inherent feature of many biological systems [22]. As its effect is accumulative over time, the degree of dispersion exhibited by contemporary life should be enormous, considering that the first forms of life in our planet arose at about 3 ∼ 3.5 Ga ago. On the other hand, assuming an approximately constant mutation rate per generation, the existence of many different generations coexisting at the same time may partially explain the robustness of life, increasing the possibilities of the different species of adapting to the more dissimilar habitats. According to that, the explicit inclusion of overlapping generations in the study of biological systems in the long term may also give new elements of how principles like the natural selection and biological evolution work in nature. In general, beyond the peculiarities of a particular group or species and considering the role of replication as a source of genetic variability, the coexistence of multiple generations may be considered as an additional opportunity for the biological systems of increasing the individual variability, the basis of natural selection. Its effects could be more marked under drastic fluctuations of the environmental conditions, for instance, as a consequence of catastrophic natural disasters [23]. Moreover, it is not discarded that, in such scenarios the existence of best fitted marginal generations may give the species additional possibilities of surviving [24]. At the end, natural selection is the outcome of a rather complicated interaction between the environment and the individual variability [6]. Furthermore, even when there not current studies about this particular issue in a more astrobiological context, the impact of overlapping generations could not be limited to the biological era. Considering that the possibility of overlapping arises for any replicative system where the period of replication is not exactly constant, most of the issues discussed above could also be extended to the evolution of the prebiotic replicators [25–27]. Similar to the biological case, the existence of overlapping generations could favor the processes of diversification and complexification of these inanimate entities [8], probably, an important step that preceded the emergence of the first forms of life in our planet.

References Koch, A.L., Development and Diversification of the Last Universal Ancestor. Journal of Theoretical Biology, 1994. 168(3): p. 269-280. Koch, A.L., Were Gram-positive rods the first bacteria? Trends in Microbiology, 2003. 11(4): p. 166-170. Bell, E.A., et al., Potentially biogenic carbon preserved in a 4.1 billion-year-old zircon. Proceedings of the National Academy of Sciences, 2015. 112(47): p. 14518–14521. Buick, R., et al., Record of emergent continental crust ∼3.5 billion years ago in the Pilbara craton of Australia. Nature, 1995. 375: p. 574. Martin, W., et al., Hydrothermal vents and the origin of life. Nature Reviews Microbiology, 2008. 6: p. 805.

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Gregory, T.R., Understanding Natural Selection: Essential Concepts and Common Misconceptions. Evolution: Education and Outreach, 2009. 2(2): p. 156–175. Walker, S.I., M.A. Grover, and N.V. Hud, Universal Sequence Replication, Reversible Polymerization and Early Functional Biopolymers: A Model for the Initiation of Prebiotic Sequence Evolution. PLOS ONE, 2012. 7(4): p. e34166. Vasas, V., et al., Primordial evolvability: Impasses and challenges. Journal of Theoretical Biology, 2015. 381: p. 29-38. Rogers, A. and A. Prügel-Bennett, Evolving Populations with Overlapping Generations. Theoretical Population Biology, 2000. 57(2): p. 121-129. Waples, R.S., T. Antao, and G. Luikart, Effects of Overlapping Generations on Linkage Disequilibrium Estimates of Effective Population Size. Genetics, 2014. 197(2): p. 769. Robertson, Michael P. and Gerald F. Joyce, Highly Efficient Self-Replicating RNA Enzymes. Chemistry & Biology, 2014. 21(2): p. 238-245. Mulkidjanian, A.Y., D.A. Cherepanov, and M.Y. Galperin, Survival of the fittest before the beginning of life: selection of the first oligonucleotide-like polymers by UV light. BMC Evolutionary Biology, 2003. 3(1): p. 12. Papadopoulos, D., et al., Genomic evolution during a 10,000-generation experiment with bacteria. Proceedings of the National Academy of Sciences, 1999. 96(7): p. 3807–3812. Stewart , G., B. Robertson, and B. Young, Tuberculosis: a problem with persistence. Nat Rev Microbiol 2003. 1: p. 8. England, J.L., Statistical physics of self-replication. The Journal of Chemical Physics, 2013. 139(12): p. 121923. Liao, C.H. and L.M. Shollenberger, Survivability and long-term preservation of bacteria in water and in phosphate-buffered saline*. Letters in Applied Microbiology, 2003. 37(1): p. 45-50. Ribeiro, S., et al., Phytoplankton growth after a century of dormancy illuminates past resilience to catastrophic darkness. Nature Communications, 2011. 2: p. 311. Phillips, B.L., G.P. Brown, and R. Shine, Life-history evolution in range-shifting populations. Ecology, 2010. 91(6): p. 1617-1627. O’Dor, R.K., K. Fennel, and E.V. Berghe, A one ocean model of biodiversity. Deep Sea Research Part II: Topical Studies in Oceanography, 2009. 56(19): p. 1816-1823. Leyva, Y., O. Martín, and C.R. García-Jacas, Constraining the Prebiotic Cell Size Limits in Extremely Hostile Environments: A Dynamical Perspective. Astrobiology, 2018. 18(4): p. 403-411. Piedrafita, G., et al., Viability Conditions for a Compartmentalized Protometabolic System: A Semi-Empirical Approach. PLOS ONE, 2012. 7(6): p. e39480. Balloux, F. and L. Lehmann, Substitution rates at neutral genes depend on population size under fluctuating demography and overlapping generations. Evolution, 2012. 66(2): p. 605–611. Maher, K.A. and D.J. Stevenson, Impact frustration of the origin of life. Nature, 1988. 331: p. 612. Lehmkuhl, J.F., Determining size and dispersion of minimum viable populations for land management planning and species conservation. Environmental Management, 1984. 8(2): p. 167-176. Gilbert, W., Origin of life: the RNA world. Nature, 1986. 319. Sievers, D. and G. von Kiedrowski, Self-replication of complementary nucleotide based oligomers. Nature, 1994. 369. Yao, S., et al., Selective amplification by auto- and cross-catalysis in a replicating peptide system. Nature, 1998. 396.

Technical and Economic Viability of Agricultural Residue-Based Power Generation in Southern Chile Through Discrete Location Models Jorge Jimenez , Cristian Rivas, and Rodrigo De La Fuente

Abstract The Chilean Energy Policy establishes that by the year 2050, 70% of its energy should come from renewable sources to reduce dependence on energy imports and to reduce emissions of greenhouse gases. Agricultural residues may contribute to producing energy from a local renewable source. The present study evaluates the economic viability of operating power plants with agricultural residue from cereal crops in southern Chile. To achieve the aim of the study, Geographic Information Systems (GIS) and discrete location models were used to identify the optimal location for a power plant based on the distance to the power grid and the access cost to biomass. The best location for a power plant with a capacity of 50 MWe was 11 km from the Victoria electric substation, with a demand of cereal straw of 293,600 t/yr. Additionally, the Levelized Cost of Electricity for a 50 MWe agricultural residue-based power plant was $77/MWh, which was greater than the average marginal cost of electricity at point-of-connection. Moreover, the results show that it is not cost-effective to generate electricity from agricultural residues, unless the cost of straw is below $12.13/t. Finally, sensitivity analysis showed the net present value is very sensitive to the marginal cost of electricity from the power grid and biomass costs. Keyword Agricultural residue · Renewable energy · Biomass burning

J. Jimenez (B) · C. Rivas · R. De La Fuente Industrial Engineering Department, University of Concepcion, Engineering faculty Edmundo Larenas 215, Concepción, Chile e-mail: [email protected] C. Rivas e-mail: [email protected] R. De La Fuente e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Cardenas et al. (eds.), Proceedings of the 3rd International Conference on BioGeoSciences, Springer Proceedings in Earth and Environmental Sciences, https://doi.org/10.1007/978-3-030-88919-7_15

