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As an important capital city of intensive urbanization and industrialization in Northeast China, Changchun has experienced extremely rapid development, with diverse sectors such as automobile manufacturing, equipment manufacturing, optoelectronics, and pharmaceutical decoration. However, data on the levels and profiles of perfluoroalkyl substances (PFASs) in urban soils of Changchun is limited. This study investigated 17 PFASs across various functional zones within the main urban area of Changchun. ∑PFAS concentrations in the soils ranged from 0.236 to 6.483 ng/g, averaging 1.820 ng/g. Perfluorocarboxylic acids (PFCAs) were more prevalent than perfluorosulfonic acids (PFSAs), and short-chain PFASs (C ≤ 6) were the predominant residues. PFAS concentrations varied across functional zones, with commercial markets exhibiting the highest levels, followed by industrial areas, residential areas, suburban zones, and transportation areas. Molecular diagnostic ratio and PCA-MLR analysis identified industrial production processes of consumer goods and wastewater treatment plants as the primary sources of soil PFAS contamination. There were no obvious health risks of soil ∑PFASs, while soil PFOS and PFHxS may have an impact on the richness and diversity of soil microbial communities in some certain locations. This study provides new data on PFAS residues in soils influenced by diverse contamination sources within a key industrial city in Northeast China, offering valuable insights for prioritizing remediation and restoration efforts.

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Recent research on fluoride in water primarily focuses on groundwater; however, the potential environmental risks of fluoride in urban rivers should not be overlooked. In 2023, this study collected 135 surface water samples from the Ershibu River in Hefei, China, during various flood periods. Through descriptive statistical analysis, correlation analysis, principal component analysis-multiple linear regression (PCA-MLR) modeling, hazard quotient (HQ) assessment, and Monte Carlo simulation analysis, the spatial and temporal distribution, potential sources, and health risks of fluoride were investigated. The results showed that fluoride concentrations in the Ershibu River ranged from 0 to 1.38 mg/L. According to the PCA-MLR calculations, industrial pollution (73.92 %) was identified as the main source, followed by hydrogeochemical evolution (16.10 %) and agricultural activities (9.98 %). The HQ analysis revealed that the average exceedance rates of HQ for the five exposed populations were as follows: infants (64.45 %) > young children (2.22 %) = adults (2.22 %) > children (0) = teenagers (0). Therefore, relevant authorities should improve defluoridation facilities to reduce fluoride levels in industrial and agricultural wastewater and implement measures to protect public health. Future research should investigate the migration processes and toxicity mechanisms of fluoride more thoroughly.

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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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A two-year (2020-2021) survey dataset of six water quality parameters (pH, dissolved oxygen (DO), chemical oxygen demand (COD), dissolved inorganic nitrogen (DIN), soluble reactive phosphate (SRP), and petroleum pollutants) was used to investigate their spatial distribution in the Bohai Sea and quantify their potential sources. There were significant differences in spatial distribution of the parameters. High concentrations of COD, DIN and SRP were found in three bays, with terrestrial input being the main pollution source. Phosphorus-limiting conditions were present in the Bohai Sea. High petroleum pollutant concentrations were identified in port areas, offshore oilfields, and adjacent areas. The pH was above the global oceanic average and there were no signs of acidification. The contribution of the mixed terrestrial inputs, maritime transportation, and offshore oil exploitation sources, oceanic and associated biotic sources, and seawater-atmosphere exchange and atmospheric deposition sources to water quality were 63.4 %, 8.0 %, and 28.6 %, respectively.

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The mechanism by which parameters influence the source apportionment results of receptor models is not well understood. Three mature receptor models, namely, principal component analysis-multiple linear regression (PCA-MLR), positive matrix factorization (PMF) and factor analysis with nonnegative constraints (FA-NNC), were comparatively employed for source apportionment of 16 polycyclic aromatic hydrocarbons in 30 street dust samples. The results indicated that the FA-NNC and PMF models produced results with a higher degree of similarity than the results obtained with the PCA-MLR model. Moreover, when the sample size was gradually decreased, similar source profiles were extracted that were consistent with results obtained from all samples. However, the overall contribution rates were not as stable as the source profiles. The PCA-MLR results remained the most stable in both aspects. FA-NNC and PMF performed better in regards to the stability of contribution rates and source profiles, respectively. Improvements in the goodness of fit of overall and individual pollutants were always accompanied by a decrease in the relevance among the variables, indicating that while the model simulation effect was improved, the credibility of the results decreased. Thus, finding an appropriate number of sample size is more appropriate than simply involving too many samples in source apportionment models.

