Positive Matrix Factorization Model Research Articles

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.

Distribution and sources of polycyclic aromatic hydrocarbons in cascade reservoir sediments: influence of anthropogenic activities and reservoir hydrology.

The construction of dams has caused disruptions to river connectivity, leading to alterations in the deposition of hydrophobic organic contaminants in reservoir sediments. Further investigation is warranted to explore the impact of cascade reservoirs with differing hydrological characteristics on polycyclic aromatic hydrocarbons (PAHs) distribution in sediment. This study examines the presence of 30PAHs in the sediments collected from six cascade reservoirs situated in the Wujiang River basin during January and July 2017. The results showed that Σ30PAHs ranged from 455-3000ng/g dw (mean 1030ng/g dw). Anthropogenic activities and reservoir hydrology determined the distribution trend of PAHs in sediments, with an overall increase from upstream to midstream and then a decrease downstream. The PAH levels were highly linked to the secondary industry (P < 0.05). This was further supported by the relationship between the PAH emissions from coal combustion and traffic sources analyzed by the positive matrix factorization model and economic parameters in the wet season (P < 0.01). At the same time, reservoir age (RA) showed a positive correlation with PAH concentrations (P < 0.05), while hydraulic retention time (HRT) exhibited a negative correlation with PAH levels (P = 0.03). The relationship between total organic carbon (TOC) and PAHs in stream sediments worldwide was nonlinear (P < 0.01), with PAH concentrations initially rising and then falling as TOC levels increased. Concerns regarding carcinogenic risk were raised due to contributions from coal and vehicular sources, with the risk increasing with RA.

Risk assessment and sources associated with potentially toxic elements in suspended particulate matter: A karst river perspective in active mining area

Study regionDaiyang River Basin, a typical karst river basin impacted by mining activities in Guizhou, Southwest China. Study focusRivers are fundamental components of regional water security, but they are facing potentially toxic elements (PTEs) pollution caused by mining activities. This study investigated the concentration, behavior, risks and sources of nine PTEs in suspended particulate matter (SPM) from mining-impacted karst rivers, which are essential for the safety and management of karst water environments. New hydrological insights for the regionThe contents of Cr, Ni, Cu, Zn, As, Cd, Sb and Hg exceeded the corresponding local soil background values, with Zn and Cu being the most important pollutants. These elements caused a very high potential toxicity risk to the basin and unacceptable carcinogenic and non-carcinogenic risks to the local residents. Correlation analysis and the positive matrix factorization (PMF) model indicated that Ni, Cd, Zn and Cu were mainly derived from mixed sources of geological background and anthropogenic activities (30.95 %), Hg, Sb, and As were related to coal mining and combustion sources (28.91 %), while Pb, As, Cr and Sb were mainly contributed by natural sources (40.15 %). Furthermore, mixed sources, mining-related sources and As were identified as priority control factors in the study area. These insights can provide powerful support for decision-makers to develop control policies and reduce PTEs pollution in karst areas.

Characteristic of PM2.5 concentration and source apportionment during winter in Seosan, Korea

Seosan is a concentrated industrial complex in the midwestern region of Korea. A study was conducted from December 2020 to January 2021, measuring PM2.5 and chemical components in Seosan using online instruments every hour. The concentration of PM2.5 during the winter season was 31.4±17.8 μg/m3, exceeding the national ambient air quality standard of Korea. The mass fraction of organic matter, elemental carbon, three major ions, five minor ions, crustal elements, and trace elements in PM2.5 accounted for 24.5%, 4.36%, 32.0%, 2.82%, 4.11%, and 5.17% of the total PM2.5 mass concentration, respectively. Source identification was conducted using positive matrix factorization modeling, revealing eight sources of PM2.5: Secondary inorganic aerosol (SIA), vehicle exhaust, industry, coal combustion, biomass burning/incinerator, oil combustion, soil, and aged sea salt. Source contributions varied during high pollution episodes (HPE), with SIA dominating in HPE1 and soil and aged sea salt in HPE2. The potential source contribution function and conditional probability function were utilized to estimate the potential local and regional emission areas for the identified sources. In Seosan, vehicle exhaust and biomass burning/incinerator were primarily influenced by local sources. SIA, industry, and oil combustion sources were significantly affected by short-range transport from eastern China. Soil and aged sea salt, which exhibited high contributions during HPE2, were associated with long-range transport from Inner Mongolia. Coal combustion was attributed to both local sources, particularly large industrial complexes near Seosan, and long-range transport from Northeast China and Inner Mongolia.Graphical