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1 Introduction The Chilean Energy Policy establishes that by 2050, 70% of its energy matrix should come from renewable sources [1]; however, nowadays it is based mainly on fossil fuels such as coal, natural gas, and oil [2] with a great dependence on foreign imports, higher greenhouse gas emissions, and price volatility from the energy markets [3]. One of the energy challenges is to diversify the energy matrix by including renewable energies from local sources to reduce dependence on imports of fossil fuels as well as to lower the emissions of greenhouse gases. Moreover, several studies [4–7] have shown that renewable energies can compete economically with fossil fuels, in terms of the Levelized Cost of Electricity (LCoE). Additionally, Chile has the potential to utilize biomass (considered a non-conventional renewable energy source) from agriculture, forestry, livestock, and municipal waste, among other sources [8]. The Araucania region of southern Chile is set apart as an ideal place to study the feasibility of producing electricity from agricultural residue due to its intensive agricultural activities including: wheat, barley and oat production, which accounts for 61% and 40% of the national production of wheat and oats, respectively [9]. Current farming practices include the use of fire, known as agricultural field burning, as an inexpensive alternative to cleaning and preparing fields for the next crop season, as well as for pest control [10]. However, field burning negatively affects the soil’s microorganism due to reductions of nutrients and energy inputs [11]. Also, it increases erosion due to water and wind erosion, affecting negatively the topsoil [12]. In addition, field burning releases great amounts of toxic compounds into the air [13, 14] contributing to the pollution observed in populated areas of southern Chile [15]. Besides, fine particulate matter, such as PM2.5 (particulate matter with aerodynamic diameter ≤2.5 µm) present in smoke from biomass burning, have a detrimental impact on human health from both acute and chronic exposure [16–20]. In addition, crop residue could be a potential source of energy for renewable energy production [21]. The present study evaluates the economic viability of locating biomass-based power plants in the Araucania region, Chile using residual agricultural biomass as a cost-effective renewable source of energy for Chile. To achieve this goal, geographic information systems (GIS) and discrete location models were used to identify an optimal location that considered the distance to the power grid and the access cost to biomass.

2 Material and Methods This research includes estimates regarding the amounts of agricultural residues available in the Araucania region, Chile. Also, several constraints were considered, such as: access to the electrical grid, roads and distance to the available biomass, among

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others. An economic analysis for each suitable location was assessed to determine the cost of electricity generation and LCoE at the selected location.

2.1 Power Plant Localization Suitable locations for power plants were determined through network analysis methodology, using the maximal coverage algorithm. As a preprocessing step, locations of potential straw supplying fields were identified, composed mainly of fields with wheat and oat crops. Locations not suitable for installing a power plant were excluded using rasterized image matching. The latter information was used to generate a gridded map of raster images for the region. Each image contained pixels with binary values, where one indicated that the location was feasible for locating a power plant and zero for a location marked as excluded or not feasible for locating a power plant (i.e., populated area, water body, roads, etc.). Then, through successive element-wise matrix multiplications (images treated as a matrix) optimal locations were obtained by matching pixels that satisfied all restrictions. Then, the distance between agricultural fields and power plants was estimated using the road network with ArcGIS software (version 3.18).

2.2 Network Analysis Using Localization-Allocation Model Potential optimal locations for power plants were determined through network analysis and used as input parameters for the maximal coverage model [22]. Thus, optimal locations were the ones with the greatest access to straw within a predefined distance between the potential power plant location and its surrounding agricultural fields. The maximum coverage model uses the following notation: let i ∈ I be a set of indices that represent I fields and j ∈ J be a set of indices that represent a set of possible J sites where to locate p power plants. The model used inputs of the potential demand of biomass from each farm given by hi , and a maximum coverage distance, d c between power plants and surrounded fields [22]. The mathematical formulation was as follows:  h i zi (1) Maximi ze st.



i∈I

ai j x j − z i ≥ 0 ∀i ∈ I

(2)

j∈J

 j∈J

xj ≤ p

(3)

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x j ∈ {0, 1} ∀ j ∈ J

(4)

yi ∈ {0, 1} ∀i ∈ I

(5)

where the parameters are given by:  ai j =

  1, if candidate power plant j can cover production of farm i di j ≤ dc 0, otherwise (6) h i = production at farm i

(7)

p = number of power plants

(8)

And the variables are described as follows:  1, if a power plant is open at a potential location j xj = 0, otherwise  1, if farmiis covered by any of the potential locations zi = 0, otherwise

(9)

(10)

The objective function (1) maximize the access to straw, constraint (2) establishes that a field or farm is covered by at least one selected power plant. Constraint (3) enforces that p power plants must be located. Constraints (4) and (5) define the domain of the variables. Suppliers of Agricultural Biomass The maximal coverage model used the total agricultural biomass (straw) available at each field (farm) as an input parameter. Information for crop types, yields, locations (coordinates), surface, and crops subject to field burning were obtained from the Office of Agricultural Studies and Policies (ODEPA in Spanish). Moreover, the total amount of straw burned in each field was estimated considering the type of crop, cultivated surface, crop yields, and the proportion of land subject to field burning. The amount of cereal produced in each farm (M i ) were estimated from Eq. (11): Mi = Si ·R h · F

(11)

Where i represents an index that belong to the set I that, in this case, contains 1774 farms that provided straw to a power plant. The index h ∈ H , represents hth district within the region where farms were located, with |H| = 32. Si denotes the land surface (hectares) of farm i subject to field burning, whereas Rh is the amount (metric ton) of cereal produced per hectare, and F, the ratio of regional yield for

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wheat crops between the agricultural years 2014–2015 and 2006–2007, being 5.50 t/ha [9] and 4.78 t/ha [23], respectively. Wheat crop was used since it is the most predominant crop in the region [9]. In addition, the amount of straw available (h i ) after harvesting at each farm was estimated from Eq. (12): h i = Mi · (1 − I C)/I C

(12)

where the term (1 − I C)/I C represents the mass of straw produced from each unit of cereal harvested, where I C corresponds to the ratio between the weight of the grain and the total weight of the plant without the roots. I C was 0.45 for wheat based on the literature [24] in detriment of the information provided by ODEPA for the agricultural burning, since it does not differentiate between wheat, oats, corn straw, or wheat. Potential Locations for Power Plants Potential locations for a biomass-based power plant were subject to satisfying basic requirements and were organized into a set of k raster layers with dimensions (M, N), with pixels containing information about the constraints. All locations (pixels) that did not meet the constraints, on each layer, were not considered within the set of potential power plant locations as seen in Fig. 1. Constraints (A)

(B)

(C)

(D)

(E)

Land cover: Land cover with rocky areas, bodies of water (lakes, lagoons, and rivers), mountains, glaciers and urban areas were excluded as possible locations. Thereby, a value of zero was assigned to the (mth, nth) pixel that was excluded. Protected areas: Areas under protection of specific laws [25] were also excluded as potential locations. These included: national parks, natural monuments, and wilderness areas for conservation. Slope and hazardous areas: Areas subject to landslide from earthquakes or heavy downpour were considered not suitable for a power plant location. Thus, the analysis excluded terrains with an angle of inclination greater than 15° [26, 27]. Proximity to the power grid: Areas closer to the power grid are more suitable for a potential location due to lower connecting costs since no extra electrical infrastructure (transmission) is required to access the grid. Then, areas located at a distance greater than 3 km [28] to an existing power grid were excluded. Proximity to roads: Proximity to roads facilitates biomass transport from the farms to power plants; therefore, areas located at a distance greater than 1.6 km from an existing road [29] were excluded as possible locations.

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Fig. 1 Selected constraints layers for potential power plant location in the Araucania region, Chile a Land cover; b Protected areas; c Slope and hazardous areas; d Proximity to roads; e Proximity to the power grid

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Suitable Locations Each constraint-layer k (or exclusion factor) was represented by a matrix Rk containing Boolean data with suitable dimensions to perform element-wise multiplication. Additionally, each entry of the matrix represented a (0.1 km × 0.1 km) region. The final grid was computed by multiplying each cell according to Eq. (13): Rf =



Rk

(13)

k∈K

In addition, the region was further aggregated into grid cell of 1 km × 1 km, and the centroids of each cell were intersected with the constrained grid R f . Finally, centroids located in non-restricted areas (cells filled with ones) were considered as suitable locations for a power plant. Transport Network The transport network was generated through ArcCatalog 10 based on the Chilean road network of the year 2014. The transport network provided information of the routes and distances between the straw supplying farms and the potential power plant locations.

