[Characterization of Metal Elements in Atmospheric Fine Particulate Matter and Their Sources in Winter in the Southern Sichuan Urban Agglomeration].

To investigate the concentration characteristics and sources of metal elements in PM2.5 during winter heavy pollution in the southern Sichuan urban agglomeration (Zigong, Luzhou, Neijiang, and Yibin), the metal elements in PM2.5 were measured using membrane sampling methods from December 30, 2018 to January 14, 2019, and the enrichment factor method (EF) and positive matrix factorization(PMF) were applied to investigate the sources of metal elements. The metal element observation data of Zigong in the same period of 2015 were also used to investigate the changes in metal element pollution and enrichment in Zigong in the middle and end of the implementation of China's Air Pollution Prevention and Control Action Plan. The main findings were as follows:① The concentrations and percentages of metal elements in particulate matter in different cities did not differ significantly. The elements with higher concentrations in the four cities showed similarities, with Al, Sb, and Fe at the top. From the comparison of different observation periods in Zigong, the concentrations of all elements except Tl changed. ② The results of the enrichment factor calculation showed that the enrichment of the elements Cr (Zigong and Yibin), Ni, Cu, As, Se, Ag, Cd, Sb, Tl, and Pb in the urban agglomeration was high. The comparison of the enrichment levels of elements in Zigong for different observation periods showed that the enrichment levels of all elements, except Cu, tended to decrease in the winter observation period of 2018. ③ The results of PMF source analysis showed that the metal elements in each city mainly originated from dust sources, coal-fired sources, industrial sources, and traffic sources, whereas there was a mixed contribution among the sources. The contribution of the main sources differed among cities, in which Zigong was dominated by traffic dust sources and mixed sources, Luzhou was dominated by industrial sources, Neijiang had a similar contribution from different sources, and Yibin was dominated by traffic sources.

Sources of acellular oxidative potential of water-soluble fine ambient particulate matter in the midwestern United States.

Ambient fine particulate matter (PM2.5) is associated with numerous health complications, yet the specific PM2.5 chemical components and their emission sources contributing to these health outcomes are understudied. Our study analyzes the chemical composition of PM2.5 collected from five distinct locations at urban, roadside and rural environments in midwestern region of the United States, and associates them with five acellular oxidative potential (OP) endpoints of water-soluble PM2.5. Redox-active metals (i.e., Cu, Fe, and Mn) and carbonaceous species were correlated with most OP endpoints, suggesting their significant role in OP. We conducted a source apportionment analysis using positive matrix factorization (PMF) and found a strong disparity in the contribution of various emission sources to PM2.5 mass vs. OP. Regional secondary sources and combustion-related aerosols contributed significantly (> 75 % in total) to PM2.5 mass, but showed weaker contribution (43-69 %) to OP. Local sources such as parking emissions, industrial emissions, and agricultural activities, though accounting marginally to PM2.5 mass (< 10 % for each), significantly contributed to various OP endpoints (10-50 %). Our results demonstrate that the sources contributing to PM2.5 mass and health effects are not necessarily same, emphasizing the need for an improved air quality management strategy utilizing more health-relevant PM2.5 indicators.

Annual variation of source contributions to PM10 and oxidative potential in a mountainous area with traffic, biomass burning, cement-plant and biogenic influences.

Toxicity of particulate matter (PM) depends on its sources, size and composition. We identified PM10 sources and determined their contribution to oxidative potential (OP) as a health proxy for PM exposure in an Alpine valley influenced by cement industry. PM10 filter sample chemical analysis and equivalent black carbon (eBC) were measured at an urban background site from November 2020 to November 2021. Using an optimized Positive Matrix Factorization (PMF) model, the source chemical fingerprints and contributions to PM10 were determined. The OP assessed through two assays, ascorbic acid (AA) and dithiothreitol (DTT), was attributed to the PM sources from the PMF model with a multiple linear regression (MLR) model. Ten factors were found at the site, including biomass burning (34, 40 and 38% contribution to annual PM10, OPAA and OPDDT, respectively), traffic (14, 19 and 7%), nitrate- and sulphate-rich (together: 16, 5 and 8%), aged sea salt (2, 2 and 0%) and mineral dust (10, 12 and 17%). The introduction of innovative organic tracers allowed the quantification of the PM primary and secondary biogenic fractions (together: 13, 8 and 21%). In addition, two unusual factors due to local features, a chloride-rich factor and a second mineral dust-rich factor (named the cement dust factor) were found, contributing together 10, 14 and 8%. We associate these two factors to different processes in the cement plant. Despite their rather low contribution to PM10 mass, these sources have one of the highest OPs per µg of source. The results of the study provide vital information about the influence of particular sources on PM10 and OP in complex environments and are thus useful for PM control strategies and actions.