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The properties and sources of soil heavy metals (Pb, Zn, Cu, Cd, As, Hg, Cr, and Ni) need to be comprehensively analyzed to take effective steps to control and reduce soil pollutants. In this research, 416 soil samples were collected on a large scale in China. Two receptor models (PCA/MLR and PMF) were utilized to identify pollutant sources and quantify the contributions. The means of soil heavy metals (Zn, Cu, As, Hg, Cr, and Ni) were lower than the corresponding screening values and intervention values. Cd was greater than the intervention value, while Pb was between the screening value and the intervention value. Source apportionments suggested that mine sources were the most polluted (64.28%), followed by traffic sources (38.98%), natural sources (11.41-39.58%), industrial sources (9.8-18.65%), and agricultural sources (2.79-14.51%). Compared to the PCA/MLR model, the PMF model had a better effect in evaluating soil heavy metal pollution. It gave corresponding weights according to the data concentration and its uncertainty, which made the result reasonable. The ecological risk assessment indicated that Cd posed a significant risk, while Hg caused a mild risk and the other six heavy metals posed a low risk. The spatial distribution of ecological risk suggested that severe risk points were mainly distributed in the central area, while high-risk points were distributed in the southern region. The SRI method was developed to link pollution sources and their potential ecological risks and indicated better applicability to the PMF model. The study findings could provide guidelines for monitoring the main sources and reducing the pollution of soil heavy metals.

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The distributions of potentially toxic elements (PTEs) among PM1, PM1-10, PM10-50, and PM50-1000 fractions of the road dust were studied in the western and eastern parts of Moscow, impacted mainly by the road transport and the industrial sector, respectively. The partitioning of PTEs in road dust can provide more precise information on pollution sources and its further interpretation regarding human health risks. The concentrations of PTEs were analyzed by mass and atomic emission inductively coupled plasma spectrometry. Differences in the results between the western and eastern parts of the city were caused by the dissimilarity between traffic and industrial emissions. The source apportionment of the PTEs was carried out using absolute principal component analysis with multiple linear regressions (PCA/APCS-MLR). The contribution from anthropogenic sources was significant to PM1 and PM1-10 particles. In coarser fractions (PM10-50, PM50-1000), it decreased due to the input with the wind-induced resuspension of soil and rock particles. In the eastern part of the city, the accumulation of PTEs (especially Mo, Sb, Cd, Sn, Bi, Co, and As) is the most active in PM1-10, while in the western part, it is most pronounced in PM1 (especially Pb, Cu, Cr, and W) which is associated with differences in the size of particles coming from traffic and industrial sources. In the eastern part of Moscow, in comparison with the western part, the contribution from industrial sources to the accumulation of PTEs in all particle size fractions was higher by 10-30%. In the western part of Moscow, the finest particles PM1 and PM1-10 demonstrate the trend of rising pollution levels with the increase in road size, while in the eastern part of the city, only coarse particles PM50-1000 show the same trend. In the fractions PM1 and PM1-10 of road dust, a significant contribution was made by anthropogenic sources; however, its role decreased in the coarse fractions-PM10-50 and especially in PM50-1000- due to the influence of roadside soils and their parent material.

