Absolute Principal Component Score-multiple Linear Regression Research Articles

Classification of Pollution Sources and Their Contributions to Surface Water Quality Using APCS-MLR and PMF Model in a Drinking Water Source Area in Southeastern China

Identifying the potential pollution sources of surface water pollutants is essential for the management and protection of regional water environments in drinking water source areas. In this study, absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models were applied to assess water quality and identify the potential pollution sources affecting the surface water quality of Xin’an River Basin. For this purpose, a 10-year (2011–2020) dataset of eight water quality indicators (including pH, EC, DO, COD, NH3-N, TN, TP, and FC) covering eight monitoring stations and 7248 monthly observations was used. The results indicated that Pukou section had the worst water quality among the eight monitoring stations, and TN was the most serious water quality index. Both the APCS-MLR and PMF models identified agricultural nonpoint source pollution, urban nonpoint source pollution and rural domestic pollution, and meteorological factors. The sum of these three sources was very close, accounting for 60% and 58%, respectively. The APCS-MLR results demonstrated that for EC, COD, and NH3-N, the major pollution sources were urban nonpoint sources and rural domestic pollution. The major contamination source of TN was agricultural nonpoint source pollution (30.4%). Meanwhile, the major pollution sources of pH, DO, TP, and FC were unidentified factors. The PMF model identified five potential sources, and pH and DO were affected by meteorological factors. NH3-N and TP were influenced mainly by agricultural nonpoint source pollution. Atmospheric deposition was the major pollution source (87.9%) of TN. FC was mostly derived from livestock and poultry breeding (88.3%). EC and COD were mostly affected by urban nonpoint sources and rural domestic pollution. Therefore, receptor models can help managers identify the major sources of pollution in watersheds, but the major factors affecting different pollutants need to be supplemented by other methods.

Characteristics and correlation analysis of heavy metal distribution in China's freshwater aquaculture pond sediments

The concentration of heavy metals (HMs) in aquaculture pond sediments significantly affects aquatic food safety and environmental quality. The contamination characteristics, drivers and potential sources of HMs in typical bulk freshwater aquaculture pond sediments in major provinces of China were systematically investigated using a variety of methods and models. Specifically, 130 surface sediment samples were collected from the study area, and the geoaccumulation index (Igeo) and potential ecological risk index (RI) were used to jointly evaluate the characteristics of the HMs. Spearman's correlation and redundancy analysis revealed the main drivers of the HMs. Additionally, the positive matrix factorization (PMF) model and absolute principal component score-multiple linear regression (APCS-MLR) model were used to identify the sources of HMs. The results revealed that the pond sediments were safe for fish culture in most of the study areas. Aquafeed protein content is an important driver of HM concentrations in sediments. The total organic carbon (TOC) content, percentage of clay particles, and pH of the aquaculture pond sediments determined the sediment HMs enrichment abilities as 13.6 %, 52 %, and 9.8 %, respectively. Cd, a significantly enriched pollutant, posed a greater ecological risk than the other five HMs (Cr, Cu, Zn, As, and Pb). Three sources of HMs were identified, including agricultural activity (e.g., aquafeeds, pesticides, and fertilizers), industrial production, and natural sources, with contributions of 44.29 %, 36.66 %, and 19.05 %, respectively. This study provides a scientific basis for minimizing the input and accumulation of HMs in freshwater aquaculture pond sediments, and this can provide insights into the prevention and control of the ecological risks posed by HMs.

Spatial distribution, sources, and human health risk assessment of elevated nitrate levels in groundwater of an agriculture-dominant coastal area in Hainan Island, China

