Watershed Regressions for Pesticides (warp) Models for Predicting Atrazine Concentrations in Corn Belt Streams1

Stone, Wesley W. and Robert J. Gilliom, 2012. Watershed Regressions for Pesticides (WARP) Models for Predicting Atrazine Concentrations in Corn Belt Streams. Journal of the American Water Resources Association (JAWRA) 48(5): 970‐986. DOI: 10.1111/j.1752‐1688.2012.00661.x Abstract: Watershed Regressions for Pesticides (WARP) models, previously developed for atrazine at the national scale, are improved for application to the United States (U.S.) Corn Belt region by developing region‐specific models that include watershed characteristics that are influential in predicting atrazine concentration statistics within the Corn Belt. WARP models for the Corn Belt (WARP‐CB) were developed for annual maximum moving‐average (14‐, 21‐, 30‐, 60‐, and 90‐day durations) and annual 95th‐percentile atrazine concentrations in streams of the Corn Belt region. The WARP‐CB models accounted for 53 to 62% of the variability in the various concentration statistics among the model‐development sites. Model predictions were within a factor of 5 of the observed concentration statistic for over 90% of the model‐development sites. The WARP‐CB residuals and uncertainty are lower than those of the National WARP model for the same sites. Although atrazine‐use intensity is the most important explanatory variable in the National WARP models, it is not a significant variable in the WARP‐CB models. The WARP‐CB models provide improved predictions for Corn Belt streams draining watersheds with atrazine‐use intensities of 17 kg/km² of watershed area or greater.     

REGRESSION MODELS FOR ESTIMATING HERBICIDE CONCENTRATIONS IN U.S. STREAMS FROM WATERSHED CHARACTERISTICS1

ABSTRACT: Regression models were developed for estimating stream concentrations of the herbicides alachlor, atrazine, cyanazine, metolachior, and trilluralin from use‐intensity data and watershed characteristics. Concentrations were determined from samples collected from 45 streams throughout the United States during 1993 to 1995 as part of the U.S. Geological Survey's National Water‐Quality Assessment (NAWQA). Separate regression models were developed for each of six percentiles (10th, 25th, 50th, 75th, 90th, 95th) of the annual distribution of stream concentrations and for the annual time‐weighted mean concentration. Estimates for the individual percentiles can be combined to provide an estimate of the annual distribution of concentrations for a given stream. Agricultural use of the herbicide in the watershed was a significant predictor in nearly all of the models. Several hydrologic and soil parameters also were useful in explaining the variability in concentrations of herbicides among the streams. Most of the regression models developed for estimation of concentration percentiles and annual mean concentrations accounted for 50 percent to 90 percent of the variability among streams. Predicted concentrations were nearly always within an order of magnitude of the measured concentrations for the model‐development streams, and predicted concentration distributions reasonably matched the actual distributions in most cases. Results from application of the models to streams not included in the model development data set are encouraging, but further validation of the regression approach described in this paper is needed.     

ATRAZINE AND METOLACHLOR OCCURRENCE IN SHALLOW GROUND WATER OF THE UNITED STATES, 1993 TO 1995: RELATIONS TO EXPLANATORY FACTORS1

ABSTRACT: Since 1991, the U.S. Geological Survey has been conducting the National Water Quality Assessment (NAWQA) Program to determine the quality of the Nation's water resources. In an effort to obtain a better understanding of why pesticides are found in shallow ground water on a national scale, a set of factors likely to affect the fate and transport of two herbicides in the subsurface were examined. Atrazine and metolachlor were selected for this discussion because they were among the most frequently detected pesticides in ground water during the first phase of the NAWQA Program (1993 to 1995), and each was the most frequently detected compound in its chemical class (triazines and acetanilides, respectively). The factors that most strongly correlated with the frequencies of atrazine detection in shallow ground‐water networks were those that provided either: (1) an indication of the potential susceptibility of ground water to atrazine contamination, or (2) an indication of relative ground‐water age. The factors most closely related to the frequencies of metolachlor detection in ground water, however, were those that estimated or indicated the intensity of the agricultural use of metolachlor. This difference is probably the result of detailed use estimates for these compounds being available only for agricultural settings. While atrazine use is relatively extensive in nonagricultural settings, in addition to its widespread agricultural use, metolachlor is used almost exclusively for agricultural purposes. As a result, estimates of agricultural applications provide a less reliable indication of total chemical use for atrazine than for metolachlor. A multivariate analysis demonstrated that the factors of interest explained about 50 percent of the variance in atrazine and metolachlor detection frequencies among the NAWQA land‐use studies examined. The inclusion of other factors related to pesticide fate and transport in ground water, or improvements in the quality and accuracy of the data employed for the factors examined, may help explain more of the remaining variance in the frequencies of atrazine and metolachlor detection.     

Regression models for estimating concentrations of atrazine plus deethylatrazine in shallow groundwater in agricultural areas of the United States

Tobit regression models were developed to predict the summed concentration of atrazine [6-chloro--ethyl--(1-methylethyl)-1,3,5-triazine-2,4-diamine] and its degradate deethylatrazine [6-chloro--(1-methylethyl)-1,3,5,-triazine-2,4-diamine] (DEA) in shallow groundwater underlying agricultural settings across the conterminous United States. The 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 affected by other factors operating within the unsaturated zone, such as soil characteristics, artificial 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. These 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.     

