Quantitative source apportionment and driver identification of soil heavy metals using advanced machine learning techniques

Quantitative Source Apportionment and Transfer Mechanism of Pb in Different Compartments of Soil-Wheat System: A Fresh Insight from Pb Isotopic Composition, Fractionation and Inverse Distance Weightings

Lead (Pb) pollution has always been a persistent and unresolved environmental issue of great concern. This study innovatively applied Pb isotopic compositions and inverse distance weighting (IDW) to quantitatively identify Pb source contributions in the soil-wheat system in Kaifeng, China. Results showed Pb concentrations followed as soil > root > stem > shell > grain, with 18.2% of grains exceeding the National food safety standard (0.2 mg kg⁻¹). Quantitative source identification displayed atmospheric deposition contributed 66.82%, 66.32% and 63.00% to grains, leaves and shells, respectively, while sewage irrigation accounted for 67.74%, 58.61% and 57.56% in roots, stems and soils. Lighter Pb isotopes from atmospheric deposition were more readily absorbed by leaves and enriched in grains, whereas roots and stems retained heavier isotopes from sewage irrigation, effectively blocking their migration to grains and reducing health risks. This study provides valuable insights into Pb uptake, migration, and mechanisms in the soil-wheat system. It is commended reasonable regulation of rhizosphere soil and atmospheric environment or physiological interference on wheat growth might be an effective way to reduce the risk of Pb enrichment in wheat grains.

Identification of the pollution sources and hidden clustering patterns for potentially toxic elements in typical peri-urban agricultural soils in southern China

Peri-urban agricultural soils are often contaminated by potentially toxic elements (PTEs) due to rapid urbanization, industrial activities, and agricultural practices. In this study, two advanced analytical methods including positive matrix factorization (PMF) model and K-means clustering algorithm were integrated to explore the potential sources and concealed contamination patterns of 8 PTEs in peri-urban soils in county Gaoming, China. Descriptive statistics showed average concentrations of arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), and zinc (Zn) as 19.11, 0.18, 35.69, 20.31, 18.26, 151.7, 67.75, and 0.29 mg/kg, respectively. The PMF model identified three primary sources: geogenic (Cr, Ni), industrial and traffic-related (Pb, Hg, Zn), and agricultural (As, Cd and Cu). The contribution of each source was quantified: geogenic sources contributed 55.6% to Cr and 52.3% to Ni, industrial sources accounted for 41.8% of Pb, 58.4% of Hg, and 41.9% of Zn, while agricultural practices contributed 88.1% of As, 77.9% of Cu, and 70.7% of Cd. Subsequently, K-means clustering classified the soil samples into three distinct clusters based on the derived factor contribution from PMF model, reflecting their clear spatial associations with different types of land use: large-scale agricultural areas (Cluster 1), natural vegetation (Cluster 2), and urbanized zones (Cluster 3). Furthermore, boxplots showed that the highest PTE concentrations were found in the third cluster, confirming the significant impact of human activities, while the lower concentrations in the second cluster indicated more natural conditions. These results underscored the dual influences of agriculture and urbanization on PTE contamination, which highlighted the need for targeted soil management strategies. Moreover, the integration of PMF and K-means clustering effectively reveals potential sources and concealed pollution patterns, providing insights for managing pollution and safeguarding environmental health in rapidly urbanized areas.

Identifying spatial drivers of soil heavy metal pollution risk integrating positive matrix factorization, machine learning, and multi-scale geographically weighted regression

Soil heavy metal (HMs) contamination poses significant ecological and health risks, yet the spatial drivers of HMs pollution remain poorly understood. This study integrates pollution risk assessment, positive matrix factorization, machine learning, and multi-scale geographically weighted regression to develop a framework for identifying the spatial drivers of soil HMs contamination risk in Yangtze River New City, China. Analysis of 7152 samples revealed that although average HMs concentrations were below national standards, As, Cd, Cr, Cu, Hg, and Ni exceeded local background levels. Four key factors were identified as drivers of HMs contamination: natural sources (30.36 %, influenced by soil type), mixed agricultural and transportation sources (29.56 %, driven by cropland, aquaculture, and road density), human activities (12.68 %, including population density and community activities), and industrial sources (27.42 %, linked to factories and enterprises). Regional variations indicated that industrial activities, transportation, and human activities primarily influenced health risks, while agriculture and natural factors had a greater impact on ecological and environmental capacity risks. These findings underscore the importance of considering spatial heterogeneity in HMs pollution risk assessments and offer insights for developing targeted, region-specific policies to mitigate pollution risks of soil HMs.

Source-oriented health risk of heavy metals in sediments: A case study of an industrial city in China

The heavy metals (HMs) pollution caused by accelerated urbanization poses a significant risk to environmental and human health. Sediments, as an important component of aquatic ecosystems, have become a global environmental problem due to their HMs pollution. In this paper, 53 surface water and sediment samples were carried out in the industrial city of Changzhou to analyze and evaluate the pollution characteristics. A comprehensive source risk source allocation and source health risk integrated method based on positive matrix factorization (PMF) and health risk assessment models is applied. We found that the average concentration of most HMs accumulated in sediments greatly exceeds the soil background value in Changzhou, posing a high ecological risk. Pollution sources contribution to the HMs contents ranked as: electronic industry and mechanical manufacturing (29.18 %) > metal smelting industry (20.97 %) > atmospheric deposition and transportation (20.07 %) > natural source (16.32 %) > agricultural source (13.46 %). The hazard index (HI) values and carcinogenic risk (CR) for adults are within an acceptable risk level range. The average HI for children is 1.589, which is an unacceptable risk. Source-oriented health risks indicate that metal mining is the main source of health risks due to the large number of arsenic emissions from metallurgical processes. This study identified pollution levels, sources, and risks of HMs and can provide supporting information for effective source regulation.

