Prediction of high-risk areas of soil heavy metal pollution with multiple factors on a large scale in industrial agglomeration areas

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.

Spatial diffusion of potentially toxic elements in soils around non-ferrous metal mines

This study focuses on the diffusion patterns of principal ore-forming elements (Pb and Zn) and associated elements (Cd, Cu, Cr, and As) in lead-zinc ore. Sampling points in upwind and downwind directions of lead-zinc ore areas at various densities (1 N/km2 - 4 N/km2) were categorized. This study analyzed the statistical relationship between the content of PTEs in the soil around lead-zinc ore and the source strength and dominant wind direction, constructed one-dimensional and two-dimensional diffusion model, and simulated the EER scope caused by PTEs. The findings indicate that: (1) concerning source strength, the content of PTEs in soils of high-density ore aggregation areas is significantly higher than in low-density ore aggregation areas. However, the impact of source strength decreases with decreasing ore grade, with a difference in Pb content of 1.71 times among principal ore-forming elements and almost consistent Cd content among associated elements. (2) Regarding the transport pathways, for most PTEs, the inverse proportion coefficients downwind are higher than upwind, approximately 1.18-3.63 times, indicating greater migration distances of PTEs downwind due to atmospheric dispersion. (3) By establishing a two-dimensional risk diffusion model, the study simulates the maximum radius of risk diffusion (r = 5.7 km), the 50% probability radius (r = 3.1 km), and the minimum radius (r = 0.8 km) based on the maximum, median, and minimum values statistically obtained from the EER. This study provides a scientific basis for implementing preventive measures for PTEs accumulation in soil within different pollution ranges. Different risk prevention and control measures should be adopted for PTEs accumulation in soil within the three ranges after cutting off pollution sources. Subsequent research should further investigate the impact and contribution of atmospheric transmission and surface runoff on the diffusion of PTEs in areas with high risk near lead-zinc ore.

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.

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.

Assessment of the migration characteristics and source-oriented health risks of heavy metals in the soil and groundwater of a legacy contaminated by the chlor-alkali industry in central China

The chlor-alkali industry (CAI) is crucial for global chemical production; however, its operation has led to widespread heavy metal (HM) contamination at numerous sites, which has not been thoroughly investigated. This study analysed 122 soil and groundwater samples from a typical CAI site in Kaifeng, China. Our aim was to assess the ecological and health risks, identify the sources, and examine the migration characteristics of HMs at this site using Monte Carlo simulation, absolute principal component score-multiple linear regression (APCS-MLR), and the potential environmental risk index (Ei). Our findings revealed that the exceedance rates for Cd, Pb, Hg, and Ni were 71.96%, 45.79%, 49.59%, and 65.42%, respectively. Mercury (Hg) displayed the greatest coefficient of variation across all the soil layers, indicating a significant anthropogenic influence. Cd and Hg were identified as having high and extremely high potential environmental risk levels, respectively. The spatial distributions of the improved Nemerow index (INI), total ecological risk (Ri), and HM content varied considerably, with the most contaminated areas typically associated with the storage of raw and auxiliary materials. Surface aggregation and significant vertical transport were noted for HMs; As and Ni showed substantial accumulation in subsoil layers, severely contaminating the groundwater. Self-organizing maps categorized the samples into two different groups, showing strong positive correlations between Cd, Pb, and Hg. The APCS-MLR model suggested that industrial emissions were the main contributors, accounting for 60.3% of the total HM input. Elevated hazard quotient values for Hg posed significant noncarcinogenic risks, whereas acceptable levels of carcinogenic risk were observed for both adults (96.60%) and children (97.83%). This study significantly enhances historical CAI pollution data and offers valuable insights into ongoing environmental and health challenges.

Available heavy metals concentrations in agricultural soils: Relationship with soil properties and total heavy metals concentrations in different industries

Heavy metal (HM) pollution in agricultural soils has arisen sharply in recent years. However, the impact of main factors on available HMs concentrations in agricultural soils of the three main industries (smelting, chemical and mining industry) is unclear. Herein, soil properties (pH, cation exchange capacity (CEC) and texture (sand, slit, clay)), total and available concentrations were concluded based on the results of 165 research papers from 2000 to 2023 in Web of Science database. In the three industries, the correlation and redundancy analysis were used to study the correlation between main factors and available concentrations, and quantitatively analyzed the contribution of each factor to available concentrations with gradient boosting decision tree model. The results showed that different factors had varying degrees of impact on available metals in the three main industries, and the importance of same factors varied in each industry, as for soil pH, it was most important for available Pb and Zn in the chemical industry, but the total concentrations were most important in the smelting and mining industry. There was no significant correlation between total and available concentrations. Soil properties involved in this paper (especially soil pH) were negatively correlated with available concentrations. This study provides effective guidance for the formulation of soil pollution control and risk assessment standards based on industry classification in the three major industrial impact areas.

A Modified Model for Quantitative Heavy Metal Source Apportionment and Pollution Pathway Identification

Current source apportionment models have successfully identified emission sources and quantified their contributions. However, when being utilized for heavy metal source apportion in soil, their accuracy needs to be improved, regarding migration patterns. Therefore, this work intended to improve the pre-existing principal component analysis and multiple linear regression with distance (PCA-MLRD) model to effectively locate pollution pathways (traffic emissions, irrigation water, atmospheric depositions, etc.) and achieve a more precise quantification. The dataset of soil heavy metals was collected from a typical area in the Chang-Zhu-Tan region, Hunan, China in 2021. The identification of the contribution of soil parent material was accomplished through enrichment factors and crustal reference elements. Meanwhile, the anthropogenic emission was identified with principal component analysis and GeoDetector. GeoDetector was used to accurately point to the pollution source from a spatial differentiation perspective. Subsequently, the pollution pathways linked to the identified sources were determined. Non-metal manufacturing factories were found to be significant anthropogenic sources of local soil contamination, mainly through rivers and atmospheric deposition. Furthermore, the influence of irrigation water on heavy metals showed a more pronounced effect within a distance of 1000 m, became weaker after that, and then gradually disappeared. This model may offer improved technical guidance for practical production and the management of soil heavy metal contamination.

