A comprehensive framework for identifying contributing factors of soil trace metal pollution using Geodetector and spatial bivariate analysis

Effects of geographical and soil factors on soilś arsenic levels: a case study in typical arsenic-contaminated paddy fields based on machine learning

Heavy metal pollution in agricultural land has emerged as a contemporary environmental issue of prominent concern. The concentration of heavy metals in soil is influenced not only by inherent soil properties but also by geographical factors. Moreover, the identification of its influencing factors is challenging because of the intricate interactive effects among them. Previous studies primarily focused on single-factor identification and spatial distribution characterization, neglecting the characteristics and spatial features of soil heavy metal concentration under the interactive effects of geographical factors and soil properties. This study assessed the influence of geographical factors, soil properties, and their interactive effects on the spatial distribution of soil arsenic (As), in a typical arsenic-contaminated paddy field area by employing machine learning, analysis of variance, and spatial analysis methods. The findings show that the prediction performance (R2) of the random forest model for soil As concentration was 0.596, and the primary factors influencing the distribution of soil As are elevation, roads, rivers, soil pH, and cation exchange capacity (CEC). Moreover, the interactive effect between elevation and soil CEC had a significant effect on soil As (p < 0.05), exhibiting spatially homogeneous characteristics. The interactive effect between rivers and both soil pH and soil CEC exhibited spatially heterogeneous effects on soil As (p < 0.1). Additionally, the interactive effect between roads and soil pH affected soil As (p < 0.05), with spatially homogeneous characteristics. By identifying the main influencing factors of As in paddy soil, this study further explores the variation characteristics of soil As concentration under the interactive effects of geographical factors and soil properties. These insights can serve as a valuable reference for the precise prevention of As pollution in paddy field area.

Identification of the primary pollution sources and dominant influencing factors of soil heavy metals using a random forest model optimized by genetic algorithm coupled with geodetector

Identifying and quantifying the dominant factors influencing heavy metal (HM) pollution sources are essential for maintaining soil ecological health and implementing effective pollution control measures. This study analyzed soil HM samples from 53 different land use types in Jiaozuo City, Henan Province, China. Pollution sources were identified using Absolute Principal Component Score (APCS), with 8 anthropogenic factors, 9 natural factors, and 4 soil physicochemical properties mapped using Geographic Information System (GIS) kernel density estimation. Geodetector and a genetic algorithm optimized random forest model (GA-RF) were employed to quantify the dominant factors and precisely identify pollution sources. A Monte Carlo model was further applied to assess source-oriented health risk probabilities across age groups in the study area. The results revealed three principal components representing pollution sources, with contribution rates of 47.2 %, 33.3 %, and 19.5 %, respectively. For pollution source 1, industrial activities were dominant, with factory density (27.7 %) and distance from the factory (36.3 %) identified as the main factors. Cr, Cu, Mn, and Ni had high loads in this source. Pollution source 2, a combination of natural and transportation influences, was primarily affected by the normalized difference vegetation index (NDVI, 37.8 %), road network density (16.8 %), and proximity to roads (15.3 %). Pollution source 3 was linked to agricultural activities, with cultivated land density (CLD) contributing 39.1 %. As exhibited a high load (91.1 %) in this source, with an exceedance rate of 93 % in cultivated soil, a moderate enrichment factor of 2.33, and a strong ecological risk index of 615.72, making it the most polluted metal in the area. The source-oriented Health Risk Assessment (HRA) showed that agricultural activities contributed 88.7 % to the carcinogenic risk from As in cultivated land. Overall, 99.3 % of the population faced an acceptable cancer risk level. Unlike traditional source apportionment methods, the GA-RF model effectively quantified the contributions of specific influencing factors (e.g., factory density) to pollution sources, rather than merely estimating the percentage contributions of the sources themselves. This approach provides a novel perspective for HM source apportionment under complex environmental conditions.