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The eastern route of the South-to-North Water Diversion Project (ER-SNWDP) is a major human health project designed to alleviate the water scarcity in the Beijing-Tianjin-Hebei region in China. Impounded lake water security is directly related to the water diversion project effectiveness. At present, there is not a thorough understanding of the sediment heavy metals in Lake Hongze, the largest impounded lake of the ER-SNWDP. Consequently, this study reports a distribution analysis of Cu, Zn, Pb, Cr, Cd, As, Hg, and Ni in 101 sediment samples from Lake Hongze; we, utilized the enrichment factor, geoaccumulation index, and potential ecological risk index for the are to determine the ecological risk of heavy metals. The heavy metal source was examined with correlation analysis and principal component analysis-multiple linear regressions. The results showed that the average heavy metal content (Cu, Zn, Pb, Cr, Cd, As, Hg, Ni) were 0.03-1.57 times greater than the Jiangsu Province background values. Cd, As, and Hg were the main contributors to the Lake Hongze ecological risk. Spatially, the open water area was the most polluted among the four lake parts, and most of the flushing area had a low ecological risk. Chengzi bay and the western lake area have similar risk profiles, but are lower than the open water area risk. Source analysis showed that nonpoint-source agricultural pollution and industrial production were important pollution sources, while a considerable portion of the heavy metal content came from atmospheric deposition and natural sources. This study identified the main contamination areas and revealed the possible sources of each heavy metal; as such, this study can serve as a reference for the remediation and management of Lake Hongze to ensure the water safety of the ER-SNWDP.

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In order to investigate the pollution characteristics and sources of water-soluble ions in atmospheric PM2.5 in different regions of central China during summer, Wuhan, Suizhou, and Pingdingshan were selected as urban, suburban, and rural monitoring stations, respectively, to collect PM2.5 samples, and the mass concentration of PM2.5 in the atmosphere and the contents of eight water-soluble ions were analyzed. The results showed that ρ(water-soluble ions) at the three sites showed obvious spatial distribution characteristics, with Pingdingshan[(36.29±9.82) µg·m-3] > Wuhan[(32.55±10.05) µg·m-3] > Suizhou[(26.10±6.23) µg·m-3], accounting for 52.47%, 51.32%, and 48.61% of the PM2.5 mass concentration, respectively. In the Pingdingshan station, the proportion of water-soluble ions was the largest due to biomass combustion in the rural area. Additionally, SNA (SO42-, NO3-, and NH4+) were the main ionic components, accounting for 95.65%, 96.12%, and 97.33% of the total water-soluble ions, respectively. The mean values of SOR of the Wuhan (0.64) and Suizhou (0.63) stations were higher than that of the Pingdingshan station (0.50), whereas the NOR values of the Wuhan (0.18) and Pingdingshan (0.19) stations were higher than that of the Suizhou station (0.15). The difference in SOR and NOR among stations was affected by the secondary conversion mechanism, the ammonia-rich environment, and the surrounding traffic sources, respectively. The PM2.5 at the Wuhan and Pingdingshan stations was in general alkaline, whereas at the Suizhou station it was neutral or weakly acidic, which was mainly caused by differences in NH4+. NH4+ mainly existed in the form of (NH4)2SO4 and NH4NO3 at the Wuhan and Pingdingshan stations, whereas at the Suizhou station it mainly existed in the form of (NH4)2SO4 or (NH4)HSO4. PCA-MLR analysis revealed that the Wuhan (89.27%) and Suizhou (67.38%) stations were the most affected by secondary conversion sources, whereas the Wuhan station was also affected by industrial sources (8.54%) and coal sources (2.27%). The pollution sources of the Suizhou station also included biomass combustion (24.42%) and dust sources (8.25%). The Pingdingshan station was most affected by biomass combustion (58.37%), followed by dust and combustion sources (38.05%) and traffic sources (3.58%). The analysis of potential sources of SNA (PSCF) showed that the main potential source areas of Wuhan were the boundary of Hubei, Henan, and Anhui and the southwest area of Anhui. Suizhou and Pingdingshan were affected by long-distance transport, and the main potential source regions were distributed in Shanghai, Jiangsu, and Anhui provinces from the east coast to the west.

