National Water-Quality Assessment (NAWQA) Project
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Jeffrey D. Martin
jdmartin@usgs.gov
October 27, 1999
Water-quality assessments for pesticides are complicated by a variety of sources of uncertainty. These include identification and quantitation of pesticides, nondetections, the type of reporting limit, the numerical value of the reporting limit, changes in the reporting limit, bias and variability of the analytical methods, and additional bias and variability contributed (potentially) by sample collection, processing, and transport in the field. Capel and others (1996) explain these sources of uncertainty and how to consider them in data analysis and interpretation. Childress and others (1999) explain reporting limits and how to consider them in data analysis and interpretation.
The National Water-Quality Assessment (NAWQA) Program and the National Water Quality Laboratory (NWQL) dedicated considerable resources to the collection and analysis of quality- control (QC) samples to ensure and document the quality of the pesticide data collected for the NAWQA Program. The purpose of this paper is to describe the quality of the NAWQA pesticide data for environmental water samples and to provide examples that show how quality-control information can be used in analysis and interpretation of environmental data.
Information on initial method performance (termed "proveout" data by NWQL) is published in the documentation for the pesticide analytical methods. Bias and variability of recovery are reported for six to seven spikes in a blank water matrix, a surface-water matrix, and a ground- water matrix at concentrations of 0.1 µg/L and 1 µg/L and for six to seven spikes at concentrations less than 0.1 µg/L in a blank-water matrix (Zaugg and others, 1995, tables 3-9; Werner and others, 1996, tables 4-9, 12). Although method performance data is published in the method documentation, the performance data provided in this paper should be used to describe the quality of the NAWQA pesticide data because: (1) the proveout data were collected over a limited period of time (weeks) at the beginning of the NAWQA program by a limited number of analysts and analytical instruments whereas the NAWQA environmental and QC data sets were collected over a much longer and more similar period of time (years) using a variety of analysts and instruments, (2) only one surface-water matrix and one ground-water matrix were used for the proveout data whereas hundreds of natural water matrixes were used for field matrix spikes, and (3) many more field matrix spikes have been analyzed than those for proveout spikes and will provide more precise estimates of data quality.
AcknowledgmentsJonathon C. Scott, U.S. Geological Survey, calculated the recovery of pesticides in field matrix spikes. Bruce R. Darnel, Ronald W. Brenton, and Chris E. Lindley, U.S. Geological Survey, provided data of the recovery of pesticides in laboratory control spikes and assisted in review of the laboratory data. David K. Mueller, U.S. Geological Survey, provided the inspiration for the calculation of confidence limits for percentiles.
DisclaimerThe field matrix-spike data have not been reviewed thoroughly, are provisional, and are subject to change. Further review of the field-spike data is expected to identify spikes that have extremely high or low recoveries because the spikes either were improperly collected or incorrectly documented in the NAWQA QC data base. The expected result of further review is a data set of field matrix spikes with fewer extreme values than the provisional data set described in this paper; consequently, the provisional data set provides a conservative estimate of the quality of the NAWQA pesticide data. Interpretations of field matrix spike data in this paper are not expected to change greatly as a result of further review of the data, however, the statistics and confidence limits reported in the text and tables will change on further review (especially for pesticides with low numbers of field spikes [less than 50]).
Data compiled for this paper include field blanks, laboratory control spikes, and field matrix spikes collected by the 1991 NAWQA Study Unit teams or the NWQL during 1992-96. This period of record was selected to correspond to the period of record for the NAWQA pesticide data sets available at URL http://wwwdwatcm.wr.usgs.gov/ccpt/pns_data/data.html. The environmental data summarized in this paper were accessed May 4, 1998.
Environmental water samples and blank and spiked QC samples were analyzed for pesticides by gas chromatography/mass spectrometry (GCMS) using the method of Zaugg and others (1995) or by high-performance liquid chomatography (HPLC) using the method of Werner and others (1996) at the U.S. Geological Survey National Water Quality Laboratory. Field QC samples were collected periodically during 1992-96 using guidelines similar those published in Koterba and others (1995) and in Mueller and others (1997).
Field blanks were collected at the field site with pesticide-grade blank water and are exposed to the field and laboratory environments and equipment similarly to environmental samples. Field blanks measure the frequency and magnitude of contamination (one type of positive bias) in environmental water samples from sources in the field and/or laboratory. Contamination is the main cause of false-positive detections (detecting a pesticide in a sample when, in truth, it is absent).
Spikes are QC samples where known amounts (mass) of pesticides are added to water and then analyzed for pesticides. The amount of a pesticide measured (recovered) in a spiked sample is expressed as a percentage (the percent recovery) of the known amount of pesticide added to the sample. Recovery (rather than concentration) is used to assess the quality of spiked samples because recovery allows comparisons among samples that were spiked with different amounts of pesticides or among samples that have different volumes of water. Recovery is a primary measure of the performance of the analytical method. Pesticides added to pesticide-grade blank water in the laboratory are laboratory control spikes. Pesticides added to environmental water samples at the field site are field matrix spikes. Spikes measure bias and variability in the measurements of pesticides. Spikes also measure the frequency of false-negative detections (the failure to detect a pesticide in a spiked sample when, in truth, it is present at the concentration spiked).
Laboratory control spikes measure the bias and variability of the analytical method at a particular concentration. One laboratory control spike is measured in each analytical set of environmental samples. The laboratory control spike has the target pesticides spiked into pesticide-grade blank water at the laboratory and extracted, processed, and analyzed like environmental samples. Laboratory control spikes analyzed by GCMS were spiked at 0.1 µg/L, whereas those analyzed by HPLC were spiked at 0.5 µg/L.
Field matrix spikes measure the bias and variability of the analytical method PLUS any potential effects caused by (1) degradation of pesticides during shipment to the laboratory, (2) inferences in the determination of pesticides from unusual characteristics of the environmental water sample ("matrix effects"), and (3) other chemical processes that cause bias or variability in the measurements of pesticides in environmental water samples. Field matrix spikes analyzed by GCMS were spiked at 0.1 µg/L, whereas those analyzed by HPLC were spiked at 1.0 µg/L.
Field matrix spikes probably are more representative of the quality of the NAWQA pesticide data than laboratory control spikes because field matrix spikes measure additional possible sources of bias and variability that could affect environmental water samples. Laboratory control spikes, however, are more representative for selected pesticides if those pesticides have degraded in spike solutions used for field spiking. Differences in recovery between field matrix spikes and laboratory control spikes have not yet been assessed and the appropriate type of spike to use to describe data quality has not yet been determined for each pesticide. Although field matrix spikes probably are most representative of the quality of the NAWQA pesticide data, data for laboratory control spikes also are presented because they (1) document method performance in a "clean" water matrix, (2) provide a basis for comparisons of data quality for NAWQA with other data-collection programs that lack field matrix spikes, (3) provide a basis for comparing data quality reported in the published method documentation with data quality achieved in practice, and (4) allow assessment of the potential importance of pesticide degradation, environmental matrix effects, or other chemical processes in the measurement of pesticides in environmental water samples.
Contamination of environmental water samples collected by the NAWQA Program was examined by Martin and others (1999) using field blanks collected from 1992-95. The need to consider contamination in data analysis and interpretation depended upon the type of water-quality assessments to be made and differed for assessments such as detection frequency, median concentrations detected, concentrations near a water-quality criterion, or maximum concentrations measured. Two criteria were used to evaluate the need to consider contamination by pesticides for two types of water-quality assessments: (1) a ratio of the frequency of pesticide detection in environmental water samples to the frequency of detection in field blanks of 5.0 or less was used to evaluate the need to consider contamination in assessing the frequency of detection, and (2) a ratio of the median concentration detected in environmental water samples to the maximum concentration detected in field blanks of 2.0 or less was used to evaluate the need to consider contamination in assessing median concentration detected.
Application of these criteria indicate that, for the majority of the pesticide data collected for the NAWQA Program, contamination probably does not need to be considered in the analysis and interpretation of the data. Contamination needs to be considered in assessing the frequency of detection of cis-permethrin, pronamide, p,p'-DDE, pebulate, propargite, ethalfluralin, and triallate in surface-water samples and fenuron, benfluralin, pronamide, cis-permethrin, triallate, chlorpyrifos, trifluralin, propanil, p,p'-DDE, bromacil, dacthal, diazinon, and diuron in ground-water samples. Contamination needs to be considered in assessing the median concentrations detected for pronamide, p,p'-DDE, propargite, napropamide, and triallate in surface-water samples and benfluralin, cis-permethrin, triallate, chlorpyrifos, trifluralin, p,p'-DDE, dacthal, and diazinon in ground-water samples (Martin and others, 1999, p. 24, 29, 38).
Three approaches and examples for considering contamination in various types of assessments were presented: (1) adjust detection frequencies in environmental samples by subtracting detection frequencies in field blanks, (2) recalculate detection frequencies in environmental samples after censoring all detections in environmental samples at a concentration equal to that of some high, infrequently occurring percentile of contamination in field blanks (the 95th or 99th percentiles for example), and (3) adjust concentrations in environmental samples after subtracting a concentration equal to that of some high, infrequently occurring percentile of contamination in field blanks (Martin and others, 1999, p. 39-40). These approaches will be discussed in the following sections.
Contamination and the Frequency of DetectionUncertainty in the frequency of contamination in environmental water samples was not assessed in Martin and others (1999). Confidence limits for the percentage of nonconforming units (Hahn and Meeker, 1991, p. 104-105) are calculated herein and used to estimate the uncertainty in the measured frequency of contamination in environmental water samples (table 1). The percentage of nonconforming units in the context of contamination is the percentage of field blanks with pesticide detections. An example interpretation of the upper confidence bound for the percentage of field blanks with pesticide detections follows.
Atrazine (parameter code 39632) was detected in 2.8 percent (4 of 145) of ground-water field blanks (table 1). The number of field blanks and the number of field blanks with detections constitute a "sample" of the unknown, true frequency of atrazine contamination in NAWQA environmental ground-water samples. Confidence limits were calculated to describe the uncertainty in the measured frequency of contamination for the "sample" of field blanks, for a selected degree of confidence. One-sided upper confidence limits (one-sided confidence limits are called confidence "bounds") were calculated because, for some types of assessments, data analysts may desire a "pessimistic" estimate of contamination in NAWQA samples ("How bad might contamination really be?"). A 95-percent confidence level was selected for calculation of the upper confidence bound. Other confidence levels could be selected (which would change the "length" of the confidence interval).
On the basis of the 95-percent upper confidence bound, data analysts are 95-percent confident that 6.2 percent or less of NAWQA ground-water samples are contaminated by atrazine. Alternately, data analysts are 95-percent confident that 93.8 percent or more of NAWQA ground-water samples are free from contamination by atrazine. Data analysts have a high degree of confidence that the vast majority of NAWQA environmental ground-water samples are not contaminated by atrazine.
Data analysts can use this estimate of uncertainty in the frequency of contamination to qualify or adjust their estimates of the frequency of detection of atrazine in environmental ground-water samples. For example, atrazine was detected in 32.5 percent (968 of 2,976) of NAWQA environmental ground-water samples. Data analysts could estimate a best case/worst case effect of contamination on detections of atrazine in ground water in the following manner. If contamination affected only environmental ground-water samples that, otherwise, were free of atrazine (the worst case), then the detection frequency in the NAWQA data set is at least 26.3 percent (32.5 percent minus 6.2 percent). If, however, contamination affected only environmental ground-water samples that, otherwise, contained atrazine anyway (the best case), then contamination had no effect on the detection of atrazine and the detection frequency in the NAWQA data set is 32.5 percent. Therefore, data analysts are 95-percent confident that the detection frequency, adjusted for the frequency of contamination, of atrazine in ground-water samples in the data set is between 26.3 percent and 32.5 percent.
Contamination and the Magnitude of ConcentrationsUncertainty in the magnitude of contamination (the concentration of the contamination) also can be estimated. Upper confidence bounds for the 95th-percentile concentration of pesticide contamination in NAWQA environmental water samples was presented in Martin and others (1991, p. 38-39). The frequency of contamination for most pesticides, however, is less than 5 percent and the upper confidence bound for the 95th percentile is nearly always a nondetection (and, therefore, not very useful for adjusting concentrations). Sample size (the number of field blanks) was insufficient to calculate uncertainty for the 99th-percentile concentration (at a reasonable degree of confidence). Consequently, efforts to adjust for the magnitude of contamination in this paper are based only on the measured magnitude of contamination in field blanks and statements about uncertainty cannot be made. Aggregation of additional field blanks collected by other Study Unit teams in the future will increase sample size and allow assessment of uncertainty in extreme percentiles of contamination.
The 99th-percentile of atrazine concentrations in ground-water field blanks was 0.004 µg/L (Martin and others, 1999, p. 16), and represents a magnitude of contamination that is infrequently exceeded in environmental ground-water samples. Data analysts could use the 99th-percentile of the magnitude of contamination in two different ways to adjust concentrations of atrazine in environmental ground-water samples for a magnitude of contamination that is exceeded only in about 1 percent of the samples.
