Effects of Sampling Strategies on Estimates of Annual Mean Herbicide

Environ. Sci. Technol. 1996, 30, 889-896

Effects of Sampling Strategies on Estimates of Annual Mean Herbicide Concentrations in Midwestern Rivers WILLIAM A. BATTAGLIN* AND LAUREN E. HAY U.S. Geological Survey, Water Resources Division, Box 25046 MS 406, D.F.C., Lakewood, Colorado 80225

The effects of 10 sampling strategies on estimates of annual mean concentrations of the herbicides atrazine, alachlor, and cyanazine in selected midwestern rivers were tested. The accuracy of the strategies was computed by comparing time-weighted annual mean herbicide concentrations calculated from water samples collected from 17 locations on midwestern rivers, with simulated annual mean concentrations calculated for each sampling strategy, using Monte Carlo simulations. Monthly sampling was the most accurate strategy tested. The U.S. Environmental Protection Agency requires quarterly sampling for municipalities using surface water as a source of drinking water. Due to the seasonality of herbicide occurrence and transport, quarterly sampling underestimates annual mean herbicide concentrations in over 40% of the simulations. Three of the strategies tested showed that, relative to quarterly sampling, a more accurate representation of annual mean concentrations could be obtained by sampling more frequently during spring and early summer runoff and assuming zero herbicide concentration during late summer and winter months.

Introduction Background. Preemergent herbicides have been used to control weeds in cropland in the United States for more than 30 years. The annual use of all herbicides increased from 50 000 t in 1966 to 225 000 t in 1982 and has remained above 200 000 t up to the present (1-3). Herbicides have been detected in groundwater, surface water, and precipitation throughout the United States (4-21). This paper will focus on three herbicides that are widely used in the United States and frequently detected in midwestern rivers: atrazine, alachlor, and cyanazine. Atrazine [2-chloro4-(ethylamino)-6-isopropylamine-s-triazine] is applied on corn (Zea mays L.), and sorghum (Sorghum bicolor L.). Atrazine use in U.S. agriculture declined during 1976-1993 by approximately 20% but it remains the most used (by weight) of all pesticides (herbicides, insecticides, and * Corresponding author telephone: (303)236-5950, ext 202; fax: (303)236-5919; e-mail address: [email protected].

This article not subject to U.S. Copyright. Published 1996 by the American Chemical Society.

fungicides) (1-3). Alachlor [2-chloro-N-(2,6-diethylphenyl)-N-(methoxymethyl)acetamide] is applied on corn, soybeans, (Glycine max L.), and sorghum. Alachlor use in U.S. agriculture has declined during 1976-1993 by approximately 45%. Alachlor is the fourth most used pesticide (1, 3). Cyanazine [2-[[4-chloro-6-(ethylamino)-s-triazin2-yl]amino]-2-methylpropionitrile] is applied on corn and cotton. Cyanazine use in U.S. agriculture has nearly tripled during 1976-1993, making it the sixth most used pesticide (1-3). Atrazine, alachlor, and cyanazine accounted for approximately 19% of the volume of pesticide active ingredient used in U.S. agriculture in 1993 (3). The use of atrazine, alachlor, and cyanazine in the midwestern United States accounts for more than two-thirds of the their total use in U.S. agriculture (11, 22). The physiochemical properties of atrazine, alachlor, and cyanazine increase the likelihood of their occurrence in surface water drinking supplies. While in contact with soil, the degradation of these herbicides occurs at a relatively rapid rate (23, 24). However, once dissolved in surface water or groundwater, they may persist for months or years (25-27). Atrazine, alachlor, and cyanazine are all relatively soluble in water (23), which contributes to their tendency to leach into groundwater (4-6) or runoff into surface water (7-18). These three herbicides have low soil sorption coefficients, so they tend to remain primarily in the dissolved phase and do not attach to suspended-sediment particles (23). Conventional water treatment practices are largely ineffective at removing dissolved herbicides from finished drinking water (28, 29), and in many cases concentrations of herbicides in finished drinking water are similar to concentrations in the supply water (23, 24). Costly alternative treatment systems, such as filtration using granular activated carbon or ozonization, may be required for water systems that fail to comply with Federal water quality standards (28-31). Pesticides in drinking water can increase cancer risk and can cause damage to the liver and nervous, cardiac, endocrine, and reproductive systems (31-33). Maximum contaminant levels (MCLs), MCL goals (MCLGs), and health advisories (HAs) have been established by the U.S. Environmental Protection Agency (EPA) for many pesticides under the authority of the Safe Drinking Water Act. MCLs are enforceable maximum permissible levels of a contaminant in water that is delivered to any user of a public water system (32, 34-38). MCLs and MCLGs are calculated with safety factors built in. Treatment feasibility and cost and analytical capabilities were considered when defining MCLs, but only health-based criteria were considered when defining MCLGs and HAs (39). The EPA has established MCLs for 56 organic chemicals including 23 pesticides and five pesticide metabolites. For pesticides, annual mean concentrations are used to determine compliance with MCLs. The EPA’s Phase II Rule established monitoring requirements for regulated pesticides (36). All community and nontransient noncommunity water systems must conduct an initial round of four consecutive quarterly samples during a 3-year compliance period. If pesticides are not detected during the initial round, states may allow systems to collect samples as infrequently as once or twice per 3-year

