Comparison of different interpolation methods and sequential Gaussian simulation to estimate volumes of soil contaminated by As, Cr, Cu, PCP and dioxins/furans

A risk-based approach for accurately delineating the extent of soil contamination: The role of additional sampling in transition zones

Accurate soil contamination delineation is crucial for deciding where remediation efforts are required. However, misjudgments, either in underestimating or overestimating contamination extents could incur different risks: underestimation may result in environmental risks, while overestimation may lead to financial risks. This study proposed an approach based on environmental and financial risks (loss risk) to improve the performance of contamination delineation. Additionally, the impact of additional sampling in the transition zones on the contamination delineation was evaluated. This approach was demonstrated in Hechi, southwest China, where the soil was polluted by arsenic and cadmium. Initially, geostatistical simulation and 512 initial soil sampling were utilized to generate two maps: the conditional coefficient of variation (CCV) and the conditional probability of exceeding a critical threshold (CPT). These two maps were integrated to quantify the uncertainty in identifying the transition zones, guiding additional sampling. Out of 189 candidate sampling sites, we selected 100 additional sites to address high uncertainty. Subsequently, the minimization risk principle was employed to delineate contamination boundaries. The results showed that contaminated areas in the initial phase were significantly underestimated. Additional sampling in the transition zones improved the performance of soil contamination delineation. The performance metrics of Recall and F1 score for arsenic exhibited a notable enhancement of 6 % and 7 %, respectively. As for cadmium, there was an enhancement with Recall and F1 scores increasing by 4 % and 7 %, respectively. Adding 100 extra samples reduced the financial risks of arsenic and cadmium by 13 % and 11 %, respectively. In comparison, the 100 additional samples reduced the environmental risks of arsenic and cadmium by 55 % and 72 %, respectively. The study demonstrates that combining CCV and CPT for additional sampling efficiently mitigates the risks of delineating contaminated areas, which could help better understand the boundaries and gradient of contamination.

Improving the mapping accuracy of soil heavy metals through an adaptive multi-fidelity interpolation method

Soil heavy metal pollution poses a serious threat to environmental safety and human health. Accurately mapping the soil heavy metal distribution is a prerequisite for soil remediation and restoration at contaminated sites. To improve the accuracy of soil heavy metal mapping, this study proposed an error correction-based multi-fidelity technique to adaptively correct the biases of traditional interpolation methods. The inverse distance weighting (IDW) interpolation method was chosen and combined with the proposed technique to form the adaptive multi-fidelity interpolation framework (AMF-IDW). In AMF-IDW, sampled data were first divided into multiple data groups. Then one data group was used to build the low-fidelity interpolation model through IDW, while the other data groups were treated as high-fidelity data and used for adaptively correcting the low-fidelity model. The capability of AMF-IDW to map the soil heavy metal distribution was evaluated in both hypothetical and real-world scenarios. Results showed that AMF-IDW provided more accurate mapping results compared with IDW and the superiority of AMF-IDW became more evident as the number of adaptive corrections increased. Eventually, after using up all data groups, AMF-IDW improved the R² values for mapping results of different heavy metals by 12.35-24.32%, and decreased the RMSE values by 30.35%-42.86%, indicating a much higher level of mapping accuracy relative to IDW. The proposed adaptive multi-fidelity technique can be equally combined with other interpolation methods and provide promising potential in improving the soil pollution mapping accuracy.

