Positive Matrix Factorization Model Research Articles

Source Apportionment of Morphine in Wastewater

It is a new approach to identify legal or illegal use of morphine through information on municipal wastewater. However, the sources of morphine in wastewater are complex, and distinguishing the contribution of different sources has become a key issue. A total of 262 influent samples from 61 representative wastewater treatment plants in a typical city were collected from October 2022 to March 2023. The concentrations of morphine, codeine, thebaine, papaverine, noscapine, and monoacetylmorphine were analyzed in wastewater and poppy straws. Combined with the proportion of alkaloids in poppy straws, the source analysis of alkaloids in wastewater was analyzed using the ratio method and positive matrix factorization model (PMF). Only five alkaloids were detected in wastewater, and monoacetylmorphine, a metabolite of heroin, was not detected. The concentrations of morphine and codeine were significantly higher than those of noscapine, papaverine, and thebaine. By constructing the ratios of codeine/(morphine + codeine) and noscapine/(noscapine + codeine), the source of poppy straw could be qualitatively distinguished. The PMF results showed that three sources of morphine for medical use, poppy straw, and codeine contributed 44.9%, 43.7%, and 9.4%, respectively. The different sources varied in these months due to the COVID-19 and influenza A outbreaks, in which the use of drugs containing poppy straws and codeine was the main source, whereas the use of morphine analgesics remained relatively stable. Inventory analysis further demonstrated the reliability of the source contributions from the PMF model, and morphine was not abused in this city.

Variations of source-specific risks for inhalable particles-bound PAHs during long-term air pollution controls in a Chinese megacity: Impact of gas/particle partitioning

Gas/particle (G/P) partitioning is a determining factor that drives the behavior of polycyclic aromatic hydrocarbons (PAHs) in the atmosphere. To comprehend the variations and impacts of PAHs G/P partitioning on inhalation risks from different sources, atmospheric PAHs in Chengdu were studied from January 2009 to January 2021. Chengdu's air quality demonstrated improvement, with a noticeable decline in PM10 (from 238 to 103 μg m−3), PM10-bound PAHs mass concentrations (from 0.08 to 0.01 μg m−3), as well as PAHs inhalation non-cancer (from 63.5 to 7.1) and cancer risks (from 6.2 × 10−5 to 7.7 × 10−6). Decreasing trends were also observed in the concentrations of organic carbon (OC) and the values of absorptive G/P partitioning coefficient (Kp, OM) from 2009 to 2020, indicating a slight decrease in the PM absorptive capacity. A positive matrix factorization (PMF) model utilized three distinct datasets: PMFpp (particle-phase PAHs only), PMFtot (total PAHs concentrations), and PMFpp/gas (particle-phase in parallel with gas-phase PAHs). PMFpp and PMFpp/gas provide reasonable source apportionment and source-specific risk results. However, daily variations in source contributions of PMFtot were found to be unreasonable. PMFpp/gas and PMFtot could identify a source of low molecular weight PAHs (LPAHs) related to temperature, and a significantly positive correlation between LPAHs and temperature was discovered. Gas/particle partitioning of PAHs showed a notable impact on source-specific risks associated with coal and biomass combustion (CC&BC). The findings contribute to our understanding of the variation in PAHs G/P partitioning and the PM absorptive capacity resulting from effective air pollution control in China. Additionally, they offer prerequisite information for incorporating semi-volatile organic compounds in source apportionment and source-specific risk assessment.

Comprehensive analysis of metal(loid)s and associated metal(loid) resistance genes in atmospheric particulate matter