Soil type data provide new methods and insights for heavy metal pollution assessment and driving factors analysis

Assessing heavy metal pollution and understanding the driving factors are crucial for monitoring and managing soil pollution. This study developed two modified assessment methods (NIPIt and NECI) based on soil type-specific background values and pollution indices, and combined them with the receptor model to evaluate pollution status. Additionally, a structural equation model was used to analyze the driving factors of soil heavy metal pollution. Results showed that the average NIPIt and NECI were 1.48 and 0.92, respectively, indicating a low pollution risk level. In some areas, Cd and Hg were the primary heavy metals contributing to pollution risk, with their highest average concentrations exceeding soil type-specific background values by 2.06 and 2.04 times, respectively. Additionally, in black soils, meadow soils, and chernozems, heavy metals primarily originated from natural sources, accounting for 48.92 %, 45.98 %, and 45.58 %, respectively. In aeolian soils, agricultural sources were predominant, contributing 43.38 %. Soil pH and organic matter were key soil properties affecting NECI and NIPIt, with direct effects of 0.36 and -0.19, respectively. This study aims to provide new methods and insights for the comprehensive assessment and driving factors analysis of soil heavy metal pollution, with the goal of enhancing pollution monitoring and reducing risk.

Potential ecological risk assessment for trace metal(loid)s in soil surrounding coal gangue heaps based on source-oriented

Coal is the predominant energy source in China, resulting in coal gangue. We used the absolute principal component score multiple linear regression (APCS-MLR) model and the geo-detector method (GDM) for determining the potential ecological risk, apportioning sources, and identifying driving factors for trace metal(loid)s (TMs) in soil surrounding coal gangue heaps. The average contents for the concerned TMs (Cd, Hg, As, Pb, Cr, Cu, Ni, and Zn) in the soil of interest were 0.48, 0.18, 11.0, 36.0, 129, 99.2, 68.3 and 141 mg/kg, respectively. Potential ecological risk indicated that the soil was primarily within the "Moderate risk" level, and Cd was the primary pollutant. "The number of coal gangue units" and "the distance between the sampling point and the coal gangue heap" were the key driving factors included in the geo-detector method. Combining APCS-MLR model and GDM, the source apportionment was enhanced in terms of accuracy and reliability. Natural, mining, and unrecognized sources contributed 41.1 %, 39.2 %, and 19.7 % of the TM distribution, respectively. Considering the relationship between TMs, their sources, and corresponding potential ecological risks, mining sources (mainly affected by gangue accumulation) presented a primary linkage with Cd, and its contribution to potential ecological risk was the highest, accounting for 58.2 %. Therefore, further research should focus on effectively managing and controlling the potential ecological risks originating from mining sources and Cd.

Spatial distribution, risk assessment, and source apportionment of soil heavy metals in a karst county based on grid survey

Soil in karst areas commonly exhibits characteristics of heavy metal enrichment. Accurate identification of soil heavy metal distribution, risks, and sources are crucial for preventing soil heavy metal pollution in karst areas. In this study, 2467 topsoil samples (0-20 cm) and 620 subsoil samples (150-200 cm) were collected using a grid-based sampling method in Tianyang County. Statistics, geo-statistics, correlation analysis, principal component analysis, and the absolute principal component-multiple linear regression model were utilized to analyze the content, spatial distribution and sources of heavy metals. The geo-accumulation index and the potential ecological risk index were employed to assess the ecological risks of heavy metals in the topsoil, with the subsoil content as baseline. The results showed that the study area's soil exhibited high heavy metal content, significantly exceeding Chinese background values. The content of heavy metals in the karst area's soil was notably higher than that in the non-karst area. The fitted semi-variogram models and the spatial distribution map revealed that the heavy metals' content was generally dominated by the geological background. As, Cr, Cu, Hg, Ni, Pb, and Zn displayed low levels of pollution in the topsoil and posed low ecological risk, with over 90 % of samples classified as unpolluted and low risk. Cd exhibited high levels of pollution and ecological risks, with 52.28 % of samples classified as polluted and 60.81 % classified as moderate to high risk. For Hg, despite only 6.94 % of samples showing polluted, the ecological risks were not negligible, with 40.65 % of samples in moderate to high risk. Natural source and anthropogenic source contribute to the heavy metals on average by 81.49 % and 18.51 %, respectively. This study provides a reference for the risk assessment of soil heavy metals, and its findings offer valuable scientific insights for the prevention of heavy metal pollution in the study area.

