Source Apportionment Of Heavy Metals Research Articles

Source apportionment of heavy metals in farmland soil with application of APCS-MLR model: A pilot study for restoration of farmland in Shaoxing City Zhejiang, China

The conditions of the sources of heavy metals are essential to assess its potential threats to human health. The identification of the origin of heavy metals is essential for planning effective measures to control long-term accumulation of heavy metals. In this study, analysis of pollution sources was performed on 100 soil samples with geostatistics and absolute principal component score-multiple linear regression (APCS-MLR) receptor model. The descriptive statistics revealed that concentrations of heavy metals (Pb, Zn, Cu, Ni) have exceeded the background value of Zhejiang Province. The coefficient of variation is Pb > Cd > Cu > Zn > Ni > Cr. The APCS-MLR and geo statistical analysis showed that sources of pollution: PC1 was Ni, Cr, Cu, Zn because of soil parent material. The contribution rates were 89.42%, 87.19%, 29.64%, and 33.58%, respectively. The PC2 was Pb, Zn and Cu which were mainly caused by anthropogenic mining activities. The contribution rates were 95.92%, 24.81%, and 40.62%, respectively. The PC3 was Cd、Zn and Cu which was mainly caused by agricultural inputs, and their contribution rates were 91.96%, 41.61%, and 30.14% respectively. According to Nemero Synthesis Index evaluation method, the Shaoxing City Zhejiang, China is heavily polluted with heavy metals.

Characterization and source apportionment of heavy metals in the sediments of Lake Tai (China) and its surrounding soils

Heavy metals (HMs) in lake sediments have aroused long-term attention due to their persistent threat to the lake ecosystems. Study into the pollution characteristics and potential sources of HMs in lakes, especially those heavily influenced by human activities, can give some insights to prevent and control the environmental risks of HMs. Lake Tai is the China's third largest freshwater lake but has been a drastic example of water pollution in recent decades. Here, we systematically investigated the characterization of pollution, risk and sources of HMs in the sediments of Lake Tai and its surrounding soils using several methods and tools. Specifically, a total of 46 sediments and 578 soils were collected from the study area, and geochemical approaches (geo-accumulation index and enrichment factor) and pollution indexes were jointly used to reveal the contamination characteristics of HMs. Meanwhile, the potential ecological risk index and probabilistic model of health risk assessment were employed to understand the environmental risk of HMs. Further, absolute principle component score-multiple linear regression and positive matrix factorization models were comparatively applied to apportion the sources of HMs. Results showed that various metals including Cd, Cr, As, Hg, Pb, and Cu appeared contamination level in varying degrees both in the sediments and soils. In particular, Cd, As and Hg were identified as the significant contaminants and presented moderate ecological risk. As a whole, the health risk levels of metals were acceptable. Source apportionment suggested that industrial discharge, agricultural activities, atmospheric deposition and natural source were the potential origins of HMs in this area. Among them, anthropogenic activities were the largest dominator of HMs with contribution of >75%. The study will advance our knowledge on HMs in lake and its surrounding soils and provide reference for policy maker to design migration strategies for protecting the lake ecosystems.

Heavy metal pollution in suburban topsoil of Nyeri, Kapsabet, Voi, Ngong and Juja towns, in Kenya

The increasing industrialization and agricultural developments in suburban areas of Kenya, has increased the threat of heavy metal contamination in soil. Until now, there are limited studies evaluating metal pollution in suburban towns. This study, therefore, investigated the concentration, distribution and environmental risk of lead (Pb), chromium (Cr), copper (Cu), nickel (Ni), zinc (Zn), mercury (Hg), arsenic (As) and cadmium (Cd) in five townships. Heavy metal source apportionment was examined using principal component analysis and validated using cluster analysis. The relationship between heavy metals was analyzed using Pearson’s correlation test and the ecological risk index calculated to determine the degree of contamination. The concentrations of heavy metals were 1.65–99.16, 0.2–12.50, n.d. (not detected)–2.28, 0.59–17.22, 0.18–4.93, 0.17–1.55, 0.01–0.23 and n.d.–0.03 mg kg−1 for Zn, Pb, As, Cu, Cr, Ni, Cd, and Hg, respectively. These values were within the toxicity limit of Tanzania soil guidelines. The results further demonstrated that natural and anthropogenic activities influenced the distribution of heavy metals. Correlation coefficients highlighted an association among heavy metals suggesting a similar origin. Pollution indices revealed that pollution decreased in the order Juja > Ngong > Kapsabet > Nyeri > Voi with the overall ecological index indicating a low level of pollution. The concentration of As and Pb were found to be high in all the study sites posing a potential hazard to the environment while Juja and Ngong regions were at a higher risk of threat. The inappropriate disposal of industrial and municipal effluents, agricultural practices, and burning of fossil fuels, was identified as the key causes of metal pollution.

