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

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To investigate the interplay between varying anthropogenic activities and sediment dynamics in an urban river (Turag, Bangladesh), this study involved 37-sediment samples from 11 different sections of the river. Neutron activation analysis and atomic absorption spectrometry were utilized to quantify the concentrations of 14 metal(oid)s (Al, Ti, Co, Fe, As, Cd, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Zn). This study revealed significant toxic metal trends, with Principal coordinate analysis explaining 62.91 % of the variance from upstream to downstream. The largest RSDs for Zn(287 %), Mn(120 %), and Cd(323 %) implies an irregular regional distribution throughout the river. The UNMIX-model and PMF-model were utilized to identify potential sources of metal(oid)s in sediments. ∼63.65-66.7 % of metal(oid)s in sediments originated from anthropogenic sources, while remaining attributed to natural sources in both models. Strikingly, all measured metal(oid)s' concentrations surpassed the threshold effect level, with Zn and Ni exceeding probable effect levels when compared to SQGs.

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Trace element pollution from anthropogenic sources is increasingly widespread. This pollution in terrestrial environments threatens agricultural crop production, while in aquatic environments, it threatens fish cultivation. The contamination of these crucial food sources raises significant concerns regarding food safety, security, and its potential adverse effects on human health. Coastal areas are particularly vulnerable to heavy metal pollution due to their proximity to industrial and urban centres, as well as their susceptibility to contamination from marine sources. In attempting to identify the sources of heavy metals (As, Cu, Cr, Cd, Fe, Hg, Mn, Ni, Pb, and Zn) and measure their contributions, we collected soil samples from thirty sites along the three coastal districts (Patuakhali, Barguna, and Bhola) in Bangladesh. Using atomic absorption spectroscopy, heavy metal concentrations in soil samples were measured and three receptor models (PMF, PCA-MLR, and UNMIX) were applied to detect their sources. Pairwise correlation analysis of metal concentrations in 30 sites across 3 coastal districts showed all possible patterns, including both significant and insignificant positive and negative relationships between different metals, except for As and Hg which did not display any significant relationships with other metals. The concentrations of Cu, Fe, Mn, Ni, and Zn exceed the US-EPA sediment quality standard. The applied PCA-MLR, PMF, and UNMIX models identified several sources of heavy metal contamination, including (i) mixed anthropogenic and natural activities: contribution of 59%, 37%, and 43%, and (ii) vehicle emissions: contribution of 23%, 26% and 29%. The recognized metal sources should be prioritised to avoid the discharge of poisonous pollutants from anthropogenic factors and any possible future exposure. This study's findings have implications for ongoing monitoring and management of heavy metal contamination in coastal environments to mitigate potential health and ecological impacts and can inform policy development and management strategies.

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Comparing results obtained by different models with different physical assumptions and constraints for source apportionment is important for better understanding the sources of pollutants. Source apportionment of PM2.5 measured at three sites located in inner urban districts of Hanoi was performed using two receptor models, UNMIX and principal component analysis with absolute principle component score (PCA/APCS). A total of 78 daily samples were collected consecutively during the dry and wet seasons in 2019 and 2020. The average PM2.5 concentration (66.26 µg/m3 ± 29.70 µg/m3 with a range from 23.57 to 169.04 µg/m3) observed in Hanoi metropolitan exceeded the National Ambient Air Quality standard QCVN 05:2013/BTNMT (50 µg/m3). Both UNMIX and PCA/APCS expressed comparable ability to reproduce measured PM2.5 concentrations. Additionally, both models identified similar potential sources of PM2.5 including traffic-related emissions, scrap metal recycling villages, crustal mixed with construction sources, coal combustion mixed with industry, and biomass burning. Both UNMIX and PCA/APCS confirmed that traffic-related emission was the most influential PM2.5 with a high percentage contribution of 59% and 55.97%, respectively. All the HQ and Cr values for both children and adults of toxic elements apportioned by both UNMIX and PCA/APCS in every source were within the acceptable range.

