Absolute Principal Component Scores Research Articles

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.

Spatial distribution, sources, and health risk assessment of elements in road dust (

This research delves into the spatial distribution of 17 elements (Al, B, Ba, Bi, Cd, Co, Cr, Cu, Fe, Li, Mg, Mn, Ni, Pb, Sr, Te, and Zn) within the < 20 μm size fraction of road dust samples collected from 177 locations within 65 grid cells (each 5×5 km2) throughout Delhi in February 2021. Notably, Fe and Al emerge as the predominant elements in the road dust samples. Using Principal Component Analysis (PCA), Absolute Principal Component Score (APCS), and Multiple Linear Regression (MLR) techniques, it is observed that crustal sources (58%), non-exhaust vehicular emissions (21%), and two distinct industrial origins (2% and 19%) are major contributors to the road dust composition. Bi and Te exhibited the highest geo-accumulation indices, indicating significant pollution levels, with notably high contamination factors, surpassing a contamination degree of 24. Elements like Cd posed the highest potential ecological risk, dominating 95% of the risk assessment. The hazard index analysis highlighted that urban populations, especially children, face the greatest risk through ingestion, followed by dermal exposure, and least through inhalation. Notably, Cd, Pb, Cr, and Ni were identified as elements posing a very high carcinogenic risk to the general populace.

Trace elements in the Upper Indus River Basin (UIRB) of Western Himalayas: Quantification, sources modeling, and impacts

This study conducted a comprehensive analysis of trace element concentrations in the Upper Indus River Basin (UIRB), a glacier-fed region in the Western Himalayas (WH), aiming to discern their environmental and anthropogenic sources and implications. Despite limited prior data, 69 samples were collected in 2019 from diverse sources within the UIRB, including mainstream, tributaries, and groundwater, to assess trace element concentrations. Enrichment factor (EF) results and comparisons with regional and global averages suggest that rising levels of Zn, Cd, and As may pose safety concerns for drinking water quality. Advanced multivariate statistical techniques such as principal component analysis (PCA), absolute principal component scores (APCS-MLR), Monte Carlo simulation (MCS), etc were applied to estimate the associated human health hazards and also identified key sources of trace elements. The 95th percentile of the MCS results indicates that the estimated total cancer risk for children is significantly greater than (>1000 times) the USEPA’s acceptable risk threshold of 1.0 × 10−6. The results classified most of the trace elements into two distinct groups: Group A (Li, Rb, Sr, U, Cs, V, Ni, TI, Sb, Mo, Ge), linked to geogenic sources, showed lower concentrations in the lower-middle river reaches, including tributaries and downstream regions. Group B (Pb, Nb, Cr, Zn, Be, Al, Th, Ga, Cu, Co), influenced by both geogenic and anthropogenic activities, exhibited higher concentrations near urban centers and midstream areas, aligning with increased municipal waste and agricultural activities. Furthermore, APCS-MLR source apportionment indicated that trace elements originated from natural geogenic processes, including rock-water interactions and mineral dissolution, as well as anthropogenic activities. These findings underscore the need for targeted measures to mitigate anthropogenic impacts and safeguard water resources for communities along the IRB and WH.

Long-term trends of ozone in the Yangtze River Delta, China: Spatiotemporal impacts of meteorological factors, local, and non-local emissions

The Yangtze River Delta (YRD) region has witnessed a consistent decrease in NO2, CO, and PM2.5 from 2016 to 2023. However, ozone has exhibited fluctuating patterns. Quantifying ozone contributions from emissions, both within and outside the YRD, is essential for understanding city-cluster-scale ozone pollution (CCSOP). To address these concerns, a comprehensive approach combining Kolmogorov-Zurbenko filtering, Empirical Orthogonal Function, Absolute Principal Component Score, and Multiple Linear Regression methods (KZ-EOF-APCs-MLR) was employed to quantify the impacts of meteorological factors, local and non-local emission contributions of ozone (LECO and NECO). Emission changes were identified as the predominant factor shaping annual fluctuations in ambient ozone. Notably, during the previous and middle stages of the COVID-19 pandemic (from 2017 to 2021), emissions reductions led to a marked decrease in YRD ozone levels (-7.01 µg/m3), with a pronounced rebound post-pandemic (2022 to 2023) (+8.04 µg/m3). Seasonally, the emission-induced ozone exhibited fluctuating upward trend during autumn and winter, suggesting a transition of ozone pollution towards colder seasons. Spatially, high LECO concentrated in the eastern YRD (EYRD) across spring, autumn, and winter, becoming prominent in the central YRD (CYRD) during summer. During CCSOP, the CYRD exhibited the highest LECO and exceedance frequency (20.82 µg/m3 and 45.27 %). LECO explained a large portion of ozone variability during CCSOP, particularly in the EYRD, while NECO showed less explanatory power but consistently high contributions (148.05 ± 15.52 µg/m3). These findings offer valuable insights for a deeper understanding of the evolving patterns of ozone pollution and the issue of CCSOP in the YRD.