In order to comprehend the groundwater nitrate contamination in Hainan Island, which is recognized as China's sole “tropical agricultural production base,” the distribution characteristics of nitrate in different aquifers and land-use types were elucidated through hydrochemical and geostatistical analysis methods. Principal component analysis (PCA), hierarchical cluster analysis (HCA), and absolute principal component score-multiple linear regression (APCS-MLR) were employed for qualitative and quantitative analysis of the nitrate source. The results showed that NO3−-high (>88.57 mg/L) groundwater accounted for about 10 % of the total distribution area of the western coastal area in Hainan Island. Moreover, the proportions of NO3−-high groundwater in pore water and fissure water were 10 % and 7 %, respectively. The order of groundwater nitrate concentration in different land-use types was as follows: bare land, cultivated land, construction land, grassland, and woodland. The main sources of elevated nitrate concentrations in pore water were animal feces and nitrogen/potassium fertilizers, accounting for 45.4 % of the overall contribution. These substances infiltrated through the vadose zone, leaching out and mobilizing selenium within this zone, ultimately leading to the characteristic enrichment patterns observed in pore water, including nitrate, arsenic, potassium, and selenium. Furthermore, the occurrence of high nitrate in fissure water was mainly attributed to the leaching of cultured manure, septic tank leakage, and infiltration of domestic sewage, accounting for a contribution rate of 32.9 %. Simultaneously, under oxidizing conditions, selenium became activated and entered groundwater through water flow, thereby exhibiting common enrichment characteristics with nitrate and potassium in fissure water. In addition, the assessment of human health risks revealed that 16 % and 40 % of groundwater samples posed unacceptable non-carcinogenic risks to adults and children respectively, with a higher risk for local children. The study recommends the reasonable application of chemical fertilizers, as well as the strengthening of sewage treatment capacity and standardized disposal of manure and feces, to reduce nitrate contamination of groundwater at source and ensure the safety of drinking water.

Source apportionment and risk assessment of soil heavy metals in the Huangshui River Basin using a hybrid model

Source apportionment and risk assessment of soil heavy metals (HMs) are essential for pollution control. However, inherent limitations in receptor models hinder accurate source apportionment, impacting outcomes of source-oriented risk assessment. A hybrid model was employed by combining two receptor models, absolute principal component score/multiple linear regression (APCS/MLR) and positive matrix factorization (PMF) models. Four primary pollution sources were identified, followed by an assessment of source-oriented environmental and human health risks in the Huangshui River Basin. Results revealed that Cr, Cd, and Ni average concentrations surpassed their background by 3.1, 2.1, and 1.9 times, respectively. Additionally, 54.29%, 13.34%, and 18.09% of Igeo values for Cr, Cd, and Ni were classified as “moderately contamination” or higher. Natural sources significantly influence Cu (90.2%) and As (71.4%). Cr (63.4%) and Ni (77.9%) mainly originated from agriculture and industry, respectively. Transportation sources emerged as the primary contributors to Pb (59.6%), Zn (48.6%), and Cd (47.2%). The environmental risk level in this study area remained acceptable. Ecological risk was mainly attributed to industrial activities (46.6%) and transportation (37.6%), with Cd being the predominant metal responsible for this risk. Although the noncarcinogenic risk was negligible for all populations, the carcinogenic risk demands attention, particularly concerning children. Industrial sources (67.0%) and Ni were identified as the main contributors to the carcinogenic risk. This study represents an attempt to develop a hybrid model, providing an effective approach to combine models for the accurate apportionment of pollution sources and the reduction of risks.

Ecological Risk Assessment and Source Analysis of Heavy Metals in Farmland Soil in Yangchun City Based on APCS-MLR and Geostatistics

Yangchun City, a typical polymetallic ore distribution area in Guangdong Province (China), was selected as the research region to study the content, distribution, source, and possible impacts of heavy metals (HMs) (Arsenic: As; Cadmium: Cd; Chromium: Cr; Copper: Cu; Mercury: Hg; Nickel: Ni; Lead: Pb; and Zinc: Zn) on the farmland soil of this City. According to our findings, the spatial distribution of HMs in Yangchun City shows higher concentrations in the north and southeast and lower in the west and other regions. Metal content in some sampled sites of the agricultural land exceeded the soil pollution risk screening values, particularly As (7.5%), Cd (12%), Cu (4%), Hg (14.5%), and Pb (3%). Additionally, the average content of As, Cu, Cd, Pb, Hg, and Zn from the studied areas surpassed the soil background value of Guangdong Province for all metals. The absolute principal component score-multiple linear regression (APCS-MLR) was used to identify potential sources of HMs in the soil samples. There are three potential sources identified by the model: traffic emissions, natural sources, and agricultural activities, accounting for 28.16%, 16.68%, and 14.42%, respectively. Based on the ecological risk assessment, the potential ecological risk (Eri = 310.77), Nemero pollution index (PN = 2.27), and multiple possible effect concentration quality (mPECQs = 0.23) indicated that the extent of heavy metal pollution in the soil samples was moderate. Three sources were identified: traffic emissions, natural sources, and agricultural activities. We suggest that by combining the above results, a monitoring and early warning system focused on Cd and Hg can be established. The system could utilize geographic information systems and remote sensing technologies to achieve dynamic monitoring and prediction of pollution. Regular testing of soils and sustainable management practices are also recommended to control and remediate contamination.

