Assessing the Integrated Uncertainty of Particulate Matter Emission Control Health Benefits and Cost. S. Yeh and M. J. Small, Carnegie Mellon University
The new PM2.5 standard will require further reductions in PM concentrations and additional expenditures for proposed emission controls. The potential impacts of emission control program are enormous, and the benefits of such action, health effects, are highly uncertain. The paper develops and applies a methodology for quantifying the health benefits and cost of potential reductions in ambient PM and most importantly, the contributions of uncertainties to the integrated assessment. Uncertainties in each component are characterized by probability distribution and incorporated into the model. Quantitative tool of formal uncertainty analysis are necessary to fully characterize the consequence (and the associate likelihood) of each possible actions. Decision analysis provides effective methods for organizing a complex problem into a structure that can be analyzed. A decision-analysis approach can help in identifying important sources of uncertainty and representing that uncertainty in a systematic and useful way. We propose a decision making framework that carry us through calculating several decisions that the policy makers propose to reduce the air quality and the consequences of those decisions. Formal uncertainty analysis and sensitivity analysis were performed to identify the most important sources of uncertainty. Parametric analysis plots were presented for decision makers when slope and threshold values were treated as value judgments. Los Angeles County, California is chosen as our case study city.
We acknowledge the support from the Carnegie Mellon University NSF Center for Integrated Study of the Human Dimensions of Global Change, grant NSF, SBR-9521914.
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