Hydrochemical Characteristics and Evolution under the Influence of Multiple Anthropogenic Activities in Karst Aquifers, Northern China

The intensification of anthropogenic activities (agriculture, industry, and exploitation of water resources) during urbanization has posed significant challenges to the aquatic environment, particularly in karst regions. Karst aquifers are highly susceptible to surface contaminants and exhibit minimal natural remediation capabilities. Our understanding of the anthropogenic activities involved in these sensitive karst systems remains limited. To address this gap, we conducted a comprehensive study, collecting 285 groundwater samples in Feicheng, northern China, from 1996 to 2015. The overexploitation of karst groundwater has resulted in several concerns. The whole dataset was classified into four groups according to land use. Water quality assessments revealed a yearly decline, particularly in industrial and agricultural areas. The water chemistry transitioned from Ca-Mg-HCO3 to Ca-Mg-HCO3-SO4. Such evolution was attributed to natural hydrogeochemical processes, atmospheric precipitation, and anthropogenic inputs. Natural factors included water-rock interactions (the mineral dissolution) and ion exchange. Absolute principal component scores with multiple linear regression (APCS-MLR) were used to quantitatively estimate the sources of pollution. The results showed that hydrogeological settings (recharge, runoff, and discharge) were crucial in the hydrochemistry evolution of karst groundwater systems. In agricultural areas, it is recognized that much of the NO3− accumulation in aquifers came from upstream inputs in the groundwater system, not just irrigation and fertilization. Urban areas were affected by Cl− pollution, primarily due to domestic waste. Industrial regions of recharge zones were more susceptible to atmospheric precipitation and industrial waste, with pollutants infiltrating through rainfall and degrading water quality. Mining areas exhibit higher SO42− and lower pH due to the oxidation of sulfur-containing minerals. Therefore, the rapid response and low self-purification capacity of groundwater in karst regions necessitate caution in urban planning to mitigate impacts on these fragile systems.

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.

Distribution, sources, contamination, and risks of toxic metals in Zijiang River, a typical tributary of the midstream of the Yangtze River in China

Excessive concentrations of toxic metals are a global threat to aquatic systems. Taking a typical tributary (Zijiang River, ZR) of the midstream of the Yangtze River as the research area, the concentration distribution and chemical fractions occurrence characteristics of five toxic metals (Cd, Cr, Cu, Pb, and Zn) were analyzed, their potential sources were explored, and their contamination and ecological risk was assessed. In the surface waters and sediments, there were high concentrations of Zn, a low concentration of Cd, and small spatial differences in concentration among the upstream, midstream, and downstream. In terms of speciation, Cd mainly existed in the acid-soluble fraction, Pb mainly existed in the reducible fraction, and Cr, Cu, and Zn mainly existed in the residue fraction. The potential sources in surface waters and sediments were determined to be industrial emissions and agricultural non-point sources through the absolute principal component scores–multiple linear regression model (APCS–MLR). Based on the assessment results of total concentration and speciation, Cd was the typical contamination element in ZR sediments. In addition, the secondary phase enrichment factor (SPEF) based on speciation underestimates the degree of Pb contamination, and the ecological risk of Zn assessed by the ratio of secondary phase and primary phase (RSP) and the risk assessment code (RAC) was higher than that of Cr, which was inconsistent with the results based on total concentrations. SOM and Al/Fe/Mn cycles in sediments influenced the geochemical behavior of toxic metals.

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.

