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

A comprehensive study of source apportionment, spatial distribution, and health risks assessment of heavy metal(loid)s in the surface soils of a semi-arid mining region in Matehuala, Mexico

BACKGROUND

This study investigates the contamination level, spatial distribution, pollution sources, potential ecological risks, and human health risks associated with heavy metal(loid)s (i.e., arsenic (As), copper (Cu), iron (Fe), manganese (Mn), lead (Pb), and zinc (Zn)) in surface soils within the mining region of Matehuala, located in central Mexico.

OBJECTIVES

The primary objectives are to estimate the contamination level of heavy metal(loid)s, identify pollution sources, assess potential ecological risks, and evaluate human health risks associated with heavy metal(loid) contamination.

METHODS

Soil samples from the study area were analysed using various indices including Igeo, Cf, PLI, mCd, EF, and PERI to evaluate contamination levels. Source apportionment of heavy metal(loid)s was conducted using the APCS-MLR and PMF receptor models. Spatial distribution patterns were determined using the most efficient interpolation technique among five different approaches. The total carcinogenic risk index (TCR) and total non-carcinogenic index (THI) were used in this study to assess the potential carcinogenic and non-carcinogenic hazards posed by heavy metal(loid)s in surface soil to human health.

RESULTS

The study reveals a high contamination level of heavy metal(loid)s in the surface soil, posing considerable ecological risks. As was identified as a priority metal for regulatory control measures. Mining and smelting activities were identified as the primary factors influencing heavy metal(loid) distributions. Based on spatial distribution mapping, concentrations were higher in the northern, western, and central regions of the study area. As and Fe were found to pose considerable and moderate ecological risks, respectively. Health risk evaluation indicated significant levels of carcinogenic risks for both adults and children, with higher risks for children.

CONCLUSION

This study highlights the urgent need for monitoring heavy metal(loid) contamination in Matehuala's soils, particularly in regions experiencing strong economic growth, to mitigate potential human health and ecological risks associated with heavy metal(loid) pollution.

Assessment of the migration characteristics and source-oriented health risks of heavy metals in the soil and groundwater of a legacy contaminated by the chlor-alkali industry in central China

The chlor-alkali industry (CAI) is crucial for global chemical production; however, its operation has led to widespread heavy metal (HM) contamination at numerous sites, which has not been thoroughly investigated. This study analysed 122 soil and groundwater samples from a typical CAI site in Kaifeng, China. Our aim was to assess the ecological and health risks, identify the sources, and examine the migration characteristics of HMs at this site using Monte Carlo simulation, absolute principal component score-multiple linear regression (APCS-MLR), and the potential environmental risk index (Ei). Our findings revealed that the exceedance rates for Cd, Pb, Hg, and Ni were 71.96%, 45.79%, 49.59%, and 65.42%, respectively. Mercury (Hg) displayed the greatest coefficient of variation across all the soil layers, indicating a significant anthropogenic influence. Cd and Hg were identified as having high and extremely high potential environmental risk levels, respectively. The spatial distributions of the improved Nemerow index (INI), total ecological risk (Ri), and HM content varied considerably, with the most contaminated areas typically associated with the storage of raw and auxiliary materials. Surface aggregation and significant vertical transport were noted for HMs; As and Ni showed substantial accumulation in subsoil layers, severely contaminating the groundwater. Self-organizing maps categorized the samples into two different groups, showing strong positive correlations between Cd, Pb, and Hg. The APCS-MLR model suggested that industrial emissions were the main contributors, accounting for 60.3% of the total HM input. Elevated hazard quotient values for Hg posed significant noncarcinogenic risks, whereas acceptable levels of carcinogenic risk were observed for both adults (96.60%) and children (97.83%). This study significantly enhances historical CAI pollution data and offers valuable insights into ongoing environmental and health challenges.

Soil pollution and elemental sources along Barapukuria coal mine, Bangladesh: Implications for eco-environmental and health risk assessment

For the first time, different pollution indices and a receptor model have been used to quantify eco-environmental and health risk assessments as well as identify the sources of potentially toxic elements in soil along the Barapukuria Coal Mine (BCM). Individual indices include enrichment and contamination factors showing the soil samples are moderately to highly contaminated by arsenic, cobalt, chromium, copper, lead, and zinc and heavily contaminated by sulfur. According to the geo-accumulation index, there is significant pollution with arsenic (1.24 ± 0.38), lead (1.49 ± 0.58), cobalt (1.49 ± 0.58), and sulfur (1.63 ± 0.38). Modified hazard quotient and ecological risk factor values also suggest low to moderate environmental risk hazards from the same elements. The nemerow pollution index, pollution load index, nemerow risk index, ecological risk index, and toxic risk index of soil range from 1.65 to 3.03, 0.82-1.23, 11-26, 77-165, and 6.82-11.76 suggest low toxic risk and moderate pollution, among other synergistic indices. Health risk assessment indicates that iron poses lower cancer risk for children than adults, while both face unacceptable cancer risks from inhaling chromium, cobalt, or arsenic. Principal component and phylogenetic cluster analysis extracted by the multiple linear regression with the absolute principal component score (APCS-MLR) model refer to the fact that manganese, iron, titanium, and nickel have originated from geogenic sources, while coal mine effluents enrich elements like arsenic, chromium, zinc, lead, uranium, sulfur, thorium, and zinc and phosphorous sourced from agriculture. In addition, geogenic and anthropogenic sources, including mine and agriculture activities, could potentially pollute the soil and ecosystem. The findings are crucial for regional and national planners in devising strategies to mitigate potentially toxic element pollution in soil along coal mine areas.

