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Chloroacetanilide Herbicide Metabolites in Wisconsin Groundwater: 2001 Survey Results J E F F R E Y K . P O S T L E , * ,† BRUCE D. RHEINECK,† PAULA E. ALLEN,† JON O. BALDOCK,‡ CODY J. COOK,† RANDY ZOGBAUM,† AND JAMES P. VANDENBROOK† Wisconsin Department of Agriculture, Trade and Consumer Protection, P.O. Box 8911, Madison, Wisconsin 53708-8911, and Agstat, 6394 Grandview Road, Verona, Wisconsin, 53593
A survey of agricultural chemicals in Wisconsin groundwater was conducted between October 2000 and April 2001 to obtain a current picture of agricultural chemicals in groundwater used for private drinking water. A stratified, random sampling procedure was used to select 336 sampling locations. Water from private drinking water wells randomly selected from within the 336 sampling locations was analyzed for 18 compounds including herbicides, herbicide metabolites, and nitrate. This report focuses on the frequency and concentration of chloroacetanilide herbicides and their metabolites. Analysis of data resulted in an estimated proportion of 38 ( 5.0% of wells that contained detectable levels of a herbicide or herbicide metabolite. The most commonly detected compound was alachlor ESA with a proportion estimate of 28 ( 4.6%. Other detected compounds in order of prevalence were metolachlor ESA, metolachlor OA, alachlor OA, acetochlor ESA, and parent alachlor. Estimates of the mean concentration for the detects ranged from 0.15 ( 0.082 µg/L for acetochlor ESA to 1.8 ( 0.60 µg/L for alachlor OA. Water quality standards have not been developed for these chloroacetanilide herbicide metabolites. The results of this survey emphasize the need for toxicological assessments of herbicide metabolite compounds and establishment of water quality standards at the state and federal levels.
Introduction Survey Objectives. The specific objectives of the 2001 Wisconsin Department of Agriculture, Trade and Consumer protection (DATCP) survey were to (i) determine the detection frequencies and concentrations of agricultural chemicals in Wisconsin groundwater, (ii) look at trends over time in atrazine and nitrate findings as compared to surveys conducted in 1994 and 1996, and (iii) complete the first comprehensive groundwater survey of pesticide metabolites in the state. This report focuses on the findings for the chloroacetanilide herbicides and their metabolites. The findings for other agricultural chemicals and the time trends analysis will be reported in a separate paper. * Corresponding author phone: (608) 224-4503; fax: (608) 2244656; e-mail:
[email protected]. † Wisconsin Department of Agriculture. ‡ Agstat. 10.1021/es040399h CCC: $27.50 Published on Web 09/03/2004
2004 American Chemical Society
Chloroacetanilide Herbicide Use. Alachlor, metolachlor, and acetochlor belong to the chloroacetanilide class of herbicides. Their main use in Wisconsin and elsewhere is for preemergence control of annual grasses in corn. Alachlor and metolachlor are also used for weed control in soybeans and certain other crops. Products containing metolachlor and alachlor have been used in Wisconsin for over 25 years. Acetochlor products have only been in use since 1994. In 2000, alachlor, metolachlor, and acetochlor were used on 560 000, 700 000, and 910 000 acres of corn in Wisconsin, respectively (1). The total amount of active ingredient applied of these three compounds was 1.15, 0.943, and 1.69 million pounds, respectively. Recent trends in the use of these three herbicides show that alachlor use is decreasing, metolachlor use is remaining fairly constant, and acetochlor use is increasing. In 2000 more acetochlor was applied to corn in Wisconsin than any other herbicide active ingredient. Chloroacetanilide herbicides are applied to corn crops throughout corn growing regions of the United States (1). In the 18 major corn producing states, corn was planted on 73 770 000 acres in 2000, and alachlor, metolachlor, and acetochlor were applied to 4, 28, and 25%, respectively, of these acres. A total of over 65 million pounds of chloroacetanilide herbicide active ingredient was applied in these states in 2000. In the Upper Midwest, Illinois, Indiana, and Iowa are major corn producing states. These three states had a combined total of 29.2 million acres of corn planted in 2000 and acetochlor and metolachlor were the most commonly applied grass herbicides. Over 29 million pounds of chloroacetanilide herbicide active ingredient were applied in these three states in 2000. Chemistry and Degradation of Chloroacetanilide Herbicides. Alachlor [2-chloro-N-(2,6-diethylphenyl)-N-(methoxymethyl) acetamide], metolachlor [2-chloro-N-(2-ethyl6-methylphenyl)-N-(methoxy-1-methylethyl) acetamide], and acetochlor [2-chloro-N-(ethoxymethyl)-N-(2-ethyl-6-methylphenyl) acetamide are three similar chloroacetanilide herbicides. Each of these three parent compounds break down into unique ethane sulfonic acid (ESA) and oxanilic acid (OA) metabolites. Figure 1 shows the chemical structures of these compounds. The chloroacetanilide herbicides are transformed to the ESA and OA metabolites primarily by microbial activity in soil (2-4). These metabolite products are probably formed by the removal of the chlorine atom on the parent compounds by glutathione and the subsequent formation of the ESA and OA metabolites by different enzymatic pathways (4). The degradation of the parent compounds to the ESA metabolites, for example, results in the removal of a chlorine atom and the addition of a sulfonic acid functional group to the molecule. This greatly increases the water solubility relative to the parent compound and contributes to the increased potential for leaching to groundwater (5). The potential for degradation of these compounds by microorganisms in groundwater appears to be limited. Once these compounds enter groundwater, they are likely to persist for a long period of time (3). Previous Groundwater Sampling for Chloroacetanilide Herbicide Metabolites. A 1994 statewide survey in Wisconsin found that 9.0% of private wells contained alachlor ESA (6). This relatively low detection rate was due to the higher limit of detection (l.0 µg/L) that was in place at the time of the survey. The mean concentration of alachlor ESA in wells with detections was 1.77 µg/L. Laboratory methods were not available for the other chloroacetanilide metabolites at the time of the 1994 survey. VOL. 38, NO. 20, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 1. Chemical structures for alachlor, metolachlor, acetochlor, and their ethane sulfonic acid and oxanilic acid metabolites.
In 1998 the United States Geological Survey (USGS) reported the occurrence of ESA and OA metabolites of chloroacetanilide herbicides in Iowa groundwater and surface water (7). These metabolites were present in almost 75% of groundwater samples obtained from municipal wells. The metabolites were detected much more frequently and at higher concentrations than their parent compounds. The authors emphasized the importance of analyzing for both parent compounds and metabolites to fully understand the fate of herbicides in hydrologic systems. A study of the occurrence of pesticides and their metabolites in near-surface aquifers in the Midwest found that alachlor ESA was detected far more frequently than parent alachlor (8). Alachlor ESA was detected in 45.8% of 153 wells, whereas parent alachlor was detected in only 5.9% of these wells. The maximum concentration of alachlor ESA detected was 8.63 µg/L. The authors noted that alachlor ESA appears to be persistent in these near-surface aquifers. Ninety percent of the wells with initial detects also had detectable levels one year later. The USGS provides a national and regional perspective on the occurrence of selected herbicides and their metabolites in groundwater based on their National Water-Quality Assessment Program and Midwest Pesticide Study (4). In the studies reviewed, the chloroacetanilide metabolites were detected up to 50 times more frequently than the parent compounds. The authors suggest that parent compounds are found in groundwater infrequently because of the their relatively low environmental persistence and relatively rapid transformation in soil to the ESA and OA metabolites.
Methods Survey Design. The target population for this and the related surveys was groundwater in rural Wisconsin exploited for private drinking water. This subset of rural Wisconsin groundwater was chosen because it would be too costly to sample all groundwater. To sample groundwater in a cost efficient manner, existing private drinking water wells were used. Because there was no comprehensive, up-to-date list 5340
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FIGURE 2. Agricultural statistics districts and sampling locations.
of private wells, the sampling frame was a list of rural civil sections (excluding those covered by water). A stratified, random sampling procedure was used to select the 336 civil sections in this survey (Figure 2). The nine Wisconsin agricultural statistics districts (ASD), which are groups of adjacent counties, formed the sampling strata (Figure 2). The number of samples collected in each ASD was based on the number of acres in agriculture in the district. Stratified random sampling is the most common approach used in similar surveys by other state and federal agencies (9-12). Previous Wisconsin surveys also used a stratified random sampling procedure to allocate samples throughout the state (6).
