Parrone Daniele, Ghergo Stefano, Preziosi Elisabetta
IRSA-CNR, Water Research Institute - National Research Council, Via Salaria km 29.300, PB 10, 00015 Monterotondo, Rome, Italy.
Sci Total Environ. 2019 Apr 1;659:884-894. doi: 10.1016/j.scitotenv.2018.12.350. Epub 2018 Dec 24.
The assessment of geochemical Natural Background Levels (NBLs) in groundwater, aims at distinguishing the naturally high levels of geogenic compounds from anthropogenic pollution. This is a fundamental issue in groundwater management, in particular when the concentration of inorganic compounds exceeds the threshold values set for the evaluation of the groundwater chemical status, as requested by environmental regulations. In this paper, we describe a new procedure that integrates the pre-selection method and statistical techniques, using the example of two case studies. The pre-selection aims to identify suitable groundwater samples for the NBLs assessment. The nitrate concentration threshold, for the removal of the groundwater samples affected by human activities, is established locally through different graphical and statistical approaches. Then, the statistical distribution of each compound is analyzed and the outliers are identified. Normality tests on the datasets allow one to select the most appropriate value, e.g. one percentile, to be adopted as NBL within the data distribution. In the selected case studies, we have defined the NBLs for As, F, Mn, Fe and SO. The two sites are part of a volcanic-sedimentary aquifer in central Italy, where the geochemical background is frequently well above the standards for human consumption. The results of the simple and easily reproducible pre-selection method are strengthened by integration with statistical techniques, notably in selecting the appropriate percentile. New criteria are suggested for the choice of the nitrate threshold to be used for the pre-selection of uncontaminated samples.
评估地下水中的地球化学自然背景值(NBLs),旨在区分地质成因化合物的自然高含量与人为污染。这是地下水管理中的一个基本问题,特别是当无机化合物的浓度超过环境法规要求的用于评估地下水化学状况的阈值时。在本文中,我们以两个案例研究为例,描述了一种整合预选方法和统计技术的新程序。预选旨在识别适合进行NBLs评估的地下水样本。通过不同的图形和统计方法在当地确定用于去除受人类活动影响的地下水样本的硝酸盐浓度阈值。然后,分析每种化合物的统计分布并识别异常值。对数据集进行正态性检验可使人们选择最合适的值,例如百分位数,作为数据分布内的NBL。在选定的案例研究中,我们确定了砷、氟、锰、铁和硫酸根的NBLs。这两个地点是意大利中部一个火山 - 沉积含水层的一部分,那里的地球化学背景经常远高于人类消费标准。通过与统计技术相结合,特别是在选择合适的百分位数时,简单且易于重现的预选方法的结果得到了加强。对于用于未受污染样本预选的硝酸盐阈值的选择,提出了新的标准。