Systematic assessment of source identification and ecological and probabilistic health risks of potentially toxic elements (PTEs) in soils of a typical coal mining area in Guanzhong region.

Mining activities may cause the accumulation of potentially toxic elements (PTEs) in surrounding soils, posing ecological threats and health dangers to the local population. Therefore, a comprehensive assessment using multiple indicators was used to quantify the level of risk in the region. The results showed that the mean values of the nine potentially toxic elements in the study area were lower than the background values only for Cr, and the lowest coefficient of variation was 17.1 % for As, and the spatial distribution characteristics of the elements indicate that they are enriched by different factors. The elements Hg and Cd, which have substantial cumulative features, are the key contributors to ecological risk in the study region, which is overall at moderate risk. APCS-MLR model parses out 4 possible sources: mixed industrial, mining and transportation sources (53.98 %), natural sources (24.56 %), atmospheric deposition sources (12.60 %), and agricultural production sources (8.76 %). The probabilistic health risks show that children are more susceptible to health risks than adults; among children, the safety criteria (HI < 1 and CR < 10-4) were surpassed by 29.29 % of THI and 8.58 % of TCR. According to source-orientated health hazards, the element Ni significantly increases the risk of cancer. Mixed sources from industry, mining, and transportation are important sources of health risks. The results of this research provide some scientific references for the management and decrease of regional ecological and health risks.

Potential ecological risk assessment for trace metal(loid)s in soil surrounding coal gangue heaps based on source-oriented.

Coal is the predominant energy source in China, resulting in coal gangue. We used the absolute principal component score multiple linear regression (APCS-MLR) model and the geo-detector method (GDM) for determining the potential ecological risk, apportioning sources, and identifying driving factors for trace metal(loid)s (TMs) in soil surrounding coal gangue heaps. The average contents for the concerned TMs (Cd, Hg, As, Pb, Cr, Cu, Ni, and Zn) in the soil of interest were 0.48, 0.18, 11.0, 36.0, 129, 99.2, 68.3 and 141 mg/kg, respectively. Potential ecological risk indicated that the soil was primarily within the "Moderate risk" level, and Cd was the primary pollutant. "The number of coal gangue units" and "the distance between the sampling point and the coal gangue heap" were the key driving factors included in the geo-detector method. Combining APCS-MLR model and GDM, the source apportionment was enhanced in terms of accuracy and reliability. Natural, mining, and unrecognized sources contributed 41.1 %, 39.2 %, and 19.7 % of the TM distribution, respectively. Considering the relationship between TMs, their sources, and corresponding potential ecological risks, mining sources (mainly affected by gangue accumulation) presented a primary linkage with Cd, and its contribution to potential ecological risk was the highest, accounting for 58.2 %. Therefore, further research should focus on effectively managing and controlling the potential ecological risks originating from mining sources and Cd.

Assessment of health risks based on different populations and sources of heavy metals on agricultural lane in Tengzhou City by APCS-MLR models.

To identify the sources of heavy metals in local soils and their risks to human health. This study quantified the concentrations of eight heavy metals in 504 soil samples collected in Tengzhou, China. The ecological risks of a single heavy metal (EI), a comprehensive ecological risk index (RI), and a health risk assessment model were used to evaluate the level of contamination in the city. The results of the research study indicate that there are different levels of heavy metal pollution in rural and urban agricultural areas in Tengzhou. Moreover, the spatial variability of mercury (Hg) is considerable, reaching 0.96, indicating a significant impact of anthropogenic activities. For the ecological risk, the heavy metal element with the highest EI value was mercury with a mean value of 67.22 and a peak value of 776.00. The heavy metal with the lowest mean EI value was Zn with only 1.03. Meanwhile, the average RI is only 128.59, but some areas have an RI as high as 842.2. The sources of heavy metals were identified using principal component analysis, correlation analysis, and an absolute principal component score multiple linear regression model (APCS-MLR). The non-carcinogenic risk for children, the carcinogenic risk for children, and the carcinogenic risk for adults were 1.23, 2.42×10-4 and 1.00×10-4, respectively, and these values exceeded their respective recommended values, and As and Cr had some carcinogenic hazards. Heavy metals in the soil come from natural, industrial, traffic and agricultural sources and represent 39.59%, 29.48%, 25.17% and 5.77%, respectively. The main source of heavy metals in local agricultural soils is the geological background, and the government needs to strengthen the monitoring of As and Cr in drinking water resources, as well as reduce traffic pollution and factory waste emissions to reduce Hg in soils.

