Effectiveness of predicting spatial contaminant distributions at industrial sites using partitioned interpolation method

Evaluation of metal pollution characteristics using water and moss in the Luanchuan molybdenum mining area, China

Luanchuan is rich in molybdenum resources, and mining activities are frequent, but over-mining can cause serious metal pollution to the local environment. To explore the degree of metal pollution caused by mining activities, the content characteristics and spatial distribution of metals in mining areas were studied by measuring the concentrations of Fe, Mn, Zn, Ba, Mo, Cu, Cr, Co, V, and W in surface water and mosses of mining areas. In addition, the metal pollution index (HPI), contamination factor (CF), and pollution load index (PLI) were used to evaluate metal pollution, and factor analysis was used to analyze the sources of metals. The results of the analysis of surface water at the mine site indicate the most abundant element in surface water, with a maximum concentration of 3713.8 μg/L, and its content far exceeds the water quality standard of Class III of the Environmental Quality Standard for Surface Water. The results of the HPI analysis showed that nearly 90% of the surface water was moderately contaminated (HPI ≥ 15). The results of the analysis of atmospheric deposition at the mine site confirm that the metal elements with a high threat to the atmospheric environment are Mo and W. The results of PLI indicate that the level of atmospheric deposition pollution in the study area is severe (PLI > 4). Factor analysis indicated that rock weathering and mining activities were the main sources of metals. This study provides a theoretical basis for the investigation and control of metal pollution in similar metal mining areas.

Optimization of spatial prediction and sampling strategy of site contamination based on Thiessen polygon coupling interpolation

Contaminated sites pose a serious threat to the ecological environment and human health. Because of the presence of multiple peaks in the pollution data of some contaminated sites, as well as strong spatial heterogeneity and skewness in their distribution, the accuracy of spatial interpolation prediction is low. This study proposes a method for investigating highly skewed contaminated sites, which uses Thiessen polygons coupled with geostatistics and deterministic interpolation to optimize the spatial prediction and sampling strategy of sites. An industrial site in Luohe is used as an example to validate the proposed method. The results indicate that using 40 × 40 m as the minimum initial sampling unit can obtain data that is representative of the regional pollution situation. Evaluation indexes reveal that the ordinary kriging (OK) method for interpolation prediction accuracy and the radial basis function_inverse distance weighted (RBF_IMQ) method for pollution scope prediction provides the best results, which can effectively improve the spatial prediction accuracy of pollution in the study area. Each accuracy indicator is enhanced by 20-70% after supplementing 11 sampling points in the suspect region, and the identification of the pollution scope approaches 95%. This method offers a novel approach for investigating highly biased contaminated sites, which can optimize the spatial prediction accuracy of pollution and reduce economic costs.

Three-dimensional distribution characteristics of multiple pollutants in the soil at a steelworks mega-site based on multi-source information

Soil pollution at steelworks mega-sites has become a severe environmental issue worldwide. However, due to the complex production processes and hydrogeology, the soil pollution distribution at steelworks is still unclear. This study scientifically cognized the distribution characteristics of polycyclic aromatic hydrocarbons (PAHs), volatile organic compounds (VOCs), and heavy metals (HMs) at a steelworks mega-site based on multi-source information. Specifically, firstly, 3D distribution and spatial autocorrelation of pollutants were obtained by interpolation model and local indicators of spatial associations (LISA), respectively. Secondly, the characteristics of horizontal distribution, vertical distribution, and spatial autocorrelations of pollutants were identified by combining multi-source information such as production processes, soil layers, and properties of pollutants. Horizontal distribution showed that soil pollution in steelworks mainly occurred in the front end of the steel process chain. Over 47% of PAHs and VOCs pollution area were distributed in coking plants and over 69% of HMs in stockyards. Vertical distribution indicated that HMs, PAHs, and VOCs were enriched in the fill, silt, and clay layers, respectively. Spatial autocorrelation of pollutants was positively correlated with their mobility. This study clarified the soil pollution characteristics at steelworks mega-sites, which can support the investigation and remediation of steelworks mega-sites.

Three-dimensional spatial prediction of Zn in the soil of a former tire manufacturing plant using machine learning and readily attainable multisource auxiliary data

Pollutants in the soil of industrial site are often highly heterogeneously distributed, which brought a challenge to accurately predict their three-dimensional (3D) spatial distributions. Here we attempt to create effective 3D prediction models using machine learning (ML) and readily attainable multisource auxiliary data for improving the prediction accuracy of highly heterogeneous Zn in the soil of a small-size industrial site. Using raw covariates from functional area layout, stratigraphic succession, and electrical resistivity tomography, and derived covariates of the raw covariates as predictors, we created 6 individual and 2 ensemble models for Zn, based on ML algorithms such as k-nearest neighbors, random forest, and extreme gradient boosting, and the stacking approach in ensemble ML. Results showed that the overall 3D spatial patterns of Zn predicted by individual and ensemble ML models, inverse distance weighting (IDW), and ordinary Kriging (OK) were similar, but their predictive performances differed significantly. The ensemble model with raw and derived covariates had the highest accuracy in representing the complex 3D spatial patterns of Zn (R² = 0.45, RMSE = 344.80 mg kg^-1), compared to the accuracies of individual ML models (R² = 0.27-0.44, RMSE = 396.75-348.56 mg kg^-1), OK (R² = 0.33, RMSE = 381.12 mg kg^-1), and IDW interpolation (R² = 0.25, RMSE = 402.94 mg kg^-1). Besides, the prediction accuracy gains of incorporating derived covariates were higher than adopting ensemble ML instead of single ML algorithm. These results highlighted the importance of developing derived covariates whilst adopting ML in predicting the 3D distribution of highly heterogeneous pollutant in the soil of small-size industrial site.

The partitioning behavior of PAHs between settled dust and its extracted water phase: Coefficients and effects of the fluorescent organic matter

The occurrence and distribution of polycyclic aromatic hydrocarbons (PAHs) in a city of Central China were determined in the settled dust and its extracted water phase from different land use types and bus stops in Nanchang City. The physicochemical properties of its water extracted dissolved organic matter (WEOM) were characterized to investigate the effect of fluorescence organic matter on the dust-water partitioning coefficients (K_d) using three-dimensional excitation-emission matrix fluorescence spectroscopy combined parallel factor analysis. Results showed that the range of ∑PAHs in settled dust and the extracted water phase was 0.05-15.92 μg·g^-1 and 2-211 ng·L^-1, respectively. These PAHs mostly came from the combustion of biomass. The risk assessment showed that PAHs in dust had no obvious health risk (less than the magnitude of 10^-6). Additionally, the high molecular weight (HMW) PAHs and the low molecular weight (LMW) PAHs were preferentially adsorbed by dust and the dissolved portion, respectively. It was confirmed by the relatively high logK_d values of 4.23 for the HMW-PAHs. Pearson correlation analysis suggested that the higher concentration of dissolved organic carbon and humic-like substance were in favor of PAHs in dust released into waters. This study can provide information on pollution control when considering the impact of fluorescent organic matter on the fate and transport of PAHs.