Positive Matrix Factorization Model Research Articles

Source Apportionment and Analysis of Potentially Toxic Element Sources in Agricultural Soils Based on the Positive Matrix Factorization and Geo-Detector Models

The potentially toxic element pollution of agricultural soils has become a significant environmental threat to food safety and human health. Accurately identifying sources of potentially toxic element pollution is key to developing effective pollution prevention and control measures. In this study, regional potentially toxic element pollution of the soils in the Nanliujiang River Basin was analyzed using the positive matrix factorization (PMF) model and the geo-detector model. First, topsoil samples from the study area were collected to analyze eight potentially toxic elements in the soil, including As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn. The PMF model was used to conduct source apportionment of the potentially toxic element data and identify the primary pollution sources and their contribution rates. Then, the geo-detector model was used to analyze the key factors affecting the spatial distribution of the potentially toxic elements and the influence of natural and human factors on the distribution of the potentially toxic elements. There are four potentially toxic element pollution sources of the agricultural soil in the study area: geological background, agricultural activities, industrial discharge, and river irrigation. The geological background contributed the most. The main factors affecting the spatial distribution of potentially toxic elements included agricultural activities, industrial discharge, and river irrigation. This integrated method can analyze the formation of potentially toxic element pollution in depth from the perspectives of source apportionment and spatial differentiation and provide a scientific basis and decision support for preventing and controlling potentially toxic element pollution in agricultural soils. This study provides a new method and scientific basis for identifying and preventing potentially toxic element pollution sources in agricultural soil and can guide the formulation of targeted soil pollution control measures.

Comparative PM2.5 Pollution Characteristics and Sources in the Autumn and Winter of 2016-2021 in Handan City

In 2018, the State Council issued the Three-year Action Plan for Winning the Blue Sky Defense War （Blue Sky Defense War）. To study the characteristics and sources of PM2.5 pollution in the early stage （first stage）, middle stage （second stage）, and late stage （third stage） of the Blue Sky Defense War in Handan City in autumn and winter, PM2.5 samples were collected in the autumn and winter from 2016 to 2021. Based on the concentration data of eight water-soluble ions, EC, and OC the source analysis was performed using the positive definite matrix factorization model, backward trajectory, and potential source contribution factor （PSCF） analysis. The results showed that in recent years, the annual and autumn-winter concentrations of PM2.5 showed an overall decreasing trend, and the concentrations of water-soluble ions and total carbon decreased stage by stage during the three stages of sampling. The concentration in the third stage （52.70 μg·m-3 and 17.17 μg·m-3） decreased by 34.31% and 63.38% compared with that in the first stage （80.23 μg·m-3 and 46.89 μg·m-3）, respectively. The concentration of SNA in the three stages accounted for 46.79%, 48.86%, and 43.22% of the total concentration of PM2.5, respectively. The proportion of SOC in PM2.5 was 12.43%, 14.05%, and 6.81%, all of which first increased and then decreased. The mean of SOR in the three stages was 0.34, 0.37, and 0.36, respectively, and NOR was 0.23, 0.28, and 0.28, indicating that the secondary transformation degree was the highest during the Blue Sky Defense War. From the perspective of pollutant sources, the main sources of PM2.5 in Handan City were secondary, combustion sources, industrial sources, traffic sources, and dust sources. During the Blue Sky Defense War, the primary emissions decreased （mainly combustion sources and industrial sources）； afterward, the end of the primary emissions increased, of which the contribution rate of industrial sources increased from 12.17% to 20.20%. Moreover, the backward trajectory clustering analysis showed that the contribution rate of secondary sources in the second stage was significantly higher than that in other stages, especially the airflow from Shandong and Shanxi. The analysis of potential sources showed that the regions with high WPSCF values in the three stages were mainly concentrated in the south of Hebei and the north of Henan. The difference was that the potential source areas in the third stage shifted from the central part of Shanxi to the south compared with the first stage. This work confirms that the environmental benefits of the Blue Sky Defense War are obvious. With the development of the Blue Sky Defense War, the contribution of secondary components is prominent. After the Blue Sky Defense War, the proportion of secondary components decreased, the influence of primary components increased, and the control of direct emissions should be strengthened.

