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 km²) 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.

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预测整个美国水体中阿特拉津的浓度：土地利用，水文学和水物理化学的重要性

r去津污染在北美地表水域无处不在，但目前尚不清楚除草剂分布对景观和湖内过程的依赖性。我们试图通过开发二项式-γ障碍模型和LASSO回归模型，确定阿特拉津的新型预测因子，并建立一个连贯的框架来模拟阿特拉津在水中的浓度。我们使用来自2012年美国EPA国家湖泊评估，2012年美国农业部CropScape和Global HydroLAB HydroLAKES数据库的数据，为美国连续900多个水体构建了模型。莠去津在美国水体的32％中检测到，具有0.17的平均浓度 μ克L- ^-1当检测到。跨栏模型分为两部分，解释了在空间和时间异质性景观中at去津的多达75％的方差。三个预测因子解释了美国水域中r去津检测的31％变异，其中流域中玉米+大豆培养物的比例是最重要的变量。一旦检测到at去津，我们的模型将解释另外29％的at去津浓度变异性，其中at去津应用面积的估计重量（kg atrazine km ²）是最重要的预测指标。在空间上，与富营养化相关的水质变量与阿特拉津污染水平的提高有关，而水温较低，自然湖泊和自然景观与污染水平的降低有关。我们的结果表明，改变土地使用方式可能是减轻水体中at去津污染的最有效方法。