RESUMO
To further the understanding and implementation of expert elicitation methods in the evaluation of public policies related to air pollution, the present study's main goal was to explore the potential strengths and weaknesses of structured expert judgment (SEJ) methodology as a way to derive a C-R function for chronic PM(2.5) exposure and premature mortality in Chile. Local experts were classified in two groups according to background and experience: physicians (Group 1) and engineers (Group 2). Experts were required to provide an estimate of the true percent change in nonaccidental mortality resulting from a permanent 1 µg/m(3) reduction in PM2.5 annual average ambient concentration across the entire Chilean territory. Cooke's Classical Model was used to combine the individual experts' assessments. Experts' mortality estimations varied markedly across groups: while experts in Group 1 delivered higher estimations than those reported in major international cohort studies, estimations from Group 2 were, to varying degrees, anchored to previous studies. Accordingly, combined distributions for each group and all experts were significantly different, due to the high sensitivity of the weighted distribution to experts' performance in calibration variables. Results of this study suggest that, while the use of SEJ has great potential for estimating C-R functions for chronic exposure to PM2.5 and premature mortality and its major sources of uncertainty in countries where no studies are available, its successful implementation is conditioned by a number of factors, which are analyzed and discussed.
Assuntos
Poluentes Atmosféricos/efeitos adversos , Exposição Ambiental/efeitos adversos , Julgamento , Mortalidade , Material Particulado/efeitos adversos , Poluentes Atmosféricos/análise , Poluição do Ar/efeitos adversos , Poluição do Ar/análise , Chile/epidemiologia , Estudos de Coortes , Humanos , Modelos Teóricos , Material Particulado/análise , IncertezaRESUMO
Chilean law requires the assessment of air pollution control strategies for their costs and benefits. Here we employ an online weather and chemical transport model, WRF-Chem, and a gridded population density map, LANDSCAN, to estimate changes in fine particle pollution exposure, health benefits, and economic valuation for two emission reduction strategies based on increasing the use of compressed natural gas (CNG) in Santiago, Chile. The first scenario, switching to a CNG public transportation system, would reduce urban PM2.5 emissions by 229 t/year. The second scenario would reduce wood burning emissions by 671 t/year, with unique hourly emission reductions distributed from daily heating demand. The CNG bus scenario reduces annual PM2.5 by 0.33 µg/m³ and up to 2 µg/m³ during winter months, while the residential heating scenario reduces annual PM2.5 by 2.07 µg/m³, with peaks exceeding 8 µg/m³ during strong air pollution episodes in winter months. These ambient pollution reductions lead to 36 avoided premature mortalities for the CNG bus scenario, and 229 for the CNG heating scenario. Both policies are shown to be cost-effective ways of reducing air pollution, as they target high-emitting area pollution sources and reduce concentrations over densely populated urban areas as well as less dense areas outside the city limits. Unlike the concentration rollback methods commonly used in public policy analyses, which assume homogeneous reductions across a whole city (including homogeneous population densities), and without accounting for the seasonality of certain emissions, this approach accounts for both seasonality and diurnal emission profiles for both the transportation and residential heating sectors.