The Evaluation of Air Quality in Albania by Moss Biomonitoring and Metals Atmospheric Deposition (SpringerBriefs in Environmental Science) 3030623572, 9783030623579

Citation preview

SPRINGER BRIEFS IN ENVIRONMENTAL SCIENCE

Pranvera Lazo · Flora Qarri · Shaniko Allajbeu · Sonila Kane · Lirim Bekteshi · Marina Frontasyeva · Trajce Stafilov

The Evaluation of Air Quality in Albania by Moss Biomonitoring and Metals Atmospheric Deposition 123

SpringerBriefs in Environmental Science

SpringerBriefs in Environmental Science present concise summaries of cutting-edge research and practical applications across a wide spectrum of environmental fields, with fast turnaround time to publication. Featuring compact volumes of 50 to 125 pages, the series covers a range of content from professional to academic. Monographs of new material are considered for the SpringerBriefs in Environmental Science series. Typical topics might include: a timely report of state-of-the-art analytical techniques, a bridge between new research results, as published in journal articles and a contextual literature review, a snapshot of a hot or emerging topic, an in-depth case study or technical example, a presentation of core concepts that students must understand in order to make independent contributions, best practices or protocols to be followed, a series of short case studies/debates highlighting a specific angle. SpringerBriefs in Environmental Science allow authors to present their ideas and readers to absorb them with minimal time investment. Both solicited and unsolicited manuscripts are considered for publication.

More information about this series at http://www.springer.com/series/8868

Pranvera Lazo Flora Qarri Shaniko Allajbeu Sonila Kane Lirim Bekteshi Marina Frontasyeva Trajce Stafilov •

•

•

•

•

•

The Evaluation of Air Quality in Albania by Moss Biomonitoring and Metals Atmospheric Deposition

123

Pranvera Lazo Department of Chemistry Faculty of Natural Sciences University of Arts Tirana, Albania Shaniko Allajbeu Department of Chemistry University of Tirana Tirana, Albania Lirim Bekteshi Department of Biochemistry University of Elbasan Elbasan, Albania

Flora Qarri Department of Chemistry University of Vlora Vlora, Albania Sonila Kane Department of Chemistry University of Vlora Vlora, Albania Marina Frontasyeva Joint Institute for Nuclear Research Frank Laboratory of Neutron Physics Dubna, Russia

Trajce Stafilov Institute of Chemistry Cyril and Methodius University Skopje, North Macedonia

ISSN 2191-5547 ISSN 2191-5555 (electronic) SpringerBriefs in Environmental Science ISBN 978-3-030-62357-9 ISBN 978-3-030-62355-5 (eBook) https://doi.org/10.1007/978-3-030-62355-5 © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 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

Foreword

Safe environment is a main goal for supporting the life. The clean and good quality of the air we breathe is an important global issue. Air monitoring is very important and cost-effective particularly for the developing countries, and the use of alternative methods such as biomonitoring is a new design that must be standardized to the quantitative measurements of pollutants in atmosphere. Air pollution strongly affects the climate change and impacts on biodiversity. The importance of including the state-of-the-art knowledge about air pollution by natural and anthropogenic sources, and the atmospheric deposition of the pollutants has been in focus of the scientists for many years in global range. The potential damage to crops by air pollution in many areas of Europe is high, and the annual economic cost may be considerable https://www.unece.org/?id=2722. This book is the first study of trace element atmospheric deposition in Albania evaluated by moss biomonitoring. It started at 2010/2011 moss biomonitoring survey conducted at the same time with similar study conducted in European scale. It is a complete research study that identified the main risk factors of air quality in Albania and contributes to other studies documenting the deposition of various elements that have been conducted in other parts of Europe. In addition, it identifies areas within the country that should be investigated further to determine whether or not they might pose risks to the health of humans as well as to different ecosystems. I endorse the work of the authors and have the pleasure to recommend this book for the reader. Tirana, Albania May 2020

Spiro Drushku

v

Preface

Global pollution and climate changes are tightly linked with air pollution which is among the most serious problems in the world that refers to the entrance of the harmful contaminants, such as chemicals or biological materials, in the atmosphere. Currently, some of the most significant sources include metal industry, other manufacturing industries and construction, electricity and heat production, road transportation and petroleum refining. Air pollution is sourced by local emission sources and long-range transport of the pollutants. It can cause long-term and short-term health effects and environmental damages, particularly to the most vulnerable groups of children, old people and the people with chronic diseases. Air pollution could also causes the damages of the ecosystems, which is followed with the damages of the crops, the loss of the agricultural productivity (Harmens et al. 2015) and the loss of the biodiversity. The studies concern the accumulation of heavy metals in ecosystems and their impacts on the environment and human health, increased during the 1980s and 1990s (Harmens et al. 2015). In the recent days, it is also hypothesized that viral problems are also linked with air pollution. An example is the Northern Italy that has been constantly exposed to chronic air pollution among the most impacted parts of the Europe from SARS-CoV-2 where the long-term air-quality data significantly correlated with cases of COVID-19 in up to 71 Italian provinces (updated 27 April 2020) (Fattorini and Regoli 2020). These providing further evidence that chronic exposure to atmospheric contamination may represent a favour context for spreading of the virus (Fattorini and Regoli 2020). This book provides an overview of air quality in Albania evaluated by moss biomonitoring and metal atmospheric deposition based on the concentration data of 51 elements in moss samples collected during 2010/2011 moss biomonitoring survey conducted at the same time with similar study conducted in European scale. The moss survey provides a complementary method to assess spatial patterns and temporal trends of atmospheric deposition of air pollutants to the vegetation (based on monitoring in the field) and to identify areas at risk from air pollution at a high spatial resolution that reports on the effects of air pollutants to the natural vegetation and crops (Harmens et al. 2015a). vii

viii

Preface

This research is a part of the international programme (ICP Vegetation Programme, UNECE) investigating the impacts of air pollutants on crops and natural vegetation. The bryophyte moss species Hypnum cupressiforme (Hedw) sps. were used as bioindicators of trace metal atmospheric deposition. Bryophytes act as precise and sensitive bioindicators as well as bioaccumulators of metal deposition in the environment. The goal of this study was to identify factors leading to the high levels of trace metals in atmospheric deposition in Albania identified by 2010/2011 moss biomonitoring surveys and to extend the study to the associations and the relationships between the element contents. It may help to identify the most probable sources of the metals origin and the areas at risk to humans and environmental ecosystems. Wide ranges of metal concentrations were found for Al, As, Hg, Cd, Pb, Ni, Co, Cr, Fe, V, Eu, Tm, Lu and Mn. The highest content of Fe, Cr, Ni, Zn and V was found in the eastern part of the country that is characterized by lower population density compared with the western part. Air quality of Albania is comparable with the neighbouring countries. The problems to the high content of Cr, Ni, V, Al, Fe and Hg, generally higher than the European countries, were identified, and the affecting factors are presented. The local emitters like iron and chromium metallurgy, cement industry, oil refinery, mining industry and long-range transport were distinguished as main contributors of trace metals in atmospheric deposition of Albania. In addition, the natural sources, from the accumulation of these metals in mosses caused by metal enriched soils, associated with wind blowing fine mineral dust particles were pointed as another possibility of local emitting factors. It was found that the local emitting sources had shown higher contribution than the long-range transport for elements with high content. Tirana, Albania May 2020

Pranvera Lazo Flora Qarri Shaniko Allajbeu Sonila Kane Lirim Bekteshi Marina Frontasyeva Trajce Stafilov

References Fattorini D, Regoli F (2020) Role of the chronic air pollution levels in the Covid-19 outbreak risk in Italy. Environ Pollut https://doi.org/10.1016/j.envpol.2020.114732 Harmens H, Mills G, Hayes F, Sharps K, Frontasyeva M, and the participants of the ICP Vegetation (2015) Air pollution and vegetation. ICP Vegetation annual report 2014/2015. ISBN: 978-1-906698-55-3

Preface

ix

Harmens H, Norris DA, Sharps K, Mills G, Alber R, Aleksiayenak Y, Blum O, Cucu-Man SM, Dam M, De Temmerman L, Ene A, Fernández JA, Martinez-Abaigar J, Frontasyeva M, Godzik B, Jeran Z, Lazo P, Leblond S, Liiv S, Magnússon SH, Maňkovská B, Pihl Karlsson G, Piispanen J, Poikolainen J, Santamaria JM, Skudnik M, Spiric Z, Stafilov T, Steinnes E, Stihi C, Suchara I, Thöni L, Todoran R, Yurukova L, Zechmeister HG (2015a) Heavy metal and nitrogen concentrations in mosses are declining across Europe whilst some “hotspots” remain in 2010. Environ. Pollut 200: 93–104

Acknowledgments

This work was carried out within the Programme of Dr. Study, University of Tirana, Faculty of Natural Sciences, Department of Chemistry. The study started in 2009 when our research group from University of Tirana joined the European Moss Survey conducted within the framework of the International Cooperative Programme on Effects of Air Pollution on Natural Vegetation and Crops, ICP Vegetation. The survey was repeated in 2015, at the same period with the European moss survey conducted under the framework of the UNECE ICP Vegetation Programme. Our thanks go to the leader of ICP Vegetation Programme, Dr. Harry Harmens, and to Professor Eiliv Steinnes for their collaboration within the programme. Their continuous collaboration is highly appreciated. We express our gratitude to the staff of the Sector of NAA and Applied Research Division of Nuclear Physics of FLNP, JINR, Dubna, Russian Federation and the Institute of Chemistry, Faculty of Science, Sts. Cyril and Methodius University, Skopje, Macedonia, for NAA and ICP-AES analysis of Albanian moss samples. The opinions expressed herein are those of the authors and should not be considered as reflecting the institutional views. Tirana, Albania May 2020

Pranvera Lazo Flora Qarri Lirim Bekteshi Marina Frontasyeva Shaniko Allajbeu Sonila Kane Trajce Stafilov

xi

Abstract

Air pollution is a global problem that has caused serious problems of the global climate changes, the worldwide epidemics, and in adverse effects in human health, environmental ecosystems and food security. Air pollution is caused by the presence of the toxic chemicals in the air. Different climatic, physical and chemical factors significantly affect to the circulation and the transfer of various pollutants in the air. The increased circulation of chemicals in the air, including toxic metals and organic compounds, had increased the interest of scientists in recognizing the factors that effect to the levels of contaminants, their emission source and the health effects of human beings and environment. It is also followed with crops damages and the loss of the agriculture productivity. This book provides an overview of air quality of Albania. Albania is a small country with an area of 28,748 sq km positioned in south-east Europe and in the western part of the Balkan Peninsula. With an average altitude of about 700 meters above the sea, Albania is characterized by mountainous landscape intersected by the valleys of seven rivers running from the east to the west and discharged to the Adriatic Sea. Albania is characterized by Mediterranean climate in the West and Mediterranean-continental climate in the East. The average annual rainfall is about 1430 mm. Due to the extensive utilization of natural resources and old technology during the past decades of 19’s (1960 to 1990), the exposure to the contaminants was relatively high. After the 1990s, when Albania changed from a totalitarian system to democracy, the old industry was closed or abandoned. The chemicals deposited in the abandoned chemical plants and/or in the processing plants of various minerals, untreated disposal wastes and dumps distributed randomly over a relatively wide area around the plants and/or mines, had caused high environmental pollution. On the other hand, the activities in ex-industrial sites of copper, chromium, iron-nickel and oil industries have produced several million tons of industrial waste impacting the surrounding environment that had adversely affected the natural resources, followed by a potential health risk for the people continuously exposed to the polluted areas. The lack of a national network for air quality monitoring as well as

xiii

xiv

Abstract

the data on morbidity caused by air pollution, makes it impossible to assess the health impact of air pollution in Albania. Air quality in Albania was evaluated by moss biomonitoring and metals atmospheric deposition based on the concentration data of 51 elements in moss samples collected during 2010/2011 moss biomonitoring survey conducted at the same time with similar study conducted in European scale. The moss survey provides a complementary method to assess spatial patterns and temporal trends of atmospheric deposition of air pollutants to the vegetation (based on monitoring in the field) and to identify areas at risk from air pollution at a high spatial resolution that reports on the effects of air pollutants to the natural vegetation and crops. This research is a part of the international programme (ICP Vegetation Programme, UNECE) investigating the impacts of air pollutants on crops and natural vegetation. The bryophyte moss species Hypnum cupressiforme (Hedw) sps. wase used as bioindicator of trace metal atmospheric deposition. Bryophytes act as precise and sensitive bioindicators as well as bioaccumulators of metal deposition in the environment. The goal of this study was to identify factors leading to the high levels of trace metals in atmospheric deposition in Albania identified by 2010/2011 moss biomonitoring surveys and to extend the study to the associations and the relationships between the elements content. It may help to identify the most probable sources of the metals origin and the areas at risk to humans and environmental ecosystems. Wide ranges of metals concentrations were found for Al, As, Hg, Cd, Pb, Ni, Co, Cr, Fe, V, Eu, Tm, Lu, and Mn. The highest content of Fe, Cr, Ni, Zn and V was found in the eastern part of the country that is characterized by lower population density compared with the western part. Air quality of Albania is comparable with the neighboring countries. The problems to the high content of Cr, Ni, V, Al, Fe and Hg, generally higher than the European countries, were identified and the affecting factors are presented. The local emitters like iron and chromium metallurgy, cement industry, oil refinery, mining industry, and long-range transport were distinguished as main contributors of trace metals in atmospheric deposition of Albania. In addition, the natural sources, from the accumulation of these metals in mosses caused by metal enriched soils, associated with wind blowing fine mineral dust particles were pointed as another possibility of local emitting factors. It was found that the local emitting sources had shown higher contribution than the long-range transport for elements with high content. This study established the first moss biomonitoring of trace metals at-mospheric deposition in Albania. The concentration level of trace metals contents in moss samples were affected by long-range transport of the pollutant and by local emission sources derived by natural and anthropogenic factors. It provided important information data of the baseline level of the elements and about the concentration level of trace metal atmospheric deposition in Albania that are compared with the neighboring countries and in a wider scale with the European countries. These data could be utilized for future biomonitoring research in atmospheric deposition of metals in Albania.

Abstract

xv

The moss survey data and the applied statistical analysis in combination with GIS technique produced a detailed and up-to-date coverage of trace metals in moss samples that directly indicate the metal atmospheric deposition of Albania. Through the maps of metal concentration data, it was possible to predict the spatial extinction of the areas with high metal concentrations where the local factors were suggested to be monitored and to control the potential threats from metal depositions. Based on the maps of metal concentration on moss samples it was also possible to investigate the differentiation between the backgrounds and the anthropogenic pollution of the study areas. The local emission sources and long-range atmospheric transport show a significant contribution to atmospheric deposition of metals in Albania. Soil dust fine particles were pointed as the main source of most trace metals in moss samples. The presence of lithogenic and crustal elements (Yb, Sc, Ta, Ce, La, Th, Nd, Hf, U, Sm, Zr, Mn, W, Co, Ti, Al, Li, Sr, V, Fe, Ba and As) in moss samples indicate the effect of long-range transport of the pollutants combined with local geochemical factors of wind blowing soil dust fine particles that produce some local differentiation of these elements along the country. Based on the moss elements concentration and the spatial distributions maps produced for the whole territory of Albania, it was found that the moss metal distribution of Albania shows diverse patterns for different elements by presenting diverse geographical variability in moss metal concentrations. The examination of the distribution profile and the pollution levels of the potential anthropogenic elements that pose a high risk to the human health (Sb, Zn, Hg, Pb, Cd, Cu) pointed out the traffic emission, oil and gas industry, shipping activity in the coastal areas, geochemical and geological factors, long-range transport and the emission from metallurgy, are the most probable sources of these elements in moss samples and in atmospheric deposition over the territory of Albania. The presence, the behavior and the relations between the most important marine tracer elements, Cl and Na, in moss samples indicate the effects of the marine environment and sea spry aerosol. Beside the important contributions of sea sprays and seawater for Na+ and Cl- in coastal regions, the natural sources of Na+ and Clthat include the atmospheric deposition, interactions between water and soil, rocks, brines and salt deposits showed a strong affect derived by different local factors such as anthropogenic sources and geogenic factors which could contribute to the increase of Na and Cl contents in moss samples. On the other hand, the heterogeneous reactions with acidic gaseous and other reactive species in the marine environment, as well as the geographical positions of sampling sites may contribute to the decline of Cl content in moss samples. The differences founded at the spatial distributions of the sea spray elements (Na, Cl, K, Br, I, Mg, and P) in moss samples indicate the effects of geographical properties of the moss sampling sites, and the local natural and anthropogenic contributions of these elements. On the other hand, the decreased contribution of sea-salt aerosols as the distance from the coastal line increased is probably linked with the increasing contribution of tropospheric aerosol, and/or with the effect

xvi

Abstract

of the weather conditions on wet or dry deposition. It was reflected on the distributions patterns of the seaspry elements in moss samples. Due to the high moss metal content in particular in the eastern regions of Albania, where some pollution tendencies and “hot spots” were identified, a continuous and detailed monitoring and assessment are suggested. Moss biomonitoring survey provides a unique opportunity for the assessment of metal contamination in atmospheric deposition in local and continental range.

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 The Methodology of the Study . . . . . . . . . . . . . . . . . . 2.1 An Overview of the Study Area . . . . . . . . . . . . . . 2.2 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Sampling . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Sample Preparation and Chemical Analysis 2.2.3 Sample Preparation and ENAA Analysis . . 2.3 Quality Control . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Data Processing and Statistical Analysis . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

. . . . . . . . .

3 The Evaluation of TM Atmospheric Deposition in Albania 3.1 Trace Metal Concentrations . . . . . . . . . . . . . . . . . . . . . . 3.2 The Evaluation of the Background Level of Elements in Moss Samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Spatial Distribution of the Elements . . . . . . . . . . . . . . . . 3.4 Spatial Distribution of Crustal Elements . . . . . . . . . . . . . 3.4.1 Aluminium, Al . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.2 Iron, Fe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.3 Lithium, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.4 Titanium, Ti . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.5 Zirconium, Zr and Hafnium, Hf . . . . . . . . . . . . 3.4.6 Lanthanides . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.7 Barium, Ba . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.8 Strontium, Sr . . . . . . . . . . . . . . . . . . . . . . . . . .

1 4

. . . . . . . . .

9 9 11 11 13 14 15 18 19

....... .......

23 23

. . . . . . . . . . .

25 28 31 31 32 33 34 35 37 39 40

. . . . . . . . .

. . . . . . . . .

. . . . . . . . . . .

. . . . . . . . .

. . . . . . . . . . .

. . . . . . . . .

. . . . . . . . . . .

. . . . . . . . .

. . . . . . . . . . .

. . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

xvii

xviii

3.4.9 3.4.10 3.4.11 3.4.12 References .

Contents

Tantalum, Ta . . . . . . . . . . . . . Uranium, U and Thorium, Th . Manganese, Mn . . . . . . . . . . . Rubidium, Rb and Cesium, Cs

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

42 43 46 48 49

4 Chalcophile (As, Cd, Cu, Hg, Ni, Pb, Zn) and Biophile (Cu, Mo, and V) Micro Elements . . . . . . . . . . . . . . . . . 4.1 Copper, Cu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Cadmium, Cd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Lead, Pb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Zinc, Zn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Vanadium, V . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Arsenic, As . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7 Antimony, Sb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.8 Molibdenum, Mo and Selenium, Se . . . . . . . . . . . . . 4.9 Silver, Ag and Gold, Au . . . . . . . . . . . . . . . . . . . . . 4.10 Mercury, Hg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . .

. . . . . . . . . . . .

. . . . . . . . . . . .

. . . . . . . . . . . .

. . . . . . . . . . . .

. . . . . . . . . . . .

. . . . . . . . . . . .

. . . . . . . . . . . .

. . . . . . . . . . . .

. . . . . . . . . . . .

51 51 53 54 56 57 58 60 61 63 64 66

5 Elements Sensitive to Red/Ox Conditions (Cr, Co, Mo, U, V, Ni and Zn) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Chromium, Cr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Nickel, Ni . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Cobalt, Co . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . .

. . . . .

. . . . .

69 70 71 73 74

6 Sea Spray Elements (Na, Cl, Mg, Ca, K, Br, I) . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

77 83

7 The Siliceous, Si and Phosphorus, P . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

85 88

8 Multivariate Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

89 95

9 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

97

. . . . .........................

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

Acronyms

AAS AMS BC Ci CVAAS DW ENAA ETAAS EWMA FA FPM GIS ICP-AES INAA Li LRTP MB NBC NC PM SSM

Atomic absorption spectroscopy Albanian moss survey Background concentration Concentration of the “ith” element Cold vapour atomic absorption spectrometry Dry weight Epithermal neutron activation analysis Electrothermal atomic absorption spectrometry Exponentially weighted moving average Factor analysis Fine particulate matter Geographic information system Inductively coupled plasma atomic emission spectrometry Instrumental neutron activation analysis Individual loading of element i Long-range transport of pollutant Moss biomonitoring National background concentration Normalized concentration Particulate matters Space series model

xix

List of Figures

Fig. 2.1

Fig. 2.2 Fig. 2.3 Fig. 2.4 Fig. 3.1

Fig. 3.2

Fig. 3.3

Fig. Fig. Fig. Fig. Fig. Fig. Fig.

3.4 3.5 3.6 3.7 3.8 3.9 3.10

The map of Albania and its geographical position (centered at the latitude 41° 00′ north of the equator and the longitude 20° 00′ east of Greenwich). Blue dots—the 1st sampling transect (St. 1–14). Green dots—the 2nd sampling transect (St. 15–33). Red dots—the 3rd sampling transect (St. 34–47) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The map of sampling sites (2010) moss survey: a 62 sampling sites, b 47 sampling sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . Views from sampling sites and moss sample Hypnium Cupressiforme (Hedv.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Views from sample preparation: a drying process, b the cleaned sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The histograms of data distribution and the predicted normal distribution curves of V, Mo, Ag and P (other elements are shown in the Appendix, Fig. A.1) . . . . . . . . . . . . . . . . . . . EWMA charts of concentration data after excluding the outlier sites (when CV% > 50%, indexed as Me_1 in the graph, N = 47—the number of outlier sites) and of original data (for CV% < 50%, indexed as Me in the graph, N = 47) (other elements are shown in the Appendix, Fig. A.2) . . . . . . GIS maps of metal distributions in moss samples of 2010 AMS. a Al, b Fe, c As, d Cd, e Cr, f Ni, g Sc, h Ce, i Pb, j Zn, k Cl, l Na, m Br, n Mn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial analysis plot of Al . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial analysis plot of Fe . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial analysis plot of Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial analysis plot of Ti . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial analysis plot of Zr and Hf . . . . . . . . . . . . . . . . . . . . . . Spatial analysis plots of Sc, La, Ce and Yb . . . . . . . . . . . . . . Linear regression plots of La versus Ce, La versus Sm, La versus Tb, La versus Yb, La versus Sc, and La versus U . . . .

..

10

..

12

..

12

..

13

..

26

..

27

. . . . . . .

. . . . . . .

29 32 33 34 35 36 38

..

40 xxi

xxii

List of Figures

Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig.

3.11 3.12 3.13 3.14 3.15 3.16 3.17 3.18 4.1

Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig.

4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10

Fig. 4.11 Fig. Fig. Fig. Fig.

5.1 5.2 5.3 6.1

Fig. 6.2

Fig. 6.3 Fig. Fig. Fig. Fig. Fig. Fig. Fig.

6.4 6.5 6.6 6.7 7.1 7.2 8.1

Fig. 8.2

Spatial analysis plot of Ba . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial analysis plot of Sr . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial Analysis plot of Ta . . . . . . . . . . . . . . . . . . . . . . . . . . . Linear regression plots of Ta versus Ce, and Ta versus Cs . . . Spatial analysis plots of Th and U . . . . . . . . . . . . . . . . . . . . . Linear regression plots of Th versus La, and Th versus U . . . Spatial analysis plot of Mn . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial analysis plots of Rb and Cs . . . . . . . . . . . . . . . . . . . . Spatial analysis plot of Cu (a), the sketch map of Cu mineralized areas (AEA-Albania 2011) (b) . . . . . . . . . . Spatial analysis plot of Cd . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial analysis plot of Pb . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial analysis plot (a) and probability plot (b) of Zn . . . . . . Spatial analysis plot of V . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial analysis plot of As . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial analysis plot of Sb . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial analysis plots of a Mo and b Se . . . . . . . . . . . . . . . . . Spatial analysis plot of a Ag and b Au. . . . . . . . . . . . . . . . . . Spatial analysis plot of Hg, a n = 47; and b n = 45 after excluding the anomalies of St. 24 and 27 . . . . . . . . . . . . . . . . Median concentration data of the elements in different transects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial analysis plot of Cr . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial analysis plot of Ni . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial analysis plot of Co . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial analysis of Na and Cl. 1–14: the 1st transect; 15–34: the 2nd transect; 35–48: the 3rd transect. . . . . . . . . . . . . . . . . Spatial analysis plot of Na/Cl molar ratios along different transects: the 1st Transect (St. 1 to 14), the 2nd Transect (St. 15 to 34), and the 3rd Transect (St. 35 to 47) . . . . . . . . . Distribution plot of Na/Cl ratios along three transect. a The 1st transect; b the 2nd transect; c the 3rd transect . . . . Spatial analysis plot of Cl/Br ratios . . . . . . . . . . . . . . . . . . . . Spatial analysis plots of a Br and b I . . . . . . . . . . . . . . . . . . . Spatial analysis plot of K . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial analysis plots of Mg and Ca . . . . . . . . . . . . . . . . . . . . Spatial analysis plot of Si . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial analysis plot of P . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial analysis plots and GIS maps of a FL1 data (Yb, Sc, Ta, Ce, La, Th, Nd, Hf, U, Sm, Zr, Mn, W, Co). Outlier sites: St. 13, 14, 24, 30, 35; b FL2 data (Al, Li, Sr, V, Fe, Ba and As). Outlier sites: St. 2, 3, 24, 38 . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial analysis plot and GIS map of FL3 data (Sb, Zn, Hg, Pb, Cd, Cu and Ca). Outlier sites: St. 14, 24, 29, 31, 41 . . . . . . .

. . . . . . . .

. . . . . . . .

41 42 42 44 45 46 47 48

. . . . . . . . .

. . . . . . . . .

52 53 55 56 58 59 61 62 64

..

65

. . . .

. . . .

66 70 72 73

..

78

..

79

. . . . . . .

. . . . . . .

79 81 82 82 83 86 87

..

93

..

94

List of Figures

Fig. 8.3 Fig. 8.4

Fig. A.1 Fig. A.2

Spatial analysis plot and GIS map of FL4 data (Ni, Cr, Mg and Co). Outlier sites: St. 14, 24, 41, 42, 47 . . . . . . . . . . . . . Spatial analysis plot and GIS maps of a FL5 data (K, Cl, P and Na). Outlier sites: St. 7, 10, 11, 12, 27, 28, 32, 33; b FL6 data (Se, I and Br). Outlier sites: St. 2, 13, 14 . . . . . . . . . . . . The histograms of data distribution of the selected elements and the predicted normal distribution curves . . . . . . . . . . . . . . EWMA charts of concentration data after excluding the outlier sites (when CV% > 50%, indexed as Me_1 in the graph, N = 47—the number of outlier sites) and of original data (CV% < 50%, indexed as Me in the graph, N = 47) . . . . . . .

xxiii

..

94

..

95

. . 101

. . 103

List of Tables

Table 2.1

Table 2.2

Table 2.3

Table 3.1 Table 3.2 Table 6.1 Table 8.1

Table A.1 Table A.2

The limits of the quantification (LOQ) of the elements (mg k−1, DW) determined with aICP-AES, bETAAS, cENAA and dCVAAS analytical methods. . . . . . . . . . . . . . . . . . . . . . The recommended values (Steinnes et al. 1997; Harmens et al. 2010) and the obtained values (Qarri et al. 2013) for element concentrations in reference moss samples M2 and M3 (in mg kg−1, ICP-AES analysis). . . . . . . . . . . . . . . . The certified and the experimental values (mean ± standard deviation) for the used reference materials (content in mg/kg, DW, ENAA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Descriptive statistic Analysis of 2010 AMS trace metal data (N = 47) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The BCi values of the elements (in mg kg−1, DW) . . . . . . . . The regression analysis of Cl (mol) versus Na (mol) . . . . . . Factor analysis of the correlation matrix of the standardized concentration data; rotated factor loadings and communalities; varimax rotation. Sorted rotated factor loadings and communalities. . . . . . . . . . . . . . . . . . . . . . . . . . Pearson correlation results between elements in moss samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spearman Rho correlation results between elements in moss samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

..

14

..

16

..

17

.. .. ..

24 27 81

..

91

. . 106 . . 110

xxv

Chapter 1

Introduction Pranvera Lazo, Flora Qarri, Shaniko Allajbeu, Sonila Kane, Lirim Bekteshi, Marina Frontasyeva, and Trajce Stafilov

Air pollution is a global problem and has negative effects on human, animal and plant health (Kanawade et al. 2010). Environmental pollution in a global scale is caused by the presence of different toxic chemicals, including different toxins, inorganic and/or organic chemicals, and is strongly linked with their content in the environment. Humans could be exposed to different metals via different pathways such as air and air dust, drinking water and the food chain (Belzile et al. 2011). The increased circulation of toxic metals through the soils, water and air and their inevitable transfer to the human food chain remains an important environmental issue which entails some unknown health risks for future generations (Nriagu and Pacyna 1988). Health concerns could be increased in locations where metals concentrations get higher. Trace metals (TM) are a group of inorganic pollutants that occur naturally in soil and minerals, and may enter the air through different pathways. Beside the natural sources, ore and metal processing, manufacturing, combustion processes of fuels and transport sectors are considered as the main anthropogenic emission sources of metals in the air (Nriagu and Pacyna 1988; Nriagu 1989; Duffus 2002; Harmens et al. 2011). The main emission sources of trace metals in the air are mostly the ore and metal processing, and manufacturing, as well as combustion processes (Duffus 2002). Atmospheric input from wet and dry deposition of 40 trace elements in Mediterranean basin is primarily originating from industrial emission and the resuspension from the Saharian dust, while the combustion of fossil fuels and the emission from the primary and secondary non-ferrous metal smelters represent the major sources of Pb, Ni, Cu, Cd and V in global scale (Pirrone et al. 1999). On the other hand, the anthropogenic activities, such as energy production, transport, industrial processes, agriculture, and waste management, emit different gaseous and particulate pollutants that are responsible for the degradation of the air quality and climate changes (Maione et al. 2016). Atmospheric transport and the deposition rate are principal factors in determining the spatial distribution and the pollution

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Lazo et al., The Evaluation of Air Quality in Albania by Moss Biomonitoring and Metals Atmospheric Deposition, SpringerBriefs in Environmental Science, https://doi.org/10.1007/978-3-030-62355-5_1

1

2

P. Lazo et al.

level of atmospheric contaminants. Metals are not biodegradable and are stable in the environment. They are notable for their wide environmental dispersion, the tendency to be accumulated in the selected tissues of the human body; and for the overall toxicity even at relatively low concentration of exposure (Hu 2002). Approximately 30 metals/metalloids (Be, B, Li, Al, Ti, V, Cr, Mn, Co, Ni, Cu, Fe, As, Se, Sr, Mo, Pd, Ag, Cd, Sn, Sb, Te, Cs, Ba, W, Pt, Au, Hg, Pb, and Bi) are potentially toxic to humans (Morais et al. 2012). Trace metals of primary concern for human health and natural environment include As, Cd, Cr, Cu, Pb, Hg, Ni, Se, V, and Zn (Dore et al. 2014). The elements As, Cd, Cr, Hg and Pb pose main threats to human health during the exposure (Järup 2003) and some others, like Zn, Cu and Fe, are essential to life and play irreplaceable roles, but may turn harmful at certain content of exposure (Hu 2002). Cd, Al, Hg, Fe, Pb and As are classified as prominent metals which may cause adverse health effects (da Silva et al. 2005), and Cr, Ni, V, Co and As ions are classified as carcinogenic because they may perform red/ox reactions in the biological systems (Rehman et al. 2017). The pollutants emitted into the atmosphere deposited at the Earth’s surface where they accumulate in soil, sediment, and biota of terrestrial and aquatic ecosystems (Schröder et al. 2016) and may enter in air as wind blown fine mineral dust particles. Several toxic metals and organic compounds are trapped to the fine particulate matter (FPM) and transported in the environment as FPM. There is increasing interest in the atmospheric transport of mineral dust that is believed to play an important role in several marine biogeochemical processes (Prospero et al. 2002), geochemical and geophysical processes, and negative effects on human health (Prospero 1999). Soil dust is a major constituent of airborne particles transported over long distances in the global atmosphere (Prospero 1999) and may show a large time and space variability of TM in the air (Lammel et al. 2003). The presence of FPM in the air may cause several health problems that make us more vulnerable to different health diseases, such as respiratory, cardiovascular and neurological disorders, and lung cancer (Kelly and Fussell 2015). The evaluation of the role and the behaviour of toxic species/or compounds associated to airborne FPM is important for formulating effective control strategies and reducing the human health risk (Schroeder et al. 1987). Air quality became an important environmental issue that needs continuous monitory. It could be monitored by measuring the concentration of pollutants in the air or directly on deposits by building models that describe the transport and the level of air pollutants. Besides the classic monitoring methods that use conventional measuring techniques, an increasing interest exist in developing the bioindicative systems that usually provide relative and integrated information which permits the evaluation and the assessment of the environment (Markert et al. 2003). The effects of air quality to the humans and/or ecosystems directly linked with different factors such as the concentration level, the deposition rate and the toxicity degree of the contaminants, the exposure degree and the vulnerability of target exposure groups to the specific contaminants. Biomonitoring uses different plants and vegetations to detect the deposition, accumulation, and distribution of contaminants in ecosystems. Different types of plants and vegetation have been successfully used to monitory the levels of atmospheric trace metal deposition (Schilling and Lehman 2002). Among

1 Introduction

3

them, the bryophyte mosses can act as sensitive bioindicators as well as bioaccumulators of metal deposition in the environment (Rühling and Tyler 1973; Fernandez and Carballeira 2000, 2002; Harmens et al. 2010, 2011, 2013a, b; Vujiˇci´c et al. 2011) are widely used as bioindicators. Moss biomonitoring is a passive biomonitoring technique that uses living organism of the terrestrial moss species to investigate the content of different contaminants (metals, nitrogen, and organic pollutants, POPs) and to assess air quality (Gerdol et al. 2014). Passive biomonitoring with terrestrial mosses, i.e., the “moss biomonitoring technique”, becames a useful tool for the study of air quality and, more specifically, the atmospheric deposition of heavy metals (Fernandez et al. 2015), organic compounds (Harmens et al. 2013a, b) and nitrogen deposition (Harmens et al. 2011) all in a large geographic scale (Harmens et al. 2010, 2011). It is a relative and integrative measurement method that is based on different sensitivity of moss living organisms to air pollution. The morphological and physiological features of moss species show a high ability to accumulate different inorganic and airborne pollutants through the wet and dry deposition. Moss biomonitoring repeated in a certain time interval, usually at five years interval, by using the same moss species in the same study area that can investigate the temporal trends of air quality and the differences on spatial distribution of the contaminants. The bryophyte mosses have no vascular root system or waxy cuticle layer and the nutrients and mineral adsorption occurs over their entire surface (Rühling and Tyler 1973) directly from wet and dry depositions. The ability to retain potentially toxic elements has led to the use of bryophyte moss as biomonitors of air pollution (Rühling and Tyler 1973, 1984, 2004; Onianwa 2001; Zeichmeister et al. 2003). Some substantial properties that make the bryophyte mosses as good indicator are: they are ectohydric species, which means that most of the species receive water as well as mineral nutrients predominantly by atmospheric depositions, have a large surface to weight ratio, the existence of large cationic exchange properties within the cell wall, show a slow growth rate, are mostly growing in groups, show minimal morphological changes during their lifetime, can survive in highly polluted environment and is possible to determine the concentrations in the annual growth segments (Zechmeister et al. 2003; Blagnyt˙e and Paliulis 2010). The use of native terrestrial ectohydric mosses as biomonitors is now a wellrecognized technique in studies of atmospheric contamination (Fernandez and Carballeira 2000, 2002; Harmens et al. 2010, 2011, 2013a, b) and is applied as a practical mode in establishing and characterizing deposition sources. Since the 1970s, mosses are used in large-scale monitoring surveys, providing valuable information on the relative spatial and temporal changes of trace metal deposition in Europe (Rühling et al. 1987; Rühling 1994; Rühling and Steinnes 1998; Frontasyeva et al. 2004; Harmens et al. 2015). Moss biomonitoring in Albania started in 2010/2011 when our research group from University of Tirana joined the European Moss Survey conducted within the framework of the International Cooperative Programme on Effects of Air Pollution on Natural Vegetation and Crops, ICP Vegetation (Harmens et al. 2013a, b) and the doctoral studies conducted in Faculty of Natural Sciences, University of Tirana, Albania. The survey was repeated in 2015, at the same period with the European moss

4

P. Lazo et al.

survey conducted under the framework of the UNECE ICP Vegetation Programme (Harmens et al. 2015). The most toxic trace metal concentrations (As, Cd, Cr, Cu, Fe, Hg, Ni, Pb, V) in moss samples of Albania have been reported in European moss survey and in several publications (Qarri et al. 2013, 2014, 2015; Bekteshi et al. 2015; Allajbeu et al. 2016, 2017; Lazo et al. 2019; Qarri et al. 2019). The present book is focused on the assessment of air quality of Albania investigated by the concentrations of 51 elements, their spatial distributions, and the possible contributions from different sources and processes. The purpose of this book was to investigate the air quality by using moss biomonitoring trace metal deposition and to assess the occurrence of relatively high concentration of specific elements that may pose high contamination and/or ecological risk. Spatial and temporal trends of trace metals deposition in Albania by using mosses as biomonitors and the problematic local sources of emissions were identified. The interfering statistic was applied to the concentration data of 51 elements in moss samples to evaluate the most probable relationships between elements, their most important sources of the origin, geochemical interpretation of the data, and the secondary effects that yielding differences in the contents and in the distribution patterns of the elements in moss samples. The differences in soil geochemistry of the area from where the dust originates, leads to the differences in the contecentration and the distribution pattern of different chemicals. On the other hand, the properties of the mineral dust particles may help to distinguish the origin from local and/or long-distance migration of the contaminants. The discussion of the individual elements is focused on the possible natural and anthropogenic sources in moss and their contribution to the air pollution.

