Contamination characteristics and source apportionment of trace metals in soils around Miyun Reservoir

Development of an integrated framework for dissecting source-oriented ecological and health risks of heavy metals in soils

Heavy metals (HMs) in soils pose significant risks on ecosystem and human health. To design targeted regulatory measures for mitigating and controlling the risk, it is necessary to accurately identify the pollution sources and environmental risks of soil HMs, as well as to reveal the linkages between them. To date, yet systematic investigation aimed at deciphering the links between source apportionment of soil HMs and their associated environmental risks is still lacking. To fill the gap, an integrated framework has been developed in this study and applied for dissecting the source-sink relationship and source-oriented ecological and health risks of soil HMs in Shanxi, a province with rich coal resource, in which long-term coal mining activities in history has resulted in soil HMs pollution and unavoidably posed environmental risks. Two advanced receptor models, multivariate curve resolution alternating least squares based on maximum likelihood principal component analysis (MCR-ALS/MLPCA) and multilinear engine 2 (ME2), have been employed for apportioning the potential sources, and their apportionment results are jointly incorporated into a modified ecological risk index and a probabilistic health risk assessment model for identifying the source-oriented ecological and health risks posed by soil metals. The results show that the soils in study area have been polluted by HMs (i.e., Cd, Cr, Hg and As) to varying degrees. Industrial activities (35%-35.8%), agricultural activities (11.1%-20.5%), atmospheric deposition (10.5%-13%) and mix source (31.5%-42.6%) are apportioned as the main contributors of soil HMs in the area. The source-oriented ecological risk assessment suggests Hg has presented significant ecological risk and largely contributed by the sources from atmospheric deposition and industrial activities. The source-oriented health risk assessment shows the non-carcinogenic hazard level and carcinogenic risk posed by soil HMs in the study area are acceptable. Relatively, industrial activities and mix source have contributed more on the health risks.

Tracing and source fingerprinting of metals from the southern coastal sediments in Bangladesh

Trace element pollution from anthropogenic sources is increasingly widespread. This pollution in terrestrial environments threatens agricultural crop production, while in aquatic environments, it threatens fish cultivation. The contamination of these crucial food sources raises significant concerns regarding food safety, security, and its potential adverse effects on human health. Coastal areas are particularly vulnerable to heavy metal pollution due to their proximity to industrial and urban centres, as well as their susceptibility to contamination from marine sources. In attempting to identify the sources of heavy metals (As, Cu, Cr, Cd, Fe, Hg, Mn, Ni, Pb, and Zn) and measure their contributions, we collected soil samples from thirty sites along the three coastal districts (Patuakhali, Barguna, and Bhola) in Bangladesh. Using atomic absorption spectroscopy, heavy metal concentrations in soil samples were measured and three receptor models (PMF, PCA-MLR, and UNMIX) were applied to detect their sources. Pairwise correlation analysis of metal concentrations in 30 sites across 3 coastal districts showed all possible patterns, including both significant and insignificant positive and negative relationships between different metals, except for As and Hg which did not display any significant relationships with other metals. The concentrations of Cu, Fe, Mn, Ni, and Zn exceed the US-EPA sediment quality standard. The applied PCA-MLR, PMF, and UNMIX models identified several sources of heavy metal contamination, including (i) mixed anthropogenic and natural activities: contribution of 59%, 37%, and 43%, and (ii) vehicle emissions: contribution of 23%, 26% and 29%. The recognized metal sources should be prioritised to avoid the discharge of poisonous pollutants from anthropogenic factors and any possible future exposure. This study's findings have implications for ongoing monitoring and management of heavy metal contamination in coastal environments to mitigate potential health and ecological impacts and can inform policy development and management strategies.

