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In order to facilitate the monitoring of groundwater quality in France, the groundwater bodies (GWB) in the Provence-Alpes-Côte d'Azur region have been grouped into 11 homogeneous clusters on the basis of their physico-chemical and bacteriological characteristics. This study aims to test the legitimacy of this grouping by predicting whether water samples belong to a given sampling point, GWB or group of GWBs. To this end, 8673 observations and 18 parameters were extracted from the Size-Eaux database, and this dataset was processed using discriminant analysis and various machine learning algorithms. The results indicate an accuracy of 67% using linear discriminant analysis and 69 to 83% using ML algorithms, while quadratic discriminant analysis underperforms in comparison, yielding a less accurate prediction of 59%. The importance of each parameter in the prediction was assessed using an approach combining recursive feature elimination (RFE) techniques and random forest feature importance (RFFI). Major ions show high spatial range and play the main role in discrimination, while trace elements and bacteriological parameters of high local and/or temporal variability only play a minor role. The disparity of the results according to the characteristics of the GWB groups (geography, altitude, lithology, etc.) is discussed. Validating the grouping of GWBs will enable monitoring and surveillance strategies to be redirected on the basis of fewer, homogeneous hydrogeological units, in order to optimize sustainable management of the resource by the health agencies. [ABSTRACT FROM AUTHOR]

Defining homogeneous units to optimize the monitoring and management of groundwater is a key challenge for organizations responsible for the protection of water for human consumption. However, the number of groundwater bodies (GWBs) is too large for targeted monitoring and recommendations. This study, carried out in the Provence-Alpes-Côte d'Azur region of France, is based on the intersection of two databases, one grouping together the physicochemical and bacteriological analyses of water and the other delimiting the boundaries of groundwater bodies. The extracted dataset contains 8627 measurements from 1143 observation points distributed over 63 GWB. Data conditioning through logarithmic transformation, dimensional reduction through principal component analysis, and hierarchical classification allows the grouping of GWBs into 11 homogeneous clusters. The fractions of unexplained variance (FUV) and ANOVA R2 were calculated to assess the performance of the method at each scale. For example, for the total dissolved load (TDS) parameter, the temporal variance was quantified at 0.36 and the clustering causes a loss of information with an R2 going from 0.63 to 0.4 from the scale of the sampling point to that of the GWB cluster. The results show that the logarithmic transformation reduces the effect of outliers and improves the quality of the GWB clustering. The groups of GWBs are homogeneous and clearly distinguishable from each other. The results can be used to define specific management and protection strategies for each group. The study also highlights the need to take into account the temporal variability of groundwater quality when implementing monitoring and management programs. [ABSTRACT FROM AUTHOR]

A 110 ha dump, located in the Akrach shallow aquifer area in Morocco, was studied in order to characterize the quality of groundwater around the landfill, to delineate the contaminated area and to determine the factors favouring the contamination of groundwater. This dump is located in the city of Oum Azza (15 km south from Rabat), (Map 1). between the Akrach River in the west and the Sidi Mohamed Ben Abdellah Dam in the east. To assess groundwater pollution from this landfill, piezometric monitoring and Hydrochemical analyses were conducted on 22 wells. The results highlight a significant degradation in the groundwater quality, especially in the parts located in the direct vicinity downstream the dump. In the impacted areas, electrical conductivity was above 1100 μS cm-1, bicarbonates were higher than 508 mg L-1, chlorides, sulphate, and nitrates contents were higher than 850, 200 and 1 mg L-1, respectively. In addition we found high cadmium (50 - 100 μg L-1) and chromium (40 - 230 μg L-1) contents, i.e. much higher than the WHO guidelines for drinking or irrigation water. A principal component analysis conducted on this dataset highlights the absence of a prevalent principal component (PC), with the variance distributed across many PCs. The mechanisms responsible for the chemical variance of the hydro system are therefore numerous and fairly balanced in terms of influence. Finally, a hierarchical classification brings out three groups of observations, each group corresponding to a level of pollution. [ABSTRACT FROM AUTHOR]