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.

Assessment of water quality and source apportionment of pollution in a tropical river in eastern India: A study utilizing multivariate statistical tools and the APCS-MLR receptor model.

The Mundeswari River, an ecologically distressed river in eastern India, has been subjected to water quality deterioration largely due to anthropogenic activities in its vicinity. This study aimed to comprehensively evaluate the current state of pollution in the river and assess the appropriateness of river water for irrigation, given its extensive use for agricultural purposes. Monthly water quality monitoring was undertaken at four distinct sampling sites (SP1-SP4) over a two-year period (2020-2022), considering seventeen water quality parameters. This research employed principal component analysis/factor analysis (PCA/FA) and absolute principal component score-multiple linear regression (APCS-MLR) receptor modelling. These methodologies were used to discern and quantify potential sources of pollution influencing the water quality of the Mundeswari River. The study revealed that the water quality of the Mundeswari River was most degraded during the pre-monsoon season. Among the four sampling sites, SP3 exhibited the highest level of pollution with mean biochemical oxygen demand (BOD) and chemical oxygen demand (COD) values of 5.36 mg/L and 44.72 mg/L, respectively. According to the one-way analysis of variance (ANOVA), there was considerable spatial and seasonal disparities (P < 0.05) in most water quality parameters. The PCA/FA extracted four latent pollution sources, accounting for 81.5% of the total variance. The primary factors influencing the quality of river water are natural weathering processes, discharge of domestic effluent and waste, and agricultural runoff. The APCS-MLR receptor model further revealed that agricultural drainage factors and the discharge of domestic effluent and waste had a greater impact on the Mundeswari River. The investigation concluded that the mean values of all indicators for irrigation suitability were below the defined threshold limits, indicating that the water of the studied river appears suitable for irrigation. The outcomes of this study may significantly contribute to the formulation of sustainable strategies for the ecological rejuvenation of the Mundeswari River.

Chronological evaluation of polycyclic aromatic hydrocarbons in sediments of tangxun lake in central China and impacts of human activities.

This study sheds light on the contamination of polycyclic aromatic hydrocarbons (PAHs) in Tangxun Lake sediments, an urban lake reflecting environmental changes in Central China. By analyzing sediment cores from both the inner and outer areas of the lake, we determined the historical trends and sources of PAHs over the past century. The results reveal a significant increase in PAHs concentrations, particularly since the 1980s, coinciding with China's rapid urbanization and industrialization. Using diagnostic ratios and Absolute principal component score-multivariate linear regression (APCS-MLR) methods, we identified petroleum combustion, coal combustion, and biomass combustion as the primary sources of PAHs in the lake sediments. The spatial analysis indicates higher PAHs levels in the inner lake, likely due to its closer proximity to industrial activities. Moreover, by comparing PAH trends in Tangxun Lake with those in other urban, suburban, and remote lakes across China, based on data from 49 sedimentary cores, we highlight the impact of regional socio-economic dynamics on PAH deposition. These insights are crucial for developing effective pollution mitigation strategies and promoting sustainable development in rapidly urbanizing regions.

Analysis of heavy metal and arsenic sources in mangrove surface sediments at Wulishan Port on Leizhou Peninsula, China, using the APCS-MLR model.

