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Polycyclic aromatic hydrocarbons (PAHs) have become a major type of persistent organic pollutant (POPs) in the world. To well understand the occurrence, spatial distribution and source apportionment of 16 PAHs in different media in Beijing, a comprehensive study was conducted based on 337 obtained and subsequently selected samples in four specific media, including soil, road dust, surface water and atmosphere covering sampling time spanning from 2005 to 2014. Mean concentrations of Σ16 PAHs in soils (878.5 μg/kg) decreased from inner city to exterior areas with different land uses decreased in the order of industrial estate (2510 μg/kg)>educational area (1331 μg/kg)>Park (783 μg/kg)>cultivated (650 μg/kg)>roadside (583 μg/kg)>residential (568 μg/kg)>green area (478 μg/kg)>forest area (125 μg/kg). Meanwhile, PAHs concentrations in branch roads were slightly higher than trunk roads and mainly concentrated in urban areas (1136 μg/kg). Affected by industrial and residential areas, PAHs concentrations in downstream surface rivers were much higher than upstream surface rivers (475 ng/L). Due to strong mobility, PAHs concentrations in atmosphere were not equally distributed around the study area (157.59 ng/m3). The result of source apportionment determined by diagnostic ratios method indicated PAHs in multiple media in Beijing are mainly from coal and biomass combustion. [ABSTRACT FROM PUBLISHER]

By means of factor analysis (FA) and positive matrix factorization (PMF) methods, groundwater pollution sources were identified in the Jinji groundwater source area, which is beside the Yellow River and is the only urban water supply source for the city of Wuzhong in Northwestern China. The sources of groundwater were quantified based on 16 samples of shallow groundwater from the source area. The source apportionment with the PMF model identified three dominant groundwater pollution sources. These were anthropogenic activities of agricultural and industrial pollution with a pollution contribution of 53.0%, water–rock interaction of 24.6%, and evaporation and concentration of 22.4%. The source apportionment with the FA model identified four sources which were evaporation and concentration, with the largest contribution (42.6%), followed by anthropogenic activities (29.2%), mineral dissolution and industrial pollution (22.4%), and natural effects (5.8%). Specific attention should be paid to these natural (fluoride, TH,etc.) and anthropogenic sources (NH4+, NO2−, turbidity, total bacterial count,etc.), and pertinent measures should be taken to control local groundwater pollution. The most significant trait of the PMF is its scientific interpretation and physical explanation of the results, depending on non-negative restriction of the pollution source profiles and contributions. [ABSTRACT FROM PUBLISHER]

Due to their toxicity and bioaccumulation, trace metals in soils can result in a wide range of toxic effects on animals, plants, microbes, and even humans. Recognizing the contamination characteristics of soil metals and especially apportioning their potential sources are the necessary preconditions for pollution prevention and control. Over the past decades, several receptor models have been developed for source apportionment. Among them, positive matrix factorization (PMF) has gained popularity and was recommended by the US Environmental Protection Agency as a general modeling tool. In this study, an extended chemometrics model, multivariate curve resolution-alternating least squares based on maximum likelihood principal component analysis (MCR-ALS/MLPCA), was proposed for source apportionment of soil metals and applied to identify the potential sources of trace metals in soils around Miyun Reservoir. Similar to PMF, the MCR-ALS/MLPCA model can incorporate measurement error information and non-negativity constraints in its calculation procedures. Model validation with synthetic dataset suggested that the MCR-ALS/MLPCA could extract acceptable recovered source profiles even considering relatively larger error levels. When applying to identify the sources of trace metals in soils around Miyun Reservoir, the MCR-ALS/MLPCA model obtained the highly similar profiles with PMF. On the other hand, the assessment results of contamination status showed that the soils around reservoir were polluted by trace metals in slightly moderate degree but potentially posed acceptable risks to the public. Mining activities, fertilizers and agrochemicals, and atmospheric deposition were identified as the potential anthropogenic sources with contributions of 24.8, 14.6, and 13.3 %, respectively. In order to protect the drinking water source of Beijing, special attention should be paid to the metal inputs to soils from mining and agricultural activities. [ABSTRACT FROM AUTHOR]

