Predicting Escherichia coli loads in cascading dams with machine learning: An integration of hydrometeorology, animal density and grazing pattern.

Accurate prediction of Escherichia coli contamination in surface waters is challenging due to considerable uncertainty in the physical, chemical and biological variables that control E. coli occurrence and sources in surface waters. This study proposes a novel approach by integrating hydro-climatic variables as well as animal density and grazing pattern in the feature selection modeling phase to increase E.

Mitigating the risk of atrazine exposure: Identifying hot spots and hot times in surface waters across Nebraska, USA.

Atrazine, one of the most widely used herbicides in the world, threatens human health along with terrestrial and aquatic biota. Recent reports have found atrazine in drinking water to be associated with increased birth defects and incidences of Non-Hodgkin's Lymphoma, with higher levels of significance from exposure to both atrazine and nitrate-N. The Midwest region of the United States, which includes Nebraska, is one of the leading regions for high nitrate-N concentrations and agrochemicals, including atrazine, in surface waters.