Health Risk Assessment of Heavy Metals in Soil of Lalu Wetland Based on Monte Carlo Simulation and ACPS-MLR

Lalu wetland is the world’s largest plateau urban wetland. It was designated as a national-level nature reserve in China in 2004 and is often called “the lung of Lhasa”. It serves as a vital recreational area for urban residents. Concerns have arisen about the potential harm to human health due to heavy metals (HMs) in the wetland soil. This study assessed the ecological risk of HMs in Lalu wetland soil by using the potential ecological risk index and geo-accumulation index techniques. The findings showed that the Lalu wetland’s overall risk level is low risk. with Cd being the primary pollutant. Through the absolute principal component scores–multiple linear regression (APCS-MLR) model and Pearson correlation analysis, three sources of HMs were identified, including traffic sources, soil parent material sources, and geothermal sources, contributing 51.13%, 32.35%, and 16.52%, respectively. The results of the probabilistic health risk assessment based on Monte Carlo simulation indicate that non-carcinogenic risks are absent for both adults and children. However, acceptable carcinogenic risks are present. For adults, the main exposure mode is through dermal contact, while children are primarily exposed through ingestion. Children manifest a heightened vulnerability to these risks compared to adults, with As being the primary contributing element. The source-oriented health risk assessment (HRA) highlights geothermal sources as the primary contributors to health risks. The research results provide insights into the current status of HMs in Lalu wetland soil, offering a scientific basis for restricting and managing contamination by HMs in wetlands.

Spatial distribution and source identification of potentially toxic elements in Yellow River Delta soils, China: An interpretable machine-learning approach

Identifying the driving factors and quantifying the sources of potentially toxic elements (PTEs) are essential for protecting the ecological environment of the Yellow River Delta. In this study, data from 201 surface soil samples and 16 environmental variables were collected, and the random forest (RF) and Shapley additive explanations (SHAP) methods were then combined to explore the key factors affecting soil PTEs. An innovative t-distributed random neighbor embedding-RF-SHAP model was then constructed, based on the absolute principal component score and multivariate linear regression model, to quantitatively determine PTE sources. Although average PTE concentrations did not exceed the risk control values, PTE distributions exhibited significant differences. It was found that sodium, soil organic matter, and phosphorus contents were the three most important factors affecting PTEs, and human activities and natural environmental factors both influence PTE contents by altering the soil properties. The proposed model successfully determined PTE sources in the soil, outperforming the original linear regression model with a significantly lower RMSE. Source analysis revealed that the parent material was the main contributor to soil PTEs, accounting for more than half of the total PTE content. Industrial and agricultural activities also contributed to an increase in soil PTEs, with average contributions of 19.91 % and 17.44 %, respectively. Unknown sources accounted for 10.83 % of the total PTE content. Thus, the proposed model provides innovative perspectives on source parsing. These findings provide valuable scientific insights for policymakers seeking to develop effective environmental protection measures and improve the quality of saline-alkali land in the Yellow River Delta.

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.

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.

Identification of Impacts from Meteorology and Local and Transported Photochemical Generation on Ozone Trends in Changsha from 2018 to 2020

Ozone (O3) pollution in Hunan province has become the most important factor among the six common conventional pollutants (i.e., NO2, SO2, CO, O3, PM10, and PM2.5) in the atmospheric environment. Further investigation has indicated that the relevant studies of O3 are insufficient. Therefore, it is essential to clarify the key driving factors of O3 variations for government regulators. In this study, a combined method consisting of a generalized additive model (GAM), empirical orthogonal function (EOF), and absolute principal component scores (APCs) model was employed to identify and quantify the impacts of meteorology and local photochemical generation (local) and that transported from outside (nonlocal) on O3 variations from 2018-2020. Simultaneously, the driving factors of O3 annual values from 2018 to 2019 and from 2019 to 2020 in Changsha were analyzed. The results showed that O3 episodes were commonly caused by meteorology when the relative contribution from precursors was high, on the short-term time scale. Overall, on the temporal scale, meteorology and local were the driving factors for the increasing annual O3 from 2018 to 2019. Additionally, the contribution from meteorology, local, and nonlocal decreased from 2019 to 2020, leading to a lower level of O3 concentration in 2020. Geographically, the east, north, and south of Changsha were mainly affected by meteorology, local, and nonlocal, respectively. Throughout the three years, nonlocal exhibited a sustained decreasing trend, whereas the tendencies from meteorology and local varied by year and geography. Local contribution in the north of Changsha increased from 2018 to 2019, which was likely attributed to the increasing biogenic volatile organic compound emission (BVOCs), and it became lower in the south owing to the strengthened consumption by NOx. Impacts from meteorology on O3 in all sites were enhanced from 2018 to 2019. By contrast, local contribution decreased in the north and increased in the south with the decline in BVOC and NOx emissions from 2019 to 2020, when the meteorological impacts on O3 in the whole area became weak.