Despite global concerns about metal(loid)s in atmospheric particulate matter (PM), the presence of metal(loid) resistance genes (MRGs) in PM remains unknown. Therefore, we conducted a comprehensive investigation of the metal(loid)s and associated MRGs in PMs in two seasons (summer and winter) in Xiamen, China. According to the geoaccumulation index (Igeo), most metal(loid)s, except for V and Mn, exhibited enrichment in PM, suggesting potential anthropogenic sources. By employing Positive Matrix Factorization (PMF) model, utilizing a dataset encompassing both total and bioaccessible metal(loid)s, along with backward trajectory simulations, traffic emissions were determined to be the primary potential contributor of metal(loid)s in summer, whereas coal combustion was observed to have a dominant contribution in winter. The major contributor to the carcinogenic risk of metal(loid)s in both summer and winter was predominantly attributed to coal combustion, which serves as the main source of bioaccessible Cr. Bacterial communities within PMs showed lower diversity and network complexity in summer than in winter, with Pseudomonadales being the dominant order. Abundant MRGs, including the As(III) S-adenosylmethionine methyltransferase gene (arsM), Cu(I)-translocating P-type ATPase gene (copA), Zn(II)/Cd(II)/Pb(II)-translocating P-type ATPase gene (zntA), and Zn(II)-translocating P-type ATPase gene (ziaA), were detected within the PMs. Seasonal variations were observed for the metal(loid) concentration, bacterial community structure, and MRG abundance. The bacterial community composition and MRG abundance within PMs were primarily influenced by temperature, rather than metal(loid)s. This research offers novel perspectives on the occurrence of metal(loid)s and MRGs in PMs, thereby contributing to the control of air pollution.

Characteristics and correlation analysis of heavy metal distribution in China's freshwater aquaculture pond sediments

The concentration of heavy metals (HMs) in aquaculture pond sediments significantly affects aquatic food safety and environmental quality. The contamination characteristics, drivers and potential sources of HMs in typical bulk freshwater aquaculture pond sediments in major provinces of China were systematically investigated using a variety of methods and models. Specifically, 130 surface sediment samples were collected from the study area, and the geoaccumulation index (Igeo) and potential ecological risk index (RI) were used to jointly evaluate the characteristics of the HMs. Spearman's correlation and redundancy analysis revealed the main drivers of the HMs. Additionally, the positive matrix factorization (PMF) model and absolute principal component score-multiple linear regression (APCS-MLR) model were used to identify the sources of HMs. The results revealed that the pond sediments were safe for fish culture in most of the study areas. Aquafeed protein content is an important driver of HM concentrations in sediments. The total organic carbon (TOC) content, percentage of clay particles, and pH of the aquaculture pond sediments determined the sediment HMs enrichment abilities as 13.6 %, 52 %, and 9.8 %, respectively. Cd, a significantly enriched pollutant, posed a greater ecological risk than the other five HMs (Cr, Cu, Zn, As, and Pb). Three sources of HMs were identified, including agricultural activity (e.g., aquafeeds, pesticides, and fertilizers), industrial production, and natural sources, with contributions of 44.29 %, 36.66 %, and 19.05 %, respectively. This study provides a scientific basis for minimizing the input and accumulation of HMs in freshwater aquaculture pond sediments, and this can provide insights into the prevention and control of the ecological risks posed by HMs.

Seasonal variations and sources of atmospheric EPFRs in a megacity in severe cold region: Implications for the influence of strong coal and biomass combustion

Environmentally persistent free radicals (EPFRs) can pose exposure risks by inducing the generation of reactive oxygen species. As a new class of pollutants, EPFRs have been frequently detected in atmospheric particulate matters. In this study, the seasonal variations and sources of EPFRs in a severe cold region in Northeastern China were comprehensively investigated, especially for the high pollution events. The geomean concentration of EPFRs in the total suspended particle was 6.58 × 1013 spins/m3 and the mean level in winter was one order of magnitude higher than summer and autumn. The correlation network analysis showed that EPFRs had significantly positive correlation with carbon component, K+ and PAHs, indicating that EPFRs were primarily emitted from combustion and pyrolysis process. The source appointment by the Positive Matrix Factorization (PMF) model indicated that the dominant sources in the heating season were coal combustion (48.4%), vehicle emission (23.1%) and biomass burning (19.4%), while the top three sources in the non-heating season were others (41.4%), coal combustion (23.7%) and vehicle emissions (21.2%). It was found that the high EPFRs in cold season can be ascribed to the extensive use of fossil fuel for heating demand; while the high EPFRs occurred in early spring were caused by the large-scale opening combustion of biomass. In summary, this study provided important basic information for better understanding the pollution characteristics of EPFRs, which suggested that the implementation of energy transformation and straw utilization was benefit for the control of EPFRs in severe cold region.