Spatial distribution, source apportionment, and health risks assessment of trace elements in pre- and post-monsoon soils in the coal-mining region of North Karanpura basin, India

Coal mining activities in the North Karanpura basin have significantly increased the trace element (TE) concentrations in the soil, resulting in soil pollution and potential health risks. To assess this, 113 soil samples, along with coal, shale, and overburden rocks, were collected from open-cast mining areas during pre-monsoon (Pre-M) and post-monsoon (Post-M) seasons. Seasonal analysis revealed higher TE concentrations in the Post-M period, especially in the SE direction, followed by NE and NW, likely due to surface runoff and deposition, demonstrating temporal variability in TE distribution which corroborated from the spatial distribution maps. Positive matrix factorization (PMF) model identified four factors: mixed sources (F1Pre-M: 37.6 %; F4Post-M: 28.9 %), coal-fired emissions (F2Pre-M: 20.5 %; F3Post-M: 26.0 %), overburden rocks (F3Pre-M: 25.5 %; F2Post-M: 16.7 %), and agricultural and lithogenic origin (F4Pre-M: 16.4 %) during the Pre-M period, attributed to coal mining. Post-M sources were similar, but agricultural and lithogenic origins were replaced by atmospheric deposition (F1Post-M: 28.4 %), enhanced by monsoon effects. Carcinogenic risk assessment revealed that As, Cr, and Ni exceeded acceptable levels for children via ingestion, though adults remained within safe limits. Inhalation and dermal contact were also considered, but ingestion posed the highest risk. The hazard index (HI) via ingestion showed that children had an HI of 1.6 in Pre-M, increasing to 2.66 in Post-M, highlighting their potential vulnerability to non-carcinogenic risks, while adults stayed within safe limits. The expansion of mining areas in the study region led to decrease in vegetative areas which could affect agriculture and local communities, raising a comprehensive environmental and public health issues. These results underline the need for implementing effective biannual soil monitoring and mitigation strategies, such as phytoremediation, bioremediation, rock dust remediation, chemical amendments and improved waste management, to reduce TE contamination.

Enhanced Assessment of Water Quality and Pollutant Source Apportionment Using APCS-MLR and PMF Models in the Upper Reaches of the Tarim River

To evaluate the pollution sources and dynamics of the upper reaches of the Tarim River, 10 typical sampling points were selected, and 23 water quality parameters from 2020 to 2022 were analyzed using one-way analysis of variance, the comprehensive Water Quality Identification Index (WQI), and Principal Component Analysis (PCA). The pollution status, sources, and contribution rates of water quality were investigated using the Absolute Principal Component-Multiple Linear Regression Model (APCS-MLR) and Positive Matrix Factorization (PMF). The results indicated that the water quality parameters of dissolved oxygen (DO), chemical oxygen demand (CODMn), biochemical oxygen demand after 5 days (BOD5), total nitrogen (TN), total phosphorus (TP), fluoride ions (F-), and ammonia-nitrogen (NH3-N) in the upper reaches of the Tarim River exceed standards, with noticeable spatial variations observed for each parameter. The water quality evaluation grades in the upper reaches of Tarim River primarily indicate “moderate” and “good” levels, with DO, TN, NH3-N, and electrical conductivity (EC) being the key parameters influencing variations in water quality. The source analysis results from APCS-MLR and PMF yielded similar outcomes, identifying six potential pollution sources. Among these, soil weathering, livestock and poultry breeding, and agricultural activities exhibited higher contribution rates. Specifically, the contribution rates for these sources according to APCS-MLR were 44.11%, 19.63%, and 11.67%, respectively; while according to PMF they are 24.08%, 17.88%, and 27.54%, respectively. Furthermore, industrial pollution sources contributed at a rate of 6.01% according to APCS-MLR, while urban living sources contributed at a rate of 2.13%. However, based on PMF analysis, the contribution rates for industrial pollution sources increased significantly to 16.71%. Additionally, APCS-MLR identified natural sources as contributing at a rate of 16.45%, whereas PMF suggested that a combination of agricultural activities and natural sources contributed at a lower rate of only 9.52%. In conclusion, the water quality within the upper reaches of the Tarim River is predominantly satisfactory. Nonetheless, localized pollution, primarily attributable to human activities, presents a substantial challenge. These observations provide critical insights into improving and protecting the fragile water quality of the Tarim River.