Distribution, Source, and Ecological Risk of Heavy Metals in Sewage Irrigation of Taiyuan, Shanxi Province, China

The pollution of agricultural soil by heavy metals is a significant environmental issue that has a serious impact on human health and food security. This study focused on investigating the presence of heavy metal pollution in sewage-irrigated soils in Taiyuan city. A total of 110 soil samples were analyzed for the presence of As, Hg, Cd, Pb, Cr, Cu and Zn. The results showed that the concentrations of these metals ranged from 0.06 to 26.74 mg/kg for As, 0.00 to 0.84 mg/kg for Hg, 0.03 to 0.69 mg/kg for Cd, 44.32 to 100.09 mg/kg for Pb, 9.85 to 42.19 mg/kg for Cr, 13.38 to 53.72 mg/kg for Cu, and 42.77 to 145.47 mg/kg for Zn. The average concentrations of these metals were found to be below the risk values specified in the "Soil environmental quality (GB15618-2018)", except for As and Cd in three sampling points in Xiaodian District. The heavy metal pollution in these areas can be attributed to various sources, such as industrial activities, the use of fertilizers and pesticides, and the irrigation process. According to the geo-accumulation index, the agricultural soil in the Taiyuan irrigation area was found to be uncontaminated by Zn, Cr, Cu, and As, and lightly contaminated by Cd, Hg, and Pb. The Nemerow Pollution Index indicated that the soil at all sampling points exhibited a slight level of pollution. Moreover, the ecological risk assessment indicated that all heavy metals posed a slight level of pollution. The findings of this study provide a scientific basis for the development of effective policies and measures for soil environmental protection and pollution control.

Impacts of land use/cover and slope on the spatial distribution and ecological risk of trace metals in soils affected by smelting emissions

The soil contamination around smelting sites shows high spatial heterogeneity. This study investigated the impacts of distance, land use/cover types, land slopes, wind direction, and soil properties on the distribution and ecological risk of trace metals in the soil around a copper smelter. The results demonstrated that the average concentrations of As, Cd, Cu, Pb, and Zn were 248.0, 16.8, 502.4, 885.6, and 250.2 g mg kg-1, respectively, higher than their background values. The hotspots of trace metals were primarily distributed in the soil of smelting production areas, runoff pollution areas, and areas in the dominant wind direction. The concentrations of trace metals decreased with the distance to the smelting production area. An exponential decay regression revealed that, depending on the metal species, the influence distances of smelting emissions on trace metals in soil ranged from 450 to 1000 m. Land use/cover types and land slopes significantly affected trace element concentrations in the soil around the smelter. High concentrations of trace metals were observed in farmland, grassland, and flatland areas. The average concentrations of trace metals in the soil decreased in the order of flat land > gentle slope > steep slope. Soil pH values were significantly positively correlated with Cd, Cu, Pb, Zn, and As, and SOM was significantly positively correlated with Cd, Pb, and Zn in the soil. Trace metals in the soil of the study area posed a significant ecological risk. The primary factors influencing the distribution of ecological risk, as determined by the Ctree analysis, were land slope, soil pH, and distance to the source. These results can support the rapid identification of high-risk sites and facilitate risk prevention and control around smelting sites.

Source identification and health risk assessment of heavy metals with mineralogy: the case of soils from a Chinese industrial and mining city

Understanding the precise sources of heavy metals (HMs) in soil and the contribution of these sources to health risks has positive effects in terms of risk management. This study focused on the HMs in the soil of five land uses in an industrial and mining city. The sources of HMs in soils were identified, and the soil mineralogical characteristics and health risks of HMs were discussed. The results showed that the HMs (Cu, Zn, Ni, Cd, Pb) found in the soil of the five land uses were affected by human activities. For example, the Cu in grassland, gobi beach, woodland, green belt, and farmland is 22.3, 3.5, 22.5, 16.7, and 21.3 times higher than the soil background values in Gansu Province, respectively. The Positive Matrix Factorization model (PMF) results revealed that traffic emissions and industrial and agricultural activities were the primary sources of HMs in the soil, with industrial sources accounting for the largest share at 55.79%. Furthermore, various characteristics proved that the studied HMs were closely related to smelting products. Concentration-oriented health risk assessments showed that HMs in the different soil types held non-carcinogenic and carcinogenic risks for children and adults. Contamination source-oriented health risk assessments of children and adults found that industrial activities controlled non-carcinogenic and carcinogenic risks. This study highlighted the critical effects of smelting on urban soil and the contribution of pollution sources to health risks. Furthermore, this work is significant in respect of the risk control of HMs in urban soils.