2.3 Power Capacity The electric power output (W B ) in MW (Megawatts) for a potential power plant facility was calculated from Eq. (14): W B = 2.77 · 10−4 · L H V · ne ·



h i /O H

(14)

i∈I

where ne is the overall efficiency for electricity generation with a value of 30% for a biomass-based power plant [30] and O H is the annual operation capacity (h/yr) of the power plant, which was equal to 7008 h/yr (plant factor of 80%) [31]. The Lower Heating Value (LHV ) (in MJ/kg) of the biomass at a specific moisture level was calculated using Eq. (15) [32]: L H V = (L H V s · (100 − w) − 2.44 · w)/100

(15)

where LHV s is the lower heating value of the fully dried biomass which is equal to 17.2 MJ/kg, and w is the moisture content (% wet basis) and equal to 15%. The calculations considered that 10% of the electricity produced was selfconsumed by auxiliary equipment [33]. Therefore, the net electric power output, W N is 90% of the gross electric power (W B ) produced by a power plant. Next, the

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relationship between the net power output and the annual operating hours (OH) of the power plant, were used to estimate the amount of electricity supplied to the power grid, E (GWh/yr) computed as in Eq. (17): E = WN · O H

(17)

2.4 Economic Analysis In order to evaluate the economic viability of a power plant, the Net Present Value (NPV) was calculated for each alternative. Thereby, in this section, p belongs to the set P ⊂ I and represents a farm within the operating range of the power plant. Capital Costs The total investment cost, IC, for a power plant was estimated following the methodology described in [33, 34], where IC is based on the net electric power output for a given power plant between 5 to 50 MWe (Megawatts of electricity). Additionally, IC includes not only direct costs (boiler, pipelines, steam turbine, auxiliary equipment, instrumentation and control, installation costs), but also indirect costs such as engineering and commissioning costs. Operational Costs The operational cost includes the cost of biomass, transport, wages and salary, administrative fees, maintenance costs, and rental costs. Cost of biomass The annual costs of biomass PC was calculated from Eq. (18) using the total annual biomass (straw) available h p (t/yr) at the producing farms. Besides, the purchased cost of biomass, SPB was $26.43/t.  h p, (18) PC = 10−6 · S P B · p∈P

Transport Costs Freight costs were not standardized since these services are arranged directly with each customer depending on the volume, distance, and type of freight. Shipping and handling fees range between $3.74 and $5.28/t of straw (bale density 233 kg/m3 ) transported up to a distance of 30 km. Therefore, assuming a linear relationship, the transport cost is determined as a function of the straw weight transported and its distance from farms to the power plant. Hence, the annual biomass transport costs, TC, is given by the Eq. (19):

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T C = 10−6 ·



203

h p · (3.74 + 0.05 · d p )

(19)

p∈P

where, m p is the biomass transport from the pth farm to the power plant, and d p corresponds to the distance between the farm p and the power plant. Maintenance Costs The annual maintenance costs, MC, of the power plant set to 1.5% of the total investment cost [33]. MC = 0.015 · I C

(20)

Wages and Salary The wages and salary, EC, for operating the power plant $0.277 million/yr [35], which considers a staff of engineers, technicians, and plant operators. Cost of Rentals The cost of renting a property depends on the location, the quality of the land, its current use, among other factors. The costs of renting were estimated for near-toroad locations, and the annual cost of renting was $443.13/ha. A required 50 ha was estimated for the project with an annual cost, RC, of $0.022 million/yr. Electricity Sales The annual income from electricity sales to the power grid, IE, was calculated using Eq. (21): I E = 10−6 · E · E P

(21)

where EP is the average marginal cost of electricity during the year 2016, for the 66 kV Victoria bar located in the region, set to $64.3/MWh [36]. Net Present Value The Net Present Value (NPV ) was calculated considering the investment cost and future cash flows (income and expenses), according to the Eq. (22): N PV = IC −

n  t=1

FCt (1 + e)t

(22)

where n is the economic lifespan of the plant estimated in 20 years [37], FCt is the annual cash flow of year t, which considered a 25% income tax rate [38]. e is the annual discount rate for the project, set to as 12% [39]. The depreciation rate of the power plant was considered equal to its lifespan (with a straight-line depreciation

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cost per year = IC/20) [40] and the residual cost was calculated as the present value of perpetuity). Levelized Costs of Electricity The Levelized Cost of Electricity (LCoE) model is helpful to compare different alternatives to generate electricity by using a standard cost of electricity generation per unit of MWh [41, 42], as defined in Eq. (23): n LCoE =

t=0

I C t +Ot +M t +Ft +T Ct (1+e)t n Et t=0 (1+e)t

(23)

where, I C t corresponds to total investment costs in year t, whereas Ot and Mt represent operation and maintenance costs in year t respectively. Ft denotes the fuel cost in year t, and T Ct , the tax credits in year t. Finally, E t is the energy available for sale in year t.

3 Results and Discussion In the Araucanía region, 1771 farms use field burning, on a regular basis, for managing wheat and oat crops. These farms were considered as potential straw suppliers for operating a power plant. Their combined contribution represents a total of 675,383 t/yr of available straw, with an energy yield of 2,700 GWh/yr. Intersecting several boolean raster images, we identified 2,767 suitable locations for installing a power plant, and by maximizing the amount of straw than can be collected from the surrounding farms (fields)—within a 30 km radius—an optimal location was obtained. This approach included a network analysis for identifying large suppling farms within the region and the definition of suitable locations according to the constraints presented in Sect. 2.2. The results are summarized in Fig. 2. More specifically, the optimal plant location was 1.8 km from the nearest transmission line (66 kV), 11 km from the nearest substation (Victoria substation) and 0.7 km from the nearest road. The location can access straw from 592 farms with a total supply of 293,600 t/yr and it represents 43% of the total available straw in the region. With this amount of biomass, it is feasible to run a power plant with a net power output of 45 MWe, capable of supplying 315 GWh/yr of electricity to the grid. Nevertheless, the amount of energy produced, the NPV for the project was −$25.86 million. The project was not profitable since the capital investment and operational costs were not compensated by future incomes from the electricity sold considering the lifespan and discount rate. The LCoE was $77/MWh, being the cost of capital the most significant cost, representing 51.2% of the total cost. Table 1 shows detailed information of the cost structure of the proposed power plant.

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Fig. 2 Optimal location for an agricultural biomass-based power plant in the Araucania region, Chile Table 1 Cost structure of the biomass-based power plant

Cost

Percent (%)

Investment cost

51.1

Biomass costs

32.7

Maintenance

5.7

Transport

5.7

Taxes and residual cost

3.4

Wages and salary

1.2

Rentals

0.1

Total

100

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For sensitivity analysis, three scenarios were evaluated to assess the economic feasibility of the power plant under different conditions, including variable price for energy, long term soft loans, and changes in the criteria for establishing the maximum distance from the power plant to the biomass supplying farms.

3.1 Scenario 1: Variable Prices of Energy On the one hand, the power plant begins to be profitable when the sale price of electricity increases by 21% with a value greater than $77/MWh (see Fig. 3). On the other hand, the power plant is profitable when the purchase price of the straw is reduced by at least 54% of the current price of biomass (a reduction of $12.13/t). The prohibition or the application of specific burning fees from field burning may reduce the price of straw since the buyers of this resource could negotiate another price with the farmers since the latter would be obliged to pay a fee in case of not having a buyer. This could lead to a reduction of the straw sale price. A burning fee of $142/ha could allow for negotiating lower prices since they would be forced to use mechanical means to clean their fields reducing the price from $26.43/t to $5.28/t, which in turn would be economically feasible for the biomass-based power plant to compete with current energy prices. In addition, the marginal cost of electricity could drop to $58/MWh, a point where the power plant becomes not economically viable. If the burning fee is equal to or greater than the cost of the straw, it may reduce the transfer cost of the straw; having a positive effect on the NPV of the power plant. If the straw is given away for free, the LCoE for the power plant is reduced to $53/MWh. However, a higher burning fee could economically affect cereal producers by increasing their production costs. Besides, it may increase imports over

Fig. 3 Relationship between Net Present Value and EP for different BC

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domestic production. This effect could reduce the domestic production of cereal, and consequently, the availability of straw to feed a biomass power plant in the region. EP = average marginal cost of electricity of the power grid; BC = Biomass cost; NPV = Net Present Value.

3.2 Scenario 2: Long-Term Financing The power plant was evaluated with a loan of $9 million, which corresponds to 10% of investment costs over a period of 20 years, with an annual interest rate of 4.1%. This resulted in a NPV of −$21.34 million (using French depreciation method).