Assessment of water quality using entropy-weighted quality index, statistical methods and electrical resistivity tomography, Moti village, northern Pakistan

In this study, twenty-two water samples were collected from boreholes (BH), and streams to evaluate drinking water quality, its distribution, identification of contamination sources and apportionment for Moti village, northern Pakistan. An atomic absorption spectrophotometer (AAS) is utilized to determine the level of heavy metals in water such as arsenic (As), zinc (Zn), lead (Pb), copper (Cu), cadmium (Cd), manganese (Mn), and ferrous (Fe). Groundwater chemistry and its quantitative driving factors were further explored using multivariate statistical methods, Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF) models. Finally, a total of eight electrical resistivity tomographs (ERTs) were acquired across i) the highly contaminated streams; ii) the villages far away from contaminated streams; and iii) across the freshwater stream. In the Moti village, the mean levels (mg/l) of heavy metals in water samples were 7.2465 (As), 0.4971 (Zn), 0.5056 (Pb), 0.0422 (Cu), 0.0279 (Cd), 0.1579 (Mn), and 0.9253 (Fe) that exceeded the permissible limit for drinking water (such as 0.010 for As and Pb, 3.0 for Zn, 0.003 for Cd and 0.3 for Fe) established by the World Health Organization (WHO, 2008). The average entropy weighted water quality index (EWQI) of 200, heavy metal pollution index (HPI) of 175, heavy metal evaluation index (HEI) of 1.6 values reveal inferior water quality in the study area. Human health risk assessment, consisting of hazard quotient (HQ) and hazard index (HI), exceeded the risk threshold (>1),indicating prevention of groundwater usage. Results obtained from the PCA and PMF models indicated anthropogenic sources (i.e. industrial and solid waste) responsible for the high concentration of heavy metals in the surface and groundwater. The ERTs imaged the subsurface down to about 40 m depths and show the least resistivity values (<11 Ωm) for subsurface layers that are highly contaminated. However, the ERTs revealed relatively high resistivity values for subsurface layers containing fresh or less contaminated water. Filtering and continuous monitoring of the quality of drinking water in the village are highly recommended.

Occurrence, source apportionment and ecological risk of bisphenol analogues in river sediments in areas with different land use patterns

Bisphenol analogues (BPs) have gained increasing attention in recent years due to their ubiquitousness and potential endocrine disrupting properties in environments. However, little information is available on their spatiotemporal distribution, source apportionment and ecological risk in river sediments, especially the case in river basins with a high population density and those typical regions with agricultural-urban gradient, where land use patterns and intensity of human activity are varying. In this study, field investigations of BPs in the sediment of the entire Qinhuai River Basin, a typical agricultural-suburban agricultural-urban gradient area, were conducted before and after the flood period. Thirty-two sites were sampled for six types of BPs, resulted in no significant difference in the concentration of ΣBPs between the two periods, with ΣBPs ranging from 3.92 to 151 ng/g and 2.16–59.0 ng/g, respectively. Bisphenol A (BPA) was the main contributor. Whereas a multivariate analysis of variance (MANOVA) suggested that the composition structure of BPs had been influenced by water periods. The land use patterns had an impact on the distribution of ΣBPs in river sediments, which was more significant in after the flood period, with ΣBPs in urban rivers was 1.85 times, 3.44 times, and 3.08 times higher than the suburban rivers, agricultural rivers, and reservoirs, respectively. Yet land use types did not significantly alter the composition structure of BPs. The correlation analysis between BPs and the physicochemical properties of sediments showed a significant positive correlation between BPA and total organic carbon (TOC). The positive matrix factorization model (PMF) suggested that BPs in sediments of the basin might be influenced by industrial coatings, textiles, electronics and biopharmaceuticals, as well as urban wastewater or solid waste generated from daily life. The ecological risk assessment posed by BPA, based on the risk quotient, indicated that the ecological risk of BPA in sediments was low for three indicator benthic organisms: crustaceans, worms, and mollusks. However, the risk of BPA in river sediments varied among different land use patterns, with the risk ranking as follows: reservoirs < agricultural rivers < suburban rivers < urban rivers.

