ji xiaowen ecological risk of heavy metals in permafrost-affected … · 2018. 7. 29. · table 1...
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Saint Petersburg State University
Ji Xiaowen
Graduate Qualification Work
Ecological risk of heavy metals in permafrost-affected soils and lichens, Yamal
Peninsula, Russia Arctic
Master program BM.5710.
“Ecology and Environmental Managements”
“Cold Region Environmental Landscapes Integrated Sciences (CORELIS)”
Scientific supervisor:
Prof. Evgeny Abakumov,
Professor, Department of Applied Ecology, Faculty of Biology,
St. Petersburg State University, St. Petersburg, Russia
Scientific consultant:
Prof. Dr. Eva-Maria Pfeiffer,
Professor, Institute of Soil Science,
Hamburg University, Hamburg, Germany
Reviewer:
Dr. Alexey Lupachev,
Institute of Physico-Chemical and Biological Problems of Soil Science,
Pushino, Moscow Oblast, Russia
St. Petersburg
2018
Contents
Contents
Abstract ................................................................................................................. 1
Резюме .................................................................................................................. 2
Acknowledgements ............................................................................................... 3
Благодарности ..................................................................................................... 4
1. Introduction ....................................................................................................... 6
2. State of the art ................................................................................................... 9
3. Investigation area and sampling ...................................................................... 10
3.1. Study sites of Yamal-Nenets autonomous region ...................................................................... 10
3.2. Sampling sites ............................................................................................................................ 11
4. Materials and methods .................................................................................... 13
4.1. Sample treatment ....................................................................................................................... 13
4.2. Heavy metals analysis ................................................................................................................ 13
4.3. Basal respiration (BR) ................................................................................................................ 13
4.4. Soil pH ....................................................................................................................................... 13
4.5. Soil particle distribution ............................................................................................................. 14
4.6. Total organic carbon (TOC) and total nitrogen (TN) ................................................................. 14
4.7. Quality assurance and quality control (QA/QC) ........................................................................ 14
4.8. Data analysis .............................................................................................................................. 14
4.8.1. Geoaccumulation index and pollution load index ...................................................................... 14
4.8.2. Baseline values .......................................................................................................................... 15
4.8.3. Transfer factor (TF) ................................................................................................................... 15
4.8.4. Risk of individual heavy metal .................................................................................................. 16
4.8.5. Accumulative risk for multiple heavy metals ............................................................................ 17
4.8.6. Positive matrix factorization (PMF) .......................................................................................... 17
4.8.7. Statistical analysis ...................................................................................................................... 18
5. Results and discussions ................................................................................... 18
5.1. Soil characterization ................................................................................................................... 18
5.2. Contamination of heavy metals in soils ..................................................................................... 20
5.3. TFs of heavy metals in lichens ................................................................................................... 27
5.4. Baseline values ........................................................................................................................... 28
5.5. Health risk of heavy metals in locals ......................................................................................... 29
5.6. Permafrost as a semi-permeable barrier ..................................................................................... 30
6. Conclusion and outlook .................................................................................. 31
Contents
References ........................................................................................................... 33
Figure list
Figure 1. location of sampling sites and study area............................................................................ 11
Figure 2. The profiles of each different soil group sampled in Yamal Peninsula. ............................. 11
Figure 3. Soil particle size distribution (%) from soil samples collected from Yamal Peninsula. ..... 20
Figure 4. Contribution (%) of individual metal (without Mn) to total concentrations of 7
metals in different sites. ...................................................................................................................... 21
Figure 5. Comparison between the average concentration of heavy metals in Yamal Peninsula
and threshold limit values (TLV) of Siberia Russia. ........................................................................... 22
Figure 6. Boxplot comparison of log-transformed concentrations (mg kg-1) for 7 heavy metals
in soils from different sampling areas in the Yamal Peninsula. ........................................................... 24
Figure 7. Geochemical baseline values for 8 heavy metals from soils from Yamal Peninsula,
calculated by relative cumulative frequency. ...................................................................................... 29
Figure 8. the vertical distribution of logarithmic transformation of 6 heavy metals in
permafrost profile (Mn and Cr are not shown due to not obvious changes for Mn and lower
than detection limit for Cr). ................................................................................................................. 31
Table list
Table 1 General sampling information of soil collected in Yamal Peninsula. ................................... 12
Table 2 Seven grades of geoaccumulation index (Igeo) for soil pollution levels. ................................ 15
Table 3 Health risk grade of HQ. ....................................................................................................... 16
Table 4 Characterizations of soils collected from Yamal Peninsula. ................................................. 19
Table 5 Levels of heavy metals (mg kg-1) ± standard deviation in different depth of soils
collected in Yamal Peninsula. ............................................................................................................. 22
Table 6 Geoaccumulation index (Igeo) and pollution load index (PLI) of heavy metals in soil
samples collected from 30 sites in Yamal Peninsula. ......................................................................... 26
Table 7 PMF matrix for soil samples in Yamal Peninsula. ................................................................ 27
Table 8 The concentrations (mg kg-1) ± standard deviation and TFs of heavy metals in lichens. ...... 28
Table 9 Average valued of HQ and AR for heavy metals in different populations. .......................... 30
Abstract
1
Abstract
A combined method was utilized to determine the pollution level and human health
risk potential of heavy metals in soils and lichens from the Arctic region of the Yamal
Peninsula, Russia. 39 soil samples and 5 lichen samples were analyzed for 8 heavy
metals (Cu, Pb, Cd, Zn, Ni, Mn, Cr, and Hg) during September of 2017. The highest
levels of heavy metals were found in mining area with a decreasing level in reference
sites. Ni and Mn were dominant metals in all samples. Regulatory threshold limit values
(TLV) for Siberian Russia were used to determine soil heavy metal pollution for
individual metal. Cu, Pb, and Mn were all lower than TLV in all areas. Yet Cd, Ni, Hg
were beyond TLV except one site for Ni. The baseline values for geology background
of 8 metals (mg kg-1) measured in Yamal Peninsula were estimated to be 3.5 for Cu, 3.8
for Pb, 0.16 for Cd, 16 for Ni, 12 for Hg, 15 for Zn, 4.67 for Cr and 1400 for Mn. Two
different indices, the Geoaccumulation Index (Igeo) and the Pollution Load Index (PLI)
were determined to assess soil sample regarding to heavy metals pollution. The results
showed that Hg is highly to extremely polluted in all sites, Cd and Ni are only
moderately polluted in mining areas. Ni and Mn were detected more in deeper
minerogenic soil horizons compared to top soil. Permafrost table acts as a geochemical
barrier, retarded migration of metals into deeper barrier. Transfer factors (TFs) of
lichens showed Cr, Hg, Cu, and Ni were accumulated in lichens more. Hazard quotient
(HQ) shows no health risk for adults for heavy metals, while Ni, Mg, Hg may cause
potential health risk for local Nets children via soil ingestion in Yamal Peninsula.
Благодарность
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Резюме
Применен комплексный подход для определения степени загрязнения
тяжелыми металлами почв и лишайников, а также вероятность риска для здоровья
человека в арктическом регионе полуострова Ямал, Россия. ( На основе
комбинированного метода были определены степень загрязнения тяжелыми
металлами почв и лишайников, а также вероятность риска для здоровья человека
в арктическом регионе полуострова Ямал, Россия.) Проанализированы 39
образцов почв и 5 образцов лишайников на наличие 8 тяжелых металлов (Cu, Pb,
Cd, Zn, Ni, Mn, Cr, и Hg) в сентябре 2017 г. Самый высокий уровень содержания
тяжелых металлов был зафиксирован (обнаружен) в районе производства горных
работ с уменьшением уровня на фоновых территориях. Во всех образцах
преобладающими металлами были Ni и Mn. Для определения уровня загрязнения
почвы отдельными видами тяжелых металлов были использованы нормативные
значения предельно допустимых концентраций (ПДК) для Сибирского региона
России. Зафиксировано содержание Cu, Pb, и Mn ниже ПДК во всех областях.
При этом содержание Cd, Ni, Hg было выше ПДК за исключением одной области
для Ni. Исходные значения фоновыхконцентраций количеств8 металлов (мг кг-1),
полученные на полуострове Ямал, были установлены на уровне 3.5 для Cu, 3.8
для Pb, 0.16 для Cd, 16 для Ni, 12 для Hg, 15 для Zn, 4.67 для Cr и 1400 для Mn.
Были определены два критерия (показателя), индекс
геоаккумуляциигеоаккумуляционный индекс (Igeo) и индекс суммарного
загрязнения (ИСЗ), для оценки уровня загрязнения образца почвы тяжелыми
металлами. Результаты показали крайне высокое содержание ртути во всех
областях, умеренное содержание Cd и Ni в районах производства горных работ.
Ni и Mn были обнаружены в основном в минеральных minerogenic почвенных
горизонтах по сравнению с почвой верхнего горизонта. Верхний горизонт
вечномерзлых грунтов выступает в качестве геохимического барьера,
задерживающим миграцию металлов в более глубокий барьер. Transfer factors
(TFs) Показатели транслокации для лишайников показали, что Cr, Hg, Cu, и Ni
накапливались в лишайниках в большей степени. Согласно индексу опасности
(ИО), риск для здоровья взрослого человека со стороны тяжелых металлов
Благодарность
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отсутствует, в то время как Ni, Mg, Hg могут причинить вред здоровью детей
местного населения путем заглатывания почвы на полуострове Ямал.