3.3 Scenario 3: Variation of the Coverage Radius Between the Power Plant and the Straw Suppling Farms As the radius between the power plant and the straw supplying farm is reduced, radius ≤30 km, the amount of available straw is reduced too, diminishing the power output (electricity) for the power plants. In addition, the LCoE increases for smaller power plants because of economies of scale. Table 2 summarizes the results for a coverage radius of 20 km, 25 km and 30 km, between the power plant and the farms. Table 2 Summary of variables for Scenario 3

Maximum distance between power plant and biomass supplying farms 20 km

25 km

30 km

Electric power (MWe )1

39.14

44.12

50.00

Electricity produced (GWh/yr)

246.89

278.30

315.34

Total capital costs ($/MWh)

45.19

41.18

38.38

Transport cost ($/MWh)

4.08

4.26

4.29

Biomass cost ($/MWh)

24.56

24.56

24.56

Maintenance cost ($/MWh)

5.06

4.61

4.30

Wages and salary ($/MWh)

1.35

1.06

0.88

Cost of renting ($/MWh)

0.11

0.08

0.07

Cost of electricity ($/MWh)2

82

78

75

NPVI (Net present value index)

−0.41

−0.34

−0.29

1 10%

for self-consumption 2 tax credits and residual cost are included

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Table 3 NPV sensitivity analysis Parameter

Base case

NPV sensitivity to the variation of parameters −10%

−5%

0%

5%

10%

Marginal cost of electricity1

$64/MWh

−38.41

−32.13

−25.86

−19.58

−13.30

Biomass costs

$26.43/t

−21.04

−23.45

−25.86

−28.27

−30.67

−25.28

−25.57

−25.86

−26.14

−26.43

Wages and salaries $0.277 million

−25.68

−25.77

−25.86

−25.94

−26.03

Cost of renting

−25.84

−25.85

−25.86

−25.86

−25.87

Freight

costs2

1 Marginal 2 Transport

$3.74/t $0.022 million

cost of electricity at the substation Victoria up to a distance of 30 km

3.4 NPV Sensitivity Table 3 shows the NPV sensitivity to variations (±10%) of the parameters for the base scenario. The NPV is very sensitive to the marginal cost of electricity and biomass costs. Parameters such as the cost of electricity depend on the marginal cost of electricity at Victoria substation, which establishes the price of electricity for the area. The cost of transport depends on the distance from the farm to the power plant and increases as farms are further away from the location.

4 Conclusions For current conditions, it is not cost-effective to generate electricity from agricultural biomass unless the LCoE for a 50 MWe biomass-based power plant is less than $77/MWh. This could be possible if the cost of straw is less than $13.22/t, which may be achieved by establishing a burning fee of $142/ha. This may reduce the cost of straw since farmers would be forced to use mechanical means to clean their fields, increasing the availability of straw in the local market and reducing its cost. The access to soft loans can leverage the investment. However, it requires lower costs of biomass or a higher marginal cost of electricity to make a power plant economically feasible to operate. The LCoE was lower for larger power plants because of economies of scale and may be attractive in terms of lower cost of electricity generation. However, as the power capacity increases, access to straw may become an issue for operating yearround due to the logistics involved for accessing a scattered resource affected by a seasonal variation in terms of availability.

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Comparative Study Between a Deterministic and Stochastic model’s for the Hematopoietic Reconstitution Dennis Lumpuy Obregón

and Miguel Ángel Martínez Hernández

Abstract The dynamics of the processes of cell maturation and regeneration is a branch currently in development for medicine, so taking advantage of the facilities of mathematics to model and solve environmental problems, different models of differential equations have been developed to describe these processes. From the existing deterministic models, the particular case of the hematopoietic cell is chosen, for of a comparative study on the influence of the medium on the process of cell maturation. For this, a probabilistic model of differential equations with six compartments is used. The stochastic term or environmental noise in this particular case modeled by a Weiner process. Using randomly selected diffusion coefficients, a preliminary mathematical comparison of the deterministic and stochastic systems is achieved for subsequent biological analysis. The inclusion of this term makes it possible to perform an analysis conditioned on the influence of the medium of the different processes of cellular maturation, in this case, of the hematopoietic cell. Keywords Deterministic models · Hematopoietic cell · Cell maturation · Stochastic model

1 Introduction Stem cells are cells that have the ability to continually renew themselves by successive divisions and to specialize (differentiate) and become many types of body cells and, therefore, produce cells from one or several perfectly functional tissues. Stem cells can be divided without losing their properties, so that in most of the tissues of an adult there are populations of stem cells that, when divided, renew dead cells and regenerate damaged tissues. When a stem cell divides, each new cell can remain a stem cell or become another type of cell with a more specialized function, such as a muscle cell, a red blood cell or a heart cell. D. L. Obregón (B) · M. Á. M. Hernández Universidad Central “Marta Abreu” de Las Villas, Carretera Camajuaní km 5 1/2Villa Clara, Santa Clara, Cuba e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Cardenas et al. (eds.), Proceedings of the 3rd International Conference on BioGeoSciences, Springer Proceedings in Earth and Environmental Sciences, https://doi.org/10.1007/978-3-030-88919-7_16

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The possibility to maintain in a culture stem cells for long periods of time (especially embryonic ones) and to differentiate into different types of cells, allows them to be used in regenerative medicine, in order to replace damaged tissues with degenerative lesions or diseases. Due to the growing interest in stem cell applications, such as stem cell-based therapies for damaged organs, degenerative diseases [1] or reconstitution of the blood structure after chemotherapy in the treatment of leukemia [2], a broad spectrum of methods has been developed to expand knowledge about the rules of this process. Several mathematical models were developed to aid in the understanding of stem cell differentiation [3–7]. The models involve different mathematical approaches to describe the processes of differentiation and self-renewal [4, 5, 7] that involve the proliferation of stem cells [3] and the mechanisms of differentiation at each stage [8]. All these models describe different parts of the homeostasis process in adult tissue. These models represent a first deterministic approach to mathematically understand this complex dynamic. Another important aspect in mathematical modeling is to incorporate those environmental and external disturbances into the system, which in the previous models are not considered, but which nonetheless positively or negatively influence the maturation and self-renewal process of the stem cells. This gives rise to stochastic models that, as a fundamental characteristic, consider the environmental disturbances of the system, which are expressed mathematically by means of diffusion coefficients and gaussian noise. These new factors considered has a direct impact on the modeling and representation of the cell maturation process. The development and study of this type of models expresses the existing interest in deepening knowledge in the applications that the area of stochastic processes has to biology. Hence, in this investigation our scientific problem derives from the interpretation of this random noise in the process of cellular maturation.

2 Materials and Methods The model is based on the assumption that the differentiation process is strictly related to cell division, that is, that differentiation takes place only during cell division, then the rate of cell differentiation is proportional to that of proliferation. To quantitatively describe the self-renewal, the so-called self-renewal fraction is introduced, ai , which describes which fraction of progeny cells (offspring) is identical to the progenitor cells, were i = 1, ..., n is the corresponding stage of the maturation process (this parameter can be interpreted as the probability that the daughter cell has the same properties as the stem cell). Furthermore, pi for i = 1, ..., n − 1 is the population proliferation rate in stage i, µi , for i = 1, ..., n represents the death rate in stage i and ci (t) represent the population density in the corresponding stage on the time t. Then:

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⎧ ⎪ ⎪ ⎪ ⎨ dC2 (t)

dC1 (t) dt

dt

⎪ ⎪ ⎪ ⎩

213

dCn (t) dt

= f 1 (s(t), C1 (t)) = f 2 (s(t), C2 (t)) + g1 (s(t), C1 (t)), with: .. .

= f n (s(t), Cn (t)) + gn−1 (s(t), Cn−1 (t)) f i(t) = (2ai − 1)∗ pi ∗ci (t)−di ∗ci (t), i < n describes the density of maturation in the stage i of all stem cells, gi (t) = 2(1 − ai−1 ) ∗ pi−1 ∗ ci−1 (t), 1 < i < n, describes the density of density of maturation from one stage i to another i + 1 of all stem cells in the process and f n(t) = −dn ∗ cn (t), this equation describes the density of death mature and stem cells. It is assumed that the feedback signal depends on the concentration of mature cells and is given by. s ≡ s(Cn (t)) = 1+kc1 n (t) , where k is a positive constant. (This dependence can be justified using a quasi-stable state of approximation of the possible dynamics of cytokine molecules, [2]). Considering different possible regulatory feedback mechanisms, different types of non-linearity are reached in the model equations. In particular in [2] 3 different regulatory mechanisms are proposed. pi and ai constant, (model1 in(9)) 1 + kcn (t) ai,max , (model 2 in(9)) M2) pi constant and ai (s) = 1 + kcn (t) pi ai,max M3) pi = and ai (s) = , (model 3 in(9)) 1 + kcn (t) 1 + kcn (t)

M1) pi =

(1)

The cellular behavior at each stage of maturation is described by parameters of mortality rate, proliferation rate and a probability of differentiation. In addition, it is assumed that the system is regulated by a single cytosine in a manner similar to the production of red blood cells is controlled by erythropoietin [9–12) or the process of granulocyte specialization is by G-CSF (colony stimulating factor of granulocytes) [13–15].