Source Apportionment of Air Quality Parameters and Noise Levels in the Industrial Zones of Blantyre City

The increase in industrial activities has raised concerns regarding air quality in urban areas within Malawi. To assess the source apportionment of air quality parameters (AQPs) and noise levels, concentrations of AQPs (CO, TSP, PM 2.5, PM10) and noise levels were monitored at 15 sites in Makata, Limbe, Maselema, Chirimba, and Maone during dry and wet seasons, respectively. Active mobile multi-gas monitors and a Dylos DC1100 PRO Laser Particle Counter (2018 model) were used to monitor AQPs, while Integrated Sound Level Meters were used to measure noise levels. Monitoring and analysis were guided by the World Health Organization (WHO) and Malawi Standards (MS). A Positive Matrix Factorization (PMF) model was used to determine source apportionment of AQPs, and matrix trajectories analysed air mass movement. In the wet season, the average concentration values of CO, TSP, PM10, and PM2.5 were 0.49 ± 0.65 mg/m3, 85.03 ± 62.18 µg/m3, 14.65 ± 8.13 µg/m3, and 11.52 ± 7.19 µg/m3, respectively. Dry season average concentration values increased to 1.31 ± 0.81 mg/m3, 99.86± 30.06 µg/m3, 24.35 ± 9.53 µg/m3, and 18.28 ± 7.14 µg/m3. Noise levels remained below public MS and WHO standards (85 dB). Positive correlations between AQPs and noise levels were observed, strengthening from weak in the dry season to moderately strong in the wet season. PMF analysis identified key factors influencing AQPs accumulation, emphasizing the need for periodic sampling to monitor seasonal pollution trends, considering potential impacts on public health and environmental sustainability. Further studies should look at factors affecting the dynamics of PMF in Blantyre City.

A Year-Long Measurement and Source Contributions of Volatile Organic Compounds in Nanning, South China

Severe ozone (O3) pollution has been recorded in China in recent years. The key precursor, volatile organic compounds (VOCs), is still not well understood in Nanning, which is a less developed city compared to other megacities in China. In this study, a year-long measurement of VOCs was conducted from 1 October 2020 to 30 September 2021, to characterize the ambient variations and apportion the source contributions of VOCs. The daily-averaged concentration of VOCs was measured to be 26.4 ppb, ranging from 3.2 ppb to 136.2 ppb across the whole year. Alkanes and oxygenated VOCs (OVOCs) were major species, contributing 46.9% and 25.2% of total VOC concentrations, respectively. Propane, ethane, and ethanol were the most abundant in Nanning, which differed from the other significant species, such as toluene (3.7 ppb) in Guangzhou, ethylene (3.8 ppb) in Nanjing, and isopentane (5.5 ppb), in Chengdu. The positive matrix factorization (PMF) model resolved six source factors, including vehicular emission (contributing 33% of total VOCs), NG and LPG combustion (19%), fuel burning (17%), solvent use (16%), industry emission (10%), and biogenic emission (5%). This indicated that Nanning was less affected by industrial emission compared with other megacities of China, with industry contributing 12–50%. Ethylene, m/p-xylene, butane, propylene, and isoprene were key species determined by ozone formation potential (OFP) analysis, which should be priority-controlled. The variations in estimated OFP and observed O3 concentrations were significantly different, suggesting that VOC reactivity-based strategies as well as meteorological and NOx effects should be considered collectively in controlling O3 pollution. This study presents a year-long dataset of VOC measurements in Nanning, which gives valuable implications for VOC control in terms of key sources and reactive species and is also beneficial to the formulation of effective ozone control strategies in other less developed regions of China.