Risk assessment of heavy metals in agricultural soil based on the coupling model of Monte Carlo simulation-triangular fuzzy number.

Soils in areas wherein agriculture and mining coexist are experiencing serious heavy metal contamination, posing a great threat to the ecological environment and human health. In this study, heavy metals (As, Cd, Cr, Cu, Ni, Pb, and Zn) in agricultural soil samples from mining areas were analyzed to explore pollution status, bioavailability, potential sources, and ecological/health risks. Particularly, the coupling model of Monte Carlo simulation-triangular fuzzy number (MCS-TFN) was established to quantify ecological/health risks accurately. Results showed that Cd was heavily enriched in soil and had the highest bioavailability based on both geo-accumulation index (Igeo) and chemical speciation analysis. Pollution sources apportioned with the absolute principal component score-multiple linear regression (APCS-MLR) model demonstrated that heavy metals were mainly derived from agricultural activities, followed by mining activities and natural sources. The MCS-TFN ecological risk assessment classified Cd into the high-risk category with a probability of 40.96%, whereas other heavy metals were categorized as the low risk. Cd was regarded as the major pollutant for the ecosystem. Moreover, the MCS-TFN health risk assessment indicated that As showed high noncarcinogenic risk (0.07% probability) and moderate carcinogenic risk (1.87% probability), and Cd presented low carcinogenic risk (80.19% probability). As and Cd were identified as the main heavy metals that pose a threat to human health. The MCS-TFN risk assessment is superior to the traditional deterministic risk assessment since it can obtain the risk level and the corresponding probability, and significantly reduce the uncertainty in risk assessment.

Profile of per- and polyfluoroalkyl substances, source appointment, and determinants in Argentinean postpartum women

BackgroundPer- and polyfluoroalkyl substances (PFAS) are a group of synthetic chemicals with potential adverse health effects. Information concerning PFAS concentrations in relation to pregnancy is scarce in South America and non-existent in Argentina. AimWe aimed to investigate an extended maternal PFAS profile herein serum concentrations in a regional and global view, source appointment, and determinants in Argentinean women. MethodsA cross-sectional study with a sampling period from 2011 to 2012 included 689 women from Ushuaia and Salta in Argentina. Serum samples collected two days postpartum were analyzed by ultra-high pressure liquid chromatography coupled to electrospray negative ionisation tandem-quadrupole mass-spectrometry. Principal Component Analysis (PCA) following absolute principal component score-multiple linear regression (APCS-MLR) was used for PFAS source appointments. Determinants of PFAS were explored through a MLR approach. A review of previous studies within the same period was conducted to compare with present levels. ResultsArgentinean PFAS concentrations were the lowest worldwide, with PFOS (0.74 ng/mL) and PFOA (0.11 ng/mL) as the dominant substances. Detection frequencies largely aligned with the compared studies, indicating the worldwide PFAS distribution considering the restrictions. The PCA revealed region-specific loading patterns of two component groups of PFAS, a mixture of replaced and legacy substances in Ushuaia and long-chain in Salta. This might relate to a mix of non-diet and diet exposure in Ushuaia and diet in Salta. Region, age, lactation, parity, household members, migration, bottled water, and freshwater fish were among the determinants of various PFAS. ConclusionThis is the first study to monitor human PFAS exposure in Argentina. Maternal PFAS concentrations were the lowest observed worldwide in the same period. Exposure contributions are suggested to be affected by restrictions and substitutions. Given the limited population-based studies and the emergence of PFAS, it is essential to conduct further monitoring of PFAS in Argentina and South America.