Distribution and Source-specific Ecological Risk Analysis of Soil Heavy Metals in South Hunan Province

To identify the spatial distribution patterns and assess the ecological risks associated with soil heavy metal pollution in the southern region of Hunan Province, a total of 362 surface soil samples were collected from the studied area. This study employed multivariate statistics and geographic information systems （GIS） to investigate the spatial distribution pattern of soil metals （Cd, Hg, As, Pb, Zn, Ni, Mn, Tl, and Sb）. Furthermore, the pollution sources and source-specific ecological risk of heavy metals were quantified by combining the positive matrix factorization （PMF） model and the comprehensive ecological risk index model. The results suggested that the mean concentration of soil metals in the study area exceeded the mean background values. Specifically, Cd, Hg, Pb, and Sb showed significant pollution levels and substantial spatial variability, with their concentrations reaching the background values by factors of 8.31, 9.12, 5.09, and 2.79, respectively. The spatial distribution of these heavy metals exhibited discernible patterns, with a higher concentration observed in western Chenzhou and a noticeable trend of increasing concentrations from the central region towards the periphery. The clusters with high concentrations of heavy metals were predominantly situated near industrial zones engaged in black metal smelting and processing and nonferrous metal mining and processing enterprises. Source apportionment indicated that the primary sources of soil heavy metals in the area were industrial activities associated with black metal mining and metallurgy （29.06%）, atmospheric deposition （22.05%）, nonferrous metal mining and processing （20.65%）, a combination of industrial and transportation sources （14.73%）, and natural sources （13.50%）. Moreover, the source-ecological risk assessment model specifically identified Cd and Hg as crucial elements requiring prioritized control measures to mitigate soil ecological risks. Industrial activities, including black metal smelting and processing and atmospheric deposition sources, were identified as the priority sources for health risks in the study region. These results provide an important theoretical basis for the development of effective strategies aimed at managing and diminishing the risks associated with heavy metal contamination in the southern region of Hunan Province.

Implications of photochemical losses of VOCs: An integrated approach for source apportionment, ozone formation potential and health risk assessment.

The increasing ozone (O3) concentration has received significant attention recently, yet the health risks posed by volatile organic compounds (VOCs) cannot be ignored. Accurately identifying the primary sources of VOCs contributing to health risks and O3 formation has been challenging due to their high reactivity with oxidants in ambient air. This study conducted field measurements of VOCs seasonally and diurnally in an urban area of central Taiwan, aiming to elucidate the effects of photochemical loss of VOCs on the source apportionment of O3, as well as health risks of VOCs under different levels of O3. The results revealed that O3 formation was sensitive to VOCs, which was diagnosed using the regional threshold of the observed VOCs/NOX ratio and was further supported by a significant positive correlation between O3 concentrations and initial O3 formation potential. The dispersion normalized positive matrix factorization model, applied to initial mixing ratios of VOCs, identified six VOC sources, with the synthetic rubber industry and solvent usage being prominent contributors to O3 formation potential. A source-attributed health risk assessment approach was developed that incorporates the effects of photochemical losses and observed mixing ratios of VOCs, enabling a more accurate evaluation of health risks from different sources. Non-carcinogenic risks associated with VOC sources remained within acceptable thresholds, while the carcinogenic risks posed by vehicle exhaust and solvent usage were above acceptable levels, particularly on O3 non-polluted days. This study highlights the importance of establishing concurrent control strategies for VOCs and O3 to effectively mitigate air pollution and improve public health.

Chemical characterization and source identification of PM2.5 in the Huaxi urban area of Guiyang