Mangrove forests are sources and sinks for various pollutants. This study analyzed the current status of heavy metal and arsenic (As) pollution in mangrove surface sediments in rapidly industrializing and urbanizing port cities. Surface sediments of mangroves at Wulishan Port on the Leizhou Peninsula, China, were analyzed using inductively coupled plasma emission spectrometry (ICP-AES) and inductively coupled plasma mass spectrometry (ICP-MS) for the presence of Cr, Pb, Ni, Zn, Cd, Cu, As, and Hg. The Pollution load index, Nemerow pollution index, and Potential ecological risk index were employed to evaluate the pollutant. Multivariate statistical methods were applied for the qualitative analysis of pollutant sources, and the APCS-MLR receptor model was used for quantification. This study indicated the following results: (1) The average content of eight pollutants surpassed the local background level but did not exceed the Marine Sediment Quality standard. The pollution levels across the four sampling areas were ranked as Ⅲ > Ⅳ > Ⅰ > Ⅱ. The area Ⅱ exhibited relatively lower pollution levels with the grain size of the sediments dominated by sand, which was not conducive to pollutant adsorption and enrichment. (2) The factor analysis and cluster analyses identified three primary sources of contamination. As, Cr, Ni, and Pb originated from nearby industrial activities and their associated wastewater, suggesting that the primary source was the industrial source. Cd, Cu, and Zn stem from the cement columns utilized in oyster farming, alongside discharges from mariculture and pig farming, establishing a secondary agricultural source. Hg originated from ship exhaust burning oil and vehicle emissions in the vicinity, representing the third traffic source. (3) The APCS-MLR receptor model results demonstrated industrial, agricultural, and traffic sources contributing 47.19 %, 33.13 %, and 13.03 %, respectively, with 6.65 % attributed to unidentified sources.

Source-specific ecological risk analysis and critical source identification of heavy metal(loid)s in the soil of typical abandoned coal mining area.

Long-term coal mining activities in abandoned coal mining areas have resulted in the migration of large quantities of heavy metals into the surrounding soil environment, posing a threat to the regional ecological environment. This study focuses on the surface soil collected from a typical abandoned coal mining area. Methods such as the pollution index (PI) and potential ecological risk index (RI) were used to comprehensively evaluate the pollution levels and ecological risks of soil heavy metals. Geostatistical analysis and the APCS-MLR model were used to quantify the sources of soil heavy metals, and Nemerow integrated ecological risk (NIRI) model was coupled to apportion the ecological risks from different pollution sources. The results indicate that the average concentrations of Cd, As, and Zn are 4.58, 2.44, and 1.67 times the soil background values, respectively, while the concentrations of other heavy metals are below the soil background values. The soil of study area is strongly polluted by heavy metals, with the pollution level and ecological risk of Cd being significantly higher than those of other heavy metals. The NIRI calculation results show that the overall comprehensive ecological risk level is considerable, with sample points classified as relatively considerable, moderate, and low at 60.53 %, 36.84 %, and 2.63 %, respectively. The sources of soil heavy metals can be categorized into four types: traffic activities, natural sources, coal gangue accumulation, and a combined source of coal mining and agricultural activities, with contribution rates of 35.3 %, 36.1 %, 19.5 %, and 9.1 %, respectively. The specific source ecological risk assessment results indicate that coal gangue accumulation contributes the most to ecological risk (36.4 %) and should be prioritized for pollution control, with Cd being the priority control element for ecological risk. The findings provide theoretical support for the refined management of soil heavy metal pollution in abandoned coal mining 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.

Quantification of sources and potential risks of cadmium, chromium, lead, mercury and arsenic in agricultural soils in a rapidly urbanizing region of southwest China: the case of Chengdu.

Rapid urbanization a major factor affecting heavy metal contamination on suburban agricultural soils. In order to assess the dynamic contamination of heavy metals in soil from agricultural land bordering a rapidly urbanizing area and the transfer of human health risks from contaminants in this process, 186 and 293 soil samples from agricultural land in suburban Chengdu were collected in September 2008 and September 2017, respectively. Several indicators, such as the integrated pollution index (PI) and the potential ecological risk index (RI), were employed for analyzing the heavy metal contamination levels, and the APCS-MLR receptor model were applied for analyzing the heavy metal sources. As a result, mean concentrations for five elements did not exceed the national soil pollution risk screening values in the two periods mentioned above. Nemerow's composite contamination index revealed an increase in soil contamination of arable land after 10 years of urbanization, with 3.75 and 1.02% of light and moderate sample plots, respectively, by 2017. The assessment for potential ecological risk indicated an increased level of eco-risk to high for most of the sample plots. Based on the APCS-MLR model, the origin and contribution to the five elements varied considerably between the two periods mentioned above. Among them, soil Pb changed from "industrial source" to "transportation source," soil Cr changed from "natural source" to "transportation source," and As and Hg changed from "industrial source" to "transportation source." As and Hg were associated with agricultural activities in both periods, and Cd was derived from industrial activities in both periods. The study suggests that inhalation has become a major contributor to non-cancer health risks in urbanization, unlike intake routes in previous periods, and that the increase in cancer risk is mainly due to children's consumption of agricultural products with As residues. The change in the main source of As to "transportation" also indicates a decrease in air quality during urbanization and the development of the transportation industry. This study provides a reference for the governments of rapidly urbanizing cities to formulate relevant highway and agricultural policies to safeguard the health of the people based on the current situation.