Combination of enrichment factor and positive matrix factorization in the estimation of potentially toxic element source distribution in agricultural soil

The study intended to assess the level of pollution of potential toxic elements (PTEs) at different soil depths and to evaluate the source contribution in agricultural soil. One hundred and two soil samples were collected for both topsoil (51), and the subsoil (51) and the content of PTEs (Cr, Cu, Cd, Mn, Ni, Pb, As and Zn) were determined using inductively coupled plasma-optical emission spectroscopy (ICP-OES). The concentrations of Zn and Cd in both soil horizons indicated that the current study levels were higher than the upper continental crust (UCC), world average value (WAV), and European average values (EAV). Nonetheless, the concentration values of PTEs such as Mn and Cu for EAV, As, Cu, Mn, and Pb for UCC, and Pb for WAV were lower than the average values of the corresponding PTEs in this study. The single pollution index, enrichment factor, and ecological risk revealed that the pollution level ranged from low to high. The pollution load index, Nemerow pollution index, and risk index all revealed that pollution levels ranged from low to high. The spatial distribution confirmed that pollution levels varied between the horizons; that is, the subsoil was considered slightly more enriched than the topsoil. Principal component analysis identified the PTE source as geogenic (i.e. for Mn, Cu, Ni, Cr) and anthropogenic (i.e. for Pb, Zn, Cd, and As). PTEs were attributed to various sources using enrichment factor-positive matrix factorization (EF-PMF) and positive matrix factorization (PMF), including geogenic (e.g. rock weathering), fertilizer application, steel industry, industrial sewage irrigation, agrochemicals, and metal works. Both receptor models allotted consistent sources for the PTEs. Multiple linear regression analysis was applied to the receptor models (EF-PMF and PMF), and their efficiency was tested and assessed using root-mean-square error (RMSE), mean absolute error (MAE), and R² accuracy indicators. The validation and accuracy assessment of the receptor models revealed that the EF-PMF receptor model output significantly reduces errors compared with the parent model PMF. Based on the marginal error levels in RMSE and MAE, 7 of the 8 PTEs (As, Cd, Cr, Cu, Ni, Mn, Pb, and Zn) analysed performed better under the EF-PMF receptor model. The EF-PMF receptor model optimizes the efficiency level in source apportionment, reducing errors in determining the proportion contribution of PTEs in each factor. The purpose of building a model is to maximize efficiency while minimizing inaccuracy. The marginal error limitation encountered in the parent model PMF was circumvented by EF-PMF. As a result, EF-PMF is feasible and useful for apparently polluted environments, whether farmland, urban land, or peri-urban land.

Human health risk apportionment from potential sources of heavy metals in agricultural soils and associated uncertainty analysis

Evaluating heavy metal pollution level in the soils and apportioning the source-specific health risk of heavy metals are critical for proposing environmental protection and remediation strategies to protection human health. This study explored heavy metal pollution and associated source-specific health risks in a typical rural industrial area, southwestern China. A total of 105 topsoil samples were collected and the concentrations of heavy metals, including As, Cd, Cr, Cu, Ni, Pb and Zn, were determined. Pollution load index and enrichment factors were used to evaluate the pollution level of heavy metals. Positive matrix factorization (PMF) model was applied to apportion the heavy metals and the associated source-specific health risks to adults and children were estimated via combining the PMF model with the health risk assessment. The results indicated that the soils were highly polluted by multiple heavy metals, especially for Cd, with the EF values of 24.94 and 22.55 in the upstream and downstream areas, respectively. Source apportionment results showed that atmospheric deposition, smelting activities, fertilizer and sewage application, and agrochemical utilization were the main anthropogenic sources, with the contributions of 37.11%, 23.69%, 19.69%, and 19.51%, respectively. Source-specific risk assessment identified atmospheric deposition as the priority source for the non-carcinogenic (NCR) and carcinogenic risks (CR) in the study area, with the contribution of 43.71% and 52.52% for adults, and 44.29% and 52.58% for children, respectively. Moreover, non-carcinogenic and carcinogenic risks posed to children (NCR: 2.84; CR: 1.31 × 10^-4) from four sources was higher than those posed to adults (NCR: 0.29; CR: 5.86 × 10^-5). The results of source-specific health risk assessment provided the valuable information on the priority sources for pollution preventing and risk controlling.