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TABLE 1

MCLs, MCLGs, and HAs and Potential Drinking Water Health Effects for Atrazine, Alachlor, and Cyanazine herbicide

drinking water health effects

atrazine alachlor cyanazine

reproductive, cardiac, mammary tumors probable carcinogen probable carcinogen, possible birth defects

MCL (µg/L)

MCL G (µg/L)

1-day HA child (µg/L)

10-day HA child (µg/L)

lifetime HA adult (µg/L)

3.0 2.0

3.0 0.0 1.0

100.0 100.0 100.0

100.0 100.0 100.0

3.0

compliance period. Detection of a regulated pesticide triggers a return to quarterly sampling. If a baseline of at least four samples (in surface water) indicates that a system is “reliably and consistently” below the MCL, then the state may reduce the sampling requirement to one sample collected during the quarter that previously yielded the highest concentrations. Failure to comply with the Federal regulation occurs if the running annual average at any point in the system exceeds an MCL or if a monitoring requirement is not met. For an MCL violation, public water systems must issue a public notice in the newspaper within 14 days and deliver notice to consumers within 45 days (36, 37). Compliance with the EPA’s Phase II regulations is expected to provide reduced exposure to approximately 3 million people at an estimated cost of $88 million per year (38). MCLGs are non-enforceable health goals for the concentration of a drinking water contaminant in public water systems. Water containing a chemical in an amount equal to or less than its MCLG is not expected to cause any health problems over the lifetime of a person drinking this water (37). The EPA has set MCLGs for all organic chemicals that have MCLs and for some that have HAs (32). HAs are nonenforceable/recommended maximum permissible levels of a contaminant in drinking water (32). HAs have been established for several pesticides that do not have MCLs, and pesticides that do have MCLs also have HAs for children and shorter-term exposures (32). There are no monitoring requirements for pesticides that do not have MCLs. The EPA ranked 48 pesticides with MCLs, MCLGs, or HAs according to their relative risk of contaminating public drinking water supplies derived from surface water. Cyanazine was ranked number one, atrazine was number three, and alachlor was number four (37). The MCLs, MCLGs, and HAs for atrazine, alachlor, and cyanazine are listed in Table 1. Numerous studies have shown that the occurrence and transport of atrazine, alachlor, and cyanazine in surface water is seasonal; concentrations are highest within a period of 3 or 4 months after application (7-9, 11, 12, 14-18, 40). In smaller river systems, a “spring flush” of these herbicides is observed, with concentrations several times the MCLs or HAs occurring for short periods of time during spring and early summer runoff events (Figure 1). In larger river systems such as the Mississippi, Missouri, or Ohio, the spring flush phenomenon is observed, but rarely are maximum concentrations twice the MCL, MCLG, or HA (Figure 2). Some herbicides are detected at low concentrations year-round in larger river systems (7, 11, 12, 18). The adequacy of quarterly monitoring for estimating annual mean herbicide concentration in surface water has recently been questioned in part due to the seasonal pattern of herbicide occurrence (31, 39-41). Objective. The objective of our research is to determine how accurately each of the 10 sampling strategies can be expected to represent annual mean concentrations of the

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FIGURE 1. Atrazine, cyanazine, and alachlor concentration time plots for the Sangamon River at Monticello, IL, April 1991-March 1992.