A Monte Carlo simulation-based health risk assessment of heavy metals in soils of the tropical region in southern China

The disturbance of ecological stability may take place in tropical regions due to the elevated biomass density resulting from heavy metal and other contaminant pollution. In this study, 62 valid soil samples were collected from Sanya. Source analysis of heavy metals in the area was carried out using absolute principal component-multiple linear regression receptor modelling (APCS-MLR); the comprehensive ecological risk of the study area was assessed based on pollution sources; the Monte-Carlo model was used to accurately predict the health risk of pollution sources in the study area. The results showed that: The average contents of soil heavy metals Cu, Ni and Cd in Sanya were 5.53, 6.56 and 11.66 times higher than the background values of heavy metals. The results of soil geo-accumulation index (Igeo) showed that Cr, Mo, Mn and Zn were unpolluted to moderately polluted, Cu and Ni were moderately polluted, and Cd was moderately polluted to strongly polluted. The main sources of heavy metal pollution were natural sources (57.99%), agricultural sources (38.44%) and traffic sources (3.57%). Natural and agricultural sources were jointly identified as priority control pollution sources and Cd was the priority control pollution element for soil ecological risk. Heavy metal content in Sanya did not pose a non-carcinogenic risk to the population, but there was a carcinogenic risk to children. The element Zn had a high carcinogenic risk to children, and was a priority controlling pollutant element for the risk of human health, with agricultural sources as the priority controlling pollutant source.

Source identification and driving factor apportionment for soil potentially toxic elements via combining APCS-MLR, UNMIX, PMF and GDM

The contamination and quantification of soil potentially toxic elements (PTEs) contamination sources and the determination of driving factors are the premise of soil contamination control. In our study, 788 soil samples from the National Agricultural Park in Chengdu, Sichuan Province were used to evaluate the contamination degree of soil PTEs by pollution factors and pollution load index. The source identification of soil PTEs was performed using positive matrix decomposition (PMF), edge analysis (UNMIX) and absolute principal component score-multiple line regression (APCS-MLR). The geo-detector method (GDM) was used to analysis drivers of soil PTEs pollution sources to help interpret pollution sources derived from receptor models. Result shows that soil Cu, Pb, Zn, Cr, Ni, Cd, As and Hg average content were 35.2, 32.3, 108.9, 91.9, 37.1, 0.22, 9.76 and 0.15 mg/kg in this study area. Except for As, all are higher than the corresponding soil background values in Sichuan Province. The best performance of APCS-MLR was determined by comparison, and APCS-MLR was considered as the preferred receptor model for soil PTEs source distribution in the study area. ACPS-MLR results showed that 82.70% of Cu, 61.6% of Pb, 75.3% of Zn, 91.9% of Cr and 89.4% of Ni came from traffic-industrial emission sources, 60.9% of Hg came from domestic-transportation emission sources, 57.7% of Cd came from agricultural sources, and 89.5% of As came from natural sources. The GDM results showed that distance from first grade highway, population, land utilization and total potassium (TK) content were the main driving factors affecting these four sources, with q values of 0.064, 0.048, 0.069 and 0.058, respectively. The results can provide reference for reducing PTEs contamination in farmland soil.

Heavy metal pollution in agricultural soils from surrounding industries with low emissions: Assessing contamination levels and sources

The potential for heavy metal (HM) pollution in agricultural soils adjacent to industries with elevated HM emissions has long been recognized. However, industries with relatively lower levels of HM emissions, such as alumina smelting and glass production, may still contribute to the pollution of surrounding agricultural soils through continuous, albeit low-level, emissions. Despite this, this issue has not garnered adequate attention thus far. Therefore, this study aimed to assess the extent of HM pollution in agricultural soils adjacent to an alumina smelting and a glass production factory, identifying contamination levels and potential sources through the analysis of input fluxes, isotope fingerprints, and receptor models. Results showed moderate cadmium (Cd) contamination in surface soil, exceeding standards at a rate of 86.36 %. Further analysis revealed that atmospheric deposition was the primary route for Cd input in both paddy fields (89.20 %) and dryland soils (91.61 %). Additionally, the δ114/110Cd values in surface soils indicated that dust played a role in influencing Cd levels in distant surface soils, while raw materials and slags were identified as primary sources near the factory. Industrial sources were considered the primary contributors of Cd in soil accounting for approximately 73.38 % and 82.67 %, respectively, according to the positive matrix factorization model (PMF) and absolute principal component scores-multiple linear regression model (APCS-MLR). Overall, this study underscores the importance of monitoring HMs from industries with relatively low emissions and provides a scientific basis for effectively managing HMs pollution in agricultural soils, ensuring the preservation of agricultural soil quality.

Source characteristics of polycyclic aromatic hydrocarbons and polychlorinated biphenyls in surface soils of Shenyang, China: A comparison of two receptor models combined with Monte Carlo simulation

The surface soil concentrations of 16 PAHs and 15 PCBs were simultaneously determined by gas chromatography-tandem mass spectrometry in 21 locations of urban areas of Shenyang. The average concentrations of PAHs and PCBs were 26.40 ± 34.68 mg/kg and 48.03 ± 27.47 μg/kg, respectively. Factor analysis with nonnegative constraints (FA-NNC) and absolute principal component score with multiple linear regression (APCS-MLR) model were used to explore and evaluate the sources of PAHs and PCBs in the study area. The results of FA-NNC showed that PAHs in soils were mainly from traffic emissions (49.64%), coal combustion (46.88%) and petrogenic source (3.49%). The PCBs in soils were mainly from commercial and high temperature combustion mixed sources (20.3%), combustion and industry emission mixed sources (21.1%), electrical equipment sources (22.2%) and traffic emission sources (36.4%). The results of APCS-MLR were consistent with those of FA-NNC. The uncertainty of FA-NNC and APCS-MLR model was analyzed by Monte Carlo simulation method. The results revealed the reliability of the two receptor models on source apportionment. The estimated carcinogenic risks indicated that the risks of PAHs in soils exceed the acceptable range (10-6-10-4), while the risks of PCBs were below the acceptable risk level of 10-6.