Seasonal and height dynamics of volatile organic compounds in rubber plantation: Impacts on ozone and secondary organic aerosol formation.

Rubber trees emit a range of volatile organic compounds (VOCs), including isoprene, monoterpenes, and sesquiterpenes, as part of their natural metabolism. These VOCs can significantly influence air quality through photochemical reactions that produce ozone and secondary organic aerosols (SOAs). This study examines the impact of VOCs detected in a rubber tree plantation in Northeastern Thailand on air quality, highlighting their role in atmospheric reactions that lead to the formation of ozone and SOAs. VOCs were collected at varying heights and seasons using Tenax-TA tubes paired with an atmospheric sampler pump and identified by gas chromatography-mass spectrometry. In total, 100 VOCs were identified, including alkanes, alkenes, terpenes, aromatics, and oxygenated VOCs. Principal Coordinate Analysis (PCoA) revealed distinct seasonal VOC profiles, with hydrocarbons, peaking in summer and terpenes in the rainy season. The Linear Mixed-Effects (LME) model indicates that VOC concentrations are more influenced by seasonal changes than by sampling heights. Secondary organic aerosol potential (SOAP) and ozone formation potential (OFP) of selected VOC species were also determined. The total SOAP ranged from 67.24 µg/m3 in summer to 17.87 µg/m3 in winter, while the total OFP ranged from 377.87 µg/m3 in summer to 139.39 µg/m3 in winter. Additionally, positive matrix factorization (PMF) analysis identified four main VOC sources: gasoline combustion (18.3 %), microbial activity (38.6 %), monoterpene emissions during latex production (15.0 %), and industrial sources (28.1 %). These findings provide essential information for managing air pollution in rubber tree plantations. By adopting focused air quality management strategies, plantation operators can mitigate the adverse effects of VOCs, promoting a healthier and more sustainable future.

Pollution characteristics and quantitative source apportionment of heavy metals within a zinc smelting site by GIS-based PMF and APCS-MLR models.

The abandoned smelters present a substantial pollution threat to the nearby soil and groundwater. In this study, 63 surface soil samples were collected from a zinc smelter to quantitatively describe the pollution characteristics, ecological risks, and source apportionment of heavy metal(loid)s (HMs). The results revealed that the average contents of Zn, Cd, Pb, As, and Hg were 0.4, 12.2, 3.3, 5.3, and 12.7 times higher than the risk screening values of the construction sites, respectively. Notably, the smelter was accumulated heavily with Cd and Hg, and the contribution of Cd (0.38) and Hg (0.53) to ecological risk was 91.58%. ZZ3 and ZZ7 were the most polluted workshops, accounting for 25.7% and 35.0% of the pollution load and ecological risk, respectively. The influence of soil parent materials on pollution was minor compared to various workshops within the smelter. Combined with PMF, APCS-MLR and GIS analysis, four sources of HMs were identified: P1(25.5%) and A3(18.4%) were atmospheric deposition from the electric defogging workshop and surface runoff from the smelter; P2(32.7%) and A2(20.9%) were surface runoff of As-Pb foul acid; P3(14.5%) and A4(49.8%) were atmospheric deposition from the leach slag drying workshop; P4(27.3%) and A1(10.8%) were the smelting process of zinc products. This paper described the distribution characteristics and specific sources of HMs in different process workshops, providing a new perspective for the precise remediation of the smelter by determining the priority control factors.

Source apportionment and specific-source-site risk of quinolone antibiotics for effluent-receiving urban rivers and groundwater in a city, China.