ORGANOCHLORINE PESTICIDE RESIDUES IN BED SEDIMENTS OF THE SAN JOAQUIN RIVER,CALIFORNIA1

ABSTRACT: Bed sediments of the San Joaquin River and its tributaries were sampled during October 7–11, 1985, and analyzed for organochiorine pesticide residues in order to determine their areal distribution and to evaluate and prioritize needs for further study. Residues of DDD, DDE, DDT, and dieldrin are widespread in the fine-grained bed sediments of the San Joaquin River and its tributaries despite little or no use of these pesticides for more than 15 years. The San Joaquin River has among the highest bed-sediment concentrations of DDD, DDE, DDT, and dieldrin residues of major rivers in the United States. Concentrations of all four pesticides were correlated with each other and with the amount of organic carbon and fine-grained particles in the bed sediments. The highest concentrations occurred in bed sediments of westside tributary streams. Potential tributary loads of DDD, DDE, DDT, and dieldrin to the San Joaquin River were computed from bed-sediment concentrations and data on streamfiow and suspended-sediment concentration in order to identify the general magnitude of differences between streams and to determine study priorities. The estimated loads indicate that the most important sources of residues during the study period were Salt Slough because of a high load of fine sediment, and Newman Wasteway, Orestimba Creek, and Hospital Creek because of high bed-sediment concentrations. Generally, the highest estimated loads of DDD, DDE, DDT, and dieldrin were in Orestimba and Hospital Creeks.     

Modeling Variability and Trends in Pesticide Concentrations in Streams1

Abstract: A parametric regression model was developed for assessing the variability and long‐term trends in pesticide concentrations in streams. The dependent variable is the logarithm of pesticide concentration and the explanatory variables are a seasonal wave, which represents the seasonal variability of concentration in response to seasonal application rates; a streamflow anomaly, which is the deviation of concurrent daily streamflow from average conditions for the previous 30 days; and a trend, which represents long‐term (inter‐annual) changes in concentration. Application of the model to selected herbicides and insecticides in four diverse streams indicated the model is robust with respect to pesticide type, stream location, and the degree of censoring (proportion of nondetections). An automatic model fitting and selection procedure for the seasonal wave and trend components was found to perform well for the datasets analyzed. Artificial censoring scenarios were used in a Monte Carlo simulation analysis to show that the fitted trends were unbiased and the approximate p‐values were accurate for as few as 10 uncensored concentrations during a three‐year period, assuming a sampling frequency of 15 samples per year. Trend estimates for the full model were compared with a model without the streamflow anomaly and a model in which the seasonality was modeled using standard trigonometric functions, rather than seasonal application rates. Exclusion of the streamflow anomaly resulted in substantial increases in the mean‐squared error and decreases in power for detecting trends. Incorrectly modeling the seasonal structure of the concentration data resulted in substantial estimation bias and moderate increases in mean‐squared error and decreases in power.     

Major herbicides in ground water: results from the National Water-Quality Assessment

To improve understanding of the factors affecting pesticide occurrence in ground water, patterns of detection were examined for selected herbicides, based primarily on results from the National Water-Quality Assessment (NAWQA) program. The NAWQA data were derived from 2,227 sites (wells and springs) sampled in 20 major hydrologic basins across the USA from 1993 to 1995. Results are presented for six high-use herbicides--atrazine (2-chloro-4-ethylamino-6-isopropylamino-s-triazine), cyanazine (2-[4-chloro-6-ethylamino-1,3,5triazin-2-yl]amino]-2-methylpropionitrile), simazine (2-chloro-4,6-bis-[ethylamino]-s-triazine), alachlor (2-chloro-N-[2,6-diethylphenyl]-N-[methoxymethyl]acetamide), acetochlor (2-chloro-N-[ethoxymethyl]-N-[2-ethyl-6-methylphenyl]acetamide), and metolachlor (2-chloro-N-[2-ethyl-6-methylphenyl]-N-[2-methoxylethyl]acetamide)--as well as for prometon (2,4-bis[isopropylamino]-6-methoxy-s-triazine), a nonagricultural herbicide detected frequently during the study. Concentrations were <1 microg L(-1) at 98% of the sites with detections, but exceeded drinking-water criteria (for atrazine) at two sites. In urban areas, frequencies of detection (at or above 0.01 microg L(-1)) of atrazine, cyanazine, simazine, alachlor, and metolachlor in shallow ground water were positively correlated with their nonagricultural use nationwide (P < 0.05). Among different agricultural areas, frequencies of detection were positively correlated with nearby agricultural use for atrazine, cyanazine, alachlor, and metolachlor, but not simazine. Multivariate analysis demonstrated that for these five herbicides, frequencies of detection beneath agricultural areas were positively correlated with their agricultural use and persistence in aerobic soil. Acetochlor, an agricultural herbicide first registered in 1994 for use in the USA, was detected in shallow ground water by 1995, consistent with previous field-scale studies indicating that some pesticides may be detected in ground water within 1 yr following application. The NAWQA results agreed closely with those from other multistate studies with similar designs.     

RELATIONSHIPS BETWEEN WATER QUALITY AND PHOSPHORUS CONCENTRATIONS FOR PUGET SOUND REGION LAKES1

Regression relationships were developed between summer mean total phosphorus (P) concentrations in near-surface water and both chlorophyll a concentrations and Secchi disc transparency for Puget Sound region lakes. Total P concentrations in the lakes studied ranged from 7 to 66 μ/L. The relationship between total P and chlorophyll a, based on data from 69 lakes, explained 57 percent of the variance in chlorophyll a. Predicted chlorophyll a concentrations and 95 percent confidence intervals ranged from 1 ⁺³_-0.5μg/L for 7 μg/L P to about ⁺³⁵_-10μ/L for 66 μ/L P. The relationship between total P and Secchi disc, based on data from 71 lakes, explained 53 percent of the variance in Secchi disc. Predicted Secchi disc transparencies and 95 percent confidence intervals ranged from 5.5 ^+5.5_-3.0 m for 7 μ/L P to 1.4 ^+1.5_-0.7 m for 66 μ/L P.