Acknowledgements
It would not be likely to write this master thesis lacking assistance and help of many
people who have been willing to support me.
I would like to thank my Russian supervisor Prof. Evgeny Vasilyevich Abakumov. I
am grateful that he gave me the topic of my master thesis and arranged the expedition to
the Yamal-Nets autonomous region for sampling soil and lichens. This was my first
time to see permafrost with my own eyes in my life. He assisted me to take samples and
gave an explanation of each soil profile. Besides, he took care of the whole expedition. I
learned a lot from this trip. I also would like to thank Prof. Marina Nadporozhskaya for
exhaustive answers to my questions reagrding soil science. I am also grateful to Prof. Dr.
Eva-Maria Pfeiffer for the suggestion about writing my thesis and for a golden
opportunity to study Hamburg she provided for the CORELIS students. I thank Ivan
Alexeev for accompanying me in Yamal and helping with sample taking. I also
appreciate that Slava Polyakov explained measurements of basic parameters of soils and
measurements of basal respiration.
Mostly I thank our coordinator, Victoria Margieva, who helped me plan a study
timetable as well as deal with delicater administrative formalities. I also thank my dear
colleagues from CORELIS: Anastasia Mischenko, Anna Cherezova, Alexandra Kiskina,
Alexandra Shadrina, Daniil Gornov, Elizaveta Yavid, Grisha Kobzar, Olga Konkova,
Veronika Kondakova, and Viktoria Pastoukhova, for staying all studying period and
helps from life. There is a big thank to Dr. Tim Eckhardt for countless assistances in
studying and life in German semester. They helped me overcome the difficulties, which
are hard to manage for a person, who cannot speak Russian.
I would like to thank my family, my parents Mrs. Shunyin Cao and Mr. Jiangang Ji
for love and supporting me spiritually and financially throughout all my master study. I
express my gratitude to my roommate, Nattanop Palahan, who always offers me help in
daily life. I am also grateful to my girlfriend Ekaterina Kuznetsova for always staying
Благодарность
4
with me during good and bad time, as well as the translation of Russian references for
me.
Благодарность
Было бы невозможно написать эту диссертацию без поддержки и помощи
некоторых людей.
Я благодарю своего научного руководителя Абакумова Евгения Васильевича.
Он всегда выручал меня в течение всего нашего сотрудничества, помог с
выбором темы магистерской диссертации и организовал экспедицию в Ямало-
Ненецкий автономный округ для исследования местных почв и лишайников.
Впервые в жизни я увидел вечную мерзлоту собственными глазами. Он объяснил,
как нужно собирать и характеризировать образцы почв. Евгений Васильевич
также заботился обо всех членах экспедиции. Я многому научился, благодаря ему
и этой поездке.
Спасибо профессору Марине Надпорожской за ее исчерпывающие ответы на
мои вопросы в почвоведении.
Я также выражаю признательность профессору Еве-Марие Пфайффер за ее
советы касательно моего исследования, а также за возможность пройти обучение
в Гамбургском университете, предоставленную ею нам, студентам программы
CORELIS.
Спасибо Алексееву Ивану за помощь в поездке на Ямал. Он также помог мне
с отбором проб. Слава Поляков объяснил измерения основных параметров
грунтов и базального дыхания.
Признателен нашему координатору программы Виктории Маргиевой, которая
помогала мне в тонких административных формальностях.
Я хотел бы также поблагодарить моих дорогих коллег: Горнова Даниила,
Кискину Александру, Кобзарь Гришу, Кондакову Викторию, Конькову Ольгу,
Мищенко Анастасию, Пастухову Викторию, Черезову Анну, Шадрину
Благодарность
5
Александру, Явид Елизавету. Они помогали мне справиться со сложностями,
которые невозможно преодолеть человеку, не владеющему русским языком.
Большое спасибо Тиму Экхарду за многократную помощь, оказанную мне в
течение всего моего пребывания в Германии.
Наконец, я хотел бы поблагодарить мою семью, моих родителей Шуньинь Цао
и Цзянан Цзи за поддержку моральную и финансовую. Выражаю признательность
моему соседу по комнате Палахану Наттанопу, который всегда помогал мне
справиться с проблемами бытового характера. Я также говорю спасибо моей
девушке Кузнецовой Екатерине. Она всегда со мной в хорошее и плохое время.
Она перевела научные материалы с русского на английский, что сыграло
немалую роль в моем исследовании.
State of the art
6
1. Introduction
The Arctic environment has been considered to be vulnerable and sensitive. Although
the Arctic regions are still regarded as marginally affected by anthropogenic activities,
local industrial development and long-range transport are leading to environmental
contamination. Since 1970s, increasing various pollutants concentrations in the Arctic
environment have been reported in several studies (Barrie et al., 1992; Akeredolu et al.,
1994; Singh et al., 2013; Abakumov et al., 2015). Heavy metals are most hazardous
pollutants in the Arctic atmosphere (Hoff et al., 1983; Shevchenko et al., 2003).
Atmospheric deposition is contributing to the Arctic soil pollutions with heavy metals
(Gong and Barrie, 2005). According to geochemical monitoring for Arctic ecosystems,
more than 50% of the observed levels of heavy metals is caused by anthropogenic
emission (Selinus, 1996).
Permafrost-affected soils are dominant in boreal and Arctic tundra consisting of
highly stored organic matter, which functions as a barrier via immobilizing heavy
metals (Antcibor et al., 2014). Organic matter enables to form organo-mineral
associations (Hofle et al., 2013). However, consequences of climate changing,
especially for snow-melting period, together with anthropogenic activities may increase
migration and leaching of heavy metals from Arctic terrestrial to aquatic ecosystem
(Antcibor et al., 2014). Several studies have reported anthropogenic heavy metals
pollution in the Arctic regions. High pollution concentrations of soils, snow, lichens and
surface waters have been observed in Norilsk industry area, west Siberia and in nickel
mining factories, Kola Peninsula (Allen-Gil et al., 2003; Boyd et al., 2009; Zhulidov et
al., 2011). In addition, heavy metals data in permafrost-affected soils from in Lena
River Delta, west Siberia shows the changes along with microrelief features (Antcibor
et al., 2014). However, the distribution of heavy metals in various environment
compartments in the Arctic is dynamic and obviously driven by processes both natural
and anthropogenic. Spatial patterns in concentrations of Permafrost-affected soil
appeared to be associated to geological provinces of the Arctic. Metals can be taken up
by vegetations in tundra revealed both geological and anthropogenic activities. For
instance, lichens are widespread and reluctantly grazed by reindeers. Cr (chromium), Pb
(lead), and Zn (zinc) show the increasing accumulation levels in Arctic lichens (C.
delisei). Hg (mercury) and Cd (cadmium) were found in some Arctic biota with the
State of the art
7
concentrations that may influence reproduction or individual health (AMAP, 2005).
Histopathological alterations in Arctic hares’ exposure to a historical mining area can be
induced by Cd in the high Arctic. However, heavy metals in Arctic vegetations have not
been sufficiently investigated.
The existence of the active layer and the permafrost layer is mostly formed in cold
regions of high latitude, which could affect the fate of trace metals (Biggar et al., 1998).
It is well known that the permafrost and active layer in this region are repetitive loops of
thawing and freezing where soil particles were processing a slow sieving that fine
particles transport from surface to the deeper, yet stones and peddles are prone to from
the deeper to surface (Wolfe and Thieme, 1964). Heavy metals are mainly attached to
the finest soil particles and lower organic content (Krauss and Wilcke, 2002; Antcibor
et al., 2014). Besides, during soil were extracted by solvents, fine particle could
correlate positively with heavy metals residing in soil matrix and correlate negatively
with heavy metals in the solvent after procedure of extraction (Carmichael and Pfaender,
1997). These studies reveal that the percentage of fine particle and their variability in
the soil matrix could be an important factor for determination of distribution and fate of
heavy metals in permafrost-affected soil. Therefore, global warming causing thawing of
active layer of permafrost would have further consequences in the distribution and fate
of heavy metals.
Some heavy metals, including Zn, copper (Cu) and nickel (Ni), are indispensable for
vital activities in human body, acting as catalytic and structural elements of enzymes
and proteins, yet may be toxic if safe threshold values are exceeded. However, some
heavy metals, such as Pb, Cr, and Cd, are toxic pollutants even at low concentration
(Bermudez et al., 2011; Zheng et al., 2015). In arctic regions, local aborigines and
industrial workers may have a health risk under exposure of heavy metals. For instance,
pollution of drinking water with heavy metals in regions of Arctic, such as mining
regions, may have adverse effects on humans. The oral ingestion of heavy metals from
soil and dietary intake from local marine and terrestrial animals also cannot be ignored.
A number of studies have revealed that heavy metals exposure to human in the Arctic.