There is a large surface-groundwater exchange downstream of wastewater treatment plants (WWTPs), and antibiotics upstream may influence sites downstream of rivers. Thus, samples from 9 effluent-receiving urban rivers (ERURs) and 12 groundwater sites were collected in Shijiazhuang City in December 2020 and April 2021. For ERURs, 8 out of 13 target quinolone antibiotics (QNs) were detected, and the total concentration of QNs in December and April were 100.6-4,398 ng/L and 8.02-2,476 ng/L, respectively. For groundwater, all target QNs were detected, and the total QNs concentration was 1.09-23.03 ng/L for December and 4.54-170.3 ng/L for April. The distribution of QNs was dissimilar between ERURs and groundwater. Most QN concentrations were weakly correlated with land use types in the system. The results of a positive matrix factorization model (PMF) indicated four potential sources of QNs in both ERURs and groundwater, and WWTP effluents were the main source of QNs. From December to April, the contribution of WWTP effluents and agricultural emissions increased, while livestock activities decreased. Singular value decomposition (SVD) results showed that the spatial variation of most QNs was mainly contributed by sites downstream (7.09%-88.86%) of ERURs. Then, a new method that combined the results of SVD and PMF was developed for a specific-source-site risk quotient (SRQ), and the SRQ for QNs was at high level, especially for the sites downstream of WWTPs. Regarding temporal variation, the SRQ for WWTP effluents, aquaculture, and agricultural emissions increased. Therefore, in order to control the antibiotic pollution, more attention should be paid to WWTP effluents, aquaculture, and agricultural emission sources for the benefit of sites downstream of WWTPs.

Distinguishing the contributions of different smelting emissions to the spatial risk footprints of toxic elements in soil using PMF, Bayesian isotope mixing models, and distance-based regression.

Toxic element pollution of soils emanating from smelting operations is an escalating global concern due to its severe impact on ecosystems and human health. In this study, soil samples were collected and analyzed to quantify the risk contributions and delineate the spatial risk footprints from smelting emissions for 8 toxic elements. A comprehensive health risk contribution and delineation framework was utilized, consisting of Positive matrix factorization (PMF), spatial interpolation, an advanced Bayesian isotope mixing model via Mixing Stable Isotope Analysis in R (MixSIAR), and distance-based regression. The results showed that the mean concentrations of As, Cd, Cu, Hg, Pb, and Zn exceeded the background levels, indicating substantial contamination. Three sources were identified using the PMF model and confirmed by spatial interpolation and MixSIAR, with contributions ranked as follows: industrial wastewater discharge and slag runoff from the smelter site (48.9 %) > natural geogenic inputs from soil parent materials (26.7 %) > atmospheric deposition of dust particles from smelting operations (24.5 %). Among the identified sources, smelter runoff posed the most significant risk, accounting for 97.9 % of the non-carcinogenic risk (NCR) and 59.9 % of the carcinogenic risk (CR). Runoff also drove NCR and CR exceedances at 7.8 % and 4.7 % of sites near the smelter, respectively. However, atmospheric deposition from smelting emissions affected soils across a larger 0.8 km radius. Although it posed lower risks, contributing just 1.1 % to NCR and 22.6 % to CR due to the limited elevation of toxic elements, deposition reached more distant soils. Spatial interpolation and distance-based regression delineated high NCR and CR exposure hotspots within 1.4 km for runoff and 0.8 km for deposition, with exponentially diminishing risks at further distances. These findings highlight the need for pathway-specific interventions that prioritize localized wastewater containment and drainage controls near the smelter while implementing broader regional air pollution mitigation measures.

Characteristics, sources, and health risks of volatile organic compounds in different functional regions of Shenyang.