Application of time series and multivariate statistical models for water quality assessment and pollution source apportionment in an Urban River, New Jersey, USA

Water quality monitoring reveals changing trends in the environmental condition of aquatic systems, elucidates the prevailing factors impacting a water body, and facilitates science-backed policymaking. A 2020 hiatus in water quality data tracking in the Lower Passaic River (LPR), New Jersey, has created a 5-year information gap. To gain insight into the LPR water quality status during this lag period and ahead, water quality indices computed with 16-year historical data available for 12 physical, chemical, nutrient, and microbiological parameters were used to predict water quality between 2020 and 2025 using seasonal autoregressive moving average (ARIMA) models. Average water quality ranged from good to very poor (34 ≤ µWQI ≤ 95), with noticeable spatial and seasonal variations detected in the historical and predicted data. Pollution source tracking with the positive matrix factorization (PMF) model yielded significant R2 values (0.9 < R2 ≤ 1) for the input parameters and revealed four major LPR pollution factors, i.e., combined sewer systems, surface runoff, tide-influenced sediment resuspension, and industrial wastewater with pollution contribution rates of 23–30.2% in the upstream and downstream study areas. Significant correlation of toxic metals, nutrients, and sewage indicators suggest similarities in their sources.Graphical

Shift from legacy to emerging per- and polyfluoroalkyl substances for watershed management along the coast of China

Per- and polyfluoroalkyl substances and their short-chain alternatives have attracted world-wide attention due to their widespread presence and persistence in the environment. However, the sources, environmental fate, and driving forces of PFAS in coastal ecosystems remain poorly understood. In this study, the spatial distribution, source apportionment, and driving mechanisms of PFAS were investigated through a comprehensive analysis of water samples collected along the China's coastline. The concentrations of Σ25PFAS in water samples followed a general pattern, with higher levels observed in northern coastal zones of China than the south, ranging from 0.72 to 1872.21 ng L−1. PFOA and PFBA were dominant. Emerging short-chain PFAS, such as PFBS, PFBA, F-53B and GenX, were frequently detected, with detection rates of 97%, 99%, 95% and 77%, respectively. This indicated a shift in coastal PFAS contamination from legacy compounds to emerging short-chain alternatives. Source apportionment using the Positive Matrix Factorization model identified key contributors to PFAS pollution, including textile production, volatile precursors, precious metal industries, aqueous film-forming foam, metal-plating, electrochemical fluorination, and fluoropolymer manufacturing. Additionally, PFAS concentrations were significantly positively correlated with cultivated land, urban area, and wastewater discharge, while negatively correlated with annual precipitation and woodland coverage (p < 0.05). Socio-economic development was identified as a major driver of PFAS emissions, while the hydrological factors and vegetation coverage can significantly enhance watershed resilience against PFAS pollution.

Temporal variation, sources, fluxes, and risk assessment of heavy metals and arsenic in rainwater from Zhanjiang Bay, Northern South China Sea: Impact of typhoons Lion and Kompasu

This study investigates the impact of typhoon-induced rainfall on coastal pollution dynamics in Zhanjiang Bay during the 2021 wet season, focusing on typhoons Lion and Kompasu. Typhoon-induced rainfall (217 mm) contributed 23 % of the total wet season precipitation. Concentrations of Cu, Zn, Cd, and As in rainwater during typhoons were significantly diluted, showing decreases of 48 %, 48 %, 54 %, and 42 %, respectively. In contrast, Pb concentrations remained consistent (29.5 vs. 29.3 μg L−1), indicating that increased local emissions offset the dilution effect. The deposition fluxes of these elements during typhoons accounted for 12–21 % of total wet season deposits. A positive matrix factorization model identified six primary sources, highlighting a rise in coal combustion contributions during typhoons. Overall, while typhoons reduced risks for Cu, Cd, and As, risks for Zn and Pb increased upon deposition on Zhanjiang Bay, illustrating the complex impact of typhoon-induced rainfall on coastal pollution dynamics.