Analysis of the Source Apportionment and Pathways of Heavy Metals in Soil in a Coal Mining Area Based on Machine Learning and an APCS-MLR Model

Long-term coal mining activities have led to severe heavy metal pollution in the soil environment of coal mining areas, posing significant threats to both ecological environments and human health. In this study, surface soil samples were collected from the overlying soil of coal gangue and the surrounding areas of the Panyi coal mine in Huainan. The concentrations of Cd, Zn, Cu, Ni, and Pb elements were determined. A self-organizing map (SOM) and an absolute principal component score multiple linear regression (APCS-MLR) receptor model were employed for the quantitative analysis of the soil’s heavy metal pollution sources and contributions. Additionally, this study focused on the analysis of the pathways of the relatively serious pollution of Cd. The results revealed that the average concentrations of heavy metals (Cd, Pb, Zn, Cu, Cr, and Ni) in the study area were 4.55, 0.59, 1.54, 0.69, 0.59, and 0.71 times the local soil background values, respectively. The concentrations of Cd and Zn exceeded the risk screening values at some sampling points, with exceedance rates of 44% and 8%, respectively, indicating a relatively serious Cd contamination. The sources of heavy metals in the soil in the study area were classified into four categories: mining activities, agricultural activities, weathering of natural matrices, and other unknown sources, with average contributions of 55.48 percent, 24.44 percent, 8.91 percent and 11.86 percent, respectively. Based on the spatial distribution of Cd, it was inferred that atmospheric deposition is one of the important pollution pathways of Cd in the study area. Cd profile distribution patterns and a surface water pollution survey showed that the farmland areas were affected by the irrigation water pathway to some extent. The vertical distribution of heavy metal content in the forest area showed a strong disorder, which was related to the absorption function of plant roots. The results of this study can help to improve the environmental management of heavy metal pollution so as to protect the ecological environment and human health.

Priority sources identification and risks assessment of heavy metal(loid)s in agricultural soils of a typical antimony mining watershed

Heavy metal(loid) (HM) pollution in agricultural soils has become an environmental concern in antimony (Sb) mining areas. However, priority pollution sources identification and deep understanding of environmental risks of HMs face great challenges due to multiple and complex pollution sources coexist. Herein, an integrated approach was conducted to distinguish pollution sources and assess human health risk (HHR) and ecological risk (ER) in a typical Sb mining watershed in Southern China. This approach combines absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models with ER and HHR assessments. Four pollution sources were distinguished for both models, and APCS-MLR model was more accurate and plausible. Predominant HM concentration source was natural source (39.1%), followed by industrial and agricultural activities (23.0%), unknown sources (21.5%) and Sb mining and smelting activities (16.4%). Although natural source contributed the most to HM concentrations, it did not pose a significant ER. Industrial and agricultural activities predominantly contributed to ER, and attention should be paid to Cd and Sb. Sb mining and smelting activities were primary anthropogenic sources of HHR, particularly Sb and As contaminations. Considering ER and HHR assessments, Sb mining and smelting, and industrial and agricultural activities are critical sources, causing serious ecological and health threats. This study showed the advantages of multiple receptor model application in obtaining reliable source identification and providing better source-oriented risk assessments. HM pollution management, such as regulating mining and smelting and implementing soil remediation in polluted agricultural soils, is strongly recommended for protecting ecosystems and humans.

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.