For example, environmental ground-water samples having concentrations of atrazine of 0.004 µg/L or less could be censored (set to nondetections) in an effort to account for contamination bias in low-concentration environmental samples where contamination could, potentially, account for all of the atrazine present. An alternate approach would be to subtract 0.004 µg/L from the measured concentrations of atrazine in all environmental ground-water samples in an effort to account for contamination bias that might occur in any sample. Adjusted concentrations that are less than the method reporting limit (or, alternately, less than zero) would be considered nondetections (a different method of censoring detections).
The detection frequency of atrazine in NAWQA environmental ground-water samples is 32.5 percent (968 of 2,976) and the median concentration of atrazine detections is 0.022 µg/L. Recalculation of these statistics using data censored at 0.004 µg/L gives a detection frequency of 27.7 percent (825 of 2,976) and the median concentration of detections of 0.036 µg/L. Recalculation using data reduced by 0.004 µg/L and censored at a method reporting limit of 0.001 µg/L gives a detection frequency of 27.7 percent (825 of 2,976) and the median concentration of detections of 0.032 µg/L. [The same rate of detection for both methods of adjustment is a fortuitous consequence of the least significant digit in the data set being equal to the method reporting limit (0.001 µg/L). For example, if the method reporting limit for atrazine was 0.002 µg/L instead of 0.001 µg/L, more samples would be adjusted to nondetections and the recalculated rate of detection would be 26.5 percent (789 of 2,976) and the median concentration of detections would be 0.035 µg/L].
Censoring detections to the 99th-percentile or subtracting the 99th-percentile from detections undoubtedly censors an unknown number of true environmental detections (environmental samples not effected by contamination). The adjustments for contamination are conservative in the sense that they provide a pessimistic estimate of data quality and an optimistic estimate of environmental quality. An optimistic estimate of data quality and a pessimistic estimate of environmental quality can be obtained by not considering contamination in water-quality assessments. On the basis of optimistic/pessimistic estimates of data quality in the example above, data analysts would conclude that the median concentration of detections of atrazine is between 0.022 µg/L and 0.036 µg/L. Data analysts must determine the need to consider contamination bias and the practical or hydrologic significance of the adjusted estimate for particular types of assessment questions. Contamination bias of 0.004 µg/L is clearly insignificant in assessments relating to a health criterion of 3 µg/L. If, however, a health criterion for atrazine was 0.03 or less, contamination bias would need to be considered.
The validity of adjusting concentrations for contamination should be evaluated in view of (1) typical magnitudes of contamination in field blanks, (2) the frequency of censored environmental detections compared to the estimated frequency of contamination, and (3) the ability of the adjusted data to address particular types of assessment questions. In the case for atrazine in ground water, the 99th-percentile concentration (0.004 µg/L) appears to be a reasonable choice for adjustment in that (1) 0.004 µg/L is not an unusually high concentration of atrazine when atrazine is detected in field blanks (Martin and others, 1991, p. 57, 80) and (2) the frequency of censoring (4.8 percent, 143 of 2,976) is less than the 95-percent upper confidence bound for the frequency of atrazine contamination in ground-water samples (6.2 percent) and, therefore, does not appear to be an excessive degree of censoring.
A false-negative detection (termed a "false negative") is a failure to detect a pesticide when, in truth, it is present. The frequency of false negatives is a function of pesticide concentration (the frequency of false negatives is much greater at low concentrations than at high concentrations). The frequency of false negative detections of pesticides in environmental water samples was estimated by calculation of the frequency of false negatives in laboratory control spikes and field matrix spikes.
The performance of analytical method is thought to be the main factor affecting the frequency of false negatives in laboratory control spikes. Laboratory control spikes measure the performance (the bias and variability of pesticide measurements) for each pesticide in the method. For a fixed amount of variability, pesticides that have a negative measurement bias (biased low) have a greater frequency of false negatives than pesticides that have a positive measurement bias (biased high). For a fixed amount of bias, pesticides that have high measurement variability will have a greater frequency of false negatives than pesticides that have low measurement variability.
The performance of analytical method also is thought to be the main factor affecting the frequency of false negatives in field matrix spikes. In addition, however, potential sources of bias and variability from sample collection and processing, water matrix effects, and pesticide degradation also affect (potentially) the frequency of false negatives in field matrix spikes.
The use of QC information in assessing the frequency of false negatives is illustrated in an example for azinphos-methyl (parameter code 82686). Azinphos-methyl is one of the most poorly performing pesticides in the NAWQA data set. On the basis of recovery in laboratory control spikes, measurements of azinphos-methyl were the tenth most biased (median recovery 53.9 percent) and the third most variable (interquartile range of recovery 62.0 percent).
Azinphos-methyl was not detected in 5 of 998 (0.5 percent) laboratory control spikes and was not detected in 3 of 304 (1.0 percent) field matrix spikes (table 2). As with field blanks, false negatives in spikes can be considered a "sample" of the unknown, true frequency of false negatives for azinphos-methyl in environmental water samples. One-sided, "pessimistic" confidence bounds can be calculated to estimate the uncertainty in the measured frequency of false negatives ("When azinphos-methyl is present in water at 0.1 µg/L, how high might the frequency of false negatives truly be?").
On the basis of laboratory control spikes, data analysts are 95-percent confident that in 1.1 percent or less of environmental samples where azinphos-methyl was present at 0.1 µg/L it would NOT be detected (table 2). Because field matrix spikes include additional sources of bias and variability, estimates of the frequency of false negatives from field spikes probably are more representative than those based on laboratory control spikes. On the basis of field matrix spikes, data analysts are 95-percent confident that in 2.5 percent or less of environmental samples where azinphos-methyl was present at 0.1 µg/L it would NOT be detected. Alternately, analysts are 95-percent confident that in 97.5 percent or more of samples where azinphos-methyl was present at 0.1 µg/L it WOULD be detected. Although azinphos-methyl is a relatively poorly performing pesticide (Zaugg and others, 1995, p. 35) data analysts have a high degree of confidence that when azinphos-methyl is in water at 0.1 µg/L it will be detected.
Bias and variability can be described by several measures of location and spread. The mean and standard deviation are common measures of location and spread, respectively, but are strongly influenced by extreme values. The median and interquartile range are common measures of location and spread, respectively, that are resistant to the influence of extreme values (Helsel and Hirsch, 1992, p. 3-9). Measures of location and spread that are resistant to the influences of extreme values are the most useful for describing the bias and variability of pesticide measurements because (1) information on typical data quality is needed for most assessment questions and (2) information on extreme values of data quality can be obtained from more direct measures (percentages for false positives or false negatives, percentiles for extreme values of concentration). Distributions of recovery for many pesticides are approximately normally distributed (distributions are symmetrical and lack extreme values). Distributions of recovery for other pesticides clearly are nonnormal (distributions are uniform, bimodal, highly skewed, or contain many extreme values). Data analysts should use measures of location and spread that are resistant to the influences of outliers for comparing bias and variability among a large number of pesticides, or when an approximately normal distribution of recovery is in doubt.
Median recovery of pesticides in laboratory control spikes ranged from 6.0 percent for chlorothalonil to 102.9 percent for metolachlor (table 3). Median recovery for 70 pesticides ranged from 70.0 to 102.9 percent and median recovery for 7 pesticides was less than 50.0 percent. Median recovery of pesticides in field matrix spikes ranged from 15.6 percent for oxamyl to 132.4 percent for propham. Median recovery for 61pesticides ranged from 70.0 to 105.7 percent and median recovery for 7 pesticides was less than 50.0 percent. No field matrix spikes were available for acetochlor.
Interquartile range of recovery of pesticides in laboratory control spikes ranged from 10.3 percent for butylate to 101.4 percent for carbaryl analyzed by GCMS (table 3). Interquartile range of recovery for 46 pesticides was less than or equal to 25.0 percent and interquartile range of recovery for 15 pesticides was greater than or equal to 40.0 percent. Interquartile range of recovery of pesticides in field matrix spikes ranged from 9.0 percent for oryzalin to 92.3 percent for propham. Interquartile range of recovery for 49 pesticides was less than or equal to 25.0 percent and interquartile range of recovery for 13 pesticides was greater than or equal to 40.0 percent.
The use of QC information in assessing the bias and variability of pesticide concentrations is illustrated in an example for azinphos-methyl. On the basis of the histograms of recovery for laboratory and field spikes (figs 1, 2), it is apparent that the normal distribution is a poor model for recovery of azinphos-methyl. The mean and standard deviation are inflated by several extreme values (very high recoveries), and measures of uncertainty (confidence limits) based upon a normal distribution are inappropriate. Measures of uncertainty based upon the binomial distribution for percentiles of pesticide recovery are presented for azinphos-methyl but are applicable for estimating uncertainty for percentiles of pesticide recovery for any pesticide. Calculation, use, and interpretation of two-sided confidence limits or one-sided confidence bounds for percentiles is described in Hahn and Meeker (1991, p. 82-90) and Helsel and Hirsch (1992, p. 70-72, 83-84).
Confidence limits for the median may be used to estimate uncertainty in the bias of recovery. The median recovery of azinphos-methyl spiked at 0.1 µg/L in 998 laboratory control spikes was 53.9 percent (table 4). Based on laboratory control spikes, data analysts are 90-percent confident that the true median recovery of azinphos-methyl in environmental water samples is between 51.2 and 57.0 percent (table 4). The median recovery of azinphos-methyl spiked at 0.1 µg/L in 304 field matrix spikes was 81.5 percent. Based on field matrix spikes, data analysts are 90-percent confident that the true median recovery of azinphos-methyl in environmental water samples is between 73.9 and 89.6 percent. Because the 90-percent confidence limits for the median (2-sided estimates of uncertainty) are the same as the 95-percent confidence bounds for the median (1-sided estimates of uncertainty), analysts also are 95-percent confident that the true median recovery of azinphos-methyl is not less than 73.9 percent. The reason for the reduced bias in field matrix spikes compared to laboratory control spikes is not known but might be caused by reduced extraction of azinphos-methyl from low ionic-strength blank water used for control spikes. As stated previously, estimates of bias and variability from field matrix spikes probably are more representative of the quality of the NAWQA environmental data than estimates from laboratory control spikes.
Confidence bounds for selected extreme percentiles may be used to estimate uncertainty in the variability of recovery. For many assessment questions, data analysts are interested in a pessimistic estimate of variability (How high might variability truly be?"). For low percentiles of recovery, analysts are interested in a lower confidence bound whereas for high percentiles, analysts are interested in an upper confidence bound. The 10th and 90th percentiles of recovery of azinphosmethyl in laboratory control spikes was 15.3 percent and 129.8 percent, respectively (table 4). Based on laboratory control spikes, data analysts are 95-percent confident that no more than 10 percent of NAWQA environmental samples have recovery of azinphos-methyl greater than 137.0 percent or less than 12.9 percent. The 10th and 90th percentiles of recovery of azinphos-methyl in field matrix spikes was 30.8 percent and 171.0 percent, respectively. Based on field matrix spikes, data analysts are 95-percent confident that no more than 10 percent of NAWQA environmental samples have recovery of azinphos-methyl greater than 214.0 percent or less than 25.6 percent.
In addition to describing and documenting bias and variability of concentrations of pesticides in NAWQA environmental water samples, QC information can be used to (1) make adjustments to data for analysis and (2) qualify interpretations of environmental data. Appropriate qualifications and adjustments depend on the SPECIFIC assessment questions that need to be answered.
Suppose, for example, that an assessment question is "how frequently does azinphos-methyl exceed 0.5 µg/L?" One approach is to adjust the NAWQA environmental data for bias. The median recovery of azinphos-methyl in field matrix spikes was 81.5 percent and is the best estimate of the true bias in the NAWQA environmental data. Data analysts could use the median recovery to adjust concentrations of azinphos-methyl (multiply all environmental detections by 100/81.5=1.23) to adjust for the bias in method performance, matrix effects, and degradation. Only 3 of 164 detections in 5,133 surface-water samples analyzed for azinphosmethyl in the NAWQA pesticide data set exceeded 0.5 µg/L. After adjustment for the median bias, 5 of 164 detections could have exceeded 0.5 µg/L.
For some types of assessment questions analysts may desire a pessimistic estimate of the frequency of exceeding 0.5 µg/L (they wish to err on the side of declaring that a measurement is greater than 0.5 when in fact it is less than 0.5 rather than to err on the side of declaring that a measurement is less than 0.5 when in fact it is more than 0.5). The main data-quality concern for this assessment question is the frequency and magnitude of "low" recoveries and some additional approaches for adjusting data and qualifying interpretations might be used.
Pessimistic estimates of environmental concentrations are obtained by adjusting environmental concentrations by pessimistic estimates of data quality. If analysts desire a pessimistic estimate of concentration, they could use the lower confidence limit for the median recovery (73.9 percent) to adjust the concentrations of azinphos-methyl (multiply all environmental detections by 100/73.9=1.35) to adjust for bias. Adjustments to concentrations of azinphos-methyl using this pessimistic estimate of median bias indicate that 6 of 164 detections could have exceeded 0.5 µg/L. Data analysts are 95-percent confident that no more than 50 percent of the adjusted concentrations for azinphos-methyl are biased low (less than 100 percent recovery).