In 2020, 123 PM2.5 samples were collected across different seasons in Huaxi District, Guiyang. The primary chemical components of PM2.5, including water-soluble ions (WSIIs), metallic elements, organic carbon (OC), and elemental carbon (EC), were analyzed. During the sampling period, the average PM2.5 concentration was 39.7 ± 22.3 µg/m2. Chemical mass closure (CMC) was used to reconstruct PM2.5 mass, yielding a reconstructed concentration of 29.1 ± 16.5 µg/m2. The major components were organic matter (OM), sulfate + nitrate + ammonium (SNA), and mineral dust (MD), with mean concentrations of 12.2 ± 6.3 µg/m2, 8.2 ± 4.0 µg/m2, and 6.3 ± 4.6 µg/m2, respectively. From clean days (CD) to lightly-moderately polluted days (LMPD), nitrate oxidation ratio (NOR) increased from 0.09 to 0.16, while sulfate oxidation ratio (SOR) and OC/EC ratio rose by 21.7% and 13.5%, indicating stronger secondary reactions on polluted days. The study also examined changes in chemical components under different atmospheric oxidizing and humidity conditions, revealing that sulfate and nitrate concentrations increased with relative humidity (RH) between 60 and 80%, while other components, especially MD, showed a declining trend due to hygroscopic growth and subsequent gravitational settling and precipitation. The average NO3−/SO42− ratio was 0.67, indicating that fixed sources such as industrial and coal emissions were the main contributors to PM2.5. This study provides insights into the chemical composition, pollution processes, and formation mechanisms of PM2.5, which are crucial for developing effective air pollution control strategies. Furthermore, source apportionment was conducted with the positive matrix factorization (PMF) model. The Coal combustion, secondary, traffic, Industrial and dust source contributions to the PM2.5 mass were approximately 30.5%, 20.0%, 18.3%,16.7% and 14.5%, respectively.

Distribution Characteristics and Formation Mechanism of Main Hydrochemical Ions in Qingshui River

Qingshui River is a vital source for human life and industrial production in Zhangjiakou City. Determination of the formation mechanism of the main hydrochemical ions is important for the sustainable development and utilization of surface water resources in the Qingshui River. In view of this, 20 surface water samples were collected from the agricultural and urban reaches of the Qingshui River in July 2022. Based on the detection of hydrochemical ions and stable isotopes （δ2H-H2O and δ18O-H2O）, the hydrogeochemical methods, multivariate statistical analysis, and positive matrix factorization model （PMF） were used to comprehensively understand the chemical formation mechanism of surface water from qualitative to quantitative perspectives. The results showed that the average pH value in the Qingshui River was 8.66, indicating weakly alkaline water. The average concentrations of cations and anions decreased in the orders of Ca2+ > Na+ > Mg2+ > K+ > NH4+ and HCO3- > SO42- > NO3- > Cl- > F- > NO2- in the river water, respectively. The main hydrochemical type was identified as HCO3-Ca·Mg water. For the natural background, surface water was mainly controlled by rock weathering and evaporation crystallization and rock weathering was identified as the primary driving factor. The main hydrochemical compositions in the Qingshui River were derived from the dissolution of carbonate rocks and silicate rocks. Regarding anthropogenic activities, the portions of river water in the agricultural reaches were mainly affected by the excessive application of chemical fertilizers. Furthermore, the concentration of NO3- （1.88-47.4 mg·L-1） exceeded the standard for drinking purposes （10 mg·L-1）. The concentration of F- （0.38-1.92 mg·L-1） was above the acceptable limit for drinking purposes （1.0 mg·L-1） in the southern urban reaches portion of the river water, which could have been attributed to the industrial sewage discharge. The obtained results from the PMF model showed that the hydrochemical compositions in the river water of agricultural reaches were mainly controlled by four factors, namely synthetic fertilizers （29.6%）, livestock manure （16.4%）, rock weathering （17.4%）, and natural geology （13.7%）, while hydrochemical compositions in the river water of urban reaches were mainly affected by the domestic pollutants （30.5%）, industrial discharges （20.1%）, natural geology （18.4%）, and rock weathering （16.7%）.

Characteristics and Source Apportionment of Ambient Volatile Organic Compounds in Shijiazhuang High-tech Industrial Development Zone in Autumn