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The identification of the source of heavy metal pollution and its quantification are the prerequisite of soil pollution control. The APCS-MLR, UNMIX and PMF models were employed to apportion pollution sources of Cu, Zn, Pb, Cd, Cr and Ni of the farmland soil in the vicinity of an abandoned iron and steel plant. The sources, contribution rates and applicability of the models were evaluated. The potential ecological risk index revealed greatest ecological risk from Cd. The results of source apportionment illustrated that the APCS-MLR and UNMIX models could verify each other for accurate allocation of pollution sources. The industrial sources were the main sources of pollution (32.41~38.42%), followed by agricultural sources (29.35~31.65%) and traffic emission sources (21.03~21.51%); and the smallest proportion was from natural sources of pollution (11.2~14.42%). The PMF model was easily affected by outliers and its fitting degree was not ideal, leading to be unable to get more accurate results of source analysis. The combination of multiple models could effectively improve the accuracy of pollution source analysis of soil heavy metals. These results provide some scientific basis for further remediation of heavy metal pollution in farmland soil.

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Eight kinds of heavy metals in soil within 0-2 km from the banks of Shuimo River in Urumqi were analyzed by using an X-ray fluorescence spectrometer and national standard detection methods. Unmix and PMF models are comprehensively used to analyze potential pollutant sources and contribution rates. Soil samples are sampled in three layers of 0-20, 20-40, and 40-60 cm, and each group of sample points in each layer is 5 m, 1 km, and 2 km away from the riverbank, respectively. Only the average concentration of Mn in each layer of soil is lower than the background value, according to the analytical results, while the average concentration of other heavy metals surpasses the background value. The highest proportion of exceeding the background value is Ni in the 40-60 cm soil layer, up to 1.92 times. Unmix and PMF models are used to analyze pollutants' source quantity and contribution rate, respectively. The results show that the two models can identify two pollution sources at the three soil layers, and their contribution rates are similar, and each index of the analysis results of the two models is within the required range of model reliability. By comparing with the Pearson correlation coefficient and distribution map of heavy metal concentration in surface soil, it is concluded that Zn, Pb, Cr, and Cu are mainly from industrial sewage and air pollution from coal combustion, while As, Mn, Ni, and V are mainly from agricultural pollution and light industrial pollution. In future research, it is necessary to investigate the change of heavy metal concentration in detail from the time dimension to further quantitatively calculate the potential pollutant source and contribution rate.

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The water-soluble concentration of heavy metals in road dust poses a considerable hazard to public health. The primary goals of the study were estimation of water-soluble contents of heavy metal, estimation of pollution indices, and source apportionment of water-soluble contents of heavy metals using UNMIX model from the road dust of Zhengzhou city. To accomplish this, inductively coupled plasma atomic emission spectroscopy (ICP-AES) was used to determine concentrations of eight heavy metals (Cr, Cu, Ni, Zn, Cd, As, Pb, and Hg), and it has been observed that Cu and Zn were the metals with the highest concentration, while Hg, Cd, and Pb were in the lowest concentration range of metals. Pollution indices, geo-accumulation index (Igeo), contamination factor (CF), and Nemerow synthetic pollution index (PIN) were calculated to assess the contamination level of water-soluble contents of these hazardous heavy metals. Igeo classified the contamination risk into a spectrum of categories ranging from unpolluted (Cr and Pb) to high polluted (Cu and Cd). For the CF results, the concentration of Cr and Pb was found to be low, similar to Igeo, while the concentrations of three heavy metals, Cu, Cd, and Hg, were found to be extremely high or excessive. The results of the PIN assessment indicated that there was an enormous risk of Hg contamination in the city and that Cu, Cd, and Zn were all within a few percent of the Hg pollution level and hence fell into the high pollution group. The UNMIX model was used for source apportionment of dissolved heavy metals and showed: Source 1 (natural sources, 10%), Source 2 (copper mine tailing contamination, 19%), and Source 3 (agricultural activities22%). Source 4 accounted for (air pollution, 15%) of the total and Source 5 accounted for (industrial activity, 34%). It is imperative that immediate and comprehensive pollution control and preventive measures be implemented in the city due to the presence of metal in the dust.

