Positive Matrix Factorization Model Research Articles

Pollution Characteristics and Source Apportionment of Heavy Metals in Topsoil of Counties Along the Shandong Section of the Yellow River

The 25 counties along the Shandong section of the Yellow River are the core areas for promoting the ecological protection and high-quality development of the Yellow River in Shandong Province. Moreover, it is of great significance to study the current situation, sources, and potential risks of heavy metal pollution in the topsoil in this region. In this study, 103 soil samples were collected from the 25 counties along the Shandong section of the Yellow River, and the contents of eight heavy metals （As, Cu, Pb, Cr, Zn, Ni, Cd, and Hg） were determined. The pollution characteristics of heavy metals were analyzed and evaluated using the geological accumulation index and potential ecological risk index. Correlation analysis and the positive matrix factorization （PMF） model were used to analyze the sources of heavy metals. The results showed that the average contents of Cu and Cr were lower than that of the background values of soils, whereas the average contents of As, Pb, Zn, Ni, Cd, and Hg were 1.16, 1.42, 1.05, 1.14, 2.29, and 1.85 times higher than that of the background values, respectively, and the average contents of all eight elements were lower than the screening value of soil pollution risk in agricultural land. In terms of different heavy metal variations, the coefficient of variation （CV） of Cu and Cd was higher than 0.500, indicating high variations, whereas As, Pb, Cr, Zn, Ni, and Hg showed moderate variation. Cd and Hg were slightly polluted, whereas the other six elements were not polluted. Cd and Hg had a moderate potential ecological risk level, whereas the other six elements were at a low level. Correlation analysis and PMF model showed that the sources of heavy metals in the study area were influenced by four factors, i.e., agricultural activities, natural sources, industrial emissions, and atmospheric dust from coal combustion and vehicle exhaust emissions, and the relative contribution rates were 32.4%, 34.9%, 16.5%, and 16.2%, respectively.

Source and Influencing Factor Analysis of Soil Heavy Metals Based on PMF Model and GeoDetector

In Lijiang City, as a typical example, 93 soil samples were collected from the study area, and soil pH； organic matter； and heavy metals arsenic （As）, mercury （Hg）, copper （Cu）, zinc （Zn）, lead （Pb）, cadmium （Cd）, and chromium （Cr） were determined. We explored the sources of heavy metals in the study area by means of Positive Definite Matrix Factorization （PMF） modeling and analyzed the impact of influencing factors by combining seven heavy metals with 13 influencing factors in a GeoDetector. The results showed that the mean values of soil heavy metals ω（As）, ω（Hg）, ω（Cu）, ω（Zn）, ω（Pb）, ω（Cd）, and ω（Cr） in the study area were 17.55, 0.19, 86.75, 164.84, 28.95, 0.39, and 167.87 mg·kg-1, respectively, which were greater than the background values of soils in Yunnan Province （except for As and Pb）. Regarding spatial distribution, the high values of Cu and Cr content were mainly concentrated in Yulong Naxi Autonomous County； the high value areas of As, Hg, Pb, and Cd were mainly concentrated in Ninglang Yi Autonomous County； and the high value of Zn content was mainly concentrated in Huaping County. Correlation analysis and PMF modeling revealed that the main sources of heavy metals As and Hg in the study area were industrial sources, Zn was from transportation pollution sources, Cr and Cu were from natural sources, and Cd and Pb were from agricultural sources. Further, the factor detector of the GeoDetector found that soil pH and organic matter （OC） had strong explanatory power for the content of seven heavy metals, and the interaction detector found that the results following the interaction of different influencing factors were nonlinear enhancement or two-factor enhancement, in which the interaction of OC and pH was the dominant factor for the spatial differentiation of heavy metals. This provides an important scientific basis for the protection of the soil environmental health and sustainable development in Lijiang City.

