Regression Models for Estimating Concentrations of Atrazine plus Deethylatrazine in Shallow Groundwater in Agricultural Areas of the United States

Abstract

Tobit regression models were developed to predict the summed concentration of atrazine [6-chloro-N-ethyl-N'-(1-methylethyl)-1,3,5-triazine-2,4-diamine] and its degradate deethylatrazine [6-chloro-N-(1-methylethyl)-1,3,5,-triazine-2,4-diamine] (DEA) in shallow groundwater underlying agricultural settings across the conterminous United States. Th e models were developed from atrazine and DEA concentrations in samples from 1298 wells and explanatory variables that represent the source of atrazine and various aspects of the transport and fate of atrazine and DEA in the subsurface. One advantage of these newly developed models over previous national regression models is that they predict concentrations (rather than detection frequency), which can be compared with water quality benchmarks. Model results indicate that variability in the concentration of atrazine residues (atrazine plus DEA) in groundwater underlying agricultural areas is more strongly controlled by the history of atrazine use in relation to the timing of recharge (groundwater age) than by processes that control the dispersion, adsorption, or degradation of these compounds in the saturated zone. Current (1990s) atrazine use was found to be a weak explanatory variable, perhaps because it does not represent the use of atrazine at the time of recharge of the sampled groundwater and because the likelihood that these compounds will reach the water table is aff ected by other factors operating within the unsaturated zone, such as soil characteristics, artifi cial drainage, and water movement. Results show that only about 5% of agricultural areas have greater than a 10% probability of exceeding the USEPA maximum contaminant level of 3.0 μg L −1 . Th ese models are not developed for regulatory purposes but rather can be used to (i) identify areas of potential concern, (ii) provide conservative estimates of the concentrations of atrazine residues in deeper potential drinking water supplies, and (iii) set priorities among areas for future groundwater monitoring.

Applying a statewide geospatial leaching tool for assessing soil vulnerability ratings for agrochemicals across the contiguous United States

“…There are some straightforward tools for assessing the leaching potential of pesticides by different input parameters and screening algorithms. Screening Concentration In GROund Water (SCI-GROW; Pereira et al, 2014), Windows Pesticide Screening Tool (WIN-PST; Brown et al, 2011), and statistical regression models of regional and national scales (Stackelberg et al, 2012) are the tools offered at the federal level from the US Environmental Protection Agency (USEPA), Department of Agriculture (USDA), and Geological Survey (USGS), respectively. Attenuation factor (AF), implemented at the State of Hawaii in the US, is a state-level tool used for pesticide evaluation procedure such as pesticide registration and certification (Ki and Ray, 2015;Stenemo et al, 2007).…”

“…All these tools, except for regression models of the USGS that used additional parameters of watershed characteristics (e.g., air temperature, prevalence of artificial drainage, etc. ), were similar in that they included basic information of chemical and soil properties (e.g., chemical half-life and organic matter content), which were widely available, for contaminant leaching assessment (Stackelberg et al, 2012). However, the results of pesticide leaching will not be exactly the same between the tools due to the difference in the assessment algorithms (e.g., linear vs non-linear regression), assumptions (e.g., the presence vs absence of advection-dominated flow), and data sets (e.g., sandy soils vs agricultural areas) used to derive them (Stackelberg et al, 2012).…”

“…), were similar in that they included basic information of chemical and soil properties (e.g., chemical half-life and organic matter content), which were widely available, for contaminant leaching assessment (Stackelberg et al, 2012). However, the results of pesticide leaching will not be exactly the same between the tools due to the difference in the assessment algorithms (e.g., linear vs non-linear regression), assumptions (e.g., the presence vs absence of advection-dominated flow), and data sets (e.g., sandy soils vs agricultural areas) used to derive them (Stackelberg et al, 2012). Although these differences are not significant in some areas, it is generally accepted that physically based screening tools which utilize basic properties that control contaminant movement tend to be more reliable and robust than subjective and empirical approaches.…”

Multiple imputation for assessment of exposures to drinking water contaminants: Evaluation with the Atrazine Monitoring Program

“…We also considered the predictors year, quarter, state (factor), county (factor), and source water type (factor); but excluded these variables because they provided no additional information or were not found to be associated with finished water quality and exhibited little variability (e.g., source water type was surface water in 4 90% of CWSs). The average atrazine concentration in groundwater was calculated for each county using the regression model of Stackelberg et al (2012), which considers local geology, hydrology, rainfall and other environmental factors. A single groundwater value was used for all years and CWSs in each county because the model was not designed to be time-varying: the primary variable in the model that can be time-varying is rainfall, but the model was developed and implemented by the investigators at the USGS using aggregated data over 10 years.…”

Extraction method for the determination of atrazine, deethylatrazine, and deisopropylatrazine in agricultural soil using factorial design

“…Because of the great importance of this subject, regression models were developed to evaluate groundwater in agricultural areas of USA for AT and DEA, using 1298 wells and explanatory variables related to the source of AT and other aspects of transport and fate of these contaminants in the subsurface. 10 Thus, based on the excessive use, and the lixiviation process of AT, DEA and DIA, the prevention and knowledge of leaching process based on the soil characteristics and the correct application are essential, in order to minimize the groundwater contamination. Therefore the understanding of the sorption behavior in soils is a fundamental aspect for the foresight of the organic pollutant release, 11 which requires appropriate and validated extraction and quantification methods.…”