Characterizing Ultrafine Particle Mobile Monitoring Data for Epidemiology.

Mobile monitoring is increasingly used to assess exposure to traffic-related air pollutants (TRAPs), including ultrafine particles (UFPs). Due to the rapid spatial decrease in the concentration of UFPs and other TRAPs with distance from roadways, mobile measurements may be non-representative of residential exposures, which are commonly used for epidemiologic studies. Our goal was to develop, apply, and test one possible approach for using mobile measurements in exposure assessment for epidemiology. We used an absolute principal component score model to adjust the contribution of on-road sources in mobile measurements to provide exposure predictions representative of cohort locations. We then compared UFP predictions at residential locations from mobile on-road plume-adjusted versus stationary measurements to understand the contribution of mobile measurements and characterize their differences. We found that predictions from mobile measurements are more representative of cohort locations after down-weighting the contribution of localized on-road plumes. Further, predictions at cohort locations derived from mobile measurements incorporate more spatial variation compared to those from short-term stationary data. Sensitivity analyses suggest that this additional spatial information captures features in the exposure surface not identified from the stationary data alone. We recommend the correction of mobile measurements to create exposure predictions representative of residential exposure for epidemiology.

Environmental forensic approach towards unraveling contamination sources with receptor models: A case study in Nakdong River, South Korea

The upstream of Nakdong River is contaminated by heavy metals such as Cd, Cu, Zn, As, and Pb. Although the origin of the contamination is unequivocal, it is suspected that the heavy metals have been leached from several mine tailings and a refinery. Here, receptor models, absolute principal component score (APCS) and positive matrix factorization (PMF), were used to identify the contamination sources. Source markers representing each source (factor) were investigated using correlation analysis for five major contaminants (Cd, Zn, As, Pb, and Cu) and identified as following: Cd and Zn for the refinery (factor 1), As for mine tailings (factor 2). The categorization of sources into two factors was statistically validated via the cumulative proportion and APCS−based KMO test score with the values >90 % and > 0.7 (p < 0.001), respectively. High R2 values of linear regressions between the predicted data from receptor models and observed data indicate the reliability of the model prediction; moreover, the predicted initial concentrations of contaminants were validated using a sediment sample collected from near the refinery (chi-test: p > 0.200). Concentration distribution and source contribution using GIS revealed the heavy metal contaminated zones affected by the precipitation.

Chemical Characterization and Source Apportionment of PM10 Using Receptor Models over the Himalayan Region of India

This study presents the source apportionment of coarse-mode particulate matter (PM10) extracted by 3 receptor models (PCA/APCS, UNMIX, and PMF) at semi-urban sites of the Indian Himalayan region (IHR) during August 2018–December 2019. In this study, water-soluble inorganic ionic species (WSIIS), water-soluble organic carbon (WSOC), carbon fractions (organic carbon (OC) and elemental carbon (EC)), and trace elements of PM10 were analyzed over the IHR. Nainital (62 ± 39 µg m−3) had the highest annual average mass concentration of PM10 (average ± standard deviation at 1 σ), followed by Mohal Kullu (58 ± 32 µg m−3) and Darjeeling (54 ± 18 µg m−3). The annual total ∑WSIIS concentration order was as follows: Darjeeling (14.02 ± 10.01 µg m−3) &gt; Mohal-Kullu (13.75 ± 10.21 µg m−3) &gt; Nainital (10.20 ± 6.30 µg m−3), contributing to 15–30% of the PM10 mass. The dominant secondary ions (NH4+, SO42−, and NO3−) suggest that the study sites were strongly influenced by anthropogenic sources from regional and long-range transport. Principal component analysis (PCA) with an absolute principal component score (APCS), UNMIX, and Positive Matrix Factorization (PMF) were used for source identification of PM10 at the study sites of the IHR. All three models showed relatively similar results of source profiles for all study sites except their source number and percentage contribution. Overall, soil dust (SD), secondary aerosols (SAs), combustion (biomass burning (BB) + fossil fuel combustion (FFC): BB+FFC), and vehicular emissions (VEs) are the major sources of PM10 identified by these models at all study sites. Air mass backward trajectories illustrated that PM10, mainly attributed to dust-related aerosols, was transported from the Thar Desert, Indo-Gangetic Plain (IGP), and northwestern region of India (i.e., Punjab and Haryana) and Afghanistan to the IHR. Transported agricultural or residual burning plumes from the IGP and nearby areas significantly contribute to the concentration of carbonaceous aerosols (CAs) at study sites.