The concentration of 56 volatile organic compounds (VOCs) in the ambient air of Shenyang was continuously monitored at four sites in 2021. The characteristics, sources, secondary pollution potential and health risks of VOCs in different functional regions of Shenyang were discussed. The results indicate that the concentration of VOCs in industrial regions was significantly higher than that in non-industrial regions, with a mean of 41.09 ± 69.82 parts per billion volumes (ppbv) compared to 19.99 ± 17.86 ppbv (commercial & residential region in urban fringe), 27.51 ± 28.81 ppbv (educational & scenic region) and 29.71 ± 23.97 ppbv (commercial & residential region in urban center). The positive matrix factorization (PMF) model was utilized to assign the sources of VOCs in Shenyang, and six factors were recognized: gasoline vehicles (34.8 %), diesel vehicles (28.3 %), combustion (11.4 %), biogenic emissions (9.7 %), industrial processes (8.2 %), and fuel evaporation (7.7 %). The results of the reactivity evaluation indicated that the ozone (O3) formation potential (OFP) was primarily influenced by industrial processes (29.2 %), diesel vehicles (25.7 %), biogenic emissions (17.0 %). These three factors were also the top three contributors to secondary organic aerosol formation potential (SOAP), accounting for 44.2 %, 9.4 % and 30.3 %, respectively. At the all four sites, the non-carcinogenic and carcinogenic risks of VOCs ranged from 1.6 × 10-2 to 3.8 × 10-2 and from 2.3 × 10-6 to 3.3 × 10-6, respectively. And the main risks can be attributed to emissions from industrial processes and gasoline vehicles. These findings suggested to strengthen the control of vehicle emissions throughout all regions in Shenyang and industrial processes emissions in industrial regions.

Characteristics and sources of chemical composition in precipitation on the Loess Plateau of China.

Knowing the sources of precipitation chemical composition is essential to understand the biogeochemical cycle and control air pollution. Despite this issue has been directly investigated with precipitation ion contents, the effects of water vapor transport have not been fully considered. Taking the Loess Plateau of China (LPC) as an example study area, this study established nine precipitation monitoring sites considering the variability in topography and rainfall amounts, and collected 435 precipitation samples during 2020-2022 to measure the chemical composition. The correlation analysis, positive matrix factorization model and backward trajectory model were combined to analyze the characteristics, sources and vapor transport effects of precipitation chemical composition. Seasonally, except for NH4+, the concentration of other ions in the dry season was significantly higher than that in the rainy season. Spatially, the concentrations of Ca2+, Na+, K+, SO42- and NO3- peaked in the Mu Us Sandy Land and industrial areas, while the high level of NH4+ was concentrated in the agricultural areas. The source apportionment found that the primary source of precipitation ions was crust (33 %), followed by coal combustion/vehicle (30 %), aged sea salt (21 %) and agriculture (16 %). The trajectory analysis showed that water vapor paths significantly varied with the seasons, but were primarily dominated by the northwestern air mass with proportions of >40 %. The dust aerosols transported by the northwestern air mass were the main contributor to crust-source precipitation ions. The eastern and southeastern air masses transported anthropogenic pollutants to the LPC, and the southeastern air mass also carried sea-salt precipitation ions. This study provides a framework to incorporate hydrochemical method with vapor source identification method for precipitation chemical source identification, and the results can be a theoretical basis for the treatment of atmospheric environmental problems.

Oxidative potential of particulate matter and its association to respiratory health endpoints in high-altitude cities in Bolivia.

Exposure to particulate matter (PM) pollution is a significant health risk, driving the search for innovative metrics that more accurately reflect the potential harm to human health. Among these, oxidative potential (OP) has emerged as a promising health-based metric, yet its application and relevance across different environments remain to be further explored. This study, set in two high-altitude Bolivian cities, aims to identify the most significant sources of PM-induced oxidation in the lungs and assess the utility of OP in assessing PM health impacts. Utilizing two distinct assays, OPDTT and OPDCFH, we measured the OP of PM samples, while also examining the associations between PM mass, OP, and black carbon (BC) concentrations with hospital visits for acute respiratory infections (ARI) and pneumonia over a range of exposure lags (0-2 weeks) using a Poisson regression model adjusted for meteorological conditions. The analysis also leveraged Positive Matrix Factorization (PMF) to link these health outcomes to specific PM sources, building on a prior source apportionment study utilizing the same dataset. Our findings highlight anthropogenic combustion, particularly from traffic and biomass burning, as the primary contributors to OP in these urban sites. Significant correlations were observed between both OPDTT and PM2.5 concentration exposure and ARI hospital visits, alongside a notable association with pneumonia cases and OPDTT levels. Furthermore, PMF analysis demonstrated a clear link between traffic-related pollution and increased hospital admissions for respiratory issues, affirming the health impact of these sources. These results underscore the potential of OPDTT as a valuable metric for assessing the health risks associated with acute PM exposure, showcasing its broader application in environmental health studies.