References Allajbeu S, Yushin NS, Lazo P, Qarri F, Duliu OG, Frontasyeva MV (2016) Atmospheric deposition of rare earth elements in Albania studied by the moss biomonitoring technique neutron activation analysis and GIS technology. Environ Sci Pollut Res 23:14087–14101. https://doi.org/10.1007/ s11356-016-6509-4 Allajbeu S, Qarri F, Marku E, Bekteshi L, Ibro V, Frontasyeva VM, Stafilov T, Lazo P (2017) Contamination scale on atmospheric deposition for assessing air quality in Albania evaluated from most toxic heavy metal and MB. Air Qual Atmos Health 10:587–599 Bekteshi L, Lazo P, Qarri F, Stafilov T (2015) Application of normalization process in the survey of atmospheric deposition of heavy metals in Albania by using moss biomonitoring. Ecol Indic 56:48–59 Belzile N, Chen YW, Filella M (2011) Human exposure to antimony: I. Sources and intake. Crit Rev Environ Sci Technol 41(14):1309–1373. https://doi.org/10.1080/10643381003608227 Blagnyt˙e R, Paliulis D (2010) Research into heavy metals pollution of atmosphere applying moss as bioindicator: a literature review. Environ Res Eng Manag 4(54):26–33 Da Silva ALO, Barrocas PRG, do Cof Jacob S, Moreira JC (2005) Dietary intake and health effects of selected toxic elements. Braz J Plant Physiol 17(1):79–93 Dore AJ, Hallsworth S, McDonald AG, Werner M, Kryza M, Abbot J, Nemitz E, Dore CJ, Malcolm H, Vieno M, Reis S, Fowler D (2014) Quantifying missing annual emission sources

1 Introduction

5

of heavy metals in the United Kingdom with an atmospheric transport model. Sci Total Environ 479(480):171–180 Duffus JH (2002) Heavy metals—a meaningless term? (IUPAC technical report). Pure Appl Chem 74(5):793–807 Fernandez JA, Carballeira A (2000) Evaluation of contamination by different elements in terrestrial mosses. Arch Environ Contam Toxicol 40:461–468. https://doi.org/10.1007/s002440010198 Fernandez JA, Carballeira A (2002) Biomonitoring metal deposition in Galicia (NW Spain) with mosses: factors affecting bioconcentration. Chemosphere 46:535–542 Fernandez JA, Boquete MT, Carballeira A, Aboal JR (2015) A critical review of protocols for moss biomonitoring of atmopheric deposition: sampling and sample preparation. Sci Total Environ 517:132–150 Frontasyeva VM, Smirnov LI, Steinnes E, Lyapunov SM, Cherchintsev VD (2004) Heavy metal atmospheric deposition study in the South Ural Mountains. J Radioanal Nucl Chem 259(1):19–26 Gerdol R, Marchesini R, Iacumin P, Brancaleoni L (2014) Monitoring temporal trends of air pollution in an urban area using mosses and lichens as biomonitors. Chemosphere 108:388–395. https:// doi.org/10.1016/j.chemosphere.2014.02.035 Harmens H, Norris DA, Steinnes E, Kubin E, Piispanen J, Alber R, Aleksiayenak Y, Blum O, Co¸skun M, Dam M, De Temmerman L, Fernández JA, Frolova M, Frontasyeva M, González-Miqueo L, Grodzi´nska K, Jeran Z, Korzekwa S, Krmar M, Kvietkus K, Leblond S, Liiv S, Magnússon SH, Maˇnkovská B, Pesch R, Rühling Å, Santamaria JM, Schröder W, Spiric Z, Suchara I, Thöni L, Urumov V, Yurukova L, Zechmeister HG (2010) Mosses as biomonitors of atmospheric heavy metal deposition: spatial and temporal trends in Europe. Environ Pollut 158:3144–3156 Harmens H, Norris DA, Cooper DM, Mills G, Steinnes E, Kubin E, Thöni L, Aboal JR, Alber R, Carballeira A, Cos, kun M, De Temmerman L, Frolova M, Gonzáles-Miqueo L, Jeran Z, Leblond S, Liiv S, Maˇnkovská B, Pesch R, Poikolainen J, Rühling Å, Santamaria JM, Simonèiè P, Schröder W, Suchara I, Yurukova L, Zechmeister HG (2011) Nitrogen concentrations in mosses indicate the spatial distribution of atmospheric nitrogen deposition in Europe. Environ Pollut 159:2852–2860 Harmens H, Foan L, Simon V, Mills G (2013a) Terrestrial mosses as biomonitors of atmospheric POPs pollution: a review. Environ Pollut 173:245–254 Harmens H, Norris D, Mills G, the participants of the moss survey (2013b) Heavy metals and nitrogen in mosses: spatial patterns in 2010/2011 and long-term temporal trends in Europe. ICP Vegetation Programme Coordination Centre, Centre for Ecology and Hydrology, Bangor, p 63. http://icpvegetation.ceh.ac.uk. Accessed 25 July 2013 Harmens H, Norris DA, Sharps K, Mills G, Alber R, Aleksiayenak Y, Blum O, Cucu-Man SM, Dam M, De Temmerman L, Ene A, Fern andez JA, Martinez-Abaigar J, Frontasyeva M, Godzik B, Jeran Z, Lazo P, Leblond S, Liiv S, Magnússon SH, Mankovska B, Pihl Karlsson G, Piispanen J, Poikolainen J, Santamaria JM, Skudnik M, Spiric Z, Stafilov T, Steinnes E, Stihi C, Suchara I, Thoni L, Todoran R, Yurukova L, Zechmeister HG (2015) Heavy metal and nitrogen concentrations in mosses are declining across Europe whilst some “hotspots” remain in 2010. Environ Pollut 200:93–104 Hu H (2002) Human health and heavy metals exposure. In: McCally M (ed) Life support: the environment and human health. MIT Press, Cambridge Järup L (2003) Hazards of heavy metal contamination. Br Med Bull 68(1):167–182. https://doi.org/ 10.1093/bmb/ldg032 Kanawade MS, Hamigi DA, Gaikwad WR (2010) Ecological effect of pollution. Int J Chem Eng Appl 1:332–335 Kelly FJ, Fussell JC (2015) Air pollution and public health: emerging hazards and improved understanding of risk. Environ Geochem Health 37:631–649. https://doi.org/10.1007/s10653015-9720-1 Lammel G, Brüggemann E, Gnauk T, Müller K, Neusüss C, Röhrl A (2003) A new method to study aerosol source contributions along the tracts of air parcels and its application to the near-ground content aerosol chemical composition in central Europe. J Aerosol Sci 34:1–25

6

P. Lazo et al.

Lazo P, Stafilov T, Qarri F, Allajbeu S, Bekteshi L, Fronasyeva M, Harmens H (2019) Spatial and temporal trend of airborne metal deposition in Albania studied by moss biomonitoring. Ecol Indic 101:1007–1017. https://doi.org/10.1016/j.ecolind.2018.11.053 Maione M, Fowler D, Monks PS, Reis S, Rudich Y, Williams ML, Fuzzi S (2016) Air quality and climate change: designing new win-win policies for Europe. Environ Sci Policy 65:48–57. https:// doi.org/10.1016/j.envsci.2016.03.011 Markert B, Breure AM, Zechmeister HG (2003) Definitions, strategies and principles for bioindication/biomonitoring of the environment. In: Markert BA, Breure AM, Zechmeister HG (eds) Bioindicators and biomonitors. Elsevier, Oxford, pp 3–39 Morais S, de Costa FG, de Lourdes Pereira M (2012) Heavy metals and human health. In: Oosthuizen J (ed) Environmental health—emerging issues and practice. InTech. ISBN: 978953-307-854-0. http://www.intechopen.com/books/environmental-health-emerging-issues-andpractice/heavy-metals-and-human-health. Accessed 29 Dec 2018 Nriagu JO (1989) A global assessment of natural sources of atmospheric trace metals. Nature 338:47–49 Nriagu JO, Pacyna JF (1988) Quantitative assessment of worldwide contamination of air, water, and soils by trace metals. Nature 333:134–139 Onianwa PC (2001) Monitoring atmospheric metal pollution: a review of the use of mosses as indicators. Environ Monit Assess 71:13–50 Pirrone N, Costa P, Pacyna JM (1999) Past, current and projected atmospheric emissions of trace elements in the Mediterranean region. Water Sci Technol 39(12):1–7 Prospero JM (1999) Long-range transport of mineral dust in the global atmosphere. Impact of African dust on the environment of the southeastern United States. Proc Natl Acad Sci U S A 96:3396–3403 Prospero MJ, Ginoux P, Torres O, Nicholson ES, Gill ET (2002) Environmental characterization of global sources of atmospheric soil dust identified with the nimbus 7 total ozone mapping spectrometer (TOMS) absorbing aerosol product. Rev Geophys 40(1):2–31. https://doi.org/10. 1029/2000RG000095 Qarri F, Lazo P, Stafilov T, Frontasyeva M, Harmens H, Bekteshi L, Baceva K, Goryainova Z (2013) Multi-elements atmospheric deposition study in Albania. Environ Sci Pollut Res 21:2506–2518. https://doi.org/10.1007/s11356-013-2091-1 Qarri F, Lazo P, Stafilov T, Bekteshi L, Baceva K, Marka J (2014) Survey of atmospheric deposition of Al, Cr, Fe, Ni, V and Zn in Albania by using moss biomonitoring and ICP-AES. Air Qual Atmos Health 7:297–307. https://doi.org/10.1007/s11869-014-0237-z Qarri F, Lazo P, Bekteshi L, Stafilov T, Frontasyeva M, Harmens H (2015) The effect of sampling scheme in the survey of atmospheric deposition of heavy metals in Albania by using moss biomonitoring. Environ Sci Pollut Res 22:2258–2271. https://doi.org/10.1007/s11356-0143417-3 Qarri F, Lazo P, Allajbeu S, Bekteshi L, Kane S, Stafilov T (2019) Evaluation of air quality in Albanian by 2015 moss biomonitroring and trace metal atmospheric deposition. Arch Environ Contam Toxicol 76(4):554–571. https://doi.org/10.1007/s00244-019-00608-x Rehman K, Fatima F, Waheed I, Akash MSH (2017) Prevalence of exposure of heavy metals and their impact on health consequences. J Cell Biochem 119(1):157–184. https://doi.org/10.1002/ jcb.26234 Rühling A (1994) Atmospheric heavy metal deposition in europe-estimations based on moss analysis. Nordic Council of Ministers, (ed.) AKA Print, A/S Arhus, p. 9. Rühling A, Tyler G (1973) Heavy metal deposition in Scandinavian. Water Air Soil Pollut 2(4):445– 455 Rühling A, Tyler G (1984) Recent changes in the deposition of heavy metals in Northern Europe. Water Air Soil Pollut 22:173–180 Rühling Å, Steinnes E (1998) Atmospheric heavy metal deposition in Europe 1995–1996, NORD environment 1998, 15. Nordic Council of Ministry, Copenhagen

1 Introduction

7

Rühling A, Tyler G (2004) Changes in the atmospheric deposition of minor and rare elements between 1975 and 2000 in south Sweden, as measured by moss analysis. Environ Pollut 131:417– 423 Rühling A, Rasmussen L, Rilegaard K, Makinen A, Steinnes E (1987) Survey of atmospheric heavy metal deposition in the Nordic countries in 1985-monitored by moss analyses. Nord 21:1–10 Schilling J, Lehman M (2002) Bioindication of atmospheric heavy metal deposition in the Southeastern US using the moss Thuidium delicatulum. Atmos Environ 36:1611–1618 Schröder W, Nickel S, Schönrock S, Meyer M, Wosniok W, Harmens H, Frontasyeva VM, Alber R, Aleksiayena J, Barandovski L, Carballeira A, Danielsson H, de Temmermann L, Godzik B, Jeran Z, Karlsson GP, Lazo P, Leblond S, Lindroos AJ, Liiv S, Magnússon SH, Mankovska B, Martínez-Abaigar J, Piispanen J, Poikolainen J, Popescu IV, Qarri F, Santamaria JM, Skudnik M, Špiri´c Z, Stafilov T, Steinnes E, Stihi C, Thöni L, Uggerud HT, Zechmeister HG (2016) Spatially valid data of atmospheric deposition of heavy metals and nitrogen derived by MS for pollution risk assessments of ecosystems. Environ Sci Pollut Res 23(11):10457–10476. https://doi.org/10. 1007/s11356-016-6577-5 Schroeder WH, Dobson M, Kane DM, Johnson ND (1987) Toxic trace elements associated with airborne particulate matter: a review. JAPCA 37(11):1267–1285. https://doi.org/10.1080/089 40630.1987.10466321 Vujiˇci´c M, Sabovljevi´c A, Sabovljevi´c M (2011) Axenically culturing the bryophytes: establishment and propagation of the moss Hypnum cupressiforme Hedw. (Bryophyta Hypnaceae) in in vitro conditions. Bot Serb 35(1):71–77 Zechmeister HG, Grodzi´nska K, Szarek-Łukaszewska G (2003) Bryophytes (chap 10). In: Markert BA, Breure AM, Zechmeister H (eds) Bioindicators and biomonitors. © 2003 Elsevier Science Ltd Zeichmeister HG, Hohenwallner D, Riss A, Hanus-Illnar A (2003) Variation in heavy metal concentrations in the moss species Abietinella abietina (Hedw.) Fleisch according to sampling time, within site variability and increase in biomass. Sci Total Environ 301:55–65

Chapter 2

The Methodology of the Study Pranvera Lazo, Flora Qarri, Shaniko Allajbeu, Sonila Kane, Lirim Bekteshi, Marina Frontasyeva, and Trajce Stafilov

2.1 An Overview of the Study Area Albania is a small country with an area of 28,748 sq km positioned in south-east Europe and in the western part of the Balkan Peninsula. It is bordered with the Adriatic and Ionian seas in the West (Fig. 2.1) with a coastline of 362 km long, and a land border of only 720 km long. With an average altitude of 700 m above the sea, it is mostly characterized by mountainous landscape which is intersected by the valleys of seven rivers running from the east to the west of Albania and discharged to the Adriatic Sea. Albania is characterized by Mediterranean climate in the West and Mediterranean-continental climate in the East. The average annual rainfall is about 1430 mm. The soil morphology of Albanian is linked with the geology of the area. The diversity in geological formations had conditioned different minerals and ore deposits in Albania. It is characterized by a complex diversity of geologic setting and soil geochemistry that are affected by different contamination inputs. Chromium, copper, ferro-chromium, nickel-ferrous, nickel-silicate ores, and petroleum are the dominant minerals of Albania. Beside these main minerals, there are alluvial deposits of heavy sand, containing Zr and REE, as well as rutile and ilmenite (Milushi 2015). The area is divided in two different mineralogical settings distinguished by different minerals. Internal tectonic zone, extended as a belt from the north to the south-east part of Albania is distinguished by a high potential of Cr, Ni, Fe and Cu minerals. The External tectonic zone, positioned along the coastal areas of Adriatic and Ionian Sea in the west, is rich in fossil fuels deposits and carbonate settings. The industrial sites with old technology, such as mining, mineral beneficiations, smelting and refining complexes (chromium, copper, iron ore, etc.), the Elbasan iron and steel plant, petroleum refineries, lignite-fired thermal electric power stations, and chemical plants had caused serious environmental pollution in the country (UNDP-Albania 2010).

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Lazo et al., The Evaluation of Air Quality in Albania by Moss Biomonitoring and Metals Atmospheric Deposition, SpringerBriefs in Environmental Science, https://doi.org/10.1007/978-3-030-62355-5_2

9

10

P. Lazo et al.

Fig. 2.1 The map of Albania and its geographical position (centered at the latitude 41° 00 north of the equator and the longitude 20° 00 east of Greenwich). Blue dots—the 1st sampling transect (St. 1–14). Green dots—the 2nd sampling transect (St. 15–33). Red dots—the 3rd sampling transect (St. 34–47)

Due to the extensive utilization of natural resources and old technology in the past decades of 19’s (1960–1990) in Albania the exposure to contaminants was relatively high. After the 1990s, when Albania changed from a totalitarian system to democracy, the old industry was in collapse and was closed or abandoned. The chemicals deposited in the abandoned chemical plants and/or in the processing plants of various minerals, untreated disposal wastes, and dumps distributed indiscriminately over a relatively wide area around the plants and/or mines, had caused high environmental pollution in different local areas. On the other hand, the activities in ex-industrial sites of copper, chromium, iron-nickel and oil industries have produced several million tons of industrial waste impacting the surrounding environment that had adversely affected the natural resources, followed by a potential health risk for the people

2 The Methodology of the Study

11

continuously exposed to the polluted areas (UNDP-Albania 2010). The lack of a national network for air quality monitoring as well as the data on morbidity caused by air pollution, makes it impossible to assess the health impact of air pollution in Albania (UNDP-Albania 2010).

2.2 Data Collection 2.2.1 Sampling Sampling was performed during the relatively dry seasons of autumn 2010 (September–October, 2010) and estate 2011 (June–July 2011). One of the recommended moss species, H. cupressiforme (Hedv.), widely spread in Albania, was used as air pollution bioindicator. Moss samples were collected and identified under the supervision of Dr. Jani Marka from the Department of Biology, Faculty of Natural Sciences, University of Tirana. Marka and Sabovljevic (2011) had listed bryophyte records from Albania. The living organisms of terrestrial mosses H. cupressiforme (Hedv.) are collected directly at each sampling site. The cleaned green and greenbrownish parts of the moss (H. cupressiforme (Hedv.) sps.) that represent the last 3 years of moss growth, were selected for analysis. Boquete et al. (2020) showed that the green parts of P. purum, and likely of other mosses with similar growth forms (like H. cupressiforme (Hedv.) sps), are the best choice for passive biomonitoring of air quality and could be used to compare the results from different studies. The use of the terrestrial mosses in metal biomonitoring enables spatial and temporal comparisons including regional biogeochemical mapping and detection of important element sources (Aboal et al. 2010) of relative and integrated concentrations of the elements. Sampling was performed according to the guidelines of the LRTAP ConventionICP Vegetation protocol and sampling strategy of the European Programme on Biomonitoring of Heavy Metal Atmospheric Deposition (ICP Vegetation 2010). Moss samples were collected at a total of 62 sampling sites. Due to the geographical diversity sampling locations were not evenly homogenously distributed and a random sampling scheme was obtained. To ensure a systematic sampling scheme (EPA QA/G-5S 2002) with a homogeneous distribution of more or less equal densities (1.5 moss samples/1000 km2 ) (Harmens et al. 2010; Qarri et al. 2014), the number of sampling sites was reduced to 47 in 2010 moss survey. The sampling locations were situated at least 300 m away from main roads or buildings and 100 m from small roads and single houses. Most of the samples were collected in open areas. Composite moss samples were formed by five to ten sub-samples collected within an area of 50 × 50 sq m. The sampling sites in mountain areas were positioned in deep valleys by keeping the altitude lower than 1000 m. Only five samples in the South-East (Korca-Pogradec region) were collected from the plateau region with an elevation lower than 1300 m. To prevent any contamination of the samples,

12

P. Lazo et al.

sampling and sample handling was performed using disposable polyethylene gloves. The distribution of the sampling sites and their geographic coordinates are shown in Fig. 2.2. Photos from a sampling site and of a moss sample Hypnium Cupressiforme (Hedv.) are shown in Fig. 2.3.

Fig. 2.2 The map of sampling sites (2010) moss survey: a 62 sampling sites, b 47 sampling sites

Fig. 2.3 Views from sampling sites and moss sample Hypnium Cupressiforme (Hedv.)

2 The Methodology of the Study

13

2.2.2 Sample Preparation and Chemical Analysis The cleaned green and green-brownish parts of the moss (H. cupressiforme (Hedv.) sps.) that represent the last 3 years of moss growth, were selected for analysis. Unwashed green and greenish-brown parts of the moss were cleaned from the adhering materials to the surface of the samples such as tree bark, lichens, soil dust, and the dead materials. The samples were dried to constant weight for 48 h at 30– 35 °C. To reduce the particle size and satisfy the conditions for homogeneity of the sample, the samples were ground and homogenized prior to the analysis using a plastic mortar and pestle. Views from sample preparation (drying process and the cleaned moss sample) are shown in Fig. 2.4. Moss samples were totally digested using a microwave digestion system (Mars, CEM, USA) according to the method presented by Stafilov et al. (2018). The instrumental parameters are published at Stafilov et al. (2018) and Balabanova et al. (2010). The content of 18 elements (Al, Ba, Ca, Cr, Co, Cu, Fe, K, Li, Mg, Mn, Na, Ni, P, Pb, Sr, V, and Zn) in the moss samples was determined by inductively coupled plasma-atomic emission spectrometric (ICP-AES) (Varian, 715ES). As and Cd were determined by electrothermal atomic absorption spectrometry (ETAAS) (Varian, SpectrAA 640Z). ICP-AES and ETAAS Analysis were performed at the Institute of Chemistry, Faculty of Science, Sts. Cyril and Methodius University, Skopje, North Macedonia. 32 other elements were analyzed by epithermal neutron activation (ENAA) method at Frank Laboratory of Neutron Physics, Joint Institute for Nuclear Research (FLNP JINR), in Dubna, Rusian Federation. Hg was determined by cold vapor atomic absorption spectrometry (CVAAS) (Varian 10+ equipped with a homemade cold vapor system) as described by Lazo and Cullaj (2002). Wet digestion of homogeneous sub-sample (0.5 g sample and 10 ml nitric acid 9:1 v/v in half pressure Teflon tubes) was applied for Hg analysis as given by Lazo et al. (2018).

Fig. 2.4 Views from sample preparation: a drying process, b the cleaned sample

14

P. Lazo et al.

Table 2.1 The limits of the quantification (LOQ) of the elements (mg k−1 , DW) determined with a ICP-AES, b ETAAS, c ENAA and d CVAAS analytical methods Elementsa

Mn

Zn

Fe

Al

Cu

Ca

Ba

LOQ

0.007

0.007

0.02

0.035

0.035

0.085

0.085

Elementsa

Mg

Sr

Cr

Li

V

Ni

Pb

LOQ

0.085

0.085

0.2

0.2

0.2

0.85

0.85

Elementsa

P

Na

K

Cdb

Asb

Hgd

LOQ

2.0

8.5

17

0.02

0.035

0.007

Elementsc

Ti

Cr

Co

Se

Rb

Sc

Zr

LOQ

63

1.21

0.024

0.071

0.123

0.01

2.79

Elementsc

Mo

Sb

Cs

La

Ce

Yb

Hf

LOQ

0.032

0.005

0.0065

0.02

0.456

0.035

0.021

Elementsc

Ta

W

Th

U

LOQ

0.002

0.019

0.005

0.0026

a,b Qarri

et al. 2013,

c,d Lazo

et al. 2018

Three replicates per moss samples were digested and three replicate measurements per digests were made during the analysis. The limits of the quantification (LOQ) for the elements determined with both ICP and ENAA methods are shown in Table 2.1. The limits of quantification were calculated as 10 SD of the lowest instrumental measurements of the blanks (Qarri et al. 2013, Lazo et al. 2018).

2.2.3 Sample Preparation and ENAA Analysis Extraneous materials, dead materials, and litter were removed from moss samples. The cleaned green and green-brownish parts of the moss (H. cupressiforme (Hedv.) sps.) that represent at last 3 years of moss growth, were selected for analysis. Samples were dried for 48 h at 40 °C till the constant weight and were analyzed by instrumental neutron activation analysis (INAA). Previous experience from the use of neutron activation analysis (NAA) in moss biomonitoring had shown that 0.3 g moss samples are sufficiently enough to be used without homogenization (Steinnes et al. 1994). This procedure is still in use for routine analysis at the Frank Laboratory of Neutron Physics, Joint Institute for Nuclear Research (FLNP JINR) (Frontasyeva et al. 2004; Barandovski et al. 2008; Marinova et al. 2010; Allajbeu et al. 2016). Epithermal NAA at the IBR-2 pulsed fast reactor of the FLNP JINR, Dubna, Russia, was used to determine contents of metals in moss samples (Frontasyeva 2011). Pulsed fast reactor IBR-2, equipped with pneumatic system REGATA for INAA, provides activation with thermal, epithermal, and fast neutrons. ENAA is an extension of INAA that enhances the activation of a number of trace elements relative to the major matrix elements (Frontasyeva and Pavvlov 2000).

2 The Methodology of the Study

15

The neutron flux density characteristics in the channel equipped with a pneumatic system are given by Frontasyeva and Pavvlov (2000). NAA is a sensitive analytical technique that is useful for performing both qualitative and quantitative multi-element analysis of major, minor, and trace elements in samples of scientific or technical interests (Biziuk et al. 2010). INAA is characterized as an analytical method relatively free from matrix effects and interferences, its high accuracy and very low or zero contributions from the blank (Filby 1995). Beside it, the INAA is a nondestructive method that provides precise and accurate results of high sensitivity and selectivity for a large number of elements in the periodic table, by making this technique attractive to multi-element analysis (Frontasyeva and Steinnes 1997). The elements Co, Cr, Fe, Ti, Se, Br, Sb, Zr, Hf, Ta, Mo, W, Sc, La, Ce, Nd, Sm, Eu, Gd, Tb, Tm, Yb, Th, U, Rb, and Cs were determined by epithermal neutron activation Analysis (ENAA) at the IBR-2 pulsed fast reactor FLNP JINR Dubna, Russia (Frontasyeva 2011). Moss samples were pelletized by using a simple press form and heat sealed in polyethylene foil for the analysis of the elements associated with short-lived radionuclides, while the samples analyzed for the elements of long-lived radionuclides were packed in aluminium cups. To determine elements associated with long-lived radionuclides the samples were irradiated for 100 h in the cadmium screened channel Ch1 with neutron flux density ϕ epi = 3.31 * 1012 n/(cm2 × s). The containers with samples were cooled for 4 days and then were repacked and measured twice, using high purity germanium detectors; the first time measurements were done directly after the repacking process and the second time measurements were done after 20 days of the end of the irradiation. The measurement time was 0.5 and 1.5 h respectively. To determine short-lived radio-nuclides (only Ti in the current work) the samples were irradiated for 3 min in the second channel (Ch2) of the reactor. After irradiation, gamma-ray spectra were recorded twice for each irradiation using a high-purity Ge detector (Frontasyeva and Pavvlov 2000). The processing of the spectral data was performed using the software developed at FLNP JINR (Ostrovnaya et al. 1993; Ostrovnaya 2000) and the content of each element in moss samples was calculated. The quantification limits of the elements calculated from the GEINE 2000 software for the concentrations range of each element in the current moss samples are shown in Table 2.1.