Towards interpretable machine learning for observational quantification of soil heavy metal concentrations under environmental constraints

Monitoring heavy metal concentrations in soils is central to assessing agricultural production safety. Satellite observations permit inferring concentrations from spectrum, thereby contributing to the prevention and control of soil heavy metal pollution. However, heavy metals exhibit weak spectral responses, particularly at low and medium concentrations, and are predominantly influenced by other soil components. Machine learning (ML)-driven modelling can produce predictions but lacks interpretability. Here, we present an interpretable ML framework for concentration quantification modelling and investigated the contributions of spectral and environmental factors-pH and organic carbon-to the estimation of metals with multiple concentration gradients, as analysed through SHAP (SHapley Additive exPlanations) data derived from four learning-based scenarios. The results indicated that scenarios SHC (spectral, pH, and organic carbon) and SH (spectral and pH) were the most optimal for chromium (Cr) [RPD = 1.42, Adj R2 = 0.62], and cadmium (Cd) [RPD = 1.80, Adj R2 = 0.80]. Under environmental constraints, the spectral predictability for Cr and Cd was improved by 67 % and 87 %, respectively. We concluded that interpretable modelling, utilising both spectral and soil environmental factors, holds significant potential for estimating heavy metals across concentration gradients. It is recommended that samples with higher organic carbon content and lower pH be selected to enhance Cr and Cd predictions. An advanced grasp of interpretable predictions facilitates earlier warning of heavy metal contamination and guides the formulation of robust sampling strategies.

Synthesis and Application of Salicylhydrazone Probes with High Selectivity for Rapid Detection of Cu2

Using the aldehyde amine condensation procedure and the triphenylamine group as the skeleton structure, the new triphenylamine-aromatic aldehyde-succinylhydrazone probe molecule DHBYMH was created. A newly created acylhydrazone probe was structurally characterized by mass spectrometry (MS), NMR, and infrared spectroscopy (FTIR). Fluorescence and UV spectroscopy were used to examine DHBYMH's sensing capabilities for metal ions. Notably, DHBYMH achieved a detection limit of 1.62 × 10-7 M by demonstrating exceptional selectivity and sensitivity towards Cu2+ ions in an optimum sample solvent system (DMSO/H2O, (v/v = 7/3); pH = 7.0; cysteine (Cys) concentration: 1 × 10-4 M). NMR titration, high-resolution mass spectrometry analysis, and DFT computation were used to clarify the response mechanism. Ultimately, predicated on DHBYMH's reversible identification of Cu2+ ions in the presence of EDTA, a molecular logic gate was successfully designed.

Integrated assessment of the pollution and risk of heavy metals in soils near chemical industry parks along the middle Yangtze River

Industrialization in riparian areas of critical rivers has caused significant environmental and health impacts. Taking eight industrial parks along the middle Yangtze River as examples, this study proposes a multiple-criteria approach to investigate soil heavy metal pollution and associated ecological and health risks posed by industrial activities. Aiming at seven heavy metals, the results show that nickel (Ni), cadmium (Cd), and copper (Cu) exhibited the most significant accumulation above background levels. The comprehensive findings from Pearson correlation analysis, cluster analysis, principal component analysis, and industrial investigation uncover the primary sources of Cd, arsenic (As), mercury (Hg), and lead (Pb) to be chemical processing, while Ni and chromium (Cr) are predominantly derived from mechanical and electrical equipment manufacturing. In contrast, Cu exhibits a broad range of origins across various industrial processes. Soil heavy metals can cause serious ecological and carcinogenic health risks, of which Cd and Hg contribute to >70 % of the total ecological risk, and As contributes over 80 % of the total health risk. This study highlights the importance of employing multiple mathematical and statistical models in determining and evaluating environmental hazards, and may aid in planning the environmental remediation engineering and optimizing the industry standards.

Assessment of metals (Ni, Ba) deposition in plant types and their organs at Mersin City, Türkiye

The increase in heavy metal concentrations in the air, especially after the Industrial Revolution, is notable for the scientific world because of the adverse effects that threaten environmental and human health. Among the trace elements, nickel (Ni) is carcinogenic, and all barium (Ba) compounds are toxic. Trace elements are critical for human and environmental health. Their threat further increases, especially in the urban areas and surroundings with a high population. In urban areas, the trace element contamination in the airborne can be reduced using plants. However, which plant and plant organs absorb trace elements could not be determined. In the present study, Ni and Ba concentrations in the branch, wood, and leaf samples of 14 species collected from the city center of Mersin province were determined. As a result, broad-leaved species' Ni and Ba concentrations in their leaf sample were generally higher than other species. Almost all species had the lowest Ni and Ba concentrations in their wood samples. Among these 14 species, it was found that Ni concentration was very high, especially in non-washed leaves of Platanus orientalis, Photinia serrulata, and Citrus reticulate, and Ba concentration was very high in Citrus reticulata, Chamaecyparis lawsoniana, Laurus nobilis, and Acer hyrcanum. Using broad-leaved species in urban areas where pollution is at high levels will significantly contribute to reducing Ni and Ba pollution. It is recommended that these points be considered in future urban landscaping projects.

Residual levels and health risk assessment of trace metals in Chinese resident diet

Large-scale metal contamination across the food web is an intractable problem due to increasing pollutant emissions, atmospheric transport, and dry and wet deposition of elements. The present study focus on several trace metals that are rarely studied but have special toxicity, including tin (Sn), antimony (Sb), gold (Au), hafnium (Hf), palladium (Pd), platinum (Pt), ruthenium (Ru), tellurium (Te) and iridium (Ir). We investigated trace metals residues and distribution characteristics, and further evaluated the potential health risks from major daily food intakes in 33 cities in China. Sn, Sb, Ir, Hf, and Au were frequently detected in food samples with the concentrations ranged from ND (not detected) to 24.78 µg/kg ww (wet weight). Eggs exhibited the highest residual level of all detected metals (13.70 ± 14.70 µg/kg ww in sum), while the lowest concentrations were observed in vegetables (0.53 ± 0.17 µg/kg ww in sum). Sn accounting for more than 50% of the total trace metals concentration in both terrestrial and aquatic animal origin foods. In terrestrial plant origin foods, Sn and Ir were the most abundant elements. Hf and Au were the most abundant elements in egg samples. In addition, Sb and Ir showed a clear trophic dilution effect in terrestrial environments, while in aquatic ecosystems, Sn, Hf, and Au exhibited obvious trophic amplification effects. The calculated average estimated daily intake (EDI) via food consumption in five regions of China was 0.09 µg/(kg·day), implying the health risk of aforementioned elements was acceptable.