Environmental risk estimation of potentially toxic metal(loid)s (PTMs) in fine particulate dust on the surface of children's playgrounds in parks

To determine the priority control factors for risk control and management of potentially toxic metal(loid)s (PTMs) in fine particulate dust (FPD) on the surface of children's playgrounds in parks, a comprehensive study was conducted on the pollution, risks, and sources of 12 PTMs in the FPD, taking Xi’an, China as a case. The pollution, ecological and health risks of PTMs were assessed using Nemerow integrated enrichment factor, modified Nemerow integrated eco–risk index, and health risk evaluation model, combined with Monte Carlo simulation. The sources of PTMs in the FPD were quantitatively apportioned using a positive matrix factorization model. Priority control factors for PTMs risk control and management were determined based on the results of source–oriented pollution and risk assessment. The results showed that the average contents (mg kg–1) of Cd (1.9), Cr (99.8), Cu (96.3), Hg (0.2), Pb (61.1), and Zn (239.4) in the FPD were considerably greater than local background values. The sources of PTMs in the FPD are industrial emission, coal combustion and medical activities, mixed source, and natural source, contributing 24.7%, 18.2%, 35.1%, and 22.1% to the total content of PTMs in the FPD, respectively. PTMs in the FPD posed a certain cancer risk to the elderly and children. Industrial emissions and mixed sources are priority control sources, while Cd and Cr are priority PTMs. It is recommended that local eco–environmental departments strengthen environmental monitoring of playgrounds, as well as management of industrial and transportation emissions, to ensure the health of residents.

Pollution area identification, receptor model-oriented sources and probabilistic health hazards to prioritize control measures for heavy metal management in soil

Identifying the primary source of heavy metals (HMs) pollution and the key pollutants is crucial for safeguarding eco-health and managing risks in industrial vicinity. For this purpose, this investigation was carried out to investigate the pollution area identification with soil static environmental capacity (QI), receptor model-oriented critical sources, and Monte Carlo simulation (MCS) based probabilistic environmental and human health hazards associated with HMs in agricultural soils of Narayanganj, Bangladesh. The average concentration of Cr, Ni, Cu, Cd, Pb, Co, Zn, and Mn were 98.67, 63.41, 37.39, 1.28, 23.93, 14.48, 125.08, and 467.45 mg/kg, respectively. The geoaccumulation index identified Cd as the dominant metal, indicating heavy to extreme contamination in soils. The QI revealed that over 99% of the areas were polluted for Ni and Cd with less uncertain regions whereas Cr showed a significant portion of areas with uncertain pollution status. The positive matrix factorization (PMF) model identified three major sources: agricultural (29%), vehicular emissions (25%), and industrial (46%). The probabilistic assessment of health hazards indicated that both carcinogenic and non-carcinogenic risks for adult male, adult female, and children were deemed unacceptable. Moreover, children faced a higher health hazard compared to adults. For adult male, adult female, and children, industrial operations contributed 48.4%, 42.7%, and 71.2% of the carcinogenic risks, respectively and these risks were associated with Ni and Cr as the main pollutants of concern. The study emphasizes valuable scientific insights for environmental managers to tackle soil pollution from HMs by effectively managing anthropogenic sources. It could aid in devising strategies for environmental remediation engineering and refining industry standards.

Watershed landscape characteristics and connectivity drive river water quality under seasonal dynamics