Prioritization of control factors for heavy metals in groundwater based on a source-oriented health risk assessment model

Heavy metals (HMs) in groundwater seriously threaten ecological safety and human health. To facilitate the effective management of groundwater contamination, priority control factors of HMs in groundwater need to be categorized. A total of 86 groundwater samples were collected from the Huangpi district of Wuhan city, China, during the dry and wet seasons. To determine priority control factors, a source-oriented health risk assessment model was applied to compare the pollution sources and health risks of seven HMs (Cu, Pb, Zn, Cr, Ni, As, and Fe). The results showed that the groundwater had higher As and Fe contents. The sources of HM pollution during the wet period were mainly industrial and agricultural activities and natural sources. During the dry period, origins were more complex due to the addition of domestic discharges, such as sewage wastewater. Industrial activities (74.10% during the wet period), agricultural activities (53.84% during the dry period), and As were identified as the priority control factors for groundwater HMs. The results provide valuable insights for policymakers to coordinate targeted management of HM pollution in groundwater and reduce the cost of HM pollution mitigation.

Source apportionment of heavy metal(loid)s in sediments of a typical karst mountain drinking-water reservoir and the associated risk assessment based on chemical speciations

As important place for water storage and supply, drinking-water reservoirs in karst mountain areas play a key role in ensuring human well-being, and its water quality safety has attracted much attention. Source apportionment and ecological risks of heavy metal(loid)s in sediments of drinking-water reservoir are important for water security, public health, and regional water resources management, especially in karst mountain areas where water resources are scarce. To expound the accumulation, potential ecological risks, and sources of heavy metal(loid)s in a drinking-water reservoir in Northwest Guizhou, China, the surface sediments were collected and analyzed based on the combined use of the geo-accumulation index (Igeo), sequential extraction (BCR), ratios of secondary phase and primary phase (RSP), risk assessment code (RAC), modified potential ecological risk index (MRI), as well as the positive matrix factorization methods. The results indicated that the accumulation of Cd in sediments was obvious, with approximately 61.9% of the samples showing moderate to high accumulation levels, followed by Pb, Cu, Ni, and Zn, whereas the As and Cr were at low levels. A large proportion of BCR-extracted acid extractable and reducible fraction were found in Cd (72.5%) and Pb (40.3%), suggesting high bioavailability. The combined results of RSP, RAC, and MRI showed that Cd was the major pollutant in sediments with high potential ecological risk, while the risk of other elements was low. Source apportionment results of heavy metal(loid)s indicated that Cd (75.76%) and Zn (23.1%) mainly originated from agricultural activities; As (69.82%), Cr (50.05%), Cu (33.47%), and Ni (31.87%) were associated with domestic sources related to residents' lives; Cu (52.36%), Ni (44.57%), Cr (34.33%), As (26.51%), Pb (24.77%), and Zn (23.80%) primarily came from natural geological sources; and Pb (47.56%), Zn (22.46%) and Cr (13.92%) might be introduced by mixed sources of traffic and domestic. The contribution ratios of the four sources were 18.41%, 36.67%, 29.48%, and 15.44%, respectively. Overall, priority control factors for pollution in relation to agricultural sources included Cd, while domestic sources are primarily associated with As. It is crucial to place special emphasis on the impacts of human activities when formulating pollution prevention and control measures. The results of this study can provide valuable reference and insights for water resources management and pollution prevention and control strategies in karst mountainous areas.

Receptor model-based source-specific health risks of toxic metal(loid)s in coal basin-induced agricultural soil in northwest Bangladesh

Toxic metal(loid)s (TMLs) in agricultural soils cause detrimental effects on ecosystem and human health. Therefore, source-specific health risk apportionment is very crucial for the prevention and control of TMLs in agricultural soils. In this study, 149 surface soil samples were taken from a coal mining region in northwest Bangladesh and analyzed for 12 TMLs (Pb, Cd, Ni, Cr, Mn, Fe, Co, Zn, Cu, As, Se, and Hg). Positive matrix factorization (PMF) and absolute principal component score-multiple linear regression (APCS-MLR) receptor models were employed to quantify the pollution sources of soil TMLs. Both models identified five possible sources of pollution: agrochemical practice, industrial emissions, coal-power-plant, geogenic source, and atmospheric deposition, while the contribution rates of each source were calculated as 28.2%, 17.2%, 19.3%, 19% and 16.3% in APCS-MLR, 22.2%, 13.4%, 24.3%, 15.1% and 25.1% in PMF, respectively. Agrochemical practice was the major source of non-carcinogenic risk (NCR) (adults: 32.37%, children: 31.54%), while atmospheric deposition was the highest source of carcinogenic risk (CR) (adults: 48.83%, children: 50.11%). NCR and CR values for adults were slightly higher than for children. However, the trends in NCR and CR between children and adults were similar. As a result, among the sources of pollution, agrochemical practices and atmospheric deposition have been identified as the primary sources of soil TMLs, so prevention and control strategies should be applied primarily for these pollution sources in order to protect human health.