Heavy Metal Pollution and Source Contributions in Agricultural Soils Developed from Karst Landform in the Southwestern Region of China

Heavy metal pollution of soil in agricultural areas is the most prominent environmental pollution problem in China, seriously affecting human health and food security. It has become one of the environmental problems to which all sectors of society attach great importance. Soil heavy metals in the weathering area of hazardous geological bodies in southwest China have naturally high background attributes. Therefore, ecological risk assessment and analysis of potential sources of soil heavy metals in southwest China is of great significance for soil health management, soil heavy metal pollution control and territorial spatial planning. In this study, we collected 787 soil samples (0-20 cm) in Xuanwei County in China and analyzed the concentrations of As, Cd, Cr, Cu, Hg, Ni, Pb and Zn in soils. Igeo, RI, HI and CR were used to calculate the pollution levels, ecological risks and human health risks. Additionally, the PMF model and one-way ANOVA were used to identify the potential sources and discuss the factors affecting the enrichment of heavy metals. The results showed that the mean contents of the surface soils were 1.190 (Cd), 139.4 (Cr), 96.74 (Cu), 0.081 (Hg), 56.97 (Ni), 46.66 (Pb) and 130.1 (Zn) mg/kg. All heavy metals exceeded the background values of the A layer soil in Yunnan Province. The I_geo showed that Cd was the most hazardous element in the study area, followed by Cu, Cr, As, Ni and Pb. The RI showed that low ecological risks, moderate ecological risks, considerable ecological risks and strong ecological risks accounted for 3.81%, 55.27%, 37.74% and 3.18%, respectively, of the total samples, and Cd was the main dominant element. The HI values of the As element in children were greater than 1, indicating a non-carcinogenic risk, and other elements' risks were acceptable. The CR values of Cr and Ni were higher than their limits (1 × 10^-4), and both had carcinogenic risks in children and adults, as did As in children. According to the PMF model, four heavy metals sources were identified: geological sources (32%), sources from mining activities (19.38%), atmospheric deposition sources (17.57%) and agricultural sources (31.05%). Thereinto, As and Pb were mainly derived from agricultural sources, Cd and Cr were mainly associated with geological sources, Cu was largely from mining activity sources, Hg was mainly from atmospheric deposition sources and Ni and Zn were mainly from geological sources, mining activities and agricultural activities. The parent material has a significant influence on the enrichment of heavy metals in the soil, and the heavy metals are significantly enriched in the carbonate parent material and quaternary parent material. Topography also plays a role in heavy metal accumulation; Cd, Cr, Cu, Ni and Zn gradually decreased with the increase in altitude, and As and Pb increased with the increase in altitude. Mn-oxide played a crucial part in the enrichment of Cu and Zn, while SOC, K₂O and pH had little influence on the accumulation of heavy metals.

