Inverse modelling for estimating sorption and degradation parameters for pesticides.

The leaching model PESTRAS was used to estimate sorption and degradation values for bentazone from three lysimeter datasets using the inverse modelling package PEST. Investigations were undertaken to assess the influence on calibration results of (1) values attributed to uncertain parameters not included in the calibration, and (2) starting values supplied to the inverse modelling package. Automatic calibrations with different realistic values for the Freundlich exponent n(f) yielded different combinations of K(om) and DT50. Similarly, the supply of different starting values for K(om) and DT50 revealed that different combinations of these two parameters equally calibrated PESTRAS for two of the three lysimeters. Examination of the error surface, ie the forward running of the model for different combinations of K(om) and DT50 values, and the calculation of the goodness-of-fit to the experimental data, was found useful for identifying those instances where non-uniqueness in the calibration is likely to occur. Although the derivation of sorption and degradation values through inverse modelling is expected to offer significant benefits over laboratory determinations, care should be exercised when examining values derived through this approach. Research is needed to identify data requirements for robust estimation of sorption and degradation parameters through calibration of pesticide fate models against leaching data.