Characteristics and pollution risks of potentially toxic elements and nematode community structure on farm soil near coal mines

As one of the most important coal-producing provinces of China, Shanxi Province has been concerned about soil potentially toxic elements (PTEs) contamination in recent years. The study aimed to determine the status and sources of PTEs contamination and evaluate the quality of the soil ecology. This study investigated the degree of 13 PTEs contamination. The sources and contributions of PTEs were traced by the absolute principal component score followed by a multiple linear regression model (APCS-MLR). And the status of the soil ecosystem was verified by evaluating the soil nematode community around the coal mining areas in Jinzhong. The results showed that the mean PTEs concentration of 5 trace elements were higher than the background values of Shanxi, and safe to considerable was indicated by the pollution and ecological risk values. Soil Hg was the most contaminated element, followed by Cd. The distribution of PTEs was determined by coal mining activities (44.72%) followed by agricultural practice (32.37%) and coal transportation (21.37%). The nematode genera Acrobeloides (4.01%), Aphelenchus (20.30%), Meloidogyne (11.95%) and Aporcelaimus (2.74%) could be regarded as bioindicators of soil PTEs contamination by their tolerance. Concentrations of soil Cr, Mn, Ti and Cd showed remarkable influences on the total nematode abundance, maturity index, enrichment index, structural index, Shannon-Wiener diversity index and Pielou index of soil nematode. It is an appropriate method to evaluate the status of soil PTEs contamination combining the response of a single nematode genus and the nematode community evaluation index.

Water quality assessment and pollution source apportionment using multivariate statistical and PMF receptor modeling techniques in a sub-watershed of the upper Yangtze River, Southwest China

Rapid industrial and agricultural development as well as urbanization affect the water environment significantly, especially in sub-watersheds where the contaminants/constituents present in the pollution sources are complex, and the flow is unstable. Water quality assessment and quantitative identification of pollution sources are the primary prerequisites for improving water management and quality. In this work, 168 water samples were collected from seven stations throughout 2018-2019 along the Laixi River, a vital pollution control unit in the upper reaches of the Yangtze River. Multivariate statistics and positive matrix factorization (PMF) receptor modeling techniques were used to evaluate the characteristics of the river-water quality and reveal the pollution sources. Principal component analysis was employed to screen the crucial parameters and establish an optimized water quality assessment procedure to reduce the analysis cost and improve the assessment efficiency. Cluster analysis further illustrates the spatiotemporal distribution characteristics of river-water quality. Results indicated that high-pollution areas are concentrated in the tributaries, and the high-pollution periods are the spring and winter, which verifies the reliability of the evaluation system. The PMF model identified five and six potential pollution sources in the cold and warm seasons, respectively. Among them, pollution from agricultural activities and domestic wastewater shows the highest contributions (33.2% and 30.3%, respectively) during the cold and warm seasons, respectively. The study can provide theoretical support for pollutant control and water quality improvement in the sub-watershed, avoiding the ecological and health risks caused by the deterioration of water quality.

Anthropogenic and natural contribution of potentially toxic elements in southwestern Ganges-Brahmaputra-Meghna delta, Bangladesh

Elemental composition, multivariate statistical analyses with the absolute principal component score-multiple linear regression (APCS-MLR) model, and different pollution indices in Upper and Lower Southwestern Ganges-Brahmaputra-Meghna (GBM) delta sediments were studied to characterize pollution, ecological risk and quantify potential toxic element sources of the area. Toxic metals concentrations were higher in Lower Delta and individual pollution indices showed Upper Delta was moderately polluted by arsenic, chromium, cobalt, copper and lead, and Lower Delta was moderately-strongly polluted by the same metals. Synergistic indices include Potential Ecological, Toxic, Nemerow, and Pollution Risk indices in Upper and Lower Delta sediment ranged from 47.17-128.07, 2.03-12.19, 29.92-65.42, 0.28-1.62, and 69.17-246.90, 8.00-13.47, 20.53-152.92, 1.18-1.58, indicated low and moderate risk pollution, respectively. Statistical models represent the metals dominantly originated from nature for Upper Delta, and both natural and anthropogenic activities contributed to Lower Delta sediment. The study found that the modern deposit in Lower Delta became more contaminated and thus enhanced ecological risk.

Risk assessment and source apportionment for metals in sediments of Kaptai Lake in Bangladesh using individual and synergistic indices and a receptor model

Metal enrichment in lake sediments originating from multiple sources can threaten both the aquatic ecosystem and human health. Therefore, assessment of the eco-environmental risks and potential sources of metals in the sediments is essential for effective lake management. Here, we analyzed the sediment metal contents of Kaptai Lake, the largest lake in Bangladesh for the first time with this study. The results indicated that only Cr and Ni contents among the metals studied exceeded the probable effect concentrations (PEC) at 25.42 % and 55.93 % of the sampling stations, respectively. All metals at most sampling stations showed low contamination and low ecological risk based on the individual indices (geoaccumulation index, contamination factor, ecological risk factor, enrichment factor and modified hazard quotient). There was no significant risk from the combined metals in the sediments of the lake according to the synergistic indices (toxic risk index, Nemerow risk index, ecological risk index, Nemerow pollution index and pollution load index). Organic matter and silt were significant sediment parameters that favored the accumulation of Cr, Fe, Cu, Pb and Mn. In the absolute principle component scores-multiple linear regression model (APCS-MLR), five potential sources of metals were identified in the sediments: Zn, Mn, Co and Cd mainly from natural sources and to a lesser extent from agricultural and aquacultural activities, Ni, Cr and Fe from parent materials, Pb and Cu mainly from natural sources and to a lesser extent from vehicle emissions, Hg and U from lithogenic sources, and As from natural sources. This study will improve our knowledge of the sedimentary metal contents of Kaptai Lake and provide helpful information for developing effective lake management and pollution control strategies.

