Application of Different Concentration-Response Functions to Estimate the Societal Benefits of Reducing PM2.5 and NOx Emissions

摘要

Objective: We assess the societal benefits of reducing air pollutant emissions that contribute to ambient fine particulate matter (PM2.5), ozone (03), and nitrogen dioxide (N02) exposure and public health impacts. Recent evidence suggests that nonlinear, multi-pollutant concentration-response (C-R) models are more appropriate than traditional, linear forms used in epidemiology. We examine the implications of alternate C-R models in an emissions reduction framework. Methods: We integrate C-R models for non-accidental mortality due to PM2.5, 03, and N02 into the Community Multiscale Air Quality Model (CMAQ). This sophisticated atmospheric model and its adjoint tool allow us to trace public health impacts back to sources of pollutant emissions. We compare the monetized public health benefits of reducing emissions from sources across Central Canada at a 12 km resolution for July 2010. We apply C-R models (single or multipollutant and linear or nonlinear) derived from the 2001 Canadian Census Health and Environment Cohort (CanCHEC). Results: Our preliminary results indicate significant and widespread benefits of PM2.5 and NOx (NO + NO2) emissions control, particularly in major urban areas of Central Canada. We find benefits ranging from $400,000-800,000 per ton of reduction in PM2.5 from sources in Toronto, while NOx control for the same location entails benefits of $2,000-200,000/ton depending on the choice of C-R model. Nonlinear models consistently produce larger benefit estimates than their linear counterparts. We estimate coefficients of variation based solely on the choice of C-R model to be 0.4-0.6 for PM2.5 and 0.6-1.6 for NOx. Conclusions: Our results show that the public health benefits of emission reductions are highly sensitive to C-R specification, and that traditional C-R models may significantly underestimate the benefits of air pollution controls. Further research is needed to determine the most appropriate C-R model to support public policy.