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The presence of fermentative hotspots in municipal waste dumps has been reported for several decades, but no study has focused on their size and shape. The uncontrolled landfill of Soub Sekt, covering an area of about 8 hectares in the Tadla plain in Morocco, is the source of a permanent pollution plume in the groundwater, detected by self-potential (SP) measurements. The study aims to detect and characterize these hotspots as well as the leachates that form within them. These hotspots are typically circular and smaller than 3 m in size, and they are concentrated within recent waste deposits. Intense electron transfer activities, particularly during redox reactions leading to metal solubilization, result in very low SP values (down to −60 mV), facilitating their detection. Several successive field campaigns suggest that they are active for 2–3 weeks. Due to the low permeability of the soils, highly mineralized leachates (average Electrical Conductivity 45 mS cm−1) rich in organic ions accumulate on the soil surface at the base of the waste windrows. There, they evolve by concentration due to evaporation and oxidation due to slow diffusion of atmospheric O2. Despite the small size of the hotspots generating the leachates, the accumulation of leachates in ponds and the low soil permeability limits the percolation rate, resulting in moderate but permanent groundwater pollution. [ABSTRACT FROM AUTHOR]

In Sahelian Africa, the characteristics of boreholes are often lost and, when several aquifers are present on the same site, it is difficult to know which one is being tapped or is likely to be contaminated, which hinders good management of the resource. In this study conducted on 153 wells distributed in the four major aquifers of Burkina Faso, the variation in chemical composition within the aquifers is high compared to that between the aquifers. In spite of this, treatment by linear statistical analysis and/or machine learning allows the discrimination of the aquifers with a success rate of about 80%. The introduction of water isotopes as an additional parameter and a dimensional reduction by principal component analysis allowed a discrimination rate of 87.6% to be achieved. The pathway of water from sedimentary to basement aquifers explains some of the confusion. [ABSTRACT FROM AUTHOR]

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]

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]