Toxic element characterization against a typical high geology background: Pollution enrichment, source tracking, spatial distribution, and ecological risk assessment.

The geological environment determines the initial content of various elements in soil, while the late input of toxic elements produced through weathering and leaching is a persistent threat to food security and human health. In this study, we selected the Lou Shao Basin, a black rock system background, and combined geostatistical analysis and multivariate statistics to quantify the specific contribution of weathering of the black rock system, and to analyze the source traces, spatial distributions, and ecological risks of the soil toxicity of elements. The results show that the soils in the study area are acidic, which is related to the weathering of sulfides in the black rock system. The concentrations of most elements in the soil were determined to exceed the soil background values, and the Cd, Se and N contents, exceeded more than five times, especially Se, Mo nearly as high as 13 times. Strong positive correlation between Se, Cu, V and P, low correlation between N and Se, Cu, V, P, Ni and Cd.72.52%, 43%, 77.79%, 82%, 77%, and 44.1% of Cd, Se, Ni, Cu, B, and Mo came from the black rock system, respectively, which were greatly affected by geogenic weathering; V, Zn, Pb, and As are mainly from biomass burning sources; N and P are mainly from agricultural surface sources. Comparison found that the Cd and Se elements in the rocks in the study area were 16.78 times and 1.36 times higher than the world shale average, respectively, and need to pay attention to the weathering process of the two, and the spatial distribution of the 12 elements in soils showed a striped and centralized block distribution pattern, specifically around the distribution of carbonate and metamorphic rocks and other high-geology blocks. The ecological risk results showed that Cd was the main element causing high ecological risk, followed by Se and N, which were at moderate to high ecological risk levels, and Se and N showed similar ecological risk patterns, which may be related to the fact that selenium can promote the uptake and transformation of nitrogen. The present results add to the endogenous sources of toxic elements, quantify the source contributions of toxic elements in soils with high geologic backgrounds, fill this knowledge gap, and provide new insights for pollution control and ecological protection in areas with high geochemical backgrounds.

Quantitative assessment of ecological risk from pollution source based on geostatistical analysis and APCS-MLR model.

The safety of human health and agricultural production depends on the quality of farmland soil. Risk assessment of heavy metal pollution sources could effectively reduce the hazard of soil pollution from various sources. This study has identified and quantitatively analyzed pollution sources with geostatistical analysis and the APCS-MLR model. The potential ecological risk index was combined with the APCS-MLR model which has quantitatively calculated the source contribution. The results revealed that As, Cr, Cd, Pb, Zn, and Cu were enriched in soil. Geostatistical analysis and the APCS-MLR model have apportioned four pollution sources. The Mn and Ni were attributed to natural sources; As and Cr were from agricultural activities; Cu and Zn were originated from natural sources; Cd and Pb were derived from atmospheric deposition. Atmospheric deposition and agricultural activities were the largest contributors to ecological risk of heavy metals in soil, which accounted for 56.21% and 36.01% respectively. Atmospheric deposition and agricultural activities are classified as priority sources of pollution. The combination of source analysis receptor model and risk assessment is an effective method to quantify source contribution. This study has quantified the ecological risks of soil heavy metals from different sources, which will provide a reliable method for the identification of primary harmfulness sources of pollution for future studies.

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.

[Spatiotemporal Distribution and Source Analysis of Heavy Metals in Surface Sediments in Lake Ulansuhai Based on PCA-APCS-MLR Model].