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Due to the spreading of the coronavirus (COVID-19) in Iran, restrictions and lockdown were announced to control the infection. In order to determine the effects of the lockdown period on the status of the water quality and pollution, the concentrations of Al, As, Ba, Cr, Cu, Mo, Ni, Pb, Se, and Zn, together with Na+, Mg2+, Ca2+ and electrical conductivity (EC), were measured in the Zarjoub River, north of Iran, in both pre-lockdown and post-lockdown periods. The results indicated that water pollution and associated human health risk reduced by an average of 30% and 39%, respectively, during the lockdown period. In addition, the multi-purpose water quality index also improved by an average of 34%. However, the water salinity and alkalinity increased during the lockdown period due to the increase of municipal wastewater and the use of disinfectants. The major sources of pollution were identified as weathering, municipal wastewater, industrial and agricultural effluents, solid waste, and vehicular pollution. PCA-MLR receptor model showed that the contribution of mixed sources of weathering and municipal wastewater in water pollution increased from 23 to 50% during the lockdown period. However, the contribution of mixed sources of industrial effluents and solid wastes reduced from 64 to 45%. Likewise, the contribution of traffic-related sources exhibited a reduction from 13% in the pre-lockdown period to 5% together with agricultural effluent in the post-lockdown period. Overall, although the lockdown period resulted in positive impacts on diminishing the level of water pollution caused by industrial and vehicular contaminants, the increase of municipal waste and wastewater is a negative consequence of the lockdown period.

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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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To determine the status and sources of contamination and phytoremediation capability of Typha latifolia L. in the Bahmanshir River of Iran, the concentration of eight potentially toxic elements (As, Cd, Cr, Cu, Mn, Ni, Pb, and Zn) in sediment and plant tissues from ten sampling sites were measured. Mean concentrations of Cd, Cr, Cu, Pb, and Zn in the sediment exceeded those of local background. PCA-MLR receptor analysis suggested that the sediment contamination was due to municipal wastewater/vehicular pollution and weathering/industrial/agricultural activities, with contributions of 66% and 34%, respectively. Average enrichment factor (EF) and modified hazard quotient (mHQ) for Pb and Cu were categorized as moderate. Modified pollution index (MPI) and modified ecological risk index (MRI) values suggested moderate to heavy pollution and low ecological risk, respectively. The values of sediment quality guidelines (SQGs), ecological contamination index (ECI), contamination severity index (CSI), and toxic risk index (TRI) were all similar, reflecting low to moderate contamination and toxicity. Typha latifolia L. showed good phytostabilization capability for Cd, Cu, and Pb, and phytoextraction capacity for Zn. Using the metal accumulation index (MAI) and the comprehensive bioconcentration index (CBCI), Typha latifolia L. was shown to have acceptable performance in the accumulation of Cd, Cu, Pb, and Zn and thus, can be considered a good candidate for bioaccumulation of these elements in the study area. Overall, this study suggests that phytoremediation using Typha latifolia L. could be a practical method for uptake and remove of potentially toxic elements from aquatic environments.

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Many source apportionment models have been applied to identify pollution sources, and differences often exist in the diagnostic results. The reasons causing these differences have not been fully elucidated. In this study, three receptor models, principal component analysis-multiple linear regression (PCA-MLR), positive matrix factorization (PMF), and factor analysis-nonnegative constraints (FA-NNC), were compared and applied for the analysis of 16 EPA priority polycyclic aromatic hydrocarbons (PAHs) adsorbed in street dust samples from Harbin City (China). The differences in the results were caused by different calculation approaches, including matrix decomposition, variable grouping extraction, and nonnegative constraints, especially between PCA-MLR and the other two models. PCA-MLR has no nonnegative constraints, making PCA-MLR less similar to the real world than the other two. Both PMF and FA-NNC have a nonnegative constraint process, which may be the main reason why their results were much more similar to each other than to those of PCA-MLR. PCA-MLR distinguishes variables into several groups that have the greatest variances from each other, whereas the other two methods find similarities among variables and extract them. In the case study of Harbin City, the contributions of mobile and industrial sources ranged from 47 to 69%, and the contributions of coal and other sources ranged from 30 to 52%. The recognized types of pollution sources were generally equivalent, but the proportional contributions were different. PCA-MLR performed best in calculating contributions, whereas PMF and FA-NNC were better in terms of source diagnosis.