Unsafe heavy metals concentrations in maternal and umbilical cord blood from Inuit,
Caucasian and other nonaboriginal habitants were observed in Artic Canada (Walker et
al., 2006). High concentrations of Hg accumulated in human body in Greenland were
proved to be related to intake of Hg polluted marine mammals as traditional food
State of the art
8
(Johansen et al., 2007). Contaminated traditional food from the diet of the Yupik people
with heavy metals were also detected in St. Lawrence island, Alaska (Moses et al., 2009;
Welfinger-Smith et al., 2011). A great deal of There is evidence of neurobehavioral
effects of Hg in children in Arctic regions (AMAP, 2005).
Industrial emissions are regarded as the major source of pollutants for heavy metals.
These heavy metals may reach the Arctic via different paths, such as anthropogenic
activity and long-distance atmospheric transport (Zhulidov et al., 2011). Local mining
activity is the main influential factor to represent a considerable amount of heavy metals
emission. Anthropogenic sources of heavy metals by mining activities were identified in
Russian Arctic of Western Siberia and Kola Peninsula (Jaffe et al., 1995; Kashulina et
al., 1997; Gregurek et al., 1998; Reimann et al., 1999; Boyd et al., 2009). These studies
showed that human activity could result to substantial contamination in Arctic
ecosystem along with several hundred kilometers. For examples, Cu and Ni shows
geochemical anomalies in soil affected by these emissions in these regions. On the other
side, heavy metals were observed to reach the Arctic by long-distance atmospheric
transport (Ottar, 1981; Barrie and Hoff, 1985). Pollutants reaching the Arctic is called
as “Arctic haze”. The occurrence of Arctic haze is mostly in winter caused by strong
transport from south to north with particle matters. This transport is dominated by
Siberian anticyclone flowing from the Eurasian to the Arctic in winter time (Barrie and
Barrie, 1990). The transportation of aerosol stretches from southern Urals to east of
Taymyr peninsula (Cottle et al., 2014).
In Russia, Ural Mountains keep numerous minerals. The most northern mining factory
of chromite in the world is located in the polar Urals (Yamal Peninsula) (Perevozchikov
et al., 2005). The exploitation way is open-pit, with production amount of three million
tons per month (News, 2015). The chromite deposits are close next to each other in
permafrost zones where miners start working at about five hundred meters above sea
level (News, 2015). Moreover, growing development of natural gas and oil extraction
industries in Yamal and Gydan peninsulas may also pose serious ecological risks and
human health (Gordeev, 2002). Soils polluted with heavy metals were found in well
drilling on Yamal Peninsula (Moskovchenko, 1998). Stationary fossil fuel combustion,
non-ferrous metal production, and waste incineration are all considered to be the
important sources of heavy metals emissions in the Arctic (AMAP, 2005).These
contaminations may cause a health risk to local indigenous people (Nenets) and workers.
State of the art
9
To our best knowledge, only two English literature about the heavy metals in soils in
Belyi Island, northern coast of the Yamal Peninsula (Abakumov et al., 2017;
Moskovchenko et al., 2017). There is no report about heavy metals pollution in the
inland of Yamal Peninsula where most mining and quarry factories are situated.
2. State of the art
In the present, Yamal Peninsula is situated at the remote area in the northwest Siberia
across Arctic circle. Meanwhile, the active development of mining industries occurs in
this region where oil and gas extraction are also booming. These industrial activities in
polar areas may cause a severe threat to the ecosystem. Especially, it is relevant to
different soils of geochemical properties. Moskovchenko (1998) observed heavy metals
(Sr, Ba, Pb and Zn) in soils which were polluted by oil products in well drilling sites of
Yamal Peninsula. Opekunov (2008) found Co, Ni, Cr, Pb, V, Mn, Ba and Zn were able
to stand for pollutants in bottom sediments from petrol extraction areas, Russian Arctic.
Arctic ecosystem has high vulnerability with increasing contamination of industrial
activities. However, mining activities causing heavy metals’ potential ecological risk in
Yamal regions have not been investigated before.
Many industries have been reported in Arctic regions. For example, it exists a huge
reserve of phosphorite and coal in the Svalbard Archipelago, in which a huge reserve of
coal and phosphorite began in 1899 AD (Sun et al., 2006). Antimony (Sb) was found in
topsoil in Ny-Alesund majorly affected by human activities (Jia et al., 2012). The
highest value of Sb was shown in the past coal mine and the concentrations decreased
due to seawater by coastline (Jia et al., 2012). Jia et al. (2012) also observed the
accumulation of Sb by moss in mining area. It has been reported that the concentrations
of heavy metals in surface sediments from Svalbard fjords were higher than estimated
background values, resulting from coal particles in terrestrial water drainage from local
coal using and other industrial activities (Holte et al., 1996). Snow and cores in the
Arctic were also revealed the strongly anthropogenic effects (Simoes and Zagorodnov,
2001; Isaksson et al., 2003). Boyle (2004) indicated that Arctic lakes are too insensitive
to record heavy metals deposition via long-distance transportation. According to AMAP
(2005), over 50% of heavy metals in Arctic air originated from anthropogenic activities.
In Russian Serbia, for instance, the Norilsk mining is the biggest nickel producer in the
global, supplying 2/3 of production in former Soviet Union (Klein and Vlasova, 1992).
Investigation area and sampling
10
The objectives of the present study were to (1) examine the distribution and pollution
status of heavy metals levels in upper layer (thawing active layer) of permafrost-
affected soil in Yamal Peninsula; (2) Examine the bioaccumulation of heavy metals by
lichens; (3) Evaluate the hazardous possibility of the heavy metals levels to different
local populations.
3. Investigation area and sampling
3.1. Study sites of Yamal-Nenets autonomous region
The study area was approximately 748,520 km2 in Yamal Peninsula where the mining
area was about 1100 km2 (Fig. 1). The mining area is located in the southwest of Yamal
Peninsula and 130 km away from Salekhard. In mining area, the prevailing wind
direction was southwest, which accounted for 35% of the total frequency. In the entire
Yamal Peninsula, the radiation balance is about 18-20 kcal cm-2 per year and relative
humidity is high (70-90%) throughout the year, which is caused by low air temperature
and adjacency to the Kara Sea. Average precipitation varies from 230-270 mm. The
number of days with snow cover is approximately 240 days. The annual amount of
evaporation is not high (about 250 mm per year). The average temperature varies
sharply (-23℃-25℃) in summer time (45 days a year) (Magnin et al., 2015).
Investigation area and sampling
11
Figure 1. location of sampling sites and study area.
3.2. Sampling sites
All upper layer permafrost-affected soils were collected in inland of the Yamal
Peninsula in the period 18-29 July, 2017. Samples (M1-M10) were collected from
anthropogenic (the vicinity of mining transportation roads) and natural landscapes in
chromic mining area. In site M7, five lichen samples (Cetrariella deliseia) were also
collected in pair within 1 m from one another. Three reference soil samples (F1-F3)
were collected in nearby one large forest which is affected by the dust of mining
transportation. The other reference soil samples (E1-E4) were collected in different
landscapes adjacent to Erkuta River. The soil samples (A1-A3) were also collected from
abandoned transferring terminals of sand quarry near Aksarka village where local
Nenets people live. The description of sampling sites, depth and soil groups is shown in
Table 1. The soil groups were determined by WRB (2015). The different soil profiles of
soil groups are shown in Figure 2.
Figure 2. The profiles of each different soil group sampled in Yamal Peninsula.
Investigation area and sampling
12
Table 1 General sampling information of soil collected in Yamal Peninsula.
No. Site
Location
Site description
Soil group
Sampling
Depth (cm)
Latitude Longitude
M1 65.51030 N 65.27737 E 2 meters away from the transportation road in
chromic mines area
Lithic Leptosol 20
M2 66.50761 N 65.27473 E 5 meters away from the transportation road in
chromic mines area
Lithic Leptosol 20
M3 66.50266 N 65.26476 E 20 meters away from the transportation road in
chromic mines area
Lithic Leptosol 20
M4 66.49633 N 62.27030 E 50 meters away from the transportation road in
chromic mines area
Lithic Leptosol 20
M5 66.51507 N 65.26574 E 2 kilometers from site 4, 5 meters away from
the transportation road in chromic mines area
Lithic Leptosol 20
M6 66.51494 N 65.26562 E 2 kilometers from site 4, 20 meters away from
the transportation road in chromic mines area
Lithic Leptosol 20
M7 66.49344 N 65.32053 E 2 kilometers from site 4, 50 meters away from
the transportation road in chromic mines area
Cambic Cryosol 25
M8 66.49350 N 65.32060 E Terrace slope in chromic mines area Cryic Arenosol 20
M9 66.49353 N 65.32072 E Lacustrine depression in chromic mines area Turbic Cryosol 30
M10 66.49439 N 65.36408 E Lacustrine depression in chromic mines area Entic Podzol 20
F1 66.48173 N 65.45421 E Large forest, 5 meters from the road in chromic
mines area
Spolic Technosols 20
F2 66.48168 N 65.45406 E Large forest, 20 meters from the road in
chromic mines area
Entic Podzols 25
F3 66.48119 N 65.45428 E Large forest, 50 meters from the road in
chromic mines area
Histic Gleysols 22
E1 66.12296 N 68.58198 E Frost mound near Erkuta River Histic Cryosol 25
E2 66.12320 N 68.58274 E Lacustrine depression near Erkuta River Reductaquic Cryosol 40
E3 66.12320 N 68.58267 E Terrace slope near Erkuta River Histic Cryosol 10
E4 68.12319 N 68.58270 E Floodplain near Erkuta River Histic Cryosol 45
A1 66.43491 N 66.16344 E abandoned sand quarry site covered by self-
growing herb-dwarf shrub near Aksarka
Reductic Technosol 50
A2 66.32105 N 67.47122 E abandoned sand quarry site covered by self-
growing herb-dwarf shrub in Aksarka
Folic Arenosol 50
A3 66.32093 N 67.47023 E dumped sand quarry near the village self-
growing in Aksarka
Gleyic Stagnosols 50
Materials and methods
13
4. Materials and methods
4.1. Sample treatment
All samples were saved in polyethylene bags at room temperature and were dried by
natural air to eliminate the influence of soil moisture on heavy metals extraction
efficiency. As soon as the weight of soil samples were stable, all samples were sieved
through a 1-mm mesh screen. The moss samples were pretreated in the same way.