Utilizing machine learning to evaluate heavy metal pollution in the world's largest mangrove forest

The world's largest mangrove forest (Sundarbans) is facing an imminent threat from heavy metal pollution, posing grave ecological and human health risks. Developing an accurate predictive model for heavy metal content in this area has been challenging. In this study, we used machine learning techniques to model sediment pollution by heavy metals in this vital ecosystem. We collected 199 standardized sediment samples to predict the accumulation of eleven heavy metals using ten different machine learning algorithms. Among them, the extremely randomized tree model exhibited the best performance in predicting Fe (0.87), Cr (0.89), Zn (0.85), Ni (0.83), Cu (0.87), Co (0.62), As (0.68), and V (0.90), achieving notable R2 values. On the other hand, the random forest outperformed for predicting Cd (0.72) and Mn (0.91), whereas the decision tree model showed the best performance for Pb (0.73). The feature attribute analysis identified FeV, CrV, CuZn, CoMn, PbCd, and AsCd relationships resembled with correlation coefficients among them. Based on the established models, the prediction of the contamination factor of metals in sediments showed very high Cd contamination (CF ≥ 6). The Moran's I index for Cd, Cr, Pb, and As were 0.71, 0.81, 0.71, and 0.67, respectively, indicating strong positive spatial autocorrelation and suggesting clustering of similar contamination levels. Conclusively, this research provides a comprehensive framework for predicting heavy metal sediment pollution in the Sundarbans, identifying key areas needing urgent conservation. Our findings support the adoption of integrated management strategies and targeted remedial actions to mitigate the harmful effects of heavy metal contamination in this vital ecosystem.

Driving factors of molybdenum (Mo) bioconcentration in maize in the Longitudinal Range-Gorge Region of Southwestern China

Molybdenum (Mo) plays an important role in maintaining plant growth and human health. Assessment studies on the driving factors of Mo migration in soil-crop systems are crucial for ensuring optimal agricultural and human health. The Mo bioconcentration factor (BCF-Mo) is a useful tool for evaluating Mo bioavailability in soil-crop systems. However, the influence pathways and degrees of different environmental factors on BCF-Mo remain poorly understood. In this context, 109 rhizosphere and maize grain samples were collected from the Longitudinal Range-Gorge Region (LRGR) in Linshui County, Sichuan Province, China, and analyzed for the contents of Mo and other soil physiochemical parameters to explore the spatial patterns of BCF-Mo and its driving factors. Areas with the highest BCF-Mo values were mainly observed in the southern and northern parts of the Huaying and Tongluo mountains. The influence degrees of the selected environmental factors in this study followed the order of normalized difference vegetation index (NDVI) < elevation (EL) < mean annual humidity (MAH) < slope (SL) < mean annual temperature (MAT). The MAH and NDVI directly influenced the BCF-Mo values. The EL and MAT mainly indirectly affected the BCF-Mo values by influencing the rhizosphere organic matter (OM) contents, while the SL mainly affected the BCF-Mo values by influencing the rhizosphere pH. Therefore, OM and pH of the rhizosphere were the main influencing factors of BCF-Mo in the study area. In summary, the selected environmental factors mainly exhibited indirect influences on BCF-Mo by directly affecting the physicochemical properties of the rhizosphere.

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.