The Sources of Polycyclic Aromatic Hydrocarbons in Road Dust and Their Potential Hazard

Polycyclic aromatic hydrocarbons (PAHs) are persistent organic pollutants (POPs) found in the environment, posing significant health concerns for the population. This research aimed to assess the PAH levels in road dust near bus stops, identify their sources, and evaluate potential health risks. The analysis involved the use of a gas chromatography–flame ionization detector (GC-FID) to measure PAHs and absolute principal component score-multiple linear regression (APCS-MLR) for source apportionment of PAHs. The results indicated that the measured PAHs concentrations in road dust ranged from 137.8 to 5813 ng g−1, with Indeno[1,2,3-c,d]pyrene having the highest PAHs concentrations. The study identified three main sources of PAHs such as oil spills, fuel combustion, and coal burning, determined through APCS-MLR modeling. Further analysis revealed that the aggregate incremental lifetime cancer risk (ILCR) for children and adults were 2.16 × 10−6 and 2.08 × 10−6, respectively. Additionally, the hazard index (HI) for children exceeded that of adults, suggesting greater vulnerability to the potential health effects of PAH exposure. The findings indicate that long-term exposure to PAHs may negatively impact lung function and increase the risk of cancer and skin diseases. As a result, it is crucial for the local government to implement effective measures aimed at improving fuel quality and promoting green public transportation within the city. These initiatives may help mitigate PAH emissions and safeguard public health.

Pollution source identification of nitrogen and phosphorus in the lower West Main Canal, the Ganfu Plain irrigation district (South China).

The degradation of surface water quality has been a widespread concern around the world. However, irrigation canal water does not attract much attention although it is important to agriculture and population. In this study, a 5-year water quality monitoring of surface water was conducted in the lower West Main Canal of the Ganfu Plain irrigation district to identify the levels and pollution sources of nitrogen and phosphorus.Over 75% of samples had total phosphorus (TP) concentrations of > 0.02mg/L, and all samples had total nitrogen (TN) concentrations of > 0.2mg/L, indicating a risk of eutrophication. The concentrations of NO3--N and NH4+-N averagely occupied 57% and 18% of TN, respectively. PCA analysis showed that phosphorus and nitrogen in canal water were associated with meteorological factors, urban life and surface runoff, agricultural cultivation, livestock-poultry breeding, and water-sediment interaction in the wet season, whereas they were affected by meteorological factors, industrial effluent, urban domestic sewage, and livestock-poultry breeding in the dry season. Absolute principal component score-multiple linear regression (APCS-MLR) model results revealed that (1) agricultural cultivation plus livestock-poultry breeding contributed 43.2% of TP in canal water in the wet season, while livestock-poultry breeding contributed 52.9% in the dry season, and (2) domestic sewage plus surface runoff contributed 29.4% of TN in the wet season, while livestock-poultry breeding contributed 45.9% in the dry season. The unidentified sources had significant contributions of > 20% for almost all variables. So further investigations are required for determining unidentified sources, and anthropogenic pollution control is imperative for canal water quality protection.

Priority control factors for heavy metal groundwater contamination in peninsula regions based on source-oriented health risk assessment

Peninsula regions in China face serious environmental issues, such as heavy metal (HM) groundwater contamination. However, attempts to investigate the pollution sources and health risks of HM contamination in such regions require considerable resources and costs. Moreover, the priority control factors for groundwater HMs remain unclear. In this study, absolute principal component score/multiple linear regression (APCS/MLR) was used to quantify the groundwater pollution sources of typical peninsular areas in China, and a health risk assessment (HRA) was performed to assess the health risks caused by different sources. The results showed that the concentrations of Mn and Fe were higher than those of other HMs, and HM pollution was high in shallow groundwater. The dominant source of HMs in groundwater was agricultural activities (31.12 %), followed by natural sources (26.33 %), industrial activities (22.47 %), and atmospheric deposition (20.09 %). The non-carcinogenic risks to residents were acceptable, whereas the carcinogenic risks were high. Agricultural sources, atmospheric deposition sources, and Cr and As were identified as the priority control factors for HM groundwater contamination. This study has implications for improving the control of groundwater HM contamination in peninsula regions and ensuring sustainable groundwater development.