4.2. Heavy metals analysis
The content of Cu, Pb, Cd, Zn, Ni, Cr, and manganese (Mn) were analyzed by
inductively coupled plasma-atomic emission spectrometry (ICP-AES) (Spectro Ciros
EOP, Germany). The content of mercury (Hg) was analyzed by atomic absorption
spectroscopy (Thermo Scientific ICE 3500, US). The extraction determination was
followed by reported references with some modifications (Nadal et al., 2011; Vilavert et
al., 2015). 2.5 g of soil was digested with 2.5 ml of concentrated HNO3 and 5 ml of
deionized water in an Erlenmeyer flask for 120 min at 20℃. The liquid phase is filtered
through an in-line membrane filter after centrifugation of the suspension. The solutions
were kept at -15℃ for the determination of heavy metals by appropriated analytical
methods.
4.3. Basal respiration (BR)
The BR was estimated under laboratory closed chambers by measuring CO2
concentrations in sodium hydroxide (NaOH) solutions saved in a plastic container
during incubation for 10 days. The metabolic quotient was described as the ratio of
respiration C-CO2 to microbial biomass per day of incubation (Jenkinson and Powlson,
1976; Vance et al., 1987).
4.4. Soil pH
The pH of soil samples were potentiometrically determined with a pH meter
(Multiparameter Bench Top Table Top pH Meter PHS-550, China) in the supernatant
suspension of a 1:2.5 soil:liquid mixture. The liquid is either distilled water (pH-H2O)
or a 1 M KCl solution (unbuffered) for.
Materials and methods
14
4.5. Soil particle distribution
According to soil textures, 1 g organic soil and 8 g mineral soil were taken. The
procedures were followed (Black et al., 1965). Soil Particle size distribution was
measured with a soil hydrometer (ASTM 152H hydrometer) using method of (Gee and
Bauder, 1979).
4.6. Total organic carbon (TOC) and total nitrogen (TN)
The TOC was measured for air-dried soil in a solution of potassium dichromate in sulfuric
acid by wet combustion followed the Walkley-Black method (Walkley, 1935). The TN was
assessed by the Kjeldahl method (Bremner and Mulvaney, 1982).
4.7. Quality assurance and quality control (QA/QC)
For the quantitative determination of heavy metals, reagent blanks and three added blank
duplicates were also analyzed in each soil and lichen sample. The recoveries of all metals were
ranged between 80% to 108%. The limits of detection of heavy metals were within range of
0.06 to 0.7 mg kg-1 dry weight.
4.8. Data analysis
4.8.1. Geoaccumulation index and pollution load index
Geoaccumulation index (Igeo) and pollution load index (PLI) are widely used to evaluate and
make grades for the soil pollution levels of heavy metals (Katsoyiannis et al., 2011; Adama et
al., 2016; Akoto et al., 2016; Tian et al., 2017). These methods consider not only the influences
of background levels, but also of the anthropogenic activities to heavy metals in soil. The
calculations are according to the following equations:
CF =𝐶𝑚
𝐵𝑚
𝐼𝑔𝑒𝑜 = log2 [𝐶𝑚
1.5𝐵𝑚]
𝑃𝐿𝐼 = √𝐶𝐹1 × 𝐶𝐹2 × ⋯ × 𝐶𝐹𝑛
Where CF represents the Concentration Factor, Cm and Bm represent the detected concentration
of the heavy metal and geochemistry background value of the heavy metal according to the
World Soil Average (Meharg, 2011), respectively. N is the number of heavy metals in this study.
Materials and methods
15
PLI values of the soil are above 1, which is considered as polluted, otherwise it is considered
as non-polluted soils. Seven grades of soil pollution levels were classified and identified by
Muller (Muller, 1969), shown in Table 2.
Table 2 Seven grades of geoaccumulation index (Igeo) for soil pollution levels.
Grade Igeo Pollution level
1 (<1) Unpolluted
2 Slightly polluted
3 Moderately polluted
4 Moderately to highly polluted
5 Highly polluted
6 Highly to extremely polluted
7 (>7) Extremely polluted
Grade PLI
< 1 Unpolluted
> 1 Polluted
4.8.2. Baseline values
The method of relative cumulative frequency was used to determine baseline values
for 8 metals. The baseline values of heavy metals can be shown in a log-normal
distribution by drawing the logarithmic scale graphs of the relative cumulative
frequencies where the bend curves can be seen as a deviation in log-normal distribution
(Meharg, 2011). It is suggested that two inflection points from graph of relative
cumulative frequency versus metal concentration (Hao et al., 2013). The first inflection
point in the curve is defined as the upper limit for background data. Therefore, non-
anthropogenically and anthropogenically affected samples can be distinguished in upper
part of the bend. If the distribution curve shows a roughly linear relationship, the sample
itself can be taken as baseline field.
4.8.3. Transfer factor (TF)
The translocation capacity of heavy metals from soil to lichens can be described as
transfer factor (TF) (Cai et al., 2015). Concentration of heavy metals from extracted
soils and plants were calculated on the condition of dry weight, as followed (Khan et al.,
2008):
TF =𝐶𝑙𝑖𝑐ℎ𝑒𝑛
𝐶𝑠𝑜𝑖𝑙
Materials and methods
16
Where Cplant and Csoil are the metal concentration in extracted lichens and soils on dry
weight basis, respectively.
4.8.4. Risk of individual heavy metal
The potential non-cancer risk of each heavy metal is determined as the hazard
quotient (HQ). The HQ for an individual heavy metal was calculated by the ratio of
evaluated amount of pollutants ingested via soil, plant, or water to the reference oral
dose (RfDo) for the local people.
HQ =𝐶𝐷𝐼𝑖𝑛ℎ
𝑅𝑓𝐷𝑜
CDI (𝑚𝑔 ∙ 𝑘𝑔−1 ∙ 𝑑𝑎𝑦−1 = ∑𝐶𝑖 × 𝐼𝑅𝑖 × 𝐸𝐹𝑖 × 𝐸𝐷𝑖
𝐵𝑊 × 𝐴𝑇𝑖
𝑛
𝑖=1
)
Where CDI is chronic daily intake via inhalation, mg of chemical per kg of body weight
per day. RfDo is an estimation of daily oral exposed to the human population that is
possible to be without the appreciable risk of deleterious influences during a lifetime.
The values of RfDo were 4×10-2, 1×10-3, 0.3, 2×10-2, 1.4×10-1 and 4×10-3 mg kg-1 day-
1 for Cu, Cd, Zn, Ni, Mn and Hg, respectively (U.S.EPA, 2000). Because RfDo value
for Pb has not been established by US protection Agency (EPA), in this study, the RfDo
for Pb was 3.6×10-3 calculated from the weekly and provisional tolerable Pb intake
limits (25 µg kg-1 bodyweight) recommended by the Food and Agriculture
Organization/World Health Organization for adults (FAO/WHO, 1984; Ostapczuk et al.,
1987). Ci and IRi represent concentration of heavy metal in exposure medium i (mg kg-1)
and daily contact rate in exposure medium i (soil: 64 mg day-1 for adults and 104 mg
day-1 for children) (U.S.EPA, 2000). EFi and EDi represent the exposure frequency (365
days year-1) and the time of exposure (70 years for adults and 6 years for children)
(U.S.EPA, 2000). BW is bodyweight on average (kg person-1) (70 kg for adults and
15kg for children) (Rosenfeld and Feng, 2011) and ATi refers to non-carcinogenic
influence in average exposure time (ED × 365 days year-1). Five grades were divided
for the health risk on the basis of HQ value shown in Table 3.
Table 3 Health risk grade of HQ.