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The extensive pattern of economic growth has an inestimable negative impact on the ecological environment, which causes the soil pollution problem to become increasingly prominent. In order to improve the effectiveness and rationality of prevention and control of heavy metal pollution in regional soil, it is necessary to understand the current situation of pollution, identify pollution sources and clarify future pollution risks. In this paper, an industrially developed city in eastern China was taken as the study region. The positive matrix factorization model (PMF) model and Unmix model was applied to identify and apportion the pollution sources of soil potential toxic elements after evaluating the ecological risk of soil potential toxic elements. The PMF model identified six factors, including single source and composite source. The Unmix model also identified six sources, including sources of nature, industrial discharge and traffic emissions. The comparison between the two models showed that Hg and Ni pollution, as well as Cr enrichment in the study region, were related to the industrial discharge from enterprises and factories. Cd pollution was related to traffic emission sources. Cu and Zn pollution were related to the multiple sources mixed with soil parent material, traffic emissions and industrial discharge from electronic enterprises. Pb pollution was related to natural sources (e.g., soil pH) but also to industrial sources (e.g., industrial wastes discharge). Enrichment was related to soil parent material and agricultural inputs. Our study also implies that soil heavy metal pollution or enrichment in the study region was mainly from anthropogenic sources and supplemented by natural sources.

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This study aimed to evaluate the degree of groundwater pollution and to assess the contribution of specific ionic sources to groundwater, thereby helping to identify the changes in groundwater chemistry and also in groundwater quality from a rural part of Telangana, India, using the comprehensive understanding of geochemical ratios (GR), pollution index of groundwater (PIG), unmix model (UM), and land use/land cover. Groundwater samples collected (22) from the study area were analysed for pH, EC, TDS, Ca2+, Mg2+, Na+, K+, HCO3-, Cl-, SO42-, NO3-, and F-. The hydrogeochemical diagram showed the dominant groundwater type of Ca2+- Mg2+- HCO3- due to the water-soil-rock interactions. GR, chloro-alkaline indices, and saturation indices revealed the groundwater chemistry that explains the mineral weathering and dissolution, ion exchange, and evaporation processes as the chief geogenic origin, and also the contamination of surface water due to the influence of household wastewater, septic tank leaks, irrigation-return-flows, chemical composts, etc. as the secondary anthropogenic sources on the aquifer system. Changes in groundwater quality from the recharge area to the discharge area and the correlation coefficient of chemical variables further supported the sources of geogenic and anthropogenic origins. According to PIG's calculations, the present study area was classified as the insignificant pollution zone (5.89%), which shows all chemical variables within their drinking water quality limits, and the low pollution zone (43.34%), medium pollution zone (27.48%), high pollution zone (17.34%), and very high pollution zone (5.95%), which exhibit the TDS, Mg2+, Na+, K+, HCO3-, Cl-, NO3-, SO42-, and F-contents above the drinking water quality standards. This indicates the gradual increase in the intensity of pollution activity. UM also classified the contribution of specific ions (>50%) into three sources: Source I (K+) measures the poor sewage conditions and potash fertilizers; Source II (SO42-, Mg2+, NO3-, Na+, and Ca2+) specifies the poor sewage conditions, irrigation-return-flows, and chemical fertilizers (gypsum and nitrate); and Source III (F- and HCO3-) represents the dissolution of fluoride minerals as a major contributor to groundwater chemistry. Furthermore, the land use/land cover observations had also supported the assessment of groundwater pollution levels and the contribution of specific ionic sources made by PIG and UM. As a result, the present study clearly indicated that groundwater quality of a geogenic origin is primarily overcome the impact of anthropogenic sources. Therefore, the present study suggested strategic measures to control groundwater pollution and improve groundwater quality.