Potential risk of heavy metals release in sediments and soils of the Yellow River Basin (Henan section): A perspective on bioavailability and bioaccessibility

The ecology of watersheds plays an important role in regulating regional climate and human activities. The sediment-soil system in the middle and lower reaches of the Yellow River Basin (Henan section) was explored. The spatial distribution characteristics of heavy metals (HMs) showed that tributaries, which are affected by anthropogenic activities, contain higher concentrations of HMs than the main channel. Sequential extraction experiments indicated that Cd had the strongest potential to be released, followed by Mn. In vitro simulation experiments showed that gastric and pulmonary fluids rendered these two orders of magnitude more bioaccessible compared to sweat. Moreover, Cd exhibited the highest bioaccessibility in both gastric and lung fluids. When bioaccessibility was considered in the evaluation of health risks, more than 82 % of reductions in non-carcinogenic and carcinogenic risk indices were observed in children and adults. A positive matrix factorization model was utilized to determine the potential sources of HMs: industrial sources, natural sources, and mixed agricultural and transportation sources were identified as the three main sources of HMs in sediments and soils. In addition, mining activities were also an HMs source in sediments.

Predicting cadmium enrichment in crops/vegetables and identifying the effects of soil factors based on transfer learning methods

Cadmium (Cd) is present in soils and can easily migrate into plants due to its various forms. This mobility allows it to be absorbed by plant roots and accumulate in edible parts, entering the food chain and posing health risks. In some regions, insufficient sampling and research, or the limited cultivation of specific vegetables and crops, make it challenging to gather adequate data for modeling. A total of 353 pairs of soil and crop/vegetable samples were collected across three regions using a unified measurement method. These samples were utilized to build predictive models to study the relationship between soil factors and cadmium (Cd) absorption in six different crops/vegetables, followed by a unified comparison. This study compares regression and probability models and determines the best feature combination, which can retain enough information to accurately predict and prevent over-fitting caused by too many features. The best feature combination is used to apply transfer learning to cadmium enrichment in crops/vegetables. The results show that the best accuracy of the random forest probability model in the rice dataset is 0.89. The best feature combination of prediction results was found by feature optimization. This feature combination has a very good effect on the prediction of cadmium in corn / vegetables by transfer learning. The accuracy of corn, rape and radish is 0.93,0.89 and 0.81, respectively. In the case of good prediction effect of transfer learning, available Cd is the most critical function, and available Cd is positively correlated with Cd in plants. It suggests that available heavy metal significantly influence predictions in crops/vegetables. In areas with less sampling and research, selecting relevant features and using transfer learning methods is more appropriate for constructing predictive models.

A data-driven framework to identify influencing factors for soil heavy metal contaminations using random forest and bivariate local Moran's I: A case study

The efficacy of traceability analysis is often limited by a lack of information on influencing factors for heavy metal (HM) contaminations in soil, such as spatial correlations between HM concentrations and influencing factors. To overcome this limitation, a novel data-driven framework was established to identify influencing factors for soil HM concentrations in an industrialised study area, in Guangdong Province, China, mainly using random forest (RF) and bivariate local Moran's I (BLMI) on the basis of the 577 soil samples and the 18 environmental covariates. The quantitative contributions of the 18 influencing factors for the Cd, As, Pb, and Cr concentrations were determined by the optimised RF. The main influencing factors of Cd were petrol stations (10.97%) and railways (9.99%), the main ones of As were groundwater depth (8.45%) and elevation (8.24%), the main ones of Pb were soil pH (8.82%) and hazardous waste disposal sites (8.02%), and the main ones of Cr were mine tailings (13.65%) and rainfall (11.88%). The eight spatial clustering maps between the four HM concentrations and the two key influencing factors were generated by BLMI. The middle part of the study area has shown the higher concentrations of Cd, As, Pb, and Cr, the more complex human activities and the more high-high clusters. Priority attention should be paid to the middle part when taking the specific prevention and control measures for their contaminations. This data-driven framework provided rich information on influencing factors, including HM concentrations, HM contaminations, quantitative contributions, and qualitative spatial clusters.

Revealing the synergistic spatial effects in soil heavy metal pollution with explainable machine learning models

The identification of factors that affect changes in the heavy metal content of soil is the basis for reducing or preventing soil heavy metal pollution. In this research, 16 environmental factors were selected, and the influences of soil heavy metal spatial distribution factors and the synergy amongst space factors were evaluated using a geographic detector (GD) and the extreme gradient boosting (XGBoost)-Shapley additive explanations (SHAP) model. Three heavy metal elements, namely, Cd, Cu and Pb, in the study region were examined. The following results were obtained. (1) XGBoost demonstrated high accuracy in predicting the spatial distributions of soil heavy metals, with each heavy metal having an R2 value of over 0.6. (2) Geological type map (Geomap) and enterprise density considerably affected the concentrations of Cd, Cu and Pb in soil in the GD and XGBoost-SHAP models. In addition, cross-detection revealed strong explanatory power when natural and human factors were combined. (3) Under the same geological background, the different trends of gross domestic product effects on heavy metals indicated that pollution control measures were effective in economically developed areas, and the economy and the environment could be balanced. Meanwhile, the interaction between the normalised difference vegetation index and enterprise density showed that vegetation could alleviate heavy metal pollution in the region. This study supports strategic decision-making, serving as a reference for the global management of soil heavy metal contamination, sustainable ecological development and assurance of people's health and well-being.

Evaluating drivers of PM2.5 air pollution at urban scales using interpretable machine learning

Reducing urban fine particulate matter (PM2.5) concentrations is essential for China to achieve the Sustainable Development Goals (SDGs). Identifying the key drivers of PM2.5 will enable the development of targeted strategies to reduce PM2.5 levels. This study introduces a machine-learning model that combines CatBoost and the Tree-Structured Parzen Estimator (TPE) to analyze PM2.5 concentration across 297 cities between 2000 and 2021. SHapley Additive exPlanations (SHAP) were employed to identify the primary factors influencing urban PM2.5 concentrations. The study revealed that the proposed model has high accuracy in predicting urban PM2.5 concentrations, achieving a coefficient of determination (R2) score of 96.44%. Socioeconomic and industrial activity are key drivers of PM2.5 concentrations. This study not only quantifies the primary factors exacerbating or alleviating pollution for each city or province during the 2000-2021 period but also evaluates the influence of operational factors such as technological and public financial expenditures. In 2000, the main contributors to pollution in four heavily polluted cities included substantial nitrogen oxide emissions, inadequate technology investments, and excessive population density and liquefied gas consumption. Due to the rapid reduction in nitrogen oxide emissions, pollution levels in these cities have improved substantially. In the future, the most effective strategies for pollution reduction in these cities will focus on controlling population density and slowing down mining development. The proposed framework serves as a robust evaluation tool and can propose tailored strategies to control PM2.5 concentrations effectively in each city.