Health risk assessment of soil trace elements using the Sequential Gaussian Simulation approach

In this study, the performance of the Sequential Gaussian Simulation (SGS) approach was studied with the aim of accurately determining local health risk distributions associated with trace elements (V, Cr, Mn, Co, Ni, Cu, Zn, As, and Pb). This study plays a crucial role in determining the distribution of health risk levels, especially from heavy metals. In the SGS approach, health risk levels (non-carcinogenic and carcinogenic) were calculated for pixel sizes of 250 × 250 m². Results were compared to the conventional Ordinary Kriging (OK) method. The cross-validation performances of both methods were compared. Non-carcinogenic health risks calculated according to SGS and OK for children were, respectively, ρ_c: 0.57 and 0.23, RMSE: 0.45 and 0.57, and MAE: 0.33 and 0.43. In the case of adults, non-carcinogenic SGS and OK results were, respectively, ρ_c: 0.53 and 0.24, RMSE: 0.06 and 0.07, and MAE: 0.04 and 0.05 for adults. Carcinogenic health risk estimates obtained by SGS and OK were, respectively, ρ_c: 0.72 and 0.31, RMSE: 4.1 × 10^-5 and 5.8 × 10^-5, and MAE: 3.2 × 10^-5 and 4.3 × 10^-5 in the case of children, and in the case of adults the results were, respectively, ρ_c: 0.71 and 0.30, RMSE: 5 × 10^-6 and 4.3 × 10^-6, and MAE: 4 × 10^-6 and 5 × 10^-6. These results indicated that SGS offered a more accurate approach in determining health risk distributions.

An improved non-stationary geostatistical method for three-dimensional interpolation of Benzo(a)pyrene at a contaminated site

Intense industrial activities and complex hydrogeological conditions at contaminated sites make accurate three-dimensional (3D) mapping challenging. The cause is the non-stationarity in the variance of soil pollutants in geographical space (G-space), making the stationary hypothesis required by the Kriging method unsatisfactory. To handle the variance non-stationarity, a Variance-Octree-Kriging (VOK) method was proposed. VOK is a spatial deformation method that constructs a stationary deformation space (D-space) by stretching and shrinking the G-spaces with low and high spatial correlation, respectively. VOK method consists of 3D stratification in G-space, space scaling and transformation, and ordinary Kriging (OK) in D-space. 3D stratification uses variance octree (VOT) to generate a set of anchor points in the G-space. The spatial scaling and transformation use the virtual force algorithm (VFA) and thin-plate spline to evenly distribute the anchor points and obtain the D-space, where the OK is implemented. The method was applied to predict the distribution of soil Benzo(a)pyrene (BaP) at a contaminated site in North China Plain. The results show that the interpolation accuracy of VOK was 9% higher than that of OK. The VOK method also changed the spatial structure from anisotropic to isotropic. The root mean squared error (RMSE) of fill, silt and clay layers decreased by 4.67%, 11.39%, and 20.46%, respectively. This method is applicable to the 3D interpolation of pollutants at contaminated sites, with the advantages of high interpolation accuracy and the ability to handle the non-stationarity in variance.

Estimation of Heavy Metal Content in Soil Based on Machine Learning Models

Heavy metal pollution in soil is threatening the ecological environment and human health. However, field measurement of heavy metal content in soil entails significant costs. Therefore, this study explores the estimation method of soil heavy metals based on remote sensing images and machine learning. To accurately estimate the heavy metal content, we propose a hybrid artificial intelligence model integrating least absolute shrinkage and selection operator (LASSO), genetic algorithm (GA) and error back propagation neural network (BPNN), namely the LASSO-GA-BPNN model. Meanwhile, this study compares the accuracy of the LASSO-GA-BPNN model, SVR (Support Vector Regression), RF (Random Forest) and spatial interpolation methods with Huanghua city as an example. Furthermore, the study uses the LASSO-GA-BPNN model to estimate the content of eight heavy metals (including Ni, Pb, Cr, Hg, Cd, As, Cu, and Zn) in Huanghua and visualize the results in high resolution. In addition, we calculate the Nemerow index based on the estimation results. The results denote that, the simultaneous optimization of BPNN by LASSO and GA can greatly improve the estimation accuracy and generalization ability. The LASSO-GA-BPNN model is a more accurate model for the estimate heavy metal content in soil compared to SVR, RF and spatial interpolation. Moreover, the comprehensive pollution level in Huanghua is mainly low pollution. The overall spatial distribution law of each heavy metal content is very similar, and the local spatial distribution of each heavy metal is different. The results are of great significance for soil pollution estimation.