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Quantitative identification of heavy metals (HM) sources in soils is key to prevention and control of heavy metal pollution. In this study, UNMIX, PMF (Positive matrix factorization) model and Pb-Zn-Cu isotopic compositions were combined to quantitatively identify heavy metal sources in a suburban agricultural area of Kaifeng, China. Using multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS) and ICP-MS, we measured Pb, Zn and Cu stable isotopic compositions, HM concentrations and HM chemical fractions in studied soils, as well as potential sources around the highly polluted site, including total suspended particle, compound fertilizer, irrigated river water and sediments. The results showed that total contents and chemical fractions of heavy metals, as well as Pb-Zn-Cu isotopic compositions presented great variation in different sites, which implied that heavy metal accumulation was obviously affected by local anthropogenic pollution source. UNMIX and PMF presented good agreement on source apportionment that industrial and agricultural activities (61.74% and 60.75% for UNMIX and PMF, respectively) were the major contributors to heavy metal accumulation in the study area. Especially, sewage irrigation and atmosphere deposition accounted for a large proportion (28.14% and 41.03% for UNMIX and PMF, respectively). Moreover, isotopic compositions of Pb, Zn and Cu in highly polluted soils and environment media gave further confirmation that sewage irrigation and atmosphere deposition were primary anthropogenic source. Therefore, combination of UNMIX, PMF model and Pb-Zn-Cu isotopic compositions showed good coordination in quantitative and specific source identification of heavy metals in agricultural soils.

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The widespread use of antibiotics has raised global concerns, but scarce information on antibiotics in the subtropical marine environment is available. In the present study, seawater and sediment samples were collected to investigate the occurrence, spatial distribution, source, and ecological risks of 22 antibiotics in the Beibu Gulf. The total concentrations of target antibiotics (∑antibiotics) were in the range of 1.74 ng/L to 23.83 ng/L for seawater and 1.33 ng/g to 8.55 ng/g dry weight (dw) for sediment. Spatially, a decreasing trend of antibiotic levels from coast to offshore area was observed, with relatively high levels at the sites close to the Qinzhou Bay and Qiongzhou Strait. Sulfamethoxazole (SMX), trimethoprim (TMP), and norfloxacin (NOX) were predominant in seawater, while NOX, enoxacin (ENX), and enrofloxacin (ENR) were the most abundant antibiotics in sediment. In general, the sediment-water partitioning coefficients (Kd) were positively correlated with log molecular weight (MW). Salinity, particle size, and pH of water were predicted to be vital factors influencing the partition of sulfadiazine (SDZ), CIX, and ENR (p < 0.05). Livestock and aquaculture were identified as dominant sources of antibiotics in the Beibu Gulf based on PCA-MLR and Unmix model. Risk assessment revealed that SMX, CIX could pose medium risks to algae in the Beibu Gulf. Overall, our results provided paramount insights into understanding the fate and transport behaviors of antibiotics in the subtropical marine environment.

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Understanding the sources, natural background levels (NBLs), and threshold values (TVs) of the major ions in groundwater is essential for the effective protection of water resources. In this study, a total of 70 shallow groundwater samples were collected in Suzhou, Huaibei Plain, China. A variety of statistical methods and cumulative probability distribution techniques were performed to identify the sources, NBLs, and TVs of the major ions. The major ion concentrations found in decreasing order as follows: HCO3- > SO42- > NO3- > Cl- and Na+ > Ca2+ > Mg2+. Piper diagram for hydrochemical types shows that groundwater types were Mg-HCO3 (36%), Ca-HCO3 (34%), and Na-HCO3 (30%). According to the factor and the Unmix model analysis, anthropogenic (agriculture-related) and geogenic source (water-rock interactions-related) were identified to be responsible for the chemical composition of the groundwater in the study area, and their mean contributions for the major ion concentrations are 47.9% and 52.1%, respectively. The NBLs for Na+, Ca2+, Mg2+, Cl-, SO42-, and NO3- were determined to be 29.5-44.2, 26.2-38.9, 18.9-39.5, 1.0-9.9, 12.9-19.4, and 2.1-16.5 mg/L, respectively, and the TVs were calculated as 122.1, 169.5, 39.5, 129.6, 134.7, and 18.3 mg/L, respectively. Moreover, this study shows the feasibility and reliability of using these multivariate statistical methods and natural background levels to evaluate the status of groundwater quality.