TABLE 1. Herbicide Parent and Metabolite Results from the 2001 Survey
compound
no. of detects (N ) 336)
limit of detection (µg L-1)
Federal maximum contaminant level (µg L-1)
no. of samples over standard
alachlor alachlor ESA alachlor OA metolachlor metolachlor ESA metolachlor OA acetochlor acetochlor ESA acetochlor OA
1 103 16 0 88 23 0 10 1
0.15 0.10 0.10 0.25 0.10 0.10 0.10 0.10 0.10
2
0
Sample Collection and Analysis. We collected 336 water samples from private drinking water wells for the survey. Sample collection began with the list of randomly selected civil sections with a randomly chosen 4.5-hectare parcel identified in each. We then visited the parcel to determine if a private well existed within that 4.5-hectare area. If so, three attempts were made to contact the owner and determine his or her willingness to participate in the survey. If we did not identify a well or willing well owner, we spiraled out in a clockwise manner to other 4.5-hectare parcels in the section. If a well or willing well owner was not found in the section, we moved to a replacement section and random 4.5-hectare parcel from a second pre-selected random list of sections in the district. Once a suitable sampling site was identified, we collected a water sample using the existing well and pump. The sampling technician interviewed the owner and inspected the plumbing system to see if there was a water treatment device. If we could not obtain a sample that did not pass through a water treatment device, we selected a replacement well using the process described above. Samples were collected through the cold water faucet after allowing the water to run for at least 5 min. We filled two 1-L glass amber bottles with Teflon-lined caps at each site and promptly placed the sample on ice. Each water sample was analyzed for 18 different compounds at the DATCP laboratory. The DATCP lab uses gas chromatography to identify and quantify these compounds and gas chromatography/mass spectrometry to confirm them. Statistical Analyses. We determined the proportion of detections and the means and variances of the concentrations for each stratum using single-sample formulas (13,14). The statewide estimates and confidence intervals were determined as the standard weighted averages over the strata with three adjustments. First, since the sample size was rather small for some districts, we used Student’s t-distribution instead of the standard-normal-distribution (13). Second, using the Student’s t required a statewide estimate of the number of degrees of freedom, which varied over the strata. To overcome this ambiguity, we followed the recommendations of Cochran (13) and Thompson (14) to use an approximation developed by Satterthwaite. Third, in the districts with no detects or zero variance, we substituted the statewide proportion or unweighted statewide variance for the zero value to avoid underestimating the standard error of the estimate. The concentration data presented issues of nonnormality and censorship that were not encountered in the proportion data. Attempts to use a logarithmic function and similar transformations did not improve the normality of data from these surveys because the nonnormality resulted from a high proportion of the data reported as being below the limit of detection. The method that was easiest to employ in this case was to calculate the means and variances of only those cases that were above the limit of detection (15). This method
concentration range (µg L-1) 0.690 0.101-14.8 0.145-13.5 0.103-10.2 0.103-5.89 0.104-0.809 0.155
FIGURE 3. Geographic distribution of chloroacetanilide metabolite detections. resulted in further subsetting the population, but it had the advantage that the detect-only data were much closer to being normally distributed.
Survey Results Herbicide Metabolite Detections. Table 1 is a summary of the chloroacetanilide herbicide metabolite results of the 2001 survey. Alachlor ESA was the most commonly found compound with 103 detects, followed by metolachlor ESA, metolachlor OA, alachlor OA, acetochlor ESA, acetochlor OA, and parent alachlor. These results show that metabolites of alachlor, acetochlor, and metolachlor were detected much more frequently than their respective parent compounds. The general order of detection frequency in this study was ESA metabolites > OA metabolites > parent compound. Parent alachlor was only detected once and parent metolachlor and acetochlor were not detected at all. Acetochlor metabolites were detected less than alachlor and metolachlor metabolites, probably because acetochlor has only been used in Wisconsin since 1994. Future groundwater surveys will be needed to determine if acetochlor metabolites will become as common in Wisconsin groundwater as the metabolites of alachlor and metolachlor. Figure 3 shows the geographic distribution of the chloroacetanilide metabolite results. Table 2 shows the number of detects in each stratum (ASD) for selected chloroacetanilide herbicide metabolites. The number of samples in each ASD varies according to the VOL. 38, NO. 20, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 2. Number of Samples, Corn Acres, and Detects, by Agricultural Statistics District
district
no. of samples
% of acres planted to corn in 2000
alachlor ESA
southeast south central southwest east central central west central northeast north central northwest total