2.1 A Special Version of the Model Let s be the concentration of signaling molecules and assuming that both processes, proliferation and differentiation are regulated by the signal (regulatory mode M2 , the following system of differential equations is obtained that describes the dynamics of n cell subpopulations:

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  dc1 = 2a1 sk1 (t) − 1 p1 c1 (t) − µ1 c1 (t) dt     dc2 = 2a2 sk1 (t) − 1 p2 c2 (t) + 2 1 − a1 sk1 (t) c1 (t) − µ2 c2 (t) dt   dcn = 2 1 − an−1 sk1 (t) pn−1 cn−1 (t) − µn cn (t) dt

(2)

The real fraction of the self-renewal at time t is then given by ai sk1 (t) and defined as a fraction of the direct progeny of Cells in stage i that are in the same stage of differentiation as their parents. Additionally, it is assumed that: t = [0, ∞), c(i,0) ≥ 0 f or i = 1 . . . n, µi = 0 f or i = 1 . . . n − 1, µn > 0, pi ≥ 0 f or i = 1 . . . n,

(3)

ai ∈ [0, 1] f or i = 1 . . . n The mortality rate, proliferation rate and initial conditions are non-negative and the self-renewal fraction is between 0 and 1, which corresponds to different types of differentiation: symmetric self-renewal, symmetric differentiation and asymmetric divisions. The model is based on the assumption that the cells divide at the rate pi , resulting in pi ci (t) of the descending cells in a unit of time t and the stage i = 1, ..., n. The ai fraction of the progeny cells remains in the same stage of differentiation as the stem cell, while the 1 − a fraction of the offspring cells differs, that is, the transfers to the highest differentiation stage. In addition, cell death at the µi rate is modeled. It is also notable that when the population of mature cells cn reaches some values,  then the term 2ai sk1 (t) − 1 pi ci (t) becomes negative and the number of the cells in stage  i decreases.  On the other hand, when the density of mature cells is low, then 2ai sk1 (t) − 1 pi ci (t) is positive and the number of cells in stage i, increases provided that the mortality rates are not too high. This shows how the dynamics of each subpopulation of cells depends on the level of mature cells. Model (2) is well laid out, the solution exists and is unique for t ∈ [0, ∞) And for the initial non-negative condition, the solution of the system (2) remains non-negative, which is demonstrated in [4]. Assuming also that µ1 < (2a1 − 1) p1 , 0 < 2a1 p1 (µi + pi ) − 2ai pi (µ1 + p1 ), for i = 2, . . . , n − 1

(4)

it is shown that the system (2) has a single positive steady state [4]. The first inequality . In other establishes that there is a level of density of mature cells such that

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words, the population of stem cells does not simply decrease and become extinct, but also replenishes itself. The second inequality of (4) says that the signal strength for the self-maintenance of the stem cell population is less than the concentration necessary at some stage of maturation i to keep the population without influx from stagei − 1. This implies that µ1 = ... = µn−1 = 0 and µn > 0. So, we get that condition (4) is equivalent to 1 , 2 a1 > ai , for i = 2, . . . , n − 1

a1 >

(5)

The number n = 6 considered in [5], corresponds to hematopoietic stem cell maturation in different stages of the process: long-term repopulating stem cells, shortterm repopulation stem cells, multipotent progenitor cells, compromised progenitor cells, precursors and mature cells.

3 Results Numerical solutions through the Mathematica package of the deterministic model with regulatory mode M2. Case of six compartments (Hematopoietic Cell), represented by. LT-HSC (long-term repopulation stem cells), ST-HSC (short-term repopulation stem cells), MPC (multipotent progenitor cells), CPC (compromised progenitor cells), precursor cells and stem cells, each type of cell represents a compartment (see Fig. 1). The parameters and initial conditions were taken from [16]. In the deterministic model, an accelerated increase in the population of mature cells in the final stage is obtained during days 10 and 20, and between days 20 and 40 this number decreases, remaining stable for more than 40 days. Clinical data report an average recovery when the patient has more than 1.5 * 10ˆ8 mature cells per liter of body blood, this system offers us an ideal vision of the result that may be in contrast to the actual data.

3.1 The Stochastic Model We now present a stochastic version of the deterministic model (2). The last deterministic number of differential equations is transformed into a system of stochastic differential equations as follows:  dξ 1 =

2a1 − 1 p1 ξ 1 (t) − μ1 ξ 1 (t) d t + α 1 ξ 1 (t)dW 1,t 1 + kξ n (t)

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Fig. 1 Case of six compartments for a1 = 0.7, a2 = 0.65, a3 = 0.65, a4 = 0.65, a4 = 0.55, a5 = 0.55, p1 = 0.8, p2 = 0.11, p3 = 0.17, p4 = 0.34, p5 = 0.69, μ = 0.3 conk = 1.2∗10−9 , c1 (0) = 105 , c2 (0) = 106 , c3 (0) = 107 , c4 (0) = 0, c5 (0) = 0, c6 (0) = 0 and final time t = 50

 2a2 a1 − 1 p2 ξ 2 (t) + 2 1 − − μ1 ξ 1 (t) d t 1 + kξ n (t) 1 + kξ n (t) + α 1 ξ 1 (t)dW 1,t   a n−1 pn−1 ξ n−1 (t) − μn ξ n (t) d t + α n ξ n (t)dW n,t dξ n = 2 1 − 1 + kξ n (t)  dξ 2 =

(6)

where the stochastic processes ξ 1 , ξ 2 , ..., ξ n describe the densities of the population of stem cells, cells at different stages of differentiation and mature cells. The coefficients a i , p i for i = 1, ..., n − 1 and μi for i = 1, ...n satisfy the assumptions (3), (4). (W 1,t , ...W n,t ) is a n-dimensional Wiener process and α i for i = 1, ..., n are positive.

3.2 Mathematical and Biological Justification of the Stochastic System This section shows the six-dimensional version (compartments) of the stochastic model of the stochastic system. For this, the noise term is maintained in each compartment and the population densities are treated as stochastic processes. The noise term is made up of the population density of the cell type of the indicated compartment, the Wiener process, independent of the rest of the processes and an α > 0, which regulates the intensity of the Wiener process in each equation. That equations represent the population dynamics of the cells in the different stages of maturation. So

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that the smaller the system is closer to the deterministic model and as it increases, the greater the Wiener process has in the solution that is gets. The deterministic model is developed in an ideal environment, without influence of the external environment for the rates of self-renewal and proliferation. For the development of this investigation the term of noise is added. It is desired to reach conclusions on the influence of the environment during the process of cell division and maturation, applying different levels of stochastic noise intensity in each compartment. The process of cell division and maturation is represented as a Markov chain. So, it is necessary to use an independent Wiener process at each stage, since the population density of each compartment i at time t only depends on its own density in that same instant of time. In addition, the Wiener process is used because the model on cell division is raised from in vitro culture or in some type of fluid, which represents a Brownian movement with statistically independent and stationary increases. In biological terms, this fluid produces different random stimuli, which together with the different feedback signals on the stem cells at each stage of the culture, can influence the speed of realization of the process of cell division and maturation and the density and quantity of cells, both of the first type (stem cells), and of the required final result (mature cells), at the end of the culture time in the applied environment. 6-dimensional model (hematopoietic cell) The following section shows the six-compartment version of the general model on cell maturation presented in the previous section, which represents the cell cycle for hematopoietic stem cells. This is solved using the parameters used in the deterministic model proposed in [2] with randomly selected noise levels, which include low and high levels compared to the models previously analyzed, because the intensity of the same can have different effects when vary the number of dimensions analyzed in the stochastic system. The model proposed for six dimensions is as follows:  2a1 p1 ξ1 [t] d t + α 1 ξ1 [t]dw1 [t] dξ1 [t] = 1 + kξ6 [t]   2a2 a1 dξ2 [t] = − 1 p2 ξ2 [t] + 2 1 − p1 ξ1 [t] d t 1 + kξ6 [t] 1 + k∗ξ6 [t] + α 2 ξ2 [t]dw2 [t]   2a3 a2 dξ3 [t] = − 1 p3 ξ3 [t] + 2 1 − p2 ξ2 [t] d t 1 + kξ6 [t] 1 + k∗ξ6 [t] + α 3 ξ3 [t]dw3 [t]   2a4 a3 − 1 p4 ξ4 [t] + 2 1 − p3 ξ3 [t] d t dξ4 [t] = 1 + kξ6 [t] 1 + k∗ξ6 [t] + α 4 ξ4 [t]dw4 [t]   a4 2a5 − 1 p5 ξ5 [t] + 2 1 − p4 ξ4 [t] d t dξ5 [t] = 1 + kξ6 [t] 1 + k∗ξ6 [t] + α 5 ξ5 [t]dw5 [t]

