Development of long-term spatiotemporal models for ambient ozone in six metropolitan regions of the United States: The MESA Air study. (December 2015)

• Record Type: Journal Article
• Authors: Wang, Meng; Keller, Joshua P.; Adar, Sara D.; Kim, Sun-Young; Larson, Timothy V.; Olives, Casey; Sampson, Paul D.; Sheppard, Lianne; Szpiro, Adam A.; Vedal, Sverre; Kaufman, Joel D.
• Is Part Of: Atmospheric environment. Volume 123:Part A(2015)
• Page Start: 79
• Page End: 87
• Publication Date: 2015-12
• Subjects: Ozone -- Spatio-temporal -- Geo-statistical model -- Multi-city -- MESA Air; AQS Air Quality System -- CALINE California line-source model -- CTM chemical transport modeling -- CO carbon monoxide -- CV cross-validation -- EM Expectation-Maximization -- LOD limit of detection -- LUR land use regression -- MESA Air Multi-Ethnic Study of Atherosclerosis and Air Pollution -- MSE mean square error -- NO nitric oxide -- NO2 nitrogen dioxide -- NOx oxides of nitrogen -- PLS partial least squares -- PM particulate matter -- SO2 sulfur dioxide -- SVD singular value decomposition -- VOCs volatile organic compounds; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2015.10.042
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: Background: Current epidemiologic studies rely on simple ozone metrics which may not appropriately capture population ozone exposure. For understanding health effects of long-term ozone exposure in population studies, it is advantageous for exposure estimation to incorporate the complex spatiotemporal pattern of ozone concentrations at fine scales. Objective: To develop a geo-statistical exposure prediction model that predicts fine scale spatiotemporal variations of ambient ozone in six United States metropolitan regions. Methods: We developed a modeling framework that estimates temporal trends from regulatory agency and cohort-specific monitoring data from MESA Air measurement campaigns and incorporates land use regression with universal kriging using predictor variables from a large geographic database. The cohort-specific data were measured at home and community locations. The framework was applied in estimating two-week average ozone concentrations from 1999 to 2013 in models of each of the six MESA Air metropolitan regions. Results: Ozone models perform well in both spatial and temporal dimensions at the agency monitoring sites in terms of prediction accuracy. City-specific leave-one (site)-out cross-validation R 2 accounting for temporal and spatial variability ranged from 0.65 to 0.88 in the six regions. For predictions at the home sites, the R 2 is between 0.60 and 0.91 for cross-validation that left out 10% of home sites in turn. The predicted ozone concentrations vary substantially over space and time in all the metropolitan regions. Conclusion: Using the available data, our spatiotemporal models are able to accurately predict long-term ozone concentrations at fine spatial scales in multiple regions. The model predictions will allow for investigation of the long-term health effects of ambient ozone concentrations in future epidemiological studies. Highlights: Few studies estimate long-term ozone exposure in large populations at fine scales. Geo-statistical exposure models are developed for ozone in six US metropolitan regions. The modeling framework incorporates land use regression with universal kriging. The models accurately predict ozone concentrations in both spatial and temporal dimension. Model predictions will be used for investigating long-term health effects.