Machine learning models with innovative outlier detection techniques for predicting heavy metal contamination in soils

Machine learning (ML) models for accurately predicting heavy metals with inconsistent outputs have improved owing to dataset outliers, which influence model reliability and accuracy. A comprehensive technique that combines machine learning and advanced statistical methods was applied to assess data outlier's effects on ML models. Ten ML models with three outlier detection methods predicted Cr, Ni, Cd, and Pb in Narayanganj soils. XGBoost with density-based spatial clustering of applications with noise (DBSCAN) improved model efficacy (R2). The R2 of Cr, Ni, Cd, and Pb was considerably enhanced by 11.11 %, 6.33 %, 14.47 %, and 5.68 %, respectively, indicating that outliers affected the model's HM prediction. Soil factors affected Cr (80 %), Ni (72.61 %), Cd (53.35 %), and Pb (63.47 %) concentrations based on feature importance. Contamination factor prediction showed considerable contamination for Cr, Ni, and Cd. LISA revealed Cd (55.4 %), Cr (49.3 %), and Pb (47.3 %) as the significant pollutant (p < 0.05). Moran's I index values for Cr, Ni, Cd, and Pb were 0.65, 0.58, 0.60, and 0.66, respectively, indicating strong positive spatial autocorrelation and clusters with similar contamination. Finally, this work successfully assessed the influence of data outliers on the ML model for soil HM contamination prediction, identifying crucial regions that require rapid conservation measures.

Heavy metal contamination of vegetables in China: status, causes, and impacts

Exposure to heavy metals from vegetable consumption poses a serious health risk to the Chinese population. The lack of knowledge on the overall status of vegetable contamination at the national level hinders the development of national regulations on preventing heavy metal exposure. To address this issue, the study presents an overview of heavy metal contamination in vegetables across China based on 96 peer-reviewed studies published over the past 20 years. The average concentrations of As, Cd, Cr, Cu, Mn, Ni, Pb, and Zn in the edible parts of vegetables are 3.7 ± 12.9, 1.6 ± 4.0, 4.3 ± 10.3, 18.6 ± 27.6, 164 ± 281, 4.5 ± 5.5, 7.7 ± 23.7, and 105 ± 283 mg kg-1 (dry weight), respectively. The associated daily exposures are 0.1-5.7, 0.1-1.7, 0.6-4.2, 4.1-20.5, 26-107, 0.7-3.0, 0.4-16.0, and 13-93 μg kg-1 d-1, respectively. General linear models explained 80%, 44%, 83%, 79%, 64%, 81%, 65%, and 55% of the total variance in As, Cd, Cr, Cu, Mn, Ni, Pb, and Zn concentrations in vegetables, respectively, based on vegetable type and selected geological, meteorological, economic, and environmental factors. Agroforestry is the main source of heavy metal contamination, accounting for 3%-30% of the total variance in heavy metal concentrations in vegetables. Mining, smelting, refining, metalworking, and electrical equipment manufacturing are important source of As, Cr, Cu, Mn, Ni, and Pb, accounting for 7%-17% of the total variance.

Exploring action-law of microplastic abundance variation in river waters at coastal regions of China based on machine learning prediction

Surface waters, particularly the river systems, constitute a vital freshwater resource for human beings and aquatic life on Earth. In economically developed and densely populated coastal regions, river water is facing severe microplastic pollution, posing a threat to public health and ecological safety. Reliable prediction of microplastic abundance (MPA) can significantly reduce the costs associated with microplastic field sampling and analysis. This study employed spatial correlation, geographical detector, principal component analysis and five mainstream machine learning models to analyze 79 datasets of MPAs in seven coastal areas of China and performed correlation, regression and attribution analyses based on 19 terrestrial influencing factors that potentially affect the MPA life cycle processes (generation, aging, and migration). The results showed that the Neural Network (NN) and the Gaussian Process Regression (GPR) models achieved the best prediction performance, with the predicted R2 close to 1. Principal component analysis and Shapley additive explanations concluded that meteorological factors, in particular the annual geotemperature, surface solar radiation, and annual relative humidity, had a key influence on the aging of microplastics. The second key factor in improving the MPA prediction ability was the dynamic description of microplastic migration, which was primarily governed by hydrological factors such as annual precipitation and average terrain slope. Unexpectedly, the effects of land use and level of urbanization were relatively small in describing the generation of microplastics. Only the percentage of built areas was strongly correlated with the MPA levels. Note that the MPA prediction and its contribution factors may vary across different basins. Nevertheless, the findings of this study are applicable to predicting and analyzing the distribution of microplastics in other coastal rivers, and for indicating the main contributing factors, ultimately serving as a basis for guiding microplastic pollution control strategies in different river basins.

Source-oriented health risk assessment and priority control factor analysis of heavy metals in urban soil of Shanghai

The characteristics and ecological risks of heavy metal pollution in urban soils were comprehensively investigated, focusing on 224 typical industries undergoing redevelopment in Shanghai. The PMF (Positive Matrix Factorization) model was used to analyze the sources of soil heavy metals, while the HRA (Health Risk Assessment) model with Monte Carlo simulation assessed health risks to humans. Health risks under different pollution sources were explored, and priority control factors were identified. Results showed that, levels of most heavy metals exceeded Shanghai soil background values. Surface soil concentrations of Cd, Hg, Pb, Cu, Zn, and Ni exceeded the background values of Shanghai's soil to varying degrees, at 5.08, 5.40, 1.81, 1.95, 1.43, and 3.53 times, respectively. Four sources were identified: natural sources (22.23 %), mixed sources from the chemical industry and traffic (26.25 %), metal product sources (36.38 %), and pollution sources from electrical manufacturing and the integrated circuit industry (15.14 %). The HRA model indicated a tolerable carcinogenic risk for adults and children, with negligible non-carcinogenic risk. Potential risk was higher for children than for adult females, and higher for adult females than for adult males, with oral ingestion as the primary exposure pathway. Metal product sources and Ni were identified as primary control factors, suggesting intensified regional control. This study provides theoretical support for urban pollution prevention and control.