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 dξ6 [t] =(2 1 −

a5 p ξ [t] − μξ6 [t])d t + α 6 ξ6 [t]dw6 [t] 1 + k∗ξ 6 [t] 5 5

(7)

evaluated for a1 = 0.51, a2 = a3 = a4 = 0.475, a5 = 0.41, p1 = 0.173, p2 = 0.231, p3 = 0.347, p4 = 0.693, p5 = 1.386, μ = 0.3, k = 1.28∗10−9 and initials conditions ξ 1 (0) = 105 , ξ 2 (0) = 106 , ξ 3 (0)= 109 , ξ 4 = ξ 5 = ξ 6 = 0, e noise intensities for α 1 = 0.011, α 2 = 0.013, α 3 = 0.015, α 4 = 0.017, α 5 = 0.019, α 6 = 0.025. Using the Euler–Maruyama method, an approximation of the Gillespie Algorism whit the functions of ItoProcess and RandomFunction of Mathematica software, to resolves the system, then is as follows (see ¡Error! No se encuentra el origen de la referencia.) The graph shows the results of 500 trajectories analyzing only the mature cell count using the stochastic SDE (Fig. 2). For this, the same parameters are used as in the deterministic system and noise is added as a random real between 0 and 0.5 for each compartment during the 500 simulations. A simulation is performed with a final time of 50 days according to the final result of the deterministic model, where stability is reached. The implementation of this noise level is a little higher than what an average patient could present, so it is done by implementing the Gillespie Algorithm to give greater accuracy. It is observed how 91 trajectories are between 1.5 and 2.5 * 108 , 106 are between 2.5 and 3.5 * 108 and 74 between 3.5 and 4.5 * 108 mature cells per liter of blood, coinciding with the clinical data taken from the bibliography and showing great accuracy due to the presence of the environmental noise factor during treatment. To verify that most of the trajectories are not null and are in the range obtained, the following representation was made (see ¡Error! No se encuentra el origen de la referencia.)

Fig. 2 Histogram of 500 trajectories of the Six-dimensional model whit a t − f i nal = 50 days, the same parameters to Fig. 1

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1.4 109 1.2 109 1.0 109 8.0 108 6.0 108 4.0 108 2.0 108 100

200

300

400

500

Fig. 3 Represent the final results of the 500 trajectories employed in the solution of a stocastic model, using the leukocyte count on the y-axis and trajectory on the x-axis

This graph shows the fluctuation of the final result of each trajectory, in which it is observable that practically none of them is null and are in the established range (Fig. 3). A spontaneous approach is performed as a final verification resource that indicates how most of it focuses in the range of 2.0 to 4.0 * 108 mature cells per liter of body blood.

4 Discussion The results of the numerical simulations of the stochastic and deterministic models show a contrast of results at the end of the cell maturation process, given for a generic patient complying with the parameters extracted from the literature [16]. There is a noticeable difference in results between the deterministic model and the stochastic system used. The latter presents a cell maturation lower than the results shown by the deterministic model in a range of 0.5 to 3*10ˆ8 cells per liter of blood. This results in an increase in the mortality of mature cells when passing from one state to the other and in the final stage of maturation. Taking into account the biological hypotheses for the maintenance of stability, this increase in mortality occurs in the last stage in a broad sense due to the appearance of external noise. In this case, the noise is expressed according to the patient’s clinical state by means of a Brownian movement as explained above. Therefore, the presence of the stochastic component and the increase in its influence have a direct effect on the mortality of the culture cells, especially the last non-proliferative stages.

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5 Conclusions Mathematical models to describe the maturation of the hematopoietic stem cell can vary depending on the desired result. The deterministic model used allows obtaining an ideal and theoretical result of performing a bone marrow transplant with exact parameters and a representation of fluctuations without alteration internal or external to the culture. This in turn offers a theoretical initial approach to perform more detailed analyzes on the behavior of bone marrow transplantation. While the stochastic model gives a more realistic view of the cell maturation process, due to the introduction of stochastic noise that represents certain clinical states of the patient or external conditions during the process. It also presents greater fluctuation than the deterministic model and great variability of the final results. These are grouped into a band that contains from the minimum results to the maximum allowed according to the stability theory applied to said model and the actual results of transplants performed. In this band, between 50 and 55% of the trajectories shown depending on the noise parameter are collected and have values between 2.0 and 4.5 ∗ 108 cells per liter, with a difference between 0.5 and 3 ∗ 108 compared to the theoretical model. As a direct consequence of the stochastic model, there is an increase in mortality in the last stages of cultivation, depending on the intensity of the stochastic noise used. That is, the greater the influence of stochastic noise, the greater the mortality of mature cells during the process. Due to this, a random noise between 0 and 0.5 was used, which represents a path from the trend to the deterministic model to a slightly higher impact of deaths than the indicators shown by [16]. The stochastic model shows greater similarity with the clinical data offered by doctors and the literature than the deterministic model, using its analysis as a starting point, to largely suppress extreme and inaccurate results. Said results can vary from extremely high noises that lead to an imbalance of the system or too close to the theoretical model that limits obtaining precise results and creates a false state of accuracy. In a broad sense, the stochastic model is superior in representation and possibilities of results to the deterministic one, but, due to the complexity of modeling the initial parameters, such as noise itself or the initial selection of a clinical recovery time, without taking into account empirical diagnoses from the collection of information from various cases treated, it is recommended to make an initial approach from the theoretical models.

References 1. A. Gratwohl HB. Trends of hematopoietic stem cell transplantation in the third millennium. Curr Opin Hematol 16. 2009:420–6. 2. A. Marciniak-Czochra TS. Mathematical models of hematopoietic reconstruction after stem cell transplantation. Model Based Parameter Estimation: Theory and Applications: Springer Verlag.

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3. J. E. Till EAM, L. Siminovitch. A stochastic model of stem cell proliferation, based on the growth of spleen colony-forming cells: Proc Natl Acad Sci U S A; 1964. 4. T. Stiehl AM-C. Characterization of stem cells using mathematical models of multistage cell lineages. Mathematical and Computer Modelling. 2010. 5. A. Marciniak-Czochra TS, W. Jaeger, A.D. Ho, W. Wagner. Modelling of asymmetric cell division in hematopoietic stem cells-regulation of self-renewal is essential for efficient repopulation. Stem Cells Dev 18. 2009:377–85. 6. Mackey CCaMC. A mathematical model of hematopoiesis: I Periodic chronic myelogenous leukemia. J Theoret Biol. 2005;237:117–32. 7. M. Doumic AM-C, B. Perthame, and J. P. Zubelli. A Structured Population Model of Cell Differentiation. SIAM J Appl Math. (71):1918–40. 8. Z. Wu KL-P, A. Bugde, L. A. Molyneux, B. Denard, W. Li,. Capacity for stochastic self-renewal and differentiation in mammalian spermatogonial stem cells. JCB vol 187.513–24. 9. Fried W. Erythropoietin and erythropoiesis. Exp Hematol. 2009;37:1007-15. 10. A. Lasota MM, M. Wazewska-Czyzewska. Minimizing therapeutically induced anemia. J Math Biol. 1981;13:149–58. 11. Adamson JW. The relationship of erythropoietin and iron metabolism to red blood cell production in humans. Sem Oncol. 1994;21 9–15. 12. M. Z. Ratajczak JR, W. Marlicz, C. H. Pletcher, Jr., B. Machalinshi, J. Moore, H. Hung, and A. M. Gewirtz. Recombinant human thrombopoietin (TPO) stimulates erythropoiesis by inhibiting erythroid progenitor cell apoptosis. Br J Haematol. 1997(98):8–17. 13. Metcalf D. Hematopoietic cytokines. Blood. 2008;111:485-91. 14. C. L. Semerad FL, A. D. Gregory, K. Stumpf, D. C. Link. G-CSF Is an Essential Regulator of Neutrophil Trafficking from the Bone Marrow to the Blood. Immunity. 2002:413–23. 15. T. H. Price GSC, and D. C. Dale. Effect of recombinant granulocyte colony-stimulating factor on neutrophil kinetics in normal young and elderly humans. Blood. 1996;88:335–40. 16. Pa´zdziorek Pl. Mathematical model of stem cell. Preprint. 2012.