Is replacing missing values of PM2.5 constituents with estimates using machine learning better for source apportionment than exclusion or median replacement?

East Asian countries have been conducting source apportionment of fine particulate matter (PM2.5) by applying positive matrix factorization (PMF) to hourly constituent concentrations. However, some of the constituent data from the supersites in South Korea was missing due to instrument maintenance and calibration. Conventional preprocessing of missing values, such as exclusion or median replacement, causes biases in the estimated source contributions by changing the PMF input. Machine learning (ML) can estimate the missing values by training on constituent data, meteorological data, and gaseous pollutants. Complete data from the Seoul Supersite in 2018 was taken, and a random 20% was set as missing. PMF was performed by replacing missing values with estimates. Percent errors of the source contributions were calculated compared to those estimated from complete data. Missing values were estimated using a random forest analysis. Estimation accuracy (r2) was as high as 0.874 for missing carbon species and low at 0.631 when ionic species and trace elements were missing. For the seven highest contributing sources, replacing the missing values of carbon species with estimates minimized the percent errors to 2.0% on average. However, replacing the missing values of the other chemical species with estimates increased the percent errors to more than 9.7% on average. Percent errors were maximal at 37% on average when missing values of ionic species and trace elements were replaced with estimates. Missing values, except for carbon species, need to be excluded. This approach reduced the percent errors to 7.4% on average, which was lower than those due to median replacement. Our results show that reducing the biases in source apportionment is possible by replacing the missing values of carbon species with estimates. To improve the biases due to missing values of the other chemical species, the estimation accuracy of the ML needs to be improved.

Anthropogenic processes drive spatiotemporal variability of sulfate in groundwater from a multi-aquifer system: Dilution caused by mine drainage.

The water quality evolution of surface and groundwater caused by mining activities and mine drainage is a grave public concern worldwide. To explore the effect of mine drainage on sulfate evolution, a multi-aquifer system in a typical coal mine in Northwest China was investigated using multi-isotopes (δ34SSO4, δ18OSO4, δD, and δ18Owater) and Positive Matrix Factorization (PMF) model. Before mining, the Jurassic aquifer was dominated by gypsum dissolution, accompanied by cation exchange and bacterial sulfate reduction, and the phreatic aquifers and surface water were dominated by carbonate dissolution. Significant increase in sulfate in phreatic aquifers due to mine drainage during the early stages of coal mining. However, in contrast to common mining activities that result in sulfate contamination from pyrite oxidation, mine drainage in this mining area resulted in accelerated groundwater flow and enhanced hydraulic connections between the phreatic and confined aquifers. Dilution caused by the altered groundwater flow system controlled the evolution of sulphate, leading to different degrees of sulfate decrease in all aquifers and surface water. As the hydrogeochemical characteristic of Jurassic aquifer evolved toward phreatic aquifer, this factor should be considered to avoid misjudgment in determining the source of mine water intrusion. The study reveals the hydrogeochemical evolution induced by mine drainage, which could benefit to the management of groundwater resources in mining areas.

Spatial distribution, compositional characteristics, and source apportionment of legacy and novel per- and polyfluoroalkyl substances in farmland soil: A nationwide study in mainland China.

China, as one of the largest global producers and consumers of per- and poly-fluoroalkyl substances (PFASs), faces concerning levels of PFAS pollution in soil. However, knowledge of their occurrence in agricultural soils of China on the national scale remains unknown. Herein, the first nationwide survey was done by collecting 352 soil samples from 31 provinces in mainland China. The results indicated that the Σ24PFASs concentrations were 74.3 - 24880.0 pg/g, with mean concentrations of PFASs in decreasing order of legacy PFASs > emerging PFASs > PFAS precursors (640.2 pg/g, 340.7 pg/g, and 154.9 pg/g, respectively). The concentrations in coastal eastern China were distinctly higher than those in inland regions. Tianjin was the most severely PFASs-contaminated province because of rapid urban industrialization. This study further compared the PFAS content in monoculture and multiple cropping farmland soils, finding the concentrations of PFASs were high in soils planted with vegetable and fruit monocultures. Moreover, a positive matrix factorization (PMF) model was employed to identify different sources of PFASs. Fluoropolymer industries and aqueous film-forming foams were the primary contributors. The contributions from different emission sources varied across the seven geographical regions. This study provides new baseline data for prevention and control policies for reducing pollution.

