Abstract

The source of inorganic pollutants in the groundwater of the Songnen Plain are complex; therefore, it is urgent to analyze the pollution sources and their contribution rates, and to determine the effects of pollutants on human health. In this study, the solution for the insufficient extraction of characteristic information and subjective weight in principal component analysis (PCA) was found using log-centralization and the entropy weight method. Based on 896 groundwater samples, the types and distribution characteristics of pollution sources were identified, and the APCS-MLR receptor model was used to quantitatively characterize the contribution of pollution sources to water quality. Considering the uncertainty of the health risk assessment process, a trapezoidal fuzzy number-Monte Carlo stochastic simulation model (TFN-MCS) was constructed. Overcoming the inapplicability of triangular fuzzy numbers in dealing with highly discrete water quality data. The results showed that: (1) The five potential pollution sources in the Songnen Plain were salinity factor (34.086 %), agricultural pollution factor (15.854 %), geological background factor (13.11 %), industrial pollution factor (10.382 %) and organic pollution factor (7.382 %). The degree of groundwater pollution in the study area is controlled by the terrain, hydrogeological conditions, land use type, and industrial layout. (2) The APCS-MLR receptor model was suitable for the allocation of potential sources of groundwater pollution. The average contributions rate of the five common factors to groundwater pollution were 38.86, 14.12, 15.73, 14.6, and 6.77 %. Salinity factor plays a major role in groundwater quality. (3) The THI of children and adults were 2.15–15.03 and 0.86–7.261, respectively, much higher than the safety threshold of one. In general, the THI of children from ingesting contaminated groundwater is twice that to adults. NO3– and F- are the primary risk control indicators for groundwater in the study area. These results support the formulation of effective groundwater pollution control strategies.

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