A novel regional-scale human health risk assessment model for soil heavy metal(loid) pollution based on empirical Bayesian kriging

A novel spatial prediction method for soil heavy metal based on unbiased conditional kernel density estimation

Soil contamination by heavy metals has emerged as a significant global problem. Accurately mapping the spatial distribution of soil heavy metal pollutant concentrations is indispensable for effective agriculture and environmental management. Nevertheless, challenges arise in obtaining comprehensive data at all desired locations, due to limitations in measuring equipment capacity and the associated capital costs. To obtain soil heavy metal maps efficiently and accurately, this paper proposes a nonparametric spatial prediction method, based on unbiased conditional kernel density estimation (UCKDE). The proposed method incorporates the advantages of both geostatistics and machine learning, including stability, adaptability, and the ability to account for various types of auxiliary information. Additionally, it can directly predict the probabilistic density function (PDF) of soil heavy metal content at the target location based on sampling data without complex parameter settings, providing both a deterministic single value and a probabilistic prediction interval. The proposed method and ordinary kriging (OK) were implemented for the spatial prediction of the six heavy metals (As, Cd, Cu, Hg, Mn, and Sb) with the greatest coefficients of variation (CV = 0.53, 1.14, 0.66, 1.05, 0.81 and 0.74, respectively) in Qingxi Town, Chongqing, China. The results showed that the predictive capability of the proposed method (with RMSE values of 5.82, 0.61, 14.76, 0.15, 383.84, and 0.85, respectively) is superior to that of OK (with RMSE values of 5.29, 0.87, 16.37, 0.22, 493.22, and 1.58, respectively) in most cases, particularly when the CV value is high. Besides, the prediction accuracy of the proposed method can be further enhanced by incorporating parent material, resulting in RMSE values of 3.02, 0.51, 8.98, 0.08, 194.16, and 0.56, respectively. The results affirm the reliability of the proposed method and suggest its effectiveness as a tool for soil heavy metal pollution prediction in practical applications.

Exploring the microbe-mediated biological processes of BTEX and toxic metal(loid)s in aging petrochemical landfills

Aging petrochemical landfills serve as reservoirs of inorganic and organic contaminants, posing potential risks of contamination to the surrounding environment. Identifying the pollution characteristics and elucidating the translocation/ transformation processes of typical contaminants in aging petrochemical landfills are crucial yet challenging endeavors. In this study, we employed a combination of chemical analysis and microbial metagenomic technologies to investigate the pollution characteristics of benzene, toluene, ethylbenzene, and xylene (BTEX) as well as metal(loid)s in a representative aging landfill, surrounding soils, and underlying groundwater. Furthermore, we aimed to explore their transformations driven by microbial activity. Our findings revealed widespread distribution of metal(loid)s, including Cd, Ni, Cu, As, Mn, Pb, and Zn, in these environmental media, surpassing soil background values and posing potential ecological risks. Additionally, microbial processes were observed to contribute significantly to the degradation of BTEX compounds and the transformation of metal(loid)s in landfills and surrounding soils, with identified microbial communities and functions playing key roles. Notably, co-occurrence network analysis unveiled the coexistence of functional genes associated with BTEX degradation and metal(loid) transformation, driven primarily by As, Ni, and Cd. These results shed light on the co-selection of resistance traits against BTEX and metal(loid) contaminants in soil microbial consortia under co-contamination scenarios, supporting microbial adaptive evolution in aging petrochemical landfills. The insights gained from this study enhance our understanding of characteristic pollutants and microbial transformation processes in aging landfills, thereby facilitating improved landfill management and contamination remediation strategies.

Perfluoroalkyl compounds in groundwater alter the spatial pattern of health risk in an arsenic‑cadmium contaminated region

