Groundwater health risk assessment and its temporal and spatial evolution based on trapezoidal fuzzy number-Monte Carlo stochastic simulation: A case study in western Jilin province.

The United States Environmental Protection Agency (USEPA) Four-step-Method (FSM) is a straightforward and extensively utilized tool for evaluating regional health risks, However, the complex and heterogeneous groundwater environment system causes great uncertainty in the assessment process. Triangular stochastic simulation (TSS) possesses certain advantages in solving uncertainty problems, but its inadequacy with discrete data reveals limitations in this aspect. To solve the above problems, this study proposes to construct trapezoidal fuzzy number-Monte Carlo stochastic simulation (TFN-MCSS) to compensate for the shortcomings of the first two methods. This method adopted trapezoidal fuzzy number (TFN) analysis to comprehensively consider the characteristics of a large dispersion of water quality monitoring data and the uncertainty of the human health risk assessment (HHRA) process. Concurrently, to overcome the subjectivity and uncertainty of artificially determining the interval of TFN in traditional methods, the slope was used to select the most probable interval value (TMPIV) of TFN combined with the α-truncated set technique (α-TST) and MCSS. Based on these, a TFN-MCSS was constructed and applied to groundwater HHRA in western Jilin Province. First, the groundwater chemical characteristic determination and water quality evaluation in western Jilin were performed to identify the main pollution indicators, and the health risk effects of pollutants in groundwater of different aquifers at different time periods on adults and children were evaluated using the TFN-MCSS. The uncertainty and sensitivity were analyzed, and the primary risk control indicators were identified and compared to FSM and TSS. The results reveal that TFN-MCSS was more sensitive to data and could reduce the uncertainty of assessment process. It indicated that over a 10-year period, the health risks associated with unconfined groundwater (UW) and confined water (CW) decreased by greater than 52 %. However, the highest total non-carcinogenic risk index (THI) was 1.3-fold higher than the safety threshold, and this posed a health risk.

Sensitivity-dependent dynamic searching approach coupling multi-intelligent surrogates in homotopy mechanism for groundwater DNAPL-source inversion.

Equifinality and premature convergence can result in considerable errors when simultaneously characterizing groundwater contamination sources and estimating contaminant transport parameters. To resolve this problem, we design a sensitivity-dependent progressive optimization system embedding ensemble-learning technique. To avoid repetitive CPU-demanding model evaluations in Sobol' global sensitivity analysis and swarm intelligence optimization inverse modeling, Kriging, support vector regression (SVR), kernel extreme learning machine (KELM), and deep convolutional neural network (DCNN) are compared and ensembled to build an accurate surrogate of the numerical model. In addition, the sensitivities of different source characteristics and contaminant transport parameters are set as important indicators to adjust the displacement vectors of the swarm in each iteration during the optimization process to achieve a balanced identification of sensitivity-varied elements. Moreover, a homotopy-based progressive searching mechanism approach to the global optimum in large areas is developed, with the aim of preventing premature convergence for multimodal search problems. The results indicate that the ensemble learning model efficiently captures the complex input-output relationship of the numerical model with an increased determination coefficient (R2 = 0.9988), while the mean relative error is limited to 0.9314%. Although the contribution of source characteristics and contaminant transport parameters to the spatial-temporal distribution of contaminants vary dramatically, the combined application of sensitivity analysis, homotopy theory, and swarm intelligence optimization provides a more stable and accurate estimation of all the elements. The mean relative error of the identification results significantly reduced from 7.2184% to 3.2718%, whereas the maximum relative error is limited to 9.9501%.

Adsorption of benzene on soils under different influential factors: an experimental investigation, importance order and prediction using artificial neural network.

The adsorption of benzene on soils is specifically associated with its migration and transformation. Although previous studies have proved that the adsorption of benzene is affected by various factors, studies simultaneously considering the effects of multiple factors are rare. This study aimed to identify the qualitative and quantitative relationships between multiple influential factors and the adsorption capacity of benzene (BC). Batch adsorption experiments considering different influential factors, including initial concentration (IC), pH, temperature (T), ion strength (IS) and organic matter content (OMC), were conducted in three kinds of soils collected in a chemical industry park. The correlation analysis between different influential factors and BC was carried out based on the experimental data. The artificial neural network (ANN) was applied to predict BC. The results showed that BC increased with the increase of T. As the pH increased, BCs on silty loam and loam increased, while that on sandy loam decreased. Besides, BCs on silty loam and loam raised with increasing OMC, while that on sandy loam remained unchanged. BCs on all three kinds of soils attained their peaks when IS was small and then become stable with an increase in IS. The sequence of correlation between BC and influential factors is listed as IC > OMC > T > IS > pH for silty loam, OMC > IC > T > IS > pH for loam and IC > T > IS > pH > OMC for sandy loam. ANN analysis showed satisfactory accuracy in predicting BC under different influential factors. These results help us understand the important factors affecting benzene adsorption and provide a tool to get the adsorption information easily in complex site conditions.

Effects of anthropogenic activities on hydrochemical characteristics of ground water of Da'an irrigation area in Western of Jilin Province.

The groundwater environment changes under the influence of anthropogenic activities. Because of the construction of the Da'an irrigation area, the amount of irrigation and fertilizer there has changed. Achieving the coordinated development of groundwater resources and economic benefits requires a deeper understanding of the impact of the construction of irrigation areas on groundwater chemistry. In this study, the variations in groundwater chemistry characteristics were studied using statistics and hydrogeochemical methods. Further, the groundwater quality was assessed using the support vector machine method. The results show that the primary water chemistry type was the HCO3 - Ca - Mg type, with local Fe3+ and F- pollution. After the construction of irrigation area, the SO42-, HCO3-, K+ + Na+, and Ca2+ contents decreased, but the Cl- and Mg2+ contents increased. The main nitrogen source in phreatic water was anthropogenic activities, and the main pollution component was NH4+. After the construction of the irrigation area, the NH4+ concentration increased significantly, and the ratio of samples exceeding the standard increased by 37.5%. The over-standard regions spread to the northwest, east, and southeast of Da'an City and east and southeast of the irrigation area. The groundwater quality was predominantly grade IV and V, which accounted for an increase of 16.35%, widely distributed in the south, east, and southwest of the irrigation area and urban areas. The construction of the irrigation area reduced the suitability of phreatic water for agricultural irrigation in the southeast but increased in the west and north.