Two different approaches for source apportionment of ambient black carbon in highly polluted environments

The aethalometer model (AM) is widely used for source apportionment (SA) of black carbon (BC) in regions with mixed BC sources despite being initially developed for a relatively simplistic and low-pollution environments. The present study interrogates the applicability of AM in highly polluted metropolitan environments by comparing its results with the more nuanced Positive Matrix Factorization (PMF) model. The measurements were conducted in Delhi during the winter and summer season. PMF apportions the BC into diverse sources by taking help from complementary trace elemental measurements, thereby acknowledging the complex pollution landscape of the Delhi region. The AM estimates BCbb (BC from biomass burning) and BCff (BC from fossil fuel combustion) contributions as 48.3% and 51.7% during the winter and 16.6% and 83.4% during the summer, respectively.In contrast, the PMF model-derived biomass burning factor is the dominant source of BC during both winter and summer seasons, contributing 53.9% and 44% of the total BC, respectively. The decrease in light absorption at UV wavelengths of biomass-burning aerosols owing to escalated ambient aging is posited to be the reason for BCbb underprediction by the AM model during summers. Furthermore, while the AM model identifies fossil fuel combustion as the only other BC source apart from biomass burning, the PMF model apportions BC to five additional sources during winter, including vehicle emissions (22.9%), Pb-rich factor (10%), power plant (5.7%), waste incineration (4%) and industrial emission (3.6%). The contribution of these BC sources during summer is vehicular emission (16.5%), power plant (14.5%), waste incineration (11.5%), Pb-rich factor (9.5%), and industrial emission (4%). Additionally, the spectral variation of the light absorption properties of black carbon (bBCabs) and brown carbon (bBrCabs), delta-C effect, and sensitivity of the AM are reported for the study period. The present study cautions that BC source apportionment can be complex in highly polluted metropolitan environments, and complementary tracer measurements are recommended for reliable results.

Pollution Characteristics and Source Apportionment of Volatile Organic Compounds in Typical Solvent-using Industrial Parks in Beijing

The BCT-7800A PLUS VOC online monitor system was employed to measure ambient volatile organic compounds （VOCs） in a typical solvent-using industrial park in Beijing. From January to June 2023, the pollution characteristics, source apportionment, and ozone formation potential（OFP）of VOCs were studied, and the results of a comparative analysis were also discussed between heating and non-heating periods. The results indicated that VOC concentrations from January to June 2023 were （104.21 ± 91.31） μg·m-3 on average. The concentrations of TVOCs under the influence of southerly and northerly winds were （214.18 ± 202.37） μg·m-3 and （197.56 ± 188.3） μg·m-3, respectively. Alkanes were the species with the highest average concentration and proportion, respectively （45.53 ± 41.43） μg·m-3. The VOC concentration during the heating period was higher than those during the non-heating period, with values of （111.57 ± 83.96） μg·m-3 and （87.92 ± 75.03） μg·m-3, respectively. Propane and ethane were the species with the highest average concentration during the heating period. Compared with those in the non-heating period, the average concentrations of three species （propane, ethane, and n-butane） in the top ten species increased during the heating period, with average concentrations increasing by 51.94%, 54.64%, and 26.32%, respectively. The source apportionment results based on the positive matrix factorization （PMF） model indicated that the major sources of VOCs in the park during the monitoring period were printing emission sources （4.95%）, oil and gas evaporation sources （9.52%）, fuel combustion sources （15.44%）, traffic emissions sources （18.97%）, electronic equipment manufacturing （24.59%）, and industrial painting sources （26.52%）. Therefore, industrial painting sources, electronic equipment manufacturing sources, and traffic emissions sources were the emission sources that the park should focus on controlling. Compared with those during non-heating periods； industrial painting, traffic emission, and fuel combustion sources contributed more during the heating period, with VOC concentrations increasing by 15.02%, 16.53%, and 24.98%, respectively. The average OFP of VOCs from May to June during the monitoring period was 198.51 μg·m-3 and OVOCs, olefins, and aromatic hydrocarbons contributed the most to OFP, which were 47.41%, 22.15%, and 18.41%, respectively. The electronic equipment manufacturing source was the largest contributor to the summer OFP of the park and its contribution rate was 30.11%, which should be strengthened in the future.