14 65 56 30 44 56 17 26 28 336
14 25 15 16 8 13 4 3 4 10
1 34 18 5 16 19 2 2 6 103
number of detects alachlor metolachlor OA ESA 0 8 1 1 5 1 0 0 0 16
2 26 19 8 6 19 0 3 5 88
metolachlor OA 0 6 5 3 3 6 0 0 0 23
TABLE 3. Statewide Proportion Estimates of Detections and 95% Confidence Intervals for Chloroacetanilide Herbicide Metabolites in the DATCP 2001 Statewide Well Sampling Program
compound alachlor ESA alachlor OA acetochlor ESA metolachlor ESA metolachlor OA any herbicide or metabolite
statewide estimate of the 95% statewide proportion confidence no. of of detects interval detects (%) (%) 103 16 10 88 23 135
28 3.7 2.6 25 6.4 38
23-32 1.9-5.6 0.9-4.3 21-30 3.8-9.1 33-43
stratified sampling design. In the 2001 survey the largest number of detections of herbicide metabolites was in the south central ASD. In this ASD, 52% of the wells had a detection of alachlor ESA and 40% had metolachlor ESA. Detection rates were next highest in the west central, southwest, and central ASDs. The higher frequency of detections in the south central ASD is probably due to the intensity of crop production and agricultural chemical use rather than the presence of unique soil or geologic features. The south central ASD has historically been the highest corn-producing district in the state. As shown in Table 2, 25% of the land area in this ASD was planted to corn in 2000 compared to less than 5% in the three northern ASDs where the predominant land cover is forest. These patterns of detections in 2001 were similar to the patterns of atrazine and nitrate-nitrogen detections in previous Wisconsin surveys (6). The idea that more intensive use of a pesticide in an area leads to a higher probability of detection in groundwater seems logical, but the lack of research data to support this theory has been noted by other researchers (4). Statewide Statistical Estimates of the Proportion of Detections. We also calculated estimates of the statewide proportions of chloroacetanilide herbicide metabolite detections. Proportion estimates were not calculated for the parent compounds because of the lack of detections. Table 3 summarizes these estimates and includes the 95% confidence intervals for these data. These estimates indicate that the metabolites of alachlor and metolachlor are present in a significant proportion of wells in Wisconsin. The proportion estimates for alachlor ESA and metolachlor ESA, for example, were 28% and 25%, respectively. The estimates for the OA metabolites and acetochlor ESA were lower, but still of concern. Alachlor and metolachlor ESA had similar proportion estimates, but metolachlor OA had a somewhat higher proportion estimate than alachlor OA. Investigators in other 5342
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FIGURE 4. Comparison of proportion estimates and 95% confidence intervals for alachlor ESA > 1.0 µg/L in 1994, 1996, and 2001. states have also reported finding chloroacetanilide metabolites at much higher frequencies than the parent compounds (7). This is probably because the metabolites have higher water solubilities than the parent compounds (5). We also compared the proportion estimate and 95% confidence interval for alachlor ESA from this survey to the estimates from earlier (1994 and 1996) surveys conducted in Wisconsin to see if there had been any change over time. To do this, we applied the 1.0 µg/L limit of detection for alachlor ESA from the earlier surveys to the 2001 data. The proportion estimates for the 1994, 1996, and 2001 surveys were 9.0, 7.7, and 9.3%, respectively, but since the 95% confidence intervals overlapped, no significant changes were noted over this time period. This analysis is shown graphically in Figure 4. This analysis was only possible for alachlor ESA because laboratory methods were not available for the other chloroacetanilide metabolites in 1994 and 1996. Concentrations. The concentration estimates are based on results from wells that contained detectable levels of chloroacetanilide herbicides and their metabolites. If wells without detections had been included, the statewide average concentration estimates would be lower. Table 4 shows the estimates and their 95% confidence intervals. A wide range of concentrations was found for the analytes detected in this study. The highest individual concentration detected in the survey was 14.8 µg/L for alachlor ESA. Estimates of the mean detect concentrations ranged from 0.15 µg/L for acetochlor ESA to 1.8 µg/L for alachlor OA. Federal Maximum Contaminant Levels have not been developed for any of these metabolites. The only chloroacetanilide metabolite with a water quality guideline in Wisconsin is alachlor ESA which has an interim health advisory level of 20 µg/L. No wells in this study exceeded this level.
Discussion Private wells may not provide a worst-case estimate of groundwater contamination by agricultural chemicals. Past
TABLE 4. Estimates of the Mean Concentration of Detects and 95% Confidence Intervals for Herbicide Metabolites in the DATCP 2001 Statewide Well Sampling Program
compound
statewide no. of detects
alachlor ESA alachlor OA acetochlor ESA acetochlor OA metolachlor ESA metolachlor OA
103 16 10 1 88 23
established for chloroacetanilide herbicide metabolites, interpretation of the findings in this survey from a public health perspective is difficult.