Incorporation of source contributions to improve the accuracy of soil heavy metal mapping using small sample sizes at a county scale

Estimating heavy metal (HM) distribution with high precision is the key to effectively preventing Chinese medicinal plants from being polluted by the native soil. A total of 44 surface soil samples were gathered to detect the concentrations of eight HMs (As, Hg, Cu, Cr, Ni, Zn, Pb, and Cd) in the herb growing area of Luanping County, northeastern Hebei Province, China. An absolute principal component score-multiple linear regression (APCS-MLR) model was used to quantify pollution source contributions to soil HMs. Furthermore, the source contribution rates and environmental data of each sampling point were simultaneously incorporated into a stepwise linear regression model to identify the crucial indicators for predicting soil HM spatial distributions. Results showed that 88% of Cu, 72% of Cr, and 72% of Ni came from natural sources; 50% of Zn, 49% of Pb, and 59% of Cd were mainly caused by agricultural activities; and 44% of As and 56% of Hg originated from industrial activities. When three-type (natural, agricultural, and industrial) source contribution rates and environmental data were simultaneously incorporated into the stepwise linear regression model, the fitting accuracy was significantly improved and the model could explain 31%–86% of the total variance in soil HM concentrations. This study introduced three-type source contributions of each sampling point based on APCS-MLR analysis as new covariates to improve soil HM estimation precision, thus providing a new approach for predicting the spatial distribution of HMs using small sample sizes at the county scale.

New insights into pollution source analysis using receptor models in the upper Yangtze river basin: Effects of land use on source identification and apportionment

To effectively control pollution and improve water quality, it is essential to accurately analyze the potential pollution sources in rivers. The study proposes a hypothesis that land use can influence the identification and apportionment of pollution sources and tested it in two areas with different types of water pollution and land use. The redundancy analysis (RDA) results showed that the response mechanisms of water quality to land use differed among regions. In both regions, the results indicated that the water quality response relationship to land use provided important objective evidence for pollution source identification, and the RDA tool optimized the procedure of source analysis for receptor models. Positive matrix decomposition (PMF) and absolute principal component score-multiple linear regression (APCS-MLR) receptor models identified five and four pollution sources along with their corresponding characteristic parameters. PMF attributed agricultural nonpoint sources (23.8%) and domestic wastewater (32.7%) as the major sources in regions 1 and 2, respectively, while APCS-MLR identified mixed sources in both regions. In terms of model performance parameters, PMF demonstrated better-fit coefficients (R2) than APCS-MLR and had a lower error rate and proportion of unidentified sources. The results show that considering the effect of land use in the source analysis can overcome the subjectivity of the receptor model and improve the accuracy of pollution source identification and apportionment. The results of the study can help managers clarify the priorities of pollution prevention and control, and provide a new methodology for water environment management in similar watersheds.

Source Analysis and Ecological Risk Assessment of Heavy Metals in the Arable Soil at the Geological High Background, Based on the Township Scale

Due to the extensive development of carbonate rocks in southwest China, heavy metals are naturally occurring elements that have high natural background levels in the environment. Therefore, it is important to conduct ecological risk assessments and identify potential sources of heavy metals in the geological high background area. Based on the township scale, a total of 307 surface soil samples were collected in Qinglong Town, Fengjie County, Chongqing. The concentrations of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn and pH were analyzed and determined. The spatial distribution and source of soil heavy metals were discussed using the geostatistical analysis and an absolute principal component score-multiple linear regression (APCS-MLR) model in the studied area. The results showed that the average values of seven heavy metals (As, Cd, Cr, Cu, Hg, Ni, and Zn) in the arable soil exceeded the background values of Chongqing, and the cumulative effect of Cd and As was obvious. The concentrations of Cd significantly exceededthe screening values in The Risk Control Standard for Soil Environmental Quality and Soil Pollution in Agricultural Land (GB 15618-2018), with the over-standard rates of 52.12%. The spatial characteristics of soil heavy metal contents exhibited a pattern of high in the south and low in the north. PCA and APCS-MLR modeling revealed that the contributions of natural sources to Cr, Cu, Ni, and Zn were 86.62%, 64.34%, 76.44%, and 85.46%, respectively. As, Pb, and Hg mainly derived from industrial activities, which accounted for 74.63%, 61.90%, and 73.49%, respectively, and Cd was affected by both natural sources and industrial activities (accounting for 47.74% and 39.56% of the total Cd content, respectively). The evaluation of the soil by the Nemerow comprehensive index (P) showed that Cd pollution was relatively serious, accounting for 27.04% of soil pollution. The potential ecological hazard index showed that Cd and Hg were the main ecological hazard elements, and the distribution range of RI was 51.77 to 2228, indicating mainly mild and moderate risks, and the moderate and above risk areas in the study area were mainly located around the southern industrial source area. Altogether, our results revealed that in the study area, the heavy metal pollution was mainly caused by industrial activities, and the heavy metal pollution caused by geological background was mainly light to moderate. In conclusion, the medium and above risk areas in the study area were mainly caused by mineral and industrial activities, whereas the heavy metal pollution caused by geological background was mainly light to moderate pollution.