Characteristics, Source Apportionment, and Health Risk of Heavy Metals in the Soils of Peri-urban Shanghai Chongming Island.

Heavy metals resulting from human activities pose significant threats to human health and the soil ecosystem. In the current study, 917 soil samples from Chongming Island in Shanghai, China, were examined for eight heavy metals. The sources of contamination were identified by using a Positive Matrix Factorization (PMF) model. Meanwhile, spatial interpolation and Moran's I index were applied to validate the model in terms of spatial linkages. The results revealed that the average concentrations of As, Cd, Hg, Pb, Cr, Cu, Zn, and Ni in the soil were 8.87, 0.19, 0.06, 28.75, 76.01, 37.74, 88.93, and 30.33 mg kg-1, respectively. The PMF analysis proved that heavy metals in the soil of the study area are mainly influenced by traffic sources (Cr and Pb), industrial sources (Zn, Cd, and Cu), station sources (Hg), and natural sources (As and Ni), with contribution rates of 22.23, 26.25, 36.38, and 15.14%, respectively. The combination of Moran's index and the spatial analysis method not only verified the analytical results of the receptor model on the one hand but also served as a supplementary explanation for the sources of heavy metals in the soil. The health risk assessment indicated that noncarcinogenic values were below the threshold values. The total carcinogenic risk (R T) of different heavy metals has a descending order of Cr > As > Ni > Cd. The R T values of multiple heavy metals for children and adults were 5.28 × 10-04 and 4.10 × 10-05, respectively, which were close to the risk threshold. Therefore, attention should be paid to the health risks, especially for children's skin contact, which is the main exposure pathway.

Characteristics and Source Apportionment of Atmospheric Volatile Organic Compounds in Zhengzhou During O3 Campaign Period

An online gas chromatograph （GC5000） was used to monitor the volatile organic compounds （VOCs） in the atmospheric environment of Zhengzhou City during the ozone campaign period from May to September of 2022. The relationship between O3 and its precursors as well as meteorology was analyzed and the pollution characteristics of VOCs during the O3 exceeding and non-exceeding the standard days were compared and explored. Different VOC activity evaluation methods of OFP and L·OH were utilized to compare and analyze the key active components and species and the ratio analysis （RA） and positive matrix factorization （PMF） analysis models were used to study the apportionment contribution of VOCs. The results showed that the O3 pollution in June and September in Zhengzhou was mainly due to the adverse meteorological conditions of high temperature and low humidity, strong radiation, and low wind speed, superimposed by the prominent concentrations of local VOCs and NO2, resulting in frequently high and excessive O3 occurrences. The VOCs concentration in Zhengzhou during the campaign period was an average of （68.3 ± 18.4） μg·m-3, whereas it was 75.7 μg·m-3 during O3 exceeding standard days and 13.4 μg·m-3 during O3 non-exceeding days, respectively. Among the VOC species, the OVOCs was 31.6%, accounting for the highest mass fraction, followed by halogenated hydrocarbon, alkane, and aromatic hydrocarbon, and the major species were ethane, n-butane, dichloromethane, propane, isopentane, toluene, chloromethane, 1,2-dichloroethane, and acetylene. VOC diurnal variation indicated that the emission of VOC pollution sources in the morning, evening peak, and at night should be paid more attention. The contribution of VOCs to OFP during the campaign period was （130.5 ± 46.4） μg·m-3, and the L·OH was （6.5 ± 2.9） s-1, among which the top 15 species with high activity were primarily acetaldehyde, isoprene, ethylene, m/p-xylene, toluene, hexal, isopentane, propanal, propylene, trans-2-butene, etc. In particular, the contributions of acetaldehyde, isoprene, ethylene, and hexal species were prominent during the O3 exceeding days. Ratio analysis showed that the B/T ratio in Zhengzhou from May to September ranged from 0.05 to 5.3, with an average value of 1.1 ± 0.6, and the regional VOCs was mainly controlled by the aging air mass with possible long-distance transports. The analysis of the PMF model showed that the major pollution sources to VOC concentration in Zhengzhou were motor vehicle exhaust emission sources and industrial solvent and secondary conversion sources, contributing 25.6% and 25.8%, respectively. The contribution rates of solvent coating sources, oil and gas volatile sources, plant emission sources, industrial solvents, and secondary conversion sources during O3 exceeding days were 5.4%, 4.7%, 3.3%, and 0.7% higher than those during O3 non-exceeding days, respectively. The research showed that the management of VOCs and NOx pollution sources should be strengthened to reduce their contribution to the O3 generation when O3 exceeds the standard.

