Materials and Methods Data. All analyses are for the year 2004. Associations of AOD and PM2.5 are weak in the western United States (Engel-Cox et al. 2004; Liu et al. 2005; Paciorek et al. 2008), so we focus on the eastern United States. Our daily exploratory analyses use data east of 100°W longitude. To limit compu- tations with large remote-sensing data sets, our longer-term analyses, including the sta- tistical modeling, focus on a mid-Atlantic region encompassing Pennsylvania and New Jersey (Figure 1), which contains the major metropolitan areas of New York, New York; Philadelphia, Pennsylvania; Washington, DC; Baltimore, Maryland; and Pittsburgh, Pennsylvania, as well as large rural areas in the north. The heterogeneity in population density and the presence of large point source emissions from power plants and industrial plants in the southwest provide a test region with substantial variability in pollution. We use AOD retrievals from three satel- lite instruments: MODIS (moderate reso- lution imaging spectroradiometer), MISR (multiangle imaging spectroradiometer), and the GOES (Geostationary Operational Environmental Satellite) aerosol/smoke prod- uct (GASP). The MODIS and MISR instru- ments are aboard the Terra satellite platform, whose polar orbit gives full coverage of the globe at regular intervals, starting in March 2000, with retrievals in the eastern United States at a constant daily time point (1030– 1045 hours local time). Both MISR (pri- marily version 15, at 558 nm) and MODIS (collection 5, at 550 nm) provide retrievals of AOD, a dimensionless measure of light extinction over the entire vertical column of air through the atmosphere (also known as aerosol optical thickness). MISR level 2 aerosol data (versions 15 and 17) were down- loaded from the U.S. National Aeronautics and Space Administration (NASA) Langley Research Center (LARC) Atmospheric Sciences Data Center (NASA 2006). MODIS aerosol data (collection 5) were downloaded from the MODIS Level 1 and Atmosphere Archive and Distribution System (LAADS) (NASA 2007). AOD generally ranges from 0 to 5, with values > 1 associated with heavy haze. MISR AOD retrievals are at a nominal spatial resolution of 17.6 km with retriev- als in the northeast United States every 4–7 days depending on location (Liu et al. 2005). MODIS provides AOD retrievals at a nomi- nal resolution of 10 km with each location covered every 1–2 days (Engel-Cox et al. 2004; Wang and Christopher 2003). AOD cannot be measured below clouds, so cloud filtering algorithms use the infrared portion of the spectrum to detect and omit obscured observations (Engel-Cox et al. 2004). Errors and uncertainties in the filtering can lead to 24 22 20 18 16 14 12 905 Remotely sensed aerosol and particulate matter erroneous AOD retrievals, and high surface reflectivity can also prevent retrievals. GASP AOD (interpolated at 550 nm) is calculated from GOES-12 (East) imager data; the U.S. National Oceanic and Atmospheric Administration (NOAA) provided their most recent version (Knapp et al. 2002; NOAA, personal communication). GASP AOD is at a nominal spatial resolution of 4 km, but retriev- als are less precise than MODIS or MISR AOD because of the coarse spectral resolu- tion and fixed viewing geometry of the sensor (Prados et al. 2007). Retrievals are attempted every half-hour during daylight, 1045–2345 hours universal time, but again, cloud cover and high surface reflectivity lead to many miss- ing observations. We use daily average GASP AOD, regardless of the number of retrievals. We use 24-hr average gravimetric [federal reference method (FRM)] measurements from the U.S. Environmental Protection Agency (EPA) Air Quality System with parameter 88101 (U.S. EPA 2009), omitting a small num- ber of IMPROVE (Interagency Monitoring of Protected Visual Environments) monitors, which tend to be placed where few people live. Although hourly data are better matched in time to the MODIS and MISR snapshots, the number of hourly monitors is limited, and there is no FRM for hourly measurements, plus our interest is in the relationship of PM2.5 and AOD at monthly and yearly periods. In our statistical modeling we use geo- graphic information system (GIS)-based and meteorologic covariates to help explain PM2.5 variation, following Yanosky et al. (2008). Covariates that may help predict PM2.5 at fine spatial scale include distance to major roads in three road classes (A1: primary roads, typically interstates; A2: primary major, noninterstate roads; and A3: smaller, secondary roads, usu- ally with more than two lanes). We also have point locations of year 2002 primary PM2.5 emissions from U.S. EPA’s 2002 National Emissions Inventory (NEI) (U.S. EPA 2006). We calculated other covariates using a GIS at the resolution of the 4-km grid used in our sta- tistical modeling. These include road density in the three road classes, population density, and elevation at the cell centroid. As a meas- ure of nonpoint source emissions in each cell, we assign the density (total emissions divided by county area) of the 2002 NEI area-level primary PM2.5 emissions in the county of the cell centroid. We based meteorologic variables on the North American Regional Reanalysis (NARR) (Mesinger et al. 2006) fields, avail- able at 32-km resolution every 3 hr. For each 3-hr value and each grid cell, we computed an inverse distance-weighted average of the NARR values from the four nearest NARR points to the cell centroid. We then averaged values to the month. Our second statistical model uses wind speed and temperature, but we also considered relative humidity (RH), planetary boundary layer (PBL) height, mean sea-level pressure, and precipitation. We also used a calibrated AOD variable (Paciorek et al. 2008), which accounts for sys- tematic effects of PBL, RH, season, and time- invariant regional variation that modify and obscure the relationship between daily PM2.5 and daily AOD. The calibration is done by regressing daily AOD values from 2004 across the eastern United States on daily PM2.5 and the variables just mentioned, matched in space and time. By including time-invariant regional variation, we cause the long-term average AOD and long-term average PM2.5 to more closely match at large spatial scales, nec- essarily increasing correlations of PM2.5 and AOD. Our hope in including this spatial term is to adjust for large-scale differences between AOD and PM2.5, allowing us to exploit com- mon patterns of AOD and PM2.5 at smaller spatial scales, to the extent that they exist. Exploratory analyses. Our goal in the exploratory analyses was to understand the association between AOD and PM2.5 at differ- ent temporal aggregations to assess the poten- tial of AOD to help predict chronic PM2.5 exposure. We started by considering associa- tions at the daily level when AOD and PM2.5 are matched such that both are available for a given day and location, mirroring analyses in the published literature. We matched avail- able PM2.5 24-hr averages with AOD retrievals from the nearest pixel for each of the three sat- ellite instruments, omitting a small number of monitors for which the nearest pixel centroid is closer to another monitor. Our interest is in fine resolution estimation of PM2.5, so unlike other analyses we used individual pixels instead of aggregating AOD across adjoining pixels. When considering prediction of long- term average PM2.5 for chronic epidemiologic A 1.0 0.8 0.6 0.4 0.2 0 B Figure 1. Example of monthly average MODIS AOD (A) and ground-level PM2.5 from monitors (B): July 2004 in our mid-Atlantic study region of the United States. Environmental Health Perspectives • volume 117 | number 6 | June 2009