2.3 Quality Control M2 and M3 international moss reference materials (Steinnes et al. 1997) were used to perform the quality control of the ICP-AES and ETAAS Analysis (Stafilov et al. 2018). Moss reference materials were analyzed together with moss samples at each 10 samples interval. The determined ICP-AES reference values reported by Stafilov et al. (2018) and Balabanova et al. (2010) were in good agreements with the recommended values. In addition, blank samples were measured in parallel to the analysis of the moss samples. International plant reference material IAEA-140/TM (Fucus homogenate) was used for checking the quality control of the CVAAS analysis (Lazo

16

P. Lazo et al.

et al. 2018). The mean content of Hg (0.036 ± 0.012 mg kg−1 , DW) is in good agreement with the mean certified value (0.038 mg kg−1 , DW). The recovery of the investigated elements was checked by standard addition method. It ranged between 98.5 and 101.2% for ICP-AES, and 96.9–103.2% for AAS. Differences between the mean contents of Hg from the respective certified values of reference material were within 15%. The recommended (Steinnes et al. 1997; Harmens et al. 2010) and the obtained values of element concentration of M2 and M3 reference moss samples are shown in Table 2.2. The quality control of ENAA results was performed by carrying out simultaneous analysis of the reference material (SRM) 2711 Montana Soil from the National Institute of Standards and Technology (NIST), 1632b (trace elements in coal) from the US, NIST, and estuarine sediment (BCR-667) from the Institute for Reference Materials and Measurements (IRMM). Three measurements of the BCR have been done for each radiation party of 30 moss samples. Because nuclear reactions and decay processes are virtually unaffected by the chemical and physical structures of the material during and after irradiation, and are relatively free from the matrix effects and the interferences, the instrumental neutron activation analyiy (INAA) allows the Table 2.2 The recommended values (Steinnes et al. 1997; Harmens et al. 2010) and the obtained values (Qarri et al. 2013) for element concentrations in reference moss samples M2 and M3 (in mg kg−1 , ICP-AES analysis) Element

M2 CV ± SD

Al

178 ± 15

M3 DV ± SD 180 ± 7 1.27 ± 0.07

CV ± SD

DV ± SD

169 ± 10

180 ± 11

0.105 ± 0.007

0.10 ± 0.01

Asa

0.98 ± 0.07

Ba

17.6 ± 0.7

16.15 ± 0.7

13.7 ± 0.6

12.1 ± 0.7

Ca

2050 ± 160

1779 ± 37

2140 ± 200

1816 ± 139

Cda

0.454 ± 0.019

0.42 ± 0.04

0.106 ± 0.05

0.09 ± 0.03

Cr

0.97 ± 0.17

0.92 ± 0.05

0.67 ± 0.19

0.59 ± 0.02

Cu

68.7 ± 2.5

69.2 ± 9.9

3.76 ± 0.23

5.3 ± 0.35

Fe

262 ± 35

259 ± 9

138 ± 12

156 ± 5

K

6980 ± 350

6470 ± 103

3510 ± 280

4235 ± 165

Mg

826 ± 52

774 ± 33

755 ± 77

731 ± 96

Mn

342 ± 17

305 ± 33

535 ± 30

466 ± 8

Na

166 ± 15

165 ± 27

133 ± 12

120 ± 6

Ni

16.3 ± 0.9

16.3 ± 1.41

0.95 ± 0.08

1.0 ± 0.20

Pb

6.37 ± 0.43

6.1 ± 0.9

3.33 ± 0.25

3.40 ± 0.05

Sr

5.31 ± 0.15

5.01 ± 0.26

4.64 ± 0.24

4.69 ± 0.27

V

1.43 ± 0.17

1.45 ± 0.37

1.19 ± 0.15

1.26 ± 0.33

Zn

36.1 ± 1.2

34.3 ± 2.83

25.4 ± 1.1

23.7 ± 1.2

CV Certified values; DV Determined values,

a ETAAS

Analysis

2 The Methodology of the Study

17

use of standards that have different compositions (and even physical state) from that of the sample (Filby 1995; Frontasyeva 2011). The certified and the experimental values (mean ± standard deviation) for the used reference materials are shown in Table 2.3. The mean contents of the elements under investigation were in good agreement with the certified data. Table 2.3 The certified and the experimental values (mean ± standard deviation) for the used reference materials (content in mg/kg, DW, ENAA)

Elements

CV ± SD

DV ± SD

Tid

7910 ± 142

7896 ± 184

Sca

13.7 ± 0.69

13.69 ± 0.74

Cra

178 ± 16

178 ± 17

Fea

44,800 ± 986

Coa

23.0 ± 1.29

23.0 ± 1.3

Sec

1.29 ± 0.109

1.29 ± 0.17

Bra

99.7 ± 2.5

Rbc Srb

5.05 ± 0.11 245.3 ± 0.7

44,819 ± 2106

99.75 ± 2.89 5.06 ± 0.90 245.13 ± 17.16

Zrb

230 ± 69.0

230 ± 70

Mob

1.60 ± 0.48

1.60 ± 0.51

Sbb

19.4 ± 1.8

19.4 ± 1.8

Csa

7.80 ± 0.70

7.81 ± 0.71

Laa

27.8 ± 1.00

27.83 ± 1.11

Cea

56.7 ± 2.49

56.63 ± 3.40

Nda

25.0 ± 1.40

24.98 ± 817

Sma

4.66 ± 0.20

4.65 ± 0.23

Eua

1.00 ± 0.01

0.99 ± 0.19

Gda

4.41 ± 0.119

4.42 ± 0.28

Tba

0.682 ± 0.017

0.681 ± 0.024

Tma

0.326 ± 0.025

0.325 ± 0.070

Yba

2.20 ± 0.09

2.19 ± 0.24

Hfb

7.30 ± 2.19

7.29 ± 2.19

Taa

0.876 ± 0.0175

0.876 ± 0.027

Wb

3 ± 0.9

3.00 ± 0.92

Thc

1.342 ± 0.036

1.34 ± 0.04

Ua

2.26 ± 0.15

2.26 ± 0.15

CV Certified value; DV Determined value Note The subscription of each element refer to the certified material used to calculate the concentration of the elements (a BCR-667; b SRM 2711; c SRM 1632b; d SRM 1633b)

18

P. Lazo et al.

2.4 Data Processing and Statistical Analysis Statistical analysis was used to investigate the variability and spatial distribution of TM atmospheric deposition, and to assess the most probable pollution sources of the elements under investigation. The concentration data of the elements were interpreted on the basis of the results of descriptive and interfering statistic analysis. The distribution type of the data of each element and their possible outlier concentrations were examined through the frequency distribution, and the frequency plots confirmed by the statistical significance levels at p > 0.05. Statistical method was applied to evaluate the background level of metals in moss samples. Matschullat et al. (2000) and Reimann et al. (2002) have used the upper concentration limits (UCL = median + 2 * SDEV) to identify the outliers of each element. The UCL values are referred as the upper limit of geochemical variation that were suggested as “threshold levels” for clean-up goals of the environmental legislation (Reimann et al. 2002). The values lower than the lower concentration limits (LCL = median – 2 * SDEV) are referred as the background content. For elements with high variation (CV % > 75%), the UCL and LCL level of each element were calculated as (median ± SDEV). The background level was calculated on the basis of the LCL level of each element that was re-plotted after excluding the outlier points higher than the respective UCL levels of the sorted original data (Qarri et al. 2015). EWMA charts were also used to detect the linear trend of the univariate variables (Bissell 1984; Aerne et al. 1991) and the potential shifts in location scale and shape parameters (Liu et al. 2013). The use of univariate control charts in environmental study make it possible to investigate the moving range of two successive observations of temporal and spatial distribution characterized by an irregular distribution of nonparametric data set (Qarri et al. 2015, Haridy and Wu 2009). Nonparametric control charts in multivariate spatial rank have been discussed by Zou et al. (2012). In this case, the median values instead of average values were used to characterize the central tendency towards the data and the variability of the data could be estimated. The upper and lower control limits (UCL and LCL) were computed for the median moving ranges by applying pooled standard deviation, the proper values of λ (the weight of EWMA that ranges from 0 to 1) and the k value. The value of λ was carefully chosen to balance the robustness to non-normality and the detection ability to various shift magnitudes (Stoumbos and Sullivan 2002). Based on the median concentration of each element, the proper k values (k = 1–3) are selected. For the elements with high values of standard deviation compared to their median values, a small k value is used. The relationship between the elements in moss was tested by Spearman correlation analysis, confirmed by the statistical significance level, P < 0.01. Factor analysis (FA) was applied as an extension of the correlation analysis to assess the relationship between elements present in moss samples and to identify the most important factors that probably affect the association of the elements in the same factor. FA may explore the hidden multivariate structures of the data (Reimann et al. 2002; Astel et al. 2008) and may clarify the link between the elements that tend to have similar origins or to subsequently develop similar associations on the data matrix. Each

2 The Methodology of the Study

19

factor was explained on the basis of the associations of the elements extracted from the correlation matrix. The statistical analysis was performed using the MINITAB 17 software package. Arc-GIS 10.2 in combination with local deterministic methods and the inverse distance weighting were used for mapping the spatial distribution of the elements and the FLisite .

References Aboal JR, Fernández JA, Boquete T, Carballeira A (2010) Is it possible to estimate atmospheric deposition of heavy metals by analysis of terrestrial mosses? Sci Total Environ 408:6291–6297. https://doi.org/10.1016/j.scitotenv.2010.09.013 Aerne LA, Champ CW, Rigdon SE (1991) Evaluation of control charts under linear trend. Commun Stat 20:334–3349 Allajbeu S, Yushin NS, Lazo P, Qarri F, Duliu OG, Frontasyeva MV (2016) Atmospheric deposition of rare earth elements in Albania studied by the moss biomonitoring technique neutron activation analysis and GIS technology. Environ Sci Pollut Res 23:14087–14101. https://doi.org/10.1007/ s11356-016-6509-4 Astel A, Astel K, Biziuk A (2008) PCA and multidimensional visualization techniques united to aid in the bioindication of elements from transplanted Sphagnum palustre moss exposed in Gdansk city area. Environ Sci Pollut Res 15(1):41–50 Balabanova B, Stafilov T, Baˇceva K, Šajn R (2010) Biomonitoring of atmospheric pollution with heavy metals in the copper mine vicinity located near Radovıš, Republic of Macedonia. J Environ Sci Health A 45:1504–1518 Barandovski L, Cekova M, Frontasyeva VM, Pavlov SS, Stafilov T, Steinnes E, Urumov V (2008) Atmospheric deposition of trace element pollutants in Macedonia studied by the moss biomonitoring technique. Environ Monit Assess 138:107–118 Bissell AF (1984) Estimating of Linear Trend from a Cusum Chart or Tabulation. Applied Statistics 33:152–157 ˙ Biziuk M, Astel K, Rai´nska E, Zukowska J, Bode P, Frontasyeva VM (2010) Nuclear actiation methods in the estimation of environmental pollution and the assessment of the industrial plant impact on the citizens of Gdansk (Poland). Anal Lett 43(7–8):1242–1253. https://doi.org/10. 1080/00032710903518666 Boquete MT, Ares A, Fernández JA, Aboal JR (2020) Matching times: trying to improve the correlation between heavy metal levels in mosses and bulk deposition. Sci Total Environ 715:136– 955. https://doi.org/10.1016/j.scitotenv.2020.1369550048-9697/ EPA QA/G-5S (2002) Guidance on choosing a sampling design for environmental data collection. United States Office of Environmental, Environmental Protection Information Agency Washington, DC 20460, EPA/240/R-02/005 Filby RH (1995) Isotopic and nuclear analytical techniques in biological systems: a critical study. Pure Appl Chem 67:1929–1941 Frontasyeva VM (2011) Neutron activation analysIs for the life sciences. Phys. Part Nucl 42:332– 378 http://www.springerlink.com/content/f836723234434m27/ Frontasyeva VM, Pavvlov SS (2000) Analytical investigation at the IBR-2 reactor in Dubna. Preprint JINR E14-2000-177, Dubna, pp 5–32 Frontasyeva VM, Smirnov LI, Steinnes E, Lyapunov SM, Cherchintsev VD (2004) Heavy metal atmospheric deposition study in the South Ural Mountains. J Radioanal Nucl Chem 259(1):19–26 Frontasyeva MV, Steinnes E (1997) Epitermal neutron activation analysis for studying the environment. Proceedings of an International Symposium on Harmonization of Health Related Environmental Measurements Using Nuclear and Isotopic Techniques (Hyderabad, India, 4–7 November, 1996), IAEA, p. 301–311

20

P. Lazo et al.

Harmens H, Norris DA, Steinnes E, Kubin E, Piispanen J, Alber R, Aleksiayenak Y, Blum O, Co¸skun M, Dam M, De Temmerman L, Fernández JA, Frolova M, Frontasyeva M, González-Miqueo L, Grodzi´nska K, Jeran Z, Korzekwa S, Krmar M, Kvietkus K, Leblond S, Liiv S, Magnússon SH, Maˇnkovská B, Pesch R, Rühling Å, Santamaria JM, Schröder W, Spiric Z, Suchara I, Thöni L, Urumov V, Yurukova L, Zechmeister HG (2010) Mosses as biomonitors of atmospheric heavy metal deposition: spatial and temporal trends in Europe. Environ Pollut 158:3144–3156 Haridy S, Wu Z (2009) Univariate and multivariate control charts for monitoring dynamic-behavior processes: a casestudy. JIEM 2(3):464–498. ISSN: 2013–0953. doi: https://doi.org/10.3926/jiem. v2n3.p464-498. ICP Vegetation (2010) Heavy metals in European mosses: survey. Monitoring manual. In: International cooperative programme on effects of air pollution on natural vegetation and crops. http://icpvegetation.ceh.ac.uk/manuals/documents/UNECEHEAVYMETAL SMOSSMANUAL2010POPsadaptedfinal_220510_.pdf. Accessed 4 Oct 2012 Lazo P, Cullaj A (2002) Determination of the different states of mercury in seawater near the Vlora and Durres Bays. Anal Chem 374:1034–1038 Lazo P, Steinnes E, Qarri F, Allajbeu S, Stafilov T, Frontasyeva M, Harmens H (2018) Origin and spatial distribution of metals in moss samples in Albania: a hotspot of heavy metal contamination in Europe. Chemosphere 190:337–349 https://doi.org/10.1016/j.chemosphere.2017.09.132 Liu L, Zi X, Zhang J, Wang Z (2013) A sequential rank-based nonparametric adaptive EWMA control chart. Commun Stat Simul Comput 42(4):841–859 Marinova S, Yurukova L, Frontasyeva VM, Steinnes E, Strelkova LP, Marinov A, Karadzhinova AG (2010) Air pollution studies in Bulgaria using the moss biomonitoring technique. Ecol Chem Eng 17:37–52 Marka J, Sabovljevic M (2011) New bryophyte records from Albania. J Biol 33(1):74–76 Markert B, Breure AM, Zechmeister HG (2003) Definitions, strategies and principles for bioindication/biomonitoring of the environment. In: Markert BA, Breure AM, Zechmeister HG (eds) Bioindicators and biomonitors. Elsevier, Oxford, pp 3–39 Matschullat J, Ottenstein R, Reimann C (2000) Geochemical background—can we calculate it? Environ Geol 39(9):990–1000 Milushi I (2015) An overview of the Albanian ophiolite and related ore minerals. Acta Geologica Sinica (English Edition) 89(supp. 2):61–64 Ostrovnaya TM (2000) Tables for identification of nuclides formed in nuclear reactors. Dubna, Russia: JINR (Preprint E14–2000-178) Ostrovnaya TM, Nefedyeva LS, Nazarov VM, Borzakov SV, Stˇrelková LP (1993) Software for INAA on the basis of relative and absolute methods using nuclear data base. Proceedings “Activation Analysis in Environment Protection”, D14-93-325. JINR, Dubna, pp 319–325 Qarri F, Lazo P, Stafilov T, Frontasyeva M, Harmens H, Bekteshi L, Baceva K, Goryainova Z (2013) Multi-elements atmospheric deposition study in Albania. Environ Sci Pollut Res 21:2506–2518. https://doi.org/10.1007/s11356-013-2091-1 Qarri F, Lazo P, Stafilov T, Bekteshi L, Baceva K, Marka J (2014) Survey of atmospheric deposition of Al, Cr, Fe, Ni, V and Zn in Albania by using moss biomonitoring and ICP-AES. Air Qual Atmos Health 7:297–307. https://doi.org/10.1007/s11869-014-0237-z Qarri F, Lazo P, Bekteshi L, Stafilov T, Frontasyeva M, Harmens H (2015) The effect of sampling scheme in the survey of atmospheric deposition of heavy metals in Albania by using moss biomonitoring. Environ Sci Pollut Res 22:2258–2271. https://doi.org/10.1007/s11356-0143417-3 Reimann C, Filzmoser P, Garrett R (2002) Factor Analysis applied to regional geochemical data: problems and possibilities. Appl Geochem 17(3):185–206. https://doi.org/10.1016/s0883-292 7(01)00066-X Stafilov T, Šajn R, Barandovski L, Baˇceva AK, Malinovska S (2018) Moss biomonitoring of atmospheric deposition study of minor and trace elements in Macedonia. Air Qual Atmos Health 11(2):137–152. https://doi.org/10.1007/s11869-017-0529-1

2 The Methodology of the Study

21

Steinnes E, Hanssen JE, Rambæk JP, Vogt NB (1994) Atmospheric deposition of trace elements in Norway: temporal and spatial trend studied by moss analysis. Water Air Soil Pollut 74:121–140 Steinnes E, Rühling Å, Lippo H, Makinen A (1997) Reference materials for large-scale metal deposition surveys. Accred Qual Assur 2:243–249 Stoumbos ZG, Sullivan JH (2002) Robustness to non-normality of the multivariate EWMA control chart. J Qual Technol 34:260–276 UNDP-Albania (2010) Ferrochrome smelter in Elbasan; Fwienv contract n_1-BH0381. In: Consultancy services to conduct environmental impact assessment of ten high priority environmental hotspots to form the basis for a major remediation programme, for the project: “Identification and Prioritization of Environmental Hot Spots in Albania” Eia Report Final Zou C, Wang Z, Tsung F (2012) A Spatial Rank-Based Multivariate EWMA Control Chart, Nav Res Logist 59. https://doi.org/10.1002/nav.21475. Wiley Online Library. zj_wang/publications/2012/zwt2012_nrl.pdf. Accessed on 23 Apr 2014

Chapter 3

The Evaluation of TM Atmospheric Deposition in Albania Pranvera Lazo, Flora Qarri, Shaniko Allajbeu, Sonila Kane, Lirim Bekteshi, Marina Frontasyeva, and Trajce Stafilov

3.1 Trace Metal Concentrations Beside the ten priority elements (V, Cr, Fe, Ni, Cu, Zn, As, Cd, Hg, Pb) 41 additional elements were investigated on 2010 moss survey. Descriptive statistic analysis was applied to the concentration data of 51 elements (Table 3.1). The most important parameters, such as mean, median, minimum, maximum, coefficient of variation (CV %), skewness and kurtosis are shown in Table 3.1. The data of the elements under the detection limit of the analytical method (LOD), such as Au and Lu, for easy calculations of the statistical parameters, were standardized by a value of 0.7 × LOD. Most of the elements showed moderate variations (CV % < 75%) by indicating that the data are relatively stable. The elements As, Hg, Cd, Pb, Ni, Co, Cr, Cl, Eu, Tm, Lu, and Mn showed high values of variability (CV % > 75%), IQR > Q1, and high values of skewness and kurtosis by indicating a high asymmetry of the concentration data, wide spreading of the data and strong geographical variations of the elements in moss samples. High metal concentrations in different locations may be associated with geochemical factors and local anthropogenic sources or long-range transport of the pollutants. The concentration data of As, Hg, Cd, Pb, Ni, Co, Cr, Cl, Gd, Eu, Tm, Lu, and Mn are characterized by higher mean concentrations than their respective medians. The disparity on the distribution of the concentration data indicates that the data are probably affected by several factors. The histograms of data distribution and the predicted normal distribution curves of V, Mo, Ag and P are shown in the Fig. 3.1. Other elements are shown in the Appendix (Fig. A.1). The sequence of the distribution of elements in moss samples was Au < Tm < Tb < Ta < Eu < Ag < W < Sb < Cd = Lu < Hg = U = Yb = Se < Gd < Hf < As < Cs < Sm < Dy < Mo < Th < Sc < Co < Li < Nd < La < Pb < V < Ce < Br < Cu < Ni < Zr < Rb < Cr < Zn < Ba < Sr < Mn < Na < Cl < Ti < P < Al < Fe < Mg < K < Ca < Si. It looks likely different from the distribution of elements in earth’s crust (Rudnick and Gao 2003) by indicating mixed origins as natural, geogenic and anthropogenic sources. On the other hand, the man-induced mobilization of trace metals into the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Lazo et al., The Evaluation of Air Quality in Albania by Moss Biomonitoring and Metals Atmospheric Deposition, SpringerBriefs in Environmental Science, https://doi.org/10.1007/978-3-030-62355-5_3

23

24

P. Lazo et al.

Table 3.1 Descriptive statistic Analysis of 2010 AMS trace metal data (N = 47) Statistical parameters

As

Cd

Hg

Cu

Pb

Zn

Meana

0.48

0.15

0.21

6.32

3.12

14.8

Mediana

0.22

0.11

0.14

6.02

2.32

14.3

St. Deviation

0.61

0.13

0.32

2.69

2.88

9.22

CV %

126

88

154

43

92

62

Kurtosis

5.9

24.0

36.0

4.4

25.0

2.3

Skewness

2.4

4.4

5.7

1.7

4.7

1.1

Minimuma

0.05

0.04

0.04

2.14

1.34

1.00

Maximuma

2.86

0.90

2.23

15.6

19.7

46.9

Statistical parameters

Se

Sb

Au

Ag

Mo

Ni

Co

Cr

V

Meana

0.16

0.12

0.0047

0.087

0.27

13.36

2.06

27.03

3.81

Mediana

0.14

0.10

0.0014

0.084

0.25

6.59

1.22

11.07

3.32

St. Deviation

0.09

0.08

0.0087

0.028

0.12

21.18

1.83

43.36

1.93

CV %

55

66

185

32

45

159

89

160

51

Kurtosis

4.3

6.9

17.1

3.8

0.9

20.9

2.3

18.2

2.6

Skewness

1.9

2.5

3.8

1.5

1.1

4.2

1.7

3.8

1.3

Minimuma

0.05

0.05

0.0005

0.04

0.11

1.56

0.39

1.47

1.15

Maximuma

0.47

0.45

0.0510

0.19

0.63

131

7.47

262

10.9

Statistical parameters

Ti

Zr

Hf

Ta

W

Li

Na

K

Rb

Meana

280

9.01

0.248

0.059

0.102

1.52

98.5

3481

7.90

Mediana

246

7.88

0.214

0.055

0.094

1.39

87.1

2869

7.98

St. Deviation

147

4.25

0.133

0.031

0.047

0.79

52.2

1698

2.58

CV %

52

47

53

52

46

52

53

49

33

Kurtosis

0.9

3.0

0.8

0.8

0.6

2.8

9.2

5.3

-0.5

Skewness

1.1

1.4

1.0

1.1

0.8

1.4

2.5

2.2

0.0

Minimuma

77

3.75

0.070

0.013

0.020

0.28

27.9

1831

2.69

Maximuma

703

25.10

0.670

0.139

0.238

4.27

338.1

10043

14.0

Statistical parameters

Cs

Cl

Br

I

Ba

Mg

Ca

Sr

Sc

Meana

0.31

245

5.21

1.59

20.6

2934

6883

21.4

0.99

Mediana

0.26

183

4.70

1.52

19.7

2960

6555

21.1

0.74

St. Deviation

0.18

201

2.02

1.08

8.13

976

1636

5.54

0.73

CV %

57

82

39

68

40

33

24

26

74

Kurtosis

1.0

4.6

−0.6

22.6

−0.9

−0.5

1.9

0.3

2.7

Skewness

1.3

2.0

0.5

4.0

0.1

0.1

1.3

0.6

1.8

(continued)

3 The Evaluation of TM Atmospheric Deposition in Albania

25

Table 3.1 (continued) Statistical parameters

Cs

Cl

Br

I

Ba

Mg

Ca

Sr

Sc

Minimuma

0.09

46

2.1

0.15

6.00

1161

4859

10.8

0.19

Maximuma

0.77

1020

10.1

7.75

39.3

5152

12433

Statistical parameters

La

Ce

Nd

Sm

Eu

Gd

Tb

Dy

Tm

Meana

2.16

4.08

2.18

0.33

0.11

0.28

0.05

0.47

0.05

Mediana

1.89

3.49

1.67

0.28

0.08

0.16

0.05

0.37

0.03

St. Deviation

1.09

2.15

1.63

0.18

0.11

0.34

0.03

0.27

0.05

36.9

3.09

CV %

50

53

75

55

103

122

56

57

95

Kurtosis

0.8

1.1

0.3

1.1

13.6

8.8

0.6

0.7

3.6

Skewness

1.0

1.1

1.2

1.1

3.5

2.8

1.1

1.2

1.9

Minimuma

0.62

0.85

0.49

0.03

0.04

0.01

0.01

0.16

0.01

Maximuma

4.95

9.88

6.50

0.89

0.60

1.79

0.12

1.20

Statistical parameters

Yb

Lu

Th

U

Mn

Fe

Al

Si

P

Meana

0.19

0.14

0.52

0.16

72.05

1916

1698

32346

791

Mediana

0.14

0.11

0.46

0.14

58.54

1618

1523

31200

752

St. Deviation

0.12

0.17

0.27

0.09

55.85

1105

884

11021

300

CV %

65

120

51

58

78

58

52

34

38

Kurtosis

2.0

1.5

0.5

2.4

7.4

2.9

0.9

-0.4

3.3

Skewness

1.6

1.4

1.0

1.4

2.6

1.7

1.0

0.3

1.6

Minimuma

0.05

0.00

0.13

0.05

22

469

535

13200

407

Maximuma

0.55

0.66

1.21

0.49

284

5488

4448

60800

1839

a in

0.21

mg kg−1 , DW

biosphere (median values in thousand tonnes t year-1 of the terrestrial plus aquatic inputs minus atmospheric emissions) comes to about Hg < Cd < Sb ≈ V < Se < Mo < As < Ni < Pb < Cu < Zn (Nriagu and Pacyna 1988). It is totally different with the distribution of trace metals into moss samples of this study comes as Sb < Cd < Hg = Se < As < Mo < Pb < V < Cu < Ni < Zn by indicating different origins of the elements mostly from the atmospheric deposition.

3.2 The Evaluation of the Background Level of Elements in Moss Samples The concentrations of elements in moss do not depend only on atmospheric deposition of pollutants from local and long-range transport (Steinnes 1995). Some other anthropogenic and natural sources such as, long-range atmospheric transport from marine environment, leaching of elements from living or dead plant material, wind erosion of local soil dust, the uptake from the ground during raining periods, may

26

P. Lazo et al. Histogram (with Normal Curve) of V

Histogram (with Normal Curve) of Mo Mean 3.813 StDev 1.927 N 48

12

10

8

8

Frequency

Frequency

Mean 0.2739 StDev 0.1244 N 48

10

6

6

4

4 2

2

0

0

2

4

6

8

0

10

-0.0

0.1

0.2

0.3

Histogram (with Normal Curve) of Ag

0.6

20

Mean 0.08651 StDev 0.02778 N 48

15

Mean 791.1 StDev 299.7 N 48

15

Frequency

Frequency

0.5

Histogram (with Normal Curve) of P

20

10

5

0

0.4

Mo

V

10

5

0.04

0.08

0.12

Ag

0.16

0.20

0

400

800

1200

1600

P

Fig. 3.1 The histograms of data distribution and the predicted normal distribution curves of V, Mo, Ag and P (other elements are shown in the Appendix, Fig. A.1)

also contribute to the observed elements in moss samples (Steinnes et al. 2016). On the other hand, the essential elements such as Zn, Cu, Co, Mo and Fe may be accumulated and transferred in different part of the moss plant and thus may contribute to the background level of these elements in the moss. The background level of all naturally occurring elements in moss samples depends on the uptake processes that are responsible for different level of metal concentrations in different sampling sites and are affected by local conditions of the area. The background level of the elements in moss is important to identify the additional contributions of the elements from atmospheric deposition or from other ways beside the air pollution. The procedure of calculating the background level is based on the space series model (SSM) exponentially weighting moving average (EWMA) charts. The average value of the outlier points lower than LCL level was used as country background content (BCi ) after excluding the outlier points higher than UCL. In this case, the data are close to the normal distribution so the average values of the concentration could be used for the calculation. The EWMA charts of concentration data after excluding the outlier sites (when CV % > 50%, indexed as Me_1 in the graph, N = 47—the number of outlier sites) and of original data (CV % < 50%, indexed as Me in the graph, N = 47) (Fig. 3.2). The EWMA charts of other elements are shown in Appendix, Fig. A.2. The BCi values of each element are shown in Table 3.2.

3 The Evaluation of TM Atmospheric Deposition in Albania

27

EWMA Chart of As_1

EWMA Chart of Cu 14

0.7 0.6

12

10

UCL=0.4515 +1SL=0.4515

0.4

__ X=0.28

0.3

EWMA

EWMA

0.5

8 +1SL=6.02 UCL=6.02 __ X=4.95

6

0.2 -1SL=0.1085 0.1

-1SL=3.88

4

LCL=3.88

LCL=0.1085 2

0.0 1

5

9

13

17

21

25

29

33

37

41

1

Sample

6

11

16

26

31

36

41

46

Sample

EWMA Chart of Ni_1

EWMA Chart of Zn_1 25

25

20

20

15

__ X=12.65

10

EWMA

+1SL=17.94 UCL=17.94

EWMA

21

15 UCL=12.27 +1SL=12.27

10

__ X=7.50

-1SL=7.36 LCL=7.36 5

5

0

0 1

5

9

13

17

21

25

29

33

37

41

-1SL=2.74 LCL=2.74 1

Sample

5

9

13

17

21

25

29

33

37

41

Sample

Fig. 3.2 EWMA charts of concentration data after excluding the outlier sites (when CV% > 50%, indexed as Me_1 in the graph, N = 47—the number of outlier sites) and of original data (for CV% < 50%, indexed as Me in the graph, N = 47) (other elements are shown in the Appendix, Fig. A.2) Table 3.2 The BCi values of the elements (in mg kg−1 , DW) As

Cd

Hg

Cu

Pb

Zn

Se

0.064

0.06

0.053

2.72

1.43

1.72

0.064

Ag

Mo

Ni

Co

Cr

V

Ti

0.055

0.139

2.06

0.41

2.21

1.54

95.3

Zr

Hf

Ta

W

Li

Na

K

4.19

0.085

0.020

0.026

0.537

36

2026

Rb

Sb

Cs

Cl

Br

I

Ba

3.61

3.75

0.095

78

2.4

0.429

7.78

Mg

Ca

Sr

Sc

La

Ce

Nd

1319

5018

13.18

0.18

0.654

1.225

0.559

Sm

Eu

Gd

Tb

Dy

Tm

Yb

0.086

0.039

0.028

0.016

0.199

0.011

0.066

Th

U

Mn

Fe

Al

Si

P

0.134

0.058

24.1

675

588

14633

462

28

P. Lazo et al.

The background level of elements As, Cd, Zn, and Cu in Albanian moss samples is among the lowest level in Europe (Harmens et al. 2013); Pb stands in the middle position, and the elements Ni, Cr, Al, Fe, V, Sb and Hg show very high BC levels classified among the three highest content together with Ukraine, Denmark and North Macedonia.

3.3 Spatial Distribution of the Elements Albania is a country with a long coast line in the west, high mountains in the east and big variations in climate and annual precipitation. The marine environment may affect the chemical composition of the precipitation that may also strongly affect to the chemical composition of the moss elements. Windblown dust, long-range transport of the pollutants, transported mineral dust and sea spry salts are the most important natural sources contributing to the aerosol loading in Europe, and particularly to the air quality in Mediterranean countries, as they are close to arid and semi-arid regions of North Africa deserts (Marelli 2007). Sea salts are the major contributors to marine aerosol mass and to marine aerosol particulate matter (PM) loadings in coastal areas. The sea salt inorganic ions consist mainly of halogens, Cl− , Br− and I− , and marine cations such as Na+ , Mg2+ , Ca2+ and K+ and SO4 2− (Marelli 2007; Steinnes and Lierhagen 2017). On the other hand the geogenic factors in the eastern part show different effects on chemical composition of the precipitation that may also strongly affect to the chemical composition of moss elements (Lazo et al. 2019). To investigate the effects of marine environment and geogenic factors, the results of present work are separated in three different transects from the south to the north direction. The 1st transects is positioned at about 1–5 km far from the coastal line, the 2nd transects is positioned in the middle of the country about 55 km far from the coastal line, and the last (the 3rd transects) one is positioned in the East about 90 km far from the coastal line. The correlations between elements (Spearman Rho correlation for non-parametric data) were calculated for complete data set and for each transect (see Table A.1, Appendix). GIS maps of selected elements (Fig. 3.3) are plotted to investigate the spatial distribution of the metals in all territory of Albania and to distinguish the areas with high content of metals that may pose risk to the humans and to the environmental ecosystems. The statistical analysis of 2010 moss surveys data (Qarri et al. 2013, 2014; Bekteshi et al. 2015; Allajbeu et al. 2017; Lazo et al. 2018) showed relatively high levels of metals(lloids) such as Al, Fe, Cr, Ni, V, Hg and somehow As, Zn, Cd, Sb, and Pb (Lazo et al. 2018) in the areas subject to local atmospheric deposition of the pollutants. It is probably linked with anthropogenic emission sources derived mostly from mining industry, the sludge and dumps from mining operation, and geogenic factors of the areas with sparse vegetation that are a great potential of wind blowing

3 The Evaluation of TM Atmospheric Deposition in Albania

29

Fig. 3.3 GIS maps of metal distributions in moss samples of 2010 AMS. a Al, b Fe, c As, d Cd, e Cr, f Ni, g Sc, h Ce, i Pb, j Zn, k Cl, l Na, m Br, n Mn

30

Fig. 3.3 (continued)

P. Lazo et al.

3 The Evaluation of TM Atmospheric Deposition in Albania

31

Fig. 3.3 (continued)

soil dust in the atmosphere. Hg is a global pollutant and its origin in the moss is mostly derived from long-range transport local factors and transboundary pollution. The local anomalies distributed geographically indicate a substantial Hg contribution from the local sources.

3.4 Spatial Distribution of Crustal Elements 3.4.1 Aluminium, Al Al is a typical crustal element that is probably supplied to the moss in the form of windblown soil dust fine particles. Aluminnium shows a moderate variation (CV % = 52%) and its spatial distribution (Fig. 3.4) looks likely uniforms over the country. It is supported by the similar values of mean concentrations among three different transects (1679, 1687 and 1690 mg kg−1 respectively), as well as by strong and significant correlations between Al (r > 0.7, p < 0.01) and typical crustal elements (Li, Fe, Hf, Ta, W, Sc, Ce, Cs, Ba, Na, Ni and Co) that are soil dust derived elements. The spatial analysis of Al (linear model) (Fig. 3.4) indicates a stable distribution of Al from the south to the north direction of the country (Al = 1767 − 4.67 × n, n represents the number of sampling site) . The highest Al concentrations were found in the areas of Fe mines (St. 38 and 41) by indicating the anthropogenic pollution from mining activity and the local geogenic factors. The next high Al content was found in St. 24 (Elbasan region) that is derived from anthropogenic emission from steel and iron metallurgy plant of Elbasan.