Priority areas identification for arable soil pollution prevention based on the accumulative risk of heavy metals

Preventing impending heavy metal pollution in arable soil is crucial for ensuring food security and avoiding challenging remediation. The key to effective prevention strategies lies in proactive identifying currently unpolluted regions that are susceptible to future pollution, which current methods, predicated on the assessment of static pollution status, inadequately characterize the potential accumulative changes in soil heavy metals. In this paper, we proposed a framework for identifying priority areas based on the discrepancy between pollution status and accumulative risk, by considering the specific factors that influence heavy metal accumulation in soil. We applied this framework to a region of Xiangtan County to pinpoint priority areas for preventing impending pollution. The result revealed certain areas exhibited a relatively higher accumulative risk of heavy metal pollution, despite not having reached severe pollution levels for the heavy metals Arsenic (As), Cadmium (Cd), Chromium (Cr), Mercury (Hg), and Lead (Pb), among which the area ratio reached nearly 6 %, 36 %, 1 %, 3 %, 4 %, respectively. The priority areas for preventing Cd pollution were primarily concentrated in the mid-southern, mid-western, and eastern regions, while that of the other four heavy metals were predominantly distributed in the mid-northern regions with varying continuous ranges. Moreover, we prioritized the main pollution risks for comprehensive prevention in the following order: Cd, As, Pb, Hg, and Cr, and investigated the key factors contributing to the pollution of these heavy metals. The insights presented in this study have significant implications for soil environmental quality management, offering valuable guidance for implementing precise measures to prevent heavy metal pollution and efficiently control pollution sources.

Endogenous phosphorus release from plateau lakes responds significantly to temperature variability over the last 50 years

The ecological environment of plateau lakes is very sensitive to temperature changes. Higher temperatures accelerate the cycling processes between lake sediments and water nutrients. Quantitatively investigating the influence mechanism of regional climate change and sediment phosphorus release over a long time series is difficult in revealing the causes of eutrophication in plateau lakes. This paper quantitatively reveals the long-term response mechanism of endogenous phosphorus release to temperature change in Dianchi, the largest plateau eutrophic lake in China, based on nearly 50 years of temperature and sediment phosphorus data from 1964 to 2013, and taking advantage of the Random Forest machine learning algorithm for deep processing of long time series and nonlinear relation. The results showed that: (1) Over the past 50 years, endogenous phosphorus release and temperature showed no trend for 22 years, followed by a consistent, significant increase in both after 1986. (2) Random Forest analysis showed that before the increase of temperature, the contribution to the phosphorus release was weak, while after the mutation, the contribution reached 52.6%, and typically was concentrated from March to August each year. (3) The response relationship between temperature and endogenous phosphorus release had non-linear variation with a threshold interval of 18.3 °C-19.2 °C. This research aims to explore the theoretical scientific knowledge of endogenous phosphorus release processes and complex mechanisms in plateau lakes under changing environments, and further explores the effects of long-term temperature variability on endogenous phosphorus loading in plateau lakes. That is, long-term temperature mutations can alter the internal cycling processes of sedimentary phosphorus by stimulating algal growth, which have a more drastic effect than short-term temperature variations.

Multimodel-based quantitative source apportionment and risk assessment of soil heavy metals: A reliable method to achieve regional pollution traceability and management

To strengthen the control of pollution sources and promote soil pollution management of agricultural land, this study constructed a comprehensive source apportionment framework, which significantly improved the reliability of potential source analysis compared with the traditional single model. The spatial distribution pattern of agricultural soil heavy metals (SHMs) content in Lintong, a typical river valley city in China, was determined and the degree of contamination was evaluated. A scientific source apportionment methodological framework was constructed through correlation analysis methods together with multiple source apportionment receptor models. Finally, the Monte Carlo simulation method was used to derive the results of the human health risk assessment (HHRA). The results revealed the following: (1) Agricultural soils were moderately and mildly polluted, accounting for 28.8 % and 71.2 % of the total number of sampling points, respectively. (2) The overall correlation of heavy metals (HMs) was strong according to the coupling analysis of the SHMs, in which a strong correlation (0.8-1) was reached among Cu, Ni, Pb, Cr and Zn, indicating that these HMs were most likely homologous or composite. (3) Multimodel analysis of the SHMs sources revealed that the first and second principal components were agricultural (41.36 %) and industrial (19.69 %) sources, respectively, and the remaining principal components were road traffic, natural factors, and atmospheric deposition or surface runoff, respectively. (4) The average comprehensive noncarcinogenic health risk indices for adults and children were 4.2259E-02 and 1.4194E-01, respectively, which were within the slight risk range, indicating that the risk caused by SHMs to the human body can be almost negligible. This study adopted a mixed method to reveal the risk of SHMs pollution and its sources, which provides some reference and technical support for traceability analysis, zoning control, and health risk studies of regional pollutants and is helpful for formulating scientific management measures and targeting control policies.

A comprehensive study of source apportionment, spatial distribution, and health risks assessment of heavy metal(loid)s in the surface soils of a semi-arid mining region in Matehuala, Mexico

BACKGROUND

This study investigates the contamination level, spatial distribution, pollution sources, potential ecological risks, and human health risks associated with heavy metal(loid)s (i.e., arsenic (As), copper (Cu), iron (Fe), manganese (Mn), lead (Pb), and zinc (Zn)) in surface soils within the mining region of Matehuala, located in central Mexico.