Pollution Characteristics and Sources of Ambient Air Dustfall in Urban Area of Beijing

Since 2016, the Ministry of Ecology and Environment and the Beijing Municipal Government have adjusted the minimum concentration limit for ambient air dustfall several times, indicating that they attach great importance to dustfall. To grasp the pollution characteristics and sources of dustfall, in this work, the filtration method was used to determine the insoluble dustfall and water-soluble dustfall in the urban area of Beijing. From our analysis, the influence of the meteorological parameters on dustfall was found, and the chemical components of dustfall were determined. The positive matrix factorization (PMF) model was also utilized to analyze the sources of dustfall. The results indicated that the average amount of dustfall in 2021–2022 was 4.4 t·(km2·30 d)−1, and the proportion of insoluble dustfall deposition was 82.4%. Dustfall was positively correlated with the average wind speed and temperature and negatively correlated with the relative humidity and rain precipitation. The impact of the meteorological parameters on insoluble dustfall and water-soluble dustfall was the opposite. The average proportions of crustal material, ions, organic matter, element carbon, trace elements, and unknown components were 48%, 16%, 14%, 1.4%, 0.20%, and 20%, respectively. The proportions of the crustal material and ions were the highest in spring (57%) and summer (37%). The contribution rates of fugitive dust source, secondary inorganic source, mobile source, coal combustion source, snow melting agent source, and other sources were 42.4%, 19.3%, 8.3%, 3.0%, 2.7%, and 24.3%, respectively. This study supported dustfall pollution control by analysing the pollutant characteristics and sources of dustfall from the standpoint of total chemical components. In order to better control dustfall pollution, control measures and evaluation standards for fugitive dust pollution should be formulated.

Isolating pollen signals from laser diode aerosol Optical Particle Counter (OPC) data through positive matrix factorization (PMF) and Unmix receptor models

Pollen, a significant natural bioaerosol and allergen for sensitized individuals, is expected to increase in prevalence due to climate change. Mitigating allergy symptoms involves avoiding pollen exposure and pre-medication, emphasizing the importance of real-time knowledge of localized ambient air pollen concentrations. Laser diode Optical Particle Counters (OPCs) are commonly used for monitoring particle number concentrations in ambient air. This study explores the hypothesis that OPCs can monitor pollen but may struggle to distinguish them from other particles. We aimed to isolate the pollen signal from collective particle number concentrations using source apportionment models, specifically Positive Matrix Factorization (PMF) and Unmix, applied to multiple bin OPC data. The pollen signals isolated using PMF show slightly better correlation values than those isolated using Unmix. PMF-derived pollen signals exhibit strong correlations with Holoptelea (r = 0.64) and total pollen (r = 0.54) concentrations, while a moderate correlation is observed with Poaceae (r = 0.47). Exclusion of low pollen events strengthens correlations for Holoptelea and Poaceae to very strong (r = 0.87) and strong (r = 0.67), respectively. Although both model types effectively isolate the pollen signal, metrics suggest that Unmix has the potential for more accurate predictions of both moderate and extreme pollen events simultaneously. The Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Relative Root Mean Square Error (RRMSE) metrics for Holoptelea are 46.2 grains m−3, 72.4 grains m−3, and 15.3; for Poaceae, 3.9 grains m−3, 4.9 grains m−3, and 13.0; and for total pollen, 43.5 grains m−3, 72.1 grains m−3, and 14.1. This study represents a significant development in the use of source apportionment models and ambient OPCs for real-time pollen monitoring, offering a cost-effective alternative to conventional automated pollen sensors. Despite challenges, the proposed methodology provides a practical and accessible solution for pollen monitoring, contributing to the advancement of bioaerosol monitoring technologies.

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.

Receptor model-based sources and risk assessment of metals in sediment of the coastal construction-oriented aquatic system in Bangladesh

Metal pollution in sediment from construction areas raises ecological and health concerns, yet source-based sediment pollution in Bangladesh remains understudied. Our investigation focused on fifteen locations in the Kohelia River and the coastal regions near the Matarbari projects (Matarbari Power Plant, Matarbari Deep Seaport), assessing metal concentrations' sources and impacts on ecology and human well-being. Sediment quality indices indicated high Cd and Cr contamination, with sites near Matarbari projects being the most polluted. The positive matrix factorization model identified three anthropogenic sources and mixed sources. Matarbari projects contributed significantly to As (67.9 %), Mn (50.25 %), Cd (48.35 %), and Cr (41.0 %), while ship-breaking yards contributed Fe (58.0 %), Zn (55.5 %), Pb (53.8 %), and Cu (36.1 %). Ecological indices showed different impacts on aquatic life from metal pollution, but cancer risk levels stayed below the threshold set by the US Environmental Protection Agency. These findings underscore the need for targeted measures to address metal pollution.

