Groundwater, a vital resource for providing drinking water to populations, must be managed sustainably to ensure its availability and quality. This study aims to assess the groundwater quality in the Bourgogne-Franche-Comté region (~50,000 km²) of France and identify the processes responsible for its variability. Data were extracted from the Sise-Eaux database, resulting in an initial sparse matrix comprising 8723 samples and over 100 bacteriological and physicochemical parameters. From this, a refined full matrix of 3569 samples and 22 key parameters was selected. The data underwent logarithmic transformation before applying principal component analysis (PCA) to reduce the dimensionality of the dataset. The analysis of the spatial structure, using both raw and directional variograms, revealed a categorization of parameters, grouping major ions according to the regional lithology. Bacteriological criteria (Escherichia coli and Enterococcus) displayed strong spatial variability over short distances, whereas iron (Fe) and nitrates showed intermediate spatial characteristics between bacteriology and major ions. The PCA allowed the creation of synthetic maps, with the first seven capturing 80% of the information contained in the database, effectively replacing the individual parameter maps. These synthetic maps highlighted the different processes driving the spatial variations in each quality criterion. On a regional scale, the variations in fecal contamination were found to be multifactorial, with significant influences captured by the first four principal components. The 22 parameters can be grouped into six categories based on their spatial and temporal variations, allowing for the redefinition of a resource management and monitoring strategy that is adapted to the identified spatial patterns and processes at the regional scale, while also reducing analytical costs.

Heavy metals in mine tailings induce severe environmental contamination of terrestrial ecosystems. They are hazardous to human health and must be cleaned up. However, ex-situ procedures are costly and soil-destroying. Phytoremediation approaches might be a cost-effective and environmentally...

This study highlights the importance of distinguishing the mechanisms driving spatial and temporal variances in groundwater database analyses, with a particular focus on bacteriological contamination processes. Groundwater quality data from the Bourgogne-Franche-Comté region of France forms the basis of this investigation. Specifically, the SISE-EAUX database includes 3569 groundwater samples collected over various dates from 989 monitoring points. The methodology involves structuring the data into three distinct sets: (1) A spatio-temporal dataset without any conditioning; (2) A spatial dataset that assigns the mean values of each parameter to each sampling point; (3) A temporal dataset that captures deviations from the mean for each sampling point and parameter. These datasets enable a separate analysis of spatial and temporal variances. Principal component analysis (PCA) and parameter hierarchical clustering were used to compare the results, yielding valuable insights into the underlying processes. This analysis helps distinguish between factors related to geological or pedological spatial distributions and those associated with climatic events, such as intense rainfall episodes exhibiting seasonal patterns. Such differentiation enhances the understanding of fecal contamination vectors and nitrate pollution, which are often linked to surface and subsurface runoff in vulnerable catchment areas. While conceptually clear, the practical separation of spatial and temporal variability presents challenges. For example, catchments sensitive to surface water inflows during rainfall events are unevenly distributed across the region, correlating with specific natural environments. As a result, areas of high temporal variability are also well-structured spatially, underscoring the interdependence of these two types of variability. This complexity is exemplified by the behavior of iron, which varies in association with surface and subsurface parameters depending on spatial and temporal contexts. Additionally, asynchronous sampling and varying frequencies across sites lead to discrepancies in the average temporal data acquisition between points. These disparities can influence spatial variability calculations, as temporal variability effects are not entirely removed. Despite these challenges, the distinction between spatial and temporal components is essential for a deeper understanding of groundwater quality mechanisms. This refined approach improves diagnostic precision and supports more targeted and effective water resource management strategies.

Few studies have quantified the complex flux of trace metals from mine tailings to rivers through water erosion, especially in the semi-arid region of North Morocco (Zaida mine) where soil erosion is a severe issue. This study applies (i) methods to understand and estimate the complex flux of trace metals from mine tailings to rivers, using the RUSLE model combined with the concentration of trace metals in the soil and additionally (ii) pollution indices and statistical analyses to assess the sediment contamination by Cd, Cu, Pb, and Zn. Our study revealed that the basin has a low erosion rate, with an average of 9.1 t/ha/yr. Moreover, the soil contamination is particularly high at the north of the mine tailings, as prevailing winds disperse particles across the basin. The assessment of the sediments indicated that Pb is the main contaminant, with concentrations exceeding 200 mg/kg specifically downstream of the tailings. This study also identified high a concentration of trace elements 14 km away from the tailings alongside the Moulouya river, due to the specific hydrological transport patterns in the area. This research contributes to a better understanding of the transport and fate of the trace metals in mining areas. It proposes a replicable method that can be applied in other regions to assess the contamination flows and thereby assist water resource management.

To optimize the management of groundwater resources in the Bourgogne-Franche-Comté (BFC, France) region, data from the Size-Eaux database were cross-referenced with the French Reference Framework for Groundwater Bodies (GWB). The information contained in this dataset was synthesized using Principal Component Analysis (PCA), followed by Agglomerative Hierarchical Clustering (AHC) of GWBs based on their average coordinates along the main factorial axes. The results reveal 11 distinct GWB groups, each internally homogeneous in terms of chemical composition and ongoing processes responsible for intra-group variability. The distribution of the groups aligns with the region’s structural geology, lithology, and agricultural activity patterns. Livestock farming areas, prone to fecal contamination, and cereal-growing areas, characterized by high nitrate concentrations, stand out distinctly. Furthermore, the analysis of GWB groups highlights regional processes such as denitrification, confirming the existence of spatial structuring of these mechanisms beyond local specificities. The major physicochemical and bacteriological zones show strong contrasts between groups while maintaining significant internal homogeneity. Despite the region’s vast size and diversity, spanning three major watersheds, further subdivision was not necessary to obtain applicable results. These findings confirm observations made in other regions and pave the way for an optimized monitoring and surveillance strategy.

The aim of this work is to assess the impact of no-till based cropping system on soil erosion using two indicators. The experiment plot was under no-tillage (NT) for four years, adjacent to a plot under conventional tillage (CT) with same other management practices. The two used indicators are Le...