To explore the spatial and temporal distribution characteristics of heavy metals in the surface sediments of lakes in cold regions during ice-sealing and non-ice-sealing periods, we analyzed the potential ecological risk degree and the pollution sources. A total of 20 sampling sites in Lake Ulansuhai in cold regions were collected from 2020 to 2021, and 120 surface sediment samples were collected during different periods. The contents of As, Cd, Cr, Cu, Ni, Pb, Zn, and Hg were determined. Correlation analysis, principal component analysis, and the absolute factor score-multiple linear regression (APCS-MLR) receptor model were used to trace the source of heavy metal pollution. The results showed that:① the distribution of heavy metals in lake surface sediments was different between the ice-sealing period and the non-ice-sealing period. The distribution of heavy metals during the ice-sealing period was higher in the north and lower in the south. Cd and Hg were mainly distributed in the central and southern lakes during the non-glacial period. ② Hg, Cd, and As were moderately polluted, and Ni was mildly polluted in the surface sediments of the lake. The overall performance was at a high risk level, and the pollution level during the non-ice-sealing period was higher than that during the ice-sealing period. The main environmental risk factors were Hg and Cd, which showed high risk and moderate risk, respectively. ③ The sources of heavy metals in lake surface sediments were mainly industrial sources from mining and transportation, agricultural sources, and natural sources. As, Ni, Pb, and Hg were mainly affected by industrial sources, with contribution rates of 62.67%, 75.31%, 77.47%, and 80.11%, respectively. The main sources of Cu and Zn were natural sources, and Cd was greatly affected by agricultural sources. The contribution rate was 81.57%. The source of Cr was mainly affected by natural factors, and the influence of human activities and unknown sources require further attention.

[Distribution Characteristics, Source Analysis and Ecological Risk Assessment of Heavy Metals in the Typical Industry Reclaimed Soil].

To further explore the characteristics of heavy metal pollution and the ecological risk of typical industries in reclaimed soil, based on data from 315 different depth profiles of soil samples collected from 49 plots in Jiading District, Shanghai, the geo-accumulation index and potential ecological risk index were used to evaluate the contents and potential ecological risk of seven heavy metals, namely Cd, Pb, Cu, Zn, Ni, Hg, and As. The APCS-MLR receptor model and PMF positive matrix factorization model were employed to analyze the pollution sources. The results showed that:① except for As, the contents of other heavy metals in the soil of the study area exceeded the Shanghai soil background values to varying degrees. The contents of Cd, Pb, Cu, Zn, Ni, and Hg in the surface soil were 3.54, 2.34, 2.91, 1.20, 3.75, and 4.40 times the background values, respectively. The contents of heavy metals in the soil decreased with the increase in depth, and heavy metals were enriched to a certain extent in the surface soil, indicating that human activities had an impact on the distribution of heavy metals in the soil. ② The APCS-MLR and PMF receptor models identified four main sources of soil heavy metals in the study area. Source 1 (Cu, Zn, and Pb) was a mixture of metal products and automobile manufacturing, source 2 (Ni and Cd) was electroplating enterprises, source 3 (Hg) was mainly from chemical enterprises, and source 4 (As) was natural. The combined use of the two receptor models further improved the accuracy and credibility of source identification. ③ The geo-accumulation index in descending order was Hg(1.54)>Ni(1.32)>Cd(1.21)>Cu(0.96)>Pb(0.64)>Zn(-0.33)>As(-1.02). The potential ecological risk index showed that the comprehensive potential ecological risk index RI value in the study area ranged from 32.50 to 4 910.97, with a mean of 321.40, indicating a strong potential ecological risk. The pollution values of heavy metals Hg, Ni, and Cd in industrial site soil deserve further attention for re-development and utilization purposes.

[Chemical Characteristics of Shallow Groundwater in the Yellow River Diversion Area of Henan Province and Identification of Main Control Pollution Sources].