Landscape characteristics and connectivity are pivotal determinants of river water quality within the context of water quality management. They play particularly important roles in the spread of non-point source pollution. However, previous research has not investigated their combined effects, especially in the topographically and ecologically volatile Loess Plateau, China. This study applied machine learning and positive matrix factorization techniques to water quality monitoring data for the Wuding River basin, China, from 2017 to 2021 to assess the seasonal impacts of environmental factors on river water quality. The results showed that there were spatial and seasonal variations in pollution sources, and specific thresholds for the key landscape indices that influence water quality were identified. Landscape composition and configuration have the greatest influence on the water quality parameters during the dry season, whereas connectivity and landscape configuration are more critical during the wet season. The connectivity contributions to chemical oxygen demand, the potassium permanganate index, and total phosphorus in the wet season were 19.07%, 27.47%, and 5.08% greater than in the dry season, respectively. Connectivity also made a significant contribution to the composite water quality index, accounting for 33.46% in the dry season and 36.22% in the wet season. The positive matrix factorization model identified agricultural non-point sources and mixed pollutants as the main factors affecting water quality during the dry season, whereas erosion and mixed pollution sources dominated the wet season. Critical thresholds were established for the landscape indices that affected water quality, such as a landscape shape index below 18.5 and a grassland proportion above 65%. The results can be used to develop more effective water quality management strategies and illustrate the essential role played by connectivity when tailoring water quality management strategies to seasonal variations.

Exposure Levels, Health Risks, Spatially Distribution, Multivariate Statistics and Positive Matrix Factorization Model of Heavy Metals from Wild solid Waste Dumpsites

Exposure Levels, Health Risks, Spatially Distribution, Multivariate Statistics and Positive Matrix Factorization Model of Heavy Metals from Wild solid Waste Dumpsites

Quantifying seasonal variations in pollution sources with machine learning-enhanced positive matrix factorization

As the pace of industrialization and urbanization accelerates, water quality management faces increasing challenges, with traditional methods for pollutant source apportionment often proving inadequate in handling complex environmental data. This study enhances the precision and reliability of pollutant source identification by integrating Positive Matrix Factorization (PMF) models with diverse machine learning techniques. Utilizing data from 17 water quality monitoring stations along the Wuding River from 2017 to 2021, we employed Random Forest (RF), Support Vector Machine (SVM), Elastic Net (EN), and Extreme Gradient Boosting (XGBoost) models to predict the Water Quality Index (WQI) during dry and wet seasons. Results indicate that the RF model exhibited optimal performance in the dry season (R2 = 0.93), while the SVM was superior in the wet season (R2 = 0.94). SHAP (SHapley Additive exPlanations) value analysis identified CODMn and NH3-N as significant influencers on WQI in the dry season, whereas COD, BOD, and TP gained prominence during the wet season. SHAP values reveal the contribution of each feature to the model output, thereby enhancing the model’s transparency and interpretability. Additionally, feature importance identified by machine learning was utilized as weights to optimize the contribution rates predicted by the PMF model. The optimised model was able to identify the contribution of domestic and farm effluent discharges more accurately in the dry season, with a significant increase in the percentage of identification from 19.4 % to 45.4 %, and an increase in the percentage of contribution from agricultural non-point sources and domestic effluent in the rainy season. This research offers a novel perspective on the characteristics of river water pollution and holds significant implications for formulating data-driven environmental management strategies.

Natural background level, source apportionment and health risk assessment of potentially toxic elements in multi-layer aquifers of arid area in Northwest China

Groundwater contaminated by potentially toxic elements has become an increasing global concern for human health. Therefore, it is crucial to identify the sources and health risks of potentially toxic elements, especially in arid areas. Despite the necessity, there is a notable research gap concerning the sources and risks of these elements within multi-layer aquifers in such regions. To address this gap, 54 phreatic and 24 confined groundwater samples were collected from an arid area in Northwest China. This study aimed to trace the sources and evaluate the human health risks of potentially toxic elements by natural background level (NBL), positive matrix factorization (PMF) model, and health risk model. Findings revealed exceeding levels of potentially toxic elements existed in phreatic and confined aquifers. Source apportionment and NBL results indicated that mineral dissolution, evaporation, redox reactions, and human activities were the main factors for elevated concentrations of potentially toxic elements. High Fe and Mn concentrations were attributed to reduction environments, while F accumulation resulted from slow runoff, and irrigation from the Yellow River. Due to high F levels, more than one-third of groundwater samples (phreatic: 33.14 %, confined: 56.22 %) posed non-carcinogenic health risks to population groups. Adults displayed higher carcinogenic risks (phreatic: 19.47 %, confined: 34.16 %) than infants (phreatic: 0 %, confined: 0 %) and children (phreatic: 1.26 %, confined: 7.97 %) owing to the toxic elements of Cr. The confined aquifer presented greater health risks than the phreatic aquifer. Consequently, controlling the levels of F and Cr in multi-layered aquifers is key to reducing health risks. These findings provide valuable insights into protecting groundwater from contamination by potentially toxic elements in multi-layered aquifers worldwide.