A novel interpolation method to predict soil heavy metals based on a genetic algorithm and neural network model

To improve the prediction accuracy of soil heavy metals (HMs) by spatial interpolation, a novel interpolation method based on genetic algorithm and neural network model (GANN model), which integrates soil properties and environmental factors, was proposed to predict the soil HM content. Eleven soil HMs (Cu, Pb, Zn, Cd, Ni, Cr, Hg, As, Co, V and Mn) were predicted using the GANN model. The results showed that the model had a good prediction performance with correlation coefficients (R²) varying from 0.7901 to 0.9776. Compared with other traditional interpolation methods, including inverse distance weighting (IDW), ordinary kriging (OK), universal kriging (UK), and spline with barriers interpolation (SBI) methods, the GANN model had a relatively lower root mean square error value, ranging from 0.0497 to 77.43, suggesting that the GANN model might be a more accurate spatial interpolation method and the soil properties together with the environmental geographical factors played key roles in prediction of soil HMs.

Evaluating source-oriented human health risk of potentially toxic elements: A new exploration of multiple age groups division

Effective source-oriented human health risk assessment (HHRA) for people in different life stages will guide pollution control and risk prevention. This work integrated three receptor models of positive matrix factorization, Unmix, and factor analysis with nonnegative constraints for accurate source-oriented HHRA of potentially toxic elements in 6 age groups of populations (0-<1 year, 1-<6 years, 6-<12 years, 12-<18 years, 18-<44 years, and 44+ years). Four sources were identified. Natural source controlled As, Cr, and Ni in dust and soil as well as Pb and Zn in soil. Industrial-traffic emissions contributed most of Cd in dust and soil as well as Pb and Zn in dust. Hg in both dust and soil originated from coal combustion. Construction works contributed more to PTEs in soil than in dust. Noncarcinogenic and carcinogenic risk for both dust and soil changed in similar trends by age. The noncancer risk reduced with increasing age for people below 44 years. Carcinogenic risk of females over 44 years were the highest, while children from 0 to 1 year faced the lowest carcinogenic risk. Among the four origins of PTEs, natural sources contributed most to health risk of PTEs, followed by industrial-traffic sources, construction works, and coal combustion. Based on sequential Gaussian simulation (SGS), the susceptible population and risk areas were identified. Children from 0 to 6 years were identified as susceptible population. The areas with noncancer risk in dust were 19.15 km² for 0-<1 year and 3.14 km² for children from 1 to <6 years, and noncancer risk areas in soil were 30.26 km² for 0-<1 year and 0.85 km² for 1-<6 years. Relevant control and management works were demanded on children from 0 to 6 years and noncancer risk areas.

Improved Mapping of Potentially Toxic Elements in Soil via Integration of Multiple Data Sources and Various Geostatistical Methods

Soil pollution by potentially toxic elements (PTEs) has become a core issue around the world. Knowledge of the spatial distribution of PTEs in soil is crucial for soil remediation. Portable X-ray fluorescence spectroscopy (p-XRF) provides a cost-saving alternative to the traditional laboratory analysis of soil PTEs. In this study, we collected 293 soil samples from Fuyang County in Southeast China. Subsequently, we used several geostatistical methods, such as inverse distance weighting (IDW), ordinary kriging (OK), and empirical Bayesian kriging (EBK), to estimate the spatial variability of soil PTEs measured by the laboratory and p-XRF methods. The final maps of soil PTEs were outputted by the model averaging method, which combines multiple maps previously created by IDW, OK, and EBK, using both lab and p-XRF data. The study results revealed that the mean PTE content measured by the laboratory methods was as follows: Zn (127.43 mg kg−1) > Cu (31.34 mg kg−1) > Ni (20.79 mg kg−1) > As (10.65 mg kg−1) > Cd (0.33 mg kg−1). p-XRF measurements showed a spatial prediction accuracy of soil PTEs similar to that of laboratory analysis measurements. The spatial prediction accuracy of different PTEs outputted by the model averaging method was as follows: Zn (R2 = 0.71) > Cd (R2 = 0.68) > Ni (R2 = 0.67) > Cu (R2 = 0.62) > As (R2 = 0.50). The prediction accuracy of the model averaging method for five PTEs studied herein was improved compared with that of the laboratory and p-XRF methods, which utilized individual geostatistical methods (e.g., IDW, OK, EBK). Our results proved that p-XRF was a reliable alternative to the traditional laboratory analysis methods for mapping soil PTEs. The model averaging approach improved the prediction accuracy of the soil PTE spatial distribution and reduced the time and cost of monitoring and mapping PTE soil contamination.