Optimization of atmospheric leaching parameters for cadmium and zinc recovery from low-grade waste by response surface methodology (RSM)

This study is focused on the optimization of effective parameters on Cadmium and Zinc recovery by atmospheric acid leaching of low-grade waste by response surface methodology (RSM) and using the Central Composite Design (CCD) method. The effects of parameters including time (0.5-2.5 h), temperature (40-80 °C), solid/liquid (S/L) (0.05-0.09 g/cc), particle size (174-44 mic), oxygen injection (0-1%) and pH (0.5-4.5) were statistically investigated at 5 surfaces. The sample of low-grade waste used in this study was mainly zinc factory waste. Two quadratic models for the correlation of independent parameters for the maximum recovery were proposed. The properties of waste were evaluated by X-ray diffraction (XRD) and X-ray fluorescence (XRF). Atomic absorption spectroscopy was used to determine the amount of Cadmium and Zinc in the leaching solution. The correlation coefficient (R2) for the predicted and experimental data of Cadmium and Zinc are 0.9837 and 0.9368, respectively. Time, S/L and size were the most effective parameters for the recovery efficiency of cadmium and zinc. 75.05% of Cadmium and 86.13% of Zinc were recovered in optimal conditions of leaching: S/L 0.08, pH 2.5, size 88 µm, 70 °C and 2.5 h. with air injection.

Effect of environmental factors on accumulation of trace metals in a typical shale gas exploitation area: A comprehensive investigation by machine learning and geodetector models

Whether a certain relationship is exist between shale gas exploitation and accumulation of trace metals in soil is a controversial issue in recent years. To date, few study clearly reveal the intrinsic contributions of natural and anthropogenic factors to accumulation of trace metals in soil. In this study, machine learning and geodetector models were integrated to investigate to contribution of environmental factors to variations of trace metals concentration. Before modeling, there are 10.33%-25.87% of soil considered as metal pollution, and the value of Pn further suggest that the Ba contribute the most to the comprehensive pollution index of trace metals in soil. The initial prediction of trace metals concentration by machine learning models is less effectively indicating the need for alternative approaches. To address this problem, post-constraints approach was used, and the post-constraint MSLR model demonstrates superior performance (R2 = 0.81) Additionally, through the utilization of geodetector model, the explanatory power (q) of CEC and SOM were identified as dominant natural factors with value of 0.055 and 0.089. respectively. Moreover, distance from working sites and working status were identified as the dominant anthropogenic factors associating to the spatial heterogeneity of trace metals in soil. The interaction between natural and anthropogenic factors showed a siginifacnt nonlinear enhancement effect on accumulation of Cr, Ba and Sr, and the highest value of q was 0.38 for SOM and distance. This study indicated that the potential metal contamination was related to shale gas exploitation and provide reference for controlling soil pollution in shale gas exploitation area and making management strategy.

Source-specific probabilistic contamination risk and health risk assessment of soil heavy metals in a typical ancient mining area

Heavy metal pollution (HMP) from mining operations severely threatens soil ecosystems and human health. Identifying the sources of soil heavy metals (HMs) and assessing source-specific risks are critical for developing effective risk mitigation strategies. In this study, a combination of methodologies including PMF, Monte Carlo analysis, soil pollution risk index, and a human health risk assessment model were utilized to investigate soil HM risks in a typical ancient mining area in Daye City, China, considering both environmental pollution and human health impacts. Cu emerged as the most significant soil pollution risk, whereas As posing the highest health risk. About 48.44 % of the multi-element integrated soil pollution risk has escalated to the heavy level. Furthermore, around 22.42 % of the non-carcinogenic risk (NCR) and 9.53 % of the carcinogenic risk (CR) exceeded unacceptable thresholds (THI > 1 for NCR and TCR > 1E-4 for CR). The PMF model identified four distinct sources: the smelting industry, traffic emissions, a combination of agricultural and natural factors, and mining activities. The mixed agricultural and natural source significantly impacted health risks, contributing 42.17 % to NCR and 53.88 % to CR, followed by the mining source, contributing 31.67 % to NCR and 24.07 % to CR. Interestingly, the mining source contributed the highest soil pollution risk at 42.45 %, while the mixed agricultural and natural source exhibited the lowest at 16.33 %. Furthermore, the study explored source-specific risk components by evaluating the contributions of different sources to specific elements. The mining source was identified as the focus for soil HMP control, followed by the mixed agricultural and natural source. Overall, this study provided an in-depth analysis of soil heavy metal risks in mining areas from the source apportionment perspective, which broadened the research framework of soil heavy metal source analysis and risk assessment, potentially providing scientific guidance for managing regional soil HMP.

Application of Cu isotopes to identify Cu sources in soils impacted by multiple anthropogenic activities

Copper (Cu) is an important micronutrient for animals and plants, but it is toxic at high concentrations in soil. Soils adjacent to industrial areas would be subjected to severe Cu pollution. Identifying Cu sources in the surface environment is crucial for understanding their pollution level and fate. This study investigated Cu content, isotope composition of topsoils, and two soil profiles with varying levels of Cu contamination and related potential Cu sources in southwest China. The difference in Cu isotope compositions of tailing (1.29 ± 0.08 ‰), smelting fly ash (0.04 ± 0.03 ‰), coal (2.44 ± 0.09 ‰), coal-burning fly ash (0.34 ± 0.03 ‰), and geogenic soil (0.10 ± 0.03 ‰) enabled us to distinguish anthropogenic Cu from geogenic Cu. The plot of δ65Cu and 1/Cu demonstrates that Cu of the polluted soils was from three end-members: the smelting fly ash, the vehicle exhaust, and the background soils. Based on the mass balance model, we calculated that the fly ash from smelting was the major anthropogenic source, contributing approximately 29 % of Cu contamination in soils, and the diesel exhaust was another important source, with a contribution rate of approximately 25 %. Additionally, soil profile results suggest that anthropogenic Cu could transport through soil profiles and influence Cu content and isotope signatures of subsurface soils, at least to a depth of ∼60 cm. Finally, our research indicates that Cu isotopes could be a promising tool for tracing industrial pollution, as significant Cu isotope fractionation would occur during the smelting process. Our research highlights the contribution of smelting and diesel exhaust to Cu contamination in the soils in a representative mining area. These findings serve as a scientific foundation for the development of policy for pollution control in industrial-affected regions.

