Spatial Heterogeneity of PM_(10) and O_3 in Sao Paulo, Brazil, and Implications for Human Health Studies

摘要

Developing exposure estimates is a challenging aspect of investigating the health effects of air pollution. Pollutant levels recorded at centrally located ambient air quality monitors in a community are commonly used as proxies for population exposures. However, if ample intraurban spatial variation in pollutants exists, city-wide averages of concentrations may introduce exposure misclassification. We assessed spatial heterogeneity of particulate matter with an aerodynamic diameter ≤10 μm (PM_(10)) and ozone (O_3) and evaluated implications for epidemiological studies in Sao Paulo, Brazil, using daily (24-hr) and daytime (12-hr) averages and 1-hr daily maximums of pollutant levels recorded at the regulatory monitoring network. Monitor locations were also analyzed with respect to a socioeconomic status index developed by the municipal government. Hourly PM_(10) and O_3 data for the Sao Paulo Municipality and Metropolitan Region (1999-2006) were used to evaluate heterogeneity by comparing distance between monitors with pollutants' correlations and coefficients of divergence (CODs). Both pollutants showed high correlations across monitoring sites (median = 0.8 for daily averages). CODs across sites averaged 0.20. Distance was a good predictor of CODs for PM_(10) (p < 0.01) but not O_3, whereas distance was a good predictor of correlations for O_3 (p < 0.01) but not PM_(10). High COD values and low temporal correlation indicate a spatially heterogeneous distribution of PM_(10). Ozone levels were highly correlated (r ≥ 0.75), but high CODs suggest that averaging over O_3 levels may obscure important spatial variations. Of municipal districts in the highest of five socioeconomic groups, 40% have ≥1 monitor, whereas districts in the lowest two groups, representing half the population, have no monitors. Results suggest that there is a potential for exposure misclassification based on the available monitoring network and that spatial heterogeneity depends on pollutant metric (e.g., daily average vs. daily 1-hr maximum). A denser monitoring network or alternative exposure methods may be needed for epidemiological research. Findings demonstrate the importance of considering spatial heterogeneity and differential exposure misclassification by subpopulation.