Correspondence/Rebuttal pubs.acs.org/est
Comment on “Aquatic Exposure Predictions of Insecticide Field Concentrations Using a Multimedia Mass-Balance Model”
K
näbel et al.1 adapted the parameters of the steady-state multimedia model (SSMM) Small Region Model2 (SRM) to approximate the conditions of the FOCUS surface water scenarios.3 The authors compared pesticide concentrations in surface water (PECsw) calculated with the SRM with maximum PECsw from FOCUS Step 3 calculations and monitoring data from literature for 21 insecticides. In addition to simulations based on FOCUS scenarios, the authors also calculated socalled realistic scenarios where parameters were chosen according to the conditions in the monitoring studies. Knäbel et al. concluded that the SRM approach has a higher level of protectiveness and predictive capability than the more complex FOCUS Step3 approach. We have several comments: (1) The materials and methods section lacks documentation. First, the authors did not document the compound properties used in their SRM Standard simulations. Second, Table S2 lists “Bulk Environmental Properties” and “Environmental Subcompartment Properties” of the SRM Standard simulations, but does not give the equally important “Intermedia Mass Transfer Coefficients” which govern the exchange velocities between compartments. These transfer coefficients would need to be adapted in order to represent FOCUS surface water scenarios. Third, the “SRM Realistic” simulations, which are the basis for the authors’ main conclusions, lack any form of documentation. Hence, it was impossible for us to reproduce the simulations performed by Knäbel et al. (2) The authors used the Ganzelmeier drift values to calculate the fraction of substance mass which is emitted to air in the SRM. However, the Ganzelmeier drift curves3,4 calculate deposition on a soil or water surface, not emissions to air. Furthermore, the drift percentages calculated by the Ganzelmeier drift function do not denote mass fractions of the application rate, but merely relative areic masses. Consequently, the emissions to air calculated by the authors are incorrect. (3) Several of the PECsw reported for the adapted SRM scenarios reach or exceed magnitudes of 1 g/L. We were unable to replicate the magnitude of the results described by Knäbel et al. using randomly selected active substances and realistic use patterns. Considering extreme worst case compound attributes (Log Kow = Log Kaw = −10, DT50 = 1000 days for all compartments) and selecting worst case environmental parameters from those given by the authors, one would still need an unrealistically large application rate of 3000 kg ha−1 y−1 of active substance to achieve PECsw in the order of g/L using the adapted SRM. With a more realistic application rate of 1 kg ha−1 y−1, one would obtain PECsw of about 400 μg/L. We therefore suggest that the authors reexamine the assumptions and inputs of their model setup. An unmodified SRM with the same substance parameters and application rate as above yields a significantly smaller PECsw of about 3 μg/L, which © XXXX American Chemical Society
(4)
(5)
(6)
(7)
A
agrees well with the understanding that SSMMs calculate long-term mean concentrations. We conclude from points 1 to 3 that the PECsw reported by Knäbel et al. contain serious flaws. The SRM-calculated PECsw show a very poor fit to the measured concentrations and deviate up to 8 orders of magnitude from the monitoring data (cf. Figure 1). Knäbel et al. state that their modified SRM has a greater protectiveness than the FOCUS Step3 approach, because their reported PECsw are usually several orders of magnitude higher than equivalent FOCUS-calculated PECsw. In this sense, the SRM is indeed more “protective” than FOCUS. But we question the usefulness of a model which predicts concentrations that are several orders of magnitude off the measured ones. A visual comparison of the reported PECsw with measured data (Figure 1) yield point clouds without any discernible correlation for both SRM and FOCUS. In addition, accuracy and precision appear worse for the SRM than for FOCUS. Knäbel et al. concluded from their analysis that the SRM yields better results than the FOCUS models, but their analysis is flawed because (i) PECsw and PECsed were plotted in the same graph; (ii) the regressions were actually not linear as stated, but log−linear; consequently, the regression lines correspond to