Information Technologies in BioGeoSciences

Image Modification to Reduce Eye Strain Vladimir Mochalov

Abstract Variants of image modification in order to reduce eye strain are considered. In this work, for the purpose of reducing the evaluation of the light emission to the eyes, it is proposed to use edge detection algorithms. Various examples of reducing eye strain based on the application of a convolution filter with a Laplace kernel, where the evaluation of the light emission to the eyes was reduced from 11.98 to 60.66 times, are given. The application of the obtained results to video files showed an average reduction in the light emission evaluation of more than 8 times compared to the original video files. Keywords Modification of images · Reduction of eye strain · Evaluation of the light emission to the eyes

1 Introduction Long-term use of a monitor or TV screen can have a negative impact on a person’s eyes, fatigue and overall health. One way to reduce eye strain is to use E-ink displays. The advantages of electronic ink include the fact that displays based on this technology operate on reflected light and do not emit light themselves, unlike LED monitors. As a result, there is no eye strain from light emission. Compared to LED monitors, the disadvantages of “electronic ink” include a limited number of colors, speed of displaying a picture, a higher cost and a significantly smaller variety of E-ink display models. In LED monitors, each pixel is made up of three LEDs: red, green, and blue. Figures 1, 2 and 3 show micro pictures of various types of LED monitor screens. One known method to reduce eye strain is to use blue light filters, which reduce the amount of blue in images. Blue light affects the eyes in a special way because blue light is the shortest wavelength range of visible radiation and has the highest energy. In the visible region of the spectrum, blue light is closest to invisible ultraviolet radiation. V. Mochalov (B) Petrozavodsk State University, Republic of Karelia, Lenin Str., 33, Petrozavodsk 185910, Russia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Cardenas et al. (eds.), Proceedings of the 3rd International Conference on BioGeoSciences, Springer Proceedings in Earth and Environmental Sciences, https://doi.org/10.1007/978-3-030-88919-7_17

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Fig. 1 Micro picture of AMOLED screen [1]

Fig. 2 Micro picture of LCD Screen Type TN + Film [1]

As shown in Fig. 4, the blue light emitted from most LED-backlit monitors contains significantly more energy than the displayed green light. Scientists have found that it is easier for the eyes to filter the green and red parts of the spectrum, since they contain less photon energy, but in the blue part of the spectrum, the eyes cannot do this due to higher energy levels, so blue light has a more pronounced effect on people. Higher levels of blue light emitted from LED backlit monitors can cause irritation, dry eyes and even more serious problems with prolonged daily exposure. Scientists have found that prolonged exposure to all types of blue light (from the sun,

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Fig. 3 Micro picture of LCD Screen Type IPS [1]

Fig. 4 Radiated energy by photons of light with different wavelengths [2]

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from monitor screens, from LED and fluorescent lamps) over time can lead to eye irritation, sleep disturbances, and even eye diseases such as cataracts and macular degeneration [2]. Another well-known technique is the use of Dark Mode in applications, which reduces eye strain by using mostly black as the background. Figure 5 shows the Google Chrome web page without using the dark theme, and Fig. 6 using the dark theme.

Fig. 5 An example of a Google Chrome web page without a dark theme

Fig. 6 An example of a Google Chrome browser web page using a dark theme

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Fig. 7 An example of created high contrast theme

Another method is to use the operating system’s tools to modify the colors of application windows. For example, Windows 10 has a configurable high contrast mode. This mode is not intended specifically to reduce eye strain, but when creating your own high contrast mode theme, this mode will perform the function we need to reduce eye strain. So, for example, Fig. 7 shows a created theme that can reduce eye strain in many Windows 10 applications by changing the background color, text, buttons, etc. Figure 8 shows an Internet Explorer browser window without using high contrast mode and Fig. 9 shows an Internet Explorer browser window using created high contrast mode theme.

2 Image Light Emission Evaluation Function In general, the evaluation of the light emission (L) of the image can be described as follows: L=

n 

(kr ri + k g gi + kb bi ),

i=0

where n is the number of pixels, kr , kg , kb are the coefficients of exposure to the eyes of red, green and blue, respectively, ri , gi , bi are the intensities, respectively, of

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Fig. 8 Internet Explorer browser window without using high contrast mode

Fig. 9 Internet Explorer browser window using custom high contrast mode theme

the red, green and blue components of the color of the i-th pixel of the image (in the RGB color model, the values of the intensities of the color components change in the range [0; 255]). The values of the coefficients kr , kg , kb can be selected based on various criteria and set, for example, in proportion to the energy emitted by color photons or in proportion to the strength of the effect of color on the eye retina, etc. Let us consider examples of calculating the evaluation of the light emission L for various images. In proportion to the energy emitted by color photons, the values of the coefficients were set to the following values: kr = 1.77, kg = 2.34, kb = 2.64.

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Images 5 and 6 are 1086 × 690 pixels in size. For the image in Fig. 5, L = 1,265,129,880.09. For the image in Fig. 6 L = 171,853,145.13. Thus, using the dark theme for the Google Chrome browser reduced the light emission evaluation by 7.36 times for the two images under consideration. Image sizes 8 and 9 are 1015 × 605 pixels. For the image in Fig. 8, L = 1,029,865,657.59. For the image in Fig. 9, L = 20,520,562.02. Thus, the use of our own theme of the high contrast mode allowed us to reduce the light emission evaluation by 50.18 times for the two images under consideration.

3 Reducing Eye Strain Based on Use of Edge Detection Algorithms There are many ways to modify the image in order to reduce the evaluation of the light emission of the image and, accordingly, reduce eye strain. In this work, for this purpose, it is proposed to use edge detection algorithms. Object edges contain basic image information. The paper [3] provides an overview of various edge detection algorithms and techniques. Let’s consider examples of image modification in order to reduce eye strain. The examples are based on the use of CSS SVG filters, where the following HTML code is used as a base:











In the above HTML-code based on CSS SVG technology, the EdgeDetect filter is implemented, which first makes the image blurry (using the feGaussianBlur filter), and then the convolution filter with the Laplace kernel is applied. Figure 10 shows the application of the above HTML code to the image of the Google Chrome web page shown in Fig. 5. For the image in Fig. 10, the light emission evaluation is L = 57,571,966.38, which is 21.97 times less than on the image in Fig. 5. Let’s create an EdgeDetectGreen filter to display only green color:

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Fig. 10 An example of reducing eye strain based on the application of a convolution filter with a Laplace kernel



Figure 11 shows an example of applying the modified green EdgeDetectGreen

Fig. 11 An example of reducing eye strain using EdgeDetectGreen filter and convolution with Laplace kernel

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Fig. 12 An example of reducing eye strain using EdgeDetectYellow filter and convolution with Laplace kernel

filter. For the image in Fig. 11, the light emission evaluation L = 20,853,690.65, which is 60.66 times less than on the image in Fig. 5. Let’s create an EdgeDetectYello filter to display only yellow color:



Figure 12 shows an example of applying the modified yellow EdgeDetectYellow filter. For the image in Fig. 12, the light emission evaluation L = 36,668,288.27, which is 34.50 times less than on the image in Fig. 5. Figure 13 shows an image of the Windows 10 operating system desktop (light emission evaluation L = 2,133,176,342.73), and Fig. 14 shows a modified image using the EdgeDetect filter (light emission estimate L = 177,982,976.73, which is 11.98 times less than the original image). As an additional way to reduce the amount of noise in the operation of EdgeDetect filters, you can increase the contrast and brightness of the original image before applying the EdgeDetect filters. So, for example, you can increase the contrast and brightness in HTML like this: ‘brightness (140%) contrast (156%)’. A promising approach to using EdgeDetect filters is to combine them with the original image, which allows you to preserve the main color of the image (but with reduced pixel brightness). Below is an example of such a filter implementation, which additionally adds a simplified blue-light filter implementation:

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Fig. 13 Image of the Windows 10 operating system desktop

Fig. 14 An example of reducing eye strain by applying the EdgeDetect filter to a Windows 10 desktop image

In order to demonstrate the application of reducing eye strain on the basis of using edge detection to video, a video modification program was written and a YouTube channel was created [4]. Also on the basis of the OpenSource project Youtube-videofilters-or-Youtube-video-effects by Dang Thanh, a fork was made and a new project “Eye Care Filters filters for YouTube” [5, 6] was created, the purpose of which is to demonstrate in real time the work of discussed in this paper CSS SVG filters on

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the example of stream modification of Youtube video images in order to reduce eye strain. The application of the obtained results to video files showed an average reduction in the light emission evaluation of more than 8 times compared to the original video files.