An Overview of Diverse Strategies To Inactivate Enterobacteriaceae-Targeting Bacteriophages

Bacteriophages are viruses that infect bacteria and thus threaten industrial processes relying on the production executed by bacterial cells. Industries bear huge economic losses due to such recurring and resilient infections. Depending on the specificity of the process, there is a need for appropriate methods of bacteriophage inactivation, with an emphasis on being inexpensive and high efficiency. In this review, we summarize the reports on antiphagents, i.e., antibacteriophage agents on inactivation of bacteriophages. We focused on bacteriophages targeting the representatives of the Enterobacteriaceae family, as its representative, Escherichia coli, is most commonly used in the bio-industry. The review is divided into sections dealing with bacteriophage inactivation by physical factors, chemical factors, and nanotechnology-based solutions.

Emerging Trends in nanostructured materials-coated screen printed electrodes for the electrochemical detection of hazardous heavy metals in environmental matrices

Heavy metal ions (HMIs) have become a significant contaminant in recent years. The increase in heavy metal pollution is a serious situation, requiring progressively robust, fast sensing, highly sensitive, and suitable techniques for heavy metal detection. Compared to other classical analytical methods, electroanalytical techniques, especially stripping voltammetric techniques with modified screen-printed electrodes (SPEs), have several advantages, such as fast sensing, great sensitivity, specificity, and long-time stability. Therefore, these techniques are more suitable for HMI detection. In this review, the nanostructured materials used to coat SPEs for the electrochemical determination of HMI are summarized. Additionally, the electrode fabrication method, modification steps, and electroanalytical study of these materials are systematically discussed. Hence, this review will support the researchers in precisely evaluating the electrochemical HMIs detection through highly sensitive stripping voltammetric techniques using SPE modified with nanostructured carbon and their allotropes, metal, metal oxides and their nanocomposites as sensor materials. Moreover, modified electrodes real time detection of HMIs in different food and environmental samples were briefly discussed.

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.

A novel approach for the prediction and analysis of daily concentrations of particulate matter using machine learning

Traditional air quality analysis and prediction methods depend on the statistical and numerical analyses of historical air quality data with more information related to a specific region; therefore, the results are unsatisfactory. In particular, fine particulate matter (PM2.5, PM10) in the atmosphere is a major concern for human health. The modelling (analysis and prediction) of particulate matter concentrations remains unsatisfactory owing to the rapid increase in urbanization and industrialization. In the present study, we reconstructed a prediction model for both PM2.5 and PM10 with varying meteorological conditions (windspeed, temperature, precipitation, specific humidity, and air pressure) in a specific region. In this study, a prediction model was developed for the two observation stations in the study region. The analysis of particulate matter shows that seasonal variation is a primary factor that highly influences air pollutant concentrations in urban regions. Based on historical data, the maximum number of days (92 days in 2019) during the winter season exceeded the maximum permissible level of particulate matter (PM2.5 = 15 μg/m3) concentration in air. The prediction results showed better performance of the Gaussian process regression model, with comparatively larger R2 values and smaller errors than the other models. Based on the analysis and prediction, these novel methods may enhance the accuracy of particulate matter prediction and influence policy- and decision-makers among pollution control authorities to protect air quality.

Assessment of land use effect, mapping of human health risks and chemometric analysis of potential toxic elements in topsoils of Aran-o-Bidgol, Iran

This study examines topsoil contamination in Aran-o-Bidgol urban region of central Iran, with a focus on potentially toxic elements (PTEs). A total of 135 topsoil samples in different land types were characterized, ranging from areas with agricultural farms, desert, industrial and residential activity, and brick kilns. The average concentrations of Cd, Pb, Cu, Ni, Cr, Co, Fe, Zn, and Mn were 0.72, 11.41, 14.82, 29.87, 51.13, 106.69, 8741.87, 48.59, and 346.42 mg kg-1, respectively, which all exceed the local background levels. The results reveal that land use significantly affected PTE concentrations. Cr, Co, Mn, and Fe concentrations in soils of residential and brick kiln areas were especially high. In contrast, concentrations of Cu, Ni, and Zn were higher in agricultural and residential areas. Risk assessment analysis showed that the sum of toxic units for PTEs for brick kilns (1.72), residential (1.82), and agricultural (1.79) areas exceeded those of other land types and that Ni and Cr contributed the most to the high toxic risk index values. Both carcinogenic and non-carcinogenic risk indices of PTEs in soils were within an acceptable limit, except for the cancer risk of Ni (3.52E-04) and Cr (3.00E-04) among children. The spatial hazard index and carcinogenic health risk of PTEs showed that samples from the southwestern parts of the study area might pose significant health problems to adults and children. This study demonstrates how combining different techniques can help spatially characterize PTE accumulation and protect populations at risk.

Spatial evaluation and zoning strategy of land use elemental conflicts in heavy industrial zones: evidence from central Liaoning Province in Northeast China

The matching imbalance of resource factors leads to land use elemental conflicts (LUECs), which has become the bottleneck restricting high-quality social and economic development. The heavy industrial zones (HIZ) have become the focus area of LUECs due to the high-resource consumption. Taking the urban group of central Liaoning Province, the area of industrial revitalization in northeast China as a case study area, the study proposed a wavelet coherence approach to identifying the influencing indicators and indicators weight of LUECs for spatial evaluation. Two-dimensional graph theory is used to cluster the evaluation results of LUECs at the plot scale and controls the main indicators to put forward the zoning strategies of LUECs. The results showed that the main indicators affecting LUECs in the western part of the HIZ are mainly human indicators, while the fierce conflicts in the east mainly come from natural indicators. The zoning strategies of LUECs in the HIZ should prevent excessive energy consumption from increasing carbon emissions in intense conflict zone and moderate conflict zone and strengthen the rural settlement arrangement and soil erosion control in mild conflict zone and structure ecological security early warnings in potential conflict zone. This study provides an important reference for land use conflicts in the global heavy industrial urban agglomeration.

