Multivariate and spatio-temporal groundwater pollution risk assessment: A new long-time serial groundwater environmental impact assessment system. (15th January 2023) - Xu, Haoli; Yang, Xing; Wang, Daqing; Hu, Yihua; Cheng, Zijian; Shi, Yue; Zheng, Pu; Shi, Liang
Abstract: Groundwater pollution risk assessment is an important part of environmental assessment. Although it has been developed for many years, there has not yet been a multi-dimensional method that takes into account long time series and spatial factors. We proposed a new method combines the advantages of remote sensing cloud computing, long-term groundwater modeling simulation and GIS technology to solve it efficiently. A coastal industrial park in Hainan was used as the study area. The depth of groundwater level, rainfall, topography and geomorphology, soil moisture, pollution source, pollution toxicity and other more than 10 parameters were used as the indexes. A comprehensive model with remote sensing cloud computing, DRASTIC model and Modflow + MT3DMS was established to assess the pollution risk from 2014 to 2021. The multi-year results indicated that the risk assessment of groundwater pollution was usually on the vertical coastal direction, and the risk increased from far away to near coast. With the discharge of pollutants in the industrial park, the pollution risk in the area 5 km away from the centre increased year by year until it became stable in 2019, and the risk in the centre of the park reached 1 level, covered an area of up to 145400 square metres, accounted for 0.012% of the whole study area. The assessment results in 2020 and 2021 fluctuate slightly compared with those in 2019. Therefore, in terms of groundwater resource protection and resource management, it is necessary to focus on the detection of pollution in the coastal zone and the pollution within 5 km of the centre to strictly control pollution discharge. In this study, the comprehensive assessment includes surface indicators, subsurface indicators, and pollutant indicators. Finally, we achieve a multivariate, spatial and long time series groundwater pollution risk assessment system, which is a new groundwater environmental impact assessment (GEIA) system. Graphical abstract: Image 1 Highlights: Combining RS cloud computing, long-time serial GMS model and GRAM model. Groundwater pollution was assessed from spatio-temporal perspectives. The results of Modflow + MT3DMS were used as index layers to participate in GRAM model. The dynamic assessment of groundwater pollution risk in coastal areas is a good example.