Utilizing the online monitoring data of volatile organic compounds （VOCs） and ozone （O3） in the Shijiazhuang high-tech industrial development zone during October 2020, the pollution characteristics of VOCs were analyzed. Subsequently, ·OH estimation, secondary organic aerosol formation potential （SOAP）, and ozone formation potential （OFP） were applied to assess the chemical reactivity of VOCs. Additionally, the source apportionment was identified using the positive matrix factorization （PMF） model. The results revealed that the hourly φ[total VOCs （TVOCs）] were （11-281）×10-9, and the components were ranked by volume fraction in the following order： oxygenated volatile organic compounds （OVOCs） （60.83%）, alkanes （19.98%）, halohydrocarbons （5.64%）, aromatics （5.91%）, alkenes （4.83%）, and alkynes （2.48%）. Although the VOC concentration levels posed negligible non-carcinogenic risks to human health, benzene and ethylbenzene presented carcinogenic risks. The m/p-xylene to ethylbenzene ratio （X/E） varied from 2.5 to 3.4, indicating short-range transport of fresh air masses. Based on the X/E value, the ·OH concentration was 1.75×106 molecule·cm-3, and the ·OH exposure from 07：00 to 14：00 was calculated as 3.77×1010 molecule·s·cm-3. Aromatics played a dominant role in SOA, with a contribution rate as high as 93.35%, while alkenes dominated O3 formation, with the OFP contribution rate reaching up to 46.79%. By applying the PMF model, five major VOC sources were identified, including industrial sources （37.20%）, process flow and solvent use （33.80%）, vehicle and oil/gas evaporation sources （15.47%）, combustion sources （8.03%）, and plant emissions （5.50%）. Industrial and process emissions emerged as the primary contributors to VOCs in the ambient air of the high-tech zone, necessitating targeted control measures within this region.

Risk Assessment of Heavy Metals in Soil Surrounding Manganese Tailings Pond Based on Source-oriented Analysis

To analyze the source apportionment, potential ecological risk, and health risk of heavy metals in soils surrounding a manganese tailings pond in Chongqing, a positive matrix factorization （PMF） model, potential ecological risk index, and health risk assessment model were used. Further, all three models were combined to explore the risks of heavy metals in soils by different pollution sources to determine the priority control factors. The results showed that except for the Cr concentration, the average values of Mn, Cd, As, Pb, Cu, Zn, and Ni concentration were higher than their corresponding background values. Using the PMF model analysis, mining, natural, agricultural, and industrial sources were identified as the determinants for the accumulation of heavy metals in soils, with contribution rates of 23.9%, 30.2%, 18.8%, and 27.1%, respectively. Using potential ecological risk index analysis, the ecological risk was predominantly categorized as "strong" risk, with the rate of 77.8%. The health risk assessment model analysis revealed that, the carcinogenic risks of adults and children were tolerable, whereas the non-carcinogenic risks were acceptable. According to the analysis results of the relationship among heavy metals, pollution sources, potential ecological risk, and health risks showed that agricultural sources were identified as the priority control sources, with Cd as the primary control factor. Mining sources and industrial sources were identified as secondary control sources, with Mn and As as the secondary control factors.

Insights into relationship of oxidative potential of particles in the atmosphere and entering the human respiratory system with particle size, composition and source: A case study in a coastal area in Northern China.

Oxidative potential (OP) of particulate matter (PM) is an important indicator of its health effects. However, the relationship between OP and its key influencing factors remains unclear. In this study, size-segregated PM samples were collected in Qingdao, China, with major components and OP of PM thoroughly examined. The PM composition and sources contributing to OP were determined by hierarchical cluster analysis and positive matrix factorization model, and deposition of size-segregated and source-specific PM in respiratory tract and its resulting OP were assessed by multiple path particle dosimetry model. Dithiothreitol (DTT) activity decreased with increase of particle size in winter, while larger particles (4.2-10.2 μm) also contributed significantly to OP in summer. WSOC strongly correlated with OP in different particle sizes, while water-soluble iron, zinc, lead, and manganese had strong correlations with DTT activity for fine particles, reflecting the co-effects of particle composition and size on OP. Coarse and fine particles were more likely to be deposited in head and pulmonary region, respectively, with traffic and industrial sources contributing significantly to the deposited OP, especially in deeper regions of respiratory tract. This study highlights that the combined effects of different factors on PM OP need to be considered in health-oriented pollution abatement.

Insights into the source characterization, risk assessment and ozone formation sensitivity of ambient VOCs at an urban site in the Fenwei Plain, China