The northern plain of Henan in the lower reaches of the Yellow River is an area where the Yellow River is frequently diverted. The shallow groundwater quality in this area is poor， and many types of components have been found to be exceeding the limit value； however， the contribution of various environmental factors to water quality needs to be further quantified. In order to clarify the genesis of water quality of shallow groundwater in the study area， 330 groups of shallow groundwater samples were collected via a regional water quality survey. The evolution of shallow groundwater quality in the Yellow River diversion area of northern Henan was revealed using the principal component-absolute principal component score-multiple linear regression （PCA-APCS-MLR） model. The results showed that the components with a shallow groundwater excess rate greater than 10% in descending order were manganese， iron， total hardness， total dissolved solids， sodium， fluoride， arsenic， chloride ions， sulfate， and ammonium. In particular， the excess rate of manganese reached 76%. The four factors of dissolution enrichment， native origin of soil， redox conditions， and agricultural activities were identified as the main reasons for poor groundwater quality， which accounted for 71.24% of the cumulative interpretation rate of variance. In addition， the recharge from the surface water also influenced the groundwater quality. The effects of dissolution between the water and aquifer matrix and redox condition in the aquifer of the Yellow River dried-riverway like Xinxiang were significantly enhanced， resulting in the increasing concentration of iron， arsenic， total hardness， TDS， and other components in groundwater. Fluoride enrichment was caused by dissolution enrichment， the origin of the soil， and lateral replenishment of the Yellow River. Groundwater with high manganese concentration was widely affected by the soil matrix. Nitrate pollution of the groundwater was caused by the extensive use of chemical fertilizers in agricultural activities in individual areas.

Chemical characteristics, morphology and source apportionment of PM10 over National Capital Region (NCR) of India.

The present study frames the physico-chemical characteristics and the source apportionment of PM10 over National Capital Region (NCR) of India using the receptor model's Positive Matrix Factorization (PMF) and Principal Momponent Mnalysis/Absolute Principal Component Score-Multilinear Regression (PCA/APCS-MLR). The annual average mass concentration of PM10 over the urban site of Faridabad, IGDTUW-Delhi and CSIR-NPL of NCR-Delhi were observed to be 195 ± 121, 275 ± 141 and 209 ± 81 µg m-3, respectively. Carbonaceous species (organic carbon (OC), elemental carbon (EC) and water-soluble organic carbon (WSOC)), elemental constituents (Al, Ti, Na, Mg, Cr, Mn, Fe, Cu, Zn, Br, Ba, Mo Pb) and water-soluble ionic components (F-, Cl-, SO42-, NO3-, NH4+, Na+, K+, Mg2+, Ca2+) of PM10 were entrenched to the receptor models to comprehend the possible sources of PM10. The PMF assorted sources over Faridabad were soil dust (SD 15%), industrial emission (IE 14%), vehicular emission (VE 19%), secondary aerosol (SA 23%) and sodium magnesium salt (SMS 17%). For IGDTUW-Delhi, the sources were SD (16%), VE (19%), SMS (18%), IE (11%), SA (27%) and VE + IE (9%). Emission sources like SD (24%), IE (8%), SMS (20%), VE + IE (12%), VE (15%) and SA + BB (21%) were extracted over CSIR-NPL, New Delhi, which are quite obvious towards the sites. PCA/APCS-MLR quantified the similar sources with varied percentage contribution. Additionally, catalogue the Conditional Bivariate Probability Function (CBPF) for directionality of the local source regions and morphology as spherical, flocculent and irregular were imaged using a Field Emission-Scanning Electron Microscope (FE-SEM).

Risk assessment and source apportionment of available atmospheric heavy metal in a typical sandy area reservoir in Inner Mongolia, China.

This study evaluated dry and wet deposition of atmospheric heavy metals (HMs) in a sandy area of Inner Mongolia, China, with the Dahekou Reservoir, Xilin Gol League, adopted as the study area. Monthly monitoring of atmospheric HM dry and wet deposition was conducted over one year (2021 to 2022) at 12 monitoring points, producing 144 dry and wet deposition samples, respectively. The sample contents of eight HMs (Cr, Ni, Pb, Cu, Zn, Mn, As, and Cd) were determined to estimate the fluxes of available forms of heavy metal (AHM) in dry and wet deposition. The potential ecological index (Eri), risk assessment coding (RAC), and ratio of secondary phase to primary phase (RSP) were used to evaluate the impact of atmospheric HM dry deposition on ecological security. Correlation analysis, principal component analysis, and the absolute principal component scores-multiple linear regression (APCS-MLR) receptor model were used to quantitatively analyze the sources of AHMs in atmospheric dry and wet deposition. The results showed that the study area experienced annual dry and wet deposition fluxes of AHMs of 1712.59 kg and 534.97 kg, respectively. Atmospheric heavy metal dry deposition over the entire year presented a strong ecological risk, with Cd contributing most to this risk. Risk assessment of HM speciation showed that the greatest risks of migration and transformation were for Cd and Pb. The APCS-MLR receptor model identified five and three sources of dry and wet deposition, respectively, in order of proportion of total contribution of: natural wind and sand > road traffic and coal combustion > mineral mining > other human activities > industrial soot.