[Pollution Characteristics, Source, and Health Risk Assessment of Metal Elements in PM2.5 Between Winter and Spring in Zhengzhou].

A total of 18 metal elements in ambient PM2.5 in Zhengzhou were continuously determined using an online heavy metal observation instrument in January and April, 2021, and the changes in element concentrations were analyzed. Metal elements were traced via enrichment factors, positive matrix factorization (PMF), and a characteristic radar chart. The US EPA health risk assessment model was used to assess the health risks of heavy metals, and the backward trajectory method and the concentration-weighted trajectory (CWT) method were used to evaluate the potential source regions of health risks. The results showed that the element concentrations were higher in spring, and the sum of Fe, Ca, Si, and Al concentrations accounted for 89.8% and 87.5% of the total element concentrations in winter and spring, respectively. Cd was enriched significantly, which was related to human activities. The concentrations of Pb, Se, Zn, Ni, Sb, and K in winter and Cr, Ni, Fe, Mn, V, Ba, Ca, K, Si, and Al in spring increased with the increasing pollution level. The results of PMF and the characteristic radar chart showed that the main sources of metal elements in winter and spring were industry, crust, motor vehicles, and mixed combustion, with industry and mixed combustion pollution occurring more often in winter and crust pollution occurring more often in spring. Significant non-carcinogenic risks existed in both winter and spring with more severe health risks in winter, and Mn caused significant non-carcinogenic risks. The health risks in winter were mainly influenced by Zhengzhou and surrounding cities and long-distance transport in the northwest, and the health risks in spring were mainly influenced by Zhengzhou and surrounding cities.

[Characteristics, Ecological Risk Assessment, and Source Apportionment of Soil Heavy Metals in the Yellow River Floodplain of Yinchuan City].

In order to study the status and sources of heavy metal pollution in Yinchuan Yellow River floodplain soils, we used inductively coupled plasma mass spectrometry (ICP-MS) to determine the presence of eight heavy metals in 92 soil samples from the Yinchuan Yellow River floodplain and used enrichment factors, geological accumulation index, and potential ecological risk index to analyze and evaluate the characteristics of heavy metal pollution in the study area. Combined correlation analysis, absolute factor analysis-multiple linear regression model (APCS-MLR), positive matrix factorization (PMF), and geostatistics were used to analyze the sources of soil heavy metals. The results showed that the content of eight heavy metals in the surface soil of the Yellow River floodplain in Yinchuan City were lower than the screening value of soil pollution risk in agricultural land; Cu and Pb contents were lower than the background value of Yinchuan City soil, and the contents of the remaining six elements were higher than the background value. The coefficients of variation of Zn and Cd were large and in the medium variation level and were influenced by anthropogenic activities. The heavy metal content varied between different land types and generally showed that wasteland > abandoned farmland > woodland > cultivated land. The average content of Cu and Pb in forest and arable soils was lower than the regional background value, whereas the rest of the heavy metals in different land types were higher than the soil background value. The analysis of enrichment factors showed that Zn and Cd were slightly enriched in the study area, and the cumulative index method and the evaluation of the potential risk of single heavy metals indicated that more than 60% of the sites in the study area were contaminated with Cd at a medium or higher potential ecological hazard. The comprehensive evaluation results of potential ecological risk showed that the overall ecological risk level of the study area was mild. From the distribution of heavy metal ecological risk comprehensive index sample points, only one point was in moderate ecological hazard, and the pollution point showed very few. Comprehensive correlation analysis, APCS-MLR model, PMF model, and geostatistical analysis results confirmed that Zn and Cd in the study area were mainly derived from human activities such as agricultural activities and transportation, and the remaining heavy metals were derived from soil parent materials. The results of this study can provide a scientific basis for the ecological protection and sustainable development of the Yellow River in Yinchuan City.

Chemical characterization and source apportionment of PM2.5 in a Northeastern China city during the epidemic period.