Sedimentary spatial variation, source identification and ecological risk assessment of parent, nitrated and oxygenated polycyclic aromatic hydrocarbons in a large shallow lake in China

Because polycyclic aromatic compounds (PACs) are persistent, universal, and toxic pollutants, understanding the potential source and ecological risk thereof in lakes is critical to the safety of the aquatic environment. Here, a total of 25 sedimentary samples were collected from Lake Taihu, China, in 2018. The total concentrations of 16 parent polycyclic aromatic hydrocarbons (PAHs), 15 nitrated PAHs (NPAHs), nine oxygenated PAHs (OPAHs), and five hydroxy-PAHs (OH-PAHs) ranged from 294 to 1243, 3.0 to 54.5, 188 to 1897, and 8.3 to 51.7 ng/g dw, with the most abundant compounds being fluoranthene, 1,8-dinitropyrene, 6H-Benzo[cd]pyren-6-one, and 2-phenylphenol, respectively. The spatial distribution of PACs in sediments of Lake Taihu showed elevated concentrations from east to west due to economic development and transportation. The positive correlations between most paired PAHs indicate that these compounds likely originated from similar sources. The total organic carbon and organic matter contents affected the distribution characteristics of PACs in sediments. Diagnostic ratios, principal component analysis-multiple linear regression (PCA-MLR), and positive matrix factorization (PMF) were integrated to identify the sources. PACs had various sources including combustion, petroleum leakage, traffic emissions, hydroxyl metabolism, and other oxidation pathways in sediments of Lake Taihu. The PMF (R² > 0.9824), which showed better optimal performance compared with PCA-MLR (R² > 0.9564) for PAHs and derivatives, is recommended as the preferred model for quantitative source analysis. Ecological risk assessment showed that the risk quotient values of OPAHs in sediments were much higher than those of other PACs and should be given special attention.

Tracing and quantifying the source of heavy metals in agricultural soils in a coal gangue stacking area: Insights from isotope fingerprints and receptor models

Historic coal gangue stacking probably brings heavy metals (HMs) into the surrounding agricultural soil, posing potential harm to human and environmental health. For better controlling and preventing agricultural soil HMs pollution, the screening of priority pollutants and identification of their pollution pathways are urgent in coal gangue stacking areas. Thus, this study selected a coal gangue stacking area in Chongqing, China as the research object and conducted the pollution evaluation, spatial distribution and source apportionment of the HMs (Cd, Cr, Ni, Cu, Zn, As, Pb and Hg) in surrounding agricultural soil. Results showed that the soil was moderately to heavily contaminated by Cd with average concentrations of 1.23 mg/kg, which were 4.1 times higher than the Environmental Quality Standards for Soils of China. Cd was considered as the soil precedent-controlled pollutant in this study area and subsequent soil δ^114/110Cd values indicated that Cd in surface soils primarily originated from the leachate of coal gangue stacking, which contributed about 89.9 % and 85.47 % to the total soil Cd according to the absolute principal component scores-multiple linear regression model (APCS-MLR) and positive matrix factorization model (PMF), respectively. In addition, other HMs mainly resulted from the leachate of coal gangue, natural and agricultural mixed pollution as well as traffic pollution. Therefore, this study provided basic information for pollution control of the HMs in agricultural soil in the coal gangue stacking area.

Heavy metal pollution in a black shale post-mining site of southern China: Pollution pattern, source apportionment and health risk assessment

Source apportionment is critical but remains largely unknown for heavy metals in the soil surrounding black shale mining areas. Herein, the distribution, potential hazards, and sources of heavy metals in the soil around a black shale post-mining site were investigated. The content of Cadmium (Cd) in topsoil samples (0.77-50.29 mg/kg, N = 84) all exceeded the Chinese agricultural soil standard (0.3 mg/kg). The majority of Cd in the soil existed in the mobile fraction posing a high potential risk to the local ecosystem. and Zn and V in soils existed in the residual form. The percentages of HQing > 1 and 0.6-1 for Vanadium (V) in soil were 8.3% and 31.0%, respectively, and the percentages of HQing > 0.5 for Cd in soil were 3.7% showed that V and Cd were the main factors that increased the potential non-cancer risk. Five potential sources were identified using the geostatistical and positive matrix factorization (PMF) model, among which Cd was mainly derived from the short-term weathering process of black shale (81.06%), most Zinc (Zn) was from the long-term weathering of black shale (67.35%), whereas V was contributed by many factors including long-term weathering of black shale (42.99%), traffic emissions (31.12%) and agricultural activities (21.05%). This study reveals the potential risk and identifies the sources of heavy metals, which is helpful to manage the contaminated soil in black shale mining areas.

Combining the multivariate statistics and dual stable isotopes methods for nitrogen source identification in coastal rivers of Hangzhou Bay, China

Coastal rivers contributed the majority of anthropogenic nitrogen (N) loads to coastal waters, often resulting in eutrophication and hypoxia zones. Accurate N source identification is critical for optimizing coastal river N pollution control strategies. Based on a 2-year seasonal record of dual stable isotopes ([Formula: see text] and [Formula: see text]) and water quality parameters, this study combined the dual stable isotope-based MixSIAR model and the absolute principal component score-multiple linear regression (APCS-MLR) model to elucidate N dynamics and sources in two coastal rivers of Hangzhou Bay. Water quality/trophic level indices indicated light-to-moderate eutrophication status for the studied rivers. Spatio-temporal variability of water quality was associated with seasonal agricultural, aquaculture, and domestic activities, as well as the seasonal precipitation pattern. The APCS-MLR model identified soil + domestic wastewater (69.5%) and aquaculture tailwater (22.2%) as the major nitrogen pollution sources. The dual stable isotope-based MixSIAR model identified soil N, aquaculture tailwater, domestic wastewater, and atmospheric deposition N contributions of 35.3 ±21.1%, 29.7 ±17.2%, 27.9 ±14.5%, and 7.2 ±11.4% to riverine [Formula: see text] in the Cao'e River (CER) and 34.4 ±21.3%, 29.5 ±17.2%, 27.4 ±14.7%, and 8.7 ±12.8% in the Jiantang River (JTR), respectively. The APCS-MLR model and the dual stable isotope-based MixSIAR model showed consistent results for riverine N source identification. Combining these two methods for riverine N source identifications effectively distinguished the mix-source components from the APCS-MLR method and alleviated the high cost of stable isotope analysis, thereby providing reliable N source apportionment results with low requirements for water quality sampling and isotope analysis costs. This study highlights the importance of soil N management and aquaculture tailwater treatment in coastal river N pollution control.