Identifying interactive effects of spatial drivers in soil heavy metal pollutants using interpretable machine learning models

The accurate identification of spatial drivers is crucial for effectively managing soil heavy metals (SHM). However, understanding the complex and diverse spatial drivers of SHM and their interactive effects remains a significant challenge. In this study, we present a comprehensive analysis framework that integrates Geodetector, CatBoost, and SHapley Additive exPlanations (SHAP) techniques to identify and elucidate the interactive effects of spatial drivers in SHM within the Pearl River Delta (PRD) region of China. Our investigation incorporated fourteen environmental factors and focused on the pollution levels of three prominent heavy metals: Hg, Cd, and Zn. These findings provide several key insights: (1) The distribution of SHM is influenced by the combined effects of various individual factors and interactions within the source-flow-sink process. (2) Compared with the spatial interpretation of individual factors, the interaction between Hg and Cd exhibited enhanced spatial explanatory power. Similarly, interactions involving Zn mainly demonstrated increased spatial explanatory power, but there was one exception in which a weakening was observed. (3) Spatial heterogeneity plays a crucial role in determining the contributions of environmental factors to soil heavy metal concentrations. Although individual factors generally promote metal accumulation, their effects fluctuate when interactions are considered. (4) The SHAP interpretable method effectively addresses the limitations associated with machine-learning models by providing understandable insights into heavy metal pollution. This enables a comparison of the importance of environmental factors and elucidates their directional impacts, thereby aiding in the understanding of interaction mechanisms. The methods and findings presented in this study offer valuable insights into the spatial heterogeneity of heavy metal pollution in soil. By focusing on the effects of interactive factors, we aimed to develop more accurate strategies for managing SHM pollution.

Exploring the environmental risks and seasonal variations of potentially toxic elements (PTEs) in fine road dust in resource-based cities based on Monte Carlo simulation, geo-detector and random forest model

The environmental pollution caused by mineral exploitation and energy consumption poses a serious threat to ecological security and human health, particularly in resource-based cities. To address this issue, a comprehensive investigation was conducted on potentially toxic elements (PTEs) in road dust from different seasons to assess the environmental risks and influencing factors faced by Datong City. Multivariate statistical analysis and absolute principal component score were employed for source identification and quantitative allocation. The geo-accumulation index and improved Nemerow index were utilized to evaluate the pollution levels of PTEs. Monte Carlo simulation was employed to assess the ecological-health risks associated with PTEs content and source orientation. Furthermore, geo-detector and random forest analysis were conducted to examine the key environmental variables and driving factors contributing to the spatiotemporal variation in PTEs content. In all PTEs, Cd, Hg, and Zn exhibited higher levels of content, with an average content/background value of 3.65 to 4.91, 2.53 to 3.34, and 2.15 to 2.89 times, respectively. Seasonal disparities were evident in PTEs contents, with average levels generally showing a pattern of spring (winter) > summer (autumn). PTEs in fine road dust (FRD) were primarily influenced by traffic, natural factors, coal-related industrial activities, and metallurgical activities, contributing 14.9-33.9 %, 41.4-47.5 %, 4.4-8.3 %, and 14.2-29.4 % to the total contents, respectively. The overall pollution and ecological risk of PTEs were categorized as moderate and high, respectively, with the winter season exhibiting the most severe conditions, primarily driven by Hg emissions from coal-related industries. Non-carcinogenic risk of PTEs for adults was within the safe limit, yet children still faced a probability of 4.1 %-16.4 % of unacceptable risks, particularly in summer. Carcinogenic risks were evident across all demographics, with children at the highest risk, mainly due to Cr and smelting industrial sources. Geo-detector and random forest model indicated that spatial disparities in prioritized control elements (Cr and Hg) were primarily influenced by particulate matter (PM10) and anthropogenic activities (industrial and socio-economic factors); variations in particulate matter (PM10 and PM2.5) and meteorological factors (wind speed and precipitation) were the primary controllers of seasonal disparities of Cr and Hg.