Elements and Pb isotopic composition as evidence for contaminant-metal dispersal in surficial soil and sediment of drinking water source in Beijing, China

Pb pollution in soils and sediments has adverse effects on human health and the environment. Identifying and quantifying the relative contribution of Pb pollution sources are key issues to control Pb pollution. In this study, U, Th and Pb concentration, Pb enrichment coefficient and Pb isotopic composition in the sediment and surface soil samples of the Miyun Reservoir and its upstream Chaohe, Baihe and Tanghe River were analysed to determine the source and relative contribution of Pb pollution. Results show a significant enrichment of Pb in the sediments of the Baihe River (2.7 ± 0.9). The enrichment of Pb in the soils in Baihe (8.0 ± 10.5) and Tanghe (313.3 ± 1139.4) is more obvious, and Pb is unevenly distributed in the soil in the Tanghe Basin. In general, soil is more seriously affected by human activities than sediment. The Pb isotope ratio indicates that mining activities and natural background are the main sources of Pb in soil and sediment. Based on the binary mixture model, the average contribution rate of mining activities to Pb pollution in the sediment is 21.5%, of which the contribution rates in the Miyun Reservoir, Chaohe, Baihe and Tanghe River are 14.86%, 17.20%, 41.03% and 26.32%, respectively. The average contribution rate of mining activities to soil Pb is 43.1%, among which the contribution rates in the Chaohe River Basin, Baihe River Basin and Tanghe River Basin are 58.79%, 60.98% and 36.24%, respectively. In summary, soils and sediments in the basin are affected by mining activities to varying degrees. Nevertheless, natural background is still the main source of Pb in the sediments in the basin and in soil in the Tanghe River. Mining activities are the main sources of Pb in soils in the Chaohe and Baihe River.

A joint method to assess pollution status and source-specific human health risks of potential toxic elements in soils

Potential toxic elements (PTEs) in soils follow various exposure pathways (e.g., ingestion, dermal contact, and inhalation) when migrating to the human body, and can threaten human health. The objective of this study was to quantitatively evaluate the pollution status and human health risk for different age groups (i.e., children and adults) caused by source-specific PTEs in the soil. A total of 425 topsoil samples (0-20 cm) were collected in the Zhangqiu District of China, and contents of eight PTEs (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) were analyzed simultaneously. The PTE sources were identified using a geostatistical method, and positive matrix factorization was used to apportion the contribution rate of each PTE source for human health risk of different age groups based on a health risk assessment model. Results showed that: (1) As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn contents all exceeded the background values in soil, indicating that there had been a slight enrichment of soil PTEs over time; (2) Cr and Ni were derived from natural sources, while Cd, Cu, and Zn were derived from transportation sources; industrial emissions and coal burning intensified pollution from Hg, Pb, and As in soils, as shown in the average contribution percentages of natural sources (24.36%), transportation sources (33.79%), and industrial sources (41.85%); (3) the non-carcinogenic risk caused by soil PTEs did not endanger human health, but the carcinogenic risk was close to the risk threshold, and should be given attention; the largest carcinogenic risk (36.53%) and non-carcinogenic risk (36.01%) for children were attributed to industrial sources, while the largest carcinogenic risk (34.98%) and non-carcinogenic risk (37.06%) for adults were attributed to transportation sources. The results presented in this study provide support for effectively preventing PTE health risks in different age groups from source-specific PTE pollution of the soil.

Incorporating bioaccessibility and source apportionment into human health risk assessment of heavy metals in urban dust of Xiamen, China

Heavy metals in urban dust could pose noticeable human health risks, but there are few studies focusing on comprehensive human health risk assessment with the incorporation of both bioaccessibility and source apportionment in urban dust. Thus, fifty-eight urban dust samples were collected from kindergartens in Xiamen to analyze the bioaccessibility-based, source-specific health risk of heavy metals (V, Co, Ni, As, Mo, Cr, Mn, Cu, Zn, and Pb). Most heavy metals, except for V and Mn, were significantly enriched in urban dust based on their values of geoaccumulation index (Igeo) and may be influenced by human activities. The oral bioaccessibility values of heavy metals, which were estimated by the Solubility/Bioaccessibility Research Consortium (SBRC) in vitro model, ranged from 1.563% to 76.51%. The source apportionment determined by applying the absolute principal component analysis-multiple linear regression (APCS-MLR) model indicated five main potential sources, coal combustion, traffic and industrial, natural, construction and furniture sources, and unidentified sources, with contributions of 34.09%, 20.72%, 18.72%, 7.597% and 18.87%, respectively, to the accumulation of heavy metals in urban dust. After incorporating bioaccessibility adjustments, lower non-carcinogenic and carcinogenic risks of heavy metals were observed than those based on total metal content, with the mean hazard index (HI) values being less than the threshold value (1) and the mean total carcinogenic risk (TCR) values exceeding the precautionary criterion (10-6) for both adults and children. By combining bioaccessibility-based health risk assessment and source apportionment, traffic and industrial emissions and coal combustion dominated the noncarcinogenic and carcinogenic risks induced by heavy metals in urban dust, respectively. This study is expected to promote the systematic integration of source apportionment and bioaccessibility into health risk estimation for heavy metal contamination in urban dust, thus providing useful implications for better human health protection.

