Spatial Heterogeneity of PM10 and O-3 in Sao Paulo, Brazil, and Implications for Human Health Studies

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 mu m (PM10) 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 PM10 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 PM10 (p < 0.01) but not O-3, whereas distance was a good predictor of correlations for O-3 (p < 0.01) but not PM10. High COD values and low temporal correlation indicate a spatially heterogeneous distribution of PM10. 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.