Acknowledgments
statewide estimate of the mean detect concn. (µg L-1)
95% confidence interval (µg L-1)
1.0 1.8 0.15
0.76-1.3 1.2-2.4 0.072-0.24
0.79 0.66
0.53-1.0 0.071-1.1
DATCP monitoring studies and other studies (16) have suggested higher concentrations of pesticides and nitrate in water table monitoring wells in or adjacent to farm fields than those found in private or municipal wells. Still, most state and federal agencies have chosen to sample private wells as their primary target population (12) because trigger levels for regulatory action are generally dependent on potential human health impacts and drinking water is a primary exposure route. In our study, well depth and construction information was not available for the majority of wells sampled. Other studies (17) have focused on sampling private wells where well logs were available or were designed to include several types of wells for comparative purposes. Although we considered limiting our sampling to wells with good construction data (such as aquifer exploited, well depth, geologic materials, etc.), we felt that this would cause a significant bias toward sampling recently installed wells that may be in better condition than older wells. While there are no sampling methods that can eliminate all biases from groundwater investigations, we believe our approach produced results that are representative of rural area groundwater exploited for private use. These results emphasize the need for toxicological assessments of the chloroacetanilide herbicide metabolites and establishment of groundwater quality standards at the state and federal levels. In Wisconsin, measures to protect groundwater from agricultural chemicals are based on comparison of results such as those presented in this paper to official trigger levels. Until these trigger levels are
We thank Justin Funk and Derek Strohl for performing the majority of the fieldwork for this survey.
Literature Cited (1) National Agricultural Statistics Service. Agricultural Chemical Usage 2000 Field Crops Summary; Agricultural Statistics Board: Washington, DC, 2001; Ag Ch 1(01) a. (2) Zimdahl, R. L.; Clark, S. K. Weed Sci. 1982, 30, 545. (3) Potter, T. L.; Carpenter, T. L. Environ. Sci. Technol. 1995, 29, 1557. (4) Barbash, J. E.; Thelin, D. P.; Kolpin, D. W.; Gilliom, R. J. USGS Water-Resources Investigations Report 98-4245; USGS Branch of Information Services: Denver CO, 1999. (5) Thurman, E. M.; Goolsby, D. S.; Aga, D. S.; Pomes, M. L.; Meyer, M. T. Environ. Sci. Technol. 1996, 30, 569. (6) LeMasters, G. S.; Baldock, J. O. A Survey of Atrazine in Wisconsin Groundwater: Final Report. Wisconsin Department of Agriculture, Trade and Consumer Protection-Agricultural Resource Management Division publication 26a, Madison, WI, 1997. (7) Kalkoff, S. J.; Kolpin, D. W.; Thurman, E. M.; Ferrer, I.; Barcelo, D. Environ. Sci. Technol. 1998, 32, 1738. (8) Kolpin, D. W.; Thurman, E. M.; Goolsby, D. A. Environ. Sci. Technol. 1996, 30, 335. (9) Scott, J. C. Computerized Stratified Random Site-Selection Approaches for Design of a Ground-Water-Quality Sampling Network; USGS Water-Resources Investigations Report 90-4101; USGS: Denver, CO, 1990. (10) Mehnert, E.; Schock, S. C.; Barnhardt, M. L.; Caughey, M. E.; Chou, S. F. J.; Dey, W. S.; Dreher, G. B.; Ray, C. Ground Water Monit. Rem. 1995, 15, 142. (11) Gilliom, R. J.; Alley, W. M.; Gurtz, M. E. USGS Circular 1112, 2002. (12) Troiano, J.; Weaver, D.; Marade, J.; Spurlock, F.; Pepple, M.; Nordmark, C.; Bartkowiak, D. J. Environ. Qual. 2001, 30, 448. (13) Cochran, William G. Sampling Techniques, 3rd ed.; John Wiley and Sons: New York, 1977. (14) Thomson, S. K. Sampling; John Wiley and Sons: New York, 1992. (15) Gilbert, R. O. Statistical Methods for Environmental Pollution Monitoring; Van Nostrand Reinhold: New York, 1987. (16) Jones, J.; Roberts, L. M. Ground Water Monit. Rem. 1999, 19,138-144. (17) Mueller, D. K.; Hamilton, P. A.; Helsel, D. R.; Hitt, K. J.; Ruddy, B. C. USGS Water-Resources Investigations Report 95-4031; 1995.
Received for review April 1, 2004. Revised manuscript received July 30, 2004. Accepted July 30, 2004. ES040399H
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