Comparative Study on Different Interpolation Methods and Source Analysis of Soil Toxic Element Pollution in Cangxi County, Guangyuan City, China

Spatial interpolation is a crucial aspect of soil toxic element pollution research, serving as a vital foundation for pollution assessment, treatment, and sustainability efforts. The selection and adjustment of interpolation methods directly influences the accuracy of spatial distribution maps and data results, thereby indirectly impacting related research. This paper conducts a comparative study of different interpolation methods and analyses the sources of soil toxic elements in the study area of Cangxi County, aiming to provide a scientific foundation for future soil management, remediation, and enhanced local sustainability. The spatial correlation of As, Cd, Hg, Mn, Pb, and Mo in 228 surface soil samples in the study area of Cangxi County is analyzed. The interpolation results, spatial distribution of OK (ordinary Kriging), IDW (inverse distance weighting), RBF (radial basis function) and the changes of pollution area after interpolation are compared. The smoothing effect is assessed based on the comparison results, interpolation accuracy, and impact on pollution assessment of OK, IDW, and RBF. The interpolation method most suitable for each metal in the study area is selected. It can be concluded that the optimal interpolation method for As, Hg, and Mn is IDW; for Cd and Mo, it is RBF; and for Pb, it is OK. After the correlation analysis of toxic elements in the soil of the study area, the PMF (positive matrix factorization) model and hotspot analysis is applied to analyzing the source of toxic elements. The analysis indicates that the predominant sources of pollution are anthropogenic, categorized into industrial activities (30.8%), atmospheric deposition caused by coal combustion and traffic exhaust (21.5%) and agricultural activities (19.5%). Natural sources, such as soil parent material, contribute to 28.2% of the pollution on average.

Rare earth elements reveal the human health and environmental concerns in the largest tributary of the Mekong river, Northeastern Thailand

The widespread application of rare earth elements (REEs) in contemporary industries and agriculture, has caused emerging contaminant accumulation in aquatic environments. However, there is a limited scope of risk assessments, particularly in relation to human health associated with REEs. This study investigated the provenance, and contamination levels of REEs, further evaluating their environmental and human health risks in river sediments from an agricultural basin. The concentrations of REEs ranged from 30.5 to 347.7 mg/kg, with showing an upward trend from headwater to downstream. The positive matrix factorization (PMF) model identified natural and anthropogenic input, especially from agricultural activities, as the primary source of REEs in Mun River sediments. The contamination assessment by the geoaccumulation index (I-geo) and pollution load index (PLI) confirmed that almost individual REEs in the samples were slightly to moderately polluted. The potential ecological risk index (PERI) showed mild to moderate risks in Mun River sediment. Regular fertilization poses pollution and ecological risks to agricultural areas, manifesting as an enrichment of light REEs in river sediments. Nevertheless, Monte Carlo simulations estimated the average daily doses of total REEs from sediments to be 0.24 μg/kg/day for adults and 0.95 μg/kg/day for children, comfortably below established human health thresholds. However, the risk of REE exposure appears to be higher in children, and sensitivity analyses suggested that REE concentration contributed more to health risks, whether the adults or children. Thus, concerns regarding REE contamination and risks should be raised considering the wide distribution of agricultural regions, and further attention is warranted to assess the health risks associated with other routes of REE exposure.

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.

PM2.5 and O3 in an Enclosed Basin, the Guanzhong Basin of Northern China: Insights into Distributions, Appointment Sources, and Transport Pathways