Increasing trace metal pollution in river sediment poses a significant threat to watershed ecosystem health. Identifying potential sources of sediment metals and apportioning their contributions are of key importance for proposing prevention and control strategies of river pollution. In this study, three advanced multivariate receptor models, factor analysis with nonnegative constraints (FA-NNC), positive matrix factorization (PMF), and multivariate curve resolution weighted-alternating least-squares (MCR-WALS), were comparatively employed for source apportionment of trace metals in river sediments and applied to the Le'an River, a main tributary of Poyang Lake which is the largest freshwater lake in China. The pollution assessment with contamination factor and geoaccumulation index suggested that the river sediments in Le'an River were contaminated severely by trace metals due to human activities. With the three apportionment tools, similar source profiles of trace metals in sediments were extracted. Especially, the MCR-WALS and PMF models produced essentially the same results. Comparatively speaking, the weighted schemes might give better solutions than the unweighted FA-NNC because the uncertainty information of environmental data was considered by PMF and MCR-WALS. Anthropogenic sources were apportioned as the most important pollution sources influencing the sediment metals in Le'an River with contributions of about 90%. Among them, copper tailings occupied the largest contribution (38.4–42.2%), followed by mining wastewater (29.0–33.5%), and agricultural activities (18.2–18.7%). To protect the ecosystem of Le'an River and Poyang Lake, special attention should be paid to the discharges of mining wastewater and the leachates of copper tailing ponds in that region. [ABSTRACT FROM AUTHOR]

Understanding the exposure risks of trace metals in contamination soils and apportioning their sources are the basic preconditions for soil pollution prevention and control. In this study, a detailed investigation was conducted to assess the health risks of trace metals in surface soils of Beijing which is one of the most populated cities in the world and to apportion their potential sources. The data set of metals for 12 elements in 240 soil samples was collected. Pollution index and enrichment factor were used to identify the general contamination characteristic of soil metals. The probabilistic risk model was employed for health risk assessment, and a chemometrics technique, multivariate curve resolution-weighted alternating least squares (MCR-WALS), was applied to apportion sources. Results suggested that the soils in Beijing metropolitan region were contaminated by Hg, Cd, Cu, As, and Pb in varying degree, lying in the moderate pollution level. As a whole, the health risks posed by soil metals were acceptable or close to tolerable. Comparatively speaking, children and adult females were the relatively vulnerable populations for the non-carcinogenic and carcinogenic risks, respectively. Atmospheric deposition, fertilizers and agrochemicals, and natural source were apportioned as the potential sources determining the contents of trace metals in soils of Beijing area with contributions of 15.5%–16.4%, 5.9%–7.7% and 76.0%–78.6%, respectively. [ABSTRACT FROM AUTHOR]

Increasing anthropogenic contamination poses a significant threat to groundwater security. Identifying potential contamination sources and apportioning their corresponding contributions are of vital importance for the prevention of contamination and management of groundwater resources. In this study, principal component analysis (PCA), modified grey relational analysis (MGRA), absolute principal component score-multiple linear regression (APCS-MLR), and positive matrix factorization (PMF) receptor modeling technologies were employed to evaluate the groundwater quality and apportion the potential contamination sources in the Lalin river basin, a main grain production district in the northeast of China. The contamination assessment with PCA and MGRA suggested that the groundwater in Lalin river basin was polluted due to human activities. The PCA method identified five and four potential contamination sources in wet and dry seasons, respectively, and the main sources were basically same. The APCS-MLR and PMF methods apportioned the source contributions to each groundwater quality variable. The final results showed that agricultural sources including waste water, agrochemicals and fertilizers were identified as the main sources of groundwater contamination both in wet and dry seasons. In addition, groundwater management strategies learned from the advanced experiences were discussed to protect the groundwater system in that region. Unlabelled Image • Groundwater contamination, risk and sources in Lain River Basin were studied in wet and dry seasons. • Agricultural activities was recognized as the significant pollution sources in the groundwater of Lalin River Basin. • The contributions of potential contamination sources of groundwater were comparative studied by APCS-MLR and PMF. • Some suggestions are presented to promote groundwater management. [ABSTRACT FROM AUTHOR]