Source apportionment of heavy metals in farmland soil of Wuwei, China: Comparison of three receptor models

Abstract Receptor models are, rarely utilized in soil but are often used to identify pollutant sources and quantify their contribution. This paper focuses on the soil in oasis farmland. A geochemical baseline is used to assess the pollution of the soil, and then three models are tentatively utilized to apportion heavy metals and compare the sources, the contributions and the operation effects. Pollution assessment indicated that the farmland soil of Wuwei was lightly contaminated by heavy metals. Source apportionments suggested that atmospheric deposition contributed the most pollution (53.95%–65.35%). The three models supplemented each other, and the grouped principal component analysis/absolute principal component scores (GPCA/APCS) was outstanding. GPCA/APCS and UNMIX suggested that agricultural activities were the prime anthropogenic source (51.06%–61.56%), followed by the combustion of fossil fuels (coal and oil) (27.92%–28.66%) and building materials-related activities source (10.52%–20.29%). Fertilizers and pesticides (67.88%–74.81%) contributed more than traffic emissions (25.19%–32.12%) in agricultural activities. Similar results were acquired via positive matrix factorization (PMF), while industrial activity was the highest individual contributor (29.91%). Therefore, combining these three models was the most effective approach, and more attention should be paid to mitigating the pollution caused by the use of fertilizers and pesticides as well as the industrial activities in Wuwei. The results of this study could provide reference in reduction of heavy metal pollution in farmland soil.

Comprehensive assessment and source apportionment of heavy metals in Shanghai agricultural soils with different fertility levels

Abstract For the purpose of pollution assessment and source apportionment of heavy metals in agricultural soils with different fertility levels, an intensive sampling (1848 samples) was conducted in Shanghai city. Various indices, including the pollution index ( PI ), Nemerow integrated pollution index ( NIPI ), geoaccumulation index ( GI ) and potential ecological risk index ( RI ), were employed to assess the pollution status caused by heavy metals. In view of the spatial heterogeneity of heavy metal concentrations, in this work a synthesis of principal component analysis (PCA) with spatial lag modeling (SLM) is proposed to explore quantitatively the sources of heavy metals and the contributions of each component. The results showed that the mean heavy metal concentrations followed a decreasing order: Cr (72.02 ± 8.90 mg/kg) > Pb (25.57 ± 7.38 mg/kg) > As (6.98 ± 1.97 mg/kg) > Cd (0.19 ± 0.10 mg/kg) > Hg (0.12 ± 0.08 mg/kg). Although the mean heavy metal concentrations did not exceed the corresponding national standards, the percentages of sampling locations exhibiting higher concentrations relative to the background values were found to be equal to 43.29% for Pb, 32.63% for Cd, 27.54% for Hg, 11.26% for Cr, and 10.93% for As, which indicate significant local accumulation of heavy metal pollutants within the study area. Industrial activities, natural sources and agricultural activities are the main pollution sources that account for 27.92%, 27.48% and 20.64% of the total pollution, respectively. Industrial activities with high Pb and Hg loadings have a large contribution to good fertility soils (39.38%). It was found that agricultural activity is the main contributor of Cd pollution, having a large contribution (33.46%) to low fertility soils. Cr and As pollution comes mainly from natural sources, with relatively equal contribution to soils with various fertility levels. The present study improves understanding of the pollution status of heavy metals in Shanghai agricultural soils, and also serves as reference for pollution source apportionment in other regions.