FIGURE 2. Atrazine, cyanazine, and alachlor concentration time plots for the Missouri River at Hermann, MO, April 1991-March 1992.

herbicides atrazine, alachlor, and cyanazine in selected midwestern rivers. It is not the intent of this study to highlight deficiencies in the currently required monitoring requirements but to perform an unbiased evaluation of the performance of several monitoring strategies. Some of the strategies tested are more economically, socially, or politically attractive than others because of the number of required samples or because of the quality of drinking water protection they could provide. The effectiveness of the sampling strategies at detecting violations of shorter term HAs is not addressed in this paper. The EPA’s Phase II Rule requires monitoring for other compounds such as nitrate, VOCs, and PCBs, which may not exhibit a spring flush. This paper only addressees appropriate sampling schemes for herbicides that display the seasonal concentration patterns as described above.

FIGURE 3. Location of study area and sampling sites. TABLE 2

Sampling Site Locations, Drainage Areas, Periods of Sample Collection, Frequency of GC/MS Analysis, and Period of Estimated Daily Herbicide Concentrations site

drainage area (km2)

period of sample collection

no. of GC/MS tests

period of estimated concn

mean daily discharge for the period of estimated concn (m3/s)

(1) Mississippi River, LA (2) Mississippi River, IL (3) Missouri River, MO (4) Ohio River, IL (5) Mississippi River, IA (6) Platte River, NE (7) Illinois River, IL (8) White River, IN (9) Cedar River, IA (10) Iroquois River, IL (11) W. Fork Big Blue, NE (12) W. Fork Big Blue, NE (13) Sangamon River, IL (14) Sangamon River, IL (15) Silver Creek, IL (16) Delaware River, KS (17) Huron River, OH (18) Old Man’s Creek, IA (19) Roberts Creek, IA

2 914 000 1 847 000 1 357 000 526 000 222 000 222 000 69 000 29 000 12 300 5 350 3 120 3 120 1 430 1 430 1 190 980 950 515 264

4/11/91-3/30/92 4/11/91-3/24/92 4/9/91-3/26/92 4/10/91-3/31/92 4/11/91-3/30/92 4/9/91-3/31/92 4/5/91-3/25/92 5/1/91-3/26/92 4/4/90-6/29/90 4/4/90-8/18/90 4/12/90-7/27/90 3/26/91-4/1/92 4/4/90-8/28/90 4/12/91-3/25/92 4/13/90-8/23/90 4/12/90-6/29/90 3/30/90-8/20/90 4/4/90-6/25/90 4/6/90-6/29/90

59 51 52 43 54 51 51 58 43 52 37 107 55 198 35 32 59 59 23

4/1/91-3/31/92 4/1/91-3/31/92 4/1/91-3/31/92 4/1/91-3/31/92 4/1/91-3/31/92 4/1/91-3/31/92 4/1/91-3/31/92 4/1/91-3/31/92 4/1/90-3/31/92 4/1/90-3/31/92 4/1/90-3/31/91 4/1/91-3/31/92 4/1/90-3/31/91 4/1/91-3/31/92 4/1/90-3/31/91 4/1/90-3/31/91 4/1/90-3/31/91 4/1/90-3/31/91 4/1/90-3/31/91

18 300 6 040 1 640 6 650 2 030 165 669 178 120 89.6 6.4 4.8 25.5 8.8 16.4 3.5 15.3 7.4 0.3

a

Discharge includes amounts diverted from the Mississippi River into the Atchafalaya River.

Experimental Methods Sampling Sites. This study uses herbicide data from water samples collected from 17 locations on midwestern rivers in 1990-1992. The samples were collected in two studies of midwestern surface water quality (17, 18). Site locations

are shown on Figure 3. The period of sample collection and number of samples collected are given in Table 2. The objective of the first study, conducted in 1990, was to determine the occurrence and detailed temporal distribution of herbicide and nutrient concentrations in the


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TABLE 3

Analytical Reporting Limits for Atrazine, Alachlor, and Cyanazine herbicide

reporting limit 100-mL sample (µg/L)

reporting limit 1-L sample (µg/L)