OBJECTIVES

The primary objectives are to estimate the contamination level of heavy metal(loid)s, identify pollution sources, assess potential ecological risks, and evaluate human health risks associated with heavy metal(loid) contamination.

METHODS

Soil samples from the study area were analysed using various indices including Igeo, Cf, PLI, mCd, EF, and PERI to evaluate contamination levels. Source apportionment of heavy metal(loid)s was conducted using the APCS-MLR and PMF receptor models. Spatial distribution patterns were determined using the most efficient interpolation technique among five different approaches. The total carcinogenic risk index (TCR) and total non-carcinogenic index (THI) were used in this study to assess the potential carcinogenic and non-carcinogenic hazards posed by heavy metal(loid)s in surface soil to human health.

RESULTS

The study reveals a high contamination level of heavy metal(loid)s in the surface soil, posing considerable ecological risks. As was identified as a priority metal for regulatory control measures. Mining and smelting activities were identified as the primary factors influencing heavy metal(loid) distributions. Based on spatial distribution mapping, concentrations were higher in the northern, western, and central regions of the study area. As and Fe were found to pose considerable and moderate ecological risks, respectively. Health risk evaluation indicated significant levels of carcinogenic risks for both adults and children, with higher risks for children.

CONCLUSION

This study highlights the urgent need for monitoring heavy metal(loid) contamination in Matehuala's soils, particularly in regions experiencing strong economic growth, to mitigate potential human health and ecological risks associated with heavy metal(loid) pollution.

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.

Characteristics and partitions of traditional and emerging organophosphate esters in soil and groundwater based on machine learning

Organophosphate esters (OPEs) pose hazards to both humans and the environment. This study applied target screening to analyze the concentrations and detection frequencies of OPEs in the soil and groundwater of representative contaminated sites in the Pearl River Delta. The clusters and correlation characteristics of OPEs in soil and groundwater were calculated by self-organizing map (SOM). The risk assessment and partitions of OPEs in industrial park soil and groundwater were conducted. The results revealed that 14 out of 23 types of OPEs were detected. The total concentrations (Σ23OPEs) ranged from 1.931 to 743.571 ng/L in the groundwater, and 0.218 to 79.578 ng/g in the soil, the former showed highly soluble OPEs with high detection frequencies and concentrations, whereas the latter exhibited the opposite trend. SOM analysis revealed that the distribution of OPEs in the soil differed significantly from that in the groundwater. In the industrial park, OPEs posed acceptable risks in both the soil and groundwater. The soil could be categorized into Zone I and II, and the groundwater into Zone I, II, and III, with corresponding management recommendations. Applying SOM to analyze the characteristics and partitions of OPEs may provide references for other new pollutants and contaminated sites.

Quantitative tracing the sources and human risk assessment of complex soil pollution in an industrial park

Pollution in industrial parks has long been characterized by complex pollution sources and difficulties in identifying pollutant origins. This study focuses on a typical industrial park consisting of 11 factories (F1-F11) including organic pigment, inorganic pigment, and chemical factories in Hunan Province, China, here, a total of 327 sample points were surveyed. Eight pollutants (Mn, Cd, As, Co, NH3-N, l, 1,2-Trichloroethane, chlorobenzene, and petroleum hydrocarbons) were classified as contaminants of concern (COCs). This study assessed the contributions of driving factors to the distribution of COCs in the soil. Pollutant source apportionment was conducted using positive matrix factorization (PMF) and random forest (RF). The results revealed that the main factors driving pollution are groundwater migration, non-compliant emissions, leaks during production, and interactions among pollutants. The primary pollution sources were four chemical factories and an inorganic pigment factory. Source 5 demonstrates significant correlations with TCA (29.6%), CB (30%), and As (31.6%). Two chemical factories (F7 and F10) are the most significant pollution source with a risk assessment contribution rate of more than 60%. The present study sheds some light on the contamination characteristics, source apportionment and source-health risk assessment of COCs in industrial park. By utilizing the proposed research framework, decision-makers can effectively prioritize and address identified pollution sources.

The heterogeneous distribution of heavy metal(loid)s at a smelting site and its potential implication on groundwater

The downward migration of soil heavy metal(loid)s (HMs) at smelting sites poses a significant risk to groundwater. Therefore, it is requisite for pollution control to determine the pollution characteristics of soil HMs and their migration risks to groundwater. 198 soil samples collected from a Pb-Zn smelting site were classified into 6 clusters by self-organizing map (SOM) and K-means clustering. Cd, Zn, As, and Pb were identified as the characteristic contaminants of the site. The driving factors for the heterogeneous distribution of HMs have been validated through the implementation of K-means clustering and multiple-hits calculation. Using ultrafiltration extraction and microscopic analysis, the soil colloids were identified as crucial carriers facilitating the migration of HMs. Specifically, the colloidal fractions of Cd, Zn, and As, Pb in deep soil (3-4 m) accounted for 91 %, 78 %, 88 %, and 82 %, respectively, consistently surpassing those found in topsoil (0-0.5 m). It was primarily attributed to the strong affinity of HMs toward soil colloids (franklinite, PbS, and kaolinite) and dissolved organic matter (humic acids and protein). The research findings highlight the potential risk of colloidal HMs to groundwater contamination, providing valuable insights for the development of targeted management and remediation strategies.

Identifying influence factors and thresholds of the next day's pollen concentration in different seasons using interpretable machine learning

The prevalence of pollen allergies is a pressing global issue, with projections suggesting that half of the world's population will be affected by 2050 according to the estimation of the World Health Organization (WHO). Accurately forecasting pollen allergy risks requires identifying key factors and their thresholds for aerosol pollen. To address this, we developed a technical framework combining advanced machine learning and SHapley Additive exPlanations (SHAP) technology, focusing on Beijing. By analyzing meteorological data and vegetation phenology, we identified the factors influencing next-day's pollen concentration (NDP) in Beijing and their thresholds. Our results highlight vegetation phenology data from Synthetic Aperture Radar (SAR), temperature, wind speed, and atmospheric pressure as crucial factors in spring. In contrast, the Normalized Difference Vegetation Index (NDVI), air temperature, and wind speed are significant in autumn. Leveraging SHAP technology, we established season-specific thresholds for these factors. Our study not only confirms previous research but also unveils seasonal variations in the relationship between radar-derived vegetation phenology data and NDP. Additionally, we observe seasonal fluctuations in the influence patterns and threshold values of daily air temperatures on NDP. These insights are pivotal for improving pollen concentration prediction accuracy and managing allergic risks effectively.