Aerosol samples (PM2.5) were collected in Xi’an (XN) from 11 August to 11 September 2021 and in Qinling (QL) from 14 July to 24 August 2021, respectively. In addition, ozone (O3) data were collected in order to investigate the characteristics and source areas of PM2.5 and O3 in the Guanzhong Basin (GB). The concentrations of PM2.5, organic carbon (OC), and elemental carbon (EC) in XN (53.40 ± 17.42, 4.61 ± 2.41, and 0.78 ± 0.60 μg m−3, respectively) were higher than those in QL (27.57 ± 8.27, 4.23 ± 1.37, and 0.67 ± 0.53 μg m−3, respectively) in summer. Total water-soluble ions (TWSIIs) accounted for 19.40% and 39.37% of the PM2.5 concentrations in XN and QL, respectively. O3 concentrations in summer were 102.44 ± 35.08 μg m−3 and 47.95 ± 21.63 μg m−3 in XN and QL, respectively, and they showed a significant correlation with Ox. The positive matrix factorization (PMF) model identified three main sources in XN and QL, including coal combustion source (COB), secondary aerosol (SA), and dust sources (DUSs). The potential source contribution function (PSCF) and a concentration weight trajectory (CWT) model with back-trajectory analysis showed that Inner Mongolia, the interior of Shaanxi, and nearby areas to the southwest were the sources and source areas of carbonaceous matter in XN and QL. The results of this study can contribute to the development of prevention and control policies and guidelines for PM2.5 and O3 in the GB. Furthermore, long-term and sustainable measuring and monitoring of PM2.5 and O3 are necessary, which is of great significance for studying climate change and the sustainable development of the environment.

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.

Hydrochemical characterization and assessment of health risks of trace elements in the Huai River Basin of China.

Basin water pollution is a global problem, especially in the densely populated areas. The Huai River Basin (abbreviated as HRB), including the Huai River system and the Yishu River system, is the sixth-largest and most densely populated watershed in China. However, there is a lack of comprehensive studies of river and well water throughout the Huai River basin, including hydrochemistry characterization and assessment of health risks. This study investigated water quality and pollution sources of river and well water in the HRB based on the hydrochemistry analysis and different water quality indices. The water body in the HRB showed weak alkalinity (pH = 8.4 ± 0.7) and had high TDS values (TDS = 339 ± 186mg/L) with water types of HCO3-Ca-Mg and SO4-Cl-Ca-Mg in the Huai River system, and SO4-Cl-Ca-Mg in the Yishu River system. Atmospheric input and evaporation had less impact on hydrochemistry. Evaporite dissolution and carbonate weathering had a greater impact on hydrochemistry. Carbonate precipitation and cation exchange also influenced the dissolved solutes, especially Ca2+ and Na+. Samples had low to medium salinity hazards and sodium absorption ratios and were suitable for irrigation. For drinking purposes, samples were fresh water and have good or excellent according to the Water Quality Index (WQI). Land use types influenced water quality with the worst river water quality from cropland. Combining different assessment indices, the water quality of the Yishu River system performed betterthanthe Huai River system. Absolute principal component analysis-multiple linear regression and the positive matrix factorization models identified the main pollutants as As, Ba, Cr, Ni, and Mn, with natural sources of As, Ba, and Ni and anthropogenic inputs of Cr, and Mn. Although the water quality of the HRB has improved in recent years, high potential risk from As, Cr, Mn, Ba, and Ni should be noted. This study provided vital information for basin hydrochemistry analysis and water quality assessment.

Novel approach for identifying VOC emission characteristics based on mobile monitoring platform data and deep learning: Application of source apportionment in a chemical industrial park

Refined volatile organic compound (VOC) emission characteristics are crucial for accurate source apportionment in chemical industrial parks. The data from mobile monitoring platforms in chemical industrial parks contain pollution information that is not intuitively displayed, requiring further excavation. A novel approach was proposed to identify VOC emission characteristics using the class activation map (CAM) technology of convolutional neural network (CNN), which was applied on the mobile monitoring platform data (MD) derived from a typical fine chemical industrial park. It converts a large amount of monitoring data with high spatiotemporal complexity into simple and interpretable characteristic maps, effectively improving the identification effect of VOC emission characteristics, supporting more accurate source apportionment of VOC pollution around the park. Using this method, the VOC emission characteristics of eight key factories were identified. VOC source apportionment in the park was conducted for one day using a positive matrix factorization (PMF) model and seven combined factor profiles (CFPs) were calculated. Based on the identified VOC emission characteristics, the main pollution sources and their contributions to surrounding schools and residential areas were determined, revealing that one pesticide factory (named LKA) had the highest contribution ratio. The source apportionment results indicated that the impact of the chemical industrial park on the surrounding areas varied from morning to afternoon, which to some extent reflected the intermittent production methods employed for fine chemicals.