If analysts wanted to account for variability as well as bias in concentrations, they could select an extreme percentile of recovery (a small one in this case) to adjust the NAWQA environmental data. Previously, it was noted (on the basis of the lower confidence bound for the 10th percentile of recovery for azinphos-methyl in field spikes) that analysts are 95-percent confident that no more than 10 percent of NAWQA environmental samples have recovery of azinphos-methyl less than 25.6 percent. A very pessimistic estimate of environmental concentrations that accounts for bias and variability in recovery could be obtained by adjusting all detections of azinphos-methyl by the pessimistic estimate of the 10th percentile of recovery (100/25.6=3.91). Adjustments to concentrations of azinphos-methyl using this pessimistic estimate of variability and bias indicate that 22 of 164 detections could have exceeded 0.5 µg/L. Data analysts are 95-percent confident that no more than 10 percent of the adjusted concentrations for azinphos-methyl are biased low (less than 100 percent recovery). (Many samples are biased high, but in this case, analysts are interested in a pessimistic estimate of concentration).
This paper describes the quality of the NAWQA pesticide data collected from 1992-96 for the 1991 Study Units and provides examples that show how quality-control information on bias and variability can be used in analysis and interpretation of environmental data. Questions regarding the quality of the NAWQA pesticide data or requests for custom statistical compilations to address specific water-quality assessment questions should be directed to the author (jdmartin@usgs.gov).
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Werner, S.L., Burkhardt, M.R., and DeRusseau, S.N., 1996, Methods of analysis by the U.S. Geological Survey National Water Quality Laboratory-Determination of pesticides in water by Carbopak-B solid-phase extraction and high-performance liquid chromatography: U.S. Geological Survey Open-File Report 96-216, 42 p.
Zaugg, S.D., Sandstrom, M.W., Smith, S.G., and Fehlberg, K.M., 1995, Methods of analysis by the U.S. Geological Survey National Water Quality Laboratory-Determination of pesticides in water by C-18 solid-phase extraction and capillary-column gas chromatography/mass spectrometry with selected-ion monitoring: U.S. Geological Survey Open-File Report 95-181, 49 p. Available at URL http://wwwnwql.cr.usgs.gov/Public/pubs/OFR95-181/OFR_95-181.html
Percentage| ** 10 + ** | ** | ** ** | ** ** 9 + ** ** ** | ** ** ** | ** ** ** | ** ** ** ** 8 + ** ** ** ** ** | ** ** ** ** ** | ** ** ** ** ** | ** ** ** ** ** ** 7 + ** ** ** ** ** ** ** | ** ** ** ** ** ** ** | ** ** ** ** ** ** ** | ** ** ** ** ** ** ** ** 6 + ** ** ** ** ** ** ** ** ** ** | ** ** ** ** ** ** ** ** ** ** | ** ** ** ** ** ** ** ** ** ** | ** ** ** ** ** ** ** ** ** ** 5 + ** ** ** ** ** ** ** ** ** ** | ** ** ** ** ** ** ** ** ** ** | ** ** ** ** ** ** ** ** ** ** | ** ** ** ** ** ** ** ** ** ** 4 + ** ** ** ** ** ** ** ** ** ** | ** ** ** ** ** ** ** ** ** ** | ** ** ** ** ** ** ** ** ** ** ** | ** ** ** ** ** ** ** ** ** ** ** ** ** 3 + ** ** ** ** ** ** ** ** ** ** ** ** ** ** | ** ** ** ** ** ** ** ** ** ** ** ** ** ** | ** ** ** ** ** ** ** ** ** ** ** ** ** ** | ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** 2 + ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** | ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** | ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** | ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** 1 + ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** | ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** | ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** | ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** -------------------------------------------------------------- 1 1 1 1 1 1 1 1 1 1 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
Spike recovery, in percent Figure 1. Histogram of recovery of azinphos-methyl in 304 field matrix spikes, 1992-96. Recoveries less than 0 percent or greater than 500 percent were deleted from the data set. Recoveries greater than 200 percent were set to 200 percent for the histograms.
Percentage
| **
12 + **
| **
| **
| **
10 + ** ** **
| ** ** **
| ** ** ** **
| ** ** ** **
8 + ** ** ** ** ** **
| ** ** ** ** ** **
| ** ** ** ** ** ** **
| ** ** ** ** ** ** **
6 + ** ** ** ** ** ** **
| ** ** ** ** ** ** ** **
| ** ** ** ** ** ** ** ** ** **
| ** ** ** ** ** ** ** ** ** ** **
4 + ** ** ** ** ** ** ** ** ** ** **
| ** ** ** ** ** ** ** ** ** ** ** **
| ** ** ** ** ** ** ** ** ** ** ** ** ** **
| ** ** ** ** ** ** ** ** ** ** ** ** ** **
2 + ** ** ** ** ** ** ** ** ** ** ** ** ** ** **
| ** ** ** ** ** ** ** ** ** ** ** ** ** ** **
| ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **
| ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **
--------------------------------------------------------------
1 1 1 1 1 1 1 1 1 1
1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9
5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
Spike recovery, in percent
Figure 2. Histogram of recovery of azinphos-methyl in 998 laboratory control spikes,
1992-96. Recoveries less than 0 percent or greater than 500 percent
were deleted from the data set. Recoveries greater than 200 percent
were set to 200 percent for the histograms.
Table 1. Contamination in field blanks of the NAWQA Program, 1992-95.
Pesticides sorted by analytical method, water type, and parameter code.
[Parm, NWIS/STORET parameter code; MRL, Method reporting limit; µg/L, microgram per liter;
GCMS, gas chromatography/mass spectrometry; HPLC, high-performance liquid chromatography;
GW, ground-water field blanks; SW, surface-water field blanks; nd, pesticide not detected]
----------------------------------------------------------------------------------------------------
95-percent
upper
confidence
Number Number bound for Maximum
of of Percent percent concen-
Analytical MRL Water field detec- detec- detec- tration
Parm Pesticide method (µg/L) type blanks tions tions tions (µg/L)
----------------------------------------------------------------------------------------------------
04024 Propachlor GCMS 0.007 GW 145 0 0.0 2.0 nd
04028 Butylate GCMS .002 GW 145 0 .0 2.0 nd
04035 Simazine GCMS .005 GW 145 2 1.4 4.3 0.001
04037 Prometon GCMS .018 GW 145 1 .7 3.2 .013
04040 Desethylatrazine GCMS .002 GW 145 1 .7 3.2 .005
04041 Cyanazine GCMS .004 GW 145 0 .0 2.0 nd
04095 Fonofos GCMS .003 GW 145 0 .0 2.0 nd
34253 alpha-HCH GCMS .002 GW 145 0 .0 2.0 nd
34653 p,p'-DDE GCMS .006 GW 145 6 4.1 8.0 .002
38933 Chlorpyrifos GCMS .004 GW 145 2 1.4 4.3 .013
39341 gamma-HCH GCMS .004 GW 145 0 .0 2.0 nd
39381 Dieldrin GCMS .001 GW 145 0 .0 2.0 nd
39415 Metolachlor GCMS .002 GW 145 2 1.4 4.3 .006
39532 Malathion GCMS .005 GW 145 0 .0 2.0 nd
39542 Parathion GCMS .004 GW 145 0 .0 2.0 nd
39572 Diazinon GCMS .002 GW 145 1 .7 3.2 .011
39632 Atrazine GCMS .001 GW 145 4 2.8 6.2 .012
46342 Alachlor GCMS .002 GW 145 0 .0 2.0 nd
49260 Acetochlor GCMS .002 GW 15 0 .0 18.1 nd
82630 Metribuzin GCMS .004 GW 145 0 .0 2.0 nd
82660 2,6-Diethylaniline GCMS .003 GW 145 0 .0 2.0 nd
82661 Trifluralin GCMS .002 GW 145 1 .7 3.2 .002
82663 Ethalfluralin GCMS .004 GW 145 0 .0 2.0 nd
82664 Phorate GCMS .002 GW 145 0 .0 2.0 nd
82665 Terbacil GCMS .007 GW 144 0 .0 2.1 nd
82666 Linuron GCMS .002 GW 145 0 .0 2.0 nd
82667 Methyl parathion GCMS .006 GW 145 0 .0 2.0 nd
82668 EPTC GCMS .002 GW 145 0 .0 2.0 nd
82669 Pebulate GCMS .004 GW 145 0 .0 2.0 nd
82670 Tebuthiuron GCMS .010 GW 145 0 .0 2.0 nd
82671 Molinate GCMS .004 GW 145 0 .0 2.0 nd
82672 Ethoprop GCMS .003 GW 145 0 .0 2.0 nd
82673 Benfluralin GCMS .002 GW 145 2 1.4 4.3 .003
82674 Carbofuran GCMS .003 GW 145 0 .0 2.0 nd
82675 Terbufos GCMS .013 GW 145 0 .0 2.0 nd
82676 Pronamide GCMS .003 GW 145 1 .7 3.2 .002
82677 Disulfoton GCMS .017 GW 145 0 .0 2.0 nd
82678 Triallate GCMS .001 GW 145 2 1.4 4.3 .001
82679 Propanil GCMS .004 GW 145 1 .7 3.2 .002
82680 Carbaryl GCMS .003 GW 145 0 .0 2.0 nd
82681 Thiobencarb GCMS .002 GW 145 0 .0 2.0 nd
82682 Dacthal GCMS .002 GW 145 1 .7 3.2 .001
82683 Pendimethalin GCMS .004 GW 145 0 .0 2.0 nd
82684 Napropamide GCMS .003 GW 145 0 .0 2.0 nd
82685 Propargite GCMS .013 GW 145 0 .0 2.0 nd
82686 Azinphos-methyl GCMS .001 GW 144 0 .0 2.1 nd
82687 cis-Permethrin GCMS .005 GW 145 1 .7 3.2 .003
04024 Propachlor GCMS .007 SW 175 0 .0 1.7 nd
04028 Butylate GCMS .002 SW 175 0 .0 1.7 nd