Integrated health risk assessment strategies for emerging organic pollutants and heavy metals that coexist in water/soil media are lacking. Contents of perfluoroalkyl compounds and potentially toxic elements in multiple media were determined by investigating a county where a landfill and a tungsten mine coexist. The spatial characteristics and sources of contaminants were predicted by Geostatistics-based and multivariate statistical analysis, and their comprehensive health risks were assessed. The average contents of perfluorooctane acid, perfluorooctanesulfonic acid, arsenic, and cadmium in groundwater were 3.21, 0.77, 1.69, and 0.14 μg L-1, respectively; the maximum content of cadmium in soils and rice highly reached 2.12 and 1.52 mg kg-1, respectively. In soils, the contribution of mine lag to cadmium was 99 %, and fertilizer and pesticide to arsenic was 59.4 %. While in groundwater, arsenic, cadmium and perfluoroalkyl compounds near the landfill mainly came from leachate leakage. Significant correlations were found between arsenic in groundwater and arsenic and cadmium in soils, as well as perfluoroalkyl compounds in groundwater and pH and sulfate. Based on these correlations, the geographically optimal similarity model predicted high-level arsenic in groundwater near the tungsten mine and cadmium/perfluoroalkyl compounds around the landfill. The combination of analytic network process, entropy weighting method and game theory-based trade-off method with risk assessment model can assess the comprehensive risks of multiple pollutants. Using this approach, a high health-risk zone located around the landfill, which was mainly attributed to the presence of arsenic, cadmium and perfluorooctanesulfonic acid, was found. Overall, perfluoroalkyl compounds in groundwater altered the spatial pattern of health risks in an arsenic‑cadmium contaminated area.

Optimizing health risk assessment for soil trace metals under low-precision sampling conditions: A case study of agricultural soil

Cost limitations often lead to the adoption of lower precision grids for soil sampling in large-scale areas, potentially causing deviations in the observed trace metal (TM) concentrations from their true values. Therefore, in this study, an enhanced Health Risk Assessment (HRA) model was developed by combining Monte Carlo simulation (MCS) and Empirical Bayesian kriging (EBK), aiming to improve the accuracy of health risk assessment under low-precision sampling conditions. The results showed that the increased sampling scale led to an overestimation of the non-carcinogenic risk for children, resulting in potential risks (the maximum Hazard index value was 1.08 and 1.64 at the 500 and 1000 m sampling scales, respectively). EBK model was suitable for predicting soil TM concentrations at large sampling scale, and the predicted concentrations were closer to the actual value. Furthermore, we found that the improved HRA model by combining EBK and MCS effectively reduced the possibility of over- or under-estimation of risk levels due to the increasing sampling size, and enhanced the accuracy and robustness of risk assessment. This study provides an important methodology support for health risk assessment of soil TMs under data limitation.

Evaluating the protective capacity of soil heavy metals regulation limits on human health: A critical analysis concerning risk assessment - Importance of localization

Heavy metal pollution of agricultural soil is a major global concern, prompting the establishment of maximum allowable limits (MALs) to ensure food safety and protect human health. This study collected and compared MALs for six heavy metals (As, Cd, Hg, Pb, Zn, and Cu) in agricultural soils from representative countries and organizations (EU and WHO/FAO). The research evaluated the critical health risks and efficacy of these MALs under the hypothetical scenario of metals concentrations reaching the maximum allowable level. Safe thresholds for heavy metals were then derived based on maximum acceptable health risk levels. The comparative analysis revealed significant variations in the specific limit values and terms of MALs across countries and organizations, even for the same metal. This suggests that there is no consensus among countries and organizations regarding the level of metal-related health risks. Furthermore, the risk analysis of metal concentrations reaching the maximum level accentuated heightened risks associated with As, suggesting that the current risk of soil As exposure was underestimated, particularly for children. However, soil Cu, Cd, and Zn limits generally resulted in low health risks, implying that the current limits may overestimate their hazard. Overall, the results highlight that the current MALs for soil heavy metals may not fully safeguard human health. There is a critical need to optimize current soil MALs based on localized risks and the actual impact of these metals on human health. It is suggested to appropriately lower the limits of metals (such as As) whose impact on health risks is underestimated, and cautiously increase the limits of metals (such as Cu, Cd, and Zn) that currently pose minor health risks. This approach aims to reduce both over and insufficient protection problems of soil heavy metal MALs, emphasizing the importance of considering the locality in setting these limits.