Source Identification of Potentially Toxic Metals in Plants of Alpine Ecosystems of Mt. Madra by Positive Matrix Factorization.

In this study, the concentrations of cadmium (Cd), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb) and zinc (Zn) in plants sampled from Mt. Madra were investigated. Furthermore, the distribution characteristics and source identification of potentially toxic metals were investigated with the application of Positive Matrix Factorization (PMF) modelling. Samples of 26 different plant species were taken from Mt. Madra at elevations ranging from 177 to 1347m using the multi-point sampling approach. The metal quantities measured by ICP-OES are the following sequences (mean ± SD) (mg/kg): Fe (974.96 ± 29.6) > Mn (111.81 ± 2.6) > Zn (27.28 ± 0.2) > Ni (2.17 ± 0.03) > Pb (0.77 ± 0.01) > Cd (0.12 ± 0.01). According to the plant samples in which the highest values were determined, the metals are as follows: Cd (Lathyrus laxiflorus, 0.401mg/kg), Fe (Ajuga orientalis, 7621.207mg/kg), Mn (Castanea sativa, 724.927mg/kg), Ni (Prunella laciniata, 6.947mg/kg), Pb (Crataegus stevenii, 3.955mg/kg) and Zn (Prunella laciniata, 50.802mg/kg). The results of the PMF model showed that Cd had an atmospheric transport factor originated and transported from industrial activites, Ni had a substrate factor, Fe, Mn, Pb and Zn were influenced by different anthropogenic factors.

Source-Oriented Health Risks and Distribution of BTEXS in Urban Shallow Lake Sediment: Application of the Positive Matrix Factorization Model

Source-Oriented Health Risks and Distribution of BTEXS in Urban Shallow Lake Sediment: Application of the Positive Matrix Factorization Model

Temporal shifts in sources and composition of particulate metals in an environmental justice community: Impacts of continued emission reductions, COVID-19, and wildfires

In this study, we evaluated trends in the composition and source contributions of ambient particulate metals, many of which are toxic air contaminants, in Paramount, California. Paramount is an environmental justice community disproportionately impacted by air pollution from multiple emission sources near sensitive receptors. Concentrations of ambient metals bound to total suspended particulates (TSP) were measured hourly using a Xact 625 ambient metal monitor between 2017 and 2020. The Positive Matrix Factorization (PMF) model identified traffic, industry, and soil dust as major contributors. Results showed that contributions of traffic and soil dust noticeably declined during the COVID-19 lockdown period (March–April 2020) due to reduced human activities but reverted to normal levels within a few months. Industry-related factors, however, dropped significantly during this period and remained low throughout the year. Contributions from industry-related sources in Paramount began declining even before 2020, despite no similar trend in regional levels, implying local emission reductions. These reductions resulted from tariffs, decreased demand, regulatory actions by the South Coast Air Quality Management District, and voluntary controls by local facilities. Consequently, Paramount is experiencing drastically lower levels of metals (up to 80%, depending on the metal and source) from metal-processing facilities and related sources. Moreover, we observed markedly elevated concentrations of several metals (e.g., Ba, Ca, Mn, K, etc.) during the unprecedented September 2020 wildfires that engulfed much of the western US, adding to the limited body of studies on this phenomenon and emphasizing the considerable impact of massive wildfires on ambient levels of particulate metals.