Single particle mass spectral signatures from on-road and non-road vehicle exhaust particles and their application in refined source apportionment using deep learning

With advances in vehicle emission control technology, updating source profiles to meet the current requirements of source apportionment has become increasingly crucial. In this study, on-road and non-road vehicle particles were collected, and then the chemical compositions of individual particles were analyzed using single particle aerosol mass spectrometry. The data were grouped using an adaptive resonance theory neural network to identify signatures and establish a mass spectral database of mobile sources. In addition, a deep learning-based model (DeepAerosolClassifier) for classifying aerosol particles was established. The objective of this model was to accomplish source apportionment. During the training process, the model achieved an accuracy of 98.49 % for the validation set and an accuracy of 93.36 % for the testing set. Regarding the model interpretation, ideal spectra were generated using the model, verifying its accurate recognition of the characteristic patterns in the mass spectra. In a practical application, the model performed hourly source apportionment at three specific field monitoring sites. The effectiveness of the model in field measurement was validated by combining traffic flow and spatial information with the model results. Compared with other machine learning methods, our model achieved highly automated source apportionment while eliminating the need for feature selection, and it enables end-to-end operation. Thus, in the future, it can be applied in refined and online source apportionment of particulate matter.

Isotopic insights and integrated analysis for heavy metal levels, ecological risks, and source apportionment in river sediments of the Qinghai-Tibet Plateau

The research was carried out to examine the pollution characteristics, ecological risk, and origins of seven heavy metals (Hg, As, Pb, Cu, Cd, Zn, and Ni) in 51 sediment samples gathered from 8 rivers located on the Qinghai-Tibet Plateau (QTP) in China. The contents of Hg and Cd were 5.0 and 1.1 times higher than their background values, respectively. The mean levels of other measured heavy metals were below those found naturally in the local soil. The enrichment factor showed that the study area exhibited significantly enriched Hg with 70.6% sampling sites. The Cd contents at 19.6% of sampling sites were moderately enriched. The other sampling sites were at a less enriched level. The sediments of all the rivers had a medium level of potential ecological risk. Hg was the major ecological risk factor in all sampling sites, followed by Cd. The findings from the positive matrix factorization (PMF) analysis shown agricultural activities, industrial activities, traffic emissions, and parent material were the major sources. The upper, middle, and low reaches of the Quanji river had different Hg isotope compositions, while sediments near the middle reaches were similar to the δ202Hg of the industrial source. At the upstream sampling sites, the Hg isotope content was very close to the background level. The results of this research can establish a strong scientific sound to improve the safety of the natural circumstances of rivers on the QTP.

Evaluation of the prediction effectiveness for geochemical mapping using machine learning methods: A case study from northern Guangdong Province in China

This study compares seven machine learning models to investigate whether they improve the accuracy of geochemical mapping compared to ordinary kriging (OK). Arsenic is widely present in soil due to human activities and soil parent material, posing significant toxicity. Predicting the spatial distribution of elements in soil has become a current research hotspot. Lianzhou City in northern Guangdong Province, China, was chosen as the study area, collecting a total of 2908 surface soil samples from 0 to 20 cm depth. Seven machine learning models were chosen: Random Forest (RF), Support Vector Machine (SVM), Ridge Regression (Ridge), Gradient Boosting Decision Tree (GBDT), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), and Gaussian Process Regression (GPR). Exploring the advantages and disadvantages of machine learning and traditional geological statistical models in predicting the spatial distribution of heavy metal elements, this study also analyzes factors affecting the accuracy of element prediction. The two best-performing models in the original model, RF (R2 = 0.445) and GBDT (R2 = 0.414), did not outperform OK (R2 = 0.459) in terms of prediction accuracy. Ridge and GPR, the worst-performing methods, have R2 values of only 0.201 and 0.248, respectively. To improve the models' prediction accuracy, a spatial regionalized (SR) covariate index was added. Improvements varied among different methods, with RF and GBDT increasing their R2 values from 0.4 to 0.78 after enhancement. In contrast, the GPR model showed the least significant improvement, with its R2 value only reaching 0.25 in the improved method. This study concluded that choosing the right machine learning model and considering factors that influence prediction accuracy, such as regional variations, the number of sampling points, and their distribution, are crucial for ensuring the accuracy of predictions. This provides valuable insights for future research in this area.

Historical construction, quantitative source identification and risk assessment of heavy metals contamination in sediments from the Pearl River Estuary, South China

Historical reconstruction of heavy metals (HMs) contamination in sediments is a key for understanding the effects of anthropogenic stresses on water bodies and predicting the variation trends of environmental state. In this work, eighteen sediment cores from the Pearl River Estuary (PRE) were collected to determine concentrations and geochemical fractions of HMs. Then, their potential sources and the relative contributions during different time periods were quantitatively identified by integrating lead-210 (210Pb) radioisotope dating technique into positive matrix factorisation (PMF) method. Pollution levels and potential ecological risks (PERs) caused by HMs were accurately assessed by enrichment factors (EF) based on establishment of their geochemical baselines (GCBs) and multiparameter evaluation index (MPE). HMs concentrations generally showed a particle size- and organic matter-dependent distribution pattern. During the period of 1958-1978, HMs concentrations remained at low levels with agricultural activities and natural processes being identified as the predominant sources and averagely contributing >60%. Since the reform and opening-up in 1978, industrial and traffic factors become the primary anthropogenic sources of HMs (such as Cu, Zn, Cd, Pb, Cr, and Ni), averagely increasing from 22.1% to 28.1% and from 11.6% to 23.4%, respectively. Conversely, the contributions of agricultural and natural factors decreased from 37.0% to 28.5% and from 29.3% to 20.0%, respectively. Subsequently, implementation of environmental preservation policies was mainly responsible for the declining trend of HMs after 2010. Little enrichment of sediment Cu, Zn, Pb, Cr and Ni with EFs (0.15-1.43) was found in the PRE, whereas EFs of Cd (1.16-2.70) demonstrated a slight to moderate enrichment. MPE indices of Cu (50.7-252), Pb (52.0-147), Zn (35.5-130), Ni (19.6-71.5), Cr (14.2-68.8) and Cd (0-9.90) highlighted their potential ecological hazards due to their non-residual fractions and anthropogenic sources.