04035 Simazine GCMS .005 SW 175 16 9.1 13.6 .007
04037 Prometon GCMS .018 SW 175 3 1.7 4.4 .004
04040 Desethylatrazine GCMS .002 SW 175 1 .6 2.7 .004
04041 Cyanazine GCMS .004 SW 175 0 .0 1.7 nd
04095 Fonofos GCMS .003 SW 175 0 .0 1.7 nd
34253 alpha-HCH GCMS .002 SW 175 0 .0 1.7 nd
34653 p,p'-DDE GCMS .006 SW 175 4 2.3 5.2 .010
38933 Chlorpyrifos GCMS .004 SW 175 1 .6 2.7 .006
39341 gamma-HCH GCMS .004 SW 175 0 .0 1.7 nd
39381 Dieldrin GCMS .001 SW 175 0 .0 1.7 nd
39415 Metolachlor GCMS .002 SW 175 8 4.6 8.1 .020
39532 Malathion GCMS .005 SW 175 3 1.7 4.4 .015
39542 Parathion GCMS .004 SW 175 0 .0 1.7 nd
39572 Diazinon GCMS .002 SW 175 4 2.3 5.2 .038
39632 Atrazine GCMS .001 SW 175 19 10.9 15.5 .009
46342 Alachlor GCMS .002 SW 175 3 1.7 4.4 .005
49260 Acetochlor GCMS .002 SW 17 0 .0 16.2 nd
82630 Metribuzin GCMS .004 SW 175 1 .6 2.7 .007
82660 2,6-Diethylaniline GCMS .003 SW 171 0 .0 1.7 nd
82661 Trifluralin GCMS .002 SW 171 1 .6 2.7 .006
82663 Ethalfluralin GCMS .004 SW 171 1 .6 2.7 .006
82664 Phorate GCMS .002 SW 171 0 .0 1.7 nd
82665 Terbacil GCMS .007 SW 164 0 .0 1.8 nd
82666 Linuron GCMS .002 SW 171 0 .0 1.7 nd
82667 Methyl parathion GCMS .006 SW 171 0 .0 1.7 nd
82668 EPTC GCMS .002 SW 171 3 1.8 4.5 .086
82669 Pebulate GCMS .004 SW 171 1 .6 2.7 .005
82670 Tebuthiuron GCMS .010 SW 171 1 .6 2.7 .053
82671 Molinate GCMS .004 SW 171 0 .0 1.7 nd
82672 Ethoprop GCMS .003 SW 171 0 .0 1.7 nd
82673 Benfluralin GCMS .002 SW 171 0 .0 1.7 nd
82674 Carbofuran GCMS .003 SW 171 0 .0 1.7 nd
82675 Terbufos GCMS .013 SW 171 0 .0 1.7 nd
82676 Pronamide GCMS .003 SW 171 2 1.2 3.6 .120
82677 Disulfoton GCMS .017 SW 171 0 .0 1.7 nd
82678 Triallate GCMS .001 SW 171 2 1.2 3.6 .004
82679 Propanil GCMS .004 SW 171 0 .0 1.7 nd
82680 Carbaryl GCMS .003 SW 171 2 1.2 3.6 .012
82681 Thiobencarb GCMS .002 SW 171 0 .0 1.7 nd
82682 Dacthal GCMS .002 SW 171 1 .6 2.7 .003
82683 Pendimethalin GCMS .004 SW 171 0 .0 1.7 nd
82684 Napropamide GCMS .003 SW 171 2 1.2 3.6 .100
82685 Propargite GCMS .013 SW 171 2 1.2 3.6 .074
82686 Azinphos-methyl GCMS .001 SW 164 0 .0 1.8 nd
82687 cis-Permethrin GCMS .005 SW 171 1 .6 2.7 .003
04029 Bromacil HPLC .035 GW 104 1 1.0 4.5 .010
38442 Dicamba HPLC .035 GW 97 0 .0 3.0 nd
38478 Linuron HPLC .018 GW 98 0 .0 3.0 nd
38482 MCPA HPLC .170 GW 97 0 .0 3.0 nd
38487 MCPB HPLC .140 GW 97 0 .0 3.0 nd
38501 Methiocarb HPLC .026 GW 98 0 .0 3.0 nd
38538 Propoxur HPLC .035 GW 96 0 .0 3.1 nd
38711 Bentazon HPLC .014 GW 97 0 .0 3.0 nd
38746 2,4-DB HPLC .240 GW 97 0 .0 3.0 nd
38811 Fluometuron HPLC .035 GW 98 0 .0 3.0 nd
38866 Oxamyl HPLC .018 GW 98 0 .0 3.0 nd
39732 2,4-D HPLC .150 GW 103 0 .0 2.9 nd
39742 2,4,5-T HPLC .035 GW 103 0 .0 2.9 nd
39762 Silvex HPLC .021 GW 103 0 .0 2.9 nd
49235 Triclopyr HPLC .250 GW 97 0 .0 3.0 nd
49236 Propham HPLC .035 GW 98 0 .0 3.0 nd
49291 Picloram HPLC .050 GW 97 0 .0 3.0 nd
49292 Oryzalin HPLC .310 GW 98 0 .0 3.0 nd
49293 Norflurazon HPLC .024 GW 98 0 .0 3.0 nd
49294 Neburon HPLC .015 GW 98 0 .0 3.0 nd
49296 Methomyl HPLC .017 GW 98 0 .0 3.0 nd
49297 Fenuron HPLC .013 GW 98 1 1.0 4.7 .010
49299 DNOC HPLC .420 GW 97 0 .0 3.0 nd
49300 Diuron HPLC .020 GW 98 1 1.0 4.7 .020
49301 Dinoseb HPLC .035 GW 97 0 .0 3.0 nd
49302 Dichlorprop HPLC .032 GW 97 0 .0 3.0 nd
49303 Dichlobenil HPLC 1.200 GW 98 0 .0 3.0 nd
49304 Dacthal monoacid HPLC .017 GW 97 0 .0 3.0 nd
49305 Clopyralid HPLC .230 GW 97 0 .0 3.0 nd
49306 Chlorothalonil HPLC .480 GW 96 0 .0 3.1 nd
49307 Chloramben HPLC .420 GW 98 0 .0 3.0 nd
49308 3-Hydroxycarbofuran HPLC .014 GW 98 0 .0 3.0 nd
49309 Carbofuran HPLC .120 GW 98 0 .0 3.0 nd
49310 Carbaryl HPLC .008 GW 98 0 .0 3.0 nd
49311 Bromoxynil HPLC .035 GW 97 0 .0 3.0 nd
49312 Aldicarb HPLC .550 GW 98 0 .0 3.0 nd
49313 Aldicarb sulfone HPLC .100 GW 98 0 .0 3.0 nd
49314 Aldicarb sulfoxide HPLC .021 GW 98 0 .0 3.0 nd
49315 Acifluorfen HPLC .035 GW 97 0 .0 3.0 nd
04029 Bromacil HPLC .035 SW 109 0 .0 2.7 nd
38442 Dicamba HPLC .035 SW 96 0 .0 3.1 nd
38478 Linuron HPLC .018 SW 97 0 .0 3.0 nd
38482 MCPA HPLC .170 SW 96 0 .0 3.1 nd
38487 MCPB HPLC .140 SW 96 0 .0 3.1 nd
38501 Methiocarb HPLC .026 SW 97 0 .0 3.0 nd
38538 Propoxur HPLC .035 SW 93 0 .0 3.2 nd
38711 Bentazon HPLC .014 SW 96 0 .0 3.1 nd
38746 2,4-DB HPLC .240 SW 96 0 .0 3.1 nd
38811 Fluometuron HPLC .035 SW 97 0 .0 3.0 nd
38866 Oxamyl HPLC .018 SW 97 0 .0 3.0 nd
39732 2,4-D HPLC .150 SW 108 1 .9 4.3 .230
39742 2,4,5-T HPLC .035 SW 108 0 .0 2.7 nd
39762 Silvex HPLC .021 SW 108 0 .0 2.7 nd
49235 Triclopyr HPLC .250 SW 91 0 .0 3.2 nd
49236 Propham HPLC .035 SW 92 0 .0 3.2 nd
49291 Picloram HPLC .050 SW 91 0 .0 3.2 nd
49292 Oryzalin HPLC .310 SW 92 0 .0 3.2 nd
49293 Norflurazon HPLC .024 SW 92 0 .0 3.2 nd
49294 Neburon HPLC .015 SW 92 0 .0 3.2 nd
49296 Methomyl HPLC .017 SW 92 0 .0 3.2 nd
49297 Fenuron HPLC .013 SW 92 0 .0 3.2 nd
49299 DNOC HPLC .420 SW 91 0 .0 3.2 nd
49300 Diuron HPLC .020 SW 92 1 1.1 5.1 .010
49301 Dinoseb HPLC .035 SW 91 0 .0 3.2 nd
49302 Dichlorprop HPLC .032 SW 91 0 .0 3.2 nd
49303 Dichlobenil HPLC 1.200 SW 92 0 .0 3.2 nd
49304 Dacthal monoacid HPLC .017 SW 91 0 .0 3.2 nd
49305 Clopyralid HPLC .230 SW 91 0 .0 3.2 nd
49306 Chlorothalonil HPLC .480 SW 91 0 .0 3.2 nd
49307 Chloramben HPLC .420 SW 92 0 .0 3.2 nd
49308 3-Hydroxycarbofuran HPLC .014 SW 92 0 .0 3.2 nd
49309 Carbofuran HPLC .120 SW 92 0 .0 3.2 nd
49310 Carbaryl HPLC .008 SW 92 0 .0 3.2 nd
49311 Bromoxynil HPLC .035 SW 91 0 .0 3.2 nd
49312 Aldicarb HPLC .550 SW 92 0 .0 3.2 nd
49313 Aldicarb sulfone HPLC .100 SW 92 0 .0 3.2 nd
49314 Aldicarb sulfoxide HPLC .021 SW 92 0 .0 3.2 nd
49315 Acifluorfen HPLC .035 SW 91 0 .0 3.2 nd
----------------------------------------------------------------------------------------------------
Table 2. False negative detections of pesticides in NAWQA field matrix spikes and NWQL
laboratory control spikes, 1992-96. Pesticides sorted by analytical method,
parameter code, and spike type. GCMS spikes at 0.1 µg/L, HPLC field matrix spikes
at 1.0 µg/L, HPLC laboratory control spikes at 0.5 µg/L. Recoveries greater than
500 percent were deleted. Recoveries less than 0 percent in field matrix spikes
were deleted.
NOTE: Data for field matrix spikes have not been reviewed thoroughly, are provisional,
and are subject to revision.
[Parm, NWIS/STORET parameter code; GCMS, gas chromatography/mass spectrometry; HPLC,
high-performance liquid chromatography; FLD, field matrix spikes; LAB, laboratory control
spikes; nc, not calculated; *, pesticide identified as poorly performing in method
documentation or NWQL memoranda]
---------------------------------------------------------------------------------------------
95-percent
upper
confidence
Number bound for
Number of Percent percent
Analytical Spike of false false false
Parm Pesticide method type spikes negatives negatives negatives
---------------------------------------------------------------------------------------------
04024 Propachlor GCMS FLD 304 0 0.0 1.0
04024 Propachlor GCMS LAB 999 0 .0 .3
04028 Butylate GCMS FLD 308 0 .0 1.0
04028 Butylate GCMS LAB 1002 0 .0 .3
04035 Simazine GCMS FLD 308 0 .0 1.0
04035 Simazine GCMS LAB 1001 0 .0 .3
04037 Prometon GCMS FLD 301 0 .0 1.0
04037 Prometon GCMS LAB 1002 11 1.1 1.8
04040 Deethylatrazine * GCMS FLD 308 0 .0 1.0
04040 Deethylatrazine * GCMS LAB 1002 0 .0 .3
04041 Cyanazine GCMS FLD 304 0 .0 1.0
04041 Cyanazine GCMS LAB 1002 0 .0 .3
04095 Fonofos GCMS FLD 304 1 .3 1.6
04095 Fonofos GCMS LAB 999 0 .0 .3
34253 alpha-HCH GCMS FLD 304 0 .0 1.0
34253 alpha-HCH GCMS LAB 1002 0 .0 .3
34653 p,p-DDE GCMS FLD 304 0 .0 1.0
34653 p,p-DDE GCMS LAB 1001 0 .0 .3
38933 Chlorpyrifos GCMS FLD 304 1 .3 1.6
38933 Chlorpyrifos GCMS LAB 1002 0 .0 .3
39341 Lindane GCMS FLD 304 1 .3 1.6
39341 Lindane GCMS LAB 1002 0 .0 .3
39381 Dieldrin GCMS FLD 303 0 .0 1.0
39381 Dieldrin GCMS LAB 1001 0 .0 .3
39415 Metolachlor GCMS FLD 306 4 1.3 3.0
39415 Metolachlor GCMS LAB 1002 0 .0 .3
39532 Malathion GCMS FLD 308 0 .0 1.0
39532 Malathion GCMS LAB 1002 0 .0 .3
39542 Parathion GCMS FLD 304 1 .3 1.6
39542 Parathion GCMS LAB 1002 0 .0 .3
39572 Diazinon GCMS FLD 308 1 .3 1.5
39572 Diazinon GCMS LAB 1002 0 .0 .3
39632 Atrazine GCMS FLD 305 0 .0 1.0
39632 Atrazine GCMS LAB 1002 0 .0 .3
46342 Alachlor GCMS FLD 308 1 .3 1.5
46342 Alachlor GCMS LAB 1001 0 .0 .3
49260 Acetochlor GCMS FLD 0 0 nc nc
49260 Acetochlor GCMS LAB 646 3 .5 1.2
82630 Metribuzin GCMS FLD 308 0 .0 1.0
82630 Metribuzin GCMS LAB 1002 0 .0 .3
82660 2,6-Diethylaniline GCMS FLD 304 2 .7 2.1
82660 2,6-Diethylaniline GCMS LAB 1002 0 .0 .3