Unraveling the impact of high arsenic, fluoride and microbial population in community tubewell water around coal mines in a semiarid region: Insight from health hazards, and geographic information systems

High arsenic (As), fluoride (F-), and microbial pathogens coexist in semiarid conditions afflicting > 240 million people worldwide including Pakistan. Groundwater quality has declined due to geogenic and manmade activities providing suitable ground for ubiquity, bioavailability, and toxicity of contaminants. We tested the health hazard, distribution, and apportionment of As, F-, and microbes in groundwater around coal mines in Quetta, Pakistan. The range of As, and F- concentrations in groundwater were 0.2–16.6 µg/L, 0.4–18.5 mg/L. Both, As and F- correlate with high HCO3-, pH, Na+, SO42-, Fe, and Mn, and negatively with Ca2+ water. The coalfield showed many folds higher As 15.8–28.5 µg/L, and F- 10.8–34.5 mg/L compared to groundwater-wells. Geochemical phases revealed saturation of groundwater with calcite, dolomite, fluorite, gypsum, and undersaturation with halite-mirabilite, and arsenopyrite minerals. The positive matrix factorization (PMF) model assessed five-factor solutions: geogenic, industrial, coal mining, sulfide & fluoride-bearing mineral-dissolution, and agriculture pollution delivered As, F-, and microbial contamination. About 24.6 % and 64.4 % of groundwater samples exceeded the WHO guidelines of As 10 µg/L, F- 1.5 mg/L. The carcinogenicity, and non-carcinogenicity of As, and F- were higher in children than adults. Therefore, health hazards in children are of great concern in achieving sustainable management goals.

Source distribution, ecological risks, and controlling factors of heavy metals in river sediments: Receptor model-based study in a transboundary river basin

In the context of transboundary rivers, which constitute intricate fluvial ecosystems, the persistent threat of heavy metals (HMs) contamination poses significant risks to ecosystem health. In this study, ecotoxicological hazards, governing factors, and the distribution of nine HMs (uranium (U), lead (Pb), cadmium (Cd), nickel (Ni), chromium (Cr), manganese (Mn), iron (Fe), zinc (Zn), and copper (Cu)), as well as sediment characteristics (sand, silt, clay, organic matter, and pH) are assessed within the sediment. The current investigation encompasses the analysis of twenty-seven sediment samples, utilizing inductively coupled plasma mass spectrometry, in the transboundary river basin of Bangladesh, specifically the Teesta River. Notably, the findings underscore the predominance of Cd as a contaminant, responsible for 51.85%, 81.84%, and 100% of the geo-accumulation index, contamination factor, and enrichment factor, respectively. The Teesta River emerges as moderately to highly polluted, with cumulative probabilities of 7.4%, 85.2%, and 7.4% denoting "medium", "high", and "priority" pollution levels, respectively. Regions in the upstream and downstream middle sections of the study area exhibit relatively higher pollution levels, particularly in proximity to Kaunia Upazila in the Rangpur district. Ecologically, the potential risk index indicates a low likelihood of ecological impacts at 77.8%, alongside a moderate risk observation of 22.2%. The current results attribute the distribution of these HMs to the pH and organic matter content within the sediment, serving as pivotal factors. To unravel the origins of the HMs, the positive matrix factorization (PMF) model successfully identifies four contributing factors, primarily from geogenic sources. Validation of the PMF model through Spearmen correlation and principal component analysis (PCA) reveals a consistent pattern, affirming its efficacy in this analysis. Within the region, HM sources are identified as originating from anthropogenic activities such as irrigation, industrial discharges, and domestic effluent, in addition to substantial inputs from geogenic sources. Recognizing the transboundary nature of metal pollution, the current study underscores the imperative for continuous and vigilant monitoring, coupled with the implementation of robust management practices. The interplay of both anthropogenic and geogenic factors necessitates a comprehensive approach to effectively and sustainably combat HM contamination.