An Integrated Approach for Source Apportionment and Health Risk Assessment of Heavy Metals in Subtropical Agricultural Soils, Eastern China

Unreasonable human activities may cause the accumulation of heavy metals (HMs) in the agricultural soil, which will ultimately threaten the quality of soil environment, the safety of agricultural products, and human health. Therefore, the accumulation characteristics, potential sources, and health risks of HMs in agricultural soils in China’s subtropical regions were investigated. The mean Hg, Cu, Zn, Pb, and Cd concentrations of agricultural soil in Jinhua City have exceeded the corresponding background values of Zhejiang Province, while the mean concentrations of determined 8 HMs were less than their corresponding risk-screening values for soil contamination of agricultural land in China. The spatial distribution of As, Cr, Ni, Cu, and Pb were generally distributed in large patches, and Hg, Zn, and Cd were generally sporadically distributed. A positive definite matrix factor analysis (PMF) model had better performance than an absolute principal component–multiple linear regression (APCS-MLR) model in the identification of major sources of soil HMs, as it revealed higher R2 value (0.81–0.99) and lower prediction error (−0.93–0.25%). The noncarcinogenic risks (HI) of the 8 HMs to adults and children were within the acceptable range, while the carcinogenic risk (RI) of children has exceeded the safety threshold, which needs to be addressed by relevant departments. The PMF based human health risk assessment model indicated that industrial sources contributed the highest risk to HI (32.92% and 30.47%) and RI (60.74% and 61.5%) for adults and children, followed by agricultural sources (21.34%, 29.31% and 32.94% 33.19%). Therefore, integrated environmental management should be implemented to control and reduce the accumulation of soil HMs from agricultural and industrial sources.

Human health risk visualization of potentially toxic elements in farmland soil: A combined method of source and probability

Human health is adversely affected by potentially toxic elements (PTEs) in the topsoil, entering the bodies via inhalation, ingestion, and dermal contact. To visualize human health risks, we investigated five PTEs (Cd, As, Pb, Hg, and Cr) in 72 farmland topsoil samples from a town in Chongqing City, southwest China. Based on the human health risk assessment model, sequential indicator simulation (SIS) and the positive matrix factorization model (PMF) were used to construct the spatial health risks and to analyze the sources of PTEs; finally, health risks were combined with the source by ArcGIS. Based on our results, the use of SIS is feasible for the prediction of the spatial distribution of PTEs. Among the risks, the non-cancer risk of As for children most likely exceeded the accepted level in some areas, making As a priority pollutant. Although the health risks of soil Cd were acceptable in the region, the spatial probability distribution of Cd> 0.3 mg/kg represents a threat as Cd enters the human food chain. Even if the industrial discharge was the lowest individual contributor (29.33%), due to the impact of industrial discharge, the total non-cancer risk with a high probability (>0.85) for children still exceeded the accepted level in the northwestern area, which should be regarded as the priority pollution source. The combined method was useful to reduce efforts in environmental management, thus providing a basis for soil remediation and pollution source control.