The ground-level O3 concentration has shown a deteriorating trend in the Fenwei Plain of China, which poses a greater challenge for formulating control strategies of O3 precursor (VOCs). To accurately control VOCs sources and effectively reduce O3 concentration from a seasonal perspective, online monitoring of 114 VOCs was conducted at Yuncheng Middle School Station from January 1, 2021 to December 31, 2021. The VOCs concentration showed a seasonal variation with the highest in winter and the lowest in summer. During the four seasons, alkanes (34.5–41.7 %) and OVOCs (36.6–46.9 %) were the most abundant species. The emission ratios of specific VOCs species indicated that vehicular exhaust, industrial source, and combustion were the major VOCs sources. The Positive Matrix Factorization (PMF) model identified that industrial source and secondary conversion were the main contributors in summer, while combustion and LPG/NG contributed more significantly in winter. The 2021-based VOCs emission inventory showed that the total VOCs emissions in the central urban area of Yuncheng was 8128.8 t, in which industrial process was the largest contributor. Alkanes, aromatics, and OVOCs accounted for 31.0 %, 25.8 %, and 25.7 % of the annual VOCs emission, respectively. In addition, the calculated relative incremental reactivity (RIR) values of O3 precursors demonstrated that alkenes and aromatics were the most sensitive groups to O3 formation during the four seasons. The ambient VOCs posed the non-carcinogenic risk across all seasons, which can be attributed to acrolein and three main sources (industrial source, secondary conversion, and combustion). However, ambient VOCs exposed to definite carcinogenic risks due to the appearance of 1,2-dichloroethane, 1,2-dichloropropane, and benzene, and the main risks arose from industrial source, vehicular exhaust, and solvent usage. These findings emphasize the necessity of undertaking scientific and systematic measures for priority species and control sources of VOCs emission.

Combination of Multiple Isotopes and PMF Model Provide Insights Into the Method Optimization of PM2.5 Source Apportionment During Haze Episodes

AbstractThe key problems with Positive Matrix Factorization (PMF) model for PM2.5 source apportionment were inconsistent results with different species selections and a lack of evaluation criteria for results accuracy. Moreover, high proportions of secondary inorganic aerosols sources (SNA) were identified by PMF without corresponding primary sources. This study develops a new method that combines multi‐isotopes (34S, 15N, 18O and 14C) and PMF model to optimize source apportionment. Data sets A–F, constructed from PM2.5 components, were input into PMF model to obtain optimal results (3–9 factors), which changed with the selection of species. Specifically, the contributions of coal combustion (CC, 3%–36%), biomass burning (BB, 11%–38%), and vehicle sources (VS, 4%–15%) showed significant differences in data sets, indicating that conventional methods cannot obtain accurate results. Then, 15N, 34S, 18O were introduced to restrict and reallocate identified SNA sources to primary sources, overcoming the influence of species on results. Additionally, 14C was used to evaluate data sets results, which showed that the combination of PMF model with more markers (data set F, 9‐factor) and multi‐isotopes techniques obtained optimized results that aligned with 14C results. Compared with the initial results, the contributions of CC, VS, and BB in the allocated 9‐factor increased by 26.4%, 5%, and 19.5%, respectively, becoming main sources of PM2.5. This study represents the first time that combination of PMF model and multi‐isotopes achieves SNA sources reapportionment and results evaluation, improving source apportionment methods.

EPA-Priority polycyclic aromatic hydrocarbons in Ashtamudi Ramsar wetland, southwest coast of India: Perspectives from the PMF modelling, health risk evaluation, and ERA technique

A detailed study was conducted on the spatial distribution, ecological hazards, identification of sources and evaluation of health risks posed by polycyclic aromatic hydrocarbons in the Ramsar coastal wetland near an urbanized area in Kollam district, Kerala, employing diverse environmental indices and methodologies. Furthermore, positive matrix factorization (PMF) and statistical methods were employed to assess the relationships among PAHs and identify their potential source. PAHs quantified in sediments showed moderate levels of pollution, with compounds ordered as Naf > Phe > Chr > BaP > Py > BbF > Fl > BkF > IcP > Bper > BaA > Flu > DbA > An > Acy > Ace, primarily originating from biomass combustion and traffic emissions (34.2 %), burning of coal (44.8 %) and petrogenic sources (21 %), adding to the overall sum of 16 PAHs. Ecological risk assessment (ERA) methods indicate that the study area poses low to moderate ecological risks. Although certain PAHs are carcinogenic, there is a limited information about their cancer risk via marine sediments, despite coastal regions being vital for world food supply. The investigation revealed that nearly all sediment samples contaminated by PAHs exceeds the USEPA's proposed ingestion route threshold (10⁻⁶), showing substantial cancer risks. The non-carcinogenic hazard index (HI) evaluation suggests that ingesting sediments from the wetland can lead to non-cancer risks for both adults and children. These conclusions are vital for developing strong environmental management practices for coastal zone of Kollam, where many livelihoods rely on these areas, and they aid efforts to move away from using petroleum-derived fossil fuels to eco-friendly fuel options like CNG (Compressed Natural Gas).