Techno-economic assessment of an hydrometallurgical process to simultaneously remove As, Cr, Cu, PCP and PCDD/F from contaminated soil

Industrial activities lead to the contamination of large amounts of soils polluted by both inorganic and organic compounds, which are difficult to treat due to different chemical properties. The efficiency of a decontamination process developed to simultaneously remove mixed contamination of industrial soils was evaluated at the pilot-scale, as well as operating costs associated to that process to define the best remediation approach. The results showed that the treatment of the coarse fractions (>0.250 mm) of 40 kg of soil by attrition in countercurrent mode allowed the removal of 17-42% of As, 3-31% of Cr, 20-38% of Cu, and 64-75% of polychlorinated dioxins and furans (PCDD/F). Removals of 60% for As, 2.2% for Cr, 23% for Cu, and 74% for PCDD/F were obtained during the treatment of attrition sludge (<0.250 mm) by alkaline leaching process. However, the results of the techno-economic evaluation, carried out on a fixed plant with an annual treatment capacity of 7560 tons of soil treated (tst), showed that the estimated overall costs for the attrition process alone [scenario 1] (CAD$ 451/tst) were lower than the costs of the process, which additionally includes an alkaline leaching step to treat attrition sludge [scenario 2] (CAD$ 579/tst). This techno-economic evaluation also showed that the process becomes competitive with current disposal options (thermal desorption and landfilling - CAD$ 600/tst) from a certain treatment capacity, which is around of 3465 tst/yr for the scenario 1 and 6930 tst/yr for the scenario 2. On the other hand, the techno-economic evaluations are crucial to selecting feasible decontamination process for a soil remediation project, with considerations of the type of contamination, site characteristics and cost effectiveness.

Identifying the influencing factors controlling the spatial variation of heavy metals in suburban soil using spatial regression models

Determining the factors that control the spatial variation of heavy metals in suburban soil is important in identifying and preventing pollution sources. Soil intrinsic factors combined with environmental variables can effectively explain the spatial distribution of heavy metals. Compared with classical statistical methods, such as multiple linear regression (MLR) models, spatial regression models that can cope with the spatial dependence of heavy metals have greater potential in establishing an accurate relationship between influencing factors and heavy metals. This study aims to identify the factors that influence the spatial variation of lead (Pb) and cadmium (Cd) in 138 topsoil samples from the suburbs of Wuhan City, China, by using spatial regression models with MLR as the reference. Moran's I values reveal the spatial autocorrelation of Pb and Cd. The spatial lag model (SLM) outperforms MLR and has higher R² and lower spatial dependence of residuals. The significant coefficients of the spatial lag term in SLMs indicate that the spatial variation of Pb and Cd depends on their surrounding observations. SLM results show that Pb content depends on the distance from the nearest industrial enterprises and suggest that industrial pollution is the main source of Pb. Cd content depends on pH, soil organic matter, and the topographic wetness index, indicating that intrinsic and topographical factors contribute to the spatial variation of Cd. Parent materials and application of phosphorus fertilizer are the most likely sources of Cd. The findings highlight the spatial autocorrelation of heavy metals and the effects of intrinsic factors and environmental variables on the spatial variation of such metals. Moreover, this study reveals the effectiveness of spatial regression models in identifying the influencing factors of heavy metals.