Distribution, risk assessment, and source analysis of heavy metals in sediment of rivers located in the hilly area of southern China

River sediment, the important sink and source of heavy metals, can provide critical information for aquatic ecosystem health. Heavy metal pollution has been a serious problem facing river systems worldwide and can adversely affect human beings via the food chain. However, no comprehensive study has been conducted on heavy metal pollution in sediments of river systems in the hilly area of southern China, which plays a key role in water supply and ecosystem balance. This study is aimed at comprehensively studying the pollution status of heavy metals in river sediments in the hilly area of southern China and apportioning sources. A total of 39 superficial sediment samples were collected from the upstream, midstream, and downstream of 13 rivers (Xiangjiang River, Zishui River, Yuanjiang River, and Lishui River located in Hunan Province; Ganjiang River, Xinjiang River, Fuhe River, Raohe River), and Xiushui River located in Jiangxi Province; Qiantangjiang River and Oujiang River located in Zhejiang Province; Minjiang River and Jiulongjiang River located in Fujian Province) in the hilly area of southern China. The total concentrations of metals of Mn, Zn, Cr, Co, Ni, Cu, As, Cd, Sb, Pb, and V were analyzed using the inductively coupled plasma-mass spectrometry method. The pollution status and potential ecological risk were assessed with the geoaccumulation index (Igeo), sediment quality guidelines (SQGs), and potential ecological risk index (RI). The source apportionment of heavy metals was performed by correlation analysis and principle component analysis (PCA). Results indicated that Mn, Zn, and Pb concentrations were significantly higher than other metals, especially in the upstream of the Jiulong River and midstream and downstream of the Xiangjiang River. Pollution assessment indicated that Cd pollution of sediments was most serious and that more than 50% of sampling sites were significantly polluted, with a very high potential ecological risk. The rivers in Hunan provinces (HN) were identified as the priority controlled rivers because of the high Igeo and RI index values. Correlation and PCA analysis indicated that Mn, Pb, and Zn originated from natural and mineral exploitation activities; As, V, Ni, and Sb originated from industrial wastewater and mineral-smelting activities; Cu and Co originated from agricultural activities; Cr and Ni originated from natural sources. While the most polluted Cd came from a combination of multiple sources described above. Results indicated that Cd was the most common heavy metal pollutant, especially in river sediments of Hunan Province. Anthropogenic activities have become the main source of heavy metals in the river sediments of the hilly area of southern China. Special attention should be paid to Cd, and measures must be taken to prevent from further anthropogenic influence on heavy metal pollution.

Accumulation, ecological-health risks assessment, and source apportionment of heavy metals in paddy soils: A case study in Hanzhong, Shaanxi, China

Contamination of agricultural soil by heavy metals has become a global issue concerning food security and human health risk. In this study, a soil investigation was conducted to evaluate metals accumulation, potential ecological and health risks as well as to identify sources of metals in paddy soils in Hanzhong City, which is located in a sedimentary basin. Ninety-two (92) surface soil samples (bulk soil) and their corresponding rice samples, 21 irrigation water samples, and 18 fertilizer samples were collected from two typical counties and quantified for the heavy metals (i.e., As, Cd, Cu, Hg, Pb, and Zn) concentrations. The results showed that As, Cd, and Zn were the main contaminants in soils in the studied area. Additionally, elevated Hg content in soils might also pose risks to the local ecosystem. Cadmium and As demonstrated high mobility, and their average contents in rice grains were slightly higher than the permissible threshold (0.20 mg kg−1). Moreover, Pb, As, and Cd intake via rice consumption might result in potential risks to local residents. Metal distribution revealed that pollution in the studied area is non-homogeneous, and agricultural activities (As, Cu, and Cd), transportation emission (Cu and Pb), coal combustion (Hg and As), and smelting activities (Zn, Pb, and Cu) were ascertained as the potential sources based on the Positive matrix factorization (PMF) analysis results.

Source apportionment of heavy metals in urban road dust in a continental city of eastern China: Using Pb and Sr isotopes combined with multivariate statistical analysis

To comprehensively analyze the characteristics and sources of heavy metals (Pb, Cd, Cu, Zn, Sr, V and Co), urban road dust samples from nine roads were collected in Nanchang, China in September 2013. The average concentrations of above heavy metals were 0.4 (V)-5.4 (Cd) times of their corresponding soil background values. The distribution patterns of Cu, Zn, Sr showed higher values mainly located in the northern and eastern parts of the study area, which indicated heavy metals in polluted industry areas migrated easily to near road dust. Enrichment factors (EFs) indicated that Cd, Cu, Sr, Zn and Pb in the road dust of Nanchang presented enrichment to some extent. Although the concentrations of Cd and Cu in the road dust were much lower than those recorded in urban areas of foreign researches, the highest mean EF of Cd (EF = 9) and the maximum EF of Cu (EF = 30) should be taken into consideration seriously. Based on isotopes source apportionment, Pb was mainly from vehicular traffic and industrial emissions, with the contribution rates of 61% and 26%, respectively, and Sr originated from anthropogenic source and soil source, with contributions of 59.7% and 40.3%, respectively. Based on multivariate statistical analysis, total heavy metals primarily originated from vehicular traffic for 43%, industrial emissions for 29%, soil source for 21%, and unknown sources for 7%, and Pb derived from traffic emissions with mean contribution of 62%, and Sr from soil-derived dust with mean contribution of 58%. The isotope tracing results were in good agreement with the multivariate statistical analysis.

