| In this paper, we develop an integrated cost-benefit analysis framework for ozone and fine particulate control, accounting for variability and uncertainty. The framework includes air quality simulation, sensitivity analysis, stochastic multi-objective air quality management, and stochastic cost-benefit analysis. This paper has two major contributions. The first is the development of stochastic source-receptor (S-R) coefficient matrices for ozone and fine particulate matter using an advanced air quality simulation model (URM-1ATM) and an efficient sensitivity algorithm (DDM-3D). The second is a demonstration of this framework for alternative ozone and PM2.5 reduction policies. Alternative objectives of the stochastic air quality management model include... |