Contamination, Source Apportionment, and Health Risk Assessment of Heavy Metals in Farmland Soils Surrounding a Typical Copper Tailings Pond

Tailings resulting from mining and smelting activities may cause soil heavy-metal pollution and harm human health. To evaluate the environmental impact of heavy metals from tailings on farmland soils in the surrounding area, heavy metals (As, Cd, Cr, Cu, Ni, Pb, and Zn) in tailings and farmland soils in the vicinity of a typical copper tailings pond were analyzed. Contamination status, potential sources, and health risks for farmland soils were investigated. The results showed that the tailings contained a high concentration of Cu (1136.23 mg/kg). The concentrations of Cd and Cu in the farmland soils exceeded the soil quality standard. The geoaccumulation index (I_geo) indicated that the soils were moderately polluted by Cu and Cd, and slightly polluted by Ni, Cr, and Zn. The absolute principal component scores-multiple linear regression (APCS-MLR) model was applied for source apportionment. The results showed that tailings release is the main source of soil heavy-metals contamination, accounting for 35.81%, followed by agricultural activities (19.41%) and traffic emission (16.31%). The health risk assessment suggested that the children in the study region were exposed to non-carcinogenic risks caused by As, while the non-carcinogenic risk to adults and the carcinogenic risk to both adults and children were at acceptable levels. It is necessary to take effective measures to control heavy-metal contamination from tailings releases to protect humans, especially children, from adverse health risks.

The Predominant Sources of Heavy Metals in Different Types of Fugitive Dust Determined by Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF) Modeling in Southeast Hubei: A Typical Mining and Metallurgy Area in Central China

To develop accurate air pollution control policies, it is necessary to determine the sources of different types of fugitive dust in mining and metallurgy areas. A method integrating principal component analysis and a positive matrix factorization model was used to identify the potential sources of heavy metals (HMs) in five different types of fugitive dust. The results showed accumulation of Mn, Fe, and Cu can be caused by natural geological processes, which contributed 38.55% of HMs. The Ni and Co can be released from multiple transport pathways and accumulated through local deposition, which contributed 29.27%. Mining-related activities contributed 20.11% of the HMs and showed a relatively high accumulation of As, Sn, Zn, and Cr, while traffic-related emissions contributed the rest of the HMs and were responsible for the enrichment in Pb and Cd. The co-applied source-identification models improved the precision of the identification of sources, which revealed that the local geological background and mining-related activities were mainly responsible for the accumulation of HMs in the area. The findings can help the government develop targeted control strategies for HM dispersion efficiency.

Spatial distribution characteristics, influencing factors, and source distribution of soil cadmium in Shantou City, Guangdong Province

A total of 511 topsoils and 139 deep soil samples were collected to analyze the distribution characteristics, regional differentiation factors, and contamination sources of Cd in Shantou City, and to assess its environmental, ecological, and human health risks. We used a combination of multivariate statistics and geostatistics to quantify the distribution and level of Cd contamination in the study area, and an absolute principal component scores-multiple linear regression model to resolve the sources of contamination and their contribution values, combined with the health risk model to assess the human health risk from each source. The result exhibited that the average value of soil Cd content was 0.100 mg/kg, which was lower than the threshold value of soil environmental quality standard, but higher than the 0.070 mg/kg background value of soil. The high-value areas of surface Cd content in the study area were distributed in the western, northern, and northeastern parts of Shantou, and the source of Cd in the soil was a mix of anthropogenic and natural contamination. The non-carcinogenic and carcinogenic risks of heavy metal Cd exposure pathways are: oral ingestion > dermal contact > inhalation. The human health risk posed by Cd is below the reference threshold, indicating that the Cd contents in the soil have no unacceptable health risk to the residents. Among industrial sources, natural sources, and unknown sources with potential carcinogenic and non-carcinogenic risks, natural sources were the main source of contamination for adults and children. Among the different soil types, paddy, and red soils had relatively high Cd content, and among the different soil-forming parent materials, the Cd content in soils developed on Quaternary sediments was significantly higher than that other parent materials. Among the different land use types, the Cd content of soil for construction land was the highest. This study provides a scientific foundation and reference for the prevention of soil Cd contamination in Shantou City and the analysis of soil contamination sources in areas with similar contamination patterns.

Apportionment and location of heavy metal(loid)s pollution sources for soil and dust using the combination of principal component analysis, Geodetector, and multiple linear regression of distance

The accurate identification of sources for soil heavy metal(loid) is difficult, especially for multi-functional parks, which include multiple pollution sources. Aiming to identify the apportionment and location of heavy metal(loid)s pollution sources, this study established a method combining principal component analysis (PCA), Geodetector, and multiple linear regression of distance (MLRD) in soil and dust, taking a multi-functional industrial park in Anhui Province, China, as an example. PCA and Geodetector were used to determine the type and possible location of the source. Source apportionment of individual elements is achieved by MLRD. The detection results quantified the spatial explanatory power (0.21 ≤ q ≤ 0.51) of the potential source targets (e.g., river and mining area) for the PCA factors. A comparative analysis of the regression equation (Model 1 and Model 3) indicated that the river (0.50 ≤ R² ≤0.78), main road (0.47 ≤ R² ≤ 0.81), and mine (0.14 ≤ R² ≤ 0.92) (p < 0.01) were the main sources. Different from the traditional source apportionment methods, the current method could obtain the exact contributing sources, not just the type of source (e.g., industrial activities), which could be useful for pollution control in areas with multiple sources.