Identifying heavy metal sources and health risks in soil-vegetable systems of fragmented vegetable fields based on machine learning, positive matrix factorization model and Monte Carlo simulation

Urban fragmented vegetable fields offer fresh produce but pose a potential risk of heavy metal (HM) exposure. Thus, this study investigated HM sources and health risks in the soil-vegetable systems of Chongqing’s central urban area. Results indicated that Cd was the primary pollutant, with 28.33 % of soil samples exceeding the screening value. Amaranth was particularly problematic, exceeding thresholds for Cd, Hg, and Cr, and both amaranth and celery showed significantly higher HM accumulation (p < 0.05). The HM pollution level in the soil-vegetable system was moderate or above. The sources of HMs identified via Positive matrix factorization (PMF) model included agricultural activities (18.19 %), natural soil parent material (25.88 %), mixed metal smelting and transportation (30.72 %), and coal combustion (25.21 %). Furthermore, evaluations using the Random Forest (RF) model revealed an intricate interaction of factors influencing the presence of HMs, where enterprise density, population density, and road density played significant roles in HMs accumulation. Monte Carlo assessments revealed higher non-carcinogenic risks for children (Pb, As) and greater carcinogenic risks for adults (Cd). Therefore, the issue of HM pollution in soils and vegetables from fragmented fields in industrial urban areas need attention, given the potential for elevated health risks with long-term vegetable consumption.

Identification Priority Source of Heavy Metals in Urban Soils Based on Source-specific Ecological Risk Analysis in a Typical Tourist City

To understand the pollution status, distribution characteristics, and pollution sources of soil heavy metals in tourist cities in northwest China, the soil content of heavy metals As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn in the main areas of Dunhuang City was collected and analyzed. The soil heavy metal pollution level was quantitatively evaluated by the methods of the geo-accumulation index and improved Nemerow pollution index, and the sources of heavy metal pollution were quantitatively analyzed using cluster analysis and the positive matrix factorization （PMF） model. The contribution rate of each pollution source to ecological risk was determined by combining the PMF model and comprehensive ecological risk index. The results showed that except for the mean contents of As and Ni, the mean contents of Cd, Cr, Cu, Hg, Pb, and Zn were all higher than the background values in Gansu Province. However, the average content of the eight elements was lower than the screening value of construction land in the Standard for Soil Pollution Risk Control of Soil Environmental Quality Construction Land （trial） （GB 36600-2018）. Among them, the enrichment of Cd, Pb, and Hg was more serious, and the exceedance rate was higher than 90%. The results of the geo-accumulation index indicated that urban soils were mainly polluted by Hg in the study area, and Cd, Cr, Cu, Pb, and Zn pollution also existed in different degrees. The improved Nemerow pollution index illustrated that the comprehensive pollution degree of the soil was clean to moderate pollution, and the overall pollution was light pollution. Based on the PMF model, we could conclude that soil heavy metals in the study area were affected by natural sources, industrial deposition sources, industrial sources, traffic sources, and comprehensive sources, and the contribution rates were 29.28%, 25.86%, 20.13%, 16.5%, and 8.23%, respectively. The specific source-integrated ecological risk assessment model found that the industrial deposition source contributed the most to the ecological risk in the study area and could be regarded as the priority control pollution source, and Hg was considered to be the priority control pollution element for ecological risk.