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Soil heavy metal(loid) (HM) source apportionment is the prerequisite to develop suitable mitigation measures and make pollution control and prevention regulations. The selection of appropriate tools (models) for source analysis is crucial, that is especially true for large-scale regions, as the Pearl River Delta (PRD), due to the high spatial variability in soil parent materials, soil topographical feature, and wide range of anthropogenic activities. The objective of this study is to evaluate the potential applications of receptor models (positive matrix factorization [PMF] and Unmix) which have been widely used in air pollution research to quantitatively apportion sources of heavy metal(loid)s in the soils. To assist the interpretation of the derived factors (sources) of the receptor models, enrichment factors and GIS mapping were used to identify the potential relationships between the factor contributions and human activities in the study area. As the models are built on completely different algorithms, a comparative approach was adopted in addition to evaluate the impact of sample size on the model results. Factor profiles generated by different receptor models were quite similar as well as their corresponding factor contributions spatial distribution. Though the stability of their results decreases with a reduced sample size, the results of PMF were less significantly influenced by the sample size than those of Unmix. Due to the difficulty (time consuming and expensive) of soil sample collection in large-scale regions, the PMF model appears to be practically more effective than Unmix. In addition, further investigation is needed for Unmix model to understand the reason for its high sensitivity and determine an appropriate sample size.

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Fatty acids (FAs) composition, 24 persistent organic pollutants (POPs), and 16 trace elements were examined in small pelagic fish (sardine, anchovy, round sardinella, chub and horse mackerels) caught by a fishing fleet for more than three years in the eastern Mediterranean Sea. Five Unmix source profiles associated with both sources, such as overlapping diet, including low-niche marine organisms and inputs from the surrounding environmental compartments were resolved. Inorganic compounds were notably more abundant in fish tissue than organochlorine xenobiotics. Comparison with the values of toxicological parameters revealed that the examined fish species are safe for human consumption, while the content of FAs emphasized the studied species as a valuable source of nutrients. A significant linear correlation was not observed between the 18 FAs and lipophilic organochlorines. Based on the obtained database, future assessments of the quality of edible fish species and the aquatic environment of the eastern Mediterranean Sea, which is known as an important fishing ground, could be significantly improved.

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The UNMIX model was used to analyze the source of heavy metals found to be present in the topsoil of parks in the main district of Lanzhou City. The Hakanson toxicity response coefficient was used concurrently to modify the traditional weights in the model, and the matter-element extension model was used to evaluate heavy metal pollution. The results of the evaluation were compared with the comprehensive pollution index (PN) and potential ecological risk index (RI). The results were as follows. ①The average heavy metal content in the topsoil at each sampling point was higher than that of the background value of soil in Lanzhou, with the proportion of Ni, Cu, and Co being 100% while the proportion of Cr, V, Pb, and As contents were 58.82%, 14.71%, 20.59%, and 2.94%, respectively. ② The results of source analysis showed that there were three major sources of heavy metal pollution in the topsoil of the parks in the study area. Source 1 is construction pollution, which contributes 56% of the Co present. Source 2 is traffic pollution, which contributes 44% and 52% of Cu and Pb, respectively. Source 3 is natural, and contributes 62%, 60%, 56%, and 56% of V, Cr, Ni, and As, respectively. Thus, this research showed that natural sources are predominant. ③ The weight correction effect for each heavy metal was significant; there was an approximately 44% reduction in both Cr and V, while the corrected weights of Ni, Cu, Pb, As, and Co increased in the order Co < Pb < Cu < Ni < As compared with the conventional weights. The most obvious change in weight was that of As, which increased by approximately 188%. ④ The results of the evaluation using the matter-element model showed that the state of 46% of the topsoil in the parks in the study area was grade Ⅴ (severely polluted), while 41% was grade Ⅳ (moderately polluted) and 3% was grade Ⅲ (lightly polluted); Co was the main pollutant. The results of the model evaluation were roughly the same as of from the PN and RI, indicating that the matter-element extension model can be used to evaluate heavy metal pollution in soil and the evaluation results are accurate and objective.