Spatial variability of heavy metals concentrations in soil of auto-mechanic workshop clusters in Nsukka, Nigeria

The indiscriminate disposal of spent engine oils and other hazardous waste at auto mechanic workshops clusters in Nsukka, Enugu State, Nigeria is an environmental concern. This study examines the concentration of heavy metals in the soil inside the workshop cluster and in the unpolluted soil outside the workshop cluster at approximately 100 m. Ten sampling points were randomly selected from within the cluster and another ten from outside the cluster. Using a hand-held Global Positioning System, the coordinates of the selected points were established and used to create a digital map. Soil samples at depths of 0-30 cm and 30-60 cm, were analyzed for Cu, Fe, Zn, Pb, As and Cd using Spectrophotometer. Moisture content determination and particle size analysis were also done on the samples. Spatial variability of heavy metals concentrations of the studied site was also mapped with ArcGIS 10.2.2 using interpolation methods. Results showed that the soil ranged from sandy loam to sandy clay loam. Cadmium and Zinc had the lowest and highest concentration, respectively, in the studied area. Comparing the concentrations of heavy metals in soils within and outside the auto mechanic cluster revealed notable differences across various depths (0-30 cm and 30-60 cm). The analysis results for soil samples within the cluster exhibited concentration levels (mg/kg) ranging from 0.716-0.751 (Cu), 2.981-3.327 (Fe), 23.464-30.113 (Zn), 1.115-1.21 (Pb), 2.6-2.912 (As), and 0.133-0.365 (Cd) demonstrating a variation pattern in the order of Zn > Fe > As > Pb > Cu > Cd. Conversely, for soil samples outside the cluster, concentration levels (mg/kg) ranged from 0.611-0.618 (Cu), 2.233-2.516 (Fe), 12.841-15.736 (Zn), 0.887-0.903 (Pb), 1.669-1.911 (As), and 0.091-0.091 (Cd). To assess the disparity in heavy metal concentration levels between samples collected within and outside the clusters, ANOVA test was performed. The test showed significant difference in heavy metal concentrations between samples within and outside the auto mechanic cluster (p < 0.05), implying auto mechanic activities significantly impact heavy metal levels within the cluster compared to outside areas. The assessment of soil pollution utilized indices including the Geo-accumulation Index (Igeo), Contamination factor (Cf), and anthropogenic metal concentration (QoC). Zinc, Cadmium, and Arsenic showed the highest contamination factors, indicating significant soil contamination likely due to anthropogenic activities. The concentrations of the metals analyzed were within WHO permissible limits while the metals concentrations were also observed to decrease as depth was increased. Using ArcGIS 10.2.2, spatial maps showing heavy metal distribution were developed, with the Kriging method proving superior. This study suggests that heavy metal levels in the soil at the area be monitored on a regular basis.

Spatio-temporal trends of mercury levels in alluvial gold mining spoils areas monitored between rainy and dry seasons in the Peruvian Amazon

Artisanal and small-scale gold mining (ASGM) in the Amazon has degraded tropical forests and escalated mercury (Hg) pollution, affecting biodiversity, ecological processes and rural livelihoods. In the Peruvian Amazon, ASGM annually releases some 181 tons of Hg into the environment. Despite some recent advances in understanding the spatial distribution of Hg within gold mine spoils and the surrounding landscape, temporal dynamics in Hg movement are not well understood. We aimed to reveal spatio-temporal trends of soil Hg in areas degraded by ASGM.,. We analyzed soil and sediment samples during the dry and rainy seasons across 14 ha of potentially contaminated sites and natural forests, in the vicinities of the Native community of San Jacinto in Madre de Dios, Peru. Soil Hg levels of areas impacted by ASGM (0.02 ± 0.02 mg kg-1) were generally below soil environmental quality standards (6.60 mg kg-1). However, they showed high variability, mainly explained by the type of natural cover vegetation, soil organic matter (SOM), clay and sand particles. Temporal trends in Hg levels in soils between seasons differed between landscape units distinguished in the mine spoils. During the rainy season, Hg levels decreased up to 45.5% in uncovered soils, while in artificial pond sediments Hg increased by up to 961%. During the dry season, uncovered degraded soils were more prone to lose Hg than sites covered by vegetation, mainly due to higher soil temperatures and concomitantly increasing volatilization. Soils from natural forests and degraded soil covered by regenerating vegetation showed a high capacity to retain Hg mainly due to the higher plant biomass, higher SOM, and increasing concentrations of clay particles. Disturbingly, our findings suggest high Hg mobility from gold mine spoil to close by sedimentary materials, mainly in artificial ponds through alluvial deposition and pluvial lixiviation. Thus, further research is needed on monitoring, and remediation of sediments in artificial to design sustainable land use strategies.