Correspondence/Rebuttal pubs.acs.org/est
Comment on “Fungicide Field Concentrations Exceed FOCUS Surface Water Predictions: Urgent Need of Model Improvement”
K
näbel et al.1 try to disprove the statement made by FOCUS2 that “The highest PECsw estimates from the ten scenarios are likely to represent at least a 90th percentile worstcase for surface water exposures resulting from agricultural pesticide use within the European Union”. First, even if we accept the validity of this statement, it is irrelevant in practice for pesticide regulation at the EU level, because the aim of FOCUS step3 at the EU level is only to demonstrate at least “one safe use”. However, the main purpose of this comment is to challenge the suitability of the methodology employed by Knäbel et al. to evaluate the protectiveness of the FOCUS surface water modeling approach for the EU. First, there has apparently been a consistent and systematic omission of nondetects. Table S1 in the Supporting Information constitutes the database of measured values used for the comparison with FOCUS simulations. Examining a number of papers selected randomly from the database and checking the data reported in these studies revealed that all combinations of substance and sampling site where the substance of concern was analyzed but not detected have been systematically and consistently excluded. Here are four examples: (a) Battaglin et al.3 detected nine fungicides at between 1 and 17 sites of the 29 that were monitored. Only the maximum and mean concentrations for each compound across all 29 sites are reported in the paper, and presumably therefore, the maximum values have been extracted to the database. Only the site/compound combinations with detections were considered. (b) Berenzen et al.4 monitored five fungicides at 18 stream sites in Germany: of the five compounds, fenpropimorph was never detected at any of the sites, while the other four were detected at between 4 and 12 sites of the 18. Only the pesticide/site combinations with detects are included in the database. (c) Rasmussen et al.5 analyzed for six fungicides at 14 stream sites but only the five detected compounds are included in the database (again fenpropimorph was not detected), presumably as maximum values, since only maximum and minimum values were reported for all sites. (d) Schäfer et al.6 monitored two fungicides in 16 stream sites in France and three (different) fungicides at 13 stream sites in Finland. In Finland, only one of the three fungicides was detected. However, these data are excluded, and only the data from France are included in the database. The data reported in the paper are maximum concentrations. Thus, the authors’ statement “From the selected field studies, all of the fungicide concentrations measured during independent events in surface waters and sediments were extracted instead of only using the maximum measured concentrations, which might rather result in a data set biased toward worst-case conditions” is obviously not true and even seriously misleading: © 2014 American Chemical Society
In fact, not all measured concentrations were extracted, and the database is therefore biased. One may argue that nondetects probably mean that the event was missed by the sampling procedure. But if in one sample several pesticides were detected and some others not, this argument is easily invalidated. To add to the confusion, in turns out that the zeros entered for “number of detections in sediment” in Table S1 represent studies where such measurements were not actually made (only water samples were analyzed). So, they are “nonmeasured” rather than “nondetects”. Entering zeros in the table in this way gives the false impression that nondetects (analyzed but not detected) are included in the database, when actually they are not. Since “not measured” is not the same as “not detected”, there should have been two separate columns in Table S1 to make things clear: “number of measurements” and “number of detections”. Excluding site/pesticide combinations where the pesticide was analyzed but not detected introduces bias. The FOCUS modeling runs should also have been performed for these site/ pesticide combinations and included in the comparison. Second, there is a mismatch of the definitions of the 90th percentile. Knäbel et al. took a 90th percentile of the data points for each substance/site combination. However, in many cases, only 1−3 values are reported for a given substance/site combination (partly a consequence of omitting the nondetects at a given site, partly because only maximum values were reported in some studies). First, the 90th percentile calculated from a sample with less than ca. 20 values is very uncertain. Moreover, for n ≤ 10, the 90th percentile will be identical to the maximum for most plotting positions. Apart from being badly estimated (for most site/substance combinations it will be identical to the reported maximum), this 90th percentile refers to time only, whereas the 90th percentile in the statement of FOCUS refers to time and space. A more correct method to establish a 90th percentile of measured concentrations and comparison with FOCUS would have been as follows: (1) For each combination of sampling site and substance, add all values to the database. Nondetects (a substance is analyzed at a given site, but not detected) must be included. (2) For a given substance, take the 90th percentile of these concentration data points over all sampling sites and sampling events, i.e. calculate the 90th percentile in space and time. (3) For a given substance, run FOCUS step3 for the crops on which this substance is used and for all scenarios in which these crops occur. Published: April 24, 2014 5345
dx.doi.org/10.1021/es500848x | Environ. Sci. Technol. 2014, 48, 5345−5346
Environmental Science & Technology
Correspondence/Rebuttal
(4) To test the FOCUS statement, compare the 90th percentile from (2) with the highest PECsw,max of FOCUS for a given substance/crop combination. Another possibility would be to include only the maximum concentration of each combination of sampling site and substance in step 1 (again including nondetects), because the maximum in time is the most relevant quantity for a comparison with PECmax. However, the maximum in time and the 90th percentile in space will not yield an overall 90th percentile. There is a further, and probably more promising, option for comparing measured and FOCUS-predicted concentrations: Given the large inherent uncertainty in assigning a FOCUS scenario to a study site (cf. Table S4), one should abandon the idea of plotting modeled vs measured data points on an X−Y plot. Instead one should rather compare distributions of measured and modeled concentrations, for example, with a Mann−Whitney U test, to see whether their means or Xth percentiles are different or not.
Stefan Reichenberger*
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Dr. Stefan Reichenberger Senior Research Expert Footways S.A.S. 10 avenue Buffon 45071 OrléansCedex 2, France
AUTHOR INFORMATION
Corresponding Author
*Phone: +33 (0)238 63 63 11; fax: +33 (0)238 63 64 60; email:
[email protected]. Notes
The authors declare no competing financial interest.
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REFERENCES
(1) Knäbel, A.; Meyer, K.; Rapp, J.; Schulz, R. Fungicide field concentrations exceed FOCUS surface water predictions: Urgent need of model improvement. Environ. Sci. Technol. 2014, 48, 455−463. (2) FOCUS. FOCUS surface water scenarios in the EU evaluation process under 91/414/EEC. Report of the FOCUS Working Group on Surface Water Scenarios; EC Document Reference SANCO/4802/ 2001-rev.2, 2001. (3) Battaglin, W. A.; Sandstrom, M. W.; Kuivila, K. M.; Kolpin, D. W.; Meyer, M. T. Occurrence of azoxystrobin, propiconazole, and selected other fungicides in US streams, 2005−2006. Water, Air, Soil Pollut. 2011, 218, 307−322. (4) Berenzen, N.; Lentzen-Godding, A.; Probst, M.; Schulz, H.; Schulz, R.; Liess, M. A comparison of predicted and measured levels of runoff-related pesticide concentrations in small lowland streams on a landscape level. Chemosphere 2005, 58, 683−691. (5) Rasmussen, J. J.; Baattrup-Pedersen, A.; Wiberg-Larsen, P.; McKnight, U. S.; Kronvang, B. Buffer strip width and agricultural pesticide contamination in Danish lowland streams: Implications for stream and riparian management. Ecol. Eng. 2011, 37, 1990−1997. (6) Schäfer, R. B.; Caquet, T.; Siimes, K.; Mueller, R.; Lagadic, L.; Liess, M. Effects of pesticides on community structure and ecosystem fuctions in agricultural streams of three biogeographical regions in Europe. Sci. Total Environ. 2007, 382, 272−285.
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