Sensitivity Analysis of Atmospheric Ozone and Source Apportionment of Volatile Organic Compounds in Yinchuan City

Based on the observation data of O3 concentration in Yinchuan in 2022, the monthly variation characteristics of O3 concentrations were analyzed. Further, based on the observation data of meteorological elements, conventional pollutants, and volatile organic compounds （VOCs） concentrations at an urban site in Yinchuan from May to July, the difference in meteorological elements and precursor concentrations between the polluted days and the non-polluted days were compared. Then, the O3 sensitivity and the VOCs sources were discussed using the Framework for 0-D Atmospheric Modeling （F0AM） and positive matrix factorization （PMF） model, respectively. The results showed that： ① The O3 pollution occurred from May to July in 2022, and the concentrations of O3-8h-90per were 156 μg·m-3, 170 μg·m-3, and 174 μg·m-3, respectively, with exceeding standard rates of 9.7%, 26.7%, and 29.0%, respectively. ② Compared with those on the non-polluted days, the hourly mean values of temperature, total solar radiation, and concentrations of various precursors on the O3-polluted days increased, including the volume concentrations of propane, isobutane, ethane, n-butane, and dichloromethane, which increased significantly by 33.1%, 29.1%, 25.0%, 22.7%, and 21.3%, respectively. The results showed that the combined increase in pollutant emissions and adverse meteorological conditions contributed to the formation of O3. ③ From May to July 2022, the top five VOCs species in terms of ozone formation potential （OFP） value on whole, non-polluted, and polluted days were the same. They were acetaldehyde, m/p-xylene, ethylene, isoprene, and toluene, mainly from solvent use sources, natural sources, and chemical industry emissions. ④ The local O3 production was mostly controlled by VOCs, and the relative incremental reactivity （RIR） results revealed that O3 production showed strong positive sensitivity to alkene and aromatic hydrocarbon but showed negative sensitivity to NOx on both polluted and non-polluted days. The relative contributions of active species such as acetone, ethylene, and isobutane to O3 production were high, and the implementation of an emission reduction scheme with the ratio of VOCs to NOx emission reduction much greater than 1 could effectively reduce the local O3 concentration. ⑤ The main sources of atmospheric VOCs in Yinchuan were motor vehicle emission sources （32.3%）, process sources （20.7%）, combustion sources （19.2%）, solvent use sources （12.7%）, gasoline volatile sources （9.1%）, and natural sources （6%）, and the contribution rate of motor vehicle emission sources on polluted days increased by 4.6% compared with that on non-polluted days, indicating that the motor vehicle emission source was an important object of summer VOCs control in Yinchuan.

Heavy metals pollution characteristics, source analysis, and risk assessment in soil based on Geographic Information System: A case study of a typical area in Three Parallel Rivers of Yunnan Protected Areas, China

Three Parallel Rivers of Yunnan Protected Areas, which was listed as World Natural Heritage, was suffering from increasingly serious heavy metal pollution. However, the existing research on heavy metal pollution in the area was almost blank. This study collected 115 surface soil samples from the local area and used inductively coupled plasma optical emission spectroscopy (ICP-OES) to determine the content of local 12 kinds of heavy metals. Results indicated that mining the tungsten-beryllium mine has caused serious heavy metal pollution to the soil in nature reserves. It poses risks to ecology and human health that cannot be ignored. There was severe pollution of tungsten (W) (Igeo = 8.30) and beryllium (Be) (Igeo = 1.92) in the topsoil. Subsequently, applied the pollution index assessment, potential ecological risk assessment (PERI), human health risk assessment (HHRA), and positive matrix factorization (PMF) models in conjunction with geographic information system (GIS) mapping to systematically evaluate the potential hazards of heavy metals in an abandoned tungsten-beryllium, where located in Haba Snow Mountain Nature Reserve that’s the core area of “Three Parallel Rivers”. The PMF model and GIS mapping identified four factors, nature, transportation, mining, and miners living, as the main sources of local heavy metals. The HHRA results indicated no health risks in the study area, but the GIS mapping identified areas where there are. It is the gaps in the systematic study of heavy metal pollution in the core area of the “Three Parallel Rivers” that the results of the study supplement and provide reference data for controlling soil heavy metal pollution in tungsten-beryllium mining areas and valuable information for managers to develop pollution control and risk management strategies.