Heavy metal pollution in lakes has attracted concerns worldwide since long retention times in lakes allow metals to accumulate and may pose significant threat to ecosystem health. For designing targeted risk mitigation strategies, it is necessary to identify the source-specific risks of heavy metals in the environment. Although previous studies have addressed either risk assessment or source identification of heavy metals in the environment, few have attempted to establish a link between them. In the study, we perform a combination of source apportionment and risk assessment for characterizing the pollution sources and source-specific risks of heavy metals in the sediments of an urban river-lake system. To this end, positive matrix factorization (PMF) was employed to apportion the potential sources of heavy metals, combined with a support vector machine classifier and the referential source fingerprints of metals in the study area. Then, the apportionment results were incorporated into the environmental risk models to evaluate the ecological and human health risks posed by heavy metals from the identified pollution sources. Results showed the river-lake system was contaminated by Cd, Cu, Zn and other metals in varying degree. Particularly, the element of Cd presented moderate to heavy pollution level. In relative, the industrial activities were identified as the largest contributor (48.0%) of heavy metals in the river-lake sediments, mainly associating with electroplating and paper making, followed by the agricultural activities (27.3%) and mix source (24.7%). Overall, the non-carcinogenic and carcinogenic risks posed by the heavy metals were acceptable, however, the element of Cd showed moderate ecological effect. Further, source-oriented risk evaluation suggested industrial processes made higher contributions to the ecological risk of heavy metals in the river-lake system. The study will provide regulators help to update the information by adding apportionment analysis in the context of risk assessment to facilitate subsequent mitigation strategies. Unlabelled Image • A combination of source apportionment and risk assessment was performed. • Source apportionment was conducted using PMF combined with SVM. • The industrial activities were the largest contributor of metals in the river-lake sediments. [ABSTRACT FROM AUTHOR]

• The study highlights the utility of partition computing in source apportionment. • Significant impact of the irrigation type was observed for soil heavy metals. • PC-PMF overcome uncertain of source apportionment results by spatial heterogeneity to a certain extent. • PC-PMF-HHRA performed much better at giving more detail source-oriented risk information than HHRA. Apportion soil heavy metal sources across large-scale regions is a challenging task. The present study developed a modified receptor model to estimate the contributions of various sources to soil heavy metals and the associated health risks at a large scale. A positive matrix factorization model based on a partition computing approach was employed; the entire study area was divided into several zones for the source apportionment and then calculated together, termed partition computing-PMF (PC-PMF). The agricultural soil in Tianjin, China, was chosen for the case study. The PC-PMF results showed that irrigation, atmospheric deposition and sludge application were the main anthropogenic sources, with contributions of 26.60 %, 19.56 % and 2.86 %, respectively. We subsequently combined PC-PMF with a human health risk assessment model (HHRA) to obtain the human health risk of every source category. The natural background was regarded as a major factor influencing human health in the study area, with contributions of 38.03 % for the noncarcinogenic risk and 28.68 % for the carcinogenic risk. The results indicated that PC-PMF performed better at the source apportionment of soil heavy metals than PMF. This study provides a good example of how the spatial variability can be utilized to reduce the uncertainty in source apportionment. [ABSTRACT FROM AUTHOR]

Here, a new integrated methodology framework has been proposed for source apportionment and source-oriented risk evaluation, and applied to identify the characteristics, sources and health risks of PAHs in the soils of a typical cold region in Northeastern China. To this end, a large-scale data set containing 1780 soil samples and 16 priority PAHs has been collected from the study area. Two advanced receptor models, positive matrix factorization (PMF) and multivariate curve resolution-weighted alternating least-squares (MCR-WALS), have been comparatively employed to apportion the pollution sources of soil PAHs, with the help of a set of modified literature PAH source fingerprints. Further, the apportionment results have been incorporated into a probabilistic incremental lifetime cancer risk model for assessing the source-specific health risk of soil PAHs. Notably, the PMF and MCR-WALS models have apportioned essentially same results. The coal combustion and gasoline engine are identified as the main contributors of soil PAHs, with contributions of 57.9–58.1% and 25.2–22.2%, respectively. The health risks posed by PAHs in the soils are negligible for both adult and children; relatively, source-oriented risk assessment shows coal combustions make the largest contribution to the total risk of PAHs (56.1%), followed by gasoline engine (22.5%) and coke oven (21.4%). [Display omitted] • A new integrated framework has been proposed for characterizing sources and risks of PAHs. • Apportionment results have been used for assessing source-specific health risk of PAHs. • The MCR-WALS model has apportioned essentially same results with PMF. • Coal combustion, gasoline engine and coke oven are the main pollution sources of PAHs. • Coal combustion has made the largest contribution to the total risk of PAHs (56.1%). [ABSTRACT FROM AUTHOR]