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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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In this study, the occurrence, spatial distribution, sources, and ecological risks of perfluoroalkyl substances (PFASs) in the surface waters of the Lingang hybrid constructed wetland were systematically investigated. Twenty-three PFASs were analyzed from 7 representative sampling zones. The obtained results indicated that PFBA, PFPeA, PFHxA, PFHpA, PFOA, PFBS, PFOS, and HFPO-DA were frequently detected; and PFBA, PFOA, and PFOS were the dominant PFASs with the relative abundances in ranges of 26.91 to 52.26%, 11.79 to 28.79%, and 0 to 31.98%, respectively. The total concentrations of 8 PFASs (Σ8PFASs) ranged from 25.9 to 56.6 ng/L, and the highest concentration was observed in subsurface flow wetland. Moreover, HFPO-DA with high toxicity was detected in wetlands for the first time. Based on the principal component analysis-multiple linear regression (PCA-MLR) analysis, three sources and their contributions were fluoropolymer processing aids (67.6%), fluororesin coatings and metal plating (17.9%), and food packaging materials and atmospheric precipitation (14.5%), respectively. According to the risk quotients (RQs), the ecological risk of 8 PFASs was low to the aquatic organisms.

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This study reports soil PAHs from a small city (Tezpur) of the mid-Brahmaputra Valley. The soil PAHs has been assessed from representative land use using detailed protocol of extracting, cleaning, and quantitative analysis by HPLC technique. The concentrations of PAHs showed minimum spatial variability and yet showed strong seasonal variability, which could be typical of small cities having weak local source strengths. On examining the air mass reaching the region, it appeared that there has been explicit effect of long-range transport. The HYSPLIT back trajectories reaching the study area during different seasons showed variations in terms of their origins and transport pathways. This might have led to differential long-range transport of PAHs, which is reflected in the seasonal variabilities of the concentrations of PAHs. The seasonal variations were much profound with the highest ΣPAHs concentration during post-monsoon (7961 ng g-1) followed by pre-monsoon (2414 ng g-1) and monsoon (773 ng g-1) season. The toxicity of the PAHs was examined as BaP equivalent (BaPeq) concentrations, which were found to be on the lower side as compared to the studies conducted elsewhere. The percentage contribution of 3- and 4-ring compounds was found to be greater. An attempt also was made to apportion the sources of the PAHs by application of diagnostic ratios, principal component analysis-multiple linear regression (PCA-MLR) and hierarchal cluster analysis (HCA), which revealed that coal and biomass burning and vehicular emissions are the major contributors to the PAHs load in Tezpur city.

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In this study, we investigated aerosol chemical composition, spectral properties of aerosol extracts, and source contributions to the aerosol light-absorbing brown carbon (BrC) in central Los Angeles from July 2018 to March 2019, during warm and cold seasons. Spectrophotometric measurements (water and methanol extracts; 200 < λ < 1100) and chemical analyses were performed on collected particulate matter (PM), and relationships of BrC light absorption (Abs365) to source tracer chemical species were evaluated. Mass absorption efficiency (MAE) of both water and methanol extracted solutions exhibited an increasing trend from warm period to cold season, with an annual average value of 0.61 ± 0.22 m2.g-1 and 1.38 ± 0.89 m2.g-1, respectively. Principal component analysis (PCA) were coupled with multiple linear regression (MLR) to identify and quantify sources of BrC light absorption in each of the seasons. Our finding documented fossil fuel combustion as the dominant source of BrC light absorption during warm season, with relative contribution of 38% to total BrC light absorption, followed by (secondary organic aerosol) SOA (30%) and biomass burning (12%). In contrast, biomass burning was the major source of BrC during the cold season (53%), while fossil fuel combustion and SOA contributed to 18% and 12% of BrC, respectively. Significantly higher contribution of biomass burning to BrC during the cold season suggested that residential heating activities (wood burning) play a major role in increased BrC concentrations. Previously collected Aethalometer model data documented fossil fuel combustion as the dominant contributing source to >90% of BC throughout the year. Finally, the solar radiation absorption ratio of BrC to elemental carbon (EC) in the ultraviolet range (300-400 nm) was maximum during the cold season with the annual corresponding values of 13-25% and 17-29% for water- and methanol-soluble BrC, respectively; which provides further evidence of the important effect of BrC light absorption on atmospheric radiative balance.