Risk grade Low Medium-low Medium Next higher High
Range of HQ 1< HQ ≤ 1.5 1.5 < HQ ≤ 2 2< HQ ≤ 2.5 2.5 < HQ ≤ 3 HQ > 3
Materials and methods
17
4.8.5. Accumulative risk for multiple heavy metals
For assessment of total potential non-cancer effects caused by more than one heavy
metal exposure, accumulative risk (AR) for several heavy metals can be calculated as
followed (Chen et al., 2018):
AR = ∑ 𝐻𝑄 =𝐶𝐷𝐼1
𝑅𝑓𝐷𝑜1+
𝐶𝐷𝐼2
𝑅𝑓𝐷𝑜2+ ⋯ +
𝐶𝐷𝐼𝑖
𝑅𝑓𝐷𝑜𝑖
4.8.6. Positive matrix factorization (PMF)
The PMF method was utilized the relationship among 8 heavy metals. This model
was developed in 1994 (Paatero and Tapper, 1994) . The PMF model defines as a n × m
data original matrix X, where n represents the number of samples and m data original
matrix X, where n represents the number of samples and m represents the number of
chemical species, can be factorized into 2 matrices, namely G (n × p) and F (p × m) with
an unexplained part E (n × m). The formula is as follows:
X=GF+E
The formula can be converted as:
X𝑖𝑗 = ∑ 𝑔𝑖𝑘
𝑝
𝑘=1𝑓𝑘𝑗 + 𝑒𝑖𝑗
Where Xij is the concentration of the jth chemical species measured in the ith sample,
gik is the contribution of source k to the i th sample, fkj is the concentration of the j th
chemical species in source k, and eij is the residual for each sample and species.
The PMF model is defined by the objective function:
Q (E) = ∑ ∑ (𝑒𝑗𝑖
𝑢𝑖𝑗)
𝑚
𝑗=1
𝑛
𝑖=1
Where uij is the uncertainty for each observation. The uncertainties for each sample
were calculated by using measurement uncertainties (MU) and method detection limits
(MDL). When the sample concentration was ≤ MDL, the uncertainty u was calculated a
Results and discussions
18
u=5/6 × MDL
and when the sample concentration was > MDL, u was calculated as:
u = √(𝑀𝑈 × 𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛)2 + (𝑀𝐷𝐿)2
In the present study, 8 heavy metals of 44 soil samples were used as the input data set.
No compound was defined as a weak category, which represents signal/noise ratio < 2.
4.8.7. Statistical analysis
Graphs were generated by GraphPad Prism 7.0 software. All data were expressed as
mean ± standard deviation (SD). The data were analyzed statistically by Dunnett’s test,
using SPSS 21.0. Statistical significance was set at a level of p < 0.05.
5. Results and discussions
5.1. Soil characterization
The results of soil characterization from different soil groups and horizons are
shown in Table 4 and Figure 3. All soil samples collected from studying sites are acid
with range of pH-KCl between 4.06 and 6.51 except the topsoil from A1 site shows pH
of 8.26. All samples were fine-texted, with a high content of silt and clay. A relatively
higher carbon content was found in four locations (13.51 and 10.02 in M4, 11.61% and
20.85% in M9, 11.97 in F3, and 10.49 in E1). High carbon content was found in hi
(histic) horizon in both F3 and E1. This is due to the low temperature, poorly drained
condition, and low mineralization which causes a surface layer of mold by humic
substances. CN ratios varied from 8.76 to 28.55 in topsoil and varied from 1.83 to 14.21
in mineral soil. From BR values, upper soil shows higher metabolic activity than lower
soils in all soil groups except leptosol shows the opposite trend.
5.2. Soil morphology and diagnostics
From soil groups, in mining area, Leposols were dominant in transport road of
mining area, with continuous rock and technic hard materials (<10 cm from surface),
which was influenced by compacting soil for road. Expect for the transport road, soil in
other landscapes was influenced by permafrost. Cambic Cryosols with pedogenetic
Results and discussions
19
alternation from weak to relatively strong were identified. Some of them were Turbic
Cryosols, which comprised a histic horizon that several cryogenic cracks, raw humus
and also relatively homogeneous mineral layer occurred. Cryic Cryosols were identified
with evidence of perennial ice segregation by processes of cryogenics.
In forest areas, both natural and human-affected soil were found. Spolic
Technosols were identified due to the significant amount of ashes accumulated in upper
layer influenced by mining trucks passing, and also subsurface showed the illuvial ashes
and high-water retention. Water-saturated in subsurface horizon with upper humus
accumulation was identified as Histic Gleysols. Entic podzol was also found with a
loose spodic horizon.
Near the Erukta river, in which Histic Cryosol and Reductaquic Cryosol were
qualified, carried with upper histic horizon and reductaquic horizon which was saturated
with water in thawing period and has reducing conditions for some time a year.
In abandoned sand quarry nearby Aksarka village. Reductic technosols were
qualified with fine particles within 100 cm and having reducing conditions. Deep sandy
soils here with folic horizon as over-moisture condition was identified as Folic Arenosol.
Soils were also influenced both by cryogenic redistribution (upper layer) and water-
saturated (gleyic horizon) were considered as Gleyic Stagnosols.
Table 4 Characterizations of soils collected from Yamal Peninsula.
No. site Horizon Depth (cm) CN PH-H20 PH-KCl TOC (%) BR (mg CO2 100 g-1 24h-1)
M1 LP (1) 0-5 8.76 7.22 5.57 0.63 57.1
LP (2) 5-20 8.36 7.08 5.54 0.78 109.6
M2 LP (1) 0-5 9.43 7.35 8.14 0.16 93.1
LP (2) 5-20 13.98 6.92 5.48 5.75 127.5
M3 LP (1) 0-5 15.13 6.37 5.56 0.63 125
LP (2) 5-20 9.56 6.4 5.76 7.77 115.5
M4 LP (1) 0-5 12.07 6.17 5.52 13.51 93.5
LP (2) 5-20 10.06 6.27 6.00 10.02 175.4
M5 LP (1) 0-5 12.69 7.01 5.99 5.13 43.5
LP (2) 5-20 11.36 7.35 5.9 2.49 50.2
M6 LP 0-20 9.88 7.19 6.17 0.54 62.1
M7
Um 0-3 10.96 5.74 5.45 1.72 52.5
Um 3-4 12.59 4.52 5.39 2.90 85.3
CR 4-25 10.03 6.88 5.36 0.80 118.7
M8 O 0-4 13.63 6.96 5.98 3.01 52.2
Um 3-20 14.51 6.36 5.98 7.44 140.4
M9 CR 0-25 12.64 6.09 5.40 20.85 109.1
tu 25-30 11.34 6.24 5.20 11.61 98.4
Results and discussions
20
M10 St 0-25 4.79 6.73 5.48 0.14 139.8
F1 AC 0-5 15.87 5.74 3.87 2.13 75.6
AC 5-20 14.21 4.64 3.69 1.46 70.2
F2
Um 0-3 19.91 5.73 4.59 4.35 130.2
PZ 3-4 16.86 5.92 3.84 7.46 83.3
St 4-25 9.79 5.43 3.57 0.53 43.1
F3 hi 0-5 14.96 5.89 5.63 11.97 115.1
GL 5-22 14.32 5.92 4.31 3.25 76.2
E1 hi 0-25 13.31 5.04 4.68 10.49 129.7
E2 hi 0-5 28.55 4.93 3.73 4.64 135.9
CR 5-40 13.32 6.32 4.33 2.52 64.8
E3 hi 0-10 24.98 4.32 2.92 2.21 170.2
E4
hi 0-15 20.96 5.52 3.61 1.50 144.8
GL(1) 15-25 12.06 8.52 4.06 0.91 37.1
GL(2) 25-45 11.65 6.52 4.20 0.93 77.2
A1 TC 0-9 12.26 8.26 7.87 0.51 34.6
rd 9-50 11.12 6.50 4.44 0.75 31.1
A2 fo 0-9 15.85 8.03 6.51 1.04 65.2
AR 9-50 1.83 6.07 5.68 0.03 120.5
A3
Um 0-2 12.40 5.70 5.71 0.88 73.2
BF 2-30 9.35 7.55 5.71 0.59 51.2
G 30-50 2.02 6.39 5.71 0.05 62.4
Figure 3. Soil particle size distribution (%) from soil samples collected from Yamal
Peninsula.
5.2. Contamination of heavy metals in soils
The concentrations of heavy metals in soil samples of different depths (horizons)
sampled in Yamal Peninsula and contribution (%) of individual metal (without Mn) to
total concentrations in different sites are shown in Table 5 and Figure 4, respectively.
The concentration of Cu, Pb, Cr and Zn on average were much lower than that in World
Results and discussions
21
Soil Average (WSA) while the content of Cd, Ni, Mn, and Hg were significantly higher
than WAS by approximately one to three orders of magnitude, respectively (Meharg,
2011). Moreover, a comparison between measured concentrations of heavy metals and
regulatory threshold limit values (TLV) for Siberian Russia revealed Cd, Ni, and Hg are
above TLV in all investigated areas expect for Ni in Erkuta River Areas and on the
contrary, Cu, Pb, Cr and Mn were all in safe values (Figure 5).
