Determining the Relevant Scale to Analyze the Quality of Regional Groundwater Resources While Combining Groundwater Bodies, Physicochemical and Biological Databases in Southeastern France

Mapping Gully Erosion Variability and Susceptibility Using Remote Sensing, Multivariate Statistical Analysis, and Machine Learning in South Mato Grosso, Brazil

In Brazil, the development of gullies constitutes widespread land degradation, especially in the state of South Mato Grosso, where fighting against this degradation has become a priority for policy makers. However, the environmental and anthropogenic factors that promote gully development are multiple, interact, and present a complexity that can vary by locality, making their prediction difficult. In this framework, a database was constructed for the Rio Ivinhema basin in the southern part of the state, including 400 georeferenced gullies and 13 geo-environmental descriptors. Multivariate statistical analysis was performed using principal component analysis (PCA) to identify the processes controlling the variability in gully development. Susceptibility maps were created through four machine learning models: multivariate discriminant analysis (MDA), logistic regression (LR), classification and regression tree (CART), and random forest (RF). The predictive performance of the models was analyzed by five evaluation indices: accuracy (ACC), sensitivity (SST), specificity (SPF), precision (PRC), and Receiver Operating Characteristic curve (ROC curve). The results show the existence of two major processes controlling gully erosion. The first is the surface runoff process, which is related to conditions of slightly higher relief and higher rainfall. The second also reflects high surface runoff conditions, but rather related to high drainage density and downslope, close to the river network. Human activity represented by peri-urban areas, construction of small earthen dams, and extensive rotational farming contribute significantly to gully formation. The four machine learning models yielded fairly similar results and validated susceptibility maps (ROC curve > 0.8). However, we noted a better performance of the random forest (RF) model (86% and 89.8% for training and test, respectively, with an ROC curve value of 0.931). The evaluation of the contribution of the parameters shows that susceptibility to gully erosion is not governed primarily by a single factor, but rather by the interconnection between different factors, mainly elevation, geology, precipitation, and land use.

An amyotrophic lateral sclerosis hot spot in the French Alps associated with genotoxic fungi

Between 1990 and 2018, 14 cases of amyotrophic lateral sclerosis (ALS) were diagnosed in residents of, and in visitors with second homes to, a mountainous hamlet in the French Alps. Systematic investigation revealed a socio-professional network that connected ALS cases. Genetic risk factors for ALS were excluded. Several known environmental factors were scrutinized and eliminated, notably lead and other chemical contaminants in soil, water or home-grown vegetation used for food, radon and electromagnetic fields. Some lifestyle-related behavioral risk factors were identified: Prior to clinical onset of motor neuron disease, some patients had a high degree of athleticism and smoked tobacco. Recent investigations on site, based on a new hypothesis, showed that all patients had ingested wild mushrooms, notably poisonous False Morels. Half of the ALS cohort reported acute illness following Gyromitra gigas mushroom consumption. This finding supports the hypothesis that genotoxins of fungal origin may induce motor neuron degeneration.