power-law functions for nonlogarithmized concentrations; (iii) it was already explained in a previous comment5 why correlation between FOCUS PECsw and measured concentrations can be expected to be low; (iv) correlation alone does not say much about predictive power, because Pearson r2 only evaluates linear relationships, not predictive accuracy;6 (v) The authors’ conclusions are based solely on the p-values of the log−linear regressions. This seems to be a misinterpretation of statistics because the goodness of fit is mainly described by the r2 values which are reported as very small (r2 ≤ 0.31). The authors use elasticity (mathematically defined as d log f(x)/d log x) to evaluate the predictive capability of both modeling approaches. However, the usefulness of elasticity as a goodness-of-fit criterion is not clear to us. From points 4−6 we conclude that the adapted SRM of Knäbel et al. does not show a higher predictive capability compared with FOCUS, and that protectiveness without predictive capability is not sufficient. Knäbel et al. state that “The simplicity and transparency of both input parameters and process descriptions of the SRM can result in a higher level of acceptance than for the complex FOCUS approach.” However, while transparency is essential (and given for FOCUS), simplicity
DOI: 10.1021/acs.est.6b04399 Environ. Sci. Technol. XXXX, XXX, XXX−XXX
Environmental Science & Technology
Correspondence/Rebuttal
does not guarantee valid results. Furthermore, acceptance by the general public should not be a decisive criterion in science. (8) We think that the proposed model is not suited for the intended task because of broad oversimplifications. For instance, the assumption of a steady-state mass balance of pesticides at the field scale, as made by the adapted SRM, can be considered unrealistic and would require in-depth justification. The SRM was originally developed to model substances on a spatial scale of hundreds of square kilometers, in contrast to the scale of 0.01 km2 modeled by Knäbel et al. In addition, we question in general the potential of SSMMs, which only calculate temporal and spatial mean values, to accurately model the fate of substances whose environmental concentrations show a very high temporal and spatial variability, as is the case for pesticides. Moreover, peak and time course of exposure are crucial information in ecological risk assessment for pesticides and should not be neglected. To summarize points 7 and 8, simplicity is not an end in itself. Models should not be simple for the sake of it, but parsimonious7 (as simple as possible, but as complex as needed for the purpose at hand).
Nils Kehrein Stefan Reichenberger*
■
Dr. Knoell Consult GmbH, Dynamostr. 19, 68165 Mannheim, Germany
AUTHOR INFORMATION
Corresponding Author
*Phone: +49 (0)621 71 88 58 189; fax: +49 (0)621 71 88 58 100; e-mail:
[email protected]. Notes
The authors declare no competing financial interest.
■
REFERENCES
(1) Knäbel, A.; Scheringer, M.; Stehle, S.; Schulz, R. Aquatic Exposure Predictions of Insecticide Field Concentrations Using a Multimedia Mass-Balance Model. Environ. Sci. Technol. 2016, 50, 3721−3728. (2) Small Models Level-III Version 2.0; Fugacity-based chemical models; developed at the Swiss Federal Institute of Technology; Zurich and Stockholm University, Sweden, coded by Matthew MacLeod, 19 June 2011. Last modified by Martin Scheringer on December 4, 2011. (3) 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. (4) Rautmann, D.; Streloke, M.; Winkler, R. New basic drift values in the authorization procedure for plant protection products. In Workshop on Risk Assessment and Risk Mitigation Measures in the Context of the Authorization of Plant Protection Products (WORMM); Forster, R.; Streloke, M., Eds.; Mitt. Biol. Bundesanst. LandForstwirtsch.: Berlin-Dahlem, 2001, 381, 133−142. (5) Reichenberger, S. Comment on “Regulatory FOCUS Surface Water Models Fail to Predict Insecticide Concentrations in the Field. Environ. Sci. Technol. 2012, 46, 8397−8404; Environ. Sci. Technol. 2013, 47, 3015−3016. (6) Legates, D. R.; McCabe, G. J., jr. Evaluating the use of “goodnessof-fit” measures in hydrologic and hydroclimatic model validation. Water Resour. Res. 1999, 35, 233−241. (7) Jarvis, N.; Larsbo, M. MACRO (V5.2): Model use, calibration and validation. Transactions of the ASABE 2012, 55, 1413−1423.
B
DOI: 10.1021/acs.est.6b04399 Environ. Sci. Technol. XXXX, XXX, XXX−XXX