Contamination characteristics and source analysis of potentially toxic elements in dustfall-soil-crop systems near non-ferrous mining areas of Yunnan, southwestern China

Potentially toxic elements (PTEs) in the dustfall-soil-crop system pose a serious threat to the ecological environment and agricultural production. However, there is still a knowledge gap in terms of better understanding the distinctive sources of PTEs by integrating various models and technologies. In this study, we comprehensively investigated the concentrations, distribution, and sources of seven PTEs in a dustfall-soil-crop system (424 samples in total) near a typical non-ferrous mining area, using absolute principal component score/multiple linear regression (APCS/MLR) combined with X-ray diffraction (XRD) and microscopy techniques. Our results showed that the mean values of As, Cd, Cr, Cu, Ni, Pb, and Zn in the soils were 211, 14, 105, 91, 65, 232, and 325 mg/kg, respectively. These values were significantly higher than the background soil values in Yunnan. Except for Ni and Cr, all elements in the soil were significantly higher than the screening values of agricultural lands in China. The spatial distribution of PTE concentrations was similar among the three media. The ACPS/MLR, XRD, and microscopy analyses further indicated that soil PTEs mainly originated from industrial activities (37 %), vehicle emissions and agricultural activities (29 %), respectively. Dustfall PTEs mainly originated from vehicle emissions and industrial activities, accounting for 40 % and 37 %, respectively. Crop PTEs mainly originated from vehicle emissions and soil (57 %), and agricultural activities (11 %), respectively. PTEs seriously threaten the safety of agricultural products and the ecological environment once they settle from the atmosphere to soil and crop leaves, further accumulate in crops, and spread through the food chain. Therefore, our study provides scientific evidence for government regulators to control PTE pollution and reduce their environmental risks in dustfall-soil-crop systems.

Distribution Characteristics and Pollution Assessment of Heavy Metals in Soils of a Testing Range

Heavy metal pollution in testing ranges is one of the most widely concerning environmental problems. The ammunition static detonation test area, the bomb falling area, and the living area of a testing range in Jilin were selected as the study objects. The contents of heavy metals (As, Cd, Cr, Cu, Ni, Pb, and Zn) in 112 topsoil samples and two soil profiles were analyzed, and their distribution characteristics and sources were analyzed in detail. After that, the pollution degree and potential ecological risk of heavy metals were investigated using multiple pollution index assessment methods. The results showed that the average contents of As, Cd, Cu, Ni, and Zn in the soil of the ammunition static detonation test area were higher than the soil background values in Jilin province, and the contents of Cu, Zn, As, and Cd showed strong spatial heterogeneity. The average concentrations of As, Cd, and Ni in the soil of the bomb falling area exceeded their background values. The average contents of As and Cd in the soil of the living area were higher than the background values, and the variation coefficients of Pb, Cd, Zn, and Cr were relatively high, indicating that they may have been affected by human activities. In different test areas, the contents of As, Cr, Cu, Ni, and Zn in the soil samples were significantly different (P<0.05). The ammunition static detonation test area was more strongly affected by the test activities than the bomb falling area, and the heavy metal contents in the surface layer of the soil profile were significantly higher. There was no obvious vertical migration of heavy metals in the soil profiles. The results of multivariate statistics and source identification analysis using absolute principal component score-multiple linear regression (APCS-MLR) showed that Zn, Pb, and Cd were mainly affected by pollution sources related to test activities; Cr and Ni were mainly affected by natural sources of soil forming materials; and the sources of As and Cu were more complicated. The geo-accumulation index showed that Cd in the three areas and Ni in the bomb falling area belonged to pollution level 1 (uncontaminated to moderately contaminated). The Nemerow comprehensive pollution index showed that the pollution levels among the different functional areas were:living area>ammunition static detonation test area>bomb falling area, and the three functional areas were slightly polluted. The potential ecological risk index showed that the study area was at moderate ecological risk level, and Cd was considered to be the main soil pollution factor.