Source Profile Analysis, Source Apportionment, and Potential Health Risk of Ambient Particle-Bound Polycyclic Aromatic Hydrocarbons in Areas of Specific Interest

Ambient particulate matter (PM10) and its chemical composition in polycyclic aromatic hydrocarbons (PAHs) were studied in areas of specific interest, between September 2015 and July 2016. The principal aim of this study was to assess the different PAH source profiles in each area, as well as their potential health risk. In particular, the studied areas were (a) the semiurban industrialized zone of the Municipality of Peloponnese (Meligalas, Messini) of Messinia prefecture, due to the intensive olive-productive activity in the extensive area, (b) the industrialized zone of Oinofyta in Voiotia prefecture, and (c) the urban/traffic center of Athens (Aristotelous). Intense spatial and seasonal variations in PAH levels were observed among the study areas collectively, but also for each one individually. During the winter period, the total PAHs average concentration was 11.45 and 9.84 ng/m3 at Meligalas–Skala (S1, S2 stations), 8.84 ng/m3 at Messini (S3 station), and 6.30 ng/m3 at the center of Athens (Aristotelous). During the summer campaign, the corresponding levels were 0.99, 1.20, and 0.70 ng/m3 (S1, S2, and S3 stations), and 5.84 ng/m3 (Aristotelous), respectively. The highest potential cancer risk of the PAHs mixture was estimated based on winter season measurements taken at the Municipality of Peloponnese. In order to determine PAH sources, two different source apportionment techniques were applied, i.e., diagnostic ratios (DRs) and the positive matrix factorization (PMF) model.

A study on source identification of contaminated soil with total petroleum hydrocarbons (aromatic and aliphatic) in the Ahvaz oil field.

The oil industry in Khuzestan province (Southwest Iran) is one of the main reasons contributing to the pollution of the environment in this area. TPH, including both aromatic and aliphatic compounds, are important parameters in creating pollution. The present study aimed to investigate the source of soil contamination by TPH in the Ahvaz oil field in 2022. The soil samples were collected from four oil centers (an oil exploitation unit, an oil desalination unit, an oil rig, and a pump oil center). An area outside the oil field was determined as a control area. Ten samples with three replicates were taken from each area according to the standard methods. Aromatic and aliphatic compounds were measured by HPLC and GC methods. The positive matrix factorization (PMF) model and isomeric ratios were used to determine the source apportionment of aromatic compounds in soil samples. The effects range low and effects range median indices were also used to assess the level of ecological risk of petroleum compounds in the soil samples. The results showed that Benzo.b.fluoranthene had the highest concentration with an average of 5667.7 ug/kg in soil samples in the Ahvaz oil field. The highest average was found in samples from the pump oil center area at 7329.48 ug/kg, while the lowest was found in control samples at 1919.4 ug/kg-1. The highest level of aliphatic components was also found in the pump oil center, with a total of 3649 (mg. Kg-1). The results of source apportionment of petroleum compounds in soil samples showed that oil activities accounted for 51.5% of the measured PAHs in soil. 38.3% of other measured compounds had anthropogenic origins, and only 10.1% of these compounds were of biotic origin. The results of the isomeric ratios also indicated the local petroleum and pyrogenic origin of PAH compounds, which is consistent with the PMF results. The analysis of ecological risk indices resulting from the release of PAHs in the environment showed that, except for fluoranthene, other PAHs in the oil exploitation unit area were above the effects range median level (ERM) and at high risk. The results of the study showed that soil pollution by total petroleum hydrocarbons (TPH), both aromatic and aliphatic, is at a high level, and is mainly caused by human activities, particularly oil activities.