32

P. Lazo et al. Variable Actual Fits

Accuracy Measures MAPE 53 MAD 686 MSD 766747

Spatial Analysis Plot for Al Linear Trend Model Al = 1767 - 4.67×n

5000

4000

Al

3000

2000

1000

0

1

5

10

15

20

25

30

35

40

45

n

Fig. 3.4 Spatial analysis plot of Al

3.4.2 Iron, Fe Fe is a typical crustal element that can be found in the earth’s crust, in rocks, and different iron ores. The Fe concentration in moss samples showed moderate variation (CV % = 58%) and the distribution pattern looks likely relatively stable over the country by indicating the effect of long-range atmospheric transport from other parts of Europe. The differences observed among different areas (Fig. 3.5) indicate the effects of additional local factors. The same as Al, the highest Fe concentrations were found in St. 24 that is mostly derived from anthropogenic pollution caused by high temperature iron and steel manufacturing. High Fe contents were found also at Librazhd and Korca-Erseka areas (St. 39, 41, 42) that are rich in Fe and Ni-Fe minerals (Lazo et al. 2018), and in the bauxite deposit areas in central and north part of the country that probably indicate the emission from mining industry and geogenic factors of the areas distributed in the form of wind blowing soil dust particles. Fe showed high and significant correlations (p < 0.01) (see the Appendix, Table A.1) with other typical crustal elements such as Li, Al, Lanthanides, U, Th, V, Ti, Zr, Hf, Ta, Mo, Se, Mn, Cr, Ni, Co and Sb. This behaviour of Fe in moss samples indicates the important role of soil dust fine particles as the main source of Fe. The uptake of these elements in moss is mainly supported by natural and anthropogenic factors. The emission of Fe from metallurgy has a strong impact in the area. On the other hand, the soil geochemistry and mining activity may explain the high levels recorded at specific points in the East. The spatial analysis of Fe (linear model) (Fig. 3.5) shows a slight increase from the south to the north, and from the western to the eastern direction of the country (Fe = 1419 + 17.8 × n, n = 47 represents the number of sampling

3 The Evaluation of TM Atmospheric Deposition in Albania Variable Actual Fits

33 Accuracy Measures MAPE 50 MAD 771 MSD 1131359

Spatial Analysis Plot for Fe Linear Trend Model Fe = 1419 + 17.8×n

6000 5000

Fe

4000 3000 2000 1000 0 1

5

10

15

20

n

25

30

35

40

45

Fig. 3.5 Spatial analysis plot of Fe

site). It is probably linked with the differences in chemical composition local soil dust particles that is controlled by different geological settingsof in different areas. The presence of Fe content in moss is probably derived from long-range atmospheric transport and is mostly dominated by from local inputs of air pollution from industry, mining activity and geochemical factors. High median value of Fe in moss samples of Albanian, higher than the median value of European moss survey (Harmens et al. 2015), supports our findings.

3.4.3 Lithium, Li Li is a typical crustal element that can be found in the earth’s crust and in different rocks. It shows a moderate variation (CV % = 52%) in current moss samples and its distribution pattern looks likely relatively stable over the country by indicating the effect of long-range atmospheric transport from the other part of Europe and wind blowing soil dust fine particles. Similar to the Al, the spatial analysis of Li (linear model) (Fig. 3.6) indicates a stable distribution of Li from the south to the north direction of the country (Li = 1.509 − 0.00165 × n, n represents the number of sampling site). The highest Li concentrations were found in the same positions with Al and Fe. Relatively high Li content was found in the areas of Fe mines (St. 35 and 38) by indicating the anthropogenic pollution from mining activity and the local geogenic factors. The next highest Li content was found in St. 24 (Elbasan region) that is derived from anthropogenic emission of fine mineral dust particulate matters from

34

P. Lazo et al. Variable Actual Fits

Accuracy Measures MAPE 54.0562 MAD 0.5726 MSD 0.6142

Spatial Analysis Plot for Li Linear Trend Model Li = 1.509 - 0.00165×n

4

Li

3

2

1

0 1

5

10

15

20

n

25

30

35

40

45

Fig. 3.6 Spatial analysis plot of Li

steel and iron metallurgy plant. Three outlier points were found also in the coastal areas (St. 5, 13, 14).

3.4.4 Titanium, Ti Ti is a crustal element that can be found in the earth’s crust, in rocks, and different mineral ores. It shows a moderate variation (CV % = 52%) in present moss samples. Ti ranged from 77 to 703 mg kg−1 in moss samples. The spatial analysis of Ti (linear model) (Fig. 3.7) shows an increase from the south to the north direction of the country (Ti = 304 − 1.42 × n, n represents the number of sampling site). Ti distribution pattern looks likely relatively stable over the country by indicating the effect of long-range atmospheric transport from the other part of Europe and local geochemistry of soil dust fine particles activated by the wind blowing dust particularly in the areas with sparse vegetation. The differences observed among different areas (Fig. 3.7) indicate the effects of the additional local factors. High Ti concentrations were found in sampling sites 11, 13, 14, 19 and 35, mostly in the same positions with Al and Fe, that are probably affected by the coastal aerosols (St. 11, 13, 14), soil geochemistry, mining activity, iron metallurgy (St. 35), and the historical dust from coal mine (St. 19) activity in the past. Al and Ti are typical mineral dust elements and their abundance in marine aerosol of Mediterranean coastal area indicate the African dust and regional and local mineral dust (Querol et al. 2019). Ti shows very strong and significant correlations (r > 0.6, p < 0.001) (see the Appendix, Tables A.1 and A.2) with Li, Al, Fe, V, Zr, Hf, W, Ta, Cs, lanthanides

3 The Evaluation of TM Atmospheric Deposition in Albania Variable Actual Fits

35 Accuracy Measures MAPE 52.2 MAD 108.6 MSD 19430.0

Spatial Analysis Plot for Ti Linear Trend Model Ti = 304 - 1.42×n

700 600 500

Ti

400 300 200 100 0 1

5

10

15

20

25

30

35

40

45

n

Fig. 3.7 Spatial analysis plot of Ti

(except Lu), Th and U that likely represent a strong lithogenic source and longrange transport of fine particulate matter. It is supported by a relatively homogenous distribution of Ti in the study area. Moderate and significant correlations (r = 0.4 − 0.6, p < 0.01) were found between Ti and As, Sb, Ni, Cr, Ba and Sr elements. High concentrations were found in the vicinity of the iron metallurgy plant of the Elbasan and in the mineralized zones of pyrite-chalcopyrite hosted by pillow lavas and agglomerate basalts, in high-Ti basalts in the North-East (Milushi 2015), as well as in the areas of the Young Neogene-Quaternary volcanism in the north, and the titanium-magnetite deposits located in the northern part of the Albanian western ophiolite (Milushi 2015). The correlation of Ti with Zr, Cr and Ni supports its origin from the igneous rocks during the weathering process. Long-range atmospheric transport from the other part of Europe is another important factor affected by coal combustion and typical industries that use Ti during their technological process such as ceramic, glass, pigment production and painting materials industry. On the other hand, the correlation of Ti with Ba and Sr may also indicate the effects of the marine ecosystem in the associations of these elements in the air.

3.4.5 Zirconium, Zr and Hafnium, Hf Zr and Hf are typical crustal elements that can be found in the earth’s crust and rocks. Ti, Zr and Hf belong to the same subgroup in the periodic table of elements. They have similar chemical properties that are reflected also in their associations with other elements in the environment. Their abundance in earth’s crust is Ti > Zr > Hf. The same sequence founded in moss samples, Ti (77−703 mg kg−1 , DW) > Zr (3.75 −

36

P. Lazo et al.

25.1 mg kg−1 , DW) > Hf (0.07 − 0.67 mg kg−1 , DW), is probably an indication of the lithogenic origin of these elements in current moss derived by local soil dust emission. Ti/Zr ratio in moss samples (average value of 28) is lower than of the chondrites (Ti/Zr = 118) that indicate the geogenic effects of local area. At the same time, the Zr/Hf ratios of moss samples ranged between 30 and 55 that probably indicate the origin from granites and zirconium granites (Wang et al. 2010). The correlations between Ti, Zr and Hf look likely very similar with the correlations of Ti, Zr and Hf with other earth’s crust elements. All these findings may indicate their natural source of origin as a dominant factor controlling their abundance in moss samples. Zr and Hf show a moderate variation (CV % = 47 and 53% respectively) in present moss samples. The spatial analysis plots of Zr and Hf (linear model) looks likely relatively consistent (Fig. 3.8) over the whole territory of the country (Zr = 8.88 − 0.0057 × n and Hf = 0.247 − 0.00045 × n, n represents the number of sampling site) (Fig. 3.8).This kind of distribution probably indicate the effects of the long-range atmospheric transport from other parts of Europe and local geochemistry of soil dust fine particles activated by the wind blowing dust particularly in the areas with sparse vegetation. The differences observed among different areas (Fig. 3.8) probably indicate the effects of additional local factors. The differences observed among different areas (Fig. 3.8) indicate the effects of additional local factors. Relatively high concentrations of Zr and Hf were found more or less in the same positions as Fe and Ti such as St. 13, 14 in coastal areas, St. 22, 24 and 29 in Elbasan and Kruja area, St. 35 and 39 in Erseka and Pogradec areas (Fig. 3.8) that are affected by similar factors; probably by mining activity, iron metallurgy, and the historical dust from coal mine activity in the past. It is also related to the soil geochemistry. The same as Ti, higher contents of Hf and Zr were found in areas of Young Neogene-Quaternary volcanism in the north part of Albania. Variable

Accuracy Measures

Spatial Analysis Plot for Zr

Actual Fits

MAPE

Linear Trend Model Zr = 8.88 - 0.0057×n

Variable

40.7507

MAD

3.1281

MSD

17.0881

Accuracy Measures

Spatial Analysis Plot for Hf

Actual Fits

MAPE

Linear Trend Model Hf = 0.247 - 0.00045×n

57.3121

MAD

0.1014

MSD

0.0163

0.7

25

0.6

0.5

0.4

15

Hf

Zr

20

0.3

10

0.2

0.1

5

0.0

1

5

10

15

20

25

30

35

40

n

Fig. 3.8 Spatial analysis plot of Zr and Hf

45

1

5

10

15

20

25

n

30

35

40

45

3 The Evaluation of TM Atmospheric Deposition in Albania

37

3.4.6 Lanthanides Lanthanides (Ln) are a group of 15 chemical elements from Lanthanum La (Z = 57) to Lutetium Lu (Z = 71). Due to the similar chemical-physical properties of Ln with Yttrium, Y (Z = 39) and Scandium, Sc (Z = 21) they form the group of rare earth elements (REE). REEs are typical representatives of the crustal elements. Due to the similar chemical-physical properties of REEs these elements are geochemicaly associated and could be found together in the environment. They are also naturally associated with minerals naturally containing radioactive elements, such as uranium and thorium. REEs play an essential role in modern technology such as metallurgical processing, alloying, electronics applications (e.g., hybrid vehicles, wind turbines, cell phones, computer components, electric motors, specialty glass and lenses) and military applications and products (EPA/600/R-12/572 2012). There are limited toxicological or epidemiological data regarding the human health effects of REEs (EPA/600/R-12/572 2012). The total REE content of soil surfaces is up to 100–200 mg kg−1 (Tyler 2004; Liang et al. 2005). Higher contents are mainly derived by human activity, low mobility of REEs and their high adsorption to soils (Cao et al. 2000; Li et al. 2013). The REEs of present study show moderate variation (CV % < 70%, except Eu, Gd and Tm which show high variations CV % ≥ 95%). High and significant correlations (r > 0.6, p < 0.01) were mostly found between REEs, except Eu, Tb and Dy that show moderate and significant correlations (r = 0.4 − 0.6, p < 0.01) with other REEs. Lu did not show correlation with other REEs. It was probably due to the very low content of Lu in moss samples, lower than the analytical LOQ of the method that leads to the high uncertainty and high RSD% (RSD % > 32%) of concentration data of Lu. The values of descriptive statistic parameters (mean, median, descriptive statistic, CV %, skewness, kurtosis, minimum and maximum) of light (LREEs; the sum of La to Eu content) and heavy (HREEs; the sum of Gd to Lu content) REEs in moss samples are summarized in Table 3.1. The concentration data of RREs show moderate variation among sampling sites suggesting consistent distributions in the all territory. The abundance of REEs showed the following sequence in moss samples: Ce > La > Nd > Gd > Dy > Yb > Eu > Tb > Tm > Lu by indicating higher content of LREEs compared to HREEs. Due to the similar ionic radii and electron configurations, REEs have very similar chemical characteristics and show similar associations or migrate as a whole group of elements (Wang et al. 2019). Under natural conditions, the behaviour and the content of REEs in environmental compartments is affected by various factors such as pH, redox conditions and organic matters (Wang et al. 2019). The spatial analysis plots of Sc, La, Ce and Yb (linear model) (Fig. 3.9) looks likely homogenous throughout the country (Sc = 0.918 − 0.00121 × n, La = 2.165 − 0.0052 × n, Ce = 4.068 − 0.0106 × n, and Yb = 0.184 − 0.00053 × n, n represents the number of sampling site). The distribution pattern of La and Ce looks almost uniform over the country, with the exception of some local variability with higher degree of selected REEs contents

38

P. Lazo et al. Variable Fits

Linear Trend Model Sc = 0.918 - 0.0012×n

MAPE

62.8625

MAD

0.4209

MSD

0.3689

Spatial Analysis Plot for La

Actual Fits

MAPE

Linear Trend Model La = 2.165 - 0.0052×n

53.2701

MAD

0.8005

MSD

1.0442

5

3.0

4

2.5 2.0

3

La

Sc

Accuracy Measures

Variable

Accuracy Measures

Spatial Analysis Plot for Sc

Actual

1.5

2 1.0 1

0.5 0.0

0 1

5

10

15

20

25

30

35

40

45

1

5

10

15

20

n

Variable Fits

30

35

Variable

Accuracy Measures

Spatial Analysis Plot for Ce

Actual

25

40

45

n

MAPE

Linear Trend Model Ce = 4.068 - 0.0106×n

1.559

MSD

4.073

Accuracy Measures

Spatial Analysis Plot for Yb

Actual

111.024

MAD

Fits

MAPE

Linear Trend Model Yb = 0.1836 - 0.00053×n

54.8187

MAD

0.0761

MSD

0.0107

0.6

10

0.5

8

0.4

Yb

Ce

6 0.3

4 0.2 2

0.1

0

0.0 1

5

10

15

20

25

30

35

40

45

n

1

5

10

15

20

25

30

35

40

45

Index

Fig. 3.9 Spatial analysis plots of Sc, La, Ce and Yb

that suppose the presence of REEs in moss is probably derived from long-range atmospheric transport of pollutants and local inputs of air pollution from wind blowing dust particles. The highest content of Ce and La was found in the same sampling sites as Ti, Zr and Hf which linked to the area of the Young Neogene-Quaternary volcanism in the north; carbonatic sandy sediments and Younger Quaternary sediments in the south, alluvial deposits of heavy sands, containing Zr and REE, as well as the rutile and ilmenite (Milushi 2015). It probably indicates the effects of wind blowing soil dust fine particles as an important local source of REEs. The effects of wind blowing soil dust fine particles are confirmed by the values of the La/Yb ratios that is an important representative parameter of the upper continental crust (Rudnick and Gao 2003). The La/Yb ratio of the current moss samples (12 ± 3) is close to the respective ratio of UCC, upper continental crust (15.5) (Rudnick and Gao 2003), by indicating similar distribution pattern of REEs in moss samples and UCC (Allajbeu et al. 2016). The local variations of REEs show also the impact of anthropogenic sources such as iron metallurgy (St. 24) of Elbasan area.

3 The Evaluation of TM Atmospheric Deposition in Albania

39

On the other hand, the distribution patterns of La and Ce in moss samples usually reflect the distribution of all rare earth elements (REEs) that show high correlations with La and Ce. In order to investigate the relationships between the elements, Pearson’s correlation analysis was performed onto REE concentration data (see the Appendix, Table A.1). Very strong and significant correlations between La and Ce with Sc and other REEs (r > 0.8, p < 0.001) indicate similar sources and spatial distribution of these elements in moss samples. Besides, the examined REEs, Sc, La, Ce and Yb, showed strong and significant correlations with U, Th, Fe, Al, Li, Zr and Hf indicating the similar behaviour and distribution patterns of REEs and all these elements among sampling sites. High correlations between REEs and U, Th, Fe, Al, Li, Zr and Hf showed the geochemical associations of REEs with radioactive elements, U and Th, and with the lithogenic elements, Fe, Al, Li, Zr and Hf by indicating high effects of geogenic factors and wind blowing soil dust particles. The obtained correlations between REEs were tested by linear regression models between the pairs of correlated elements (Fig. 3.10).

3.4.7 Barium, Ba Ba is a naturally occurring element. It is found in small quantity in the earth’s crust, particularly in igneous rocks, sandstone, shale, and coal. Barium enters the environment naturally through the weathering of rocks and minerals, and anthropogenically through the releases from the industrial processes (ATSDR 2007). The air contains about 0.0015 ppb Ba, and in industrial areas it goes to 0.33 ppb (ATSDR 2007). The background level of Ba in current moss was 7.78 mg kg−1 . Organometallic Ba compounds added as diesel fuel’ additives may reduce the smoke emission from the diesel engines that are followed with Ba emission in the air mostly in the form of Ba sulfate and carbonate (Miner 1969). The estimated maximum emission of Ba from a diesel engine would be on the order of 48 µg m−3 of exhaust (Miner 1969). Ba showed a moderate variation (CV % = 41%) in moss samples of this study. It showed strong and significant correlations (r > 0.6, p < 0.01) with typical crustal elements such as Li, Sr, Ti, and Al, and moderate and significant correlations (r = 0.4 − 0.6, p < 0.01) with V, Zr, Hf, Sc, La, Ce, Yb, Th, U, Fe, Ta, W, and Cs that probably indicate the lithogenic origin of Ba derived from wind blowing soil dust particles. The spatial analysis of Ba (linear model) (Fig. 3.11) indicated a stable and uniform distribution over the entire territory of the country (Ba = 20.63 − 0.002 × n, n represents the number of sampling site) by indicating the presence of long-range transport and wind blowing fine dust particles.

40

P. Lazo et al. 1.0

Fitted Line Plot

Fitted Line Plot

10

Sm = 0.00786 + 0.1495 La

Ce = - 0.0745 + 1.925 La 0.8

8 0.6

Ce

Sm

6

0.4

4 Regression

0.2

Regression

95% CI

95% CI

2

95% PI

95% PI S

0 0

1

2

3

0.472795

R-Sq

95.1%

R-Sq(adj)

95.0%

4

S

0.0

5

0

1

2

La

81.5%

4

5

Fitted Line Plot

0.6

Tb = - 0.003732 + 0.02528 La

Yb = - 0.01677 + 0.09649 La

0.12

0.5

0.10

0.4

0.08

0.3

Yb

Tb

81.9%

R-Sq(adj)

La

Fitted Line Plot

0.14

3

0.0762544

R-Sq

0.06

0.2

0.04

0.1

Regression 95% CI

0.02 S R-Sq

0.00

1

2

3

95% PI

0.0

0.0081964 91.8%

R-Sq(adj)

0

Regression 95% CI

95% PI

S

-0.1

91.6%

4

5

0

1

2

La

4

3

0.0644655

R-Sq

72.5%

R-Sq(adj)

71.9%

4

5

La

0.5

Fitted Line Plot

Fitted Line Plot U = - 0.00203 + 0.07501 La

Sc = - 0.2191 + 0.5547 La 0.4

3

0.3

Sc

U

2 0.2

1 0.1

Regression 95% CI

Regression 95% CI 95% PI

95% PI

0 S R-Sq R-Sq(adj)

0

1

2

3

La

4

0.412268 68.1%

0.0

S R-Sq R-Sq(adj)

67.4%

5

0

1

2

3

4

0.0434761 77.8% 77.3%

5

La

Fig. 3.10 Linear regression plots of La versus Ce, La versus Sm, La versus Tb, La versus Yb, La versus Sc, and La versus U

3.4.8 Strontium, Sr Strontium is a naturally occurring element that is present in rocks, soil, dust, coal, oil, water, air, plants and animals, and is found in the form of stable radioactive compounds in the air dust particles (ATSDR 2004). Its content in the earth’s crust goes up to 0.02–0.03% (ATSDR 2004), in the same level with Ba. Strontium can be

3 The Evaluation of TM Atmospheric Deposition in Albania Variable Actual Fits

41 Accuracy Measures MAPE 45.6968 MAD 7.1506 MSD 69.5593

Spatial Analysis Plot for Ba Linear Trend Model Ba= 20.63 - 0.0020×n

40 35 30

Ba

25 20 15 10 5 1

5

10

15

20

25

30

35

40

45

n

Fig. 3.11 Spatial analysis plot of Ba

found in all plant species. The uptake level of air-borne strontium by plants depends on physico-chemical form of Sr, the type of deposition (wet or dry), the time between fallout and first rain and the weather illumination (Burger and Lichtscheidl 2019). After incorporated by plants, it enters the food chain and causes threat to human health and the environment. Stable strontium can be released into the atmosphere from natural activities and then redeposited on the earth by dry fall or wet deposition. The emissions from burning of coal and oil may increase the strontium level in the air. Sr showed a moderate to small variation (CV % = 26% or close to the low variation range of 25%). The spatial distribution pattern of Sr looks likely homogenous over the country with a narrow range of variation (from 6.0 to 39.3 mg kg−1 ) and a small value of skeweness and kurtosis (0.57 and 0.51 respectively). The spatial analysis of Ba (linear model) (Fig. 3.12) indicates a very slight decline from the south to the north direction of the country (Sr = 23.31 − 0.0817 × n, n represents the number of sampling site). Higher Ba concentration was found in St. 24 (Elbasan area) by indicating the anthropogenic pollution from iron metallurgy. The next high Sr contents were found in the coastal area (St. 2, 5 and 6) that is probably derived from the sea spry emission in the area. Sr showed strong and significant correlations (r > 0.6, p < 0.01) with typical crustal elements such as Li, Ba, and V, and moderate and significant correlations (r = 0.4 − 0.6, p < 0.01) with V, Zr, Hf, Sc, La, Ce, Yb, Th, U, Fe, Ta, W, and Cs by probably indicating its lithogenic origin from wind blowing soil dust particles.

42

P. Lazo et al. Variable Actual Fits

Accuracy Measures MAPE 22.9931 MAD 4.5844 MSD 29.3298

Spatial Analysis Plot for Sr Linear Trend Model Sr = 23.3 - 0.0817×n

40 35

Sr

30 25 20 15 10 1

10

5

15

20

25

30

35

40

45

n

Fig. 3.12 Spatial analysis plot of Sr

3.4.9 Tantalum, Ta Ta is another typical crustal element that can be found in the earth’s crust, in rocks, and different ore minerals. It shows a moderate variation (CV % = 52%) in present moss samples. Its content ranged from 0.013 to 0.29 mg kg−1 in moss samples. The spatial analysis of Ta (linear model) (Fig. 3.13) shows an increase from the south to Variable Actual Fits

Accuracy Measures MAPE 59.1316 MAD 0.0278 MSD 0.0018

Spatial Analysis Plot for Ta Linear Trend Model Ta = 0.045 + 0.00066×n

0.30

0.25

Ta

0.20

0.15

0.10

0.05

0.00 1

5

10

15

20

25

n

Fig. 3.13 Spatial Analysis plot of Ta

30

35

40

45

3 The Evaluation of TM Atmospheric Deposition in Albania

43

the north direction of the country (Ta = 0.045 + 0.00066 × n, n = 47, represents the number of sampling site). Beside few anomalies of St. 13, 14, 24 and 46, the distribution pattern of Ta looks likely stable over the country by indicating the effect of long-range atmospheric transport from the other part of Europe. The differences observed among different areas (Fig. 3.13) indicate the effects of additional local factors. Higher concentrations were found in the same sampling sites as Zr that are probably affected by soil geochemistry, mining activity, iron metallurgy, and the carbonate formations of coastal areas. The highest content found at St. 46, in Has, Kukesi region, that is rich in Cr and Cu minerals. High Ta content in this sampling sites has a strong affect to the slope of the linear trend model of Ta obtained through its spatial analysis plot that is highly affected by a very high Ta content in the North (St. 46). Ta shows very strong and significant correlations (r > 0.6, p < 0.001) with most of the crustal elements such as Li, Al, Fe, V, Zr, Hf, W, Ti, Cs, Rb, most of lanthanides (except Lu), Th and U that likely represent strong lithogenic source of Ta. It showed moderate and significant correlations (r = 0.4 − 0.6, p < 0.01) with Cu, Sb, Hg and Mn, and with I, Br, Ba, Mg and Cr. The second group of correlated elements is probably affected by the marine environment and shipping activity in coastal areas. High Ta content is probably linked with the soil geochemistry derived from Triassic volcanic rocks, middle Jurassic basalts of the area of the Young NeogeneQuaternary volcanism in the north, as well as the titano-magnetite deposits located in the northern part of the Albanian western ophiolite (Milushi 2015). Very good linear regression lines were obtained between Ta and Ce, and between Ta and Cs (r2 = 0.91 and 0.86 respectively). The obtained correlations between Ta and Ce, and Cs were tested by linear regression models between the pairs of correlated elements (Fig. 3.14) that resulted with high values of the linearity coefficients that confirm the high correlations between them.

3.4.10 Uranium, U and Thorium, Th Th and U are typical crustal elements that represent the main radioactive elements in earth’s crust. They occur naturally in soils (at 1 – 10 mg kg−1 for Th and about 2 mg kg−1 for U), in different geochemical formations, and in crude oil (Rudnick and Gao 2003). Th ranged from 0.13 to 1.21 mg kg−1 , and U from 0.05 to 0.49 mg kg−1 and showed moderate variations (CV % = 51 and 58% respectively) in present moss samples. The spatial analysis of Th and U (linear model) (Fig. 3.15) show similar behaviour that looks likely homogenous over all territory of the country, but different intercept or higher background level for Th compared to U (Th = 0.551 − 0.00227 × n and U = 0.148 + 0.0002 × n, n represents the number of sampling site). The homogeneously distributions patterns of these elements probably indicate the effects of long-range atmospheric transport from other part of Europe and from local soil dust fine particles. The differences observed among different sampling sites (Fig. 3.15) indicate the effects of additional local factors. High concentrations of both elements were mostly found in the same sampling pozitions that are probably

44

P. Lazo et al. 12

Fitted Line Plot

Ce = 0.2409 + 65.90 Ta

10

8

Ce

6

4 Regression 95% CI 95% PI

2

S R-Sq R-Sq(adj)

0 0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.649797 90.8% 90.6%

0.14

Ta

1.0

Fitted Line Plot

Cs = - 0.00278 + 5.297 Ta 0.8

Cs

0.6

0.4

0.2

Regression 95% CI 95% PI S R-Sq R-Sq(adj)

0.0

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.0654808 86.3% 86.0%

0.14

Ta

Fig. 3.14 Linear regression plots of Ta versus Ce, and Ta versus Cs

affected the same factors. St. 24 of Elbasan area with high concentrations for both elements could be affected by iron metallurgy, and geochemical properties of local soils (St. 35 and 39). High content of Th and U founded in the coastal areas (St. 11, 13 and 14) are probably affected by geogenic factors (titano-magnetites, and the area of the Young Neogene-Quaternary volcanism), soil geochemistry, and the historical dust from mining activity in the past. Th and U showed strong and significant correlations (r > 0.6, p < 0.001) with most of the crustal elements such as Li, Al, Fe, V, Zr, Hf, W, Ti, Cs, Rb, and lanthanides (except Lu) that likely represent their strong lithogenic sources. Th and U showed moderate and significant correlations (r = 0.4 − 0.6, p < 0.01) with As, Sb, Mo, Cr,

3 The Evaluation of TM Atmospheric Deposition in Albania Variable Actual Fits

45 Accuracy Measures MAPE 52.9785 MAD 0.1942 MSD 0.0629

Spatial Analysis Plot for Th Linear Trend Model Th = 0.551 - 0.0023×n

1.2 1.0

Th

0.8 0.6 0.4 0.2 0.0 1

5

10

15

20

25

30

35

40

45

n Variable Actual Fits

Accuracy Measures MAPE 54.1037 MAD 0.0646 MSD 0.0075

Spatial Analysis Plot for U Linear Trend Model U = 0.148 + 0.0002×n

0.5

0.4

U

0.3

0.2

0.1

0.0 1

5

10

15

20

25

30

35

40

45

n

Fig. 3.15 Spatial analysis plots of Th and U

Rb, Ba, Sr and Mn (see the Appendix, Tables A.1 and A.2) that probably indicate their mixed origin mainly driven by both regional geochemistry and global atmospheric deposition. The correlation Th and La, and between Th and U was tested by the linear regression model (Fig. 3.16). Good linear regression were found between Th versus La and between Th versus U (r2 = 0.94 and 0.77 respectively). Two outlier points were evident at Th versus U regression line caused by high Th content at St. 24 (Elbasan area) that is affected by anthropogenic sources from iron metallurgy, and the next one at St. 27 (Kruja area) that show relatively high U content. After excluding the outlier points better linear regression were found between Th and U (r2 = 0.83).

46

P. Lazo et al. 1.4

Fitted Line Plot

Th = 0.00634 + 0.2388 La

1.2

1.0

Th

0.8

0.6

0.4

Regression 95% CI 95% PI

0.2

S R-Sq R-Sq(adj)

0.0 0

1

2

3

0.0649394 94.1% 94.0%

4

5

La

0.5

Fitted Line Plot

U = 0.00158 + 0.3033 Th 0.4

U

0.3

0.2

0.1

Regression 95% CI 95% PI

0.0

S R-Sq R-Sq(adj)

0.0

0.2

0.4

0.6

0.8

1.0

0.0441347 77.1% 76.7%

1.2

Th

Fig. 3.16 Linear regression plots of Th versus La, and Th versus U

3.4.11 Manganese, Mn Mn is a naturally occurring element that is found in rock, and water. The Mn content in earth’s crust is about 1000 mg kg−1 and the natural “background” levels in soil ranges from 1 to 4000 mg kg−1 , dry weight (Howe et al. 2004). Weathering process is the major sources of Mn in the atmosphere. Ocean spray, forest fires, vegetation, and volcanic activity are other major natural sources of manganese in the atmosphere (Schroeder et al. 1987). The major anthropogenic sources of Mn in the air include

3 The Evaluation of TM Atmospheric Deposition in Albania

47

mining and mineral processing, emissions from steel and iron production, and the combustion of fossil fuels (Howe et al. 2004). Besides, the Mn, Rb and Cs considered as elements with no connection to air pollution (Steinnes et al. 2016). Mn content in moss samples ranged from 22 to 284 which are within the normal background range of Mn in soil. It showed higher variations (CV % = 78%) than other crustal elements present in current moss samples. The spatial analysis of Mn (linear model) (Fig. 3.17) shows a slight increase (Mn = 57.4 + 0.256 × n, n represents the number of sampling site) from the south to the north direction of the country. The differences observed among different areas (Fig. 3.17) are probably indicates the effects of additional local factors. The highest concentration of Mn was found in St. 29 (Mirdita), the area of chromium mining and chromium processing industry, and also Cu mineralizations by indicating the effects of geogenic sources. The next high Mn contents were found in St. 12 and 14 of coastal area, and in the East (St. 35 and 39) that are probably affected by marine environment, the vegetation of the area and geogenic factors (of bauxite, coal and iron mineralization areas), as well as the effects of mining industry. Mn shows strong and significant correlations (r => 0.6, p < 0.01) with Fe, and moderate and significant correlations (r = 0.4 − 0.6, p < 0.01) with As, Sb, Mo, Cr, Rb, Ba and Sr (see the Appendix, Tables A.1 and A.2) that probably indicate the mixed origin mainly driven by both regional soil, geochemistry and/or geological factors, effects of higher plants and global atmospheric deposition. Variable Actual Fits

Accuracy Measures MAPE 51.18 MAD 26.90 MSD 1976.79

Spatial Analysis Plot for Mn Linear Trend Model Mn = 57.4 + 0.256×n

300 250

Mn

200 150 100 50 0 1

5

10

15

20

25

n

Fig. 3.17 Spatial analysis plot of Mn

30

35

40

45

48

P. Lazo et al.

3.4.12 Rubidium, Rb and Cesium, Cs Trace elements showed higher variations than major elements from study to study, but the lithophile crustal trace elements, such as REEs, Li, Sr, Rb, Cs, Zr, Hf, Th and U, are relatively constant and do not vary beyond 50% between studies (Rudnick and Gao 2003). The same behaviour was found in current moss samples for most of the litophile elements, including also Rb and Cs. Both elements showed moderate variation (CV % = 33% and 58% for Rb and Cs respectively). Rb ranged from 2.7 to 14 mg kg−1 , with a mean concentration of 7.8 mg kg−1 , and Cs ranged from 0.086 to 0.77 mg kg−1 , with a mean concentration of 0.31 mg kg−1 . The spatial analysis of Rb and Cs (linear model) (Fig. 3.18) showed different depletions from the south to the north direction of the country (Rb = 8.26 − 0.026 × n and Cs = 0.312 − 0.0011 × n, n represents the number of sampling site). The spatial ditribution pattern of Rb looks likely more stable than of Cs over the country by indicating the effect of long-range atmospheric transport from the other part of Europe and less from the local soil dust. The differences of both elements observed among different areas are situated more or less in the same sampling sites. St. 24 of Elbasan area is characterized by the highest concentration for both elements. It is probably affected by the anthropogenic emission from iron metallurgy. The small anomalies founded at St. 11, 13, 14, 27, 31 and 33 for Rb, and St. 13, 14, 27 and 29 for Cs are probably affected by higher plant effects and soil geochemistry. Rb showed strong and significant correlation with Cs (r = 0.66, p < 0.001), and moderate and significant correlations (r = 0.4 − 0.6, p < 0.01) (see the Appendix, Tables A.1 and A.2) with most of the crustal elements such as Li, Al, Fe, V, Zr, Hf, W, Ti, and most of the lanthanides by indicating its crustal origin probably emitted as wind blowing soil dust fine particles from long-range transport and local areas. In contrary, Cs showed strong and significant correlation with most of lithogenic elements (r = 0.66, p < 0.001), such as Th, U, Ta, Sc, Hf, W, La, Ce (and most of lanthanides), Li, Fe, V, Ti and Al, by indicating strong lithogenic sources of the origin Variable Fits

Variable

Accuracy Measures

Spatial Analysis Plot for Rb

Actual

Linear Trend Model Rb = 8.26 - 0.0258×n

MAPE

33.2991

MAD

2.0424

MSD

6.5093

Accuracy Measures

Spatial Analysis Plot for Cs

Actual Fits

MAPE

Linear Trend Model Cs = 0.312 - 0.0011×n

50.1379

MAD

0.1142

MSD

0.0246

0.8

14

0.7

12 0.6 0.5

Cs

Rb

10

8

0.4 0.3

6 0.2

4

0.1

2

0.0

1

5

10

15

20

25

30

35

40

45

n

Fig. 3.18 Spatial analysis plots of Rb and Cs

1

15

10

15

20

25

n

30

35

40

45

3 The Evaluation of TM Atmospheric Deposition in Albania

49

probably derived from longe-range transport and local effect of wind blowing soil dust fine particles. Cs showed also moderate and significant correlations (r = 0.4 − 0.6, p < 0.01) with As, Sb, Mo, Cr, Rb, Ba, Sr and Mn that probably indicate the mixed origin mainly driven by both regional geochemistry and global atmospheric deposition. The associations of Cs with As, Sb, Mo, and Cr probably indicate their association as anthropogenic elements entrapped to soil dust particles in the air. Several toxic metals and organic compounds are trapped to the fine particulate matter (FPM) and transported in the environment as FPM.