Source identification and risk assessment of trace metals in surface sediment of China Sea by combining APCA-MLR receptor model and lead isotope analysis.

This study aimed to investigate the distribution, pollution, risk and sources of trace metals in sediments along China Sea. Clear spatial variations were found for Cr, Mn, Co, Ni, Cu, Zn, Se, Mo, Ag, Cd, and Pb, whereas As did not show spatial variation. East China Sea (ECS) contained the highest concentrations of Mn, Co, Ni, Cu, Zn, South China Sea (SCS) shallow sea contained the highest concentrations of Zn, Se, Mo, Ag, Cd, and Pb, whereas coral reefs contained the lowest concentrations of trace metals. Spatial variations could be explained by economic development characteristics along China Sea. As, Se and Cd exhibited low to moderate pollution in China Sea sediment, yet pollution for Cu, Zn, Ni, and Ag appeared in some regions. Sediment in ECS had moderate ecological risks and other regions at low ecological risks. The absolute principle component score-multiple linear regression (APCS-MLR) and Pb stable isotope indicated that 43-74% of trace metals (Ni, Cu, Zn, As, Se, Cd, and Pb) were derived from anthropogenic sources like traffic emission, agricultural activities, industrial source. No pollution and ecological risk were observed in coral reefs, yet 39-71% (Pb) was derived from anthropogenic activities such as motor vessels.

[Hydrochemical Characteristics and Control Factors of Surface Water in Kuaize River Basin at the Upper Pearl River].

The Kuaize River is a small typical karst watershed in the source area of the Pearl River as well as an important coal mining area in Eastern Yunnan with a fragile ecological environment. Strengthening the research on the water environment in the region plays an important role in supporting the comprehensive management of the ecological environment and water resources in the source region of the Pearl River. Through the systematic collection of surface water, karst groundwater, and mine water samples, mathematical statistics analysis, correlation analysis, ion ratio analysis, absolute principal component scores multiple linear regression(APCS-MLR), and other methods were used to study the characteristics of hydrochemical evolution and control factors in Kuaize River Basin. The results showed that the average pH value of surface water in Kuaize River Basin was 7.8, which was weakly alkaline. The main cations were Ca2+ and Na+, showing the characteristics of Ca2+>Na+>Mg2+>K+. The main anions were HCO3- and SO42-, showing the characteristics of HCO3->SO42->NO3->Cl-. The variation coefficients of Na+, SO42-, and NO3- in surface water were high, showing strong spatial variability. The water chemical type of the trunk stream was mainly HCO3-Ca, whereas the water chemical type of the tributary was relatively complex, mainly HCO3-Ca, HCO3-Ca·Na, and HCO3·SO4-Ca·Na. The chemical composition of surface water was mainly affected by rock weathering, cation exchange, and human activities. Ca2+, Mg2+, Na+, and HCO3- in surface water mainly came from the weathering of carbonate rock and silicate rock; SO42- mainly came from the oxidation of sulfide, such as pyrite in coal seams; K+, Cl-, and NO3- mainly came from domestic sewage and agricultural activities. The APCS-MLR receptor model analysis results showed that the surface water in the Kuaize River Basin was mainly affected by sulfide oxidation, carbonate weathering, weathering of silicate rock in mine water, domestic sewage, agricultural activities, and unknown sources. In general, the contribution rate of human activities such as mining, domestic sewage, and agricultural activities to the surface water reached 47.17%, indicating that human activities were the key driving factor of surface water chemistry in the Kuaize River Basin.