Probabilistic health risk assessment for residents exposed to potentially toxic elements near typical mining areas in China

Public health problems caused by toxic elements in mining areas have always been an important topic worldwide. However, existing studies have focused on single exposure routes and common toxic elements, which might underestimate the risks faced by residents. In this study, three typical mining areas in central China were selected to assess the health risks of 14 potentially toxic elements through five exposure routes using Monte Carlo simulations. The results indicated that the 95th percentile non-carcinogenic risk values to humans via rice and vegetable ingestion ranged from 9.8 to 26.0 and 6.2 to 19.0. The corresponding carcinogenic risks ranged from 1.4E-2 to 6.3E-2 and from 2.9E-3 to 2.3E-2, respectively. Therefore, residents face serious health risks. Multi-element analysis showed that cadmium (Cd), boron (B), and arsenic (As) were the main contributors to rice non-carcinogenicity, whereas Cd and nickel (Ni) were the main elements of rice carcinogenicity. B and lead (Pb) played an essential role in the non-carcinogenesis of vegetables, and B, Ni, and Cd played an essential role in carcinogenesis. Accidental ingestion is the main route of soil exposure. In these three areas, the probability of non-carcinogenic risk faced by adults was 40%, 0%, and 1%, respectively, while the probabilities for children were 100%, 62%, and 83%, respectively. Regarding carcinogenicity, the risk for both adults and children was up to 100%. This study emphasizes the overall health risks in polluted areas via multi-route and multi-element analysis. This conclusion is helpful to comprehensively assess the potential health risks faced by residents in mining areas and provide baseline data support and a scientific basis for formulating reasonable risk control measures.

Health Risk Assessment of Heavy Metals in Groundwater of Hainan Island Using the Monte Carlo Simulation Coupled with the APCS/MLR Model

Groundwater is a significant component of water resources, but drinking groundwater with excessive heavy metals (HMs) is harmful to human health. Currently, quantitative source apportionment and probabilistic health risk assessment of HMs in groundwater are relatively limited. In this study, 60 groundwater samples containing seven HMs were collected from Hainan Island and analyzed by the coupled absolute principal component scores/multiple linear regression (APCS/MLR), the health risk assessment (HRA) and the Monte Carlo simulation (MCS) to quantify the pollution sources of HMs and the health risks. The results show that the high-pollution-value areas of HMs are mainly located in the industry-oriented western region, but the pollution level by HMs in the groundwater in the study area is generally low. The main sources of HMs in the groundwater are found to be the mixed sources of agricultural activities and traffic emissions (39.16%), industrial activities (25.57%) and natural sources (35.27%). Although the non-carcinogenic risks for adults and children are negligible, the carcinogenic risks are at a high level. Through analyzing the relationship between HMs, pollution sources, and health risks, natural sources contribute the most to the health risks, and Cr is determined as the priority control HM. This study emphasizes the importance of quantitative evaluation of the HM pollution sources and probabilistic health risk assessment, which provides an essential basis for water pollution prevention and control in Hainan Island.

Source apportionment and risk assessment for available occurrence forms of heavy metals in Dongdahe Wetland sediments, southwest of China

Urban wetland ecosystems are easily influenced by heavy metals (HMs) because of their functional properties. In this study, absolute principal component scores-multivariate linear regression (APCS-MLR) and positive matrix factorization (PMF) receptor models were applied for the source apportionment of available occurrence forms of heavy metals (AHMs) of surface sediments in a typical urban wetland of Dianchi Lake, southwest of China. The risk assessment was conducted to evaluate the potential ecological/human health risks of HMs. Results indicated that Zn, Pb, and Cr were the major pollutants affected by anthropogenic activities in sediments and their concentrations were significantly exceeding the background value. Most of the highly AHMs-polluted area was close to the river in wetland, and the concentration distribution of all AHMs were generally low in the southwest and high in the northeast. Both APCS-MLR and PMF models identified three comparable classes of potential sources, namely (1) agricultural fertilizer/insecticide, atmospheric deposition, and traffic emissions; (2) natural transitions; and (3) industrial and sewage wastes. Moreover, the comparison results implied that the PMF model was more feasible for quantifying AHMs sources in wetland sediments since it is capable to analyze one more source, namely plant maintenance and waterfowl feeding, and has higher accuracy in predicting the concentrations of AHMs. In addition, the risk assessment model revealed that all these HMs were within the acceptable ranges of ecological and carcinogenic/non-carcinogenic human health risks. Among these, ingestion was the major exposure pathway of HMs from local areas, followed by dermal exposure and oral or nasal inhalation. However, children were more easily exposed to HMs than adults by ingestion due to their hand-to-mouth behaviors. This study aims to assess the HM pollution status in a plateau urban wetland, and provides a practical case for modeling source apportionment and risk assessment of HMs in wetland sediments.

Concentration, source apportionment and potential carcinogenic risks of polycyclic aromatic hydrocarbons (PAHs) in roadside soils

PAHs are organic pollutants that have carcinogenic and mutagenic impacts on human health and are a subject of great concern. The soil-bound polycyclic aromatic hydrocarbons (PAHs) in the urban areas can be very lethal to human health. The concentrations, sources, and possible cancer risks of 15 PAHs were analysed by collecting roadside soil samples in Lucknow, India. The range of ∑₁₅PAHs was found to be 478.94 ng/g to 8164.07 ng/g with a mean concentration of 3748.23 ng/g. The highest contribution (32.5%) was found to be from four-ring PAHs, followed by six-ring (24.5%) and five-ring (16.7%) PAHs. The source apportionment through diagnostic ratios ANT/(ANT + PHE) against FL-2/(FL-2+PYR) highlighted the dominance of petroleum, wood, coal, and grass combustion as sources of PAHs in the study area. Source apportionment was also done through positive matrix factorization, confirming the dominance of 'vehicular emissions' (49%), followed by 'coal and biomass combustion' (∼39%), and 'leakages, volatilization and petroleum combustion' (∼12%) as potential sources. The results from lifetime cancer risks (ILCR) varied in the range of 7.5 × 10^-4 and 1.3 × 10 × ^-2 illustrating 'high cancer risk'. The total cancer risk susceptibility of children was found to be 31% more than that of adults. The highest risk associated with toxic equivalent concentration (TEQ) was found at site S8 highlighting the impact of the presence of an international airport, and huge traffic load. The present study will prove to be useful for information related to human exposure to PAHs content in soil in the study area and as baseline study for policy makers, stakeholders, and researchers.