References Allajbeu S, Yushin NS, Lazo P, Qarri F, Duliu OG, Frontasyeva MV (2016) Atmospheric deposition of rare earth elements in Albania studied by the moss biomonitoring technique neutron activation analysis and GIS technology. Environ Sci Pollut Res 23:14087–14101. https://doi.org/10.1007/ s11356-016-6509-4 Allajbeu Sh, Qarri F, Marku E, Bekteshi L, Ibro V, Frontasyeva VM, Stafilov T, Lazo P (2017) Contamination scale of atmospheric deposition for assessing air quality in Albania evaluated from most toxic heavy metal and moss biomonitoring. Air Qual Atmos Health 10:587–599 ATSDR (2004) Toxilogical profile for strontium. CAS#: 7440-24-6. U.S. department of health and human services. Public health service agency for toxic substances and disease registry. https:// www.atsdr.cdc.gov/toxprofiles/tp159.pdf ATSDR (2007) Toxicological profile for barium and barium compounds. CAS#: 7440-39-3. https:// www.atsdr.cdc.gov/ToxProfiles/tp24.pdf Bekteshi L, Lazo P, Qarri F, Stafilov T (2015) Application of normalization process in the survey of atmospheric deposition of heavy metals in Albania by using moss biomonitoring. Ecol Indic 56:48–59 Burger A, Lichtscheidl I (2019) Strontium in the environment: review about reactions of plants towards stable and radioactive strontium isotopes. Sci Total Environ 653:1458–1512. https://doi. org/10.1016/j.scitotenv.2018.10.312 Cao X, Chen Y, Gu Z, Wang X (2000) Determination of trace rare earth elements in plant and soil samples by inductively coupled plasma-mass spectrometry. Int. J. Environ Anal Chem 76:295– 309. https://doi.org/10.1080/03067310008034137 EPA/600/R-12/572. EPA Office of Research and Development (2012) Revised rare earth elements: a review of production, processing, recycling, and associated environmental issues. www.epa. gov/ord Harmens H, Norris D, Mills G (2013) Heavy metals and nitrogen in mosses: spatial patterns in 2010/2011 and long term temporal trends in Europe, ICP Vegetation Programme Coordination Centre, Centre for Ecology and Hydrology, Bangor, UK, p 63. http://icpvegetation.ceh.ac.uk. Accessed 25 July 2013 Harmens H, Norris DA, Sharps K, Mills G, Alber R, Aleksiayenak Y, Blum O, Cucu-Man SM, Dam M, De Temmerman L, Ene A, Fern andez J.A., Martinez-Abaigar J, Frontasyeva M, Godzik B, Jeran Z, Lazo P, Leblond S, Liiv S Magnússon SH, Mankovska B, Pihl Karlsson G, Piispanen J, Poikolainen J, SantamariaJM, Skudnik M, Spiric Z, Stafilov T, Steinnes E, Stihi C, Suchara I, Thoni L, Todoran R, Yurukova L, Zechmeister HG (2015) Heavy metal and nitrogenconcentrations in mosses are declining across Europe whilst some “hotspots” remain in 2010. Environ Pollut 200:93–104 Howe P, Malcolm H, Dobson S (2004) Manganese and its compounds: environmental aspects. World Health Organization, Geneva, p 63. http://www.inchem.org/documents/cicads/cicads/cic ad63.htm#1.0

50

P. Lazo et al.

Lazo P, Steinnes E, Qarri F, Allajbeu S, Stafilov T, Frontasyeva M, Harmens H (2018) Origin and spatial distribution of metals in moss samples in Albania: a hotspot of heavy metal contamination in Europe. Chemosphere 190:337–349. https://doi.org/10.1016/j.chemosphere.2017.09.132 Lazo P, Stafilov T, Qarri F, Allajbeu Sh, Bekteshi L, Fronasyeva M, Harmens H (2019) Spatial and temporal trend of airborne metal deposition in Albania studied by moss biomonitoring. Ecol Ind 101:1007–1017. https://doi.org/10.1016/j.ecolind.2018.11.053 Li X, Chen Z, Chen Z, Zhang Y (2013) A human health risk assessment of rare earth elements in soil and vegetables from a mining area in Fujian Province, Southeast China. Chemosphere. 93:1240–6. https://doi.org/10.1016/j.chemosphere.2013.06.085 Liang T, Zhang S, Wang L, Kung H, Wang Y, Hu A, Ding S (2005) Environmental biogeochemical behaviors of rare earth elements in soil-plant systems. Environ. Geochem. Health 27:301–311. https://doi.org/10.1007/s10653-004-5734-9 Marelli L (2007) Contribution of natural sources to air pollution levels in the EU—a technical basis for the development of guidance for the Member States. EUR 22779 EN. ISSN 1018-5593. http:// ies.jrc.ec.europa.eu Milushi I (2015) An overview of the Albanian ophiolite and related ore minerals. Acta Geologica Sinica (English Edition) 89(supp. 2):61–64 Miner S (1969) Air pollution aspects of barium and its compounds. Prepared for the National Air Pollution Control Administration Consumer Protection & Environmental Health Service, Department of Health, Education, and Welfare (Contract No. PH-22-58-25) Nriagu JO, Pacyna JM (1988) Quantitative assessment of worldwide contamination of air, water and soils by trace metals Nature 333(12):134–139 Qarri F, Lazo P, Stafilov T, Frontasyeva M, Harmens H, Bekteshi L, Baceva K, Goryainova Z (2013) Multi-elements atmospheric deposition study in Albania. Environ Sci Pollut Res 21:2506–2518. https://doi.org/10.1007/s11356-013-2091-1 Qarri F, Lazo P, Stafilov T, Bekteshi L, Baceva K, Marka J (2014) Survey of atmospheric deposition of Al, Cr, Fe, Ni, V and Zn in Albania by using Moss biomonitoring and ICP-AES. Air Qual Atmos Health 7:297–307. https://doi.org/10.1007/s11869-014-0237-z Querol X, Pérez N, Reche C, Ealo M, Ripoll A, Tur J, Pandolfi M, Pey J, Salvador P, Moreno T, Alastuey A (2019) African dust and air quality over Spain: is it only dust that matters? Sci Total Environ 686:737–752. https://doi.org/10.1016/j.scitotenv.2019.05.349 Rudnick RL, Gao S (2003) The composition of the continental crust. In: Holland HD, Turekian KK (eds) Treatise on geochemistry, vol 3. The Crust, Elsevier-Pergamon, Oxford, pp 1–64 http://dx. doi.org/10.1016/b0-08-043751-6/03016-4 Schroeder WH, Dobson M, Kane DM, Johnson ND (1987) Toxic trace elements associated with airborne particulate matter: a review. JAPCA 37(11):1267–1285. https://doi.org/10.1080/089 40630.1987.10466321 Steinnes E (1995) A critical evaluation of the use of naturally growing moss to monitor the deposition of atmospheric metals. Sci Total Environ 160(161):243–249 Steinnes E, Lierhagen S (2017) Geographical distribution of trace elements in natural surface soils: atmospheric influence from natural and anthropogenic sources. Appl Geochem. https://doi.org/ 10.1016/j.apgeochem.2017.03.013 Steinnes E, Uggerud HT, Pfaffhuber KA, Berg T (2016) Atmospheric deposition of heavy metals in norway-country moss survey 2015. M-595. Norwegian Institute for Air Research, Trondheim Tyler G (2004) Rare earth elements in soil and plant systems—a review. Plant Soil 267:191–206. https://doi.org/10.1007/s11104-005-4888-2 Wang X, Griffin WL, Chen J (2010) Hf contents and Zr/Hf ratios in granitic zircons. Geochem J 44:65–72 Wang Z, Yin L, Xiang H, Qin X, Wang S (2019) Accumulation patterns and species-specific characteristics of yttrium and rare earth elements (YREEs) in biological matrices from Maluan Bay, China: implications for biomonitoring. Env Res 179:108804. https://doi.org/10.1016/j.env res.2019.108804

Chapter 4

Chalcophile (As, Cd, Cu, Hg, Ni, Pb, Zn) and Biophile (Cu, Mo, and V) Micro Elements Pranvera Lazo, Flora Qarri, Shaniko Allajbeu, Sonila Kane, Lirim Bekteshi, Marina Frontasyeva, and Trajce Stafilov

Based in the values of the median concentrations, the order of the distribution of calcophile and biophile elements in moss samples was Cd < Hg < As < Mo < Pb < V, Cu < Ni < Zn. The concentration data of Cu, Zn, Mo and V showed moderate variation, while the concentration data of As, Hg, Cd, Ni and Pb showed high variation by indicating high geographical variation of these elements. This is probably linked with the local enrichment of these elements and the effects of different factors.

4.1 Copper, Cu Cu is a common crustal element that is found naturally in the earth’s crust, rocks, and in different mineral ores, and is emitted by natural and anthropogenic sources. It shows a moderate variation (CV% = 43%) and the spatial distribution pattern (Fig. 4.1) looks likely relatively stable over the country. It is probably affected by long-range atmospheric transport from the other parts of Europe as an important source of Cu in moss samples. The spatial analysis of Cu (linear model) (Fig. 4.1) showed an increase in Cu content from the south to the north direction of the country (Cu = 5.171 + 0.036 × n, n represents the number of sampling site). Soil geochemistry and mining activity may explain the high levels recorded at specific points in the North-East part of Albania. The Cu deposits are mainly located in six main areas of Albania such as Mirdita, Puka, Shkodra, Kukes, Has and Korca (Fig. 4.1) mostly positioned in the north, except Korca. Among those, Has, Mirdita and Puka regions are known for their important Cu potential. On the other hand, the beneficiated products of Cu mineral concentrates mainly smelted at the Rubik, Gjegjan (Kukes) and Laci pyrometallurgical smelting plants with old technology had emitted high amount of waste materials that may generate soil dust fine particulate maters rich in Cu. The anomalies of Cu in mosses positioned in NE-SE part of the country are probably affected by the geological factors of the area that are known as Cu and Ni sulfide minerals. Another © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Lazo et al., The Evaluation of Air Quality in Albania by Moss Biomonitoring and Metals Atmospheric Deposition, SpringerBriefs in Environmental Science, https://doi.org/10.1007/978-3-030-62355-5_4

51

52

P. Lazo et al.

Variable Actual

Accuracy Measures MAPE 30.6433

Spatial Analysis Plot for Cu

Fits

Linear Trend Model Cu = 5.171 + 0.036×n

MAD

1.5871

MSD

5.1946

16 14 12

Cu

10 8 6 4 2 1

a.

5

10

15

20

25

Index

30

35

40

45

b.

Fig. 4.1 Spatial analysis plot of Cu (a), the sketch map of Cu mineralized areas (AEA-Albania 2011) (b)

probable emitting source should be the wind blowing dust from the industrial waste deposits and sulfide mineral dumps (Lazo et al. 2018). Finally, the presence of Cu content in moss is probably derived from long-range atmospheric transport and by local inputs of air pollution from industry, mining activity, geogenic factors and crude oil industry. The differences observed among different areas (Fig. 4.1) may indicate the effects of additional local factors. Higher concentrations of Cu were found in sampling sites 8, 10, 11, 13, 14, 24, 27, 35, 39, 40, 41 and 44 that are probably affected by soil geochemistry, mining activity, iron metallurgy and kiln operations in cement plants. The highest Cu content was found in St. 14 (Miloti region) that is probably affected by the geogenic factors and the traffic emission, and in St. 30 (Mirdita region) that is rich in Cu sulfide minerals (Milushi 2015). The next hight Cu content was found in moss samples in St. 24 (Elbasan region) that is probably affected by the emissions from iron and steel metallurgy, cement plant and traffic of the area. The emissions of trace metals during various human activities, mostly as fine particles, are assumed to be the cause of the increase of the metals concentrations (Pacyna and Pacyna 2001). Cu showed strong and significant correlations with Zn and Mg (r = 0.82 and 0.64 respectively, p = 0.000) and moderate and significant correlations (r = 0.4 − 0.6, p < 0.05) with Pb, Cd, Sb, Hg, Mo and Ni by indicating similar sources with Zn and Mg, and partly with Pb, Cd, Sb, Hg, Mo and Ni. The association of Cu with this group of elements (except Ni and Zn) is far from the group of elements Fe, Co, Ag, Ni and Zn that shows strong geochemical similarity with Cu. On the other hand, as a calcophile element, the association of Cu with other calcophile elements (Pb, Cd, Sb, Hg, Mo and Ni) is probably derived by the presence of chalcophile elements in sulfide minerals in the north part of Albania. This behaviour of Cu in moss samples

4 Chalcophile (As, Cd, Cu, Hg, Ni, Pb, Zn) …

53

indicates the important role of soil dust fine particles and longe-range transport from the other part of Europe as the main sources of Cu. On the other hand, this group of elements is also typical for traffic emission by indicating the role of traffic emission as an addition source of Cu in moss samples.

4.2 Cadmium, Cd Cadmium is a rare, but widely dispersed element of the earth’s crust. It shows similar geochemical properties with Zn, Hg and In (UNEP 2013) and is often associated with zinc ores (Cheng et al. 2014). Cd is a volatile element and its main anthropogenic atmospheric emissions are mostly derived from high-temperature processes, such as coal and oil combustion in electric power stations and industrial plants, roasting and smelting of ores in non-ferrous metal smelters, melting operations in ferrous foundries, refuse incineration, and kiln operations in cement plants (Pacyna and Pacyna 2001; UNEP 2013). Impacts of the former nonferrous metals smelting, iron and steel manufacturing, cement production, coal and liquid fuels combustion, biomass and municipal solid waste burning are the most important Cd sources in Albania. Cd showed high variation (CV % = 88%) in present moss samples by indicating the effects of different factors. Its spatial distribution pattern looks likely relatively stable over the country by indicating the effect of long-range atmospheric transport from the other part of Europe. The differences observed among different areas (Fig. 4.2) indicate the effects of additional local factors. Variable Actual Fits

Accuracy Measures MAPE 57.8647 MAD 0.0735 MSD 0.0163

Spatial Analysis Plot for Cd Linear Trend Model Cd = 0.1110 + 0.00144×n

0.9 0.8 0.7

Cd

0.6 0.5 0.4 0.3 0.2 0.1 0.0 1

5

10

15

20

25

n

Fig. 4.2 Spatial analysis plot of Cd

30

35

40

45

54

P. Lazo et al.

The spatial analysis plot of Cd (linear model) (Fig. 4.2) showed a very slight increase from the south to the north direction of the country that is highly affected by high Cd content in St. 41 (Cd = 0.111 + 0.00144 × n, n represents the number of sampling sites). Soil geochemistry and mining activity may explain the high levels recorded at specific points in the N-E part of Albania. High Cd content was found in NE-SE metal mineralization belt mostly of Cr, Cu, Ni and Fe deposits, and the lower in Western coastal areas (Fig. 4.2) which is characterized by carbonate rocks and sediments of Adriatic and Ionian Seas (NAMR 2010). The highest Cd content was found in moss sample of St. 41 (Librazhdi area) which probably derived from the local mining industry. High Cd content in the South (Fig. 4.2) is probably derived from fuel combustion, oil refinery, and oil and gas drilling industry of this area. Weathering and erosion of cadmium-bearing rocks are probably the most important natural source of Cd in Albania. The next high content of Cd was found in St. 27 (Kruja region) probably affected by cement production plant and strong anthropogenic effect of kiln operations that burn waste materials for heating, such as tyre wears, unselected urban wastes, woods etc., during CaO production. The outlier sites at St. 1, 4, 8, 10, 14, 15, 23 and 31 positioned in coastal areas (St. 1–14) are probably affected by shipping activity, oil and gas industry, and by heavy traffic zones (St. 14). Besides, the high Cd content at St. 20, 23 and 31 are probably affected by soil geochemistry, as well as from iron metallurgy and kiln operations in cement plants (St. 24). These findings may indicate that the presence of Cd content in moss samples of Albania is probably derived from long-range atmospheric transport and by local inputs of air pollution from industry, mining activity, traffic, geogenic factors and crude oil industry. Cd showed moderate and significant correlation with Cu, Pb and Zn (r = 0.4 − 0.6, p < 0.05) by probably indicating the effect of traffic emission and municipal waste burning in open and non-regulated landfills. Weak and significant correlation (r = 0.3 − 0.4, p < 0.05) were found between Cd and Na, Mg, Rb and Nd. The elemental contents which are captured by moss samples can be part of the emissions within particulate matter from different sources that create a very complex mixture in the air and some times the associations between elements is not easy to be explained.

4.3 Lead, Pb Pb is not a common element of earth’s crust and is mostly present in different ores and minerals (UNEP 2013). It can enter in the air through the natural and the anthropogenic processes. Pb contents in moss samples varied from 1.34 to 19.7 mg kg−1 , with an average concentration of 3.12 mg kg−1 . The concentration of Pb was lower in moss samples of Albania compared to the mosses of the most European countries (Harmens et al. 2013; Lazo et al. 2018). The same situation was found for the atmospheric deposition of Pb in regional scale of Mediterranean area which is following a downward trend, and in the country basis, Albania with only 0.3% of Pb emission is listed at the end of other Mediterranean Countries (Pirrone et al. 1999).

4 Chalcophile (As, Cd, Cu, Hg, Ni, Pb, Zn) … Variable Actual Fits

55 Accuracy Measures MAPE 44.5745 MAD 1.3879 MSD 8.0796

Spatial Analysis Plot for Pb Linear Trend Model Pb = 2.308 + 0.0335×n

20

Pb

15

10

5

0 1

5

10

15

20

25

30

35

40

45

n

Fig. 4.3 Spatial analysis plot of Pb

The spatial analysis plot of Pb (linear model) indicates a stable and homogenous distribution of Pb throughout the country (Pb = 2.308 + 0.0335 × n, n represents the number of sampling site) (Fig. 4.3) by indicating the Pb content in moss is mostly derived from long-range atmospheric transport. The distribution pattern of Pb in moss samples looks likely uniforms throughout the whole territory. The main sources of Pb in Albania are traffic and industrial emission sources. While the traffic shows a similar extent throughout the country, some specific industrial activities are focused in the central part of the country (Lazo et al. 2018). The outlier points with high Pb content are probably indicating the local inputs of air pollution from industry, mining activity, traffic, geogenic factors and crude oil industry. Soil geochemistry and mining activity may explain the high levels recorded at specific point of St. 31 (Mirdita region). Next high contents of Pb concentration were found in SE (St. 23 and 24) is probably linked with anthropogenic emission from iron and steel metallurgy. High Pb content founded in Elbasan and Librazhdi regions (St. 23, 24 and 31) are probably derived from local sources of steel, iron and ferrochromium metallurgy, emission from the cement factory, as well as from geogenic factors, mining industry and traffic emission. Thus, industrial emissions, road transport, and long-range transport could be the main factors affecting higher contents of Pb in these areas. Pb emissions from iron metallurgy in Elbasan area should be in consideration due to the old technology used in this plant. Moderate and significant correlation (r = 0.4 − 0.6, p < 0.005) were found between Pb and Cu, Zn, Hg, Cd, Mg and Nd, and weak and significant correlation (r = 0.3 − 0.4, p < 0.01) with Sb, Mo, V, Li, Tm (see the Appendix, Tables A1 and A2).The association of Pb with chalcophile (sulfide) elements such as Cu, Zn, Hg, Cd, Sb and Mo is probably derived by mining activity, soil geochemistry and mineral wastes of

56

P. Lazo et al.

mining industry in the north which may be enter atmosphere as wind blowing fine soil and mineral particles. High-temperature processes in steel and iron industry, municipal waste burning, and cement production could be in consideration as strong anthropogenic emitters of Pb (Pacyna 1988) that enter in the atmosphere as fine particulate matter from point sources. The chemical composition of the particulate matter and the manner of the distribution of all sources regarding the space, place or location, time, magnitude, characteristics of pollutants, and meteorological and climatic conditions (Ávila-Pérez et al. 2018) may explain the associations between the elements. On the other hand, traffic should be considered as a major contributor to Pb emission.

4.4 Zinc, Zn Zn is a widely distributed chemical element which is naturally occurring in the earth’s crust. The Zn content in the moss samples ranged from 1.0 to 46.9 mg kg−1 , with an average content of 14.3 mg kg−1 . The distribution of Zn throughout the whole country looks like homogeneous. The same as Pb, the concentration of Zn in moss samples of Albania was lower than the mosses of the most European countries (Harmens et al. 2013). Zn shows a moderate variation in the moss samples (CV% = 66%) and small difference between average (14.3 mg kg−1 ) and median (13.9 mg kg−1 ) values by indicating the data should be close to the normal distribution (p = 0.064 > 0.05) (Fig. 4.4b). It should be mostly derived from longe-range transport. The outlier sites may indicate the effects of some additional factors. Anthropogenic emission of Zn can be intensive in areas of the abandoned mine sites and metal production industries (UNEP 2013). The spatial analysis of Zn (linear model) (Fig. 4.4) showed an increase from the south to the north direction of the country (Zn = 10.47 + 0.167 × n, n represents the number of sampling site). Variable Actual

99

Accuracy Measures MAPE 111.218

Spatial Analysis Plot for Zn

Fits

Linear Trend Model Zn = 10.47 + 0.1673×n

MAD

6.532

MSD

77.891

Probability Plot of Zn Normal - 95% CI

95 90

50

80 70

Percent

40

30

60 50 40

Zn

30 20

20

10

10

Mean StDev

5

N AD

46

P-Value 0.064

0 5

a.

14.32 9.434 0.697

10 1

15

20

25

n

30

35

40

1

45

-20

-10

b.

Fig. 4.4 Spatial analysis plot (a) and probability plot (b) of Zn

0

10

20

Zn

30

40

50

4 Chalcophile (As, Cd, Cu, Hg, Ni, Pb, Zn) …

57

The geographical distribution of Zn is dominated by point sources of air pollution (Fig. 4.4a). The areas of high Zn content were located in the central part, in the area surrounding the metallurgical and cement plants, in the North-East and in the SouthEast that show the effects of geogenic factors, mining activity and the wind blowing dust from mineral dumps and former smelting industry in this area. Zn is an essential plant element with low retention capacity in bryophyte moss, so its concentration level in moss is usually high and only in very high environmental concentrations would be produced high uptake of Zn (Rühling and Steinnes 1998). The highest Zn content (46.9 mg kg−1 ) was found in moss samples collected at Kruja region (St. 27) that is probably affected by a strong anthropogenic effect of kiln operations that were using waste materials for heating such as tire wear, unselected urban wastes, woods etc., during the CaO production. The next area with high Zn content in moss samples (19.6 – 37.7 mg kg−1 ) is located in Elbasan (St. 23, 24) and Librazhd (St. 41) region that are strongly affected by iron and steel production plants, and strong geogenic factors, respectively. Relatively high Zn content was also found in Western coastal part of the country (St. 8, 10, 11, 13, 14) that is probably affected by the emission from crude oil and gas industry, and by shipping traffic of the area. The highest Zn content in coastal areas was found in St. 14 (21.0 mg kg−1 ). These findings indicate that the presence of Zn content in moss is probably derived from long-range atmospheric transport and by local inputs of air pollution from industry, mining activity, geogenic factors crude oil industry and shipping activity. Zn showed strong and significant correlation with Cu (r = 0.82, p = 0.000), and moderate and significant correlations with Cd, Pb, Hg and Sb (r = 0.4 − 0.6, p < 0.01) (see the Appendix, Tables A1 and A2). These associations are probably indicating the presence of chalcophile elements in sulfide minerals in the north part of Albania, the effect of the traffic emission and shipping activity in coastal area, the municipal waste burning in open and non-regulated landfills.

4.5 Vanadium, V Vanadium is a crustal element that can be found in the earth’s crust, in rocks, some iron ores, and crude petroleum. The spatial distribution of V (Fig. 4.5) is similar with Al. Vanadium shows a moderate variation (CV% = 46%) and its spatial distribution pattern looks likely reasonably uniforms over the country. The spatial analysis of V (V = 3.841 − 0.005 × n, n represents the number of sampling site) (Fig. 4.5) shows a stable distribution throughout the country (intercept = 3.87 and slope = 0.005). The vanadium anomalies, appeared in different sampling points are probably affected by local factors such as the geochemistry of the local area, crude oil and gas industry, mining activity and iron metallurgy. The highest concentration of V was found in Elbasan area (St. 24) that is probably affected by high temperature iron and steel processing industry and cement production plant. High V contents founded in moss samples of St. 19, 21, 27, 29, 30, 34, 35, 39 and 41 are probably derived by geogenic factors and mining activity, followed by the next high content in coastal

58

P. Lazo et al. Variable Actual Fits

Accuracy Measures MAPE 47.4119 MAD 1.4433 MSD 3.6184

Spatial Analysis Plot for V Linear Trend Model V = 3.841 - 0.005×n

12 10

V

8 6 4 2 0 1

5

10

15

20

25

30

35

40

45

n

Fig. 4.5 Spatial analysis plot of V

areas (St. 5, 6, 11, 13 and 14) that were probably affected by shipping activity and oil industry, as well as in St. 19, that is probably derived by wind blowing soil dust particles from the abandoned coal mine of Memaliaj. The mean concentrations of vanadium in Lines 1, 2 and 3 (3.75, 3.84 and 3.68 mg kg−1 respectively) do not differ from each other by indicating similar trend of distribution in each line. V shows strong and significant correlations (r > 0.7, p < 0.01) with the typical crustal elements such as Li, Al, Fe, Ti, Hf, Ta, W, Sc and the elements of Lanthanides, Co, Cr, Se, Th and U, as well as with Ni, Zr, Sb, Rb (r = 0.5 − 0.7, p < 0.05) that probably represent the soil dust origin of this elements derived from wind blowing fine mineral dust particles (see the Appendix, Tables A1 and A2). The presence of V content in moss was probably derived from long-range atmospheric transport and from local inputs of air pollution that show a higher impact compared to the longrange atmospheric transport, as well as from soil geochemistry. Titano-magnetite deposits in the north of Albania are associated with V2 O5 (Milushi 2015). High median value of V in Albanian moss, higher than the median value of European moss survey (Harmens et al. 2015), supported our findings.

4.6 Arsenic, As Arsenic is a common element of the Earth’s crust. It occurs naturally in the environment, such as soil, water, air and plants and can be released through natural and anthropogenic sources such as volcanic activity, erosion of rocks, forest fires, and different human activities like metal smelters, agriculture and burning of woods treated with arsenic preservatives. Generally, the As content in soil is considered of the geological origin, with higher background concentration in clayey soils (Tóth

4 Chalcophile (As, Cd, Cu, Hg, Ni, Pb, Zn) …

59

et al. 2016) and in sulfide ores and/or metal arsenates which are also present in Albania, and respectively located in the South and in the Northern part of Albania (Lazo et al. 2007). The country differences of As content in moss samples were very high (CV% = 126% that were followed by high values of skeweness and kurtosis) by indicating a high disparity in its distribution pattern throughout the country. It is probably sourced by the local emission points and long-range atmospheric transport. The content of As in moss samples ranged from 0.05 to 2.86 mg kg−1 , with an average concentration of 0.48 mg kg−1 . The spatial analysis plot of As (linear trend model) shows a small decline from the south to the north-east direction (As = 0.595 − 0.00595 × n, n = 47 represent the number of sampling sites) (Fig. 4.6). The local enrichment in different areas, in the South, North, and in the central part of the country are probably linked with the combination of the long-range transport and strong effects of local emission sources such as geogenic factor and windblowing dust from industrial and mining waste deposits in the north, emission from iron and steel metallurgy in the central part, and the use of pesticides in the south particularly in the past decades. The highest As polluted sites were found in the coastal areas in the south (St. 2 and 3) was probably derived by the use of pesticides during agriculture activity in the S-W part of the country, and probably by historical deposition and the accumulation of As in phosphorus minerals of this area (St. 2). The next high contents were found in St. 39 (Pogradeci region) and in St. 24 (Elbasan region) that are probably derived by geogenic factors, mining activity and the wind blowing mineral fine particles from the mineral waste deposits and iron and steel high temperature processing industry (St. 24). High As content in St. 44 (Peshkopi) was probably derived by geogenic factors, mining activity, former non ferrous metal Variable Actual Fits

Accuracy Measures MAPE 192.141 MAD 0.387 MSD 0.348

Spatial Analysis Plot for As Linear Trend Model As = 0.595 - 0.00595×n

3.0 2.5

As

2.0 1.5 1.0 0.5 0.0 1

5

10

15

20

25

n

Fig. 4.6 Spatial analysis plot of As

30

35

40

45

60

P. Lazo et al.

smelting industry, and the wind blowing mineral fine particles from the mineral wastes deposits. As shows strong and significant correlations with lithogenic elements (Al, Li and V), (r > 0.6, p < 0.001), and moderate and significant correlations (r = 0.4 − 0.6, p < 0.01) (see the Appendix, Tables A1 and A2) with the same group of elements (Ti, Zr, Hf, Cs, Sr, Sc, La, Ce, Yb, U, Th and Fe), by indicating strong effects of wind blowing fine soil dust particles of local and/or long-range transport of the pollutants throughout the country. On the other hands, As did not correlate with the elements that are typical of long-range atmospheric transport such as Cd, Hg, Zn, Cu, by indicating a higher local emission sources compared to long-range transport of pollutants.

4.7 Antimony, Sb Sb is naturally occurring in the environment at very low levels. It is emitted to the environment by both, the natural and the man-made processes. The exposure to antimony and its daily intake by humans is usually not a health concern because the concentrations in surrounding air and air particulate matter, drinking water, and food are low or very low (Belzile et al. 2011). Volcanism and weathering are the main natural sources of Sb. However, the metallic-iron ore mining, smelting industry and coal combustion are the main sources of Sb pollution (He et al. 2019). Anthropogenic mobilization of Sb is calculated more or less 1000 times higher than the natural mobilization (UNEP 2013), by indicating high anthropogenic sources of Sb. Antimony shows similar geochemical properties with As that is attributed to their geochemically associated elements. Sb travels through the atmosphere as part of its global biogeochemical cycling process (Belzile et al. 2011). Sb content in moss samples ranged from 0.05 to 0.45 mg kg−1 , DW, with an average content of 0.12 mg kg−1 , lower than the most reported values of the Sb content in plants, 1.0 mg kg−1 , DW and some of them show a linear relationship between the Sb concentration in soils and that in the plant leaves (Belzile et al. 2011) by indicating higher Sb content in soils than in atmospheric deposition in Albania. Sb shows a moderate distribution in moss samples (CV% = 66%), and high values of skeweness (2.7) and kurtosis (8) by indicating the presence of some outliers from the homogenous distribution of Sb throughout the country. The spatial analysis plot of Sb showed a very slight increasing gradient from the south to the north (Sb = 0.118 + 0.00014 × n, n represent the number of sampling sites) (Fig. 4.7). It is probably linked with the combination of the long-range transport and strong effects of local emission sources such as geogenic factor and windblowing dust from Cu industrial and mining waste deposits in the north, emission from iron and steel metallurgy, and waste burning in open urban wastes disposals. The highest Sb contents were found in St. 24 (0.412 mg kg−1 ) and St. 27 (0.452 mg kg−1 ) (respectively Elbasan and Kruja), both positioned in central part of Albania and only 20−30 km far from Tirana. High enrichment in this area is probably derived by historical deposition and the accumulation of Sb from iron and

4 Chalcophile (As, Cd, Cu, Hg, Ni, Pb, Zn) … Variable Actual Fits

61 Accuracy Measures MAPE 46.8228 MAD 0.0530 MSD 0.0064

Spatial Analysis Plot for Sb Linear Trend Model Sb = 0.118 + 0.00014×n

0.5

0.4

Sb

0.3

0.2

0.1

0.0 1

5

10

15

20

25

30

35

40

45

n

Fig. 4.7 Spatial analysis plot of Sb

steel metallurgy (St. 24). The same as Zn, high Sb content (0.452 mg kg−1 ) was found in moss samples collected at Kruja region (St. 27) that is probably linked with a strong anthropogenic effect of kiln operations that were using waste materials such as tires wear, unselected urban wastes, woods etc., as burning materials during the CaO production. The next high Sb contents were found in mosses of St. 4, 13, 14, 22, 25 and 41 that are probably derived by marine environment, geogenic factors, mining activity, former non ferrous metal smelting industry, and the wind blowing mineral fine particles from the mineral waste deposits. Sb showed moderate and significant correlations (r = 0.4 − 0.6, p < 0.01) (see the Appendix, Tables A1 and A2) with Cu, Co, Cr, Ti, Ta, W, Sc, most of the light lanthanides, Th and U that are typical elements of coal burning by indicating strong effect of long-range transport of the pollutants throughout the country.

