Optimizing spatial interpolation method and sampling number for predicting cadmium distribution in the largest shallow lake of North China

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.

Mapping soil trace metal distribution using remote sensing and multivariate analysis

Trace metal soil contamination poses significant risks to human health and ecosystems, necessitating thorough investigation and management strategies. Researchers have increasingly utilized advanced techniques like remote sensing (RS), geographic information systems (GIS), geostatistical analysis, and multivariate analysis to address this issue. RS tools play a crucial role in collecting spectral data aiding in the analysis of trace metal distribution in soil. Spectroscopy offers an effective understanding of environmental contamination by analyzing trace metal distribution in soil. The spatial distribution of trace metals in soil has been a key focus of these studies, with factors influencing this distribution identified as soil type, pH levels, organic matter content, land use patterns, and concentrations of trace metals. While progress has been made, further research is needed to fully recognize the potential of integrated geospatial imaging spectroscopy and multivariate statistical analysis for assessing trace metal distribution in soils. Future directions include mapping multivariate results in GIS, identifying specific anthropogenic sources, analyzing temporal trends, and exploring alternative multivariate analysis tools. In conclusion, this review highlights the significance of integrated GIS and multivariate analysis in addressing trace metal contamination in soils, advocating for continued research to enhance assessment and management strategies.

Screening and optimization of interpolation methods for mapping soil-borne polychlorinated biphenyls

There is yet no scientific consensus, and for now, on how to choose the optimal interpolation method and its parameters for mapping soil-borne organic pollutants. Take the polychlorinated biphenyls (PCBs) for instance, we present the comparison of some classic interpolation methods using a high-resolution soil monitoring database. The results showed that empirical Bayesian kriging (EBK) has the highest accuracy for predicting the total PCB concentration, while root mean squared error (RMSE) in inverse distance weighting (IDW) is among the highest in these interpolation methods. The logarithmic transformation of non-normally distributed data contributed to enhance considerably the semivariogram for modeling in kriging interpolation. The increasing of search neighborhood reduced IDW's RMSE, but slightly affected in ordinary kriging (OK), while both of them resulted in over smooth of prediction map. The existence of outliers made the difference between two points increase sharply, and thereby weakening spatial autocorrelation and decreasing the accuracy. As predicted error increased continuously, the prediction accuracy of different interpolation methods reached unanimity gradually. The attempt of the assisted interpolation algorithm did not significantly improve the prediction accuracy of the IDW method. This study constructed a standardized workflow for interpolation, which could reduce human error to reach higher interpolation accuracy for mapping soil-borne PCBs.

Exploring the accumulation capacity of dominant plants based on soil heavy metals forms and assessing heavy metals contamination characteristics near gold tailings ponds

Heavy metal contamination of soil commonly accompanies problems around gold mine tailings ponds. Fully investigating the distribution characteristics of heavy metals and the survival strategies of dominant plants in contaminated soils is crucial for effective pollution management and remediation. This study aims to investigate the contamination characteristics, sources of heavy metals (As, Cd, Pb, Hg, Cu, Zn, Cr, and Ni) in soils around gold mine tailings ponds areas (JHH and WZ) and to clarify the form distribution of heavy metals (As, Cd, Pb, Hg) in contaminated plots as well as their accumulation and translocation in native dominant plants. The results of the study showed that the concentrations of As, Pb, Cd, Cu, and Zn in soil exceeded the national limits at parts of the sampling sites in both study areas. The Nemerow pollution index showed that both study areas reached extreme high pollution levels. Spatial analysis showed that the main areas of contamination were concentrated around metallurgical plants and tailings ponds, with Cd exhibiting the most extensive area of contamination. In the JHH, As (74%), Cd (66%), Pb (77%), Zn (47%) were mainly from tailings releases, and Cu (52%) and Hg (51%) were mainly from gold ore smelting. In the WZ, As (42%), Cd (41%), Pb (73%), Cu (47%), and Zn (41%) were mainly from tailings releases. As, Cd, Pb, and Hg were mostly present in the residue state, and the proportion of water-soluble, ion-exchangeable, and carbonate-bound forms of Cd (19.93%) was significantly higher than that of other heavy metals. Artemisia L. and Amaranthus L. are the primary dominating plants, which exhibited superior accumulation of Cd compared to As, Pb, and Hg, and Artemisia L. demonstrated a robust translocation capacity for As, Pb, and Hg. Compared to the concentrations of other forms of soil heavy metals, the heavy metal content in Artemisia L correlates significantly better with the total soil heavy metal concentration. These results offer additional systematic data support and a deeper theoretical foundation to bolster pollution-control and ecological remediation efforts in mining areas.