Analysis of heavy metal and polycyclic aromatic hydrocarbon pollution characteristics of a typical metal rolling industrial site based on data mining

With the transformation and upgrading of industries, the environmental problems caused by industrial residual contaminated sites are becoming increasingly prominent. Based on actual investigation cases, this study analyzed the soil pollution status of a remaining sites of the copper and zinc rolling industry, and found that the pollutants exceeding the screening values included Cu, Ni, Zn, Pb, total petroleum hydrocarbons and 6 polycyclic aromatic hydrocarbon monomers. Based on traditional analysis methods such as the correlation coefficient and spatial distribution, combined with machine learning methods such as SOM + K-means, it is inferred that the heavy metal Zn/Pb may be mainly related to the production history of zinc rolling. Cu/Ni may be mainly originated from the production history of copper rolling. PAHs are mainly due to the incomplete combustion of fossil fuels in the melting equipment. TPH pollution is speculated to be related to oil leakage during the industrial use period and later period of vehicle parking. The results showed that traditional analysis methods can quickly identify the correlation between site pollutants, while SOM + K-means machine learning methods can further effectively extract complex hidden relationships in data and achieve in-depth mining of site monitoring data.

Application of machine learning in atmospheric pollution research: A state-of-art review

Machine learning (ML) is an artificial intelligence technology that has been used in atmospheric pollution research due to their powerful fitting ability. In this review, 105 articles related to ML and the atmospheric pollution research are critically reviewed. Applications of ML in the prediction of atmospheric pollution (mainly particulate matters) are systematically described, including the principle of prediction, influencing factors and improvement measures. Researchers can improve the accuracy of the prediction model through three main aspects, namely considering the geographical features of the study area into the model, introducing the physical characteristics of pollutants, matching and optimizing ML models. And by using interpretable ML tools, researchers are able to understand the mechanism of the model and gain in-depth information. Then, the state-of-art applications of ML in the source apportionment of atmospheric particulate matter and the effect of atmospheric pollutants on human health are also described. In addition, the advantages and disadvantages of the current applications of ML in atmospheric pollution research are summarized, and the application perspective of ML in this field is elucidated. Given the scarcity of source apportionment applications and human health research, standardized research methods and specialized ML methods are required in atmospheric pollution research to connect these two disciplines.

Assessment and machine learning prediction of heavy metals fate in mining farmland assisted by Positive Matrix Factorization

The accurate pollutant prediction by Machine Learning (ML) is significant to efficient environmental monitoring and risk assessment. However, application of ML in soil is under studied. In this study, a Positive Matrix Factorization (PMF) assisted prediction method was developed with Support Vector Machine (SVM) and Random Forest (RF) for heavy metals (HMs) prediction in mining farmland. Principal Component Analysis (PCA) and Redundancy Analysis (RDA) were selected to pretreat data. Experiment results illustrated Cd was the main pollutant with heavy risks in the study area and Pb was easy to migrate. The method effects of HMs total concentration predicting were PMF > Simple > PCA > PCA - PMF, and RF predicted better than SVM. Data pretreatment by RDA prior inspection improved the model results. Characteristic HMs Tessier fractions prediction received good effects with average R value as 0.86. Risk classification prediction performed good in Cd, Cu, Ni and Zn, however, Pb showed weak effect by simple model. The best classifier method for Pb was PMF - RF method with relatively good effect (Area under ROC Curve = 0.896). Overall, our study suggested the combination between PMF and ML can assist the prediction of HMs in soil. Spatial weighted attribute of HMs can be provided by PMF.

Machine learning combined with Geodetector quantifies the synergistic effect of environmental factors on soil heavy metal pollution

The critical prerequisite for the prevention and control of soil heavy metal (HM) pollution is the identification of factors that influence soil HM accumulation. The dominant factors have been individually identified and apportioned in existing studies. However, the accumulation of soil HMs results from a combination of multiple factors, and the influence of a single factor is less than the interaction of multiple parameters on soil HM pollution. In this study, we employed Geodetector to delve into the interaction effect of the influencing factors on the variations of soil HMs. We performed partial dependence plot to depict how these factors interact with each other to affect the HM content. We found that both individually and interactively, pH and agricultural activities significantly impact soil HM content. Except for Hg and Cu, the pairs with the most significant interaction effects all involve pH. For Pb, As and Zn, interaction with pH has the most significant driving force compared to the other factors. For Cu, Hg, and Ni, all environmental factor interactions increased their explanatory power, while for Cr, the single most significant driver decreased its driving power when interacting with other factors. Additionally, the study area exhibited a widespread prevalence of changes in HM concentration being governed by the synergistic effect of two factors. For the response of HMs to the interaction of pH and fertilizer, soil HM concentration was sensitive to pH, while fertilizer had less effect. These results provide a dependable method of investigating the interaction of environmental factors on soil HM content and put forth efficacious and potent tactical measures for soil HM pollution prevention and control based on the interaction type.