Ecological and health risk assessment and quantitative source apportionment of dissolved metals in ponds used for drinking and irrigation purposes.

In this study, dissolved metal levels of 10 different ponds used as irrigation and drinking water sources in the north of Saros Bay (Türkiye) were evaluated using multivariate statistical methods, contamination and ecological risk indices, and absolute principal component score-multiple linear regression (APCS-MLR). The mean levels of metals in the ponds ranged from 0.045µg/L (Cd) to 127µg/L (Mn). Pond 7 used for drinking water source had the lowest total metal level. Only Mn levels in two ponds (P1 and P2) slightly exceeded the critical value set by EU Drinking Water Directive. However, the levels of all metals in all ponds were lower than the critical values set for irrigation water and aquatic life. According to the heavy metal pollution index (HPI), five ponds showed low metal pollution in terms of drinking water quality, four ponds showed moderate metal pollution, and one pond (P1) showed moderate to heavy pollution. According to the Nemerow pollution index (NPI) values (0.26-1.82), ponds P1 and P2 showed slight metal pollution, while other ponds showed insignificant metal pollution. Contamination degree (CD) values of ponds varied between 0.95 and 3.33, indicating that all ponds showed low pollution. In terms of irrigation water quality, all ponds showed low or insignificant metal pollution according to the HPI, NPI, and CD values. According to the ecological risk index (ERI) values, metals in all ponds posed low ecological risks for both drinking and irrigation purposes. Factor analysis identified two potential sources: mixed sources and natural sources. The APCS-MLR model results revealed that mixed sources and natural sources contributed 78.99% and 21.01% to dissolved metals in the ponds, respectively. Health risk assessment results indicated that both individual and combined metals in the ponds would not cause non-carcinogenic risks to both adults and children. Similarly, it was found that Cr and As would not cause carcinogenic risks to the residents of the region.

Water quality assessment and pollution source apportionment using multivariate statistical techniques: a case study of the Laixi River Basin, China.

Identifying potential sources of pollution in tributaries and determining their contribution rates are critical to the treatment of water pollution in main streams. In this paper, we conducted a multivariate statistical analysis on the water quality data of 12 parameters for 3years (2018-2020) at six sampling sites in the Laixi River to qualitatively identify potential pollution sources and quantitatively calculate the contribution rates to reveal the tributaries' pollution status. Spatio-temporal cluster analysis (CA) divided 12months into two parts, corresponding to the lightly polluted season (LPS) and highly polluted season (HPS), and six sampling sites were divided into two regions, corresponding to the lightly polluted region (LPR) and highly polluted region (HPR). Principal component analysis (PCA) was used to determine the potential sources of contamination, identifying four and three potential factors in the LPS and HPS, respectively. The absolute principal component score-multiple linear regression (APCS-MLR) receptor model quantitatively analyzed the contribution rates of identified pollution sources, and the importance of the different pollution sources in LPS can be ranked as domestic sewage and industrial wastewater and breeding pollution (33.80%) > soil weathering (29.02%) > agricultural activities (20.95%) > natural influence (13.03%). HPS can be classified as agricultural cultivation (41.23%), domestic sewage and industrial wastewater and animal waste (33.19%), and natural variations (21.43%). Four potential sources were identified in LPR ranked as rural domestic sewage (31.01%) > agricultural pollution (26.82%) > industrial effluents and free-range livestock and poultry pollution (25.13%) > natural influence (14.82%). Three identified latent pollution sources in HPR were municipal sewage and industrial effluents (37.96%) > agricultural nonpoint sources and livestock and poultry wastewater (33.55%) > natural sources (25.23%). Using multivariate statistical tools to identify and quantify potential pollution sources, managers may be able to enhance water quality in tributary watersheds and develop future management plans.