Spatial response relationship between mining and industrial activities and eco-environmental risks in mineral resource-based areas

Mining and industrial activities in mineral resource-based areas are important sources of potentially toxic elements (PTEs) in the soil, which lead to spatial heterogeneity in regional eco-environmental risks. In this study, we analysed the spatial response relationship between mining and industrial activities and eco-environmental risks using Anselin local Moran's I index and bivariate local Moran's I index. The results showed that the proportions of moderate, moderate to strong, and strong pollution of PTEs in the study area reached 30.9%. The high clusters of PTEs ranged from 5.4 to 13.6%, and were mainly distributed around cities. The influence of different types of metal mines on eco-environmental risks was nonferrous metal mines > precious metal mines > ferrous metal mines. In turn, that of different pollution enterprises was manufacturing industry > other industries > power and thermal industries. Our research demonstrates that there was a significant spatial response relationship between densities of mines and enterprises and eco-environmental risk level. High-density metal mines (5.3/100 km2) and high-density pollution enterprises (10.3/100 km2) resulted in the local high risk. Consequently, this study provides a basis for regional eco-environmental risk management of mineral resource-based areas. With the gradual depletion of mineral resources, high-density pollution enterprise area should be paid more attention to, which would pose a threat not only to the environment but also to population health.

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.

High-resolution risk mapping of heavy metals in soil with an integrated static-dynamic interaction model: A case study in an industrial agglomeration area in China

Heavy metal pollution of soils in industrial agglomeration areas is an increasing concern worldwide. In this study, we traced the sources of heavy metal emissions using a positive matrix factorization (PMF) model. Accordingly, we proposed a novel static-dynamic risk interaction model incorporating multiple risk-related factors to quantify the spatial interaction of emission sources and the probability of accumulation of heavy metals on a large scale. This model was further classified using the Jenks optimization technique to predict the spatial distribution of high-risk hotspots. Our results determined four primary emission sources of heavy metals: industrial (35.01 %), natural (28.61 %), agricultural (26.07 %), and traffic (10.31 %) sources. Five levels were classified by the integrated risk coefficient (IRC), namely, from extremely high to extremely low risk. The extremely high- and high-risk hotspots constituting 41.52 % of the total area of the Zhenhai District, with IRC values ranging from 0.221 to 0.413, were mainly generated by multiple sources linked to PMF-based factors. This quantitative evaluation framework can generate a high-resolution spatially distributed pollution risk map at the grid scale (1 km), which can provide a relatively precise basis for policymaking for point-to-point soil pollution management.

A review on control and abatement of soil pollution by heavy metals: Emphasis on artificial intelligence in recovery of contaminated soil

"Save Soil Save Earth" is not just a catchphrase; it is a necessity to protect soil ecosystem from the unwanted and unregulated level of xenobiotic contamination. Numerous challenges such as type, lifespan, nature of pollutants and high cost of treatment has been associated with the treatment or remediation of contaminated soil, whether it be either on-site or off-site. Due to the food chain, the health of non-target soil species as well as human health were impacted by soil contaminants, both organic and inorganic. In this review, the use of microbial omics approaches and artificial intelligence or machine learning has been comprehensively explored with recent advancements in order to identify the sources, characterize, quantify, and mitigate soil pollutants from the environment for increased sustainability. This will generate novel insights into methods for soil remediation that will reduce the time and expense of soil treatment.

Metal ion endogenous cycles of CoFe2O4-x induced boosted photocatalytic/PMS degradation toward polycyclic aromatic hydrocarbons

The rational design of photocatalytic nanomaterials with unique structures is critical for remediating environmental problems and thus reducing ecological risks. In this work, we employed H₂ temperature-programmed reduction to modify MFe₂O₄ (M = Co, Cu, and Zn) photocatalysts for obtaining additional oxygen vacancies. After activation of PMS, naphthalene and phenanthrene degradation rates in the soil phase were increased by 3.24-fold and 1.39-fold, respectively, and 1.38-fold for naphthalene in the aqueous phase by H-CoFe₂O_4-x. The extraordinary photocatalytic activity is attributed to the oxygen vacancies on the H-CoFe₂O_4-x surface, which promote electron transfer and thus enhance the redox cycle from Co(III)/Fe(III) to Co(II)/Fe(II). In addition, oxygen vacancies are used as electron traps to hinder the recombination of photogenerated carriers and accelerate the generation of hydroxyl and superoxide radicals. Quenching tests showed that the addition of p-benzoquinone resulted in the greatest decrease in the degradation rate of naphthalene (inhibition of about 85.5%), demonstrating that O₂˙^- radicals are the main active species in the photocatalytic degradation of naphthalene. H-CoFe₂O_4-x showed improved degradation performance in synergy with PMS (82.0%, k_app = 0.00714 min^-1) while maintaining excellent stability and reusability. Hence, this work provides a promising approach for the design of efficient photocatalysts to degrade persistent organic pollutants in soil and aqueous environments.

Analysis of driving factors of spatial distribution of heavy metals in soil of non-ferrous metal smelting sites: Screening the geodetector calculation results combined with correlation analysis

Heavy metals (HMs) discharged from smelting production may pose a major threat to human health and soil ecosystems. In this study, the spatial distribution characteristics of HMs in the soil of a non-ferrous metal smelting site were assessed. This study employed the geodetector (GD) by optimizing the classification condition and supplementing the correlation analysis (CA). The contribution of driving factors, such as production workshop distributions, hydrogeological conditions, and soil physicochemical properties, to the distribution of HMs in soil in the horizontal and vertical dimensions was assessed. The results showed that the main factors underlying the spatial distribution of As, Cd, Hg, Pb, Sb, and Zn in the horizontal direction were the distance from the sintering workshop (the maximum q value of that factor, q=0.28), raw material yard (q=0.14), and electrolyzer (q=0.29), while those in the vertical direction were the soil moisture content (q=0.17), formation lithology (q=0.12), and soil pH (q=0.06). The findings revealed that the CA is a simple and effective method to supplement the GD analysis underlying the spatial distribution characteristics of HMs at site scale. This study provides useful suggestions for environmental management to prevent HMs pollution and control HMs in the soil of non-ferrous metal smelting sites.