Local contributions and climate change effects on organochlorine pesticide levels in soil and sediments in Svalbard

The Arctic region, including Svalbard, faces unique environmental challenges from the presence and persistence of organochlorine pesticides (OCPs), pollutants known for their long-range atmospheric transport and potential local sources. In Svalbard, the melting of sea ice and glaciers due to climate change may release OCPs trapped over decades, while human activities in the area could contribute additional local contamination. This study aimed to identify and quantify different sources of OCPs in soil and marine sediments at Svalbard. Samples were collected from Kongsfjorden, Rijpfjorden, and in Ny-Ålesund. The concentrations of 23 OCPs in sediments sampled were in the range of 0.36–0.90 ng/g, which were lower than those in the soils from Ny-Ålesund (0.28 ng/g to 3.6 ng/g). The highest OCP levels were detected at locations near the research station in Ny-Ålesund, where local contamination from research activities, mining, and dumpsites could occur. Hexachlorobenzene (HCB) were the most prominent compound, followed by various DDTs and HCHs. Dignostic ratios and the Positive Matrix Factorization (PMF) model were employed to determine the primary sources of OCPs. The results from modeling showed that historically used pollutants were the primary contributor, accounting for 90% of OCPs present, while recently input OCPs were a minor contributor. However, newly input pollutants significantly contributed to HCHs (43%). It is suggested that the contribution of legacy OCPs mainly comes from the melting of sea ice and glaciers. This was especially true for Rijpfjorden (95%), while it was also significant for Kongsfjorden (55%). The local contamination and fresh inputs played a substantial role in the area near the research station in Ny-Ålesund. The study emphasizes the importance of secondly source, especially the role of melting sea ice and glaciers as well as local contaminations as sources of OCPs in Svalbard's marine sediment, which highlight the urgent need to address the impact of climate change on the Arctic environment.

Chemical composition, multiple sources, and health risks of PM2.5: A case study in Linyi, China's plate and logistics capital

Elucidating the chemical composition, sources, and health risks of fine particulate matter (PM2.5) is crucial for effectively preventing and controlling air pollution. This study collected PM2.5 samples in Linyi from November 10, 2021, to October 15, 2022, spanning the period of the 2022 Winter Olympics and Paralympics. The analysis focused on seasonal variations in the chemical composition of PM2.5, including water-soluble ions, inorganic elements, and carbonaceous aerosols. Results from the random forest model indicated that control measures during the Olympics and Paralympics reduced PM2.5 concentrations by 21.5% in Linyi. Organic matter was the dominant component of PM2.5, followed by NO3−, SO42−, and NH4+. Among secondary inorganic ions, SO42− exhibited the highest concentration in summer, while NO3− and NH4+ showed the lowest concentrations. The inorganic elements S, K, Fe, and Si had high mean annual concentrations, underscoring the need for targeted control measures for plate production, bulk coal burning, and biomass combustion in Linyi. The organic carbon (OC) to elemental carbon ratio (17.7–20.5) in Linyi was high, highlighting the importance of addressing secondary OC pollution. According to the positive matrix factorization model, coal burning, and the secondary formation processes of sulfate and nitrate were the dominant sources of PM2.5. Backward air mass trajectories revealed substantial contributions from the southeastern, local, and southwestern regions of Linyi. This suggests the need for enhanced regional joint prevention and control efforts between Linyi and neighboring cities, such as Rizhao and Jining in Shandong Province, as well as northern cities in Jiangsu Province. The highest non-carcinogenic and carcinogenic risks (CRs) were associated with As. coal burning posed significant noncarcinogenic risks and a moderate CR, contributing 41.7% and 44.0% of the total health risk, respectively. These findings are crucial for developing effective air pollution prevention and control strategies.

Heavy metal(loid)s pollution in soils of a typical agricultural and rural area: Source apportionment and derived risk quantification.