0.05 0.05 0.2

0.010 0.015 0.050

months following application at nine locations across the midwest (17). The rivers sampled in this study had drainage basins that ranged in size from 264 to 12 299 km2. Automatic samplers and manual sampling were used in data collection. Several hundred samples were collected for analysis at each site. Automatic samplers were programed to collect a sample every other day during nonrunoff periods and as frequently as every few hours during runoff periods (7, 17). Two of the sites (West Fork Big Blue, NE, and Sangamon River, IL) were sampled again in 19911992 (Table 2). For simplicity, these two sites will be referred to separately by year, resulting in a total of 19 sites. At several sites, samples were only collected during spring and early summer (Table 2). These sites were included because samples were collected during the time period when the bulk of herbicide occurrence was expected. The objective of the second study, conducted in 19911992, was to determine the occurrence, temporal distribution, and annual mass transport of herbicides and nutrients in the Mississippi River and its major tributaries (18, 42). The eight sites sampled in this study had drainage basins that ranged in size from 29 000 to 2 914 000 km2. Samples were collected using equal-discharge-increment or equalwidth-increment procedures (43) at all sites except the Mississippi River at Baton Rouge, LA. Samples were collected about once per week on average, with more frequent collection (every 3 days) during late spring and early summer and less frequent collection (biweekly) in winter (18). In both studies, all samples for herbicide, insecticide, and nutrient analysis were filtered to remove particulate and colloidal matter. Samples for herbicides collected in 1990 were filtered through 1-µm glass-fiber filters using a peristaltic pump. Those collected in 19911992 were filtered through 0.7-µm glass-fiber filters using either nitrogen gas or a ceramic piston fluid-metering pump. On-site and laboratory quality assurance and quality control procedures required extensive cleaning of bottles and sampling equipment and analysis of bottle blanks, field blanks, blind spikes, and split samples (17, 18, 42). Water Sample Analyses. All water samples collected during 1990 were analyzed for triazine and chloroacetanilide herbicides by enzyme-linked immunosorbent assay (ELISA) (44) and about 25% of the samples were analyzed for 11 herbicides and two herbicide metabolites by GC/MS (17, 45, 46). Only the herbicide concentrations obtained by GC/MS were used in this paper. Samples collected during 1991-1992 were analyzed by GC/MS for 12 herbicides and two herbicide metabolites following solid-phase extraction of a 100-mL water sample or by a more sensitive solidphase extraction of a 1-L water sample for 28 herbicides, 16 insecticides, and two fungicides (17, 18, 45-47). Analytical reporting limits for the two methods for the herbicides addressed in this paper are given in Table 3. Time-Weighted Annual Mean Concentrations. Timeweighted annual mean concentrations of atrazine, alachlor, and cyanazine were computed from the sample data by

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FIGURE 4. Time-weighted annual mean concentration bar graphs for atrazine at the 19 sites ordered by decreasing basin area.

FIGURE 5. Time-weighted annual mean concentration bar graphs for alachlor at the 19 sites ordered by decreasing basin area.

FIGURE 6. Time-weighted annual mean concentration bar graphs for cyanazine at the 19 sites ordered by decreasing basin area.

constructing estimates of daily concentration at each site. During the period of sample collection (Table 2), concentration on days with samples was computed as either the value from a single analysis, the average of several analytical results for the day, or zero if concentration was less than analytical reporting limits. Concentrations were estimated by linear interpolation for days within the period of sample collection when no sample was collected. Concentrations were set to zero for all days that were outside the period of sample collection, but within the uniform 1-year period of estimated concentrations (Table 2). Time-weighted annual mean herbicide concentrations were calculated at the 19 sites as the mean of the 365 estimated daily concentrations. Figures 4-6 show the time-weighted annual mean concentration bar graphs for atrazine, alachlor, and cyanazine, respectively. One site out of 19 exceeds an MCL or MCLG for each of the three herbicides. Annual mean herbicide concentration estimates are time-weighted not flow-weighted because the intent was to produce conservative estimates that are representative of annual mean exposure via drinking water. Time-