Pollution characteristics, human health risk through multiple exposure pathways, and source apportionment of heavy metals in PM10 at Indo-Gangetic site

This study assesses the heavy metal (Pb, Zn, Ni, Fe, Mn, Cr, Cd, As, Co and Cu) concentrations in PM10 in Agra, India. The analysis of heavy metals was carried out by Inductively Coupled Plasma-Mass Spectrometer (ICP-MS) after digestion of quartz fiber filter paper. The annual mean concentration for PM10 was 214.61 μg m−3 and higher than WHO and NAAQS limit. The results showed mean value of heavy metals followed the order: Fe > Zn > Cr > Mn > Pb > Ni > Cu > Co > As>Cd. The carcinogenic risks of As, Cd, Co, Cr, Pb and Ni for both children and adults via dermal contact and ingestion exposure were within the acceptable level (<1 × 10−4). Co showed potential carcinogenic risk only for adults and Cr for both children and adults via inhalation. The HQ values for Cr, Mn, Fe, Ni, Cu, Zn, As, Pb and Cd via ingestion and dermal contact exposure for both children and adults were all lower than the safe level. The HQ values for Mn, Co, Ni and As to both children and adults via inhalation and for Co to only children via ingestion exposure were higher than the safe level. The source identification was confirmed through enrichment factor (EF), cluster analysis (CA) and principal component analysis (PCA).

Spatial distribution and source apportionment of heavy metals in soil from a typical county-level city of Guangdong Province, China

The contents of ten heavy metals (Cr, Hg, As, Pb, Ni, Cd, Ti, Cu, Zn and V) in 413 topsoil samples from Puning City, Guangdong Province, China were investigated. Obvious enrichment of Hg, As, Pb, Cd and Zn were presented, and the contents of Hg and As in 5.8% and 3.4% samples respectively were higher than the guideline values recommended by the Chinese Environmental Quality Standard for Soils. Chromium and V were presented no enrichment and no pollution. According to one-way analysis of variance, the mean contents of Hg, Pb, Cu and Zn in land for construction were significantly higher than farmland and natural vegetation, but the land use had no obvious effect on other heavy metals. Furthermore, the potential sources of ten heavy metals were identified and apportioned in combination with geostatistics, correlation analysis and positive matrix factorization model. The results were following as: a) Pb, Zn and Cu mainly origin from vehicle emission and atmosphere deposition, and the hotspots approximately distributed in the areas of intensive traffic and near main roads; b) Hg and Cd were derived to industrial activities related to pharmaceutical industries, the textile and dyeing industries and e-waste recycling industries, and high-value areas were mainly concentrated in the northeast of the urban area where the industrial parks have been distributed; c) Soil parent material (Jurassic shale) was the main source of Cr, Ni, V and Ti; d) As mainly came from agricultural inputs such as pesticides or herbicides, livestock and fertilizers. Meanwhile, the contributions of four sources were 33.08%, 24.04%, 27.11% and 15.77% of the total contribution, respectively.

Assessment of the Geographical Detector Method for investigating heavy metal source apportionment in an urban watershed of Eastern China