82661 Trifluralin GCMS FLD 304 0 .0 1.0
82661 Trifluralin GCMS LAB 1002 0 .0 .3
82663 Ethalfluralin GCMS FLD 304 0 .0 1.0
82663 Ethalfluralin GCMS LAB 1002 0 .0 .3
82664 Phorate GCMS FLD 304 1 .3 1.6
82664 Phorate GCMS LAB 1002 0 .0 .3
82665 Terbacil * GCMS FLD 302 3 1.0 2.5
82665 Terbacil * GCMS LAB 1000 0 .0 .3
82666 Linuron GCMS FLD 303 0 .0 1.0
82666 Linuron GCMS LAB 1001 0 .0 .3
82667 Parathion-methyl GCMS FLD 304 1 .3 1.6
82667 Parathion-methyl GCMS LAB 1002 0 .0 .3
82668 EPTC GCMS FLD 304 0 .0 1.0
82668 EPTC GCMS LAB 1002 0 .0 .3
82669 Pebulate GCMS FLD 303 0 .0 1.0
82669 Pebulate GCMS LAB 1001 0 .0 .3
82670 Tebuthiuron GCMS FLD 302 1 .3 1.6
82670 Tebuthiuron GCMS LAB 1000 0 .0 .3
82671 Molinate GCMS FLD 303 0 .0 1.0
82671 Molinate GCMS LAB 999 0 .0 .3
82672 Ethoprophos GCMS FLD 304 0 .0 1.0
82672 Ethoprophos GCMS LAB 1002 0 .0 .3
82673 Benfluralin GCMS FLD 304 0 .0 1.0
82673 Benfluralin GCMS LAB 1002 0 .0 .3
82674 Carbofuran * GCMS FLD 301 0 .0 1.0
82674 Carbofuran * GCMS LAB 1002 0 .0 .3
82675 Terbufos GCMS FLD 304 0 .0 1.0
82675 Terbufos GCMS LAB 1002 0 .0 .3
82676 Propyzamide GCMS FLD 304 1 .3 1.6
82676 Propyzamide GCMS LAB 999 0 .0 .3
82677 Disulfoton GCMS FLD 304 1 .3 1.6
82677 Disulfoton GCMS LAB 1002 4 .4 .9
82678 Tri-allate GCMS FLD 304 0 .0 1.0
82678 Tri-allate GCMS LAB 1002 0 .0 .3
82679 Propanil GCMS FLD 304 0 .0 1.0
82679 Propanil GCMS LAB 1002 0 .0 .3
82680 Carbaryl * GCMS FLD 306 0 .0 1.0
82680 Carbaryl * GCMS LAB 1000 0 .0 .3
82681 Thiobencarb GCMS FLD 304 0 .0 1.0
82681 Thiobencarb GCMS LAB 1002 0 .0 .3
82682 Dacthal GCMS FLD 298 0 .0 1.0
82682 Dacthal GCMS LAB 1002 0 .0 .3
82683 Pendimethalin GCMS FLD 304 0 .0 1.0
82683 Pendimethalin GCMS LAB 1002 0 .0 .3
82684 Napropamide GCMS FLD 304 0 .0 1.0
82684 Napropamide GCMS LAB 1002 0 .0 .3
82685 Propargite GCMS FLD 304 0 .0 1.0
82685 Propargite GCMS LAB 1001 0 .0 .3
82686 Azinphos-methyl * GCMS FLD 304 3 1.0 2.5
82686 Azinphos-methyl * GCMS LAB 998 5 .5 1.1
82687 cis-Permethrin GCMS FLD 303 2 .7 2.1
82687 cis-Permethrin GCMS LAB 1002 0 .0 .3
04029 Bromacil HPLC FLD 81 0 .0 3.6
04029 Bromacil HPLC LAB 694 5 .7 1.5
38442 Dicamba HPLC FLD 73 4 5.5 12.1
38442 Dicamba HPLC LAB 703 17 2.4 3.6
38478 Linuron HPLC FLD 72 1 1.4 6.4
38478 Linuron HPLC LAB 573 4 .7 1.6
38482 MCPA HPLC FLD 76 0 .0 3.9
38482 MCPA HPLC LAB 709 21 3.0 4.2
38487 MCPB HPLC FLD 21 0 .0 13.3
38487 MCPB HPLC LAB 709 83 11.7 13.9
38501 Methiocarb HPLC FLD 67 6 9.0 16.9
38501 Methiocarb HPLC LAB 559 8 1.4 2.6
38538 Propoxur HPLC FLD 79 0 .0 3.7
38538 Propoxur HPLC LAB 610 7 1.1 2.1
38711 Bentazon HPLC FLD 75 0 .0 3.9
38711 Bentazon HPLC LAB 700 13 1.9 2.9
38746 2,4-DB HPLC FLD 79 0 .0 3.7
38746 2,4-DB HPLC LAB 700 43 6.1 7.9
38811 Fluometuron HPLC FLD 79 0 .0 3.7
38811 Fluometuron HPLC LAB 621 5 .8 1.7
38866 Oxamyl HPLC FLD 70 9 12.9 21.4
38866 Oxamyl HPLC LAB 684 17 2.5 3.7
39732 2,4-D HPLC FLD 77 0 .0 3.8
39732 2,4-D HPLC LAB 702 16 2.3 3.4
39742 2,4,5-T HPLC FLD 72 1 1.4 6.4
39742 2,4,5-T HPLC LAB 662 20 3.0 4.4
39762 Silvex HPLC FLD 80 0 .0 3.7
39762 Silvex HPLC LAB 707 13 1.8 2.9
49235 Triclopyr HPLC FLD 21 0 .0 13.3
49235 Triclopyr HPLC LAB 700 17 2.4 3.6
49236 Propham HPLC FLD 57 1 1.8 8.1
49236 Propham HPLC LAB 444 9 2.0 3.5
49291 Picloram HPLC FLD 58 2 3.4 10.5
49291 Picloram HPLC LAB 668 31 4.6 6.2
49292 Oryzalin HPLC FLD 21 0 .0 13.3
49292 Oryzalin HPLC LAB 707 5 .7 1.5
49293 Norflurazon HPLC FLD 21 0 .0 13.3
49293 Norflurazon HPLC LAB 700 3 .4 1.1
49294 Neburon HPLC FLD 81 0 .0 3.6
49294 Neburon HPLC LAB 719 4 .6 1.3
49296 Methomyl HPLC FLD 72 1 1.4 6.4
49296 Methomyl HPLC LAB 675 16 2.4 3.6
49297 Fenuron HPLC FLD 76 0 .0 3.9
49297 Fenuron HPLC LAB 663 4 .6 1.4
49299 DNOC * HPLC FLD 72 1 1.4 6.4
49299 DNOC * HPLC LAB 632 56 8.9 10.9
49300 Diuron HPLC FLD 82 0 .0 3.6
49300 Diuron HPLC LAB 696 5 .7 1.5
49301 Dinoseb HPLC FLD 80 0 .0 3.7
49301 Dinoseb HPLC LAB 707 20 2.8 4.1
49302 Dichlorprop HPLC FLD 80 0 .0 3.7
49302 Dichlorprop HPLC LAB 707 15 2.1 3.2
49303 Dichlobenil * HPLC FLD 21 0 .0 13.3
49303 Dichlobenil * HPLC LAB 691 114 16.5 19.0
49304 Dacthal monoacid HPLC FLD 20 0 .0 13.9
49304 Dacthal monoacid HPLC LAB 674 13 1.9 3.0
49305 Clopyralid HPLC FLD 14 4 28.6 54.0
49305 Clopyralid HPLC LAB 660 60 9.1 11.1
49306 Chlorothalonil * HPLC FLD 24 1 4.2 18.3
49306 Chlorothalonil * HPLC LAB 693 229 33.0 36.1
49307 Chloramben HPLC FLD 8 0 .0 31.2
49307 Chloramben HPLC LAB 496 3 .6 1.6
49308 3-Hydroxycarbofuran HPLC FLD 18 0 .0 15.3
49308 3-Hydroxycarbofuran HPLC LAB 678 14 2.1 3.2
49309 Carbofuran HPLC FLD 78 0 .0 3.8
49309 Carbofuran HPLC LAB 668 5 .7 1.6
49310 Carbaryl HPLC FLD 81 4 4.9 10.9
49310 Carbaryl HPLC LAB 710 6 .8 1.7
49311 Bromoxynil HPLC FLD 72 0 .0 4.1
49311 Bromoxynil HPLC LAB 657 14 2.1 3.3
49312 Aldicarb * HPLC FLD 66 4 6.1 13.3
49312 Aldicarb * HPLC LAB 702 32 4.6 6.1
49313 Aldicarb sulfone * HPLC FLD 70 15 21.4 31.1
49313 Aldicarb sulfone * HPLC LAB 655 57 8.7 10.7
49314 Aldicarb sulfoxide * HPLC FLD 70 7 10.0 18.0
49314 Aldicarb sulfoxide * HPLC LAB 660 35 5.3 7.0
49315 Acifluorfen HPLC FLD 21 0 .0 13.3
49315 Acifluorfen HPLC LAB 704 13 1.8 2.9
---------------------------------------------------------------------------------------------
Table 3. Bias and variability of recovery of pesticides in NAWQA field matrix spikes and NWQL laboratory control spikes, 1992-96.
Pesticides sorted by analytical method and parameter code. GCMS spikes at 0.1 µg/L, HPLC field matrix spikes at
1.0 µg/L, HPLC laboratory control spikes at 0.5 µg/L. Recoveries greater than 500 percent were deleted. Recoveries less
than 0 percent in field matrix spikes were deleted.
NOTE: Data for field matrix spikes have not been reviewed thoroughly, are provisional, and are subject to revision.
[Parm, NWIS/STORET parameter code; N, number of spikes; pct, percent; IQR, interquartile range; Min, minimum; Max, maximum; SD,
standard deviation; GCMS, gas chromatography/mass spectrometry; HPLC, high-performance liquid chromatography; FLD, field matrix
spikes; LAB, laboratory control spikes; nc, not calculated; *, pesticide identified as poorly performing in method documentation
or NWQL memoranda]
-----------------------------------------------------------------------------------------------------------------------------------
Percentiles of recovery
Relative ----------------------------- Relative
Analytical Spike Median IQR IQR Min 10 25 50 75 90 Max Mean SD SD
Parm Pesticide method type N (pct) (pct) (pct) (pct) (pct) (pct) (pct) (pct) (pct) (pct) (pct) (pct) (pct)
-----------------------------------------------------------------------------------------------------------------------------------
04024 Propachlor GCMS FLD 304 94.9 19.8 20.9 6 78 86 95 106 117 211 97.0 19.1 19.7
04024 Propachlor GCMS LAB 999 96.8 20.3 21.0 7 74 87 97 107 117 324 96.5 21.9 22.6
04028 Butylate GCMS FLD 308 91.6 13.3 14.5 32 80 85 92 99 107 250 93.5 16.6 17.7
04028 Butylate GCMS LAB 1002 90.6 10.3 11.4 7 79 85 91 95 99 420 90.5 20.2 22.3
04035 Simazine GCMS FLD 308 90.9 25.7 28.2 18 68 77 91 103 111 245 91.2 22.2 24.4
04035 Simazine GCMS LAB 1001 93.7 19.0 20.2 5 72 84 94 103 109 254 92.1 18.5 20.1
04037 Prometon GCMS FLD 301 93.2 21.5 23.1 20 71 84 93 105 114 167 92.7 19.3 20.8
04037 Prometon GCMS LAB 1002 71.8 36.8 51.2 0 22 50 72 87 99 182 66.6 28.4 42.6
04040 Deethylatrazine * GCMS FLD 308 31.5 21.8 69.2 3 15 22 31 44 56 127 34.8 19.4 55.8
04040 Deethylatrazine * GCMS LAB 1002 38.7 14.8 38.2 2 23 30 39 45 56 110 39.3 14.1 36.0
04041 Cyanazine GCMS FLD 304 96.5 31.4 32.5 22 70 83 96 114 136 194 99.5 27.9 28.1
04041 Cyanazine GCMS LAB 1002 97.6 29.6 30.4 7 64 82 98 112 123 250 96.2 26.8 27.8
04095 Fonofos GCMS FLD 304 86.8 16.8 19.4 0 70 78 87 95 107 184 88.1 17.6 19.9
04095 Fonofos GCMS LAB 999 88.4 16.2 18.4 7 70 80 88 96 103 322 87.6 18.5 21.2
34253 alpha-HCH GCMS FLD 304 88.9 21.2 23.9 51 73 79 89 101 109 194 90.5 16.7 18.4
34253 alpha-HCH GCMS LAB 1002 89.3 18.8 21.1 6 68 79 89 98 109 228 88.8 19.0 21.4
34653 p,p-DDE GCMS FLD 304 66.3 18.8 28.3 5 52 58 66 77 86 175 67.9 16.5 24.3
34653 p,p-DDE GCMS LAB 1001 59.7 15.1 25.3 5 46 52 60 67 75 158 60.0 13.7 22.9
38933 Chlorpyrifos GCMS FLD 304 88.8 24.7 27.8 0 69 78 89 103 116 230 91.0 23.2 25.6
38933 Chlorpyrifos GCMS LAB 1002 89.9 20.1 22.4 5 67 79 90 99 111 230 89.0 21.8 24.5
39341 Lindane GCMS FLD 304 89.8 18.2 20.2 0 74 82 90 100 114 183 93.0 20.4 21.9
39341 Lindane GCMS LAB 1002 89.0 19.0 21.3 9 70 80 89 99 109 219 89.3 19.1 21.4