Diversity and activity of soil biota at a post-mining site highly contaminated with Zn and Cd are enhanced by metallicolous compared to non-metallicolous Arabidopsis halleri ecotypes

Hyperaccumulators' ability to take up large quantities of harmful heavy metals from contaminated soils and store them in their foliage makes them promising organisms for bioremediation. Here we demonstrate that some ecotypes of the zinc hyperaccumulator Arabidopsis halleri are more suitable for bioremediation than others, because of their distinct influence on soil biota. In a field experiment, populations originating from metal-polluted and unpolluted soils were transplanted to a highly contaminated metalliferous site in Southern Poland. Effects of plant ecotypes on soil biota were assessed by measurements of feeding activity of soil fauna (bait-lamina test) and catabolic activity and functional diversity of soil bacteria underneath A. halleri plants (Biolog® ECO plates). Chemical soil properties, plant morphological parameters, and zinc concentration in shoots and roots were additionally evaluated. Higher soil fauna feeding activity and higher bacterial community functional diversity were found in soils affected by A. halleri plants originating from metallicolous compared to non-metallicolous ecotypes. Differences in community-level physiological profiles further evidenced changes in microbial communities in response to plant ecotype. These soil characteristics were positively correlated with plant size. No differences in zinc content in shoots and roots, zinc translocation ratio, and plant morphology were observed between metallicolous and non-metallicolous plants. Our results indicate strong associations between A. halleri ecotype and soil microbial community properties. In particular, the improvement of soil biological properties by metallicolous accessions should be further explored to optimize hyperaccumulator-based bioremediation technologies.

Assessment of Heavy Metal Content in Soil and Lycopersicon esculentum (Tomato) and Their Health Implications

In this study, the content of lead (Pb), cadmium (Cd), chromium (Cr) and manganese (Mn) was evaluated in soils and tomatoes (Lycopersicon esculentum) collected from rural areas of Dobrogea province, South-East of Romania. The risk to human health due to the heavy metal exposure via tomato consumption was also assessed.The results suggest that based on the contamination factor, the soils are moderately contaminated with Cd and Mn (Cf values of 1.266. and 1.40) and poorly contaminated with Pb and Cr. The bioconcentration factor (BAF) was below 1 and indicated that the studied species of Lycopersicon esculentum did not accumulate the monitored elements. Person's correlation analysis showed that there were significant relations between soil pH and BCF values of Cd, Pb, Cr and Mn in analysed tomatoes. The estimated daily intake of each metal was below the oral reference dose. The hazard quotient (HQ) and hazard index (HI) were below the acceptable level (< 1), and the cancer risk (CR) for Pb, Cd and Cr was found within acceptable levels (1.0 × 10^-6-1.0 × 10^-4). Based on health guidance values, it may be concluded that the analysed tomatoes do not present health risks to consumers in terms of content and accumulation of heavy metals. It is important to monitor the other toxic metals as well, in order to evaluate the heavy metal accumulation variation and the toxicity value of each metal in agricultural soils from both rural and industrial areas.

Risk assessment of trace metals in Solanum lycopersicum L. (tomato) grown under wastewater irrigation conditions

Heavy metal contamination of food crop plants is viewed as a global issue. Heavy metals like cadmium (Cd), copper (Cu), lead (Pb), chromium (Cr), zinc (Zn), nickel (Ni), arsenic (As), cobalt (Co), and mercury (Hg) are poisonous. Depending on their concentration and capacity for bioaccumulation, they can provide a range of health risks.This research sought to investigate the effects of toxic metals (TMs) on the growth characteristics of produced tomatoes grown under wastewater irrigation. Additionally, it looked into the potential repercussions of both domestic and foreign individuals consuming this plant. In south Cairo, Egypt, two study locations were looked into: a control site in Abu Ragwan, which received water from tributaries of the Nile River, and a contaminated site in El-Shobak El-Sharky, which had raw industrial wastewater. The nutrients of soil and tomato plants (N, P, and K) decreased (P < 0.01), while TMs increased (P < 0.001) significantly as a result of using wastewater for irrigation. Except for Cu, all examined TM accumulating in tomato plants' roots as opposed to shoots had a bioaccumulation factor (BF) > 1. However, the tomato plant's shoot had solely undergone Pb and Ni translocation and storage, with a translocation factor (TF) > 1. A significant amount of Fe (5000.1 mg kg-1), Pb (360.7 mg kg-1), and Mn (356.3 mg kg-1) were present in the edible fruits. The ingestion of contaminated crops increases the daily intake rate of metals (DIR). The values of the high hazard quotient (HQ) were obtained (2073.8 and 2558.9 for Pb, 574.0 and 708.3 for Cd, and 41.1 and 50.7 for Fe for adults and children, respectively). Therefore, tomato plants grown in soils irrigated with untreated wastewater may offer a greater danger to human health, indicating that they should not be grown as a crop for human consumption.

Research on health risk assessment of heavy metals in soil based on multi-factor source apportionment: A case study in Guangdong Province, China

Environmental problems caused by heavy metal pollution in soil have attracted widespread attention worldwide. Identifying and quantifying the heavy metal pollution sources and risks is crucial for subsequent soil management. In this study, an integrated source-risk method for source apportionment and risk assessment based on the PMF model, the geodetector model and the health risk assessment model (HRA) was proposed and applied. Analysis of Hg, As, Pb, Cd, Cu, Ni, Cr, and Zn in 208 topsoils showed that the average contents of eight heavy metals were 1.87-5.86 times greater than corresponding background values, among which Cd and As were relatively high, which were higher than the specified soil risk screening values, high-value areas of heavy metals are mainly concentrated in the central part of the study area. The source apportionment showed that the accumulation of heavy metals was affected by five sources: atmospheric deposition (16.3 %), natural sources (33.1 %), industrial activities dominated by metal mining (15.1 %), industrial activities dominated by metal smelting (12.6 %) and traffic sources (22.9 %). The results of the health risk assessment showed that the carcinogenic risks (adult: 4.74E-05, children: 7.41E-05) of heavy metals in soil to the study population were both acceptable, the non-carcinogenic risk of adult (THI = 0.277) was within the limit, while the non-carcinogenic risk of children (THI = 1.70) was higher than the limit value. Ingestion (89.5 %-95.9 %) contributed the greatest health risk among all exposure routes. Source 3 (arsenic-related industrial activities dominated by metal mining) contributed the most to the HI and CRI of adults and children (all above 50 %), therefore, in the formulation stage of soil management strategy in this area, priority should be given to the control and management of this pollution source. These results can provide more detailed support for environmental protection departments to propose targeted soil pollution control measures.