Source apportionment and source-specific health risk assessment of heavy metals in size-fractionated road dust from a typical mining and smelting area, Gejiu, China

Source-specific health risk apportionment for heavy metals is critical for pollution prevention and risk management in mining and smelting areas. An integrated method combining health risk assessments with the positive matrix factorization model was proposed to evaluate source-specific health risks for adults and children. A typical mining and smelting area was taken as an example in the present study to apportion the source-specific health risks to humans. A total of 37 road dust samples collected from the industrial (IA) and residential areas (RA) of Gejiu (China) were analyzed for heavy metals (Cd, Cr, Cu, Ni, Pb, and Zn). The results indicated that road dust in the study area was mainly contaminated with Cd, Cu, Pb, and Zn. Three potential sources, including atmospheric deposition, industrial waste, and natural sources, were identified and quantified, with contributions of 43.32%, 30.83%, and 25.85%, respectively. For non-carcinogenic risks, a similar trend of the source contribution was found for adults and children under the same land use; atmospheric deposition made the greatest contribution to the non-carcinogenic risk in both IA and RA. However, for carcinogenic risk, natural sources were the greatest contributor to human health risks in both IA and RA, followed by atmospheric deposition and industrial waste. The investigation in this study allowed the evaluation of health risks from potential contamination sources and the results provide valuable information on health risk mitigation strategies for environmental managers.

Quantitative source identification and apportionment of heavy metals under two different land use types: comparison of two receptor models APCS-MLR and PMF

At present, many researchers are increasingly aware of the importance of using models to identify heavy metal (HM) pollution sources. However, on the performance and application of different source identification models to HMs under different land use types had been studied little. In this study, comparison of absolute principal component scores-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models and their application characteristics in identifying pollution sources were carried out by using 11 HMs in Zhongwei City farmland and Shizuishan industrial park, Ningxia. The results indicated that HM pollution in farmland mainly came from pesticides, fertilizers, and deposition of the Yellow River, while the pollution in industrial park mainly originated from atmospheric deposition and various industrial productions. The APCS-MLR model had the problem of less identification sources and the difficulty to explain the complex pollution, while the PMF model not only identified more pollution sources, but also distinguished heavy metal-related sources for two different land use types and different industrial production conditions. It is of great significance the formulation of agricultural-related pesticides' and chemical fertilizers' rational use and various industrial production-related raw materials put in and emission control strategies.

Source apportionment of potentially toxic elements in soils using APCS/MLR, PMF and geostatistics in a typical industrial and mining city in Eastern China

Source apportionment of potentially toxic elements in soils is a critical step for devising soil sustainable management strategies. However, misjudgment or imprecision can occur when traditional statistical methods are applied to identify and apportion the sources. The main objective of the study was to develop a robust approach composed of the absolute principal component score/multiple linear regression (APCS/MLR) receptor model, positive matrix factorization (PMF) receptor model and geostatistics to identify and apportion sources of soil potentially toxic elements in typical industrial and mining city, eastern China. APCS/MLR and PMF were applied to provide robust factors with contribution rates. The geostatistics coupled with the variography and kriging methods was used to present factors derived from these two receptor models. The results indicated that mean concentrations of As, Cd, Cr, Cu, Hg, Ni, Pb and Zn exceeded the local background levels. Based on multivariate receptor models and geostatistics, we determined four sources of eight potentially toxic elements including natural source (parent material), agricultural practices, pollutant emissions (industrial, mining and traffic) and the atmospheric deposition of coal combustion, which accounted for 68%, 12%, 12% and 9% of the observed potentially toxic element concentrations, respectively. This study provides a reliable and robust approach for potentially toxic elements source apportionment in this particular industrial and mining city with a clear potential for future application in other regions.