4.8 Molibdenum, Mo and Selenium, Se Mo and Se are naturally occurring in the environment. Se is an essential metalloid that has beneficial effects to human health and plants. Mo and Se show similar geochemical behaviours and are often appear as associated elements of the metal sulfides (Cu, Ag and sulfide minerals) that are also present in the North and the North-East part of Albania particularly as Cu sulfides (Lazo et al. 2007). Mo can also be found in Cu, U and Zn ore deposits, usually as metal-molybdates. The main anthropogenic sources of Mo and Se are mining, agriculture, soils with high content of Mo and Se, coal and petroleum burning, and forest fires.

62

P. Lazo et al. Variable Fits

Variable

Accuracy Measures

Spatial Analysis Plot for Mo

Actual

Linear Trend Model Mo = 0.2776 - 0.00031×n

MAPE

41.7559

MAD

0.0967

MSD

0.0157

Fits

0.7

Accuracy Measures

Spatial Analysis Plot for Se

Actual

MAPE

Linear Trend Model Se = 0.189 - 0.00149×n

43.1193

MAD

0.0564

MSD

0.0058

0.5

0.6

0.4

0.5

Se

Mo

0.3

0.4

0.2

0.3 0.1

0.2

0.1

0.0

1

a.

5

10

15

20

25

30

35

40

45

n

1

b.

5

10

15

20

25

30

35

40

45

n

Fig. 4.8 Spatial analysis plots of a Mo and b Se

The spatial distribution of Mo was very stable. It showed a moderate coefficient of variation (CV% = 45%) and small values of kurtosis, by indicating the concentration data show a relatively central tendency. Mo concentration data show a lognormal distribution (p = 0.524 > 0.05) that usually indicates a tendency of crustal origin. Se is also showed a moderate coefficient of variation (CV% = 52%), but it is followed by high positive value of kurtosis (aprox. 5.6) by indicating the concentrations data were shifted right with a positive tendency in their distribution. The spatial analysis plot of Mo (linear trend model) (Fig. 4.8a) showed a very small increasing gradient (slope = 0.0003 < intercept = 0.278) from the south to the north (Mo = 0.278 − 0.0003 × n, n represent the number of sampling sites). Similar to Mo, the linear spatial analysis of Se (Fig. 4.8b) showed similar increasing gradient (slope = 0.00168 < intercept = 0.193) from the south to the north (Se = 0.189 − 0.00149 × n, n represent the number of sampling sites). Very high intercept values compared to the slope of the regression lines indicate a relatively stable tendency of the concentrations data that was probably linked with the combination of the long-range transport and small effects of local emission sources. High Mo contents in moss samples were found in coastal areas (St. 11, 13, 14), in Memaliaj area that is mentioned for its abandoned lignite mine (St. 19), Kruja area (St. 27), and Shkodra region (St. 31, 32 and 33), that is probably linked with soil geochemistry. The highest Mo contents were found in St. 24 (0.606 mg kg−1 ) and St. 33 (0.628 mg kg−1 ) (Elbasan and Shkodra), positioned in central part of Albania and only 30 km far from Tirana, and in the North part of the country. High Mo enrichment in this area was probably derived by historical deposition and the accumulation of Mo from iron and steel metallurgy (St. 24) and geogenic factors. The highest Se contents in moss samples were found in St. 13 and 14. The next relatively high Se contents in moss samples were found in St. 2 and 4 positioned in the Western part of the country, that should be affected by oil refine industry and coastal sea salts effects. All these findings indicate that Mo and Se are mostly derived by atmospheric deposition supply and only some selected points are affected by local factors. It can be explained by contributions from long-range transport of pollutants in a similar way as other elements.

4 Chalcophile (As, Cd, Cu, Hg, Ni, Pb, Zn) …

63

The correlation analysis (see the Appendix, Tables A1 and A2) showed moderate and significant correlations (r = 0.4 − 0.6, p < 0.05) of Mo with lithogenic elements such as Zn, V, W, Li, Rb, Cs, Ca, Sr, LREE, Th and Fe by indicating strong effect of long-range transport of the pollutants throughout the country. It shows weak and significant correlation (r = 0.3 − 0.4, p < 0.05) with As, Cu, Pb, Sb, Cl, I and Se that is probably derived by marine environment and geogenic factors of sulfide minerals. Se shows weak and significant correlation (r = 0.3 − 0.4, p < 0.05) with V, Sb, Mo and Sr. Selenium is probably affected by stronger impact of the marine environment compared to the geogenic influences in moss samples.

4.9 Silver, Ag and Gold, Au Ag is a naturally occurring rare element. The Ag content in the earth’s crust is estimated as 0.07 mg/kg and is predominantly concentrated in basalt (0.1 mg/kg) and igneous rocks (0.07 mg/kg) (Howe and Dobson 2002). It is often found in association with other elements (ATSDR 1990) such as Cu, Ni, Pb and Zn ores, and Pt and Au deposits. Silver is released to the environment by natural and anthropogenic sources, long-range transport of fine particles in the atmosphere, wet and dry deposition, and sorption to soils and sediments (ATSDR 1990). It may be emitted to the atmosphere by anthropogenic sources such as coal burning, metal smelting, mining industry, metal wastes disposal and photo-processing industry. Gold (Au) is a noble element that is very scarce in the earth. It occurs in different rocks and geological settings. Au is found as a free metal and almost associated with pyrite and/or some other sulfides, and in the alluvial deposits (UNEP 2013). It is extremely resistant to weathering and its content in the seawater ranged between 0.1 and 2 µg/L (UNEP 2013). The Au contents in moss samples varied from 0.00045 to 0.051 mg kg−1 or 0.45 to 51 µg kg−1 , with an average content of 0.0046 mg kg−1 . The concentration data of Au in moss samples of this study showed a high disparity (CV% = 191%) followed by high positive values of skeweness and kurtosis. Ag content in moss samples is more stable. Its contents varied from 0.036 to 0.451 mg kg−1 , with an average content of 0.094 mg kg−1 . The concentration data of Ag show a moderate variation (CV% = 63%) followed by high positive values of skeweness and kurtosis that indicate the data are skewed right. The spatial analysis of Ag (Fig. 4.9a) and Au (Fig. 4.9b) show different distribution throughout the country. Ag shows an increase from the south to the North (Ag = 0.0753 + 0.00046 × n) with a strong anomaly at St. 30, Puka area, and two weak anomalies at St. 43 and 47. Au shows a decline from the south to the North (Au = 0.00777 − 0.000127 × n), with a strong anomaly at St. 8 and another one in St. 20 both in the vicinity of oil and gas industry areas. All these findings are probably indicating Au and Ag are mostly sourced from longe range transport of trace elements. Local anomalies of Au are mostly originated from marine environment and geogenic factors. The median concentration (0.084 mg kg-1 ) of Ag is similar with Ag content in the earth’s crust

64

P. Lazo et al. Variable Fits

Variable

Accuracy Measures

Spatial Analysis Plot for Ag

Actual

Linear Trend Model Ag= 0.0753 + 0.00046×n

MAPE

22.6073

MAD

0.0183

MSD

0.0007

Accuracy Measures

Spatial Analysis Plot for Au

Actual Fits

MAPE

Linear Trend Model Au = 0.00777 - 0.000127×n

0.200

282.499

MAD

0.005

MSD

0.000

40

45

0.05

0.175 0.04

0.150 0.03

Ag

Au

0.125

0.100

0.02

0.075 0.01

0.050 0.00

1

a.

5

10

15

20

25

30

35

40

45

n

1

b.

5

10

15

20

25

30

35

n

Fig. 4.9 Spatial analysis plot of a Ag and b Au

(about 0.07 mg/kg) by giving an impression that Ag content was mostly affected by fine particles of soil dust atmospheric deposition.

4.10 Mercury, Hg Hg is a naturally occurring metal that is widely used in different processes and manmade products (ATSDR 1999). It is a global pollutant that occurs in the environment in different forms such as metallic, inorganic and organic mercury. Long-range transport has been pointed as an important source of mercury in Europe (Harmens et al. 2015). About 60% of the total Hg emission in the atmosphere in global scale originate from natural sources, while the anthropogenic emission of Cu, Ni and Zn are counted of several orders of magnitude higher than the natural sources (Pirrone et al. 1999). Hg distribution looks likely homogeneous throughout the country. It ranged from 0.04 to 2.230 mg kg−1 with an average content of 0.204 mg kg−1 . The concentration data of Hg show a high disparity (CV% = 158%) followed by high positive values of skeweness and kurtosis. Only two outlier points were observed from the diagram of Spatial analysis, St. 24, in Elbasan region, and St. 29, in Kruja region, both of them affected by anthropogenic emission of Hg from iron and steel metallurgy and waste burning. In different situation, after excluding the concentration data of these outlier points (St. 24 and 27), Hg content in moss samples looks likely more stable. Its contents varied from 0.036 to 0.371 mg kg−1 , with an average content of 0.149 mg kg−1 . In this case, the concentration data of Hg show a moderate variation (CV% = 62%) followed by low values of skeweness and kurtosis that indicate the data are relatively stable and are mostly affected by long-range transport from other part of Europe and Asia. As it is expected, a relatively uniform distribution should be anticipated since the total Hg predominately depend on the relatively stable Global/Hemispheric background concentration and only the influence from major sources may be resulted in higher values (Wangberg et al. 2001). Beside two strong

4 Chalcophile (As, Cd, Cu, Hg, Ni, Pb, Zn) …

65

Hg anomalies of St. 24 and 29 (Fig. 4.10a), moderate Hg anomalies were observed along the western coastal line of Albania, St. 5, 11, 13, 14 (Fig. 4.10b). It is likely derived by shipping emission and coal combustion facilities as the main local source of Hg emissions in this area (EMEP Report 2015). Higher Hg contents were found in the cross border between Albania, Greece and Macedonia (Fig. 4.10a), St. 37, 38 and 39, Kapshtic, Pogradec and Prespa). Similar Hg anomalies were reported by the EMEP maps obtained by the EMEP gridded emission data (EMEP Report 2015). A relatively high impact from Greece in Hg atmospheric deposition in the cross border between Albania, Greece and Macedonia was reported (EMEP Report 2015) that may indicate the possibility of trans-boundary Hg emission. Spatial analysis of Hg concentration data (n = 47 and n = 45; St. 24 and 27 are excluded) shows a slight increase of the Hg content from the south to the North (Hgn = 47 = 0.185 + 0.00086 × n, and Hg’n = 45 = 0.133 + 0.0008 × n) (Fig. 4.10) by indicating higher effects from geogenic and anthropogenic factors compared to the emission from the coastal areas. Thus, the main emission source of Hg atmospheric deposition in Albania can be pointed from the long-range transport of the pollutants. The local anomalies of Hg are mostly originated from anthropogenic factors such as iron and steel metallurgy, geogenic factors, transboundry pollution, crude oil and gas industry, and shipping activity in the coastal areas. The sequence of the distribution of calcophile elements in different axis (Fig. 4.11) resulted as following: 1st Transect: Au(I) > Au(II) > Au(III); As(I) > As(II) > As(III); Se(I) > Se(II) > Se(III); by indicating higher effects of coastal factors than of geogenic and industrial emission of inland area (the 2nd and the 3rd Transects). 2nd Transect: Pb(II) > Pb(III) > Pb(I); Mo(II) > Mo(III) > Mo(I); Hg(II) > Hg(III) > Hg(I) by indicating higher effects of industrial emission positioned mostly at the 2nd Line than of coastal and geogenic factors (the 1st and the 3rd Transects). Variable

Fits

MAPE

Linear Trend Model Hg = 0.1851 + 0.00086×n

0.144

MSD

0.101

Spatial Analysis Plot for Hg

Actual

108.765

MAD

Accuracy Measures

Variable

Accuracy Measures

Spatial Analysis Plot for Hg

Actual

Fits

Linear Trend Model Hg' = 0.1333 + 0.0008×n

2.5

MAPE

70.0001

MAD

0.0724

MSD

0.0080

0.4

2.0

0.3

Hg

Hg

1.5

0.2

1.0

0.1

0.5

0.0

0.0 1

a.

5

10

15

20

25

n

30

35

40

4

45

b.

8

12

16

20

24

28

32

36

40

44

Index

Fig. 4.10 Spatial analysis plot of Hg, a n = 47; and b n = 45 after excluding the anomalies of St. 24 and 27

66

P. Lazo et al. 0.27

Mo Ag Au Sb Se Zn Pb Cu Hg Cd As

0.08 0.01

0.31 0.10

0.003

0.11

0.12

0.19 12.08

0.15

0%

20%

1st Transect 2nd Transect

2.58

5.68

3rd Transect

6.33

0.24

0.20

0.14

0.17 0.37

40%

0.12 16.21

4.07

6.09 0.12 0.58

0.002

0.12

14.50

2.33 0.16

0.24 0.24

60%

0.46

80%

100%

Fig. 4.11 Median concentration data of the elements in different transects

3rd Transect: Zn(III) > Zn(II) > Zn(I); Cu(III) > Cu(I) > Cu(II); Cd(III) > Cd(II) > Cd(I) by indicating higher effects of geogenic factors (the 3rd Line) than of coastal and industrial factors (the 1st and the 2nd Transects). The elements Ag and Sb do not differ among three Transects.

References AEA-Albania (2011) Mineral resources and mining activity in Albania. Albanian Energy Association. pp 19. https://aeaal.org/mineral-resources-and-mining-activity-in-albania/ ATSDR (1990) Toxicological profile for silver. Atlanta, GA, US Department of Health and Human Services, Public Health Service, Agency for Toxic Substancesand Disease Registry (TP-90–24). https://www.atsdr.cdc.gov/ToxProfiles/tp.asp?id=539&tid=97 ATSDR (1999) Public health statement mercury CAS#: 7439-97-6. https://www.atsdr.cdc.gov/tox profiles/tp46.pdf Ávila-Pérez A, Longoria-Gándara LC, García-Rosales G, Zarazua G, López-Reyes C (2018) Monitoring of elements in mosses by instrumental neutron activation analysis and total X-ray fluorescence spectrometry. J Radioanal Nucl Chem 317(1):367–380. https://doi.org/10.1007/s10967018-5896-z Belzile N, Chen YW, Filella M (2011) Human exposure to antimony: I. Sources and intake. Crit Rev Environ Sci Technol 41(14):1309–1373. https://doi.org/10.1080/10643381003608227 Cheng K, Tian HZ, Zhao D, Lu L, Wang Y, Chen J, Liu XG, Jia WX, Huang Z (2014) Atmospheric emission inventory of cadmium from anthropogenic sources. Int J Environ Sci Technol 11:605– 616. https://doi.org/10.1007/s13762-013-0206-3 EMEP (2015) Country-specific report for Albania within the CLRTAP and its related protocols. http://www.msceast.org/index.php/albania Harmens H, Norris D, Mills G and the participants of the moss survey (2013) Heavy metals and nitrogen in mosses: spatial patterns in 2010/2011 and long-term temporal trends in Europe. In: ICP vegetation programme coordination centre, centre for ecology and hydrology, Bangor, UK, p 63. http://icpvegetation.ceh.ac.uk. Accessed 25 July 2013

4 Chalcophile (As, Cd, Cu, Hg, Ni, Pb, Zn) …

67

Harmens H, Norris DA, Sharps K, Mills G, Alber R, Aleksiayenak Y, Blum O, Cucu-Man SM, Dam M, De Temmerman L, Ene A, Fern andez JA, Martinez-Abaigar J, Frontasyeva M, Godzik B, Jeran Z, Lazo P, Leblond S, Liiv S, Magnússon SH, Mankovska B, Pihl Karlsson G, Piispanen J, Poikolainen J, Santamaria JM, Skudnik M, Spiric Z, Stafilov T, Steinnes E, Stihi C, Suchara I, Thoni L, Todoran R, Yurukova L, Zechmeister HG (2015) Heavy metal and nitrogen concentrations in mosses are declining across Europe whilst some “hotspots” remain in 2010. Environ Pollut 200:93–104 He M, Wang N, Long X, Zhang C, Ma C, Zhong Q, Wang A, Wang Y, Pervaiz A, Shan J (2019) Antimony speciation in the environment: recent advances in understanding the biogeochemical processes and ecological effects. J Environ Sci 75:14–39 Howe P, Dobson S (2002) SIlver and silver compounds: environmental aspects. Concise International Chemical Assessment Document 44. Published under the joint sponsorship of the United Nations Environment Programme, the International Labour Organization, and the World Health Organization, and produced within the framework of the Inter-Organization Programme for the Sound Management of Chemicals. https://www.who.int/ipcs/publications/cicad/en/cicad44.pdf Lazo P, Cullaj A, Deda T, Shehu A (2007) Arsenic in soil environment in Albania. In: Battacharia P, Mukherjee AB, Bundschuh J, Zevenhoven JR, Loeppert RH (eds) Arsenic in soils and groundwater environment. Elsevier, Trace metals and other contaminants in the environment, pp 237–256 Lazo P, Steinnes E, Qarri F, Allajbeu S, Kane S, Stafilov S, Frontasyeva M, Harmens H (2018) Origin and spatial distribution of metals in moss samples in Albania: a hotspot of heavy metal contamination in Europe. Chemosphere 190:337–349. https://doi.org/10.1016/j.chemosphere. 2017.09.132 Milushi I (2015) An overview of the Albanian ophiolite and related ore minerals. Acta Geologica Sinica (English Edition) 89(supp. 2):61–64 NAMR (National Agency of Natural Resources) (2010) Mineral Resources in Albania. http://www. akbn.gov.al/images/pdf/publikime/Minierat.pdf Pacyna JM (1988) Atmospheric lead emissions in Europe in 1985. Norwegian Institute for Air Research, NILU OR: 19/88 Pacyna JM, Pacyna EG (2001) An assessment of global and regional emissions of trace metals to the atmosphere from anthropogenic sources worldwide. Environ Rev 9(4):269–298. https://doi. org/10.1139/a01-012 Pirrone N, Costa P, Pacyna JM (1999) Past, current and projected atmospheric emissions of trace elements in the mediterranean region. Wat Sci Tech 39(12):1–7 Rühling Å, Steinnes E (1998) Atmospheric heavy metal deposition in Europe 1995–1996, NORD Environment 1998, 15. Nordic Council of Ministry, Copenhagen, 1007–1017 1016 Denmark Tóth G, Hermann T, Da Silva MR, Montanarella L (2016) Heavy metals in agricultural soils of the European Union with implications for food safety. Env Int 88:299–309. https://doi.org/10.1016/ j.envint.2015.12.017 UNEP (2013) Environmental risks and challenges of anthropogenic metals flows and cycles. In: van der Voet E, Salminen R, Eckelman M, Mudd G, Norgate T, Hischier R (eds) A report of the working group on the global metal flows to the international resource panel. United Nations Environment Programme Wangberg I, Munthe J, Pirrone N, Iverfeldt A, Bahlman E, Costa P, Ebinghaus R, Feng X, Ferrara R, Gardfeldt K, Kock H, Lanzillotta E, Mamane Y, Mas F, Melamed E, Osnat Y, Prestbo E, Sommar J, Schmolke S, Spain G, Sprovieri F, Tuncel G (2001) Atmospheric mercury distribution in Northern Europe and in the Mediterranean region. Atmos Environ 35:3019–3025

Chapter 5

Elements Sensitive to Red/Ox Conditions (Cr, Co, Mo, U, V, Ni and Zn) Pranvera Lazo, Flora Qarri, Shaniko Allajbeu, Sonila Kane, Lirim Bekteshi, Marina Frontasyeva, and Trajce Stafilov

The variation on the content of redox sensitive elements (U, V, Mo, Co, Cr) and the elements sensitive to redox conditions, such as Ni and Zn, in moss samples may reflect the regional redox conditions that may affect to the composition of mineral dust particles (Lazo et al. 2018). Most of these elements are classified also as litophile, calcophile and/or biophile elements (U, V, Mo and Zn) and are discussed before; only three elements (Cr, Ni and Co) will be analyzed here. The geology of Albania has a fundamental two-fold division, with a western domain of monotonous sediments and an eastern domain of basic and acid volcanic rocks and ultramafic massifs (Shallari et al. 1998). The soils derived by weathering of ultramafic rocks are characterized by high contents of geogenic potentially toxic elements, in particular Cr, Ni, and Co which usually exceed their maximum permitted limits of soils, and representing serious environmental risks for ecosystems and human health (Kelepertzis et al. 2013; Marescotti et al. 2019) directly by soil dermal contact or through the inhalation of wind blowing soil dust fine particles that are present in the air. The mechanisms of air pollution induced several health effects through the fine particulate and ultrafine particles (PM ≤ 2.5 µm and PM ≤ 0.1 µm), ozone, nitrogen oxides, and transition metals which are potent oxidants or able to generate reactive oxygen species (ROS) (Lodovici and Bigagli 2008). The generation of hydroxyl radicals (·OH) and other reactive oxygen species (ROS) through transition metal-mediated pathways is one of the main hypotheses for PM toxicity (Vidrio et al. 2008). High content of redox metals in plants could induce both oxidative and genotoxic stress response, leading to cytotoxicity and damage to different cellular components, including proteins, membranes, and nucleic acids, therefore, generating typical abiotic stress response in plants (Panda and Choudhury 2005; Dutta et al. 2018). On the other hand, the excessive Cr content in plants could induce the oxidative stress leading to the disruption of plant cellular functions and structure, and lipid peroxidation that is considered as an indication of oxidative damage by which the integrity and functionality of the membrane is lost (Panda and Choudhury 2005).

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Lazo et al., The Evaluation of Air Quality in Albania by Moss Biomonitoring and Metals Atmospheric Deposition, SpringerBriefs in Environmental Science, https://doi.org/10.1007/978-3-030-62355-5_5

69

70

P. Lazo et al.

5.1 Chromium, Cr Cr is naturally occurring and widely distributed in the earth’s crust. It is a common constituent of many minerals (UNEP 2013). Cr is emitted to the atmosphere via natural sources such as windblown dust, forest fires, and anthropogenic sources such as mining, smelting, refining, fossil fuel combustion, and waste incineration (ATSDR 2005, 2012). Chromium contents in moss samples varied from 1.47 to 262 mg kg−1 (INAA Analysis), with an average concentration of 27 mg kg−1 . It shows a high variation (CV % = 168%) and high positive values of skeweness and kurtosis that indicate high disparity of the concentration data with a tendency of skewed right. High differences were observed among different areas (Fig. 5.1) that indicate the effects of different factors. The spatial analysis plot of Cr (linear model) (Fig. 5.1) shows a high increase from the south to the north direction of the country (Cr = 3.7 + 0.928 × n, n represents the number of sampling site) that is characterized by a high slope in the linear model (b = 0.928, very close to the maximum value of b = 1). This trend with very high slope in regression line, is dominately affected by the significant Cr maximum concentration at the site 42 (Bulqiza area). High Cr concentrations were found in St. 11, 13, 14, 24, 27, 41, 42, 43 and 47 positioned in the south, central part and in the North-East of the country. The highest Cr content was found in St. 42, Bulqiza region that is the area of main Cr deposits of Albania. High Cr contents in moss samples of St. 41, 43 and 47, respectively at Librazhd, Burrel and Bujan, are strongly linked with geogenic factors of these areas that are known for their chromite deposits and Cr mining industry. These findings indicate the important role of soil dust fine mineral particles as the main source of Cr in these areas. Moss samples at the areas with very high of certain trace elements in Variable Actual Fits

Accuracy Measures MAPE 255.09 MAD 23.07 MSD 1609.73

Spatial Analysis Plot for Cr Linear Trend Model Cr = 3.7 + 0.928×n

250 200

Cr

150

100 50

0 1

5

10

15

20

25

n

Fig. 5.1 Spatial analysis plot of Cr

30

35

40

45

5 Elements Sensitive to Red/Ox Conditions …

71

soil are probably affected too, by the interaction between the substrate soils and the terrestrial moss (Schröder et al. 2010). High Cr content was also found in St. 24, in Elbasan region which is affected by anthropogenic pollution from ferro-chromium plant of Elbasan metallurgical combine, and wind blowing fine mineral dust particles emitted from huge amount of mineral wastes deposited in this area for more than 50 years. The next area with high Cr content was found in St. 14 (Miloti area) that is probably derived from wind blowing fine mineral dust particles emitted from Cr mineral deposited in this area. The presence of Cr content in moss is probably derived mostly from local inputs of air pollution from industry, mining activity and geogenic factors, as well as from long-range atmospheric transport. High median value of Cr in moss samples of Albanian, higher than the median value of European moss survey (Harmens et al. 2015), supports our findings. Cr showed high and significant correlation (r > 0.6, p < 0.001) with Ni, Co, Fe, Mg and Ti (the last r = 0.541, p = 0.001) (see the Appendix, Table A.1 and A.2) that is probably derived from local variability of Cr and geochemical association of Cr, Fe, Ni, Co, Ti, and Mg, that tends to be present in mafic minerals (UNEP 2013) which are present in N-E part of Albania (Milushi 2015). Cr showed moderate and significant correlation (r = 0.4 − 0.6, p < 0.005) with typical crustal elements such as Zr, Hf, Ta, W, Sc, Lanthanides, U, and Th that are probably derived from the soil dust fine particles and long-range transport as an additional source of Cr in current moss samples. Soil geochemistry and mining activity, and the emission of ferro-chromium metallurgy may explain high levels of Cr recorded at specific points and high Cr background level in moss samples of Albania.

5.2 Nickel, Ni Ni is a naturally occurring element widely distributed in the environment. It enters in the air mostly as wind blowing fine particles, both from natural and anthropogenic sources. Ni contents in moss samples varied from 1.56 to 131 mg kg−1 , with an average concentration of 13.3 mg kg−1 . Ni shows high variation (CV % = 161%) and high positive values of skeweness and kurtosis that indicate high disparity of the concentration data with a tendency of skewed right. High differences were observed among different areas (Fig. 5.2) that indicate the effects of different factors. The spatial analysis plot of Ni (linear model) shows a high increase from the south to the N-E direction of the country (Ni = −1.61 + 0.618 × n, n represents the number of sampling site) (Fig. 5.2) that is characterized by a high slope in the linear model (b = 0.618). The same with Cr, the distribution trend of Ni dominately affected by the significant maximum concentration of Ni at the site 42, Bulqiza region which is the area of main Cr deposits in Albania. High Ni concentrations were found in the same sampling sites as Cr (St. 22, 24, 37, 39, 40, 41, 42 and 43), positioned in the central (St. 22 and 24) and in the Eastern part of the country. High Ni contents in moss samples of St. 37, 39, 40, 41 and 43 are strongly linked with geogenic factors of these areas that are

72

P. Lazo et al. Variable Actual Fits

Accuracy Measures MAPE 169.208 MAD 10.648 MSD 379.878

Spatial Analysis Plot for Ni Linear Trend Model Ni = -1.61 + 0.618×n

140 120 100

Ni

80 60 40 20 0 1

5

10

15

20

25

30

35

40

45

n

Fig. 5.2 Spatial analysis plot of Ni

known for their chromite, nickel silicate deposits and nickel-ferrous ores (Milushi 2015). These findings indicate the important role of soil dust fine mineral particles as the main source of Ni and Cr in these areas. High Ni concentration was also found in St. 24, in Elbasan region which is affected by anthropogenic pollution from steel and iron metallurgy and ferro-chromium plant of Elbasan metallurgical combine. The wind blowing fine mineral dust particles emitted from huge amount of mineral wastes deposited in this area is an important factor of Ni high concentration in moss samples of these sites. The variation of Ni content in moss is probably derived mostly from local inputs of air pollution from industry, mining activity and geogenic factors and long-range atmospheric transport. High median value of Ni concentration data, higher than the median value of the European moss survey (Harmens et al. 2015) support our findings. Ni showed high and significant correlation (r > 0.6, p < 0.001) with Cr, Co, Fe and Mg (the last r = 0.569, p = 0.001) (see the Appendix, Table A.1 and A.2) that is probably derived from local variability of Ni and geochemical association of Ni, Cr, Fe, Co, and Mg that tends to be present in mafic minerals (UNEP 2013) and ultramafic rocks that contain appreciable content of Ni, Cr, Mg, etc. (SGS Minerals Services 2005) and are present in N-E and the Easter part of Albania (Milushi 2015). Ni showed moderate and significant correlation (r = 0.4 − 0.6, p < 0.001) with typical crustal elements such as Zr, Hf, Ta, W, Sc, Lanthanides, U, and Th that is probably derived from the soil dust fine particles and long-range transport as an additional source of Ni in current moss samples. A weak and significant correlation (r = 0.3 − 0.4, p < 0.005) was found between Ni and the most important group of calcophile elements. Ni could mainly be found as nickel sulfide deposits in association with chalcophile elements such as Pd, Co, Cu, Zn, Cd and Pb. Ni sulfides often contain lower Ni content that weathered relatively slowly compared to the Ni silicate and

5 Elements Sensitive to Red/Ox Conditions …

73

ultramafic rocks, the last contain appreciable Cr, Ni and Mg (SGS Minerals Services 2005). At last, the soil geochemistry, mining activity, and the emission from ferrochromium metallurgy may explain high Ni levels recorded at specific points relatively high Ni background level in moss samples of Albania.

5.3 Cobalt, Co Cobalt is a naturally-occurring trace element widely dispersed in the environment in low concentrations. It may enter the environment from both natural and anthropogenic sources. Co may enter air from windblown dust, seawater spray, volcanic eruptions, forest fires and rock containing cobalt. Soils near to the ore deposits implies a particular rather than a common source, phosphate rocks, or ore smelting facilities, soils contaminated by airport traffic, highway traffic, or other industrial pollution may contain high concentrations of cobalt (ATSDR 2004). Small amounts of cobalt may be released into the atmosphere from coal-fired power plants and incinerators, vehicular exhaust, industrial activities relating to the mining and processing of cobaltcontaining ores (ATSDR 2004). Co contents in moss samples varied from 0.389 to 7.47 mg kg−1 , with an average concentration of 1.844 mg kg−1 . It shows a high variation (CV % = 87%) and positive values of skeweness and kurtosis that indicate a high disparity tendency of the concentration data. The differences observed among different areas (Fig. 5.3) may indicate the effects of different factors. The spatial analysis plot of Co (linear model) (Fig. 5.3) shows an increase from the south to the north direction of the country (Co = 1.149 + 0.029 × n, n represents the number Variable Actual Fits

Accuracy Measures MAPE 89.6143 MAD 1.1033 MSD 2.3581

Spatial Analysis Plot for Co Linear Trend Model Co = 1.149 + 0.0289×n

8 7 6

Co

5 4 3 2 1 0 1

5

10

15

20

25

n

Fig. 5.3 Spatial analysis plot of Co

30

35

40

45

74

P. Lazo et al.

of sampling site) that is characterized by a positive slope in the linear model (b = 0.029, and a = 1.149). High Co concentrations were found in the same sampling sites as Ni and Cr (St. 22, 24, 27, 29, 40, 42, 43 and 47), and in St. 13, 14 and 24. The highest Co content was found in the St. 29, Mirdita region that is the area of chromium and sulfide minerals and Cr ore deposits (Milushi 2015). High Co contents in moss samples of St. 40, 41, 42, 43 and 47, are strongly linked with geogenic factors of these areas that are known for their chromite, nickel silicate deposits and nickel-ferrous ore exploited (Milushi 2015). These findings indicate the important role of soil dust fine mineral particles as the main source of Co in these areas. High Co content was also found in St. 24, in Elbasan region, which is affected by anthropogenic pollution from steel and iron metallurgy and ferro-chromium plant of Elbasan metallurgical combine. The next area with high Co contents in moss samples positioned in the Western part of the country (St. 13 and 14) were probably affected by sea spray emission. The wind blowing fine mineral dust particles emitted from huge amount of mineral wastes deposited in different areas may be an important factor of Co distribution pattern. The variation of Co content in moss was probably derived from local inputs of air pollution from industry, mining activity and geogenic factors and long-range atmospheric transport. Co showed high and significant correlation (r > 0.6, p < 0.001) with Cr, Ni, Fe, Ti, Mg (the last r = 0.569, p = 0.001), Ta, Sc, mostly with light lanthanides, Th and U. The geochemical association of the group of elements (Co, Ni, Cr, Fe, Ti and Mg) that tends to be present in mafic minerals (UNEP 2013) and ultramafic rocks that contain appreciable content of Ni, Cr, Mg, etc. (SGS Minerals Services 2005) may indicate the geogemic and geochemical source of these elements. Co showed moderate and significant correlation (r = 0.4 − 0.6, p < 0.001) with typical crustal elements such as Zr, Hf, W, Li, that is probably derived from the soil dust fine particles and longrange transport as an additional source of Co in the current moss samples. Finally, the long-range transport of the pollutants, soil geochemistry, mining activity, and the anthropogenic emission from the ferro-chromium metallurgy may explain the high Co levels recorded at specific points.