Heavy metal pollution has been a global concern and key points of environmental pollution prevention and control due to the growing problems of urbanization and industrialization. Rapidly and correctly apportioning sources of heavy metal is still a great challenge because of the stability of source fingerprint and complex interaction of multiple contaminants and sources. In this study, we perform a combination of optimization of pollution source fingerprint and source apportionment through jointly utilizing two machine classification and screening methods for characterizing the pollution sources of heavy metal in the sediments of an urban river and its surrounding soils. Dominance-based rough set model (DRS), content optimization tools, and multivariate curve resolution-alternating least squares model (MCR-WALS) were employed to screen representative pollution source samples, optimize pollution source fingerprint, and apportion the potential sources of heavy metals, respectively. Further, Support vector machine (SVM) was adopted to correspondence analysis results and pollution fingerprint based on the factor characteristics for achieving source apportionment accurately. Results showed that the pollution source pollution source fingerprints optimized by DRS and optimization tools are more representative and stable, and the results obtained by SVM and MCR-WALS are more accurate comparing with traditional methods. As whole, source apportionment suggested that printing and dyeing, chemical, electroplating, metal processing were the main origins of heavy metals in this area and the proportions of them in sediment and soil pollution sources were 67.05% and 28.43%, respectively. Besides, coal combustion was also the main sources of heavy metal pollution in soils, accounting about 34.16%. Results of the study can advance our knowledge to better understand the characterization of heavy metal pollution in the peri-urban ecosystem and to design effective targeted strategies for reducing heavy metal pollution diffusion. [Display omitted] • A combination of fingerprint screening and source apportionment was performed. • Characteristics and source-sink relationship of heavy metals in the river-soil system was studied. • Source apportionment was executed using MLR-WALS combined with SVM. • Multiple complex industrial activities were the dominant same type contributor of heavy metals in the river sediments. [ABSTRACT FROM AUTHOR]

This study aims to better understand the effects of heavy metal enrichment on microbiomes and resistomes and the source-specific ecological risks of metals in the sediments of an urban river. Geo-accumulation index and enrichment factor suggested the river sediments were contaminated by Cd, Cu, Pb, and Zn in varying degrees. High-throughput sequencing-based metagenomics analysis identified 430 types of antibiotic resistance genes (ARGs), dominated by the multidrug, MLS, bacitracin, quinolone, and aminoglycoside ARGs, and 52 metal resistance genes (MRGs) mainly conferring resistance to zinc, copper, cadmium, lead, mercury and multiple metals. Spearman correlation analysis and Mantel test showed the heavy metal enrichment exerted significant effects on the microbial community, ARGs and MRGs. Source apportionment using positive matrix factorization revealed that natural source (42.8%) was the largest contributor of metals in the river sediments, followed by urban activities (35.4%) and a mixed source (21.7%). However, when incorporating the apportionment results into a modified risk model to evaluate the source-specific ecological risks, results showed human activities dominated the risks of metals. Comparatively, the urban activities majorly caused moderate- and considerable- ecological risks, while the mixed source with respect to agricultural and industrial activities contributed higher percentages on high- and extremely high- ecological risks. ga1 • Effects of heavy metal enrichment on microbiomes and resistomes were studied. • A modified ecological risk index was constructed to evaluate the source-specific risks. • Heavy metal enrichment exerted effects on the microbial community, ARGs and MRGs. • Human activities dominated the ecological risks of heavy metals in the river sediments. [ABSTRACT FROM AUTHOR]