Pollution Assessment and Source Apportionment of Trace Metals in Urban Topsoil of Xi'an City in Northwest China

Sixty-two topsoil samples were collected within the third ring road of Xi'an City in Northwest China and analyzed by X-ray fluorescence spectrometry for the concentrations of As, Ba, Co, Cr, Cu, Mn, Ni, Pb, V, and Zn. The pollution levels of trace metals were assessed by pollution index (PI) and Nemerow pollution index (NPI). Meanwhile, the sources of trace metals were apportioned by receptor models, including positive matrix factorization (PMF), UNMIX, and principal component analysis-multiple linear regression (PCA-MLR). The average concentrations of the trace metals analyzed in the urban soil exceeded the corresponding soil element background values of Shaanxi Province, especially for Co, which was 2.38 times higher than the corresponding background value. The mean of PI was 2.38 for Co, reflecting a moderate pollution level, and ranged from 1.07 to 1.72 for other trace metals, presenting slight pollution levels. The NPI of trace metals varied between 1.20 and 3.50 with an average of 2.00, indicating that trace metals presented slight pollution in 62.90% of soil samples, moderate pollution in 30.65% of soil samples, and heavy pollution in 6.45% of soil samples, respectively. Three sources of trace metals apportioned by the three receptor models were mixed nature and anthropogenic source, traffic exhaust, and industrial emissions. The contributions of them were 38.58%, 32.72%, and 28.70% from the PMF, 65.36%, 17.76%, and 16.88% through the UNMIX and 49.16%, 38.90%, and 11.94% via the PCA-MLR, respectively. Meanwhile, the study results suggested that the combined usage of multiple receptor models is a good method to apportion the source compositions and contributions of trace metals in urban soil.

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.

A new exploration of health risk assessment quantification from sources of soil heavy metals under different land use

Heavy metals in the topsoil affected adversely human health through inhalation, ingestion and dermal contact. The health risk assessment, which are quantified from soil heavy metals sources under different land use, can provide an important reference basis for preventing and controlling the soil heavy metals pollution from the source. In this study, simple statistical analysis and Positive Matrix Factorization (PMF) were used to quantify sources of soil heavy metals; then a health risk assessment (HRA) model combined with PMF was proposed to assess quantificationally the human health risk (including non-cancer risk and cancer risk) from sources under residential-land, forest-land and farm land. Xiang River New District (XRNQ) was chosen as the example and four significant sources were quantitatively analyzed in the study. For cancer risk, industrial discharge was the largest source and accounted for about 69.6%, 69.7%, 56.5% for adults under residential-land, forest-land and farm-land, respectively. For non-cancer risk, industrial discharge was still the largest significant source under residential-land and forest-land and accounted for about 41.7%, 39.2% for adult, respectively; while agricultural activities accounted for about 51.8% for adult under farm-land. The risk trend of children, including cancer risk and non-cancer risk, was similar with adults. However, the non-cancer risk areas of adults affected by industrial discharge was higher than that of children, while the cancer risk areas of adults were on the contrary. The new exploration was useful to assess health risk quantification from sources under different land use, thus providing certain reference in preventing and controlling the pollution from the source for local authorities effectively.

Contamination characteristics and source apportionment of heavy metals in topsoil from an area in Xi'an city, China

As soil-extractable elements potentially pose ecological and health risks, identifying their contamination characteristics and sources is crucial. Therefore, to understand topsoil trace elements in the urban ring zone from the Second Ring Road to the Third Ring of Xi'an city in China, we determined the concentrations of Zn, Co, V, As, Cu, Mn, Ba, Ni and Pb, and analyzed the sources of the contamination. The results showed that the individual pollution indices of Pb, Co, Cu, Zn, Ba, Ni, Mn, As, and V were 1.79, 1.48, 1.41, 1.33, 1.20, 1.07, 1.04, 0.99, and 0.99, respectively. Evaluation with the aid of the pollution load index (PLI) indicated slight soil contamination by these elements in the study area. Using the positive matrix factorization (PMF) method, we identified four sources of contamination, namely (1) a natural source, (2) traffic emission source, (3) industrial emission source, and (4) mixed source. PMF is an effective tool for source apportionment of heavy metals in topsoil. The contribution rates of the natural source, traffic source, mixed source, and industrial source to the heavy metal contamination were specified as 25.04%, 24.71%, 24.99%, and 25.26%, respectively. Considering the above, any attempt to reduce the soil environmental cost of urban development, has to take into account the heavy metal contamination of the topsoil from industries, traffic, and other activities.