Sensitivity-based research prioritization through stochastic characterization modeling

Purpose: Product developers using life cycle toxicity characterization models to understand the potential impacts of chemical emissions face serious challenges related to large data demands and high input data uncertainty. This motivates greater focus on model sensitivity toward input parameter variability to guide research efforts in data refinement and design of experiments for existing and emerging chemicals alike. This study presents a sensitivity-based approach for estimating toxicity characterization factors given high input data uncertainty and using the results to prioritize data collection according to parameter influence on characterization factors (CFs). Proof of concept is illustrated with the UNEP-SETAC scientific consensus model USEtox. Methods: Using Monte Carlo analysis, we demonstrate a sensitivity-based approach to prioritize data collection with an illustrative example of aquatic ecotoxicity CFs for the vitamin B derivative niacinamide, which is an antioxidant used in personal care products. We calculate CFs via 10,000 iterations assuming plus-or-minus one order of magnitude variability in fate and exposure-relevant data inputs, while uncertainty in effect factor data is modeled as a central t distribution. Spearman’s rank correlation indices are used for all variable inputs to identify parameters with the largest influence on CFs. Results and discussion: For emissions to freshwater, the niacinamide CF is near log-normally distributed with a geometric mean of 0.02 and geometric standard deviation of 8.5 PAF m³ day/kg. Results of Spearman’s rank correlation show that degradation rates in air, water, and soil are the most influential parameters in calculating CFs, thus benefiting the most from future data refinement and experimental research. Kow, sediment degradation rate, and vapor pressure were the least influential parameters on CF results. These results may be very different for other, e.g., more lipophilic chemicals, where Kow is known to drive many fate and exposure aspects in multimedia modeling. Furthermore, non-linearity between input parameters and CF results prevents transferring sensitivity conclusions from one chemical to another. Conclusions: A sensitivity-based approach for data refinement and research prioritization can provide guidance to database managers, life cycle assessment practitioners, and experimentalists to concentrate efforts on the few parameters that are most influential on toxicity characterization model results. Researchers can conserve resources and address parameter uncertainty by applying this approach when developing new or refining existing CFs for the inventory items that contribute most to toxicity impacts.