39381 Dieldrin GCMS FLD 303 89.2 21.1 23.6 49 73 80 89 101 113 183 91.0 17.3 19.0
39381 Dieldrin GCMS LAB 1001 82.4 17.2 20.9 7 64 74 82 91 103 208 83.0 18.3 22.1
39415 Metolachlor GCMS FLD 306 105.7 16.9 16.0 0 89 97 106 114 126 455 107.5 30.1 28.0
39415 Metolachlor GCMS LAB 1002 102.9 19.9 19.3 8 85 94 103 114 122 265 103.3 20.7 20.1
39532 Malathion GCMS FLD 308 88.6 26.8 30.3 10 59 76 89 103 113 184 88.4 22.6 25.6
39532 Malathion GCMS LAB 1002 93.8 25.2 26.9 5 62 79 94 104 112 204 90.0 22.4 24.9
39542 Parathion GCMS FLD 304 94.6 27.5 29.0 0 65 80 95 108 124 287 97.7 31.3 32.0
39542 Parathion GCMS LAB 1002 92.2 25.0 27.1 9 63 79 92 103 118 229 91.0 23.4 25.8
39572 Diazinon GCMS FLD 308 87.9 16.4 18.7 0 70 79 88 96 105 146 87.9 16.3 18.6
39572 Diazinon GCMS LAB 1002 86.4 16.8 19.5 7 69 78 86 94 106 278 86.6 19.9 23.0
39632 Atrazine GCMS FLD 305 94.9 18.8 19.9 8 76 85 95 104 113 184 94.5 19.3 20.4
39632 Atrazine GCMS LAB 1002 94.1 17.6 18.7 6 75 85 94 102 110 258 93.4 18.7 20.0
46342 Alachlor GCMS FLD 308 102.0 17.1 16.8 0 87 93 102 110 121 204 101.7 18.6 18.3
46342 Alachlor GCMS LAB 1001 99.5 17.7 17.8 8 79 90 100 107 114 224 97.6 18.2 18.7
49260 Acetochlor GCMS FLD 0 nc nc nc nc nc nc nc nc nc nc nc nc nc
49260 Acetochlor GCMS LAB 646 97.1 12.1 12.5 0 86 91 97 103 112 228 98.1 16.2 16.5
82630 Metribuzin GCMS FLD 308 74.3 23.7 31.9 24 47 62 74 86 95 175 73.6 20.3 27.6
82630 Metribuzin GCMS LAB 1002 73.9 18.9 25.6 7 54 64 74 83 94 252 73.8 18.5 25.1
82660 2,6-Diethylaniline GCMS FLD 304 89.1 12.0 13.4 0 76 82 89 94 100 232 88.9 17.7 19.9
82660 2,6-Diethylaniline GCMS LAB 1002 87.8 10.6 12.1 1 77 83 88 93 99 223 87.5 15.2 17.4
82661 Trifluralin GCMS FLD 304 77.3 24.1 31.2 27 56 64 77 88 99 204 78.5 21.9 27.9
82661 Trifluralin GCMS LAB 1002 71.7 22.6 31.4 7 48 59 72 81 94 201 71.2 20.3 28.6
82663 Ethalfluralin GCMS FLD 304 87.3 23.1 26.4 29 61 76 87 99 116 241 89.6 25.8 28.8
82663 Ethalfluralin GCMS LAB 1002 81.0 24.8 30.7 8 54 68 81 93 107 218 80.8 22.9 28.3
82664 Phorate GCMS FLD 304 76.7 28.6 37.2 0 52 63 77 92 103 184 77.0 22.7 29.4
82664 Phorate GCMS LAB 1002 82.3 23.8 29.0 4 50 69 82 93 105 204 80.4 23.3 29.0
82665 Terbacil * GCMS FLD 302 86.8 51.6 59.5 0 39 61 87 113 183 426 100.4 66.3 66.1
82665 Terbacil * GCMS LAB 1000 74.2 36.5 49.2 4 37 55 74 92 109 261 75.6 33.1 43.8
82666 Linuron GCMS FLD 303 87.7 40.7 46.4 13 47 68 88 109 140 403 94.0 49.3 52.4
82666 Linuron GCMS LAB 1001 91.5 36.0 39.4 7 55 72 91 108 125 395 95.3 44.9 47.1
82667 Parathion-methyl GCMS FLD 304 84.7 28.2 33.3 0 61 72 85 100 120 330 89.6 32.8 36.7
82667 Parathion-methyl GCMS LAB 1002 85.2 31.1 36.5 7 50 69 85 100 111 232 84.6 27.1 32.0
82668 EPTC GCMS FLD 304 91.6 15.3 16.7 2 76 84 92 100 107 204 91.9 17.4 19.0
82668 EPTC GCMS LAB 1002 91.8 10.6 11.6 1 81 87 92 97 103 226 91.5 16.0 17.4
82669 Pebulate GCMS FLD 303 93.4 18.5 19.8 30 77 84 93 102 107 204 93.2 16.1 17.3
82669 Pebulate GCMS LAB 1001 91.0 10.9 12.0 7 80 86 91 97 103 445 91.2 22.2 24.3
82670 Tebuthiuron GCMS FLD 302 84.4 32.0 38.0 0 57 71 84 103 120 430 90.2 42.1 46.6
82670 Tebuthiuron GCMS LAB 1000 99.7 42.7 42.8 7 63 79 100 121 140 353 101.2 32.8 32.4
82671 Molinate GCMS FLD 303 94.6 18.8 19.8 29 76 86 95 104 113 192 95.1 16.5 17.4
82671 Molinate GCMS LAB 999 93.7 10.4 11.1 6 82 89 94 99 104 357 93.3 18.0 19.3
82672 Ethoprophos GCMS FLD 304 93.7 20.4 21.8 47 74 84 94 104 114 202 94.8 17.7 18.6
82672 Ethoprophos GCMS LAB 1002 92.3 18.5 20.0 6 72 83 92 101 109 215 91.2 19.5 21.3
82673 Benfluralin GCMS FLD 304 77.8 26.8 34.5 33 53 60 78 87 104 204 77.7 22.8 29.4
82673 Benfluralin GCMS LAB 1002 70.4 22.2 31.6 6 45 58 70 80 91 201 69.7 21.0 30.1
82674 Carbofuran * GCMS FLD 301 105.6 50.8 48.1 8 58 83 106 134 189 466 114.4 55.9 48.8
82674 Carbofuran * GCMS LAB 1002 96.7 66.8 69.0 1 32 59 97 126 159 395 95.8 48.4 50.5
82675 Terbufos GCMS FLD 304 89.7 25.0 27.9 30 63 75 90 100 113 194 89.3 22.1 24.7
82675 Terbufos GCMS LAB 1002 86.0 27.2 31.6 8 56 72 86 99 112 244 85.6 24.5 28.6
82676 Propyzamide GCMS FLD 304 83.7 20.9 25.0 0 67 74 84 95 109 177 86.6 20.2 23.3
82676 Propyzamide GCMS LAB 999 84.4 20.5 24.2 6 62 73 84 94 103 374 83.4 20.7 24.8
82677 Disulfoton GCMS FLD 304 86.3 55.2 64.0 0 54 65 86 120 143 290 94.1 40.5 43.1
82677 Disulfoton GCMS LAB 1002 85.4 52.8 61.9 0 30 61 85 114 159 305 91.1 51.4 56.3
82678 Tri-allate GCMS FLD 304 90.0 13.2 14.7 4 76 84 90 97 105 165 90.7 14.5 16.0
82678 Tri-allate GCMS LAB 1002 89.2 15.6 17.5 7 73 81 89 97 103 242 88.3 16.8 19.1
82679 Propanil GCMS FLD 304 94.2 23.7 25.2 37 77 83 94 107 118 204 96.8 20.1 20.8
82679 Propanil GCMS LAB 1002 101.3 26.0 25.6 5 74 87 101 113 122 276 99.3 22.8 22.9
82680 Carbaryl * GCMS FLD 306 98.5 75.4 76.5 13 40 67 99 142 200 456 115.0 72.6 63.1
82680 Carbaryl * GCMS LAB 1000 98.4 101.4 103.1 1 20 44 98 145 185 329 99.6 62.0 62.2
82681 Thiobencarb GCMS FLD 304 98.4 19.4 19.7 54 83 88 98 107 120 187 100.1 18.7 18.7
82681 Thiobencarb GCMS LAB 1002 98.3 18.9 19.2 7 77 88 98 107 114 272 96.9 19.4 20.0
82682 Dacthal GCMS FLD 298 105.1 24.2 23.1 66 87 94 105 118 143 223 109.8 23.4 21.3
82682 Dacthal GCMS LAB 1002 100.7 18.8 18.7 4 82 91 101 110 121 250 100.9 20.9 20.7
82683 Pendimethalin GCMS FLD 304 76.6 29.8 38.9 35 52 62 77 91 103 213 79.5 28.6 36.0
82683 Pendimethalin GCMS LAB 1002 70.2 26.0 37.0 7 47 58 70 84 98 218 72.1 23.2 32.2
82684 Napropamide GCMS FLD 304 100.9 18.6 18.4 47 83 91 101 109 117 218 101.2 17.8 17.6
82684 Napropamide GCMS LAB 1002 97.0 16.8 17.4 9 80 88 97 105 115 260 97.0 18.4 19.0
82685 Propargite GCMS FLD 304 86.6 40.1 46.3 11 59 70 87 110 155 330 97.7 44.2 45.3
82685 Propargite GCMS LAB 1001 73.4 27.9 38.1 5 50 61 73 89 110 315 78.4 30.9 39.4
82686 Azinphos-methyl * GCMS FLD 304 81.5 64.8 79.6 0 31 51 81 116 171 473 96.7 72.8 75.3
82686 Azinphos-methyl * GCMS LAB 998 53.9 62.0 115.1 0 15 30 54 92 130 453 67.6 53.1 78.6
82687 cis-Permethrin GCMS FLD 303 58.3 73.8 126.6 0 38 47 58 121 258 390 96.6 83.5 86.4
82687 cis-Permethrin GCMS LAB 1002 37.5 41.9 111.8 2 10 13 37 55 80 177 39.7 32.3 81.2
04029 Bromacil HPLC FLD 81 77.0 19.2 25.0 11 63 68 77 88 105 173 80.9 22.0 27.2
04029 Bromacil HPLC LAB 694 81.0 21.0 25.9 0 58 71 81 92 105 154 80.9 20.5 25.3
38442 Dicamba HPLC FLD 73 65.8 30.2 45.9 0 8 45 66 75 82 213 60.4 35.0 57.9
38442 Dicamba HPLC LAB 703 73.0 31.0 42.5 0 26 53 73 84 93 128 66.2 25.8 38.9
38478 Linuron HPLC FLD 72 116.0 45.8 39.5 0 84 91 116 137 157 195 116.1 33.4 28.7
38478 Linuron HPLC LAB 573 81.0 24.0 29.6 0 51 68 81 92 105 173 79.1 22.9 28.9
38482 MCPA HPLC FLD 76 60.2 16.2 26.9 19 46 53 60 69 77 95 61.2 12.9 21.1
38482 MCPA HPLC LAB 709 73.0 33.0 45.2 0 24 52 73 85 95 141 66.3 27.2 41.0
38487 MCPB HPLC FLD 21 73.7 20.9 28.4 59 66 67 74 88 90 107 77.6 12.3 15.8
38487 MCPB HPLC LAB 709 42.0 62.0 147.6 0 0 12 42 74 85 128 43.3 32.9 75.9
38501 Methiocarb HPLC FLD 67 45.1 66.5 147.3 0 2 16 45 82 89 150 48.7 36.0 73.8
38501 Methiocarb HPLC LAB 559 77.0 37.0 48.1 0 25 54 77 91 103 178 71.7 30.2 42.0
38538 Propoxur HPLC FLD 79 62.2 26.0 41.8 24 42 50 62 76 96 138 64.9 21.3 32.8
38538 Propoxur HPLC LAB 610 75.0 25.0 33.3 0 47 63 75 88 100 149 74.2 21.7 29.2
38711 Bentazon HPLC FLD 75 72.0 19.5 27.0 16 49 61 72 80 92 119 69.8 19.9 28.5
38711 Bentazon HPLC LAB 700 80.0 26.0 32.5 0 40 65 80 91 101 154 75.2 25.2 33.5
38746 2,4-DB HPLC FLD 79 51.6 39.8 77.1 13 31 37 52 77 90 97 55.4 22.0 39.6
38746 2,4-DB HPLC LAB 700 47.0 60.0 127.7 0 3 18 47 78 91 129 48.2 32.7 67.8
38811 Fluometuron HPLC FLD 79 77.0 20.9 27.2 21 59 68 77 89 101 131 79.0 18.2 23.1
38811 Fluometuron HPLC LAB 621 84.0 27.0 32.1 0 53 71 84 98 111 162 83.2 24.1 29.0
38866 Oxamyl HPLC FLD 70 15.6 33.4 213.9 0 0 7 16 40 68 102 26.3 25.8 97.8
38866 Oxamyl HPLC LAB 684 64.0 44.3 69.1 0 14 35 64 79 91 146 57.8 28.9 50.0
39732 2,4-D HPLC FLD 77 70.0 20.7 29.6 42 53 61 70 81 87 114 70.6 13.9 19.6
39732 2,4-D HPLC LAB 702 75.0 29.0 38.7 0 36 57 75 86 97 143 70.2 25.0 35.6
39742 2,4,5-T HPLC FLD 72 75.5 20.9 27.6 0 53 68 76 88 106 166 78.9 25.8 32.7
39742 2,4,5-T HPLC LAB 662 75.0 23.0 30.7 0 41 62 75 85 98 193 72.9 26.2 35.9
39762 Silvex HPLC FLD 80 76.1 17.9 23.5 44 60 66 76 84 91 116 75.8 13.4 17.7
39762 Silvex HPLC LAB 707 80.0 25.0 31.3 0 46 64 80 89 100 139 75.1 23.5 31.2
49235 Triclopyr HPLC FLD 21 49.3 9.5 19.3 31 36 44 49 53 65 91 51.2 14.6 28.5
49235 Triclopyr HPLC LAB 700 73.0 35.0 47.9 0 28 50 73 85 95 120 66.8 25.7 38.5
49236 Propham HPLC FLD 57 132.4 92.3 69.7 0 55 77 132 169 191 199 122.7 51.5 42.0
49236 Propham HPLC LAB 444 62.0 34.0 54.8 0 30 46 62 80 91 134 61.8 24.4 39.5