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In this study, 17 legacy and emerging PFASs were investigated in soil and plant leaves near a valley-type landfill, which has been in operation for over 20 years. ΣPFASs concentrations ranged from 5.31 to 108 ng/g dw and 11.9 to 115 ng/g dw in the soil and leaf samples, respectively, and perfluorobutanoic acid (PFBA) was dominant in both soil and leaves. The concentrations of hexafluoropropylene oxide dimer acid (HFPO-DA), 6:2 chlorinated polyfluorinated ether sulfonic acid (F-53B) and 6:2 fluorotelomer sulfonic acid (6:2 FTS) were significantly higher than those of legacy PFOA and PFOS, indicating emerging alternatives were widely applied in the region. The integrated approach of PCA analysis, field investigation of relevant industrial activities in the study area, along with the Unmix model analysis quantitatively revealed that factories producing consumer products and the landfill were the major sources of PFASs in soil, accounting for 57% of total PFASs detected. Bioaccumulation factors (BAFs) of ΣPFASs in leaves varied from 0.37 to 8.59, and higher BAFs were found in camphor leaves. The log10BAFs in all plant leaves showed a linear decrease with increasing carbon chain lengths for individual PFCAs (C4-C8). The BAF values of HFPO-DA, F-53B and 6:2 FTS were 0.01-3.39, 0.04-6.15 and 0.01-6.33, respectively. The human health risk assessment of EDIs showed a decreasing trend with the increasing carbon chain lengths of PFCAs (C4-C9), and the PFASs EDI indicated further study on the human health risk via vegetable consumption be warranted.

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The present work deals with the seasonal variations in the contribution of sources to PM2.5 and PM10 in Delhi, India. Samples of PM2.5 and PM10 were collected from January 2013 to December 2016 at an urban site of Delhi, India, and analyzed to evaluate their chemical components [organic carbon (OC), elemental carbon (EC), water-soluble inorganic components (WSICs), and major and trace elements]. The average concentrations of PM2.5 and PM10 were 131 ± 79 µg m-3 and 238 ± 106 µg m-3, respectively during the entire sampling period. The analyzed and seasonally segregated data sets of both PM2.5 and PM10 were used as input in the three different receptor models, i.e., principal component analysis-absolute principal component score (PCA-APCS), UNMIX, and positive matrix factorization (PMF), to achieve conjointly corroborated results. The present study deals with the implementation and comparison of results of three different multivariate receptor models (PCA-APCS, UNMIX, and PMF) on the same data sets that allowed a better understanding of the probable sources of PM2.5 and PM10 as well as the comportment of these sources with respect to different seasons. PCA-APCS, UNMIX, and PMF extracted similar sources but in different contributions to PM2.5 and PM10. All the three models extracted 7 similar sources while mutually confirmed the 4 major sources over Delhi, i.e., secondary aerosols, vehicular emissions, biomass burning, and soil dust, although the contribution of these sources varies seasonally. PCA-APCS and UNMIX analysis identified a less number of sources (besides mixed type) as compared to the PMF, which may cause erroneous interpretation of seasonal implications on source contribution to the PM mass concentration.

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The occurrence of atmospheric fine particles (PM2.5)-associated polycyclic aromatic hydrocarbons (PAHs), trace metals and organic molecular markers was investigated by conducting an intensive sampling campaign at the Eastern Mediterranean urban area of Nicosia (Cyprus). Sixty-two 24-hr PM2.5 samples were collected and analyzed for fifty parent and alkylated PAHs, twenty-five long chain n-alkanes, seventeen hopanes and twelve steranes used for source apportionment. The same number and kind of samples were analyzed to determine twenty-eight trace metals. Emphasis was given to investigate the air levels of the scarcely monitored although highly carcinogenic PAHs such as dibenzopyrenes, dibenzoanthracenes, 7H-benzo[c]fluorene and 5-methyl-chrysene, not included in the USEPA's sixteen PAH priority list (USEPA-16). UNMIX receptor model was applied to apportion the sources of atmospheric emissions of the determined organic compounds and trace metals and evaluate their daily contributions to the corresponding PM2.5 associated concentrations. For comparison purposes, principal component analysis with multiple linear regression (PCA/MLR) was also applied and its results are reported. The UNMIX receptor model, compared to PCA/MLR, offered a more precise source profile and more reliable daily mass source distributions by eliminating negative contributions. The individual and cumulative multi-pathway lifetime cancer risk (posed via inhalation, ingestion and dermal contact) by exposure to PM2.5-associated USEPA-16 listed and non-listed PAHs and selected airborne trace metals (As, Cd, Co, Ni, and Pb) were assessed. To estimate the contribution of each emission source to the total cancer risk, multiple linear regression analysis was performed, using as independent variables the daily source mass contributions and as dependent variables the respective cancer risk units. The estimated total cumulative cancer risk comprising all toxic PAHs, besides those included in the priority list, and metals was higher than the USEPA's threshold by a factor of eight, denoting a potential risk for long-term exposure of a population in the urban environment.

