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

Source tracing analysis of the exceedance of NH3-N and CODMn in shallow groundwater in the central typical area of the Yangtze river delta

With the accelerated urbanization process in the Yangtze River Delta region, shallow groundwater has received increasing attention. In this work, the exceedances of the ammonium nitrogen (NH3-N) and chemical oxygen demand (CODMn) in shallow groundwater in the central typical area of the Yangtze River Delta were investigated. With the utilization of the national monitoring well (QY10A) as a focal point, a combination of methods, including onsite sampling, hydrogeological surveys, leaching tests, water quality analysis, and isotope tracing, was employed to comprehensively examine groundwater pollution. The study addressed the history of groundwater exploitation, changes in surface water quality, and the influence of stratigraphic structure on groundwater contamination. It has been observed that the NH3-N levels in the silty chalky clay layer and the lower grayish black chalky clay layer in the study area are notably elevated, with concentrations reaching up to 87.5 mg/kg and 97.4 mg/kg in some boreholes. The NH3-N concentration in the silty clay with silty sand can reach as high as 87.2 mg/kg, whereas the concentration is lower in the underlying layers. In the other strata, NH3-N values remain low. The results indicated that the NH3-N and CODMn in the QY10A monitoring well resulted primarily from the inherently high organic nitrogen content in the local geological environment rather than from anthropogenic sources such as industrial parks, domestic sewage, or agricultural activities. This finding highlights the critical role of geological conditions in influencing groundwater quality, emphasizing the necessity of considering these natural factors in pollution prevention and management strategies. Our research provides valuable insights for environmental management in similar geological settings and demonstrates the importance of scientifically rigorous methods for advancing environmental research and policy-making.

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.

Comparative assessment on ammonia nitrogen adsorption onto a saline soil-groundwater environment: distribution, multi-factor interaction, and optimization using response surface methodology and artificial neural network

The adsorption of soil can reduce the leaching of NH₄⁺-N from the external environment into groundwater. The adsorption of NH₄⁺-N is affected by many factors. It is critical to use statistical model to quantitatively describe the effects of interaction between two or more factors on the system response. In this study, HJ-Biplot was used to analyze the correlation characteristics of soil water, salt, and nitrogen, and the response surface methodology and artificial neural network were used to statistically visualize the interaction between factors, including concentration, total dissolved solids (TDS), temperature, and pH. The results showed that the study soil was a typical saline soil, with maximum soil NH₄⁺-N content of 85.45 mg/kg. For the adsorption experiments of NH₄⁺-N on saline soils, the effects of factors on the adsorption capacity were assessed using the RSM model. The RSM model was coupled with an ANN to predict the adsorption of NH₄⁺-N by saline soils. The NH₄⁺-N concentration and water pH were both significant at a linear level (p < 0.0001). The interaction between NH₄⁺-N concentration and pH was also more significant (p < 0.01). Under optimal conditions (concentration: 800 mg/L; temperature: 24 °C; TDS: 637 mg/L; pH: 7.83), the NH₄⁺-N adsorption capacity was 1650.2 ug/g, which was in general agreement with the calculated values from the Box-Behnken and RSM model. In addition, a statistical error criterion for the model showed that the RSM-ANN model had greater predictive ability than RSM model.