M 1 M 2 M 3 M 4 M 5 M 6 M 7 M 8 M 9 M 1 0 F 1 F 2 F 3 E 1 E 2 E 3 E 4 A 1 A 2 A 3
0 %
2 0 %
4 0 %
6 0 %
8 0 %
1 0 0 %
C u
P b
C d
Z n
N i
H g
C r
Figure 4. Contribution (%) of individual metal (without Mn) to total concentrations of 7
metals in different sites.
Results and discussions
22
Figure 5. Comparison between the average concentration of heavy metals in Yamal
Peninsula and threshold limit values (TLV) of Siberia Russia (Abakumov et al., 2016;
Alekseev et al., 2016).
Table 5 Levels of heavy metals (mg kg-1) ± standard deviation in different depth of
soils collected in Yamal Peninsula.
No. sample Horizon Depth (cm) Cu Pb Cd Zn Ni Mn Hg Cr
M1
LP (1) 0-5 13.6±2.7 4.9±0.8 3.8±0.1 24±4 1300±200 760±220 8±3 1.05±0.1
LP (2) 5-20 11.6±2.3 4.3±0.7 4.3±0.09 26±2.5 1500±500 840±260 9±4 0.51±0.05
M2
LP (1) 0-5 11.1±2.2 0.94±0.6 3.6±0.08 2.5±2.2 1300±400 140±40 13±3 60.40+6.3
LP (2) 5-20 17±3.0 15±1.8 3±0.60 4.4±1.6 1000±400 750±250 29±9 0.21±0.02
M3
LP (1) 0-5 18±4.0 13±0.9 3.9±0.08 63±8 1400±500 830±250 42±19 0.46±0.05
LP (2) 5-20 18±4.0 10.2±0.7 4.3±0.08 57±3 1500±500 870±260 39±18 0.37±0.04
M4
LP (1) 0-5 15±3.0 19±0.5 3.5±0.07 58±2.0 1200±300 1400±400 103±30 3.27±0.33
LP (2) 5-20 13±3.0 9.4±1.5 4.1±0.18 43±7 1400±500 1400±400 54±24 1.08+0.11
M5
LP (1) 0-5 18±4.0 11.4±1.2 3.1±0.4 41±4 1000±400 670±200 37±17 0.72+0.07
LP (2) 5-20 13±2.6 6.3±1.3 3.6±0.1 27±5 1100±400 540±160 26±10 0.27±0.03
M6 LP 0-20 11.2±2.2 4.9±1.3 3.3±0.06 27±7 1100±100 920±280 9±3 0.37±0.04
M7
Um 0-3 15±3.0 8±0.5 3.7±0.01 32±1.3 900±300 950±280 14±6 0.92±0.09
Um 3-4 14.7±2.9 10±0.6 3.6±1.8 32±2.8 1000±400 860±260 58±26 1.30±0.13
CR 4-25 13.5±2.3 5.1±0.5 3.9±2.3 24±1.2 1400±500 1100±300 12±5 3.51±0.35
M8
O 0-4 11.5±2.3 4.8±2.5 3.7±2.1 39±6 69±24 530±160 16±7 0.82±0.08
Um 3-20 21±4.0 16±3 3±0.06 60±10 1000±400 1100±300 64± 0.51±0.05
M9
CR 0-25 24±5.0 17±4 4.2±0.5 54±11 1000±400 3300±1000 90±40 4.35±0.43
tu 25-30 20±4.0 13±0.5 4.6±0.5 48±1.7 1400±400 6500±2000 52±23 1.05±0.11
M10 St 0-25 3.1±0.6 2.2±1.9 0.11±2.0 8.7±8 6.7±2.3 96±29 5.9±2.7 0.18±0.02
F1
AC 0-5 22±4.0 7.5±1.7 1±1.5 41±6 100±40 560±170 16±7 4.67±0.47
AC 5-20 21±4.0 7.6±2.3 0.8±1.6 39±9 69±24 530±160 16±7 1.11±0.11
Results and discussions
23
F2
Um 0-3 29±6.0 7.4±4 1.1±0.02 45±12 200±70 610±50 33±15 0.78±0.08
PZ 3-4 42±8.0 9.7±1.2 1.1±0.03 52±5 110±40 500±200 42±19 3.71±0.37
St 4-25 18±4.0 6.7±3 0.7±0.02 38±13 120±40 420±130 114±29 1.61±0.16
F3
hi 0-5 79±19.0 6.8±1.3 1±0.15 32±5 100±40 560±160 16±7 2.16±0.22
GL 5-22 59±12.0 7.8±1.1 1.2±0.16 38±5 120±40 420±130 114±29 3.57±0.36
E1 hi 0-25 170±30.0 7.4±4 1.1±0.6 42±9 100±40 730±220 62±28 0.69±0.07
E2
hi 0-5 2.1±0.4 3.3±2.9 0.2±0.18 19±8 6.8±2.4 660±200 52±23 1.05±0.10
CR 5-40 3.5±0.7 3.8±1.8 0.16±0.09 7.9±9 7.4±2.6 68±20 10±5 0.16±0.02
E3 hi 0-10 2±0.4 1.9±0.10 0.14±0.4 10.2±12 12±4 160±50 70±30 1.86±0.19
E4
hi 0-15 1.9±0.4 2.6±1.8 0.15±0.22 15±5 6.9±2.4 250±80 50±22 0.38±0.04
GL(1) 15-25 3.4±0.7 2.9±1.2 0.18±0.06 12.7±2.2 9±3 66±20 9±3 1.23±0.12
GL(2) 25-45 3.3±0.7 2.4±0.23 0.16±0.01 11±9 7.1±2.5 66±20 12±5 0.87±0.09
A1
TC 0-9 6±1.2 6.1±1.2 0.35±0.18 33±8 6.8±2.4 260±80 5±2 0.58±0.06
rd 9-50 10.8±2.2 5±1.7 0.36±0.17 24±9 16±5 250±70 20±9 0.29±0.03
A2
fo 0-9 62±12.0 5±2.6 0.8±0.06 36±12 21±7 630±190 15±7 0.84±0.08
AR 9-50 2.1±0.4 2.1±0.2 0.1±0.02 5.8±5 7.2±2.5 41±12 5±2 0.48±0.05
A3
Um 0-2 2.5±0.5 2.5±1.6 0.13±0.01 14±8 5.4±1.9 120±40 14± 0.23±0.02
BF 2-30 64±13.0 4.7±1.6 2.6±0.08 22±4 710±200 570±170 7±3 5.62±0.56
G 30-50 1.5±0.3 1.9±0.10 0.1±0.02 6.5±4 4.2±1.5 29±9 5±2 0.35±0.03
By comparing the distribution of log-transformed concentrations in different sites
(Figure 6), Mn and Ni had the highest concentration in mining areas (including the
forest inside this zone), whereas a high level of Hg was found in the Erkuta River areas,
and Cr and Cd were at their lowest level for all the sites. This is may be due to Hg being
air-transported to the slope depression terrace in the Erkuta River areas, because there
are no pollution sources in this area.
Results and discussions
24
Figure 6. Boxplot comparison of log-transformed concentrations (mg kg-1) for 7 heavy
metals in soils from different sampling areas in the Yamal Peninsula.
In transportation sites in chromic mining areas, M1 to M7 showed the similar trend
that Ni has the highest concentration (mean=1221 mg kg-1) followed by Mn (mean=859
mg kg-1) and mean concentrations for Cu, Pb, Cd, Zn, Cr and Hg were 14.48, 8.75, 3.69,
32.92, 5.32, and 32.36 mg kg-1, respectively. However, the significantly higher
concentration (60.40 mg kg-1) for Cr was observed in M2. In other investigated areas
(natural landscapes) within chromic mining areas (M8 to M10), the content of Mn
(2305.2 mg kg-1) was much higher than Ni (695.14 mg kg-1). Cr shows a relatively
lower concentration (1.38 mg kg-1) in this area. As for other heavy metals, Cu (15.92
mg kg-1), Pb (10.6 mg kg-1), Cd (3.12 mg kg-1), Zn (41.94 mg kg-1), and Hg (45.58 mg
kg-1) were the same pattern as M1-7. Cr was assumed to be high level metal in this
chromic mining areas. However, the mean concentration from M1-10 (3.35 mg kg-1) is
significantly lower than those observed in Belyi island, Yamal Peninsula (23.5 mg kg-1)
(Moskovchenko et al., 2017), Svalbard (15.9 mg kg-1) (Marques et al., 2017),
Norwegian Arctic (19.0 mg kg-1) (Halbach et al., 2017). This level of Cr observed might
be considered as the background level for this region.