References ATSDR (2004) Toxicological profile for Cobalt. CAS#: 7440-48-4. https://www.atsdr.cdc.gov/Tox Profiles/tp33.pdf ATSDR (2005) Public health statement. Nickel CAS#: 7440-02-0. https://www.atsdr.cdc.gov/Tox Profles/tp15-c1-b.pdf. Accessed 6 Jan 2019 ATSDR (2012) Public health statement. Chromium CAS# 7440-7-3. https://www.atsdr.cdc.gov/ ToxProfiles/tp7-c1-b.pdf. Accessed 6 Jan 2019 Dutta S, Mitra M, Agarwal P, Mahapatra K, De S, Sett U, Roy S (2018) Oxidative and genotoxic damages in plants in response to heavy metal stress and maintenance of genome stability. Plant Signal Behav 13(8): e1460048 (17 pages). https://doi.org/10.1080/15592324.2018.1460048

5 Elements Sensitive to Red/Ox Conditions …

75

Harmens H, Norris DA, Sharps K, Mills G, Alber R, Aleksiayenak Y, Blum O, Cucu-Man SM, Dam M, De Temmerman L, Ene A, Fern andez JA, Martinez-Abaigar J, Frontasyeva M, Godzik B, Jeran Z, Lazo P, Leblond S, Liiv S, Magnússon SH, Mankovska B, Pihl Karlsson G, Piispanen J, Poikolainen J, Santamaria JM, Skudnik M, Spiric Z, Stafilov T, Steinnes E, Stihi C, Suchara I, Thoni L, Todoran R, Yurukova L, Zechmeister HG (2015) Heavy metal and nitrogen concentrations in mosses are declining across Europe whilst some “hotspots” remain in 2010. Environ Pollut 200:93–104 Kelepertzis E, Galanos E, Mitsis I (2013) Origin, mineral speciation and geochemical baseline mapping of Ni and Cr in agricultural topsoils of Thiva Valley (central Greece). J of Geochem Expl 125:56–68. https://doi.org/10.1016/j.gexplo.2012.11.007 Lazo P, Steinnes E, Qarri F, Allajbeu S, Kane S, Stafilov S, Frontasyeva M, Harmens H (2018) Origin and spatial distribution of metals in moss samples in Albania: a hotspot of heavy metal contamination in Europe. Chemosphere 190:337–349. https://doi.org/10.1016/j.chemosphere. 2017.09.132 Lodovici M, Bigagli E (2008) Oxidative stress and air pollution exposure. J Toxicol 2011(487074): 9 pages https://doi.org/10.1155/2011/487074 Marescotti P, Comodi P, Crispini L, Gigli L, Zucchini A, Fornasaro S (2019) Potentially toxic elements in ultramafic soils: a study from metamorphic ophiolites of the Voltri Massif (Western Alps, Italy). Minerals 9: 502. https://doi.org/10.3390/min9080502. https://www.mdpi.com/jou rnal/minerals Milushi I (2015) An overview of the Albanian ophiolite and related ore minerals. Acta Geologica Sinica 89(supp 2):61–64 (English edition) Panda SK, Choudhury S (2005) Chromium stress in plants. Braz J Plant Physiol 17(1):95–102. https://doi.org/10.1590/S1677-04202005000100008 Schröder W, Holy M, Pesch R, Harmens H, Ilyin I, Steinnes E, Alber R, Aleksiayenak Y, Blum O, Coskun M, Dam M, De Temmerman L, Frolova M, Frontasyeva M, Miqueo LG, Grodzi´nska K, Jeran Z, Korzekwa S, Krmar M, Kubin EJ, Kvietkus K, Leblond S, Liiv S, Magnússon S, Maˇnkovská B, Piispanen J, Rühling Å, Santamaria JM, Spiric Z, Suchara I, Thöni L, Zechmeister HG (2010) Are cadmium, lead and mercury concentrations in mosses across Europe primarily determined by atmospheric deposition of these metals? J Soils Sediment 10:1572–1584 SGS Minerals Services (2005) Mmi geochemistry for nickel exploration. Tech Bull Mmi Tb17. https://www.sgs.com/-/media/global/documents/technical-documents/sgs-technicalpapers/sgs-min-mmitb17-mmi-nickel-en-11-10.pdf Shallari S, Schwartz C, Hasko A, Morela JL (1998) Heavy metals in soils and plants of serpentine and industrial sites of Albania. Sci Total Environ 209:133–142 UNEP (2013) Environmental risks and challenges of anthropogenic metals flows and cycles. In: van der Voet E, Salminen R, Eckelman M, Mudd G, Norgate T, Hischier R (eds) A report of the working group on the global metal flows to the international resource panel. United Nations Environment Programme Vidrio E, Jung H, Anastasio C (2008) Generation of hydroxyl radicals from dissolved transition metals in surrogate lung fluid solutions. Atmos Environ 42(18):4369–4379. https://doi.org/10. 1016/j.atmosenv.2008.01.004

Chapter 6

Sea Spray Elements (Na, Cl, Mg, Ca, K, Br, I) Pranvera Lazo, Flora Qarri, Shaniko Allajbeu, Sonila Kane, Lirim Bekteshi, Marina Frontasyeva, and Trajce Stafilov

Sea salt is a major contributor of Na+ , Mg2+ , Ca2+ , K+ , Cl− , SO4 2− in coastal areas (Xiao et al. 2018). Chloride (Cl) and sodium (Na) are generally used as tracers of marine origin (Boatta et al. 2014). Beside the important contributions of sea spray and seawater for Na+ and Cl− in coastal regions, the natural sources of Na+ and Cl− include the atmospheric deposition, interactions between water and soil, rocks, brines and salt deposits (Long et al. 2015). Different concentration ranges of Na and Cl were found in the moss samples collected at three sampling transects. The content of Na in moss samples along three sampling transects ranged from 31 to 338 mg kg−1 (average concentration of 117 mg kg−1 ), 28 to 231 mg kg−1 (average concentration of 95 mg kg−1 ) and 74 to 142 mg kg−1 (average concentration of 88 mg kg−1 ) in the 1st, 2nd and the 3rd transects respectively. The content of Cl in moss samples showed higher variation than Na. Cl contents along three sampling transects ranged from 46 to 1020 mg kg−1 (average concentration of 338 mg kg−1 ), 88 to 606 mg kg−1 (average concentration of 259 mg kg−1 ) and 84 to 388 mg kg−1 (average concentration of 148 mg kg−1 ) in the 1st, 2nd and the 3rd trasect respectively. The spatial analysis of Na and Cl (linear model) showed a decline from the coastal areas (St. 1–14) or from the south to the north direction of the country (Na = 114 − 0.629 × n and Cl = 331 − 3.66 × n, n represents the number of sampling site) (Fig. 6.1) that is characterized by a higher negative slope in the linear trend model of Cl (b = −3.66, and a = 331) compared to Na (b = −0.629, and a = 114). The differentiations in Cl and Na spatial distribution patterns were observed also among different sampling transects positioned at different distances from the coast. It was found that the Cl and Na content in moss samples were decreased as the distances of sampling sites from the coastal areas were increased. High Cl and Na concentrations were found in the coastal areas (1st Transect), followed by the 2nd Transect, and lower content in inland (the 3rd Transect).

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Lazo et al., The Evaluation of Air Quality in Albania by Moss Biomonitoring and Metals Atmospheric Deposition, SpringerBriefs in Environmental Science, https://doi.org/10.1007/978-3-030-62355-5_6

77

78

P. Lazo et al. Variable Actual

Variable Actual

Accuracy Measures MAPE 42.82

Spatial Analysis Plot for Na

Fits

Linear Trend Model Na = 114 - 0.629×n

MAD

33.12

MSD

2671.95

350

Accuracy Measures MAPE 82.4

Trend Analysis Plot for Cl

Fits

Linear Trend Model Cl = 331 - 3.66×n

MAD

141.1

MSD

37334.9

1000

300 800

200

600

Cl

Na

250

150

400

100 200

50

0

0 1

5

10

15

20

25

n

30

35

40

45

1

5

10

15

20

25

30

35

40

45

n

Fig. 6.1 Spatial analysis of Na and Cl. 1–14: the 1st transect; 15–34: the 2nd transect; 35–48: the 3rd transect

The molar Na/Cl ratio in precipitation is a typical sea salt parameter with a value of 0.857 (Neal and Kirchner 2000). This value was proved to be useful also in sites far from the sea (Thimonier et al. 2008). Having in the consideration different possible natural or anthropogenic effects to Na and Cl behaviours in sea spray aerosols and during the transport from the coastal areas to the inland areas, the range of the possible Na/Cl values was defined between 0.5 and 1.5 by ICP Forests Manual (Ulrich et al. 2006; Thimonier et al. 2008). The Na to Cl molar ratio (Na/Cl) and the content of major elements, Na, Mg, Ca, K, and Cl were investigated separately in three different parallel transects positioned in different distances from the coastal areas, i.e. 2–8 km (the 1st Transect), about 50 km (the 2nd Transect), and approximately 120 km, the 3rd Transect. The range of the variation of Na/Cl molar ratio in moss samples was 0.215–4.17, with an average value of 0.967, higher than the theoretical value of 0.857. It shows a wide spread of the values, with several outlier points up and down the range of the acceptable limits (Fig. 6.2). In total, 31 moss samples, or 66% of the Na/Cl molar ratios were within the proposed range of 0.5–1.5 by indicating the natural sea spray as important source of Na and Cl in the moss samples. The outlier sites (16 in total) could probably be affected by different local factors such as anthropogenic sources and geogenic factors that may contribute to the increase of Na content in moss samples by heterogeneous reactions with acidic gaseous and other reactive species that may contribute to the decline of Cl content in the moss samples; and/or geographical positions of sampling sites. It is confirmed by the moderate and significant correlation, r = 0.521, p = 0.000 (Table A1), between Cl and Na. It which is probably indicate a high effect of human activity and/or geogenic dust effect to Na and Cl content. Four moss samples, or 8.3% of the total samples, resulted with Na/Cl molar ratio higher than 1.5 (Fig. 6.3). Different factors affect the Na/Cl molar ratios. The uncertainty in Na and Cl determinations is reported as an important factor that may show a certain affect in Na/Cl values, and in such cases, it was seen appropriate to extend the range of the acceptable values for Na/Cl molar ratio (Thimonier et al. 2008). The determination of Na and Cl of our study was characterized by low uncertainty (RSD

6 Sea Spray Elements (Na, Cl, Mg, Ca, K, Br, I) Variable Actual Fits

79 Accuracy Measures MAPE 90.1580 MAD 0.5889 MSD 0.7641

Spatial Analysis Plot for Na/Cl Linear Trend Model Na/Cl = 0.99 - 0.00012×n

4

Na/Cl

3

2 1 1 0 0 1

10

5

15

20

25

30

35

40

45

n

Fig. 6.2 Spatial analysis plot of Na/Cl molar ratios along different transects: the 1st Transect (St. 1 to 14), the 2nd Transect (St. 15 to 34), and the 3rd Transect (St. 35 to 47) Variable Actual

Accuracy Measures MAPE 84.9884

Spatial Analysis Plot for Na/Cl_2

Fits

Linear Trend Model Na/Cl-2 = 1.015 - 0.0076×n

MAD

0.6187

MSD

0.8324

4

Na/Cl_2

3

2

1

Variable

Accuracy Measures

Spatial Analysis Plot for Na/Cl_1

Actual Fits

MAPE

Linear Trend Model Na/Cl-1 = 1.504 - 0.0679×n

0

136.501

MAD

0.765

MSD

1.168

2

4

6

8

10

12

14

16

18

n

b. Variable Actual Fits

4

Accuracy Measures MAPE 42.3604 MAD 0.3131 MSD 0.1285

Spatial Analysis Plot for Na/Cl_3 Linear Trend Model Na/Cl-3 = 1.254 - 0.0234×n

4

3

2

Na/Cl_3

N a/ C l_1

3

1.

2

1 1

0. 0 1

a.

2

3

4

5

6

7

8

n

9

10

11

12

13

14

0

15

c.

1

2

3

4

5

6

7

n

8

9

10

11

12

13

14

Fig. 6.3 Distribution plot of Na/Cl ratios along three transect. a The 1st transect; b the 2nd transect; c the 3rd transect

80

P. Lazo et al.

% = 3% for Na and 9% for Cl respectively in INAA Analysis), so this factor is considered as negligible. Other factors such as the anthropogenic factors, the altitude of sampling sites, the distance from the coast, seasonal weather variability, nature of precipitation etc., may also affect the Na/Cl molar ratio. In general, the mean Na/Cl ratio in through-fall was reported lower than in bulk precipitation, except the sites with high elevation (Thimonier et al. 2008). On the other hand, the Na/Cl molar ratio in bulk precipitation was substantially higher than the respective ratio in sea-water (Thimonier et al. 2008). The outlier points with Na/Cl molar ratio higher than 1.5 (4 in total, St. 4, 5, 20 and 30) are mostly positioned at the altitude of 800–1200 m and in different distances from the coastal line. Two moss samples with high Na/Cl molar ratios were positioned far from the coastal line (the 2nd and the 3rd transect Lines, St. 20 and 30), by indicating higher effect of bulk deposition than the sea spray origin. Two other moss samples (St. 4 and 5) were positioned in the coastal areas. On the other hand, the anthropogenic factors have a strong effect to the Na/Cl molar ratio (Xiao et al. 2018). Low Na/Cl molar ratios (0.22–0.5), lower than the minimum value of 0.5, were found in eleven moss samples (Fig. 6.3). Station 44 belongs to the 3rd transect (about 120 km far from the coastal line). Other stations were positioned just in coastal line (1st transect, St. 2, 3, 10 and 11) and at the 2nd transect (St. 15, 18, 19), about 50 km far from the coastal line. As the distance from the coastal line is not high, the decline in Na/Cl molar ratios is probably linked with the regional sources of HCl production, such as coal combustion, waste burning or sea-salt dechlorination process during which HCl is produced from the interaction of sea-salt aerosols and atmospheric acid gases, such as H2 SO4 and HNO3 (Hara et al. 2004; Thimonier et al. 2008; Xiao et al. 2018). Theoretically, Na (mol) versus Cl (mol) plot is a straight line with a slope of 0.857, or Cl (mol) versus Na (mol) is a straight line with a slope of 1.17 (Boatta et al. 2014). The slope of Cl versus Na in current moss samples positioned in the coastal line (the 1st Line) is 1.33, higher than 1.17. The highest value of Cl versus Na was found from the regression data of the 1st Line (average value of Cl/Na = 3.22). It was far from the theoretical value of 1.17 that is probably derived from the effects of the additional factors such as human activity, effects from the geogenic factors and/or geographic position of sampling sites. The outlier points (St. 4 and 5, Llogora and Orikum) at the distribution pattern of Cl/Na molar ratios of the data of the 1st Line were characterized by low Cl content. Both sites are positioned at about 1200 m of altitude that could affect to the decline of the Cl content. In contrary, the next outlier point, St. 11 (Golem) was characterized by high Cl content. Station 11 is positioned very close to the coastal line of Durres Bay, the main harbor of Albania, that is affected by high anthropogenic sources of shipping activity, human activity in urban area and heavy traffic emission. The sequences of the average values of Cl/Na molar ratios are Cl/Na1st Transect = 2.96 > Cl/Na2nd Transect = 1.95 > Cl/Na3rd Transect = 1.12. The decrease of the Cl/Na molar ratio as the distance from the coast is increast is derived by additional factors beside the sea spry effect such as human activity, geogenic factor and the geochemistry of soil duast particles, the weather conditions and the geographic properties and the pozitions of sampling sites. It is

6 Sea Spray Elements (Na, Cl, Mg, Ca, K, Br, I)

81

Table 6.1 The regression analysis of Cl (mol) versus Na (mol) The 1st transect

The 2nd transect

The 3rd transect

The regression equation: Cl (mol) = 2.97 + 1.33 Na (mol) S = 7.1, R-sq = 0.32

The regression equation: C l (mol) = 5.136 + 0.499 Na (mol) S = 4.3, R-sq = 0.05

The regression equation: Cl (mol) = 1.14 +0.79 Na (mol) S = 2.6, R-sq = 0.07

Variable Actual Fits

Accuracy Measures MAPE 112.7 MAD 75.6 MSD 10652.2

Spatial Analysis Plot for Cl/Br Linear Trend Model Cl/Br = 134 - 0.58×n

500

400

Cl/Br

300

200

100

0 1

5

10

15

20

25

30

35

40

45

n

Fig. 6.4 Spatial analysis plot of Cl/Br ratios

probably indicate very strong effects of the additional factors such as human activity, geogenic dust or geographic position of sampling sites (Table 6.1). Cl/Br ratio (297) is another important sea spray indicator which can be used as a tracer of sea spray atmosphere only in the low contents of organic matter where they are free from the reaction with the aquifer matrix (Klassen et al. 2014). The Cl/Br ratio in current moss samples (Fig. 6.4) ranged from 13.3 to 509.5. The values lower than 297 may indicate the decreased contribution of sea-salt aerosol as the distance from the coastal line is increased and as an increasing contribution of tropospheric aerosol, and/or the effect of weather condition on wet or dry deposition (Short et al. 2017). The values higher than 297, beside the effect of sea spray aerosol, may also indicate the anthropogenic sources like sewage effluents or the pesticides application in agriculture activity (Alcala and Custodio 2008). The Cl/Br values higher than 297 were found in coastal areas (St. 8, 10 and 11) and in the inland area (St. 23 and 32). Lower Cl/Br values are probably linked with the use of Br-based pesticides, the distance from the coastal areas and the weather conditions relating wet and dry deposition (Alcala and Custodio 2008). The effect of the distance of sampling sites from the coastal areas is clearly showed by spatial analysis of the Cl/Br molar ratio data (Fig. 6.4). The linear trend model

82

P. Lazo et al. Variable Fits

Variable Actual

Accuracy Measures MAPE 33.3049

Spatial Analysis Plot for Br

Actual

Linear Trend Model Br = 6.658 - 0.0699×n

MAD

1.4371

MSD

3.0160

11

8

10

7

9

Linear Trend Model I = 1.569 + 0.0034×n

MAD

0.6909

MSD

1.5603

6

8

5

I

7

Br

Accuracy Measures MAPE 72.5603

Spatial Analysis Plot for I

Fits

4

6

3

5

2

4

1

3 2

0 1

5

10

15

20

25

30

35

40

1

45

n

a.

5

10

15

20

25

30

35

40

45

n

b.

Fig. 6.5 Spatial analysis plots of a Br and b I

Variable Actual Fits

Accuracy Measures MAPE 35 MAD 1211 MSD 2891534

Spatial Analysis Plot for K Linear Trend Model K = 4228 - 27.5×n

10000

8000

K

6000

4000

2000

0

1

5

10

15

20

25

30

35

40

45

n

Fig. 6.6 Spatial analysis plot of K

of Br showed a strong decline of Br from the coastal areas (1st Line, St. 1–14) to the 2nd and the 3rd Lines (Br = 6.658 − 0.0699 × n) (Fig. 6.5a). The similar linear trend model of iodine, (I = 1.569 + 0.0034 × n) (Fig. 6.5b) showed a stable and homogenous distribution of I by indicating this effect is negligible to the iodine distribution pattern. The sources and the reason of two outlier points (St. 12 and 46) with high I contents, are not clear. In order to better understand the sea spray processes, beside Na and Cl, other sea spray elements are also discussed. K+ is a typical ion in the marine atmosphere. It showed a strong correlation with Na+ (r = 0.68, p < 0.01) (see the Appendix, Tables A.1 and A.2) that is probably indicate their similar origin and/or behaviour in the environment. The variation of K among sampling sites is shown in Fig. 6.6. The spatial analysis of K (linear model) showed a high decline from the coastal areas (St. 1–14) or from the south to the north direction of the country (K = 4228 − 27.5 ×

6 Sea Spray Elements (Na, Cl, Mg, Ca, K, Br, I) Variable

Accuracy Measures MAPE 30

Spatial Analysis Plot for Mg

Actual Fits

83

Linear Trend Model Mg = 1978 + 37.22×n

MAD

726

MSD

709308

Variable

Accuracy Measures

Spatial Analysis Plot for Ca

Actual Fits

MAPE

Linear Trend Model Ca = 7120 - 8.9×n

17

MAD

1197

MSD

2589603

13000

5000

12000 11000 10000 9000

Ca

Mg

4000

3000

8000 7000

2000

6000 5000

1000

4000

1

5

10

15

20

25

30

35

40

45

n

1

5

10

15

20

25

30

35

40

45

n

Fig. 6.7 Spatial analysis plots of Mg and Ca

n, n represents the number of sampling site). High K concentrations were found in the coastal areas (St. 11, 13 and 14 in the 1st Line), and in St. 16, 24, 27, 31 and 33 (2nd Line). It is probably affected by the sea spray and wind blowing soil dust fine particles of the crustal origin. Mg2+ and Ca2+ are also considered as typical ions in the marine atmosphere. They are often treated as crustal-derived ions and as tracers of crustal material. On the other hand, calcium is an essential plant nutrient and the most abundant cation in plants (Burger and Lichtscheidl 2019). No relationship was found between Ca2+ , Mg2+ and other typical sea spray ions, such as Na, Cl, Br, I and K in present moss samples by indicating their strong crustal origin and the effects of other additional factors. The average molar ratio of Mg/Ca (0.35) in current moss samples was lower than the average ratio of seawater (5.2) and close to the average ratio for river water (0.42), indicating that Ca is more stable compared to Mg in the marine atmosphere of coastal areas (1st Transect). It is proved by spatial analysis of both elements where Mg shows a high increment from the 1st Transect (St. 1–14) to the 2nd and the 3rd Transect (St. 15–47) (Mg = 1978 + 37 × n and Ca = 7120 − 8.9 × n) (Fig. 6.7a, b respectively). It indicates enrichment of Mg in inland areas derived mostly from different geogenic factors of the areas and less from the sea spray sources.

References Alcala FJ, Custodio E (2008) Using the Cl/Br ratio as a tracer to identify the origin of salinity in aquifers in Spain and Portugal. J Hydrol 359:189–207. https://doi.org/10.1016/j.jhydrol.2008. 06.028 Boatta F, Calabrese S, D’alessandro W, Parello F (2014) Chemical composition of atmospheric bulk deposition at the industrial area of Gela (Sicily, Italy). In: 10th international hydrogeological congress of Greece/Thessaloniki. https://pdfs.semanticscholar.org/dbbc/5d95ee57665d3ce 43cb451cef56dcca6016f.pdf

84

P. Lazo et al.

Burger A, Lichtscheidl I (2019) Strontium in the environment: review about reactions of plants towards stable and radioactive strontium isotopes. Sci Total Environ 653:1458–1512. https://doi. org/10.1016/j.scitotenv.2018.10.312 Hara K, Osada K, Kido M, Hayashi M, Matsunaga K, Iwasaka Y, Yamanouchi T, Hashida G, Fukatsu T (2004) Chemistry of sea-salt particles and inorganic halogen species in Antarctic regions: compositional differences between coastal and inland stations. J Geophys Res 109:D20208. https://doi.org/10.1029/2004JD004713.2004 Klassen J, Allen DM, Kirste D (2014) Chemical indicators of saltwater intrusion for the Gulf Islands, British Columbia. Final report submitted to: BC Ministry of Forests, Lands and Natural Resource Operations and BC Ministry of Environment. https://www.sfu.ca/personal/dallen/Chemical%20I ndicators%20of%20SWI_Final.pdf Long TD, Voice CT, Chen A, Xing F, Li SG (2015) Temporal and spatial patterns of Cl− and Na+ concentrations and Cl/Na ratios in salted urban watersheds. Elem Sci Anthr 3:000049. https:// doi.org/10.12952/journal.elementa.000049.elementascience.org Neal C, Kirchner JW (2000) Sodium and chloride levels in rainfall, mist, stream water and groundwater at the Plynlimon catchments, mid-Wales; inferences on hydrological and chemical controls. Hydrol Earth Syst Sci 4(2):295–310 Short MA, de Caritat P, McPhail DC (2017) Continental-scale variation in chloride/bromide ratios of wet deposition. Sci Total Environ 1(574):1533–1543. https://doi.org/10.1016/j.scitotenv.2016. 08.161 Thimonier A, Schmitt M, Waldner M, Schleppi P (2008) Seasonality of the Na/Cl ratio in precipitation and implications of canopy leaching in validating chemical analysis of throughfall samples. Atmos Environ 42:9106–9117 Ulrich E, Mosello R, Derome J, Derome K, Clarke N, Konig N, Lovblad G, Draaijers GPJ (2006) Part VI. Sampling and analysis of deposition. In: Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. UN-ECE, Convention on Long-Range Transboundary Air Pollution (LRTAP). International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP-Forests), Hamburg. Available from www.icp-forests.org, 74 pp Xiao HW, Xiao HY, Shen CY, Zhang ZY, Long AM (2018) Chemical composition and sources of marine aerosol over the Western North Pacific Ocean in winter. Atmosphere 9(298):13. https:// doi.org/10.3390/atmos9080298

Chapter 7

The Siliceous, Si and Phosphorus, P Pranvera Lazo, Flora Qarri, Shaniko Allajbeu, Sonila Kane, Lirim Bekteshi, Marina Frontasyeva, and Trajce Stafilov

Si is a major element of earth’s crust and a main soils’ consistuent. Si content in soil varies from 1 to 45%, but it is non-soluble and is not bio-available for plants. It is found as silicate minerals, alumino-silicates and silicon dioxide. Beside it, Si is also sourced from silicon-rich materials of industrial wastes and plant biomass. Environmental emissions of silica can arise from natural and anthropogenic sources (Richards et al. 2009) particularly from industrial and farming activities (EPA/600/R95/115 1996). Si should be uptake by plants only in the form of silicic acid [Si(OH)4 ] or as the ionized form, Si(OH)3 O2− (Currie and Perry 2007; Tubaña and Heckman 2015). Silicon is also used as nutrient for fertilization in improving overall crop productivity and health, but the amount of literature that documents the benefits of silicon on the growth of a wide variety of agronomic and horticultural crops is vast and continues to increase (Tubaña and Heckman 2015), and however, the process that plants are able to transport silicon and to control its polymerization are not fully understood (Currie and Perry 2007). Si contents in moss samples varied from 1.32 to 6.08% (INAA Analysis), with an average concentration of 3.23%. It shows a moderate variation (CV % = 35%), low positive values of skeweness and negative value of kurtosis that indicate small disparity of the concentration data with a centered tendency. Low differences were observed among different areas (Fig. 7.1) and the distribution over the country looks likely homogenous. The spatial anlysis of Si (linear model) shows a small decline from the south to the north direction of the country (Si = 34659 − 130 × n, n represents the number of sampling site) that is characterized by a small slope in the linear model (a = 130  b = 34659 and n = 47) (Fig. 7.1). Higher concentrations of Si compared to the other sampling sites were found in the sampling sites (St. 2, 8, 16, 28, 45) positioned in the south, central part of the country and in the North-East. The highest Si content was found in the moss sample of St. 45, positioned in the North. Si did not show any correlation with other elements in moss samples by indicating different behaviour from those, and or high uncertainty of Si determination by ENAA analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Lazo et al., The Evaluation of Air Quality in Albania by Moss Biomonitoring and Metals Atmospheric Deposition, SpringerBriefs in Environmental Science, https://doi.org/10.1007/978-3-030-62355-5_7

85

86

P. Lazo et al. Variable Actual

Accuracy Measures MAPE 36

Spatial Analysis Plot for Si

Fits

Linear Trend Model Si = 34659 - 130×n

MAD

9350

MSD

126915263

60000

50000

Si

40000

30000

20000

10000 1

5

10

15

20

25

30

35

40

45

n

Fig. 7.1 Spatial analysis plot of Si

Phosphorus is a major element in soil, organic matter, and in natural terrestrial ecosystems (Lajtha et al. 1999). It is derived from the weathering of minerals in parent rock material and could be emitted to the environment from different sources such as phosphorus industry, phosphate rock mining, phosphoric acid, the use of phosphate fertilizers, cement production, iron and steel industry, wind-blown soil dust, refuse incineration, fuel oil and coal combustion, agricultural and forest burning etc. (Lajtha et al. 1999). The increased soil erosion and runoff from fields, recycling of crop residues and manures, discharges of urban and industrial wastes, and the applications of inorganic fertilizers are the major sources of the P (Smil 2000). Phosphorus is redistributed among terrestrial, freshwater and marine ecosystems by atmospheric emission, transport, and deposition, and at large scales, the dust from deserts and soils contain P that can move over long distances by providing a significant nutrient source to the oceans, tropical forests and remote peat lands (Tipping et al. 2014). Natural mobilization of the phosphorus element, a part of the grand geotectonic denudation-uplift cycle, is slow, and low solubility of phosphates and their rapid transformation to insoluble forms make the element commonly the growth-limiting nutrient (Smil 2000). Phosphorus to the atmosphere could also sourced by organisms and plant material, and spray from bodies of water. Plants are mostly assimilating inorganic P (Shen et al. 2011). P contents in moss samples varied from 407 to 1839 mg kg−1 (INAA Analysis), with an average concentration of 798 mg kg−1 . It shows a moderate variation (CV % = 39%), and positive values of skeweness and kurtosis that indicate relatively high disparity of the concentration data with a tendency of skewed right. The distribution over the country looks likely homogenous except some local differences observed among different areas (Fig. 7.2) which should be affected by different local

7 The Siliceous, Si and Phosphorus, P Variable Actual Fits

87 Accuracy Measures MAPE 28.5 MAD 213.5 MSD 89734.8

Spatial Analysis Plot for P Linear Trend Model P = 789.6 - 0.24×n

2000 1750 1500

P

1250 1000 750 500 1

5

10

15

20

25

30

35

40

45

n

Fig. 7.2 Spatial analysis plot of P

factors. The spatial analysis plot of P (linear model) shows a stable distribution throughout the country (P = 790 + 0.24 × n, n represents the number of sampling site) (Fig. 7.2) that is characterized by a small slope in the linear model (b = 0.24 ≪ a = 790, n = 47). Our findings are similar with the data of the study reported by Tipping et al. (2014) that revealed no systematic spatial variation and significant temporal variations in P deposition, over periods of up to 19 years. Higher concentrations of P compared to the other sampling sites were found in St. 11, 12, 28, 32 and 33 positioned in the south, central part of the country and in the North-East. The highest P contents were found in St. 11 and 12, positioned in Gjorm-Selenic and Memaliaj that are under the effects of bituminous production and the abandoned lignite coal mine. The next high P contents in moss were founded in St. 28 (Librazhd) and St. 32, 33. It was probably linked with mining industry and agriculture emissions from livestock farming. P and K are essential elements of plants and probably it may explain the high and significant correlation (r2 = 0.724, p = 0.000) founded between them. This finding probably indicates their similar origin from plants, forest burning and other similar factors. On the other hand, phosphorus did not show any correlation with other elements, particularly with the elements linked with traffic emission by indicating no phosphorus contribution from traffic emission due to the phosphorus contained additives added to the gasoline.