Determination of high-risk factors and related spatially influencing variables of heavy metals in groundwater

Identifying high-risk factors (heavy metals (HMs) and pollution sources) by coupling receptor models and health risk assessment model (HRA) is a novel approach within the field of risk assessment. However, this coupled model ignores the contribution of spatial differentiation to high-risk factors, resulting in the assessment being subjective. Taking Dongting Plain (DTP) as an example, a coupling framework by jointly using the positive matrix factorization model (PMF), HRA, Monte Carlo simulation, and geo-detector was developed, aiming to identify high-risk factors in groundwater, and further explore key environmental variables influencing the spatial heterogeneity of high-risk factors. The results showed that at least 82.86 % of non-carcinogenic risks and 97.41 % of carcinogenic risks were unacceptable for people of all ages, especially infants and children. According to the relationships among HMs, pollution sources, and health risks, As and natural sources were defined as high-risk HMs and sources, respectively. The interactions among Holocene thickness, oxidation-reduction potential, and dissolved organic carbon emerged as the primary drivers of spatial variability in high-risk factors, with their combined explanatory power reaching up to 74%. This proposed framework provides a scientific reference for future studies and a practical reference for environmental authorities in developing effective pollution management measures.

A method of gas-related pollution source layout based on multi-source data: A case study of shaanxi province, China

The location and layout of enterprises have an important impact on local air quality. However, a few studies on exploring of the optimal layout of gas-related enterprises from the perspective of optimizing the layout of air pollution sources. This study developed a method for the evaluation of air pollution source layout based on air pollutant emission inventory data, atmospheric self-purification capacity data, and satellite remote sensing air quality data. Taking Shaanxi Province as an example, the Moran's I index and GIS spatial analysis techniques were used to evaluate the layout of air pollution sources, analyze the spatial variation characteristics of air pollution sources, and propose specific countermeasures to optimize the layout of air pollution sources. Results showed that northern Shaanxi and Guanzhong Plain are the most unsuitable for the distribution of NOx and CO sources, accounting for 13.78% and 21.77% of the total area, respectively. The most suitable area for the distribution of NOx is southern Shaanxi, accounting for 65.77% of the total area, mainly concentrated in Hanzhong and Ankang regions. The most suitable area for the distribution of CO is southern Shaanxi, accounting for 40.97% of the total area, mainly concentrated in Hanzhong and Shangluo regions. The findings of this study could supplement and improve the evaluation of the layout of industrial enterprises in China from technical and methodological aspects, and provide new insight for local governments to adjust and optimize the layout of air pollution sources.

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.

Source-specific risk judgement and environmental impact of potentially toxic elements in fine road dust from an integrated industrial city, North China

The contamination of potentially toxic elements (PTEs) in road dust of large industrial cities is extremely serious. Determining the priority risk control factors of PTE contamination in road dust is critical to enhance the environmental quality of such cities and mitigate the risk of PTE pollution. The Monte Carlo simulation (MCS) method and geographical models were employed to assess the probabilistic pollution levels and eco-health risks of PTEs originating from different sources in fine road dust (FRD) of large industrial cities, and to identify key factors affecting the spatial variability of priority control sources and target PTEs. It was observed that in FRD of Shijiazhuang, a typical large industrial city in China, more than 97% of the samples had an INI > 1 (INI_mean = 1.8), indicating moderately contaminated with PTEs. The eco-risk was at least considerable (NCRI >160) with more than 98% of the samples, mainly caused by Hg (E_{i (mean)} = 367.3). The coal-related industrial source (NCRI_(mean) = 235.1) contributed 70.9% to the overall eco-risk (NCRI_(mean) = 295.5) of source-oriented risks. The non-carcinogenic risk of children and adults are of less importance, but the carcinogenic risk deserves attention. The coal-related industry is a priority control pollution source for human health protection, with As corresponding to the target PTE. The major factors affecting the spatial changes of target PTEs (Hg and As) and coal-related industrial sources were plant distribution, population density, and gross domestic product. The hot spots of coal-related industrial sources in different regions were strongly interfered by various human activities. Our results illustrate spatial changes and key-influencing factors of priority source and target PTEs in Shijiazhuang FRD, which are helpful for environmental protection and control of environmental risks by PTEs.