Assessing heavy metal pollution in river sediments and identifying the key factors contributing to metal pollution are critical components for devising river environmental protection and remediation strategies to protect human and ecological health. This is especially important in urban areas where metals from a wide range of sources contribute to sediment pollution. In this study, the metal enrichment factor (EF) was used to measure the watershed distribution of Cu, Zn, Pb and Cd in sediments in the Wen-Rui Tang urban river system in Wenzhou, Eastern China. The Geographical Detector Method (GDM) was specifically evaluated for its ability to analyze spatial relationships between metal EFs and their anthropogenic and natural control factors, including densities of industry (DI), livestock (DL), service industries (DS), population (DP), and roads (DR), along with agricultural area (AG), sediment total organic carbon (TOC), and soil types (ST). Results showed that the watershed was highly contaminated by all metals with an EF trend of Cd ≫ Zn > Cu > Pb. The spatial distribution of EFs demonstrated high contamination of all metals in the southwestern region of the watershed where industrial activities were concentrated, and higher Cu and Zn concentrations in the northeastern region having a high density of livestock production. GDM results identified DI as the dominant determinant for all metals, while TOC and ST were determined to have a moderate secondary influence for Zn, Pb and Cd. Additionally, GDM revealed several additive and nonlinear interactions between anthropogenic and natural factors influencing metal concentrations. Compared to other correlation, multiple linear regression and geographically weighted regression, GDM demonstrated distinct advantages of being able to assess both categorical and continuous variables and determine both single and multiple factor interactions. These attributes provide a more comprehensive understanding of metal spatial distributions while avoiding multicollinearity issues when identifying significant contributing factors at the watershed scale.

Uncertainty analysis in source apportionment of heavy metals in road dust based on positive matrix factorization model and geographic information system

Based on 36 road dust samples from an urbanized area of Beijing in September 2016, the information about sources (types, proportions, and intensity in spatial) of heavy metals and uncertainties were analyzed using positive matrix factorization (PMF) model, bootstrap (BS), geographic information system (GIS) and Kriging. The mean concentration of most heavy metals was higher than the corresponding background, and mean concentration of Cd was six times of its background value. Types and proportions of four sources were identified: fuel combustion (33.64%), vehicle emission (25.46%), manufacture and use of metallic substances (22.63%), and use of pesticides, fertilizers, and medical devices (18.26%). The intensity of vehicle emission and the use of pesticides, fertilizers, and medical devices were more homogeneous in spatial (extents were 1.285 and 0.955), while intensity of fuel combustion and the manufacture and use of metallic substances varied largely (extents were 4.172 and 5.518). Uncertainty analysis contained three aspects: goodness of fit, bias and variability in the PMF solution, and impact of input data size. Goodness of fit was assessed by coefficient of determination (R2) of predicted and measured values, and R2 of most species were higher than 0.56. Influenced by an outlier, R2 of Ni decreased from 0.59 to 0.11. Result of bootstrap (BS) showed good robust of this four-factor configuration in PMF model, and contributions of base run of factors to most species were contained in the small interquartile range and close to median values of bootstrap. Size of input data also had influence on results of PMF model. Residuals changed largely with the increase of number of site, it varied at first and then kept stable after number of site reached 70.

Multivariate receptor models and robust geostatistics to estimate source apportionment of heavy metals in soils

Absolute principal component score/multiple linear regression (APCS/MLR) and positive matrix factorization (PMF) were applied to a dataset consisting of 10 heavy metals in 300 surface soils samples. Robust geostatistics were used to delineate and compare the factors derived from these two receptor models. Both APCS/MLR and PMF afforded three similar source factors with comparable contributions, but APCS/MLR had some negative and unidentified contributions; thus, PMF, with its optimal non-negativity results, was adopted for source apportionment. Experimental variograms for each factor from two receptor models were built using classical Matheron's and three robust estimators. The best association of experimental variograms fitted to theoretical models differed between the corresponding APCS and PMF-factors. However, kriged interpolation indicated that the corresponding APCS and PMF-factor showed similar spatial variability. Based on PMF and robust geostatistics, three sources of 10 heavy metals in Guangrao were determined. As, Co, Cr, Cu, Mn, Ni, Zn, and partially Hg, Pb, Cd originated from natural source. The factor grouping these heavy metals showed consistent distribution with parent material map. 43.1% of Hg and 13.2% of Pb were related to atmosphere deposition of human inputs, with high values of their association patterns being located around urban areas. 29.6% concentration of Cd was associated with agricultural practice, and the hotspot coincided with the spatial distribution of vegetable-producing soils. Overall, natural source, atmosphere deposition of human emissions, and agricultural practices, explained 81.1%, 7.3%, and 11.6% of the total of 10 heavy metals concentrations, respectively. Receptor models coupled with robust geostatistics could successfully estimate the source apportionment of heavy metals in soils.

