Helmholtz Centre for Environmental Research, Department of Environmental Informatics, Permoserstr.15, 04318 Leipzig, Germany; Technical University Dresden, Faculty of Environmental Sciences, Helmholtzstr.10, 01069 Dresden, Germany.
Chinese Research Academy of Environmental Sciences, Dayangfang, Beiyuan 8, 100012 Beijing, China.
Sci Total Environ. 2017 Dec 15;605-606:598-609. doi: 10.1016/j.scitotenv.2017.06.126. Epub 2017 Jun 30.
The main objective of this study is to quantify the groundwater contamination risk of Songhua River Basin by applying a novel approach of integrating public datasets, web services and numerical modelling techniques. To our knowledge, this study is the first to establish groundwater risk maps for the entire Songhua River Basin, one of the largest and most contamination-endangered river basins in China. Index-based groundwater risk maps were created with GIS tools at a spatial resolution of 30arc sec by combining the results of groundwater vulnerability and hazard assessment. Groundwater vulnerability was evaluated using the DRASTIC index method based on public datasets at the highest available resolution in combination with numerical groundwater modelling. As a novel approach to overcome data scarcity at large scales, a web mapping service based data query was applied to obtain an inventory for potential hazardous sites within the basin. The groundwater risk assessment demonstrated that <1% of Songhua River Basin is at high or very high contamination risk. These areas were mainly located in the vast plain areas with hotspots particularly in the Changchun metropolitan area. Moreover, groundwater levels and pollution point sources were found to play a significantly larger impact in assessing these areas than originally assumed by the index scheme. Moderate contamination risk was assigned to 27% of the aquifers, predominantly associated with less densely populated agricultural areas. However, the majority of aquifer area in the sparsely populated mountain ranges displayed low groundwater contamination risk. Sensitivity analysis demonstrated that this novel method is valid for regional assessments of groundwater contamination risk. Despite limitations in resolution and input data consistency, the obtained groundwater contamination risk maps will be beneficial for regional and local decision-making processes with regard to groundwater protection measures, particularly if other data availability is limited.
本研究的主要目的是通过整合公共数据集、网络服务和数值建模技术，量化松花江河域地下水污染风险。据我们所知，这是首次针对中国最大且污染最严重的流域之一的整个松花江河域建立地下水风险图。利用 GIS 工具，以 30 弧秒的空间分辨率，结合地下水脆弱性和危害评估的结果，创建基于指数的地下水风险图。利用 DRASTIC 指数法，根据公共数据集评估地下水脆弱性，该数据集具有最高可用分辨率，同时结合数值地下水模型。作为克服大数据集数据稀缺的一种新方法，应用基于网络的地图服务数据查询，获取流域内潜在危险点的清单。地下水风险评估表明，<1%的松花江河域处于高或极高污染风险。这些地区主要位于广阔的平原地区，热点地区尤其集中在长春大都市区。此外，地下水水位和污染点源在评估这些地区时的影响明显大于指数方案所假设的影响。27%的含水层被评估为中度污染风险，主要与人口较少的农业区有关。然而，人口稀少的山区大部分含水层的地下水污染风险较低。敏感性分析表明，该新方法适用于地下水污染风险的区域评估。尽管分辨率和输入数据一致性存在限制，但获得的地下水污染风险图将有利于地下水保护措施的区域和地方决策过程，特别是在其他数据可用性有限的情况下。