To investigate the influence of COVID-19 lockdown measures on PM2.5 and its chemical components in Shenyang, PM2.5 samples were continuously collected from January 1 to May 31, 2020. The samples were then analyzed for water-soluble inorganic ions, metal elements, organic carbon, and elemental carbon. The findings indicated a significant decrease in PM2.5 and its various chemical components during the lockdown period, compared to pre-lockdown levels (p < 0.05), suggesting a substantial improvement in air quality. Water-soluble inorganic ions (WSIIs) were identified as the primary contributors to PM2.5, accounting for 47% before the lockdown, 46% during the lockdown, and 37% after the lockdown. Ionic balance analysis revealed that PM2.5 exhibited neutral, weakly alkaline, and alkaline characteristics before, during, and after the lockdown, respectively. NH4+ was identified as the main balancing cation and was predominantly present in the form of NH4NO3 in the absence of complete neutralization of SO42- and NO3-. Moreover, the higher sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR), along with the significant increase in PM2.5/EC, suggested intense secondary transformation during the lockdown period. The elevated OC/EC ratio during the lockdown period implied higher secondary organic carbon (SOC), and the notable increase in SOC/EC ratio indicated a significant secondary transformation of total carbon. The enrichment factor (EF) results revealed that during the lockdown, 9 metal elements (As, Sn, Pb, Zn, Cu, Sb, Ag, Cd, and Se) were substantially impacted by anthropogenic emissions. Source analysis of PMF was employed to identify the sources of PM2.5 in Shenyang during the study period, and the analysis identified six factors: secondary sulfate and vehicle emissions, catering fume sources, secondary nitrate and coal combustion emissions, dust sources, biomass combustion, and industrial emissions, with secondary sulfate and vehicle emissions and catering fume sources contributing the most to PM2.5.

Identification and health risk evaluation of soil contaminated by polycyclic aromatic hydrocarbons at shale gas extraction sites based on positive matrix factorization.

The impact of shale gas extraction on surrounding environmental media remains unclear. In this study, the current state of contamination by polycyclic aromatic hydrocarbons (PAHs), which are high-frequency contaminants of shale gas, was investigated in the soil surrounding emerging shale gas development sites. The source analysis of PAHs was conducted in the soils of shale gas extraction sites using positive matrix factorization (PMF). The health risk assessment (HRA) was calculated for ingestion, dermal contact, and inhalation exposures, and the priority sources of PAHs in the soil were jointly identified by PMF and HRA to refine the contribution level of different individual PAHs to the carcinogenic risk. The results showed that both Sichuan and Chongqing mining site soils were contaminated to different degrees. Shale gas extraction has an impact on the surrounding soil, and the highest contributing source of PAHs in the mining site soil of Sichuan was anthropogenic activity, accounting for 31.6%, whereas that in the mining site soil of Chongqing was biomass combustion and mixed automobile combustion, accounting for 35.9%. At the two mining sites in Sichuan and Chongqing, none of the three exposure pathways (ingestion, dermal contact, and inhalation) posed a carcinogenic risk to children, whereas the dermal exposure pathway posed a carcinogenic risk to adults. Health risk assessments based on specific source assignments indicate that when managing soil pollution, the control of fossil fuel combustion and vehicular emissions should be prioritized.

Lagging pollution of polycyclic aromatic hydrocarbons in the rebuilt e-waste site: From the perspective of characteristics, sources, and risk assessment.

Little information is known regarding how the lagged pollution of polycyclic aromatic hydrocarbon (PAH) influenced the environment and human health after an e-waste dismantling site was rebuilt. This study investigated the characteristics, sources, and risk assessment of PAHs in a rebuilt e-waste site and its surrounding farmland by analyzing the samples of soil, dust, water, and vegetable. Concentrations of PAHs in soil, vegetable and water in the rebuilt site were relatively higher than in its surrounding farmland. The concentrations in surface soils, soil columns, dust, vegetables, and water varied from 55.4 to 3990 ng g-1, 1.65 to 5060 ng g-1, 2190 to 2420 ng g-1, 2670 to 10,300 ng g-1, and 46.8 to 110 µg L-1 in the e-waste site, respectively. On the farmland, PAH concentrations in surface soils, vegetables, and water ranged from 41.5 to 2760 ng g-1, 506 to 7640 ng g-1, and 56.6 to 89.2 µg L-1, respectively. A higher proportion of high-molecular-weight PAHs (HMW-PAHs) appeared in all multimedia compared with low-molecular-weight PAHs (LMW-PAHs). Diagnostic ratio together with positive matrix factorization (PMF) revealed that vehicle emission was the primary source in this area, and the activity of e-waste disposal was another important source in the rebuilt e-waste site. Based on the deterministic health risks, people working in the reconstructed e-waste site were exposed to low risks, whereas the residents living near the surrounding farmland were exposed to low risk. Sensitivity analyses indicated that exposure frequency and PAH concentrations were the main factors that influenced exposure risk. This study provides valuable insight into the comprehension of the lagging pollution effects of PAH on the environment and human health after the e-waste site was rebuilt.