Multivariate linear regression model for source apportionment and health risk assessment of heavy metals from different environmental media

The study evaluated source apportionment of heavy metals in vegetable samples from the potential sources of fertilizer, water and soil samples collected along the Changjiang River delta in China. The results showed that 25.72% of vegetable samples (Brassica chinensis L.) containing Pb, and Cd, Cu, Hg and Zn at relatively serious levels were from soil. Combined with principle component analysis (PCA) and cluster analysis (CA), the results of the spatial distribution of heavy metals in different environmental media indicated that fertilizer, water and soil were the main sources of heavy metals in vegetables. The results of multivariate linear regression (MLR) using partition indexes (P) showed that fertilizer contributed to 38.5%, 40.56%, 46.01%, 53.34% and 65.25% of As, Cd, Cu, Pb and Zn contents in vegetables, respectively. In contrast, 44.58% of As, 32.57% of Hg and 32.83% of Pb in vegetables came from soil and 42.78% of Cd and 66.97% of Hg contents in vegetables came from the irrigation water. The results of PCA and CA verified that MLR using P was suitable for determining source apportionment in a vegetable. A health risk assessment was performed; As, Cd and Pb contributed to more than 75% of the total hazard quotient (THQ) values and total carcinogenic risk values (Risktotal) for adults and children through oral ingestion. More than 70% of the estimated THQ and Risktotal is contributed by water and fertilizer. Therefore, it is necessary to increase efforts in screening limits/levels of heavy metals in fertilizer and irrigation water and prioritize appropriate pollution management strategies.

Comprehensive risk assessment and source apportionment of heavy metal contamination in the surface sediment of the Yangtze River Anqing section, China

The Yangzi River has been seriously affected by intensive anthropogenic activities in recent years. In this study, the levels and distribution, risk assessment, and source apportionment of heavy metals in sediments were investigated in the Yangtze River (Anqing section). The heavy metal concentration and chemical characteristics (Cu/Pb/Zn/Cd/Cr/As/Hg) of 16 samples over four sections were measured using Atomic Fluorescence Spectrometry and Graphite Furnace Atomic Absorption Spectrometry. The metal concentration in the study area showed variance in different sampling sites. The mean heavy metal content was also higher than the background value. Vertical analysis of metal concentration showed the decreasing order from surface sediment. The analysis of Hakanson’s potential risk assessment indicated that the total potential ecological risk of these elements amounted to a considerable pollution level in the study area. The result of geo-accumulation index indicated that the risk of the studied metals had the following decreasing order: Cd > Hg > Pb > Cu > Zn > As > Cr. Based on the result of enrichment factor and principal component analysis, Cr, As, Hg, and partly Zn, Cu, and Cd mostly originated from natural sources. Pb and partly Cu and Zn mostly originated from gas companies, industries, and traffic sources, whereas Cd and partly Zn represented agricultural activities, such as fertilizer and pesticide consumption.

Source apportionment of heavy metals and their ecological risk in a tropical river basin system.

Surface water and sediment samples were collected from Ajay River basin to appraise the behavior of heavy metals with surrounding environments and their inter-elemental relationships. Parameters like pH and organic carbon are having a minimal role in heavy metal distribution while some elements like Fe and Cu showed great affinity for organic matter based on linear regression analysis (LRA). Ficklin diagram justified that river basin is not contaminated through acidic pollutants. The river basin is highly enriched with Cu, Cd, Pb, and Ni which were much higher than world average values, average shale standard, effect range low (ERL), and threshold effect level (TEL). PCA and LRA verified that Cu, Cd, Pb, and Ni were mainly derived from anthropogenic inputs, and others like Fe, Mn, Zn, and Co came from geogenic sources. Pollution indices revealed that river basin is moderately to highly contaminated by Cu, Cd, and Ni. Furthermore, Ajay River basin is under strong potential ecological risk based on the obtained value of risk index and probable effect level/effect range median quotient index. However, river basin is strongly influenced by lithological properties, diversified hydrogeological settings, mineralization and mobilization of subsurface materials, and urban and industrial effluents which are controlling the heavy metals.

