title: Spatial heterogeneity of PM10 and O3 in São Paulo, Brazil, and implications for human health studies.
abstract: 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 < or = 10 microm (PM10) and ozone (O3) and evaluated implications for epidemiological studies in São 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 O3 data for the Sāo 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 O3, whereas distance was a good predictor of correlations for O3 (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 > or = 0.75), but high CODs suggest that averaging over O3 levels may obscure important spatial variations. Of municipal districts in the highest of five socioeconomic groups, 40% have > or = 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.
authors.affiliation: School of Forestry and Environmental Studies, Yale University, New Haven, CT 06511, USA. mercedes.bravo@yale.edu
authors.forename: Mercedes A
authors.initials: MA
authors.lastname: Bravo
authors.forename: Michelle L
authors.initials: ML
authors.lastname: Bell
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D001938:Brazil; D002947:Cities; D004784:Environmental Monitoring / Q000706:statistics & numerical data*; D006801:Humans; D010126:Ozone / Q000032:analysis* / Q000592:standards; D052638:Particulate Matter / Q000032:analysis* / Q000592:standards; D012959:Socioeconomic Factors
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter; D010126:Ozone