Fate and transport of chromium in industrial sites: Dynamic simulation on soil profile

Direct discharge of chromium-containing waste water and improper disposal of waste residues in industrial sites may lead to the vertical migration of metals into aquifers, posing serious threat to soil-groundwater system. The heterogeneity in soil profile further aggravates the complexity and unpredictability of this transport process. However, topsoil was the main focus of most studies. Herein, the vertical transport and transformation of Cr in soils at different depths in three industrial sites (i.e., Shijiazhuang, Zhuzhou, and Guangzhou) were investigated to delineate Cr transport and retention characteristics under complex conditions. Regional and vertical differences in soil properties led to the specificity in Cr migration behaviors among these three sites. Correlation analysis showed that soil pH (r = -0.909, p < 0.05) and Fe content (r = 0.949, p < 0.01) were the major controlling factors of Cr(VI) migration and transformation in aquifers. Furthermore, the soil of Zhuzhou site showed the maximum adsorption capacity for Cr(VI) (0.225 mol/kg), and the strongest reduction ability of Cr(VI) was observed in the Guangzhou soil. Results of model-based long-term forecast indicated that the Cr(III) concentration in the liquid phase of Guangzhou subsoil could reach 0.08 mol/m³ within 20 years. Heavier rainfall condition exacerbated the contamination due to an increased pollutant flux and enhanced convection. Specially, Cr was fixed in the topsoil of Zhuzhou site with the formation of PbCrO₄ and presented least vertical migration risk. The conclusions above can provide scientific theoretical guidance for heavy metal pollution prevention and control in industrial contaminated regions.

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

The accurate identification of pollution sources is important for controlling soil pollution. However, the widely used Positive matrix factorization (PMF) model generally relies on knowledge and experience to accurately identify pollution sources; thus, this method faces significant challenges in objectively identifying soil pollution sources. Herein, we established a comprehensive source analysis framework using factor identification and geospatial analysis, and revealed the factors contributing to trace metal(loid) (TM) pollution in soil in the Pearl River Delta (PRD), China. Using the PMF model, we initially considered that the PRD may be affected by natural, atmospheric, traffic and industrial, and agricultural sources. Moreover, Geodetector model detected the relationship between TMs and 12 environmental variables based on the strong spatial "source-sink" relationship of pollutants. The parent material and digital elevation model were the key factors predicting the accumulation of Cr, Ni, and Cu. Industries and roads were the most important determinants of Pb, Zn, and Cd, whereas atmospheric deposition was more important for Hg accumulation. The accumulation of As was found to be closely related to agricultural activities such as the application of chemical fertilizers and pesticides. The spatial autocorrelation between soil TM pollution and environmental variables further supports this hypothesis. Overall, the obtained results showed that proposed approach improved the accuracy of source apportionment and provided a basis for soil pollution control.

An interpolation method incorporating the pollution diffusion characteristics for soil heavy metals - taking a coke plant as an example

The existing spatial interpolation methods in the prediction of soil heavy metal distribution are generally based on spatial auto correlation theory, rarely considering the pollution patterns. By contrast, in polluted sites, heavy metals have a strong heterogeneity even within a very small area, which is not exactly in line with auto correlation theory. This contradiction may lead to inaccuracy in spatial prediction. Atmospheric diffusion and deposition are one of the main sources of soil heavy metal pollution caused by coal-related production activities. To improve the prediction accuracy, the diffusion patterns of pollutants were considered in this paper by integrating Geodetector, Co-Kriging (COK), and partition interpolation. Geodetector was used to identify the main driving factors of soil pollution, based on which, the main driving factors were used as covariates introduced into the interpolation method (COK). Specifically, the amount of particulate matter deposition obtained by a pollutant diffusion model (AERMOD) was used as a covariate. For comparison, the distances to quenching, coke oven, and ammonium sulfate section were also used as covariates. Compared with the Ordinary Kriging method, the method COK-AERMOD established here decreased the root mean square error values of As (2.05 reduced to 1.89), Cd (0.18 reduced to 0.16), Cr (19.07 reduced to 12.97), Cu (6.92 reduced to 4.72), Hg (0.32 reduced to 0.28), Ni (16.92 reduced to 16.10), Pb (18.29 reduced to 16.62), and Zn (159.68 reduced to 153.66). This method in this paper is informative for the interpolation of soil elements in contaminated areas with known pollution source and diffusion patterns.

Spatial variation, sources identification and risk assessment of soil heavy metals in a typical Torreya grandis cv. Merrillii plantation region of southeastern China

Torreya grandis (Torreya grandis cv. Merrillii) is a unique nut tree species in China. Currently, researches on Torreya grandis focus on nuts quality and yield, while few works are related to the soil quality of Torreya grandis plantation. In this study, the typical Torreya grandis production areas of Zhuji, Shengzhou, Keqiao and Dongyang cities along the Kuaiji Mountain were selected. A total of 121 topsoil samples (0-20 cm) were collected based on a grid of 1 km × 1 km. The results indicated that the average concentrations of Cd, Cr, Cu, As, Ni and Pb in soils were 0.12, 49.01, 27.95, 14.28, 26.97 and 40.28 mg kg^-1, respectively. The concentrations of six heavy metals all exceeded the background values, and there were different degrees of pollution levels. The results of Moran's I indicated that the spatial high-high clusters of soil heavy metals were mainly distributed in Zhuji and the junction of Shengzhou and Keqiao. The partial least squares path analysis of structural equation modeling (PLS-SEM) showed that OM and soil nutrients had extremely significant effects on soil heavy metals. Sources identification of principle component analysis (PCA) and positive matrix factorization model (PMF) revealed that agricultural activities, natural factors and mining were the main sources of soil heavy metals. The human health risks caused by soil heavy metals pollution were generally acceptable based on Monte Carlo simulation method. For the heavy-metal polluted area, management measures should be considered in order to protect human health.