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This study investigates the elemental characteristics and sources of aerosol fine particulate matter (PM2.5) samples obtained from Pingdingshan, Suizhou, and Wuhan, Central China, in June 2017. Thirteen kinds of elements (Ti, Zn, Cu, Cr, As, Pb, Fe, Ni, Se, V, Sb, Cd, and Co) were analyzed by inductively coupled plasma mass spectrometry (ICP-MS), and three source identification methods-enrichment factor, principle component analysis and multiple linear regression (PCA-MLR), and backward trajectory clustering-were applied. The results showed that Zn was the highest trace element in PM2.5 in samples from Pingdingshan, Suizhou, and Wuhan, and that the concentration of As exceeded the annual limit of Chinese air quality standards (GB 3096-2012). Concentrations of Pb and Cd in PM2.5 in samples from the three cities during the summer were low. The enrichment factor coefficients for Se, Sb, Cd, As, Cu, and Zn exceeded 10, which suggests that summer pollution from human activities was serious, for example, the enrichment factor coefficient for Se was>600. PCA-MLR and backward trajectory clustering analysis results showed that the main pollution sources in Pingdingshan during the summer were industrial fuel oil (57.90% of total), traffic pollution (24.40%), coal combustion (6.10%), and mine soil (11.60%). The main pollution source in Suizhou was fuel, which contributed 54.30% of the total. Wuhan was mainly affected by industrial emissions (60.80% of the total) and motor vehicle pollution (39.20%). Hence, Wuhan and Suizhou were mainly affected by local source emissions, whereas Pingdingshan was jointly affected by local emissions and regional inputs during the summer.

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The presence of phthalates, polycyclic aromatic hydrocarbons (PAHs) and semi-volatile chlorinated organic compounds (SVCOC) in toilet tissue papers may be detrimental to the health of consumers upon exposure. This study therefore, sought to investigate the levels of these toxicants in toilet tissue papers on the Ghanaian market and the associated risk of exposure. The study also sought to conduct source apportionments for analytes. A total of 32 composite toilet tissue samples from 8 different brands were analysed in replicates for PAHs, phthalates and SVCOCs. Analysis was conducted using Shimadzu GCMS QP 2020 with the MS operated in SIM mode. The results showed elevated levels of PAHs, phthalates, and appreciable levels of SVCOCs in the toilets tissue papers. The risk assessment conducted, showed an associated elevated cancer risk >10-4 for PAHs in all samples and DEHP in samples NN, BB and SF. The risk associated with the levels of carcinogenic SVCOCs were found to be > 10-5 but < 10-4.The hazard indices (HI) calculated for non-cancer effects, showed risk levels < 1.0 for phthalates in most toilet paper samples except for samples BB and SF. The HI recorded for chlorophenols were all <1. Cumulatively, these values suggested elevated cancer and non-cancer risk associated with the dermal use of the toilet tissue papers on the Ghanaian market. The PCA-MLR source apportionment suggested two significant sources of SVOCs in the toilet tissue papers. PAHs, phthalates and 2-chloronaphthalene were of one source (oil base source) whereas SVCOCs were of another source (bleaching process).

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Organic aerosols have attracted increasing attention recently due to their significant contribution to fine particles (PM2.5) and their complex components and sources. In this study, a total of 40 PM2.5 samples were collected simultaneously with high-volume samplers in Changchun from 16th Oct to 29th Nov 2016. Organic carbon (OC), elemental carbon (EC), non-polar organic compounds including n-alkanes, polycyclic aromatic hydrocarbons (PAHs), and hopanes, and levoglucosan in atmospheric fine particles were analyzed. The main sources of organic aerosols were identified by molecular markers, diagnostic ratios, and a principal component analysis-multiple liner regression (PCA-MLR) model. The results showed that the average mass concentration of PM2.5 was (79.0±55.7) µg·m-3, and the averaged OC and EC mass concentrations were (20.7±15.6) µg·m-3 and (2.2±1.1) µg·m-3, which accounted for 26.2% and 2.8% of PM2.5, respectively. The total average concentration of the tested non-polar organic compounds was (186.3±104.5) ng·m-3 and, in descending order, this was composed of n-alkane (101.3±67.0) ng·m-3, polycyclic aromatic hydrocarbons (81.4±46.0) ng·m-3, hopanes (3.8±1.9) ng·m-3. The PCA-MLR model results showed that the relative contributions of the main sources of organic aerosols were coal combustion (47.0%), biomass burning (42.6%), and traffic emission (10.4%).