Antibiotics have been widely used in the past decades and caused global public health concerns due to the growing problems of antimicrobial resistance. The peri-urban rivers are always receiving massive wastes containing antibiotics and appear to be a reservoir of antibiotics and antibiotic resistance genes in the environment. To prevent and control the pollution of antibiotics, it is essential to correctly identify the potential sources of antibiotics in peri-urban rivers. Currently, systematic knowledge on risk characteristics and source apportionment of antibiotics in peri-urban rivers is still lacking. In the study, we addressed this problem and focused on exploring the ecotoxicological risk and potential sources of antibiotics in a peri-urban river in Beijing (Chaobai River). To this end, the waters and sediments were collected from the river, as well as the potential source types including domestic sewage, WWTP effluent, chicken manure, pig manure and cattle manure. The occurrence and concentration levels of 16 antibiotics in the waters and sediments of the river were comprehensively characterized, as well as the correlation of antibiotics with environmental factors. Then, risk quotients and mixture risk quotients were used to assess the ecotoxicological risk of single compound and the mixture toxicity of antibiotics, respectively. The synergistic effects of antibiotic mixtures were also analyzed. Further, positive matrix factorization was employed to apportion the potential sources of antibiotics based on the multilinear engine (ME-2) algorithm. The target antibiotics were widely detected in the peri-urban river and several antibiotics posed moderate ecotoxicological risks on aquatic organisms. Apportionment analysis identified four potential sources of antibiotics in the waters of Chaobai River, including domestic sewage (31.5%), chicken waste (26.4%), WWTP effluent (22.2%) and a mix source (20.0%). Additionally, WWTP effluent (~58%) and sewage effluent (41%) were apportioned as the main contributors of antibiotics in the sediments. Unlabelled Image • Characterization and source apportionment of antibiotics in a peri-urban river was studied. • TOC had significantly positive correlation with antibiotics in the river. • Antibiotics posed moderate ecotoxicological risk in the river. • Sewage effluent was the largest contributor of antibiotics in the river. [ABSTRACT FROM AUTHOR]

Heavy metals (HMs) in lake sediments have aroused long-term attention due to their persistent threat to the lake ecosystems. Study into the pollution characteristics and potential sources of HMs in lakes, especially those heavily influenced by human activities, can give some insights to prevent and control the environmental risks of HMs. Lake Tai is the China's third largest freshwater lake but has been a drastic example of water pollution in recent decades. Here, we systematically investigated the characterization of pollution, risk and sources of HMs in the sediments of Lake Tai and its surrounding soils using several methods and tools. Specifically, a total of 46 sediments and 578 soils were collected from the study area, and geochemical approaches (geo-accumulation index and enrichment factor) and pollution indexes were jointly used to reveal the contamination characteristics of HMs. Meanwhile, the potential ecological risk index and probabilistic model of health risk assessment were employed to understand the environmental risk of HMs. Further, absolute principle component score-multiple linear regression and positive matrix factorization models were comparatively applied to apportion the sources of HMs. Results showed that various metals including Cd, Cr, As, Hg, Pb, and Cu appeared contamination level in varying degrees both in the sediments and soils. In particular, Cd, As and Hg were identified as the significant contaminants and presented moderate ecological risk. As a whole, the health risk levels of metals were acceptable. Source apportionment suggested that industrial discharge, agricultural activities, atmospheric deposition and natural source were the potential origins of HMs in this area. Among them, anthropogenic activities were the largest dominator of HMs with contribution of >75%. The study will advance our knowledge on HMs in lake and its surrounding soils and provide reference for policy maker to design migration strategies for protecting the lake ecosystems. Unlabelled Image • Contamination risk and sources of HMs in the sediments of Lake Tai were studied. • Cd, As and Hg were recognized as the significant contaminants. • Contamination risk of HMs in the sediments of Lake Tai was higher than surrounding soils. • Anthropogenic activities were the largest dominator of HMs with contributions of >75%. [ABSTRACT FROM AUTHOR]