Ni is believed to be highly linked with Mn oxides for the most soils (Norrish, 1975),
which may explain that comparable content of Mn was also found in each sample. Mean
Results and discussions
25
concentrations of Ni in this area are obviously higher than those observed in other
Arctic regions, such as Ny-Ålesund (21.2 mg kg-1) (Halbach et al., 2017), Pyramiden
(25.1 mg kg-1) (Marques et al., 2017), Brogger Peninsula (4.3 mg kg-1) (Headley, 1996),
Billefjorden (1.1) mg kg-1 (Gulinska et al., 2003) in Svalbard, Lena River Delta (30.2)
(Antcibor et al., 2014), West Siberia (1.8 mg kg-1) (Raudina et al., 2017). The Ni
content in soils is highly dependent on its parent rocks. In no particular pollution sites of
central Serbia, the highest Ni concentration observed for Leptosol was 662 mg kg-1
(Pavlovic et al., 2017), which is still lower than the highest sample (1500 mg kg-1) in
this area. However, intensive metal processing operations and combustion of coal and
oil from local oil and gas extraction industries in this area can release Ni as well, and
besides the mining vehicles are also significant emission source in those sites. In
addition, From M8 to M10, which are not Leptosol, the concentrations of Ni were
relatively lower than those from M1 to M7. This may indicate that the background Ni
values of rocks in this mining area may not be the most contributor to Ni content in the
soil. For TLV, Cd (1610%), Ni (1274), and Hg (524%) all exceeded the safe status in
this mining area, which may have adverse effects on miners and local tundra terrestrial
system.
In the nearby forest of mining areas, F1 to F3 showed a decreasing Ni concentration
with an increasing concentration of Mn. The mean concentration of each metal is 530,
116.5, 41.1, 39.5, 35.1, 7.6, 2.51, and 0.95 mg kg-1 for Mn, Ni, Zn, Hg, Cu, Pb, Cr, and
Cd. The TLV status of F1 to F3 were also beyond the same levels for Cd, Ni, Hg,
suggesting air transportation of these metals is an important pathway. In the reference
sites nearby Erkuta River E1 to E3, the mean concentration of each metal was 30.65
(Cu), 4.01(Pb), 0.41 (Cd), 19.75 (Zn), 33.65 (Ni), 302.5 (Mn), 0.89 (Cr) and 47.38 (Hg)
mg kg-1. The contribution of Mn was the most abundant except for E2 with dominant Ni
contribution, which may be due to different geology background. Only TLV of Cd and
Hg in the references were beyond the safe values, which might be caused by long-range
atmospheric transport as pervious reports for Cd and Hg (Nygard et al., 2012). In sites
A1 to A3 where local Nets people reside, the mean concentration of heavy metals
showed that Mn (274.42 mg kg-1) > Ni (110.09 mg kg-1) > Cu (21.27 mg kg-1) > Zn
(20.19 mg kg-1) > Pb (3.9 mg kg-1) > Cr (1.2 mg kg-1) > Cd (0.63 mg kg-1) and Cd, Ni,
and Hg exceeded the TLV values. The ready bioavailability of Hg, Cd, Ni can create an
important health hazard (Kot et al., 1999). The volatilization of Hg in Hg-enriched soils
Results and discussions
26
can also have an adverse influence on human health when exceeding inhalation
reference concentration (300 mg m-3) by the EPA (Henry et al., 1999). Cd content in
topsoil is also reported to be very high (3.2-1871 mg kg-1) in the vicinity of mines and
quarries, especially smelting operation (Scokart et al., 1983), which is same with this
study. Besides, the Cd content is much higher than the greatest concentration (0.11 to
2.5 mg kg-1) observed in flooded soils from Belyi Island, in Kara Sea to north of the
Yamal Peninsula (Moskovchenko et al., 2017).
The results of Igeo and PLI indices are presented in Table 6. According to Igeo values,
all areas were highly and extremely by Hg. M1 to M9 were moderately polluted by Cd.
M1-7 and M9 were moderately to highly polluted by Ni, while soil from F2 was slightly
polluted by Ni. M9 and E1 were also slightly polluted by Mn and Cu, respectively. Thus,
only M9 seems to be polluted by 4 metals. Meanwhile, the highest PLI value was also
for M9 (891.63). M10, E2-4, as well as A1 and A3 showed PLI values were less than 1,
with the lowest value of 0.03 (M10) in spite of highly polluted soil by Hg.
Table 6 Geoaccumulation index (Igeo) and pollution load index (PLI) of heavy metals in
soil samples collected from 30 sites in Yamal Peninsula.
No.
sample Cu Pb Cd Zn Ni Mn Hg Cr PLI
M1 -2.10 -3.05 2.61 -2.13 4.90 0.05 6.05 -6.41 4.958
M2 -2.39 -5.43 2.53 -5.39 4.90 -2.39 6.75 -0.56 2.562
M3 -1.70 -1.64 2.64 -0.73 5.01 0.18 8.44 -7.59 25.154
M4 -2.00 -1.10 2.49 -0.85 4.79 0.93 9.74 -4.76 126.580
M5 -1.70 -1.83 2.31 -1.35 4.53 -0.13 8.26 -6.94 15.121
M6 -2.38 -3.05 2.40 -1.96 4.66 0.33 6.22 -7.92 2.824
M7 -1.96 -2.34 2.57 -1.71 4.37 0.37 6.86 -6.59 8.753
M8 -2.34 -3.08 2.57 -1.42 0.67 -0.47 7.05 -6.76 1.366
M9 -1.28 -1.26 2.75 -0.96 4.53 2.17 9.54 -4.35 241.735
M10 -4.24 -4.21 -2.50 -3.59 -2.70 -2.93 5.61 -8.94 0.001
F1 -1.41 -2.44 0.68 -1.35 1.20 -0.39 7.05 -4.25 3.720
F2 -1.01 -2.46 0.82 -1.22 2.20 -0.27 8.09 -6.83 4.026
F3 0.44 -2.58 0.68 -1.71 1.20 -0.39 7.05 -5.36 4.305
E1 1.54 -2.46 0.82 -1.32 1.20 -0.01 9.00 -7.01 9.409
E2 -4.80 -3.62 -1.64 -2.46 -2.67 -0.15 8.75 -6.40 0.056
E3 -4.87 -4.42 -2.16 -3.36 -1.85 -2.20 9.18 -5.58 0.026
E4 -4.94 -3.97 -2.06 -2.80 -2.65 -1.55 8.69 -7.87 0.013
A1 -3.28 -2.74 -0.83 -1.67 -2.67 -1.50 5.37 -7.25 0.033
A2 0.09 -3.02 0.36 -1.54 -1.05 -0.22 6.96 -6.72 0.853
A3 -4.55 -4.02 -2.26 -2.90 -3.01 -2.61 6.86 -8.85 0.003
Results and discussions
27
A factor analysis was conducted to show similar degrees among the measured metals
that would suggest the common sources. Three extracted factors for all soil samples are
presented in Table 7. F1 includes TOC, Pb, Cd, Zn, Ni, and Mn, with a significant
correlation among them, which suggested that they are affected by bedrock, as well as
other mining activities. It also indicates that Pb is prone to bind to MnO2 and soil
organic matter plays a key role in retention/adsorption of Pb, Cd, Zn, Ni, and Mn. F2
could reflect that the lower correlation coefficient of Cd, Ni, and Cr with other metals
which is likely due to relevant processes of anthropogenic contamination. F3 may
reflect the chemical association of Cu and Cr, which is the natural background or from
atmospheric deposition by aerosols.
Table 7 PMF matrix for soil samples in Yamal Peninsula.
Component
F1 F2 F3
TOC 0.793 -0.307 0.139
Cu 0.240 -0.529 0.593
Pb 0.894 -0.120 -0.125
Cd 0.737 0.601 0.006
Zn 0.800 -0.260 -0.071
Ni 0.666 0.679 -0.011
Mn 0.743 0.114 -0.124
Hg 0.582 -0.435 0.122
Cr -0.078 0.511 0.771
Variance explained (%) 44.569 19.355 11.303
5.3. TFs of heavy metals in lichens
The concentration and TF of heavy metals in lichens are given in Table 8. The TFs of
lichen were in order: Cr > Hg > Cu > Ni > Cd > Pb > Zn > Mn. The TF for Cr, Hg, Cu,
and Ni all exceeds 1. No data available on presence of heavy metals in vegetations of
the Yamal Peninsula. However, a comparison of the results of concentration of 8 heavy
metals in thalli of the lichens (Cetrariella delisei) in Spitsbergen, Svarlbard (Norwegian
Arctic) with the present results, which revealed that levels of Cr, Cd, Ni, Mn, and Hg
are more than 1 to 3 orders of magnitudes higher in Yamal Peninsula (Wegrzyn et al.,
2016). This is perhaps due to the study areas in Svalbard not inhabited and influenced
by human activities. Some fragmented available data from Arctic can be found in
Greenland (Riget et al., 2000) and Russia (AMAP, 2005) while there are different
lichen species. Cr has the highest TFs value (2.21) among all metals. However, there is
no evidence yet of an essential role of Cr in plant metabolism, although the positive
Results and discussions
28
effects on plant growth of Cr applications to soils having a low soluble content of Cr
(Mertz, 1969). Therefore, Cr in lichens may be bioaccumulated via food chain.
Table 8 The concentrations (mg kg-1) ± standard deviation and TFs of heavy metals in
lichens.