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A better understanding of the sources of organophosphate esters (OPEs) is a prerequisite for OPE control and the establishment of related environmental policies. Sources of OPEs in 35 major inflow rivers to the Bohai Sea of China were quantitatively analyzed using three effective receptor models (principal component analysis-multiple linear regression (PCA-MLR), positive matrix factorization (PMF), and Unmix) in this paper. The similarities and differences in results from PCA-MLR, PMF, and Unmix were discussed in depth. All three models well predicted the spatial variability of the total concentrations of nine OPEs (triethyl phosphate, tri-n-butyl phosphate, triisobutyl phosphate, tri (2-ethylhexyl) phosphate, tri (2-chloroethyl) phosphate, tris(1-chloro-2-propyl) phosphate, tris(1,3-dichloro-2-propyl) phosphate, triphenyl phosphate, and triphenylphosphine oxide) (∑9OPEs) (r2 = 0.90-0.96, p = 0.000) and explained 98.4%-101.2% of the observed ∑9OPEs. The predicted ∑9OPEs values from each pairwise model were significantly correlated (r2 = 0.88-0.91, p = 0.000). Three OPE sources were extracted by all three models: rigid and flexible polyurethane foam/coating, cellulosic/acrylic/vinyl polymer/unsaturated polyester, and polyvinyl chloride, contributing 49.9%, 29.7%, and 20.5% by PCA-MLR, 57.9%, 28.6%, and 13.5% by PMF, and 47.9%, 30.8%, and 22.4% by Unmix to the ∑9OPEs, respectively. PMF was recommended as the preferred receptor model for analyzing OPE sources in water during the monitoring period because of its optimal performance.

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Sources of airborne particulates (PM10) were investigated in two contrasting sites over central Indo-Gangetic Plain (IGP), one representing a rural background (Mirzapur) and another as an urban pollution hotspot (Varanasi). Very high PM10 concentration was noted both in Varanasi (178 ± 105 µgm-3; N:435) and Mirzapur (131 ± 56 µgm-3; N:169) with 72% and 62% of monitoring days exceeded the national air quality standard, respectively. Particulate-bound elements contribute significant proportion of PM10 mass (15%-18%), with highest contribution from Ca (7%-10%) and Fe (2%-3%). Besides, presence of Zn (1%-3%), K (1%-2%) and Na (1%-2%) was also noted. Water-soluble ionic species contributed 15%-19% of particulate mass, primarily by the secondary inorganic aerosols (SIA). Among the SIA, sulphate (5%-7%) and nitrate (4%) were prominent, contributing 59%-62% of the total ionic load, especially in winter. Particulate-bound metallic species and ions were selectively used as signatory molecules and source apportionment of PM10 was done by multivariate factor analysis. UNMIX was able to extract particulate sources in both the locations and crustal resuspensions (dust/-soil) were identified as the dominant source contributing 57%-63% of PM10 mass. Secondary aerosols were the second important source (17%-23%), followed by emissions from biomass/-refuse burning (10-19%). Transport of airborne particulates from upper IGP by prevailing westerly were identified as the important contributor of particulates, especially during high particulate loading days. Health risks associated to particulate-bound toxic metal exposure were also assessed. Non-carcinogenic health risk was within the permissible limit while there is possibility of elevated risk for PM10-bound Cr and Cd, if adequate control measures are not in place.

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