Heavy metal contamination assessment and source attribution in the Vicinity of an iron slag pile in Hechi, China: Integrating multi-medium analysis

Heavy metals, such as mercury, cadmium, and nickel, may contaminate human inhabited environments, with critical consequences for human health. This study examines the health impacts of heavy metal pollution from an iron slag pile in Hechi, China, by analyzing heavy metal contamination in water, sediment, soil, and crops. Here, the Nemerow pollution index (NI) indicated severe pollution at most sampling sites, the mean NI of groundwater, and surface water had reached 594.13 and 26.79, respectively. Bioaccumulation of mercury (Hg), cadmium (Cd), and nickel (Ni) was noted in crops, cucumbers showed comparatively lower risk levels. Logarithmic surface water-sediment partition coefficient calculations indicated that heavy metals such as chromium (Cr), ferrum (Fe), zinc (Zn), copper (Cu), Ni, arsenic (As), and lead (Pb) tend to accumulate in sediments. There was a high risk in groundwater (67.48–6590.54) and surface water (13.73–2500.85). Variably influenced by rainfall, these metals can be diluted and mobilized from surface water and sediments, thereby changing the contamination levels and ecological risks. Probabilistic health risk assessments indicated that health risks were higher in children than in adults, the mean total carcinogenic risk values of soil, groundwater, and surface water, were 6.79E-04, 4.20E-06, and 1.15E-6 for children, respectively. Moderate soil pollution is the main health hazard. A Positive Matrix Factorization model attributed over 60% of the pollution to slag stacking. Biotechnologies, solidification/stabilization techniques, field management, and institutional controls, driven by principles of green, low-carbon, and economic efficiency may mitigate. These findings contribute to the management of heavy metal pollution in iron slag pile areas.

Pollution characteristics and probabilistic risk assessment of heavy metal(loid)s in agricultural soils across the Yellow River Basin, China

Heavy metal(loid)s (HMs) contamination in farmland soils, an environmental issue of global concern, has undergone extensive research across local and regional scales in the Yellow River Basin (YRB), China, but comprehensive studies at the basin scale remain scarce. This study, utilizing published data from 2000 to 2023, analyzed the concentrations, distribution patterns, potential sources, probabilistic ecological risks, and source-specific health risks of eight HMs (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) in farmland soils across the YRB. Results indicated that while most agricultural soils are safe, 21.7% of samples for Cd, 5.6% for As, and 1.2% for Zn exceed China’s risk screening levels for agricultural soil pollution, with Hg and Cd showing the highest pollution levels. Ecological risks were generally high, with Cd and Hg identified as primary risk factors. Correlation analysis and the positive matrix factorization model identified four potential origins, contributing in descending order: traffic-industrial atmospheric deposition (37.0%), mixed agricultural-natural sources (26.7%), industrial-natural sources (14.6%), and mixed agricultural-industrial sources (14.6%). The probabilistic health risk assessment identified negligible noncarcinogenic threats to the population; however, carcinogenic hazards were concerning, particularly due to industrial activities and As. Children are more vulnerable to hazards than adults. These findings enhance our insight into soil HM pollution risks in the YRB and offer a scientific foundation for targeted pollution management and control strategies at the basin scale.

Characterization and Sources of VOCs during PM2.5 Pollution Periods in a Typical City of the Yangtze River Delta