4 Conclusions The results presented in the work can become the basis for creating various filters to reduce eye strain based on using of edge detection algorithms. It is interesting to further develop the results to implement of software and hardware solutions for various operating systems, monitors, devices, video cards, etc. A promising technique is to combine different approaches to reduce eye strain in order to better adapt to the requirements of specific users. It should also be noted the importance of developing work to improve the function the evaluation function of the light emission, which, for example, will take into account the strength of the effect of different colors on the eye retina.

References 1. How are e-book displays different from those of smartphones and tablets? https://www.viewso nic.com/ru/products/lcd/blue-light-filter/ 2. Blue Light Filter from ViewSonic. https://www.viewsonic.com/ru/products/lcd/blue-light-filter/ 3. Kumar M, Saxena R (2013) Algorithm and technique on various edge detection: a survey. Signal Image Process: Int J (SIPIJ) 4(3) 4. YouTube channel “Eye Care Movies”. https://www.youtube.com/channel/UCF3tyzOvcoieHs gU88aND_Q 5. Source code in github of “Eye Care Filters filters for YouTube” project. https://github.com/sen sorlife/Youtube-filters 6. Eye Care Filters filters for YouTube. https://chrome.google.com/webstore/detail/eye-care-fil ters-for-yout/jcpkjjkeihijhliooempibldlpnhoddd

Problems of the Commutative and Grouping Properties of the Addition of Floating Point Numbers in Modern Programming Languages Vladimir Mochalov and Anastasia Mochalova

Abstract The problems of precision of floating point computations of simple data types of modern programming languages are considered. Examples of loss of computational accuracy when performing addition operations are given. The examples under consideration are based on the accumulation of computational errors with a large number of operations performed. In modern programming languages, the commutative and grouping property for simple floating point data types does not always work. Examples are given in which it is shown that the sum changes significantly from the permutation of the numbers. When adding floating point numbers, grouping them together has different results. The problem considered in the work is of great importance for the organization of computations and, in particular, for the processing of massive data of measurements of the parameters of the external environment. Keywords Floating point numbers · Computation problems · Commutative property · Grouping property · Accumulation of computation error

1 Introduction The work is devoted to the problems of floating point calculations. Many programmers do not think about the precision of calculations, using floating point numbers. Let’s consider the first universal example for the Java [1], C/C++ [2] programming languages.

V. Mochalov (B) Petrozavodsk State University, Republic of Karelia, Lenin Str., 33, Petrozavodsk 185910, Russia A. Mochalova Institute of Cosmophysical Research and Radio Wave Propagation FEB RAS, Mirnaya str., 7, Kamchatka region, Elizovskiy district, Paratunka 684034, Russia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Cardenas et al. (eds.), Proceedings of the 3rd International Conference on BioGeoSciences, Springer Proceedings in Earth and Environmental Sciences, https://doi.org/10.1007/978-3-030-88919-7_18

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Listing 1 for the Java, C / C ++ programming languages. int a = 0; float total = 0; float add = 0.7f; int cycles = 1000000; for (int i = 0; i < cycles; i++) total = total + add; if(total > 693000) a = 1; The sum of the total variable did not go beyond the range of admissible values for the float type ~3.402823466 * 1038 . Question to the code from Listing 1: What will be stored in the variable a after executing the above code? The answer in the variable a will be stored 0 since the sum in the variable total (of float type), calculated by the computer by adding a million times by 0.7, turned out to be equal to ~692,988.56 regardless of the programming language used. Surprisingly, the sum of a million times 0.7 should be equal to 700,000. Where did more than 1% of the total go? Let’s consider the second universal example for the Java, C/C++ programming languages. Listing 2 for the Java, C / C ++ programming languages. int a = 0; double total = 0; double add = 0.9; int cycles = 1000000000; double calc = add * cycles; for (int i = 0; i < cycles; i++) total = total + add; double dif = calc - total; if (dif > 15) a = 1; The sum of the total variable did not go beyond the range of valid values for the double type ~1.7976931348623158 * 10308 . Question to the code from Listing 2: What will be stored in the variable a after executing the above code? The answer in the variable a will be stored 1, since the number 15.475845 will be stored in the dif variable (of type double). Those, the amount calculated by the computer by adding a billion times 0.9 is 15.475845 less than multiplying the number 0.9 by a billion. The two examples above demonstrate the accumulation of computational error with a large number of operations performed. It should be noted that some issues of error in results when using mathematical calculations are considered in [3] as a consequence of data error, rounding or truncation. In [4], the problems of performing correct calculations and the problems of developing requirements and creating tests for the implementations of mathematical functions working with floating point numbers in the formats of the IEEE 754 standard [5] are considered. The work

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[6] provides a method for implementing the IEEE 754 adaptive precision floating point calculations. Next, we will consider the problems of using the commutative and grouping properties of addition. All examples will be considered without leaving the variables outside the range of acceptable values.

2 Examples of Accumulation Errors that Occur When Using the Commutative Addition Property From the properties of addition and subtraction, the commutative property of addition is known, which states that the sum does not change from the permutation of the terms. Let us show that the proxy addition property does not always work for floating point numbers of simple data types in modern programming languages. Listings 3 and 4 show slightly modified examples of Listing 1. For example, Listing 3 adds 1,000,000 to total before the addition loop, and Listing 4 adds 1,000,000 to total after the addition loop. Those the usual permutation of the summands has occurred and the total sum, according to the commutative property of addition, should not change. Listing 3 for the Java, C / C ++ programming languages. float total = 0; total = total + 1000000; float add = 0.7f; int cycles = 1000000; for (int i = 0; i < cycles; i++) total = total + add; Listing 4 for the Java, C / C ++ programming languages. float total = 0; float add = 0.7f; int cycles = 1000000; for (int i = 0; i < cycles; i++) total = total + add; total = total + 1000000; The value of total after running the code in Listing 3 is total = 1,745,584.0. The value of total after running the code in Listing 4 is total = 1,692,988.5. The results are not only different, but also imprecise (the exact value of the variable total should be 1,700,000). Based on the results obtained, we can conclude that when using the float type, the sum may change from the permutation of the terms. In the example under consideration, the permutation of only one term has led to a significant change in the total. Let’s look at a modified example for the double variable from Listing 2.

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Listing 5 for the Java, C / C ++ programming languages. double total = 0; total = total + 1000000000; double add = 0.9; int cycles = 1000000000; for (int i = 0; i < cycles; i++) total = total + add; Listing 6 for the Java, C / C ++ programming languages. double total = 0; double add = 0.9; int cycles = 1000000000; for (int i = 0; i < cycles; i++) total = total + add; total = total + 1000000000; The value of total after running the code in Listing 5 is total = 1,900,000,085.5999753. The value of total after running the code in Listing 6 is total = 1,899,999,984.5241551. The results are not only different, but also inaccurate (the exact value of the variable total should be 1,900,000,000). The difference between the total in Listing 5 and the total in Listing 6 was 101.0758202. The difference between the total and the exact value was 85.5999753 and −15.4758449 for Listings 5 and 6, respectively. Based on the results obtained, we can conclude that when using the double type, the sum may change from the permutation of the terms.

3 Examples of Accumulation Errors that Occur When Using the Grouping Property of Addition From the properties of addition and subtraction, the grouping property of addition is known, which states that when adding numbers, they can be combined into groups and rearranged as desired, and the total amount will not change. Let us show that the grouping property of addition does not always work for floating point numbers of simple data types in modern programming languages. Listing 7 shows a slightly modified example of Listing 1. For example, a million terms are split into groups of N terms. The sum of each group is calculated and the resulting value is added to the total. According to the grouping property of addition, the combination into groups of terms should not lead to a change in the total amount. But the value of total after executing the code in Listing 7 with N = 1000 (that is, the groupSum(1000) function is called) is total = 700,001.44, which is much closer to the exact value 700,000 than in the resulting code of Listing 1 without grouping (692,988.56).

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Listing 7 for the Java, C / C ++ programming languages. float groupSum(int N) { float total = 0; float curSum = 0; float add = 0.7f; int cycles = 1000000; for (int i = 0; i < cycles; i++) { curSum = curSum + add; if(i % N == N - 1) { total = total + curSum; curSum = 0; } } total = total + curSum; return total; } Listing 8 for the Java, C / C ++ programming languages. for (int N = 1; N