Application of a combined sensitivity analysis approach on a pesticide environmental risk indicator

• Authors: Yu Zhan;Minghua Zhang
• Venue: Environmental Modelling & Software
• Year: 2013

Sensitivity analysis aims to characterize factors (i.e., model inputs) accounting for the amount of uncertainty in model output. Input factors are usually assumed to be independent, which may lead to incorrect conclusions. In this study, a combined sensitivity analysis approach, composed of the Sobol' and Importance Measurement (IM) methods, is applied on a pesticide environmental risk indicator (called PURE), where main, interaction, and correlation effects (i.e., the effects of factor correlations on sensitivity indices) are all addressed. PURE calculates pesticide risk scores for air, soil, groundwater, and surface water based on pesticide properties and surrounding environmental conditions. The Sobol' method calculates the first-order sensitivity index (S"i) and the total-effect sensitivity index (S"T"i) in noncorrelated-factor setting to address the main and interaction effects; while the IM method calculates S"i in both noncorrelated-factor and correlated-factor settings to show the correlation effects. In the tested case, the S"i estimations in noncorrelated-factor setting by the Sobol' and IM methods are very similar, which not only cross-validates the main effect estimations by the two different methods, but also provides the common ground for combining the two methods to address both interaction and correlation effects. In addition, the S"i estimations in correlated-factor setting are relatively different from the ones in noncorrelated-factor setting, which demonstrates that it is cautious to assume all factors are independent in sensitivity analysis. Take the soil risk evaluation as an example, the positive correlation between the chronic no-observed-effect concentration and acute 50%-lethal concentration to earthworms largely increases the S"i of the latter factor. The results of S"i estimations show that the risk scores for air, soil, groundwater, and surface water are most sensitive to the application rate of pesticide product, the application rate of pesticide active ingredient, the organic carbon sorption constant, and the monthly maximum daily water input, respectively. In summary, while this study enhances the understanding of PURE, it also provides an option for investigating both interaction and correlation effects, and hence promotes sensitivity analysis with factor-correlation structures in environmental modeling.