To investigate the characteristics and sources of volatile organic compounds (VOCs) as well as their impacts on secondary organic aerosols (SOAs) formation during high-incidence periods of PM2.5 pollution, a field measurement was conducted in December 2019 in Hefei, a typical city of the Yangtze River Delta (YRD). During the whole process, the mixing ratios of VOCs were averaged as 21.1 ± 15.9 ppb, with alkanes, alkenes, alkyne, and aromatics accounting for 59.9%, 15.3%, 15.0%, and 9.8% of the total VOCs, respectively. It is worth noting that the contributions of alkenes and alkyne increased significantly during PM2.5 pollution periods. Based on source apportionment via the positive matrix factorization (PMF) model, vehicle emissions, liquefied petroleum gas/natural gas (LPG/NG), and biomass/coal burning were the main sources of VOCs during the research in Hefei. During pollution periods, however, the contribution of biomass/coal burning to VOCs increased significantly, reaching as much as 47.6%. The calculated SOA formation potential (SOAFP) of VOCs was 0.38 ± 1.04 µg m−3 (range: 0.04–7.30 µg m−3), and aromatics were the dominant contributors, with a percentage of 96.8%. The source contributions showed that industrial emissions (49.1%) and vehicle emissions (28.3%) contributed the most to SOAFP during non-pollution periods, whereas the contribution of biomass/coal burning to SOA formation increased significantly (32.8%) during PM2.5 pollution periods. These findings suggest that reducing VOCs emissions from biomass/coal burning, vehicle, and industrial sources is a crucial approach for the effective control of SOA formation in Hefei, which provides a scientific basis for controlling PM2.5 pollution and improving air quality in the YRD region.

Source-risk and uncertainty assessment of trace metals in surface sediments of a human-dominated seaward catchment in eastern China

Current total concentration-based methods for source attribution and risk assessment often overestimate metal risks, thereby impeding the formulation of effective risk management strategies. This study aims to develop a framework for source-specific risk assessment based on metal bioavailability in surface river sediments from a human-dominated seaward catchment in eastern China. Metal bioavailability was quantified using chemical fractionation results, and source apportionment was conducted using the positive matrix factorization (PMF) model. Risk assessment integrated these findings using two indices: the Potential Ecological Risk Index (PERI) and the Mean Probable Effect Concentration Quotient (mPEC-Q), with uncertainty addressed via Monte Carlo simulations. Results indicated that average total concentrations of Cu, Pb, Zn, Cr, Hg, Cd, and As exceeded their respective background levels by 1.63 to 15.00 times. The residual fraction constituted the majority, accounting for 53.84 % to 77.79 % of total concentrations, suggesting significant natural origins. However, source apportionment revealed a predominant contribution from anthropogenic activities, including industrial smelting, agricultural practices, and atmospheric deposition. The contributions were found to vary between 5.35 % and 40.03 % when the total concentration was adjusted to bioavailable content. Total concentration-based PERI/mPEC-Q assessments indicated high/moderate risk levels, decreasing to considerable/low risk levels with bioavailability adjustment. Hg and Cd were identified as priority metals. Further incorporating source appointment parameters into the risk assessment, industrial smelting was identified as the primary contributor, accounting for 66.06 % of total risk by total concentration and 65.63 % by bioavailability. This underscores the role of bioavailability in mitigating risk overestimation. Monte Carlo simulations validated industrial smelting as a major risk contributor. This study emphasizes the importance of considering bioavailability in the source-risk assessment of sediment-metals, crucial for targeted risk management in urbanized catchment areas.

Machine learning-driven assessment of heavy metal contamination in the impounded lakes of China's South‐to‐North Water Diversion Project: Identifying spatiotemporal patterns and ecological risks

The Eastern Route of China's South-to-North Water Diversion Project (SNWDP-ER) traverses through impounded lakes that are potentially vulnerable to heavy metals (HMs) contamination although the understanding remains elusive. This study employed machine learning approaches, including super-clustering of Self-Organizing Map (SOM) and Robust Principal Component Analysis (RPCA), to elucidate the spatiotemporal patterns and assess ecological risks associated with HMs in the surface sediments of Gao-Bao-Shaobo Lake (GBSL) and Dongping Lake (DPL). We collected 184 surface sediments from 47 stations across the two important impounded lakes over four seasons. The results revealed higher HMs concentrations in the south-central GBSL and west-central DPL, with a notable increase in contamination in autumn. The comprehensive risk assessment, utilizing various indicators such as the Sediment Quality Guidelines (SQGs), Improved Potential Ecological Risk Index (IPERI), Geo-accumulation Index (Igeo), Contamination Factor (CF), and Enrichment Factor (EF), identified arsenic (As), cadmium (Cd), nickel (Ni), and chromium (Cr) as primary contaminants of concern. Positive Matrix Factorization (PMF) model, coupled with Spearman analysis, attributed over 70 % of HMs pollution to anthropogenic activities. This research provides a nuanced understanding of HMs pollution in the context of large-scale water diversion projects and offers a scientific basis for targeted pollution mitigation strategies.