Predictive analysis and risk assessment of potentially toxic elements in Beijing gas station soils using machine learning and two-dimensional Monte Carlo simulations

Gas stations not only serve as sites for oil storage and refueling but also as locations where vehicles frequently brake, significantly enriching the surrounding soil with potentially toxic elements (PTEs). Herein, 117 topsoil samples from gas stations were collected in Beijing to explore the impact of gas stations on PTE accumulation. The analysis revealed that the average Pollution Index (PI) values for Cd, Hg, Pb, Cu, and Zn in the soil samples all exceeded 1. The random forest (RF) model, achieving an AUC score of 0.95, was employed to predict PTE pollution at 372 unsampled gas stations. Additionally, a Positive Matrix Factorization (PMF) model indicated that gas station operations and vehicle emissions were responsible for 70 % of the lead (Pb) enrichment. Probabilistic health risk assessments showed that the carcinogenic risk (CR) and noncarcinogenic risk (NCR) for PTE pollution to adult females were the highest, at 0.451 and 1.61E-05 respectively, but still within acceptable levels. For adult males at contaminated sites, the Pb-associated CR and NCR were approximately twice as high as those at uncontaminated sites, with increases of 107 % and 81 %, respectively. This study provides new insights for managing pollution caused by gas stations.

Health Risk Assessment of Road-Dust-Bound Heavy Metals via Ingestion Exposure from One Typical Inland City of Northern China: Incorporation of Sources and Bioaccessibility

Heavy metals in road dust pose potential health risks to humans, while oral bioaccessibility and sources are all important factors influencing this health risk. However, few prior studies have combined them for health risk analysis. In this study, road dust samples were collected from different geographical locations of Jinan (west area, WA; central area, CA; and east area, EA) to analyze the source-specific and bioaccessibility-based health risks of heavy metals. The mean concentrations of heavy metals in the three areas were CA &gt; EA &gt; WA, with Cd, Cu, Mn, Pb, and Zn exceeding their corresponding background values. A source analysis using a Positive Matrix Factorization (PMF) model showed that traffic emissions were the main source of heavy metals in the WA and CA, while industrial activities were the main source in the EA. The mean bioaccessibility of heavy metals extracted using the Solubility Bioaccessibility Research Consortium (SBRC) method followed the order of Cd (75.5%) &gt; Zn (42.2%) &gt; Mn (42.1%) &gt; Pb (42.0%) &gt; Cu (32.9%) &gt; As (23.6%) &gt; Ni (20.1%) &gt; V (16.8%) &gt; Cr (13.3%). According to the combined source analysis, traffic was the primary risk factor in the WA (54.5 and 58.3% of NCR and CR, respectively) and CA (61.8 and 51.2%), with solid waste being the main risk factor in the EA (41.9 and 51.3%). In oral bioaccessibility testing, lower non-carcinogenic (&lt;1.0) and carcinogenic risks (&lt;1 × 10−6) of heavy metals were observed than those based on the total metal content. More importantly, As (43.4%) was replaced by V (29.7%) as the main contributor to NCR. Source-specific and bioaccessibility-based health risk assessments can accurately identify priority pollutants and heavy metals in urban road dust that need to be controlled. This provides more effective and accurate urban environmental risk management recommendations for sustainable urban development and population health.

Identify the seasonal differences in water quality and pollution sources between river-connected and gate-controlled lakes in the Yangtze River basin

The river-connected Dongting Lake (DT) and Poyang Lake (PY), and the gate-controlled Taihu Lake (TH) and Chaohu Lake (CH) are the four important lakes in the Yangtze River Basin. The comprehensive Water Quality Index (WQI), the Eutrophication Integrated Index (TLI(Σ)), and the Positive Matrix Factorization (PMF) model were employed to evaluate water quality and the contribution of pollution sources for these lakes. The results show that WQI for all lakes indicated generally good water quality, with DT scoring 73.52–86.18, the highest among them. During the wet season, the eutrophication degree of river-connected lake was medium, and that of gate-controlled lakes was high. The surface runoff and agricultural non-point sources are the main pollution sources for both types of lakes, but their impact is more pronounced in gate-controlled lakes during the wet season. The study provides evidence support for scientific understanding of water quality problems and management strategies in these areas.