Cu Pb Cd Zn Ni Mn Hg Cr
Concentration 21±4 6.6±1.6 3.4±1.7 22±4 1100±400 540±160 26±11 2.037±0.3 TF 1.40 0.83 0.92 0.69 1.22 0.57 1.86 2.21
Wegrzyn et al. (2016) observed that Cd accumulation in lichens is strong while
opposite tendency was found for Cu, Mn, Ni, which is also different from Cu and Ni in
current study. This may be explained by too low background concentrations in Svalbard.
In addition, Osyczka et al. (2016) found lichen species of the genus Cladonia absorb
heavy metals intensively in highly contaminated areas. Since lichens, an exception from
most vegetations absorbing heavy metals from contamination substrates, are able to
accumulate heavy metals both from the ground and the air (Garty, 2001; Wegrzyn et al.,
2013; Klimek et al., 2015). Thus, more studies are needed to understand accumulation
fate in different lichens.
5.4. Baseline values
From the relative cumulative frequency versus concentration (Figure 7), the baseline
values for 8 metals (mg kg-1) measured in the Yamal Peninsula were: 3.5 for Cu; 3.8 for
Pb; 0.16 for Cd; 16 for Ni; 12 for Hg; 15 for Zn; 4.67 for Cr; and 1400 for Mn. These
values are in the range of estimated Clarke world values (mg kg-1), which are as follows:
1-70 for Cu; 0.1-10 for Pb; < 0.3 for Cd; 1400-2000 for Ni; 50-77 for Hg; 10-120 for Zn;
5-120 for Cr; and 350-2000 for Mn (Meharg, 2011). The proposed geochemical
baseline values in this region, which are affected by the current mining exploitation of
the Yamal Peninsula, allow for the quick identification of the sites that may be affected
by pollution processes in this region.
Results and discussions
29
Figure 7. Geochemical baseline values for 8 heavy metals from soils from Yamal
Peninsula, calculated by relative cumulative frequency.
5.5. Health risk of heavy metals in locals
The mean values of HQ for each heavy metal via ingestion of soil and lichens are
shown Table 9. Generally, the results showed that HQ values of 8 heavy metals through
exposure pathway (soil and lichen) were all higher in children than in adults, which is
same with other studies for soil and vegetation (Albering et al., 1999; Chen et al., 2018).
The consequences of HQ in soils are significantly higher than in lichens. Lichen is one
of food sources for reindeers adapted by local Nets people who takes meats including
organs from reindeers, which will result in bioaccumulations of heavy metals in human.
Thus, lichen is also an indirect potential exposure to local people. However, HQ values
for both adults and children are far from 1, suggesting exposure of analyzed heavy
metals from lichens is not directly associated. However, there is no data about
bioaccumulation and biomagnification for Arctic terrestrial biotic system so far. Further
studies for Arctic vegetations accumulated by heavy metals to human exposure risks are
still required. In soil, the highest HQ value (7.73) for Ni was found for children,
indicating high health risk of Ni for children in this region. The HQ values for Mn and
Hg were in medium-low level of health risk for children. The average AR of 8 metals
and lichen was 1.66 (adult) and 12.54 (children) by exposure of soil, and 0.06 and 0.48
Results and discussions
30
by lichen. Therefore, multi heavy metals may lead to potential health risk for both
adults and children in Yamal regions.
Table 9 Average valued of HQ and AR for heavy metals in different populations.
Cu Pb Cd Zn Ni Mn Hg Cr ARi
HQsoil (Adult) 2.02×10-2 7.12×10-2 2.46×10-2 3.70×10-3 1.02 2.02×10-1 3.12×10-1 1.24×10-6 1.66
HQsoil (Children) 1.53×10-1 5.40×10-1 1.86×10-1 2.81×10-2 7.73 1.53 2.36 9.41×10-6 12.54
HQlichen (Adult) 4.80×10-4 1.67×10-3 1.03×10-3 6.70×10-5 5.02×10-2 3.52×10-3 5.94×10-3 5.94×10-3 0.06
HQlichen (Children) 3.63×10-3 1.27×10-2 7.85×10-3 5.08×10-4 3.81×10-1 2.67×10-2 4.50×10-2 4.50×10-2 0.48
5.6. Permafrost as a semi-permeable barrier
The last work was finding of pattern of heavy metals within permafrost. The highest
values found for Cu within under interface between the permafrost and the active layer.
Taking the progressive decreasing in detected samples into permafrost, which showed
permafrost behaves as a low-permeability barrier of mobility of heavy metal in this area.
Other studies also showed some pollutants in some certain conditions could penetrate
the permafrost table. Biggar et al. (1998) observed that vertical profile of migrating
petroleum hydrocarbons in Canadian Arctic and suggested these chemicals could
migrate into the permafrost, yet which occurred in some sites and not all investigated
areas. Biggar et al. (1998) indicated that gravity drainage of pollutants by interspace of
interconnected air in frozen ground. To a minor extent, the diffusion of heavy metals,
such as Hg, Cr and Cu, may diffuse in unfrozen water, which presents possible
mechanism of heavy metals to enter permafrost from upper active layer. McCarthy et al.
(2004) carried out the study in Alaskan Arctic and found in despite that permafrost acts
as an effective barrier to water flow, petroleum hydrocarbons and heavy metals still
could penetrate permafrost. They suggested migrated heavy metals maintained in a very
thin continuous layer, also isolation of each small discrete reservoirs with one another
through impermeable permafrost. However, mechanism in this study were based on fate
of fuel-derived heavy metals, which is not very similar as in Yamal Peninsula where
high water-soluble and high variable temperature changes in summer time. To our best
knowledge, no other studies conducted within permafrost -affected soil of heavy metals
into permafrost in this region.
The result of vertical distribution in Cryosols is shown in Figure 8. All heavy metals
declined within depth to permafrost table. Cu, Pb, Cd and Zn were in very minor level
Results and discussions
31
near the permafrost table. This indicates mobility of them were not that strong. However,
as we know, that warming occurs gradually, which leads to more snow and rain
precipitation, as well as increasingly consequent drainage water during thawing period
via highly porous soils. Besides, warming may influence the level of permafrost table
resulting to migration of heavy metals to deeper level, decreasing its thickness, and at
last its disappearing. All these factors will mobilize heavy metals from active layer
downwards upper layer of permafrost. Since global warming affects Russian Arctic at
present days, thawing of permafrost increases leaching, which would lead to enhancing
flow of heavy metals with unpredictable ecological consequences.
Figure 8. the vertical distribution of logarithmic transformation of 6 heavy metals in
permafrost profile (Mn and Cr are not shown due to not obvious changes for Mn and
lower than detection limit for Cr).
6. Conclusion and outlook
1. This is the first study focusing on the heavy metals pollution status in the interior
area of the Yamal Peninsula. From the TLVs and Igeo, Hg qualifies as the only
heavy metal viewed as having polluted the areas in all the measured sites.
References
32
2. The baseline values for the geological background of the 8 metals (mg kg-1)
measured in the Yamal Peninsula were estimated: 3.5 for Cu; 3.8 for Pb; 0.16 for
Cd; 16 for Ni; 12 for Hg; 15 for Zn; 4.67 for Cr; and 1400 for Mn.
3. Other metals were quite varied at different sites. In the mining areas, the highest
concentration of Ni (1221 mg kg-1) and Mn (2305.2 mg kg-1) was found in the
periphery of mining transportation roads (Leptosol), in which both Cd and Ni
exceeded the Threshold Limitation Value of Russian Siberia.
4. All the sites in the mining area are highly and extremely polluted by Ni and Hg
according to Igeo. Furthermore, the Ni concentration in the mining area is not
primarily due to the background values of Leptosol, because the same levels are
found in other soil groups (far from the mining transportation road).
5. From the reference areas, a decreasing concentration of Ni and a slight pollution
of Ni, Mn, and Cu were observed in large forest sites where Cd and Ni were also
beyond the threshold value. In other reference sites (Erkuta River), Cd also
exceeded the threshold value. It can be inferred that the sources of Cd and Hg are
likely from air transportation.
6. In the Aksarka village areas where dumped sand quarries were located nearby,
Cd and Ni exceeded the threshold value, but there were no Igeo values (>1) except
for Hg. For lichens, the transfer factors showed that Cr had the highest value
followed by Hg and Ni.
7. Moreover, the concentration of metals the lichens accumulated was higher than
that in soil. This might be due to its capacity to capture Hg from the air since Hg
is the only metal which can be transported in a gaseous form in the atmosphere,
and specifically lead to bioaccumulation of Cr and Ni. However, the source of
the heavy metals in different lichen species necessities further studies.
8. Ni and Mn were detected more in deeper minerogenic soil horizons compared to
top soil. This may suggest permafrost table acting as a geochemical barrier,
retarded migration of metals into deeper barrier.
9. From the HQ values, it can be ascertained that the potential health risks for all
heavy metals in soil and lichen for adults are at a safe level. However, the HQs
References
33
for Ni, Mn, and Hg in soil depict a specific health risk for children. Moreover,
AR reveals that multi-metals in soil can have adverse effects on both adults and
children.
10. The current results strongly demonstrate the significance and need for
environmental monitoring of heavy metals in this area, especially around mining
areas. Lastly, there is a need for some specific regulations to limit the local
industrial emission sources.
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