In light of the pervasive contamination of soils with heavy metals (HMs) on a global scope, the precise identification and assessment of sources and areas of contamination are crucial for the effective management and utilization of land. This study combines several models to quantitatively apportion possible sources of HMs and environmental risks in agricultural and rural areas of Langfang City, Hebei Province. The findings indicate that the accumulation of HMs is influenced by human activities, with varying degrees of accumulation observed. The Positive Matrix Factorization (PMF) model identified five sources, with the order of magnitude of their contributions to the soil HMs content being as follows: coal combustion source (24.9%), industrial activities source (23.1%), agricultural activities source (19.2%), natural source (16.6%), and traffic emissions source (16.3%). The ecological risks present in the study area are not significant; however, the human health risks exceed the acceptable limits. The primary sources of pollution that pose a risk to human health are industrial, followed by natural source. As, Cr and Ni were responsible for the non-carcinogenic risk, while Cr was the dominant factor in the carcinogenic effect. The risk distribution map indicates that the areas exhibiting the highest risk are situated in the east-central region of the study area. This study had identified the priority control factors (including HMs, sources of pollution and high-risk areas) for the use and management of the land.

Unravelling integrated groundwater management in pollution-prone agricultural cities: A synergistic approach combining probabilistic risk, source apportionment and artificial intelligence

Groundwater is vital for agricultural cities, but intensive farming and fertilizer use have increased contamination risks, particularly for non-carcinogenic health hazards. This study reveals the sources of contaminants in groundwater, their health impacts, and targeted strategies in such cities. The study analyzed 115 groundwater samples, with the main groundwater chemical type being HCO₃-Na·Ca. Significant exceedances were found in Mg²⁺, HCO₃−, F−, total hardness (TH), and Mn, with HCO₃− and Mg²⁺ surpassing standards in nearly all samples. The average Comprehensive Environmental Water Quality Index (CEWQI) was 100.68, indicating that overall groundwater quality in the study area is good. High-quality water is mainly found near reservoirs and rivers, while urban and eastern regions have relatively poorer water quality. The proportion of groundwater unsuitable for drinking is low. Monte Carlo risk assessments revealed that F− and NO₃− pose non-carcinogenic risks to both adults and children, with NO₃− presenting a higher potential health risk. The Positive Matrix Factorization (PMF) model identified that groundwater pollution primarily results from natural geological processes and human activities, with agriculture being the major anthropogenic factor. AI-based zoning strategies highlighted industrial areas and high-fluoride zones as critical areas requiring enhanced prevention and control measures.

Improvements in source apportionment of multiple time-resolved PM2.5 inorganic and organic speciation measurements using constrained Positive MatrixFactorization.

The equation of Positive MatrixFactorization (PMF) has been modified to resolve multiple time resolution inputs and applied in numerous field studies. The refined modeling results provide a solution with an increased number of factors and enriched profile features. However, the incorporation of low time-resolved data may retrieve unfavorable mixed factor profiles, introducing high uncertainties into the PMF solution computations. To address this issue, a dual-stage PMF modeling procedure with predefined constraints was proposed. Multiple time-resolved PM2.5 inorganic and organic speciation measurements were collected from autumn of 2022 to summer of 2023 in Taipei, Taiwan. Without using the proposed approach, a mixed factor of vehicle/biomass burning and an unphysically meaningful factor of sodium ion- and ammonium ion-rich were identified. After implementing the proposed approach, a refined number of eight factors with separated and reasonable profiles were retrieved. Over the sampling period, the largest contributor to PM2.5 and organic carbon was vehicle (contribution = 26% and 47%, respectively), while those for secondary inorganic aerosols of SO42-, NO3-, and NH4+ were industry (27%, 25%, and 31%, respectively), highlighting the importance of regulating these two sources. The low vehicle contribution to NO3- may be due to time-lag effects from gas-to-particle conversion, which led to different temporal patterns between NO3- and primary species. Addressing this issue is crucial in future studies for better apportionment of secondary aerosols.