Receptor model-based source apportionment and ecological risk of metals in sediments of an urban river in Bangladesh

Metal accumulation (As, Cd, Cr, Cu, Fe, Hg, Mn, Ni, Pb, and Zn) in Korotoa River sediment was studied in order to determine the metal content, distribution, sources, and their possible ecological impacts on the riverine ecosystem. Our study found significant spatial patterns of toxic metal concentration and principal coordinate analysis (PCoA) accounted for 45.2% of spatial variation from upstream to downstream. Metal contents were compared to sediment quality standards and found all studied metal concentrations exceeded the Threshold Effect Level (TEL) whereas Cr and Ni surpassed probable effect levels. All metal concentrations were higher than Average Shale Value (ASV) except Mn and Hg. The positive matrix factorization (PMF) and absolute principal component score-multiple linear regression models (APCS-MLR) were applied to identify promising sources of metals in sediment samples. Both models identified three potential sources i.e. natural source, traffic emission, and industrial pollution, which accounted for 50.32%, 20.16%, and 29.51% in PMF model whereas 43.56%, 29.42%, and 27.02% in APCS-MLR model, respectively. Based on ecological risk assessment, pollution load index (7.74), potential ecological risk (1078.45), Nemerow pollution index (5.50), and multiple probable effect concentrations quality (7.73) showed very high contamination of toxic metal in sediment samples.

Source identification of surface water pollution using multivariate statistics combined with physicochemical and socioeconomic parameters

Accurate identification of potential contamination sources of river water is a basis for effective pollution control and sustainable water management. Pollution source identification based on physicochemical-parameters-only method may lead to uncertainty and subjectivity. In this study along with hydrochemistry parameters (HPs), socioeconomic parameters (SPs) were considered as an auxiliary in multivariate statistics to achieve a comprehensive assessment on pollution sources with accurate estimates of source identification and apportionment. Fifteen physicochemical parameters were combined with twelve socioeconomic parameters in multivariate statistics to quantitatively assess potential pollution sources and their contributions to river water pollution. Multivariate statistics in the study included regression analysis, principal component analysis (PCA), and absolute principal component score-multiple linear regression (APCS-MLR). Regression analysis between hydro-chemical parameters and socioeconomic parameters indicated that industrial and population growths were the main factors related to ammonium nitrogen (NH₄⁺-N), total nitrogen (TN) contamination, while total phosphorus (TP) was more correlated with domestic discharge and poultry breeding. Based on the results of PCA, four latent factors were extracted for hydrochemistry parameters (HPs) and socioeconomics parameters (SPs), accounting for 68.59% and 82.40% of the total variance, respectively. With integrating the PCA results of the two parameter groups, pollution sources were ranked as industrial effluents > rural wastewater > municipal sewage > phytoplankton growth and agricultural cultivation. Source apportionment in APCS-MLR revealed that industrial wastewater and rural wastewater averagely contributed 35.68% and 25.08% of pollution, respectively, followed by municipal sewage (18.73%) and phytoplankton pollution (15.13%) with relatively small percentage of unrecognized source. It is concluded that socioeconomic parameters assisting hydrochemistry parameters in multivariate statistics can improve the accuracy and certainty of pollution source identification, supporting decision makers to formulate strategies on protection of river water quality.

Spatiotemporal Trends, Sources and Ecological Risks of Heavy Metals in the Surface Sediments of Weitou Bay, China

Heavy metals are extremely harmful materials to marine ecosystems and human health. To determine the anthropogenic contributions and ecological risks in Weitou Bay, China, the spatiotemporal variations in the concentrations of heavy metals in surface sediment were investigated during spring 2008 and 2017. The results indicated that high concentrations of pollutants were generally located near the river mouths and along the coast of industrial areas. Principal component analysis indicated that heavy metal contents were mainly affected by industrial waste drainage, urban development, natural weathering and erosion, and interactions between organic matter and sulfides. The potential ecological risk assessment demonstrated that, in 2008, 82% of the sampling sites were at low risk, while 18% were at moderate risk. The situation had deteriorated slightly by 2017, with 73%, 18%, and 9% of stations in Waytou Bay at low, moderate, and very high risk, respectively. Cd was the most harmful metal, followed by Hg. These two elements accounted for more than 80% of the potential ecological risk index (RI) value. The present work analyzed the source of heavy metals, identified the major pollution elements and high risk areas, and provides guidance for pollution control and ecological restoration in Weitou Bay.

Soft Computing Techniques for Appraisal of Potentially Toxic Elements from Jalandhar (Punjab), India

The contamination of potentially toxic elements (PTEs) in agricultural soils is a serious concern around the globe, and modelling approaches is imperative in order to determine the possible hazards linked with PTEs. These techniques accurately assess the PTEs in soil, which play a pivotal role in eliminating the weaknesses in determining PTEs in soils. This paper aims to predict the concentration of Cu, Co and Pb using neural networks (NNs) based on multilayer perceptron (MLP) and boosted regression trees (BT). Statistical performance estimation factors were rummage-sale to measure the performance of developed models. Comparison of the coefficient of correlation and root mean squared error suggest that MLP-established models perform better than BT-based models for predicting the concentration of Cu and Pb, whereas BT models perform better than MLP established models at predicting the concentration of Co.