Grants and Fellowships | 2014

43 Post-Doctoral Fellowships Particle Exposure Assessment The use of satellite data expands spatial coverage, enhancing our ability to estimate location- and/or subject-specific exposures to PM 2.5 . The National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite (GOES) has a long history of observations, thus its unique data is potentially extremely valuable for future epidemiological analyses. In our study we applied a daily calibration technique to aerosol optical depth (AOD) retrievals from the GOES Aerosol/Smoke Product (GASP) AOD data to predict PM 2.5 concentrations within the New England area of the United States. With this approach we could control for the inherent day-to-day variability in the AOD-PM 2.5 relationship, which depends on time-varying parameters such as particle optical properties, their vertical and diurnal concentration profiles and ground surface reflectance among others. The model-predicted values of PM 2.5 mass concentration were highly correlated with the actual observations, with a coefficient of determination of 0.89. Furthermore, based on the high quality predictions we investigated the spatial patterns of particle concentrations within the study area as they related to population and traffic densities. In another study we reported on pollution spatial patterns derived from previously unavailable higher resolution (1km) Moderate Resolution Imaging Spectroradiometer (MODIS) AOD data. A new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for MODIS which provides AOD above bright urban areas at high spatial resolution. Using MAIAC data, the relationship between MAIAC AOD and PM 2.5 (as measured by the EPA ground monitoring stations) was investigated at varying spatial scales. Our analysis suggested that the correlation between PM 2.5 and AOD decreased significantly as AOD resolution degraded despite the intrinsic mismatch between PM 2.5 ground level measurements and AOD vertically integrated measurements. The fine resolution results indicated spatial variability in particle concentration at a sub-10 kilometer scale. This spatial variability of AOD within the urban domain was shown to depend on PM 2.5 levels and wind speed. Furthermore, this study was expanded and statistical models were developed to predict PM 2.5 concentrations. It was found that land use and meteorology impact pollution levels within the New England area. Research publications (1) Chudnovsky, A., Lee, H.J., Kostinski, A., Kotlov, T., & Koutrakis, P. (2012). Prediction of daily fine particulate matter concentrations using aerosol optical depth retrievals from the Geostationary Operational Environmental Satellite. Journal of the Air & Waste Management Association, 62 (9), 1022-1031. (2) Kloog, I., Chudnovsky, A., Koutrakis, P., & Schwartz, J. (2012). Temporal and spatial assessments of minimum air temperature using satellite surface temperature measurements in Massachusetts, USA. Science of the Total Environment, 432 , 85-92. (3) Chudnovsky, A., Kostinski, A., Lyapustin, A., & Koutrakis, P. (2013). Spatial scales of pollution from variable resolution satellite imaging. Environmental Pollution, 172 , 131-138. (4) Chudnovsky, A., Lyapustin, A., Wang, Y., Tang, C., Schwartz, J., & Koutrakis, P. (2013). High resolution aerosol data from MODIS satellite for urban air quality studies. Central European Journal of Geosciences, 6 (1), 17-26. (5) Chudnovsky, A., Tang, C., Lyapustin, A.,Wang, Y., Schwartz, J., & Koutrakis, P. (2013). A critical assessment of high-resolution aerosol optical depth retrievals for fine particulate matter predictions. Atmospheric Chemistry and Physics, 13 , 10907-10917. (6) Nordio, F., Kloog, I., Coull, B.A., Chudnovsky, A., Grillo, P., Bertazzi, P.A., Baccarelli, A.A., & Schwartz, J. (2013). Estimating spatio-temporal resolved PM 10 aerosol mass concentrations using MODIS satellite data and land use regression over Lombardy, Italy. Atmospheric Environment, 74 , 227-236. (7) Alexeeff, S.E., Schwartz, J., Kloog, I., Chudnovsky, A., Koutrakis, P., & Coull, B.A. (2014). Consequences of kriging and land use regression for PM 2.5 predictions in epidemiologic analyses: Insights into spatial variability using high-resolution satellite data. J ournal of Exposure Science and Environmental Epidemiology . Advance online publication. doi:10.1038/jes.2014.40 (8) Chudnovsky, A., Koutrakis, P., Kloog, I., Melly, S., Nordio, F., Lyapustin, A., Wang, Y., & Schwartz, J. (2014). Fine particulate matter predictions using high resolution Aerosol Optical Depth (AOD) retrievals. Atmospheric Environment, 89 , 189–198. (9) Kloog, I., Chudnovsky, A.A., Just, A.C., Nordio, F., Koutrakis, P., Coull, B.A., Lyapustin, A., Wang, Y., & Schwartz, J. (2014). A new hybrid spatio-temporal model for estimating daily multi-year PM 2.5 concentrations across northeastern USA using high resolution aerosol optical depth data. Atmospheric Environment, 95 , 581-590. Fellow Alexandra Chudnovsky Harvard University, USA Supervisor Petros Koutrakis 2010-2012