weighted concentration estimates are also more relevant to public water suppliers, who need to comply with federal regulations. The method of estimating annual mean concentrations is conservative. Zeros are substituted for concentrations that are less than analytical reporting limits and for all days outside of the period of sample collection even though, for several sites, the period of sample collection is short (Table 2) and ends in June when the concentrations of herbicides are still significantly above detection limits. Also, the concentration of herbicides in rivers can change dramatically over a period of hours, and daily concentration averages are often less than peak concentrations (17). Sampling Strategy Testing Procedure. Ten sampling strategies were tested to determine the accuracy with which they could be expected to represent annual mean herbicide concentrations in midwestern rivers. Monte Carlo simulations were used to estimate annual mean herbicide concentrations using each of the ten sampling strategies. The strategies tested were: [1] one sample collected during the second quarter (April, May, or June); [2] two samples, one in each of April-May and JuneJuly; [3] three samples, one in each of January-April, MayAugust, and September-December (seasonal (31)); [4] four samples, one in each quarter (quarterly); [5] six samples, one collected every 2 months (bimonthly); [6] 12 samples, one collected every month (monthly); [7] one sample collected in June averaged with 11 zeros representing the remaining 11 months; [8] two samples, one each in May and June, averaged with 10 zeros for the remaining months; [9] three samples, one each in April, May, and June, averaged with nine zeros for the remaining months; and [10] four samples, one each in April, May, June, and July, averaged with eight zeros for the remaining months. Quarterly sampling (sampling strategy 4) represents the base monitoring requirements for public water systems under current EPA regulations. Sampling strategies 7-10 were tested to validate the hypothesis that more accurate representation of annual mean concentration could be obtained by sampling more frequently during spring runoff and then assuming small or zero herbicide concentrations during late summer, winter, and early spring months (3941). Sampling strategy 1 represents the monitoring requirements for small water systems in which the state has determined that contaminant concentrations are “reliably and consistently” below the MCL (37). For these water systems, an annual sample must be collected during the quarter which previously yielded the highest concentrations (36). Seasonal sampling 3 was proposed by Wiles and others (31) as a means for stratifying existing water quality data so as not to “skew exposure estimates upwards”. Monte Carlo Analysis. A total of 1000 Monte Carlo simulations were run for each sampling strategy for each site. For each simulation, a day was selected within each of the sampling periods for each sampling strategy by randomly sampling from a uniform distribution (for example, a day was randomly chosen within each month for the monthly sampling strategy). For each selected day, the corresponding concentration values from the daily time series of 365 measured and interpolated concentration values (described in the previous section) were used to calculate annual mean concentrations. This resulted in 1000 simulated annual

TABLE 4

Percentage of Monte Carlo Simulations That Are within, over, or under the Specified Tolerance about Time-Weighted Annual Mean Atrazine ConcentrationsAverage for All Sitesa percent of simulations (0.3 µg/L

sampling strategy

no.

name

1 1 in second quarter 2 1 each in April-May, June-July 3 seasonal 4 quarterly 5 bimonthly 6 monthly 7 1 in June (11 zeros) 8 1 each in May, June (10 zeros) 9 1 each in April, May, June (9 zeros) 10 1 each in April, May, June, July (8 zeros) a

(0.75 µg/L

within over under within over under 16 19

55 65

27 14

34 39

48 53

16 7

28 31 39 62 15 48

26 27 23 17 2 9

45 41 36 20 83 42

59 63 69 85 58 81

17 16 14 7 1 5

22 20 17 8 40 14

51

10

37

82

5

13

58

15

26

84

6

9

Boldface numbers indicate highest percentage.

mean concentration values for each of the 10 sampling strategies at each site. These simulated annual mean concentration values were compared with the timeweighted annual mean concentrations to determine how accurately each of the 10 sampling strategies represented the time-weighted annual mean herbicide concentrations. The accuracy of results for the different herbicides was computed as the percentage of simulated annual mean concentrations that were within a tolerance of the timeweighted annual mean. Tolerances are based on a percentage of each herbicide’s MCL (atrazine and alachlor) or MCLG (cyanazine) (Table 1). Computed annual mean herbicide concentrations at each site were not used in defining tolerance levels due to problems with tolerances being smaller than the analytical accuracy of the chemical analysis for some sites. In addition, tolerance levels based on the MCLs are more relevant to regulatory standards. Two tolerance levels were computed for each herbicide: (1) a tolerance of (10% of the herbicide’s MCL or MCLG ((0.3 µg/L for atrazine, (0.2 µg/L for alachlor, and (0.1 µg/L for cyanazine) and (2) a tolerance of ( 25% of the herbicide’s MCL or MCLG ((0.75 µg/L for atrazine, (0.5 µg/L for alachlor, and (0.25 µg/L for cyanazine). Also, for each sampling strategy, the percentage of all simulated concentrations (all sites) that exceeded an MCL or MCLG was calculated and compared with the percentage of sites that had time-weighted annual mean herbicide concentrations that exceeded an MCL or MCLG.