Immobilization on anionic metal(loid)s in soil by biochar: A meta-analysis assisted by machine learning

Metal pollution in soil has become one of the most serious environmental problems in China. Biochar is one of the most widely used remediation agents for soil metal pollution. However, the literature does not provide a consistent picture of the performance of biochar on the immobilization of anionic metal(loid)s, especially arsenic, in soil. To obtain a baseline understanding on the interactions of metals and biochar, 597 data records on four metal(loid)s (As, Cr, Sb and V) were collected from 70 publications for this meta-analysis, and the results are highlighted below. Biochar has a significant immobilization effect on anionic metal(loid)s in soil and reduces the bioavailability of these metals to plants. Subgroup analysis found that biochar could decrease the potential mobility of Cr, Sb and V, but the immobilization effect on As was not always consistent. Meanwhile, biochar pH and soil pH are the most key factors affecting the immobilization effect. To summarize, biochar can effectively immobilize Cr, Sb and V in soil, but more attention should be given to As immobilization in future applications. By regulating the properties of biochar and appropriate modification, anionic metal(loid)s in soil can be immobilized more effectively. Hence, both of the soil quality and crop quality can be improved.

Source Identification and Health Risk Assessment of Heavy Metals in Soil: A Case Study of Lintancang Plain, Northeast China

To investigate the concentration, source, and potential health risk of soil heavy metals (V, Cr, Ni, Cu, Zn, Pb, Hg), this study determined the concentration of these seven metals in 37 soil samples from Linyi City, southeast of Shandong Province, China. The mean concentrations of the investigated heavy metals followed the sequence: Cr (76.2 mg/kg) > V (70.5 mg/kg) > Zn (70.1 mg/kg) > Ni (34.0 mg/kg) > Pb (31.4 mg/kg) > Cu (23.2 mg/kg) > Hg (1.7 mg/kg). The enrichment factor (EF) and geo-accumulation index (Igeo) indicated an extreme enrichment of Hg (EF > 10, Igeo > 4) within the study area, while a slight enrichment of other metals. According to the toxic risk index (TRI), Hg accounted for the strongest soil toxicity (TRI = 8.07, 64.3%). The risk assessment with hazard index (HI) suggested that the health risks of all metals were acceptable, and the HI of adults was generally lower compared with that of the children. In addition, two principal components (PC) calculated by principal component analysis (PCA) were used to identify the sources of these heavy metals, which were 57.73% for PC 1 (Pb, Cr, Zn, Ni, Hg, Cu and V) and 21.63% for PC 2 (Hg, Cu and V), respectively. Moreover, PC 1 was mainly controlled by anthropogenic inputs, while PC 2 was contributed to by natural sources. Combined with the correlation matrix, it was concluded that there were three different sources for all seven heavy metals.

Geochemical characteristics and source apportionment of toxic elements in the Tethys-Himalaya tectonic domain, Tibet, China

Toxic elements (TEs) in soil threaten the eco-environmental system and human health. The identification and prediction of sources and high-risk areas of TEs in soil are fundamental for regional pollution prevention and control. In this study, geostatistical methods and GIS-based approaches were used to quantitatively analyze the spatial distribution, geochemical characteristics, key driving factors, and their interactive effects of TEs in soil from a typical area of the Tethys-Himalaya tectonic domain in Tibet based on an integrated approach combining positive matrix factorization and GeoDetector models. The mean contents of chromium, arsenic (As), cadmium, mercury and lead in the soil exceeded the Tibetan background values, with 66.20% of As being higher than the screening values. The spatial distribution of TEs content in the soil was primarily affected by geogenic source factors (primarily geology types, soil parent materials, soil types, and soil pH), and environmental source factors (primarily precipitation and vegetation types) and anthropogenic source factors (primarily income of residents and land-use types) also had the same contribution approximately. Compared with that for individual driving factors, the interaction between most pairs of driving factors enhanced their explanatory power. The high-risk areas for soil As pollution were primarily distributed in the valley areas of the upper reaches of the Longzi River Basin. Therefore, to guarantee the health of residents and the security and sustainability of agricultural production in the study area, regular monitoring and soil remediation should be used to reduce the migration and transformation of As in the local biogeochemical cycle. This study provides new ideas for the regional prediction of high-risk areas for soil pollution, which has guiding importance and reference value for the control and management of large-scale soil pollution.

Occurrence status of microplastics in main agricultural areas of Xinjiang Uygur Autonomous Region, China

A large number of plastic products are used in the process of agricultural production, and the recycling efficiency is low, which leads to the production of a large number of microplastics. Therefore, the microplastic contamination in agricultural areas requires being investigated urgently. In addition, the occurrence characteristics of microplastics are also different in agricultural areas with various land use modes. In this study, the main agricultural areas in Xinjiang are taken as the research object. The abundance of microplastics in the main agricultural areas in Xinjiang ranges from 288 to 1452 items/kg. The shape of microplastics is mainly bulks, and white microplastics account for the highest proportion, and the majority of their sizes are less than 0.5 mm. The risk assessment results show that the contamination risk index of microplastics in this area is 108.92 and the risk level is grade III. The research shows that there is little difference in the abundance of microplastics between paddy field and garden land, which may be because there are few sources of microplastics in the land of these two utilization modes, and the potential pollution sources are similar, such as the atmospheric deposition of microplastics, the falling of fibers on people's clothes during farming, and the agricultural use of sludge. This study can provide a reference for further study on the existing circumstances of microplastics in agricultural areas.

Ranking of Basin-Scale Factors Affecting Metal Concentrations in River Sediment

River sediments often contain potentially harmful pollutants such as metals. Much research has been conducted to identify factors involved in sediment concentrations of metals. While most metal pollution studies focus on smaller scales, it has been shown that basin-scale parameters are powerful predictors of river water quality. The present study focused on basin-scale factors of metal concentrations in river sediments. The study was performed on the contiguous USA using Random Forest (R.F.) to analyze the importance of different factors of the metal pollution potential of river sediments and evaluate the possibility of assessing this potential from basin characteristics. Results indicated that the most important factors belonged to the groups Geology, Dams, and Land cover. Rock characteristics (contents of K2O, CaO, and SiO2) and reservoir drainage area were strong factors. Vegetation indices were more important than land cover types. The response of different metals to basin-scale factors varied greatly. The R.F. models performed well with prediction errors of 16.5% to 28.1%, showing that basin-scale parameters hold sufficient information for predicting potential metal concentrations. The results contribute to research and policymaking dependent on understanding large-scale factors of metal pollution.