88

P. Lazo et al.

References Currie HA, Perry CC (2007) Silica in plants: biological, biochemical and chemical studies. Botanical briefing. Ann Bot 100:1383–1389. https://doi.org/10.1093/aob/mcm247. Available online at www.aob.oxfordjournals.org EPA/600/R-95/115 (1996) Ambient levels and non-cancer health effects of inhaled crystalline and amorphous silica: health issue assessment. https://nepis.epa.gov/Exe/ZyPDF.cgi/P100CTTF. PDF?Dockey=P100CTTF.PDF Lajtha K, Drscoll TC, Jarell MW, Elliot TE (1999) Soil phosphorus characterization and total element analysis; soil chemical properties. In: Robertson GP, Coleman DC, Bledsoe CS, Sollins P (eds) Standard methods for long-tem ecological research. Oxford University Press, Oxford, UK, pp 115–142. https://andrewsforest.oregonstate.edu/sites/default/files/lter/pubs/pdf/pub2710.pdf Richards JR, Brozell TT, Rea C, Boraston G, Hayden J (2009) PM4 crystalline silica emission factors and ambient concentrations at aggregate-producing sources in California. J Air Waste Manag Assoc 59(11):1287–1295. https://doi.org/10.3155/1047-3289.59.11.1287 Shen J, Yuan L, Zhang J, Li H, Bai Z, Chen X, Zhang W, Zhang F (2011) Phosphorus dynamics: from soil to plant. Plant Physiol 156:997–1005 www.plantphysiol.org Smil V (2000) Phosphorus in the environment: natural flows and human interferences. Annu Rev Energy Environ 25:53–88 Tipping E, Benham E, Boyle JF, Crow P, Davies J, Fischer U, Guyatt H, Helliwell R, JacksonBlake L, Lawlor AJ, Monteith DT, Roweg EC, Toberman H (2014) Atmospheric deposition of phosphorus to land and freshwater. Environ Sci Process Impacts, p 11. https://doi.org/10.1039/ c3em00641g Tubaña BS, Heckman JR (2015) Silicon in soils and plants. In: Rodrigues FA, Datnoff LE (eds) Silicon and plant diseases. Springer International Publishing, Switzerland. https://doi.org/10. 1007/978-3-319-22930-0_2

Chapter 8

Multivariate Analysis Pranvera Lazo, Flora Qarri, Shaniko Allajbeu, Sonila Kane, Lirim Bekteshi, Marina Frontasyeva, and Trajce Stafilov

The environment is a multidimensional system characterized by multiple chemical, physical and biological parameters which display very complex relationships between them. A complex study in environmental researches that produce big data matrixes with a large number of sampling sites and several investigated parameters is not easy to give a solution to the raised environmental hypothesis, to discover the structure and the properties of the data and to explain the factors affecting to the structure of these data. Multivariate analysis is a strong tool widely used in environmental research for the exploration of the multidimensional data. The main goal of multivariate analysis is determine the most important factors that affect to the state of natural ecosystems. Correlation analysis of parametric (Pearson linear correlation) or non-parametric (Spearman rank correlation) datasets are useful statistical tools to identify the relationship between pollutants, that leads to the identification of the factors affecting the association of the chemical parameters and to understand the most probable phenomena and the sources of these parameters. In addition, factor analysis (FA) is used to explore the hidden multivariate structures of the data (Reimann et al. 2002; Astel et al. 2008) and to clarify the link between the elements with similar origins or similar associations on the data matrix. Each factor was explained on the basis of the associations of the elements extracted from the correlation matrix. The correlation analysis (Spearman Rho and Pearson correlation, shown in the Appendix, Tables A.1 and A.2) carried out to the concentration data of the elements, could give an insight on the strength of the association between the variables (elements). Similar values of Spearman Rho and Pearson correlations were found to the pairs of elements which showed strong and significant Pearson correlation coefficients (r2 > 0.4, P < 0.001). The differences founded to the weak correlation coefficients did not affect the factor Analysis (FA) that was carried out to the original concentration data since such weak correlations are neglected on FA process. The

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Lazo et al., The Evaluation of Air Quality in Albania by Moss Biomonitoring and Metals Atmospheric Deposition, SpringerBriefs in Environmental Science, https://doi.org/10.1007/978-3-030-62355-5_8

89

90

P. Lazo et al.

main criteria in selecting the number of optimal factors are Kaiser dropping factor and that of Eigenvalues equal and/or larger than 1. The correct choice of the meaningful number of factors is based on the choice of the model that may describe the population in factors structure after varimax rotation, such as factor loadings and elements correlations. It may help to identify the sources of the elements in moss samples and in the study area. After all, the associations of the elements with high loadings in the same factor (Reimann et al. 2002) are assumed to be affected by different characteristics, such as chemical properties of the elements, the geochemical associations of elements in soil and dust, local and long-range transport of the elements, the inventory of the local emission sources of the elements in the study area and the previous knowledge of the atmospheric concentrations (Miranda et al. 2015). The emission inventory of air pollutants could supply the essential information to understand regional and local emission sources of the pollutants (Qiu et al. 2014). These are important reasons for linking the metals with their sources of origins or factors affecting their presence in the study area. Factor loadings (FL) larger than 0.4 (Lazo et al. 2019) are recommended for interpreting each factor. As the variances of the elements differ significantly, from low variation to very high variation, and the number of elements under investigation is high by producing a big data of correlation matrices, the factor loadings (FL) larger than 0.5 were selected for interpreting each factor (Table 8.1, Figs. 8.1, 8.2, 8.3, 8.4). The model that we applied consists in two steps. 1st: Factor analysis was applied to the standardized concentration data obtained as the ratio of the concentration of the element against its respective median concentration; 2nd: Aiming to avoid the high variability of the concentration data the outlier sites obtained from the score plot diagram of the original concentration data (Factor analysis), were excluded. Six main factors that represent 76% of the total variance were identified from the standartized data after exluding the outlier points. The associations of metals within the same factor are as follows: Factor 1 (F1) and Factor 2 (F2) are the strongest factors representing 41% of the total variance, 24 and 17% respectively. F1 and F2 are characterized by high loadings of lithogenic and crustal elements such as Yb, Sc, Ta, Ce, La, Th, Nd, Hf, U, Sm, Zr, Mn, W, Co and Ti (F1, FL > 0.59), and Al, Li, Sr, V, Fe, Ba and As (F2, FL > 0.61). These elements are naturally distributed as typical soil elements (Rudnick and Gao 2003) and crustal materials that may indicate the soil dust as their origin. The presence of Al in this factor confirms this assumption, since Al compounds are insoluble and most of the Al found in biological systems comes from soil and dust contamination (Qarri et al. 2013). Similar associations of the elements were reported to the 2010 AMS and for other regions of Europe (Harmens et al. 2015) and Balkan countries (Špiri´c et al. 2013; Barandovski et al. 2015; Stafilov et al. 2018). It indicates the origin of the elements from local emission and/or long-range transport of the pollutants. Spatial analysis plots and GIS maps of FL1 and FL2 data are shown in Fig. 8.1.

8 Multivariate Analysis

91

Table 8.1 Factor analysis of the correlation matrix of the standardized concentration data; rotated factor loadings and communalities; varimax rotation. Sorted rotated factor loadings and communalities Var

F1

F2

F3

F5

F6

Yb

0.875

0.000

0.000

F4 0.000

0.000

0.000

Comm 0.833

Sc

0.871

0.000

0.000

0.000

0.000

0.000

0.920

Ta

0.777

0.000

0.000

0.000

0.000

0.000

0.934

Ce

0.769

0.000

0.000

0.000

0.000

0.000

0.945

Mn

0.767

0.000

0.000

0.000

0.000

0.000

0.678

La

0.749

0.000

0.000

0.000

0.000

0.000

0.942

Th

0.745

0.521

0.000

0.000

0.000

0.000

0.938

Nd

0.719

0.000

0.000

0.000

0.000

0.000

0.748

Hf

0.697

0.595

0.000

0.000

0.000

0.000

0.886

U

0.694

0.000

0.000

0.000

0.000

0.000

0.820

Sm

0.671

0.000

0.000

0.000

0.000

0.000

0.831

Zr

0.648

0.582

0.000

0.000

0.000

0.000

0.817

W

0.636

0.000

0.000

0.000

0.000

0.000

0.771

Co

0.632

0.000

0.000

−0.599

0.000

0.000

0.837

Ti

0.593

0.000

0.000

0.000

0.000

0.000

0.674

Al

0.000

0.868

0.000

0.000

0.000

0.000

0.914

Li

0.000

0.811

0.000

0.000

0.000

0.000

0.912

Sr

0.000

0.759

0.000

0.000

0.000

0.000

0.701

V

0.000

0.754

0.000

0.000

0.000

0.000

0.887

Fe

0.000

0.675

0.000

0.000

0.000

0.000

0.905

Ba

0.000

0.653

0.000

0.000

0.000

0.000

0.630

As

0.000

0.612

0.000

0.000

0.000

0.000

0.499

Sb

0.000

0.000

0.736

0.000

0.000

0.000

0.758

Zn

0.000

0.000

0.704

0.000

0.000

0.000

0.767

Hg

0.000

0.000

0.672

0.000

0.000

0.000

0.681

Pb

0.000

0.000

0.631

0.000

0.000

0.000

0.414

Cd

0.000

0.000

0.602

0.000

0.000

0.000

0.486

Cu

0.000

0.000

0.594

0.000

0.000

0.000

0.672

Ca

0.000

0.000

0.507

0.000

0.000

0.000

0.461

Ni

0.000

0.000

0.000

0.881

0.000

0.000

0.861

Cr

0.000

0.000

0.000

0.853

0.000

0.000

0.805

Mg

0.000

0.000

0.000

0.741

0.000

0.000

0.815

K

0.000

0.000

0.000

0.000

0.882

0.000

0.848

Cl

0.000

0.000

0.000

0.000

0.819

0.000

0.713

P

0.000

0.000

0.000

0.000

0.769

0.000

0.711 (continued)

92

P. Lazo et al.

Table 8.1 (continued) Var

F1

F2

F3

F5

F6

Na

0.000

0.000

0.000

F4 0.000

0.675

0.000

Comm 0.536

Se

0.000

0.000

0.000

0.000

0.000

0.849

0.778

I

0.000

0.000

0.000

0.000

0.000

0.730

0.664

Br

0.000

0.000

0.000

0.000

0.000

0.726

0.634

Var

9.403

6.613

4.296

3.461

3.168

2.688

29.628

% Var*

0.241

0.170

0.110

0.089

0.081

0.069

0.760

Var Variable; Var* Variance; Cumm Cummulative

Factor 3 (F3) is the next factor, explaining 11% of the total variance. It is characterised by high loadings of Sb, Zn, Hg, Pb, Cd, Cu and Ca (FL > 0.51). Sb, Zn, Hg, Pb, Cd, Cu are typical elements of traffic emission, oil and gas industry, shipping activity in the coastal areas, and the geogenic factors. These elements are also distinguished as typical elements derived from anthropogenic sources that are mainly linked with traffic emissions, geogenic origin, and wind-blown mineral dust particles of sulfide mineral mine wastes (Lazo et al. 2018). The presence of Pb in this factor is probably derived from long-range transport (Harmens et al. 2015), traffic emission and the emission from metallurgy, as another Pb potential origin (Harmens et al. 2010, 2015). Spatial analysis plot and GIS map of FL3 data is shown in Fig. 8.2. Factor 4 (F4) represents 8.9% of the total variance. It is characterized by high loading of Ni, Cr, Mg and Co (FL > 0.60). All these elements are typical elements present in chromites and nickel ores, and are probably derived from mining industry, ferro-chromium and ferro-nickel metallurgy of Elbasan (Lazo et al. 2013), from geogenic origin and wind-blown mineral dust particles of mine wastes (Lazo et al. 2018). Spatial analysis plot and GIS map of FL4 data is shown in Fig. 8.3. Factor 5 (F5) and Factor (F6) are the weakest factors that represents only 15% of the total variance, 8.1 and 6.9% respectively. F5 and F6 are characterized by high loadings of sea spray and anthropogenic elements such as K, Cl, P and Na (F5, FL > 0.68), and Se, I and Br (F6, (FL > 0.73). These elements are typical sea salt elements that probably originate from the Adriatic and Ionian coastal areas in the western part of Albania. On the other hand, a wide potassium anomaly (St. 27, 28, 32 and 33, Fig. 8.4) was present in the 2nd transect, in N-W and central part of Albania. High K content of this area is probably linked with geochemical settings, mostly from serpentine soils in the East, where soils were derived from K feldspars, gabbros and ultrabasic rocks; the latter is also rich in K (Shallari et al. 1998). Spatial analysis plots and GIS maps of FL5 and FL6 data are shown in Fig. 8.4.

8 Multivariate Analysis

Variable Actual Fits

93

Accuracy Measures MAPE 42.7162 MAD 0.2987 MSD 0.1759

Spatial Analysis Plot for F1 Linear Trend Model F1 = 0.819 + 0.0013×n

2.0

F1

1.5

1.0

0.5

0.0 1

5

10

15

20

25

30

35

40

45

n

a.

Variable Actual Fits

Accuracy Measures MAPE 46.3395 MAD 0.3422 MSD 0.2220

Spatial Analysis Plot for F2 Linear Trend Model F2 = 0.977 - 0.00289×n

2.5

2.0

F2

1.5

1.0

0.5

0.0 1

b.

5

10

15

20

25

30

35

40

45

n

Fig. 8.1 Spatial analysis plots and GIS maps of a FL1 data (Yb, Sc, Ta, Ce, La, Th, Nd, Hf, U, Sm, Zr, Mn, W, Co). Outlier sites: St. 13, 14, 24, 30, 35; b FL2 data (Al, Li, Sr, V, Fe, Ba and As). Outlier sites: St. 2, 3, 24, 38

94

P. Lazo et al.

Variable Actual

Accuracy Measures MAPE 35.9485

Spatial Analysis Plot for F3

Fits

Linear Trend Model F3 = 0.658 + 0.00398×n

MAD

0.2684

MSD

0.1948

3.0

2.5

F3

2.0

1.5

1.0

0.5

0.0 1

5

10

15

20

25

30

35

40

45

n

Fig. 8.2 Spatial analysis plot and GIS map of FL3 data (Sb, Zn, Hg, Pb, Cd, Cu and Ca). Outlier sites: St. 14, 24, 29, 31, 41

Variable Actual Fits

Accuracy Measures MAPE 98.4033 MAD 0.9637 MSD 2.8108

Spatial Analysis Plot for F4 Linear Trend Model F4= 0.294 + 0.0472×n

12 10

F4

8 6 4 2 0 1

5

10

15

20

25

30

35

40

45

n

Fig. 8.3 Spatial analysis plot and GIS map of FL4 data (Ni, Cr, Mg and Co). Outlier sites: St. 14, 24, 41, 42, 47

8 Multivariate Analysis

Variable Actual

95

Accuracy Measures MAPE 37.0564

Spatial Analysis Plot for F5

Fits

Linear Trend Model F = 1.082 - 0.00668×n

MAD

0.3365

MSD

0.1831

2.5

F5

2.0

1.5

1.0

0.5 1

5

10

15

20

25

30

35

40

45

n

a.

Variable Actual

Accuracy Measures MAPE 28.8460 MAD 0.3044

Spatial Analysis Plot for F6

Fits

Linear Trend Model F6 = 1.384 - 0.01128×n

MSD

0.2237

4.0 3.5 3.0

F6

2.5 2.0 1.5 1.0 0.5 1

5

b.

10

15

20

25

30

35

40

45

n

Fig. 8.4 Spatial analysis plot and GIS maps of a FL5 data (K, Cl, P and Na). Outlier sites: St. 7, 10, 11, 12, 27, 28, 32, 33; b FL6 data (Se, I and Br). Outlier sites: St. 2, 13, 14

References Astel A, Astel K, Biziuk A (2008) PCA and multidimensional visualization techniques united to aid in the bioindication of elements from transplanted Sphagnum palustre moss exposed in Gdansk city area. Environ Sci Pollut Res 15(1):41–50 Barandovski L, Frontasyeva VM, Stafilov T, Šajn R, Ostrovnaya MT (2015) Multielement atmospheric deposition in Macedonia studied by the moss biomonitoring technique. Environ Sci Pollut Res 22:16077–16097. https://doi.org/10.1007/s11356-015-4787-x

96

P. Lazo et al.

Harmens H, Norris DA, Steinnes E, Kubin E, Piispanen J, Alber R, Aleksiayenak Y, Blum O, Co¸skun M, Dam M, De Temmerman L, Fernández JA, Frolova M, Frontasyeva M, González-Miqueo L, Grodzi´nska K, Jeran Z, Korzekwa S, Krmar M, Kvietkus K, Leblond S, Liiv S, Magnússon SH, Maˇnkovská B, Pesch R, Rühling Å, Santamaria JM, Schröder W, Spiric Z, Suchara I, Thöni L, Urumov V, Yurukova L, Zechmeister HG (2010) Mosses as biomonitors of atmospheric heavy metal deposition: spatial and temporal trends in Europe. Environ Pollut 158:3144–3156 Harmens H, Norris DA, Sharps K, Mills G, Alber R, Aleksiayenak Y, Blum O, Cucu-Man SM, Dam M, De Temmerman L, Ene A, Fern andez JA, Martinez-Abaigar J, Frontasyeva M, Godzik B, Jeran Z, Lazo P, Leblond S, Liiv S, Magnússon SH, Mankovska B, Pihl Karlsson G, Piispanen J, Poikolainen J, Santamaria JM, Skudnik M, Spiric Z, Stafilov T, Steinnes E, Stihi C, Suchara I, Thoni L, Todoran R, Yurukova L, Zechmeister HG (2015) Heavy metal and nitrogen concentrations in mosses are declining across Europe whilst some “hotspots” remain in 2010. Environ Pollut 200:93–104 Lazo P, Bekteshi L, Shehu A (2013) Active moss biomonitoring technique for atmospheric deposition of heavy metals study in Elbasan city. Fresenius Environ Bull 22(1a):213–219 Lazo P, Steinnes E, Qarri F, Allajbeu S, Kane S, Stafilov S, Frontasyeva M, Harmens H (2018) Origin and spatial distribution of metals in moss samples in Albania: a hotspot of heavy metal contamination in Europe. Chemosphere 190:337–349. https://doi.org/10.1016/j.chemosphere. 2017.09.132 Lazo P, Stafilov T, Qarri F, Allajbeu Sh, Bekteshi L, Fronasyeva M, Harmens H (2019) Spatial and temporal trend of airborne metal deposition in Albania studied by moss biomonitoring. Ecol Ind 101:1007–1017. https://doi.org/10.1016/j.ecolind.2018.11.053 Miranda A, Silveira C, Ferreira J, Monteiro A, Lopes D, Relvas H, Borrego C, Roebeling P (2015) Current air quality plans in Europe designed to support air quality management policies. Atmos Pollut Res 6:434–443. https://doi.org/10.5094/APR.2015.048 Qarri F, Lazo P, Stafilov T, Frontasyeva M, Harmens H, Bekteshi L, Baceva K, Goryainova Z (2013) Multi-elements atmospheric deposition study in Albania. Environ Sci Pollut Res 21:2506–2518. https://doi.org/10.1007/s11356-013-2091-1 Qiu P, Tian H, Zhu C, Liu K, Gao J, Zhou J (2014) An elaborate high resolution emission inventory of primary air pollutants for the central plain urban agglomeration of China. Atmos Environ 86:9–101. https://doi.org/10.1016/j.atmosenv.2013.11.062 Reimann C, Filzmoser P, Garrett R (2002) Factor Analysis applied to regional geochemical data: problems and possibilities. Appl Geochem 17(3):185–206. https://doi.org/10.1016/s0883-292 7(01)00066-X Rudnick RL, Gao S (2003) The composition of the continental crust. In: Holland HD, Turekian KK (eds) Treatise on geochemistry, vol 3. The Crust, Elsevier-Pergamon, Oxford, pp 1–64. http://dx. doi.org/10.1016/b0-08-043751-6/03016-4 Shallari S, Schwartz C, Hasko A, Morel JL (1998) Heavy metals in soils and plants of serpentine and industrial sites of Albania. Sci Total Environ 209:133–142 Špiri´c Z, Vuˇckovi´c I, Stafilov T, Kušan V, Frontasyeva VM (2013) Air pollution study in Croatia using moss biomonitoring and ICP-AES and AAS analytical techniques. Arch Environ Contam Toxicol 65:33–46. https://doi.org/10.1007/s00244-013-9884-6 Stafilov T, Šajn R, Barandovski L, Baˇceva AK, Malinovska S (2018) Moss biomonitoring of atmospheric deposition study of minor and trace elements in Macedonia. Air Qual Atmos Health 11(2):137–152. https://doi.org/10.1007/s11869-017-0529-1

Chapter 9

Conclusions Pranvera Lazo, Flora Qarri, Shaniko Allajbeu, Sonila Kane, Lirim Bekteshi, Marina Frontasyeva, and Trajce Stafilov

This study established the first moss biomonitoring of trace metals atmospheric deposition in Albania. It showed the TM contents in moss samples were affected by long-range transport of the pollutant and by the local emission sources derived by local natural and anthropogenic factors. It provided important information data of the baseline level of the elements. The concentration level of trace metal atmospheric deposition in Albania was compared with the neighboring countries and in a wider scale with the European countries. These data could be utilized for future biomonitoring research in atmospheric deposition of metals in Albania. The moss survey data and the applied statistical analysis in combination with GIS technique produced a detailed and up-to-date coverage of trace metals in moss samples that directly indicate the metal atmospheric deposition of Albania. Through the maps of metal concentration data, it was possible to predict the spatial extinction of the areas with high metal concentrations where the local factors were suggested to be monitored and to control the potential threats from metal depositions. Based on the maps of metal concentration on moss samples it was also possible to investigate the differentiation between the backgrounds and the anthropogenic pollution of the study areas. The local emission sources and long-range atmospheric transport show a significant contribution to atmospheric deposition of metals in Albania. Soil dust fine particles were pointed as the main source of most trace metals in moss samples. The presence of lithogenic and crustal elements (Yb, Sc, Ta, Ce, La, Th, Nd, Hf, U, Sm, Zr, Mn, W, Co, Ti, Al, Li, Sr, V, Fe, Ba and As) in moss samples indicate the effect of long-range transport of the pollutants combined with local geochemical factors of wind blowing soil dust fine particles that produce some local differentiation of these elements along the country. Based on the moss elements concentration and the spatial

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Lazo et al., The Evaluation of Air Quality in Albania by Moss Biomonitoring and Metals Atmospheric Deposition, SpringerBriefs in Environmental Science, https://doi.org/10.1007/978-3-030-62355-5_9

97

98

P. Lazo et al.

distributions maps produced for the whole territory of Albania, it was found that the moss metal distribution of Albania shows diverse patterns for different elements by presenting diverse geographical variability in moss metal concentrations. The content of the elements Al, Fe, Cr, Ni, and V were higher, and the concentrations of Zn, Cd and Pb were lower in moss samples of Albania compared with those of the most European countries. The eastern part of Albania could be classified as a highly polluted area from anthropogenic elements derived mostly by geological and anthropogenic emission sources that may affect the crop cultivation for human or animal consumption that may accumulate trace metals and thus may pose a risk to humans and animals health. It was found that the western agricultural land area could be considered as a safer area for food production. The anthropogenic sources of the elements are likely higher than their natural background. Significant geographical differences were found in the concentrations of As, Cr, Cu, Hg, Ni, Pb, and Zn in moss samples. The examination of the distribution profile and the pollution levels of the potential anthropogenic elements that pose a high risk to the human health (Sb, Zn, Hg, Pb, Cd, Cu) pointed out the traffic emission, oil and gas industry, shipping activity in the coastal areas, geochemical and geological factors, long-range transport and the emission from metallurgy as the most probable sources of these elements in moss samples and in atmospheric deposition over the territory of Albania. Very high contents of Ni and Cr in moss samples were mostly derived from local factors and lower from the long-range atmospheric transport of these elements. High correlations of these elements with Mg and Co indicate their similar behavior and similar associations between Ni, Cr, Co and Mg in moss and in the atmospheric deposition of Albania. All these elements are typical elements present in chromites and nickel ores and are probably derived from mining industry, ferro-chromium and ferro-nickel metallurgy, from geogenic origin and wind-blown mineral dust particles of mine wastes. The presence, the behavior and the relations between the most important marine tracer elements, Cl and Na, in moss samples may indicate the effects of the sea spray and seawater of Adriatic and Ionian coastal areas positioned in the western part of Albania. Beside the important contributions of sea sprays and seawater for Na+ and Cl− in coastal regions, the natural sources of Na+ and Cl− that include the atmospheric deposition, interactions between water and soil, rocks, brines and salt deposits showed a strong affect derived by different local factors such as anthropogenic sources and geogenic factors which could contribute to the increase of Na and Cl contents in moss samples. On the other hand, the heterogeneous reactions with acidic gaseous and other reactive species in the marine environment, as well as the geographical positions of sampling sites may contribute to the decline of Cl content in moss samples. The differences founded at the spatial distributions of the sea spray elements (Na, Cl, K, Br, I, Mg, and P) in moss samples indicate the effects of geographical properties of the moss sampling sites, and the local natural and anthropogenic contributions of these elements. On the other hand, the decreased contribution of sea-salt aerosols as the distance from the coastal line increased is probably linked with the increasing

9 Conclusions

99

contribution of tropospheric aerosol, and/or with the effect of the weather conditions on wet or dry deposition. It was reflected on the distributions patterns of the seaspry elements in moss samples. Due to the high moss metal content in particular in the eastern regions of Albania, where some pollution tendencies and “hot spots” were identified, a continuous and detailed monitoring and assessment are suggested. Moss biomonitoring survey provides a unique opportunity for the assessment of metal contamination in atmospheric deposition in local and continental range.

Appendix

See Figs. A.1 and A.2.

Fig. A.1 The histograms of data distribution of the selected elements and the predicted normal distribution curves © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Lazo et al., The Evaluation of Air Quality in Albania by Moss Biomonitoring and Metals Atmospheric Deposition, SpringerBriefs in Environmental Science, https://doi.org/10.1007/978-3-030-62355-5

101

102

Fig. A.1 (continued)

Appendix

Appendix

103

Fig. A.2 EWMA charts of concentration data after excluding the outlier sites (when CV% > 50%, indexed as Me_1 in the graph, N = 47—the number of outlier sites) and of original data (CV% < 50%, indexed as Me in the graph, N = 47)

104

Fig. A.2 (continued)

Appendix

Appendix

105

Correlation Analysis Note 1. Factor analysis (FA) was applied to the standardized concentration data obtained as the ratio of the concentration of the element against its respective median Ci . concentration, Cst = Cmedian 2. The number of the meaningful factors is based on the choice of the model that may describe the population of the factors structure after varimax rotation, such as factor loadings and elements correlation matrix. 3. Aiming to avoid high variability of the concentration data the outlier sites obtained from score plot diagram of the original concentration data (Factor analysis), were excluded (Tables A.1 and A.2).

0.107

−0.243

−0.021

−0.090

−0.111

−0.142

0.120

0.124

0.139

0.188

0.043

0.374d −0.173

V

Ti

Zr

Hf

Ta

W

Li

0.250

0.212

−0.173

0.256

Cr

−0.124

0.146

0.058

−0.176

Co

Rb

0.113

−0.105

−0.138

Ni

0.150

0.103

−0.048

−0.055

Mo

−0.261

0.195

−0.034

0.040

Sb

K

0.138

−0.010

−0.100

Se

Na

0.259

0.015

0.220

Zn

0.563a

0.083

0.433b

−0.37d

−0.40c

0.252

0.310d

−0.150

Pb

0.024

0.037

0.050

0.254

0.320d

−0.309d

−0.112

0.287

0.145

0.345

−0.030

0.119

0.124

0.010

0.109

0.059

−0.053

0.174

0.140

0.253

0.272

0.329d

0.779a

0.582a

Cu

0.344d

0.122

0.344d

−0.36d

0.331d

0.129

−0.200

Cu

Hg

Hg

Cd

−0.282

Cd

As

0.299d

−0.030

−0.013

−0.029

0.037

0.067

0.000

−0.131

−0.005

−0.012

−0.060

−0.071

−0.099

0.275

0.227

0.009

0.239

Pb

0.153

0.269

0.453b

−0.043

0.049

−0.056

−0.099

0.138

0.009

−0.115

0.217

0.080

0.224

0.300d

0.138

−0.294d

Zn

0.033

−0.070

−0.152

0.018

−0.159

−0.119

−0.053

−0.215

−0.019

0.013

−0.139

−0.231

−0.397c

0.133

0.262

Se

Table A.1 Pearson correlation results between elements in moss samples

0.162

−0.087 0.143

−0.196

−0.224

−0.142

0.150

0.254

0.401c

0.382c −0.066

0.242

−0.064

0.358d

0.214

0.372d 0.010

0.209

0.314d

0.229

0.339d

0.323d

0.201

0.435b

0.308d

0.072

−0.268

−0.211

0.144

0.496a

0.534a

0.348d

0.158

0.128

0.433b

0.140

0.067

0.248

0.050

0.757a

0.773a

Co

0.362d

0.815a

Ni

−0.085

0.079

0.266

−0.081

0.415b

Mo

0.216

0.144

Sb

0.010

−0.111

−0.061

−0.064

0.246

0.303d

0.225

0.258

0.376c

−0.083

Cr

0.957a

0.550a

0.708a

0.757a

0.634a

0.598a

0.254

−0.097

−0.253

V

(continued)

0.281

0.073

−0.229

0.562a

0.570a

0.685a

0.677a

0.597a

Ti

106 Appendix

−0.175

0.601a

−0.169

−0.010

0.390c

0.206

−0.041

−0.067

−0.141

0.200

−0.169

−0.197

0.300d

0.220

I

Ba

Mg −0.335

0.139

0.113

0.188

−0.023

0.284

0.016

−0.242

−0.053

−0.136

−0.261

0.057

−0.058

0.308d −0.174

−0.137

0.196

0.231

0.445b

−0.213

Ce

Yb

Th

U

Mn −0.021

0.222

La

Fe

Al

P

0.853a

0.685a

0.778a

0.854a

0.748a

0.653a

0.685a

Hf

Ta

W

Li

Hf

0.102

0.367d −0.145

−0.049

0.087

Sc

Zr

0.037

0.249

Sr

0.720a

0.778a

Ta

0.035

0.197

−0.094

0.190

Ca

0.090

0.561a

W

0.256

−0.244

0.185

0.133

0.061

−0.021

−0.047

0.022

−0.073

Li

0.131

−0.109

−0.027

−0.152

0.122

0.017

0.064

0.121

0.054

0.022

−0.138

0.075

0.183

−0.316d

0.096

−0.100

0.046

0.020

0.083 −0.021

−0.198

0.048

0.030

0.034

0.285

0.331d

0.255

0.065

Pb

0.058

−0.121

Cu

Br

0.079

Hg

Cl

Cd

−0.055

0.248

Cs

As

Table A.1 (continued) Zn

Na

K

−0.077

0.059

0.200

−0.171

−0.317d

−0.202

−0.140

−0.048

−0.204

−0.074

−0.053

−0.106

0.397c

0.161

Rb

−0.238

0.018

0.129

−0.159

0.270

0.228

−0.031

0.191

0.236

0.119 Cs

Cl

0.308d −0.146

0.208

0.458b

Br

−0.199

0.109

0.361d

0.028

0.523a 0.083

0.428b

0.255

0.394c

0.375c

0.746a

0.028

0.335d

0.337d

0.595a

0.193

0.071

−0.176

−0.042

0.165

Cr

I

−0.051

−0.095

0.157

−0.122

0.369d

0.151

0.125

0.259

0.205

0.507a

0.029

0.288d −0.006

0.547a

0.114

−0.129

0.258

0.205

0.188

0.326d

0.217

0.219

0.484b

−0.109

0.035

0.593a

0.138

−0.123

0.365d 0.145

−0.212

0.445b

Co

−0.378c −0.204

−0.222

0.201

Ni

0.268

0.355d 0.178

0.239

0.084

0.303d

0.064

0.311d

0.334d

−0.012

0.378c

0.266

Mo

0.317d

0.434b

0.248

−0.306d

−0.202 −0.009

0.257

0.077

0.221

−0.223

0.538a

−0.177

Sb 0.255

Se −0.003

0.179

0.146

−0.051

−0.126

−0.044

−0.054

−0.174

−0.051

−0.168

−0.217

0.658a

−0.097

0.143

−0.294d

0.509a

−0.096 0.712a

Ba

−0.142

0.922a

0.876a

0.510a

0.587a

(continued)

Mg

0.055

0.564a

0.589a

0.348d

0.649a

0.741a

0.486b 0.783a

0.759a 0.586a

0.710a

0.739a

0.416b

0.160

0.402c

0.487b

0.137

0.060

0.066

0.606a

Ti

0.742a

0.706a

0.617a

0.565a

0.285

−0.180

0.618a

−0.155

0.067

−0.207

V

Appendix 107

0.866a

−0.167

0.768a

−0.107

0.084

0.654a

0.087

−0.106

Cs

Cl

Br

I

0.148

−0.001

0.163

0.435b

0.761a

0.804a

0.854a

0.583a

0.857a

0.761a

0.473b

0.693a

0.757a

−0.038

0.170

0.000

0.296d

0.665a

0.683a

0.760a

0.532a

0.769a

0.678a

0.483b

0.683a

0.615a

−0.021

Ba

Mg

Ca

Sr

Sc

La

Ce

Yb

Th

U

Mn

Fe

Al

P

−0.167

0.669a

0.696a

0.335d

0.859a

0.916a

0.490a

0.885a

0.886a

0.881a

0.356d

0.284

0.488b

0.553a

−0.024

−0.153

0.474b

0.149

−0.061

Rb

0.364d

0.374d

0.396c

−0.199

−0.011

0.083

K

Ta

−0.194

Hf

−0.087

0.116

Na

Zr

Table A.1 (continued)

W

−0.201

0.477b

0.567a

0.294d

0.800a

0.779a

0.600a

0.716a

0.739a

0.781a

0.291d

0.252

0.195

0.248

−0.011

0.106

−0.028

0.721a

0.282

−0.230

−0.196

Li

−0.142

0.920a

0.902a

0.530a

0.632a

0.802a

0.555a

0.746a

0.728a

0.596a

0.572a

0.224

−0.144

0.654a

−0.078

0.041

−0.157

0.723a

0.259

−0.125

−0.287

0.679a

0.521a

0.764a

−0.050

0.306d

−0.153

−0.326d

−0.171

−0.339d −0.245

−0.059

−0.250 0.259

−0.016

−0.237

0.055

−0.131

−0.152

−0.230 −0.034

−0.106

−0.276

−0.189

−0.087

−0.247

−0.305d

−0.161

0.190

−0.227 0.266

−0.043

−0.213

−0.103

−0.055

−0.169 0.122

0.463b

K

0.248

0.507a

Na

0.428b

0.204

0.151

0.313d

0.333d

0.431b

0.040

0.448b

0.352d

0.391c

0.038

0.237

−0.032

0.295d

0.015

0.187

0.313d

0.557a

Rb

−0.083

0.682a

0.609a

0.346d

0.835a

0.899a

0.407b

0.833a

0.824a

0.869a

0.389c

0.397c

−0.058

0.489a

−0.086

0.270

−0.056

Cs

0.542a

−0.237

−0.155

0.024

−0.111

−0.056

−0.098

−0.023

−0.086

−0.125

−0.181

−0.105

0.104

−0.083

0.381c

−0.013

Cl

I

0.033

0.056

0.058

0.105

−0.085

0.115

0.082 −0.189

−0.183 −0.111

0.337d

−0.241 −0.098

0.112

0.652a

0.527a

0.487b

0.535a

0.382c

0.553a

0.509a

0.444b

0.743a

0.079

0.088

0.024

0.138 −0.015

−0.191 −0.106

0.117

0.177

0.082 −0.073

0.229

Ba

0.063 −0.212 0.260 −0.094

−0.259

0.011

−0.003

Br

(continued)

−0.031

−0.39c

0.169

0.068

0.072

−0.073

0.099

0.014

−0.070

0.199

−0.230

−0.161

Mg

108 Appendix

0.867a

0.852a

0.285

0.622a

0.560a

−0.135

0.435b

0.364d

0.084

0.396b

0.529a

−0.305

−0.026

0.301d

0.240

−0.035

0.191

0.302d

0.047

Yb

Th

U

Mn

Fe

Al

P

Cell contents pearson correlation; P-value:

0.498a

0.295d

−0.050

0.723a −0.033

0.713a

0.684a

0.381c

0.647a

0.876a

0.382c

0.920a

0.631a

Ce

0.886a

0.931a

0.610a

0.946a

La

a