Results Effects of Sampling Strategy. Tables 4-6 show the percentage of simulated annual mean herbicide concentrations that are within, over, or under the two specified tolerances for each herbicide, averaged for all sites. Boldface numbers indicate the highest percentage for each strategy. For all three herbicides tested, time-weighted annual mean herbicide concentrations were most accurately represented by sampling strategy 6 and least accurately represented by sampling strategy 1 or 7. In general, alachlor concentrations were more accurately simulated than atrazine or cyanazine concentrations; the highest percentage of simulations were always within the two tolerance levels set for alachlor (Table 5).


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TABLE 5

Percentage of Monte Carlo Simulations That Are within, over, or under the Specified Tolerance about Time-Weighted Annual Mean Alachlor ConcentrationsAverage for All Sitesa percent of simulations (0.2 µg/L

sampling strategy

no.

name


(0.5 µg/L


40 41

16 10

63 68

28 25

8 6

61 65 68 78 69 77

13 12 13 10 3 7

25 22 18 11 28 15

82 83 84 89 88 89

7 7 6 5 1 4

10 9 9 6 10 6

77

8

14

89

4

6

77

9

13

89

5

6


TABLE 6

Percentage of Monte Carlo Simulations That Are within, over, or under the Specified Tolerance about Time-Weighted Annual Mean Cyanazine ConcentrationsAverage for All Sitesa percent of simulations (0.1 µg/L

sampling strategy

no.

name


(0.25 µg/L


53 59

33 20

32 41

46 47

21 11

25 30 37 54 20 44

23 24 22 20 4 13

50 44 40 25 75 42

55 60 63 81 57 78

15 15 14 9 2 7

28 24 21 9 40 15

45

14

39

78

7

14

51

17

31

80

9

11


The effects of sampling strategies on annual mean herbicide concentration estimates were similar for atrazine, alachlor, and cyanazine. Sampling strategies 1 and 2 tended to overestimate annual mean concentrations. Quarterly 4 and seasonal 3 sampling strategies tended to underestimate rather than overestimate for both tolerances. For the smaller tolerance band, the highest percentage of annual mean atrazine and cyanazine concentration estimates simulated using quarterly and seasonal sampling strategies were underestimates. For the larger tolerance band, the highest percentage of simulations fell within the band for all three herbicides. Using 5, the highest percentage of simulations were within the tolerance bands, with the exception of cyanazine concentration simulations, which were more likely to be underestimates than fall within the smaller tolerance band. Using 6, the highest percentage of simulations fell within the tolerance bands for all three herbicides (Tables 4-6). Sampling strategies 7-10 were tested to validate the hypothesis that an accurate estimate of annual mean

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FIGURE 7. Percentage of all simulated annual mean herbicide concentrations that exceed an MCL or MCLG.

herbicide concentration could be obtained by sampling more frequently during spring and early summer runoff and assuming zero herbicide concentration during the rest of the year. Using sampling strategy 7, the highest percentage of simulations were within the larger tolerance band for all three herbicides. Atrazine and cyanazine concentration simulations were more likely to be underestimates than estimates that were within the smaller tolerance band. For sampling strategies 8-10, the highest percentage of simulations fell within both tolerance bands, with little chance of overestimation (e17%). Figure 7 shows for each sampling scheme the percentage of all simulated annual mean concentrations (all sites) that exceeded an MCL or MCLG. Separate bars are shown for atrazine, alachlor, and cyanazine. The line at 5.3% represents the percentage of sites (1 of 19) that had timeweighted annual mean concentrations of atrazine, alachlor, or cyanazine that exceeded an MCL or MCLG. Using any of the sampling strategies except 7 resulted in a tendency to simulate concentrations that exceeded an MCL or MCLG more frequently than when using the time-weighted annual mean concentrations. Sampling strategy 1, which represents the monitoring requirements for small water systems in which the state has determined that contaminant concentrations are “reliably and consistently” below the MCL, has the strongest tendency to erroneously simulate concentrations that exceed an MCL or MCLG. Sampling strategies 8 and 9 have the weakest tendency to erroneously simulate concentrations that exceed an MCL or MCLG. Effects of Basin Size and Discharge. Time-weighted annual mean concentrations of atrazine and alachlor tended to be inversely related to basin area and mean daily discharge for the period of estimated herbicide concentrations (Q). Time-weighted annual mean cyanazine concentrations showed no significant relations to basin area or Q. Figures 4-6 show time-weighted annual mean concentration bar graphs for the three herbicides, with sites ordered by decreasing basin area. Results of linear regressions of time-weighted annual mean concentration with basin area and Q are shown in Table 7. Atrazine and alachlor show a stronger inverse relation with Q than with basin area. The relations between annual mean herbicide concentration and basin area or Q may in part be the result of two factors: (1) in this study, basin area is inversely correlated with percentage of land in agricultural production (Pearson correlation coefficient ) -0.60 with p ) 0.007) (Figure 8); and (2) larger discharges in larger basins result in dilution of the herbicides and lower concentrations, even