Source apportionment of soil PTE in a northern industrial county using PMF model: Partitioning strategies and uncertainty analysis.

Positive matrix factorization (PMF) has commonly been applied for source apportionment of potentially toxic elements (PTE) in agricultural soil, however, spatial heterogeneity of PTE significantly undermines the accuracy and reliability of PMF results. In this study, a representative industrial-agricultural hub in North China (Xuanhua district, Zhangjiakou City) was selected as the research subject, multiple partition processing (PP) strategies and uncertainty analyses were integrated to advance the PMF modeling and associated algorithm mechanisms were comparatively discussed. Specifically, we adopted three methods to split the research area into several subzones according to industrial density (PP-1), population density (PP-2), and the ecological risk index (PP-3) respectively, to rectify the spatial bias phenomenon of PTE concentrations and to achieve a more interpretable result. Our results indicated that the obvious enrichment of Cd, Pb, and Zn was found in the agricultural soil, with Hg and Cd accounted for 83.49% of the overall potential ecological risk. Combining proper PP with PMF can significantly improve the modelling accuracy. Uncertainty analysis showed that interval ratios of tracer species (Cd, Pb, Hg, and Zn) calculated by PP-3 were consistently lower than that of PP-1 and PP-2, indicating that PP-3 coupled PMF can afford the optimal modeling results. It suggested that natural sources, fertilizers and pesticides, atmosphere deposition, mining, and smelting were recognized as the major contributor for the soil PTE contamination. The contribution of anthropogenic activities, specifically fertilizers and pesticides, and atmosphere deposition, increased by 1.64% and 5.91% compared to PMF results. These findings demonstrate that integration of proper partitioning processing into PMF can effectively improve the accuracy of the model even at the case of soil PTE contamination with high heterogeneity, offering support to subsequently implement directional control strategies.

[Characteristics and Identification Priority Source of Heavy Metals Pollution in Farmland Soils in the Yellow River Basin].

Assessments of the soil environmental quality of farmland and pollution source apportionment are the foundation for ensuring national food security and agricultural sustainable development, as well as an important prerequisite for the pursuit to keep our lands clean. This study evaluated the characteristics of heavy metal pollution in farmland soils in the Yellow River Basin from 2000 to 2023, based on the data of heavy metal contents including As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn, using the geo-accumulation index method. Source apportionment was conducted by employing a positive matrix factorization (PMF) model. The probabilistic health risks were evaluated by coupling Monte Carlo simulation with a human health risk assessment model, and priority pollution sources and elements were identified. The results showed that:① the average content of all heavy metals in farmland soils within the study area was lower than the screening values specified in the soil environment quality risk control standard for soil contamination of agriculture land (GB 15618-2018) (pH>7.5). However, the contents of Cd, As, and Zn in the samples exceeded their screening values, with percentages of 21.69%, 5.56%, and 1.23%, respectively, with Cd having the highest rate of exceedance. ② Hg and Cd were moderately polluted, Cu and Pb were slightly polluted, and the other elements were not polluted. ③ The main sources of heavy metals in farmland soil were traffic-industrial sources, natural-agricultural sources, industrial-natural sources, and agricultural-industrial sources, with contribution rates of 37.04%, 26.69%, 21.72%, and 14.55%, respectively. ④ Heavy metals in farmland soil posed carcinogenic health risks to adults and children but did not have non-carcinogenic risks; As and Cd were priority control elements for human health risks, and industrial-natural sources and agricultural-industrial sources were priority control sources in the study area.