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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_Ti) in noncorrelated-factor setting to address the main and interaction effects; while the IM method calculates S_i in both …