TABLE 7

Regression Results from Time-Weighted Annual Mean Herbicide Concentration with Drainage Basin Area and Mean Daily Discharge R 2 from regression of time-weighted annual mean herbicide concentration with herbicide

drainage basin areaa

mean daily discharge for the period of estimated concna


0.413 0.461 0.051

0.598 0.621 0.061

a

Concentration, discharge, and area values are in log space.

FIGURE 8. Percentage of agricultural land at the 19 sites ordered by decreasing basin area.

FIGURE 9. Variance in time-weighted annual mean atrazine concentration versus basin area (km2) and mean daily discharge (m3/s).

though the mass of herbicides transported out of larger basins is greater. Other factors including soil type and climate are also likely to affect herbicide concentrations in midwestern rivers. The accuracy of estimates of annual mean herbicide concentrations for all sampling strategies is inversely related to the variance of the daily herbicide concentrations, as are basin area and Q (Figures 9 and 10). Figure 10 shows the percentage of simulations within the smaller tolerance band for three sampling strategies for the 19 sites, with the sites ordered by decreasing basin area. Figure 10 indicates that the three sampling strategies shown tend to be less accurate in smaller basins; the same basins that are likely to have herbicide concentrations with higher annual means and larger daily variances.

Conclusions Water samples collected at frequent intervals from 17 locations on midwestern rivers were used to develop 1-year-

FIGURE 10. Percentage of annual mean concentration simulations for (A) atrazine, (B) alachlor, and (C) cyanazine that fall within the smaller tolerance band. Three sampling strategies are shown: (1) monthly sampling; (2) three samples, one each in April, May, and June, averaged with 9 zeros; and (3) quaterly sampling. The 19 sites are ordered by decreasing basin area.

long, daily time series of the concentration of three commonly detected herbicides: atrazine, alachlor, and cyanazine. How accurately 10 sampling strategies could be expected to represent the annual mean concentration of these three herbicides was tested by comparing timeweighted annual mean herbicide concentrations, computed from the daily concentration time series, with simulated annual mean concentrations calculated using Monte Carlo techniques. The seasonal nature of herbicide occurrence and transport in midwestern rivers makes it difficult to calculate accurate estimates of annual mean concentrations using calendar-based sampling strategies with a limited number of samples. In general, monthly sampling was the most accurate strategy tested. The EPA-required quarterly sampling generally underestimated annual mean herbicide concentrations due to the seasonality of herbicide occurrence. Four strategies were tested that sampled more frequently during spring and early summer runoff: 7 one sample collected in June averaged with 11 zeros for the remaining months; 8 two samples, one in each of May and


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June, averaged with 10 zeros; 9 three samples, one in each of April, May, and June, averaged with nine zeros; and 10 four samples, one in each of April, May, June, and July, averaged with eight zeros. Sampling strategies 8-10 simulated annual mean herbicide concentrations more accurately than quarterly sampling, making them attractive because they require collection of few samples, but provide nearly the same accuracy as monthly sampling in estimating annual mean herbicide concentrations. Using any of the 10 sampling strategies except 7 resulted in a tendency to simulate concentrations that exceeded an MCL or MCLG more frequently than when using the time-weighted annual mean concentrations. In general, the accuracy of all estimates of annual mean herbicide concentration decreased as drainage basin area and discharge decreased. These results only apply to appropriate sampling schemes for compounds that display a spring flush seasonal concentration pattern. Other sampling schemes may be more appropriate to use when determining the concentration of other regulated contaminants such as nitrate or PCBs.

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Received for review May 24, 1995. Revised manuscript received October 2, 1995. Accepted October 3, 1995.X ES950351R X

Abstract published in Advance ACS Abstracts, December 15, 1995.

Effects of Sampling Strategies on Estimates of Annual Mean Herbicide

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