Differentiating environmental scenarios to establish geochemical baseline values for heavy metals in soil: A case study of Hainan Island, China

Soil heavy metal distributions exhibit regional heterogeneity due to the complex characteristics of parent materials and soil formation processes, emphasizing the need for appropriate regional standards prior to assessing soil risks. This study focuses on Hainan Island and employs the Multi-purpose Regional Geochemical Survey dataset to establish heavy metal geochemical baseline and background values for soil using an iterative method. Geographical detector analysis reveals that parent materials are the primary factor influencing heavy metal distribution, followed by soil types and land use. Heavy metal geochemical baseline values are established for the island's three environments and administrative regions. Notably, a universal geochemical baseline value cannot adequately represent regional variations in heavy metal distribution, with parent materials playing a crucial role in various scenarios. Locally applicable values based on parent material are the most representative for Hainan Island. This study provides a reference framework for developing region-specific environmental baseline values for soil heavy metal assessments.

Heavy metals contamination, receptor model-based sources identification, sources-specific ecological and health risks in road dust of a highly developed city

The present study quantified Ni, Cu, Cr, Pb, Cd, As, Zn, and Fe levels in road dust collected from a variety of sites in Tangail, Bangladesh. The goal of this study was to use a matrix factorization model to identify the specific origin of these components and to evaluate the ecological and health hazards associated with each potential origin. The inductively coupled plasma mass spectrometry was used to determine the concentrations of Cu, Ni, Cr, Pb, As, Zn, Cd, and Fe. The average concentrations of these elements were found to be 30.77 ± 8.80, 25.17 ± 6.78, 39.49 ± 12.53, 28.74 ± 7.84, 1.90 ± 0.79, 158.30 ± 28.25, 2.42 ± 0.69, and 18,185.53 ± 4215.61 mg/kg, respectively. Compared to the top continental crust, the mean values of Cu, Pb, Zn, and Cd were 1.09, 1.69, 2.36, and 26.88 times higher, respectively. According to the Nemerow integrated pollution index (NIPI), pollution load index (PLI), Nemerow integrated risk index (NIRI), and potential ecological risk (PER), 84%, 42%, 30%, and 16% of sampling areas, respectively, which possessed severe contamination. PMF model revealed that Cu (43%), Fe (69.3%), and Cd (69.2%) were mainly released from mixed sources, natural sources, and traffic emission, respectively. Traffic emission posed high and moderate risks for modified NIRI and potential ecological risks. The calculated PMF model-based health hazards indicated that the cancer risk value for traffic emission, natural, and mixed sources had been greater than (1.0E-04), indicating probable cancer risks and that traffic emission posed 38% risk to adult males where 37% for both adult females and children.

Genetic programming-based predictive model for the Cr removal effect of in-situ electrokinetic remediation in contaminated soil

Soil electrokinetic remediation is an emerging and efficient in-situ remediation technology for reducing environmental risks. Promoting the dissolution and migration of Cr in soil under the electric field is crucial to decrease soil toxicity and ecological influences. However, it is difficult to establish strong relationships between soil treatment and impact factors and to quantify their contributions. Machine learning can help establish pollutant migration models, but it is challenging to derive predictive formulas to improve remediation efficiency, describe the predictive model construction process, and reflect the importance of the predictors for better regulation. Therefore, this paper established a predictive model for the electrokinetic remediation of Cr-contaminated soil using genetic programming (GP) after determining the characteristic parameters which influenced the remediation effect, described the model's adaptive optimization process through the algorithm's function, and identified the sensitivity factors affecting the Cr removal effect. Results showed that the Cr(VI) and total Cr concentrations predicted by GP were in satisfactory agreement with the experimental values, 92% of the training data and 90% of the validation data achieved errors within 1%, and could fully reflect the target ions' content variation in different soil layers. By substituting the above prediction formulas into Sobol sensitivity analysis, it was determined that conductivity, pH, current, and moisture content dramatically affected the Cr content variation in distinguished regions. For overall contaminated area, the system current and soil pH were the most sensitive factors for Cr(VI) and total Cr contents. Remediation efforts throughout the contaminated area should focus on the role of current versus soil pH. GP and sensitivity analysis can provide decision support and operational guidance for in-situ soil electrokinetic treatment by establishing a remediation effect prediction model, expediting the development and innovation of electrokinetic technology.

Contamination and source apportionment of metals in urban road dust (Jinan, China) integrating the enrichment factor, receptor models (FA-NNC and PMF), local Moran's index, Pb isotopes and source-oriented health risk

Contamination and source identifications of metals in urban road dust are critical for remediation and health protection. Receptor models are commonly used for metal source identification, whereas the results are usually subjective and not verified by other indicators. Here we present and discuss a comprehensive approach to study metal contamination and sources in urban road dust (Jinan) in spring and winter by integrating the enrichment factor (EF), receptor models (positive matrix factorization (PMF) and factor analysis with nonnegative constraints (FA-NNC)), local Moran's index, traffic factors and Pb isotopes. Cadmium, Cr, Cu, Pb, Sb, Sn and Zn were the main contaminants, with mean EFs of 2.0-7.1. The EFs were 1.0-1.6 times higher in winter than in spring but exhibited similar spatial trends. Chromium contamination hotspots occurred in the northern area, with other metal contamination hotspots in the central, southeastern and eastern areas. The FA-NNC results indicated Cr contamination primarily resulting from industrial sources and other metal contamination primarily originating from traffic emissions during the two seasons. Coal burning emissions also contributed to Cd, Pb and Zn contamination in winter. FA-NNC model-identified metal sources were verified via traffic factors, atmospheric monitoring and Pb isotopes. The PMF model failed to differentiate Cr contamination from other detrital metals and the above anthropogenic sources, largely due to the model grouping metals by emphasizing hotspots. Considering the FA-NNC results, industrial and traffic sources accounted for 28.5 % (23.3 %) and 44.7 % (28.4 %), respectively, of the metal concentrations in spring (winter), and coal burning emissions contributed 34.3 % in winter. Industrial emissions primarily contributed to the health risks of metals due to the high Cr loading factor, but traffic emissions dominated metal contamination. Through Monte Carlo simulations, Cr had 4.8 % and 0.4 % possibilities posing noncarcinogenic and 18.8 % and 8.2 % possibilities posing carcinogenic risks for children in spring and winter, respectively.