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首页> 外文期刊>Journal of water resource and protection >Modelling Nitrate Pollution Vulnerability in the Brussel's Capital Region (Belgium) Using Data-Driven Modelling Approaches
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Modelling Nitrate Pollution Vulnerability in the Brussel's Capital Region (Belgium) Using Data-Driven Modelling Approaches

机译：利用数据驱动建模方法在布鲁塞尔首都地区（比利时）建模硝酸盐污染脆弱性

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Groundwater vulnerability for nitrate pollution of groundwater in the Brussel's Capital Region was modelled using data-driven modelling approaches. The land use in the study area is heterogeneous. The South South-Eastern part of the region is forested, while the remaining part is urbanised. Ground-water nitrate concentration data were determined at 48 measurement stations distributed over the study area. In addition, oxygen and nitrogen isotope concentration of the nitrates were determined. The data show that the groundwater body is degraded, particularly in the urbanised part of the study area. The contamination with nitrates at degraded stations is slightly decreasing, while the opposite is true for the nitrate contamination at the less degraded stations. We modelled the contamination and trends of nitrate contamination using linear and non-linear statistical modelling techniques. In total, we defined 23 spatially distributed proxy variables that could explain nitrate contamination of the groundwater body. These proxy variables were defined at the grid size of 10 m, and averaged over the influence zone of each measurement station. The influence zones were identified using a simplified particle tracking algorithm from the groundwater piezometric map. The calculated influence zones were consistent with results obtained from a detailed numerical groundwater flow and transport model. Stepwise regression allowed explaining 56% of the observed variability of nitrate contaminations, while non-linear artificial neural network modelling allows explaining nearly 60% of the variability. The dominant explaining variables are the percentage of impermeable surface, the percentage of the sewage system that is in a degradation state, the number of urban infrastructure construction permits with a high pollution risk, the size of the influence zone, and the depth of the groundwater sampling. These results illustrate the important role of urban infrastructure on groundwater degradation and are consistent with the isotopic signature of nitrates determined on the sampling stations. The overlay of the nitrate contamination data with the DRASTIC vulnerability model shows that this latter conceptual model captures partially the spatial signature of the observed contamination.
机译：使用数据驱动的建模方法建模布鲁塞尔资本区域硝酸盐地下水的地下水脆弱性。研究区域的土地使用是异质的。该地区的南东部部分是森林的，而剩下的部分是城市化的。在分布在研究区域的48个测量站确定邻水硝酸盐浓度数据。另外，测定硝酸盐的氧和氮同位素浓度。数据表明，地下水体劣化，特别是在研究区域的城市化部分。硝酸盐处于降解的站的污染略微降低，而相反的是在不太降解的站处于硝酸盐污染的情况下是正确的。我们使用线性和非线性统计建模技术建模了硝酸盐污染的污染和趋势。总共定义了23个空间分布的代理变量，可以解释地下水位的硝酸盐污染。这些代理变量在网格尺寸为10米，并在每个测量站的影响区上平均。使用来自地下水压电图的简化粒子跟踪算法识别影响区。计算出的影响区与从详细的数控地下水流量和运输模型获得的结果一致。逐步回归允许解释硝酸盐污染的观察变异的56％，而非线性人工神经网络建模允许解释近60％的变异性。主导解释变量是不可渗透表面的百分比，污水系统的百分比，处于退化状态，城市基础设施建设的数量具有高污染风险，影响区的大小，以及地下水的深度采样。这些结果说明了城市基础设施对地下水降解的重要作用，并且与在采样站确定的硝酸盐的同位素特征一致。硝酸盐污染数据的覆盖与激烈的漏洞模型表明，该后一概念模型部分捕获观察到的污染的空间签名。

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