Characteristics, Sources, and Health Risk Changes of Heavy Metal Pollution Carried by PM2.5 in Beijing and Evaluation of Policy Effectiveness

To evaluate the effectiveness of air pollution prevention and control measures in Beijing, this study measured the content of 13 metal elements, including seven heavy metal elements [As, Cd, Co, Cr（Ⅵ）, Ni, Pb, and V], through daily PM2.5 sampling （n = 934） in the urban area of Beijing for four years. We analyzed the interannual changes in the concentration levels of various metals and the differences between heating and non-heating seasons, used a positive definite matrix factorization （PMF） model for quantitative source analysis, and used health risk assessment methods to evaluate the health risks of six metals. The results showed that, except for a few metal elements such as Cr（Ⅵ） and Ni, which showed an increase in concentration in individual years, the overall concentration of each element showed a downward trend. The concentrations of ten metals, including V, Co, Pb, and Mn, during the heating season were significantly higher than those during the non-heating season （P<0.05）. There were five main types of atmospheric heavy metal pollution sources in Beijing： dust sources, transportation sources, coal sources, industrial sources, and fuel oil combustion sources. Among them, the proportion of coal sources was generally decreasing. The HQ values of each metal were all less than 1, indicating no non-carcinogenic health risk. The carcinogenic risk of Ni and Cd could be ignored, while the R values of As, Co, and Cr（Ⅵ） in each year were between 10-6 and 10-4, indicating a certain carcinogenic risk. The interannual trends in atmospheric concentration, sources, and health risks indicate that the relevant measures for air pollution prevention and control in Beijing have achieved positive results.

Characterization and source apportionment of heavy metal pollution in soil around red mud disposal sites using absolute principal component scores-multiple linear regression and positive matrix factorization models.

In recent years, industrial waste and agrochemicals have reduced soil fertility and productivity, significantly impacting food security and ecosystems. In China, areas near red mud deposits from the aluminum industry show severe heavy metal contamination. This study examines agricultural soil near a red mud site in Shanxi Province, analyzing Cd, Cr, Hg, Ni, Pb, As, Cu, and Zn levels and distribution. Geostatistical methods and GIS are utilized to assess heavy metal pollution using the single factor index, the Nemerow integrated index, and the Hakanson potential ecological risk index. Absolute Principal Component Scores-Multiple Linear Regression (APCS-MLR) and Positive Matrix Factorization (PMF) models are used for quantitative analysis of pollution sources. Research indicates that the average concentrations of eight heavy metals exceed the natural background values of Shanxi, placing them at a severe pollution level with moderate ecological risk. Specifically, indices for As, Pb, and Cr are 3.79, 3.38, and 3.26, indicating severe pollution; Cd, Cu, and Hg at 2.36, 2.62, and 3.00 suggest moderate pollution; Ni at 1.87 shows mild pollution, while Zn at 0.97 is not polluted. Hg presents the highest ecological risk with a coefficient of 120.00, followed by Cd (70.69) and As (37.92). Spatial analysis shows significant correlations among Pb, Zn, Cu, and Ni, while Cr, Cd, Hg, and As show greater variability and weaker correlations. Both models identify five main sources: industrial activities, agricultural fertilizers, red mud leachate, energy combustion, and natural geological backgrounds, with respective contribution rates in the APCS-MLR model at 27.7%, 24.6%, 18.1%, 15.2%, and 14.4%, and in the PMF model at 29.2%, 21.5%, 16.9%, 16.7%, and 15.7%. This study offers a scientific basis for controlling soil pollution in the region, filling a literature gap.

National investigation of bisphenols in the surface soil in China

The long-term production and extensive use have resulted in the widespread presence of bisphenols (BPs) in the environment. In order to clarify the pollution characteristics and potential sources of BPs, the national scale surface soil samples were collected in China in 2019. The results demonstrated that 32 target BPs existed widely in soil with the highest concentration for bisphenol A (BPA), and at least 2 BPs were detected in each sample. The total concentration of Σ32BPs (from 0.387 to 713 ng/g) exhibited a stepwise decrease from southeastern coast to inland regions, and due to the presence of more pollution sources concentrations of Σ32BPs in urban areas were slightly higher than rural areas. The different industrial structures (such as plastics, epoxy resins, and thermal paper) may be the important factors for the different pollution levels between southeastern coast and other regions. In addition, the high organic matter content in soil and low temperature may be the reasons for high concentrations of Σ32BPs in Northeast China. The results of source identification using the Positive Matrix Factorization model indicated that BPs in soil were originated from three sources: old sources represented by BPA, relatively new sources characterized by bisphenol S, and sources from specific industries using bisphenol F. The estimated daily intake indicated that BPA exposure through soil accounted for only a small proportion of the total exposure compared to other exposure routes, and the risk by BPA in soil can be negligible to human health. In summary, the study provided basic pollution information of BPs in Chinese surface soil for future related studies.