Assessments of surface water quality through the use of multivariate statistical techniques: A case study for the watershed of the Yuqiao Reservoir, China

In light of the fact that water quality has been threatened by human activities, apportionments of potential pollution sources are essential for water pollution control. Multivariate methods were used to assess the water quality in the Yuqiao Reservoir and its surrounding rivers in northern China to identify potential pollution sources and quantify their apportionment. Fifteen variables at 10 sites were surveyed monthly in 2015–2016. The quality at this location was acceptable according to the water quality index (WQI), except for special parameters including chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), and chlorophyll (chlα). Cluster analysis (CA) grouped these datasets into three seasonal groups, July–September, December–March, and the remaining months. Principal component analysis/factor analysis (PCA/FA) identified seven factors that accounted for 79.7%–86.4% of the total variance, and the main sources included cities, rural districts, industries, weather, fertilizers, upstream areas, and vehicles. Absolute principal component scores and multiple linear regression (APCS–MLR) modeling results show that the hierarchical contribution of main pollution sources was ranked in the following order: upstream (26.6%) &amp;gt; urban district pollution source (21.5%) &amp;gt; vehicle emission pollution source (10.9%) in the flood season, upstream (22.3%) &amp;gt; rural district pollution (19.8%) &amp;gt; fertilizer erosion (15.8%) in the normal season, and upstream (26.4%) &amp;gt; urban district pollution (19.0%) &amp;gt; fertilizer erosion (18.8%) in the dry season. Sources from upstream and urban districts explained the most proportion. The matrix was also subjected to positive matrix factorization (PMF). A comparison of PMF and APCS–MLR results showed significant differences in the contribution of potential pollution sources. The APCS–MLR model performed better, as evidenced by a more robust R2 test. Measures should be discussed and implemented in managing upstream areas, sewage treatment facilities, and fertilizer and industrial application.

Source apportionment of river water pollution in a typical agricultural city of Anhui Province, eastern China using multivariate statistical techniques with APCS–MLR

The deterioration of the surface water environment has become a serious challenge for water resources management due to increasing anthropogenic disturbance. Water resources protection requires control of potential pollution sources. In this study, 99 water samples were collected from a river in a typical agricultural city of Anhui Province in eastern China, and these samples were analyzed in terms of pH, electrical conductivity, and the concentrations of F−, Cl−, SO42−, Na+, K+, Mg2+, Ca2+, As, Cr, Cu, Zn, and Pb. Cluster analysis, co-occurrence network analysis, and principal component analysis/factor analysis were conducted to qualitatively identify the potential sources of river water pollution in the study area. An absolute principal component score–multiple linear regression receptor model was used to quantitatively evaluate the contribution of each source to water quality parameters. The results showed that all observed water quality indices met the quality criteria specified in the Chinese drinking water standards, except for pH, ρ(F−), ρ(SO42−), and ρ(As). The heat map showed that the frequent recharge of pollutants from the tributaries during the wet season was the main reason for the deterioration of water quality. Five sources of river water pollution were identified, and their contribution ratios in a descending order were as follows: the geogenic process (24%) > agricultural activities (21%) > poultry farming sources (17%) > domestic pollution (9%) > transportation pollution (5%). Therefore, controlling pollution from agricultural activities, strengthening the regulation of livestock farming, and improving the sewage network are the recommended strategies for improving the quality of surface water resources in this area.

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.

Risk assessment, geochemical speciation, and source apportionment of heavy metals in sediments of an urban river draining into a coastal wetland

Thirty sediment samples were collected from the Gohar Rood River (Iran) to assess the elemental concentrations, origins, and probable environmental risks in the riverine system. In this study, fifteen elements were analyzed by inductively coupled plasma mass spectrometry (ICP-MS). Cr at all sites were exceeded the SEL (Severe Effect Level) value. Zn, Mn, Co, and Cr showed a moderate level of contamination, based on pollution index (PI), modified pollution index (MPI), and enrichment factor (EF). The modified hazard quotient (mHQ) represented low to extreme severity of pollution for some elements. The multi-linear regression of the absolute principal component score model indicated that largest contributors of Zn, Cu, Pb, Sb, and Mo to the riverine sediment were from agricultural runoff, domestic, and municipal sewage. Based on the modified BCR (the European Community Bureau of Reference) fractionation scheme, Mn, Co, and Zn indicated a medium to high risk to the local environment.