Predicting atrazine concentrations in waterbodies across the contiguous United States: The importance of land use, hydrology, and water physicochemistry

Abstract

Atrazine contamination is ubiquitous in North American surface waters, but the dependency of the herbicide's distribution on landscape and within-lake processes is currently poorly known. We sought to identify novel predictors of atrazine and to build a coherent framework to model its concentration in waterbodies through the development of binomial-gamma hurdle models and LASSO regression models. We constructed models for over 900 waterbodies in the contiguous United States using data from the 2012 U.S. EPA National Lake Assessment, the 2012 U.S. Department of Agriculture CropScape and the Global HydroLAB HydroLAKES databases. Atrazine was detected in 32% of U.S. waterbodies, with a mean concentration of 0.17 μg L −1 when detected. The two-part hurdle model explained as much as 75% of the variance in atrazine across a spatially and temporally heterogeneous landscape. Three predictors explained 31% of the variability in atrazine detection in U.S. waterbodies, where the proportion of corn + soy cultures in the watershed was the most important variable. Once atrazine was detected, our models explained an additional 29% of the variability in atrazine concentrations, where the estimated areal weight of atrazine application (kg atrazine km 2) in the watershed was the most important predictor. Spatially, water quality variables associated with eutrophication were linked to increased levels of atrazine contamination while cooler water temperatures and natural lakes and landscapes were associated with decreased levels of contamination. Our results suggest that changes in land-use practices may be the most effective way to mitigate atrazine contamination in waterbodies.

A Two-Stage Interpretable Machine Learning Framework for Accurate Prediction of Trace Pollutants: With an Application to Microcystin

“…The two steps of the two-stage machine learning model framework can be implemented by (1) converting MICX values to a binary variable, TRUE if above the detection limit and FALSE if not; then using the binarized variable along with other observational data as input variables to build a binary classifier to predict the presence of MICX; and (2) only using the data instances where MICX concentrations are above the detection limit to build a regressor to predict the concentration of MICX. Note that this two-stage machine learning model described above is not explicitly designed for MICX but can be applied to all left-censored response variables, such as heavy metal and volatile organic pollutants. , …”

Atrazine and estradiol effects on development of Acris blanchardi (Blanchard's cricket frog) exposed in outdoor enclosures

“…We based our E2 concentrations in the present study of 2.3 and 25 ug/L on these results. Atrazine concentrations were based on environmentally relevant concentrations 6,27 and concentrations reported to elicit feminization in A. blanchardi and Rhinella arenarum. 4,28 Methanol (0.04 mL, 0.0002% of exposure water) was used as a carrier for all treatments and added to the negative control.…”

Synthesis and Characterizations of Molecularly Imprinted Polymer of Atrazine: Removal of Atrazine from Aqueous Samples

“…[20] Non-target plants, including important wildflowers and crops, can also be affected by the uncontrolled usage of ATZ, leading to reduced biodiversity and agricultural yield losses. Concerns regarding human health including the disruption of the human endocrine system [21] and increased risks of teratogenesis, [8,22,23] mutagenesis, [24] and carcinogenesis [25,26] are due to the ATZ-contaminated water that enters the human body through the food chain. [27] The persistence of ATZ in the environment, its ecological disruptions, and potential health risks highlight the need to develop sustainable alternatives to cope with the issues of ATZ contamination.…”