49291 Picloram HPLC FLD 58 61.7 31.0 50.3 0 38 48 62 79 89 103 61.9 23.0 37.2
49291 Picloram HPLC LAB 668 63.0 40.5 64.3 0 20 40 63 81 89 167 59.0 27.7 46.9
49292 Oryzalin HPLC FLD 21 77.8 9.0 11.6 33 44 72 78 81 92 111 73.8 20.4 27.7
49292 Oryzalin HPLC LAB 707 70.0 28.0 40.0 0 43 58 70 86 98 202 70.9 22.7 32.0
49293 Norflurazon HPLC FLD 21 83.5 11.0 13.1 60 75 79 84 90 98 110 84.8 11.9 14.0
49293 Norflurazon HPLC LAB 700 83.0 24.0 28.9 0 56 72 83 96 107 159 82.4 21.6 26.2
49294 Neburon HPLC FLD 81 66.7 29.6 44.3 31 47 51 67 81 89 106 67.0 17.4 26.0
49294 Neburon HPLC LAB 719 76.0 27.0 35.5 0 45 62 76 89 102 148 74.9 23.0 30.7
49296 Methomyl HPLC FLD 72 71.3 21.2 29.7 0 56 64 71 85 97 113 72.8 20.3 27.9
49296 Methomyl HPLC LAB 675 80.0 25.0 31.3 0 46 68 80 93 106 164 78.3 26.4 33.6
49297 Fenuron HPLC FLD 76 78.4 31.6 40.3 27 58 68 78 100 140 212 90.6 35.7 39.4
49297 Fenuron HPLC LAB 663 80.0 41.0 51.3 0 37 53 80 94 104 212 75.5 27.9 37.0
49299 DNOC * HPLC FLD 72 63.8 24.3 38.1 0 37 49 64 73 82 128 61.1 20.4 33.5
49299 DNOC * HPLC LAB 632 36.0 27.0 75.0 0 4 21 36 48 61 96 35.1 20.6 58.5
49300 Diuron HPLC FLD 82 55.8 36.7 65.7 2 33 40 56 77 89 102 59.0 21.8 37.0
49300 Diuron HPLC LAB 696 71.0 34.0 47.9 0 36 52 71 86 96 178 68.1 24.3 35.6
49301 Dinoseb HPLC FLD 80 70.3 25.4 36.1 39 50 57 70 83 91 120 70.5 16.9 24.0
49301 Dinoseb HPLC LAB 707 76.0 25.0 32.9 0 36 61 76 86 94 146 70.5 24.2 34.4
49302 Dichlorprop HPLC FLD 80 74.6 19.2 25.7 23 57 64 75 83 93 112 74.3 14.8 20.0
49302 Dichlorprop HPLC LAB 707 78.0 26.0 33.3 0 34 62 78 88 98 138 72.3 25.7 35.5
49303 Dichlobenil * HPLC FLD 21 61.7 12.7 20.6 32 44 56 62 68 70 81 60.4 11.4 18.9
49303 Dichlobenil * HPLC LAB 691 29.0 49.0 169.0 0 0 6 29 55 70 117 32.6 27.6 84.5
49304 Dacthal monoacid HPLC FLD 20 74.2 23.3 31.4 6 28 59 74 82 87 93 66.1 23.1 34.9
49304 Dacthal monoacid HPLC LAB 674 79.0 22.0 27.8 0 46 66 79 88 96 131 74.7 21.6 29.0
49305 Clopyralid HPLC FLD 14 48.8 59.5 121.9 0 0 0 49 60 87 87 39.7 32.8 82.6
49305 Clopyralid HPLC LAB 660 66.0 40.5 61.4 0 2 41 66 81 94 155 59.3 31.1 52.4
49306 Chlorothalonil * HPLC FLD 24 49.9 36.9 74.0 0 7 24 50 61 70 93 43.5 24.7 56.9
49306 Chlorothalonil * HPLC LAB 693 6.0 36.0 600.0 0 0 0 6 36 54 129 18.7 24.2 129.7
49307 Chloramben HPLC FLD 8 62.3 12.8 20.5 55 55 59 62 71 76 76 64.4 7.8 12.2
49307 Chloramben HPLC LAB 496 61.0 24.5 40.2 0 34 50 61 74 84 128 61.0 19.1 31.3
49308 3-Hydroxycarbofuran HPLC FLD 18 79.6 29.3 36.8 14 55 66 80 96 111 115 79.7 24.5 30.7
49308 3-Hydroxycarbofuran HPLC LAB 678 70.0 33.0 47.1 0 26 49 70 82 95 184 65.6 26.7 40.7
49309 Carbofuran HPLC FLD 78 66.0 29.2 44.2 23 34 53 66 82 110 173 69.4 27.8 40.0
49309 Carbofuran HPLC LAB 668 79.0 27.0 34.2 0 50 66 79 93 110 449 80.8 32.0 39.6
49310 Carbaryl HPLC FLD 81 32.5 61.0 187.7 0 6 13 32 74 84 103 41.9 31.4 75.0
49310 Carbaryl HPLC LAB 710 77.0 40.0 51.9 0 23 50 77 90 100 201 69.3 29.0 41.8
49311 Bromoxynil HPLC FLD 72 76.6 19.8 25.9 51 61 67 77 87 92 115 78.0 13.8 17.7
49311 Bromoxynil HPLC LAB 657 81.0 25.0 30.9 0 42 66 81 91 101 145 75.4 24.9 33.0
49312 Aldicarb * HPLC FLD 66 50.5 37.3 73.7 0 9 31 51 69 92 117 51.5 28.5 55.4
49312 Aldicarb * HPLC LAB 702 50.0 35.0 70.0 0 14 32 50 67 84 150 50.3 27.0 53.7
49313 Aldicarb sulfone * HPLC FLD 70 18.2 24.0 131.7 0 0 7 18 31 40 65 20.0 15.7 78.4
49313 Aldicarb sulfone * HPLC LAB 655 50.0 33.0 66.0 0 15 34 50 67 83 150 50.3 26.7 53.1
49314 Aldicarb sulfoxide * HPLC FLD 70 61.0 37.4 61.4 0 2 41 61 79 103 122 58.7 31.8 54.2
49314 Aldicarb sulfoxide * HPLC LAB 660 88.0 41.5 47.2 0 39 70 88 111 134 204 87.7 38.6 44.1
49315 Acifluorfen HPLC FLD 21 81.4 12.2 14.9 35 74 78 81 90 97 103 81.6 13.6 16.7
49315 Acifluorfen HPLC LAB 704 84.0 25.0 29.8 0 49 69 84 94 108 200 80.5 26.3 32.7
-----------------------------------------------------------------------------------------------------------------------------------
Table 4. Uncertainty in selected measures of bias and variability of recovery of pesticides in NAWQA field matrix spikes and NWQL
laboratory control spikes, 1992-96. Pesticides sorted by analytical method and parameter code. GCMS spikes at 0.1 µg/L,
HPLC field matrix spikes at 1.0 µg/L, HPLC laboratory control spikes at 0.5 µg/L. Recoveries greater than 500 percent were
deleted. Recoveries less than 0 percent in field matrix spikes were deleted.
NOTE: Data for field matrix spikes have not been reviewed thoroughly, are provisional, and are subject to revision.
[Parm, NWIS/STORET parameter code; N, number of spikes; pct, percent; IQR, interquartile range; Min, minimum; Max, maximum; SD,
standard deviation; GCMS, gas chromatography/mass spectrometry; HPLC, high-performance liquid chromatography; FLD, field matrix
spikes; LAB, laboratory control spikes; nc, not calculated; *, pesticide identified as poorly performing in method documentation
or NWQL memoranda; iss, insufficient sample size to calculate a 95-percent confidence bound]
--------------------------------------------------------------------------------------------------------------------------------------
95-percent 95-percent
lower upper
confidence confidence
95-percent bound or bound or 95-percent
lower 90-percent 90-percent upper
confidence lower upper confidence
bound for confidence confidence bound for
10th 10th limit for limit for 90th 90th
percentile percentile median Median median percentile percentile
Analytical Spike recovery recovery recovery recovery recovery recovery recovery
Parm Pesticide method type N (pct) (pct) (pct) (pct) (pct) (pct) (pct)
--------------------------------------------------------------------------------------------------------------------------------------
04024 Propachlor GCMS FLD 304 75.5 78.3 93.5 94.9 96.0 116.9 121.0
04024 Propachlor GCMS LAB 999 72.6 74.4 95.7 96.8 98.2 116.7 118.6
04028 Butylate GCMS FLD 308 77.4 79.5 90.9 91.6 92.6 107.0 110.0
04028 Butylate GCMS LAB 1002 78.0 79.4 90.1 90.6 91.0 99.4 100.3
04035 Simazine GCMS FLD 308 65.0 68.0 89.1 90.9 92.7 111.0 116.8
04035 Simazine GCMS LAB 1001 69.6 72.0 92.7 93.7 94.6 108.9 110.0
04037 Prometon GCMS FLD 301 66.3 71.0 91.1 93.2 95.2 114.0 115.9
04037 Prometon GCMS LAB 1002 17.0 21.8 70.5 71.8 73.3 98.8 100.2
04040 Deethylatrazine * GCMS FLD 308 12.1 14.9 29.6 31.5 33.9 55.7 59.8
04040 Deethylatrazine * GCMS LAB 1002 22.6 23.4 37.9 38.7 39.4 55.7 58.5
04041 Cyanazine GCMS FLD 304 64.8 69.8 93.9 96.5 99.0 135.9 142.7
04041 Cyanazine GCMS LAB 1002 60.5 63.9 96.2 97.6 98.9 123.4 125.1
04095 Fonofos GCMS FLD 304 69.1 70.4 85.0 86.8 87.9 107.4 112.3
04095 Fonofos GCMS LAB 999 68.2 70.3 87.6 88.4 89.0 102.8 104.0
34253 alpha-HCH GCMS FLD 304 71.3 73.3 86.7 88.9 90.7 108.7 112.1
34253 alpha-HCH GCMS LAB 1002 66.3 68.3 88.4 89.3 90.1 109.5 111.4
34653 p,p-DDE GCMS FLD 304 48.3 51.9 64.4 66.3 68.6 85.8 87.3
34653 p,p-DDE GCMS LAB 1001 43.9 46.1 59.1 59.7 60.5 75.3 76.5
38933 Chlorpyrifos GCMS FLD 304 64.8 68.7 87.3 88.8 90.9 115.8 119.6
38933 Chlorpyrifos GCMS LAB 1002 64.5 67.1 88.6 89.9 90.7 110.5 112.9
39341 Lindane GCMS FLD 304 71.6 74.3 88.4 89.8 91.5 114.2 124.4
39341 Lindane GCMS LAB 1002 68.3 69.5 87.8 89.0 90.0 109.4 110.7
39381 Dieldrin GCMS FLD 303 69.6 72.7 87.3 89.2 90.9 113.2 115.3
39381 Dieldrin GCMS LAB 1001 62.7 64.1 81.0 82.4 83.2 103.3 105.5
39415 Metolachlor GCMS FLD 306 86.4 88.5 104.7 105.7 106.9 126.5 130.8
39415 Metolachlor GCMS LAB 1002 83.0 85.3 101.9 102.9 104.0 121.8 122.8
39532 Malathion GCMS FLD 308 54.7 59.0 87.3 88.6 89.7 113.0 114.9
39532 Malathion GCMS LAB 1002 59.7 62.2 92.6 93.8 94.5 112.1 113.6
39542 Parathion GCMS FLD 304 63.2 65.3 92.0 94.6 97.2 123.8 134.3
39542 Parathion GCMS LAB 1002 60.0 62.9 91.0 92.2 93.5 117.6 120.5
39572 Diazinon GCMS FLD 308 67.7 69.8 86.6 87.9 89.3 105.5 109.2
39572 Diazinon GCMS LAB 1002 66.4 68.7 85.5 86.4 87.1 105.8 107.7
39632 Atrazine GCMS FLD 305 72.9 75.6 92.9 94.9 96.1 112.6 116.8
39632 Atrazine GCMS LAB 1002 73.7 75.2 93.5 94.1 94.8 109.5 110.7
46342 Alachlor GCMS FLD 308 84.1 87.0 100.0 102.0 103.5 120.8 122.3
46342 Alachlor GCMS LAB 1001 76.9 79.4 98.6 99.5 100.4 113.9 115.4
49260 Acetochlor GCMS FLD 0 nc nc nc nc nc nc nc
49260 Acetochlor GCMS LAB 646 84.5 85.6 96.3 97.1 97.8 111.5 114.2
82630 Metribuzin GCMS FLD 308 40.8 46.8 72.2 74.3 76.3 95.0 99.4
82630 Metribuzin GCMS LAB 1002 52.1 53.6 72.8 73.9 74.7 93.8 95.4
82660 2,6-Diethylaniline GCMS FLD 304 72.5 76.2 88.0 89.1 89.7 100.0 104.7
82660 2,6-Diethylaniline GCMS LAB 1002 75.1 76.5 87.2 87.8 88.4 99.0 99.9
82661 Trifluralin GCMS FLD 304 54.0 56.2 74.5 77.3 78.7 99.1 106.5