A modified receptor model for source apportionment of heavy metal pollution in soil

Source apportionment is a crucial step toward reduction of heavy metal pollution in soil. Existing methods are generally based on receptor models. However, overestimation or underestimation occurs when they are applied to heavy metal source apportionment in soil. Therefore, a modified model (PCA-MLRD) was developed, which is based on principal component analysis (PCA) and multiple linear regression with distance (MLRD). This model was applied to a case study conducted in a peri-urban area in southeast China where soils were contaminated by arsenic (As), cadmium (Cd), mercury (Hg) and lead (Pb). Compared with existing models, PCA-MLRD is able to identify specific sources and quantify the extent of influence for each emission. The zinc (Zn)-Pb mine was identified as the most important anthropogenic emission, which affected approximately half area for Pb and As accumulation, and approximately one third for Cd. Overall, the influence extent of the anthropogenic emissions decreased in the order of mine (3 km) > dyeing mill (2 km) ≈ industrial hub (2 km) > fluorescent factory (1.5 km) > road (0.5 km). Although algorithm still needs to improved, the PCA-MLRD model has the potential to become a useful tool for heavy metal source apportionment in soil.

Combining emission inventory and isotope ratio analyses for quantitative source apportionment of heavy metals in agricultural soil

Two quantitative methods (emission inventory and isotope ratio analysis) were combined to apportion source contributions of heavy metals entering agricultural soils in the Lihe River watershed (Taihu region, east China). Source apportionment based on the emission inventory method indicated that for Cd, Cr, Cu, Pb, and Zn, the mean percentage input from atmospheric deposition was highest (62–85%), followed by irrigation (12–27%) and fertilization (1–14%). Thus, the heavy metals were derived mainly from industrial activities and traffic emissions. For Ni the combined percentage input from irrigation and fertilization was approximately 20% higher than that from atmospheric deposition, indicating that Ni was mainly derived from agricultural activities. Based on isotope ratio analysis, atmospheric deposition accounted for 57–93% of Pb entering soil, with the mean value of 69.3%, which indicates that this was the major source of Pb entering soil in the study area. The mean contributions of irrigation and fertilization to Pb pollution of soil ranged from 0% to 10%, indicating that they played only a marginally important role. Overall, the results obtained using the two methods were similar. This study provides a reliable approach for source apportionment of heavy metals entering agricultural soils in the study area, and clearly have potential application for future studies in other regions.

Risk assessment, spatial distribution, and source apportionment of heavy metals in Chinese surface soils from a typically tobacco cultivated area

The heavy metals (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) in the surface soils of tobacco (Nicotiana tabacum L.) fields in Jiangxi Province were analyzed, and the mean heavy metal concentrations were 3.55, 0.19, 25.89, 14.96, 0.25, 10.89, 27.80, and 44.00mg/kg, respectively. Spatial distribution analysis showed that the highest concentrations were recorded in the north-western, south-western, and mid-eastern parts of the study area. The index of geo-accumulation and pollution index indicated modest enrichment with Cd and Hg, which were the only two metals posing a potentially high ecological risk to the local agricultural environment. The health risk assessment showed no considerable non-carcinogenic or carcinogenic risks for children and adults from these elements. The principal component analysis (PCA) and cluster analysis (CA) found that the variations in the Cr and Ni concentrations were largely on account of the soil parent rocks, but the As, Cd, Cu, and Hg variations in the soil were largely owing to agricultural practices of years. However, the main factor influencing Pb and Zn was atmospheric deposition.

Source Apportionment of Heavy Metals in Farmland Soils Around Mining Area Based on UNMIX Model

Forty-two farmland soil samples were collected from the mining area of Huize City, Yunnan Province, to identify the sources of metals in the farmland soils, and 14 selected elements in farmland soils were detected. The UNMIX model was applied to identify the sources of the metals. The inverse distance weighted interpolation method of statistical analysis in ArcGIS was used to develop a spatial distribution map of the six severely polluted metals to validate the source apportionment results of the UNMIX model. The results indicated that:① the metals, Pb, Zn, and Cd, in the studied farmland soils were seriously polluted according to the soil background values of Yunnan Province and the Chinese environmental quality standard; ② there were three potential sources of metals according to UNMIX model:source 1 was the anthropogenic sources caused by industrial activities with a source contribution rate of 16.32%; source 2 was the anthropogenic sources caused by coal combustion and fertilization with a source contribution rate of 68.26%; and source 3 was the combined sources caused by mining related activities and soil parent materials with a source contribution rate of 15.42%; and ③ the spatial distribution of selected metals was in accordance with the local land-use and the results of the UNMIX model. Therefore, the UNMIX model can be effectively applied to the source apportionment of heavy metals in farmland soils around the mining area.