Masetti Marco, Sterlacchini Simone, Ballabio Cristiano, Sorichetta Alessandro, Poli Simone
Department of Earth Sciences, University of Milan, Via Mangiagalli 34, 20143 Milan, Italy.
Sci Total Environ. 2009 Jun 1;407(12):3836-46. doi: 10.1016/j.scitotenv.2009.01.055. Epub 2009 Apr 5.
Statistical techniques can be used in groundwater pollution problems to determine the relationships among observed contamination (impacted wells representing an occurrence of what has to be predicted), environmental factors that may influence it and the potential contamination sources. Determination of a threshold concentration to discriminate between impacted or non impacted wells represents a key issue in the application of these techniques. In this work the effects on groundwater vulnerability assessment by statistical methods due to the use of different threshold values have been evaluated. The study area (Province of Milan, northern Italy) is about 2000 km(2) and groundwater nitrate concentration is constantly monitored by a net of about 300 wells. Along with different predictor factors three different threshold values of nitrate concentration have been considered to perform the vulnerability assessment of the shallow unconfined aquifer. The likelihood ratio model has been chosen to analyze the spatial distribution of the vulnerable areas. The reliability of the three final vulnerability maps has been tested showing that all maps identify a general positive trend relating mean nitrate concentration in the wells and vulnerability classes the same wells belong to. Then using the kappa coefficient the influence of the different threshold values has been evaluated comparing the spatial distribution of the resulting vulnerability classes in each map. The use of different threshold does not determine different vulnerability assessment if results are analyzed on a broad scale, even if the smaller threshold value gives the poorest performance in terms of reliability. On the contrary, the spatial distribution of a detailed vulnerability assessment is strongly influenced by the selected threshold used to identify the occurrences, suggesting that there is a strong relationship among the number of identified occurrences, the scale of the maps representing the predictor factors and the model efficiency in discriminating different vulnerable areas.
统计技术可用于解决地下水污染问题，以确定观测到的污染（受影响的水井代表必须预测的事件）、可能影响污染的环境因素与潜在污染源之间的关系。确定用于区分受影响或未受影响水井的阈值浓度是这些技术应用中的一个关键问题。在这项工作中，评估了由于使用不同阈值而导致的统计方法对地下水脆弱性评估的影响。研究区域（意大利北部米兰省）面积约2000平方公里，通过约300口水井组成的网络持续监测地下水中的硝酸盐浓度。除了不同的预测因子外，还考虑了三个不同的硝酸盐浓度阈值，以对浅层无压含水层进行脆弱性评估。选择似然比模型来分析脆弱区域的空间分布。对三张最终脆弱性地图的可靠性进行了测试，结果表明所有地图都识别出了水井中平均硝酸盐浓度与水井所属脆弱性等级之间的总体正相关趋势。然后，使用kappa系数评估不同阈值的影响，比较每张地图中所得脆弱性等级的空间分布。如果在宏观尺度上分析结果，使用不同阈值并不会导致不同的脆弱性评估，即使较小的阈值在可靠性方面表现最差。相反，详细脆弱性评估的空间分布受到用于识别事件的所选阈值的强烈影响，这表明识别出的事件数量、代表预测因子的地图比例尺与区分不同脆弱区域的模型效率之间存在很强的关系。