82661 Trifluralin GCMS LAB 1002 46.1 47.6 70.5 71.7 72.7 93.5 95.7
82663 Ethalfluralin GCMS FLD 304 56.4 61.3 84.9 87.3 89.3 115.6 128.4
82663 Ethalfluralin GCMS LAB 1002 51.0 54.0 79.7 81.0 82.0 107.1 109.4
82664 Phorate GCMS FLD 304 46.3 51.7 75.1 76.7 79.3 102.7 105.8
82664 Phorate GCMS LAB 1002 48.2 49.9 81.3 82.3 83.5 105.1 109.3
82665 Terbacil * GCMS FLD 302 35.3 38.9 81.5 86.8 89.7 182.8 202.8
82665 Terbacil * GCMS LAB 1000 34.3 37.1 72.9 74.2 75.4 109.1 112.5
82666 Linuron GCMS FLD 303 37.0 47.0 85.3 87.7 91.6 140.3 146.7
82666 Linuron GCMS LAB 1001 51.7 54.9 89.8 91.5 93.4 124.8 130.5
82667 Parathion-methyl GCMS FLD 304 58.2 60.9 83.3 84.7 87.0 120.3 132.5
82667 Parathion-methyl GCMS LAB 1002 47.8 50.0 84.0 85.2 86.6 111.4 114.9
82668 EPTC GCMS FLD 304 71.6 76.3 90.2 91.6 92.7 107.2 112.6
82668 EPTC GCMS LAB 1002 80.2 81.1 91.3 91.8 92.3 103.0 103.6
82669 Pebulate GCMS FLD 303 74.7 77.3 91.6 93.4 94.5 107.3 108.8
82669 Pebulate GCMS LAB 1001 78.1 79.5 90.5 91.0 91.7 102.7 103.6
82670 Tebuthiuron GCMS FLD 302 50.5 57.3 81.5 84.4 87.9 119.6 133.1
82670 Tebuthiuron GCMS LAB 1000 60.8 62.8 97.6 99.7 102.0 139.6 142.3
82671 Molinate GCMS FLD 303 72.0 75.9 93.2 94.6 95.9 113.2 114.2
82671 Molinate GCMS LAB 999 80.9 81.7 93.1 93.7 94.3 103.9 105.0
82672 Ethoprophos GCMS FLD 304 71.5 74.2 91.7 93.7 94.9 114.2 118.2
82672 Ethoprophos GCMS LAB 1002 69.6 71.8 91.4 92.3 93.2 108.8 110.3
82673 Benfluralin GCMS FLD 304 51.2 53.0 75.1 77.8 80.0 103.5 108.7
82673 Benfluralin GCMS LAB 1002 43.8 45.1 69.4 70.4 71.3 91.3 93.2
82674 Carbofuran * GCMS FLD 301 53.2 57.6 101.8 105.6 109.8 188.8 200.0
82674 Carbofuran * GCMS LAB 1002 27.5 31.6 93.6 96.7 99.6 158.7 163.1
82675 Terbufos GCMS FLD 304 60.9 63.0 86.3 89.7 91.5 113.2 118.2
82675 Terbufos GCMS LAB 1002 54.6 56.5 85.0 86.0 87.2 112.0 114.6
82676 Propyzamide GCMS FLD 304 65.2 67.2 82.1 83.7 85.7 108.9 112.9
82676 Propyzamide GCMS LAB 999 60.6 62.2 83.5 84.4 85.3 102.8 104.3
82677 Disulfoton GCMS FLD 304 48.7 53.7 79.9 86.3 92.0 142.8 149.4
82677 Disulfoton GCMS LAB 1002 22.7 30.0 82.7 85.4 88.2 158.7 165.9
82678 Tri-allate GCMS FLD 304 74.0 76.1 88.8 90.0 90.9 104.7 110.5
82678 Tri-allate GCMS LAB 1002 70.6 72.9 88.5 89.2 89.8 103.3 104.8
82679 Propanil GCMS FLD 304 74.5 76.6 92.2 94.2 96.5 117.6 122.8
82679 Propanil GCMS LAB 1002 71.6 73.7 100.0 101.3 102.4 122.4 123.7
82680 Carbaryl * GCMS FLD 306 33.9 40.2 94.4 98.5 103.7 199.9 225.5
82680 Carbaryl * GCMS LAB 1000 18.6 19.9 93.0 98.4 101.6 185.1 190.4
82681 Thiobencarb GCMS FLD 304 78.6 82.5 96.5 98.4 100.0 120.0 124.2
82681 Thiobencarb GCMS LAB 1002 74.6 77.0 97.5 98.3 99.3 114.0 115.2
82682 Dacthal GCMS FLD 298 84.1 87.5 104.1 105.1 107.0 142.6 145.4
82682 Dacthal GCMS LAB 1002 79.5 81.7 99.8 100.7 101.4 121.1 123.0
82683 Pendimethalin GCMS FLD 304 47.1 51.7 73.3 76.6 79.3 102.8 111.2
82683 Pendimethalin GCMS LAB 1002 44.5 47.2 69.0 70.2 71.7 97.8 99.9
82684 Napropamide GCMS FLD 304 80.4 83.1 99.4 100.9 103.0 116.8 120.8
82684 Napropamide GCMS LAB 1002 79.6 80.3 96.2 97.0 97.5 114.6 116.7
82685 Propargite GCMS FLD 304 53.0 59.2 82.4 86.6 90.0 155.3 182.6
82685 Propargite GCMS LAB 1001 47.8 49.7 72.7 73.4 74.5 110.4 116.3
82686 Azinphos-methyl * GCMS FLD 304 25.6 30.8 73.9 81.5 89.6 171.0 214.0
82686 Azinphos-methyl * GCMS LAB 998 12.9 15.3 51.2 53.9 57.0 129.8 137.0
82687 cis-Permethrin GCMS FLD 303 34.3 38.0 56.7 58.3 62.2 257.8 273.3
82687 cis-Permethrin GCMS LAB 1002 9.8 10.1 32.0 37.5 40.4 80.2 85.0
04029 Bromacil HPLC FLD 81 52.1 63.0 74.3 77.0 81.4 105.2 127.1
04029 Bromacil HPLC LAB 694 55.0 58.0 79.0 81.0 82.0 105.0 108.0
38442 Dicamba HPLC FLD 73 .0 7.7 56.8 65.8 71.2 81.7 90.2
38442 Dicamba HPLC LAB 703 17.0 26.0 71.0 73.0 74.0 93.0 95.0
38478 Linuron HPLC FLD 72 69.0 83.7 101.3 116.0 131.3 156.9 190.3
38478 Linuron HPLC LAB 573 45.0 51.0 79.0 81.0 83.0 105.0 106.0
38482 MCPA HPLC FLD 76 40.8 46.5 57.6 60.2 64.8 77.4 81.6
38482 MCPA HPLC LAB 709 18.0 24.0 70.0 73.0 75.0 95.0 97.0
38487 MCPB HPLC FLD 21 iss 65.7 68.1 73.7 85.7 90.4 iss
38487 MCPB HPLC LAB 709 .0 .0 34.0 42.0 49.0 85.0 87.0
38501 Methiocarb HPLC FLD 67 .0 1.8 30.9 45.1 69.1 89.3 92.0
38501 Methiocarb HPLC LAB 559 17.0 25.0 75.0 77.0 80.0 103.0 106.0
38538 Propoxur HPLC FLD 79 28.5 41.8 58.0 62.2 65.4 95.6 104.5
38538 Propoxur HPLC LAB 610 44.0 46.5 73.0 75.0 76.0 100.0 101.0
38711 Bentazon HPLC FLD 75 18.9 48.6 68.0 72.0 74.8 92.0 103.4
38711 Bentazon HPLC LAB 700 34.0 40.0 79.0 80.0 82.0 101.0 102.0
38746 2,4-DB HPLC FLD 79 26.2 31.3 43.3 51.6 57.0 89.6 93.1
38746 2,4-DB HPLC LAB 700 2.0 3.0 42.0 47.0 51.0 91.0 93.0
38811 Fluometuron HPLC FLD 79 52.8 59.0 74.4 77.0 80.5 100.7 119.7
38811 Fluometuron HPLC LAB 621 48.0 53.0 82.0 84.0 85.0 111.0 114.0
38866 Oxamyl HPLC FLD 70 .0 .0 11.1 15.6 26.6 68.5 75.8
38866 Oxamyl HPLC LAB 684 11.0 14.0 62.0 64.0 66.0 91.0 94.0
39732 2,4-D HPLC FLD 77 47.0 53.2 66.0 70.0 72.6 86.8 97.5
39732 2,4-D HPLC LAB 702 32.0 36.0 74.0 75.0 77.0 97.0 99.0
39742 2,4,5-T HPLC FLD 72 37.4 53.5 71.3 75.5 78.8 105.6 142.0
39742 2,4,5-T HPLC LAB 662 37.0 41.0 74.0 75.0 76.0 98.0 103.0
39762 Silvex HPLC FLD 80 55.9 59.5 72.2 76.1 78.5 91.2 101.2
39762 Silvex HPLC LAB 707 42.0 46.0 78.0 80.0 81.0 100.0 101.0
49235 Triclopyr HPLC FLD 21 iss 36.4 43.9 49.3 52.9 65.3 iss
49235 Triclopyr HPLC LAB 700 26.0 28.0 72.0 73.0 75.0 95.0 97.0
49236 Propham HPLC FLD 57 15.6 55.3 96.5 132.4 147.3 190.9 197.7
49236 Propham HPLC LAB 444 25.0 30.0 60.0 62.0 65.0 91.0 95.0
49291 Picloram HPLC FLD 58 .0 38.4 56.6 61.7 70.9 88.8 100.7
49291 Picloram HPLC LAB 668 14.0 20.0 61.0 63.0 64.0 89.0 92.0
49292 Oryzalin HPLC FLD 21 iss 43.9 72.9 77.8 80.8 92.2 iss
49292 Oryzalin HPLC LAB 707 40.0 43.0 69.0 70.0 72.0 98.0 100.0
49293 Norflurazon HPLC FLD 21 iss 74.8 79.4 83.5 90.2 97.7 iss
49293 Norflurazon HPLC LAB 700 51.0 56.0 82.0 83.0 85.0 107.0 109.0
49294 Neburon HPLC FLD 81 38.6 46.7 60.9 66.7 71.0 89.2 98.2
49294 Neburon HPLC LAB 719 41.0 45.0 75.0 76.0 78.0 102.0 105.0
49296 Methomyl HPLC FLD 72 18.0 55.9 68.3 71.3 78.1 97.0 104.0
49296 Methomyl HPLC LAB 675 41.0 46.0 79.0 80.0 82.0 106.0 109.0
49297 Fenuron HPLC FLD 76 54.8 57.7 75.4 78.4 84.2 140.0 155.5
49297 Fenuron HPLC LAB 663 35.0 37.0 78.0 80.0 83.0 104.0 107.0
49299 DNOC * HPLC FLD 72 18.3 37.0 58.6 63.8 67.1 81.7 97.9
49299 DNOC * HPLC LAB 632 .0 4.0 34.0 36.0 38.0 61.0 64.0
49300 Diuron HPLC FLD 82 30.7 33.4 50.7 55.8 61.5 89.0 94.3
49300 Diuron HPLC LAB 696 33.0 36.0 68.0 71.0 74.0 96.0 97.0
49301 Dinoseb HPLC FLD 80 45.0 50.2 65.4 70.3 75.5 91.4 96.8
49301 Dinoseb HPLC LAB 707 29.0 36.0 75.0 76.0 77.0 94.0 95.0
49302 Dichlorprop HPLC FLD 80 53.4 57.1 70.7 74.6 77.0 92.8 99.2
49302 Dichlorprop HPLC LAB 707 28.0 34.0 77.0 78.0 80.0 98.0 101.0
49303 Dichlobenil * HPLC FLD 21 iss 44.2 58.3 61.7 67.5 70.1 iss
49303 Dichlobenil * HPLC LAB 691 .0 .0 27.0 29.0 33.0 70.0 73.0
49304 Dacthal monoacid HPLC FLD 20 iss 28.4 62.0 74.2 80.3 87.0 iss
49304 Dacthal monoacid HPLC LAB 674 44.0 46.0 78.0 79.0 80.0 96.0 97.0
49305 Clopyralid HPLC FLD 14 iss .0 .0 48.8 59.5 87.0 iss
49305 Clopyralid HPLC LAB 660 .0 2.0 64.0 66.0 68.0 94.0 96.0
49306 Chlorothalonil * HPLC FLD 24 iss 6.8 33.7 49.9 59.0 70.0 iss
49306 Chlorothalonil * HPLC LAB 693 .0 .0 5.0 6.0 7.0 54.0 60.0
49307 Chloramben HPLC FLD 8 iss 54.7 57.2 62.3 74.6 75.8 iss
49307 Chloramben HPLC LAB 496 32.0 34.0 60.0 61.0 63.0 84.0 86.0
49308 3-Hydroxycarbofuran HPLC FLD 18 iss 54.8 71.9 79.6 89.8 110.9 iss
49308 3-Hydroxycarbofuran HPLC LAB 678 24.0 26.0 68.0 70.0 71.0 95.0 97.0
49309 Carbofuran HPLC FLD 78 25.7 34.4 62.0 66.0 71.5 110.0 115.1
49309 Carbofuran HPLC LAB 668 46.0 50.0 78.0 79.0 81.0 110.0 113.0
49310 Carbaryl HPLC FLD 81 .0 6.5 21.8 32.5 54.9 84.3 90.3
49310 Carbaryl HPLC LAB 710 20.0 23.0 74.0 77.0 78.0 100.0 102.0
49311 Bromoxynil HPLC FLD 72 57.5 61.3 74.6 76.6 80.8 91.9 109.4
49311 Bromoxynil HPLC LAB 657 35.0 42.0 80.0 81.0 82.0 101.0 102.0
49312 Aldicarb * HPLC FLD 66 .0 9.1 42.9 50.5 60.7 92.0 99.0
49312 Aldicarb * HPLC LAB 702 11.0 14.0 48.0 50.0 52.0 84.0 89.0
49313 Aldicarb sulfone * HPLC FLD 70 .0 .0 14.8 18.2 22.7 40.5 49.1
49313 Aldicarb sulfone * HPLC LAB 655 .0 15.0 48.0 50.0 53.0 83.0 88.0
49314 Aldicarb sulfoxide * HPLC FLD 70 .0 2.1 56.0 61.0 65.2 103.1 116.0
49314 Aldicarb sulfoxide * HPLC LAB 660 24.0 39.0 86.0 88.0 91.0 133.5 138.0
49315 Acifluorfen HPLC FLD 21 iss 73.8 78.7 81.4 87.0 97.0 iss
49315 Acifluorfen HPLC LAB 704 44.0 49.0 83.0 84.0 85.0 108.0 110.0
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