We analyzed daily MODIS AOD data collected by the Terra and Aqua satellites during 2005 because that was the year with the highest average data coverage per day (27%). The Level 2 MODIS science data include many parameters associated with location and time, solar and viewing geometry, science, cloud mask and quality assurance [12]. Optical Depth Land and Ocean (ODLO) is a science parameter that includes AOD values at 0.55 μm for both ocean and land.

We downloaded Terra and Aqua daily ODLO measurements for 2005 (Collection 5.1) from NASA's Level 1 and Atmospheric Archive and Distribution System (LAADS) as Level-2 AOD data sets [12]. The MOD04 is the first data set to monitor global AOD over land [13]. The Level-2 AOD data are derived from Level-1 products at a 10 × 10 km² nominal spatial resolution. The AOD values of the ODLO parameter are stored as 2-byte integers, which require a conversion procedure to obtain real physical AOD values using the equation with a scale factor (a) and offset value (b).

Physical AOD values of ODLO parameter = a × (integer value − b) where a is equal to 0.001 and b is equal to 0 (personal communication with Bill Ridgway in NASA, August 19, 2009). AOD values generally range from 0 to 5, with values greater than 1 being associated with heavy haze [7, 13].

We obtained hourly ground-level PM_2.5 concentrations across the contiguous US states from EPA's Air Quality System (AQS), which collects ambient air quality data from state, local, and tribal agencies [14]. All AQS PM_2.5 data are collected in accordance with EPA-approved reference and equivalent sampling methods, and all monitors in the contiguous US are included in the AQS.

We also obtained monitor site information from the AQS, including the category of land use near the sites (agricultural, commercial, industrial, residential, or other), the population density category of site areas (rural, suburban, or urban), and the latitude and longitude of the sites. We adopted an exact pixel-to-point match procedure to extract AOD values. All the points, representing each ground-level station, were overlaid on the MODIS ODLO parameter imagery to derive an AOD value for each pixel where a monitoring station is located by its latitude and longitude. The linked data set with both daily AOD and hourly PM_2.5 has 66,768 records (36,587 for Terra and 30,181 for Aqua). The monthly- and seasonal-average AOD values of individual ground stations were derived from the daily AOD data set.

We used a linear mixed model with a site-level random effect to evaluate the correlation between AOD measurements and hourly PM_2.5 measurements in the District of Columbia and all contiguous states except Vermont, Wisconsin, and West Virginia (which had no matched AOD and PM_2.5 site-level data). Among states included in our study, the sample sizes of PM_2.5 measurement sites for which matching AOD data were available ranged from 116 in Wyoming to 7232 in California. For each state, the model had the following form:

Hourly PM_2.5∼ Land Use + Site Setting + Season + AOD + Site (random effect).

Land use categories were agriculture, commercial, industrial, residential (reference) and “other”; site setting (i.e., population density) categories were rural, suburban, and urban (reference); season categories were spring, summer, fall, and winter (reference). All models also adjusted for site-level random effects of unobserved or unmeasured factors associated with PM_2.5 measurements. We used regression coefficients associated with AOD to evaluate the direction and magnitude of the association between AOD and PM_2.5 and considered p-values <0.05 to be indicative of a statistically significant association between AOD and PM_2.5 concentrations. All the models were implemented in SAS 9.3 using Proc MIXED.

We found that the mean number of days with valid AOD data was higher with use of the combined Terra and Aqua datasets (117) than with use of either the Terra dataset (91) or the Aqua dataset (78) alone (Figure 1). Terra and Aqua provided similar AOD coverage in the western and central regions of country; however, Terra provided better coverage in the eastern region. We also found that the use of data from both satellites generally resulted in more valid observations than the use of data from either satellite alone (Figure 2), although the overall increase in spatial coverage with both satellites was primarily attributable to greater coverage during winter. During the other three seasons, the use of data from both satellites resulted in coverage little different from that with each satellite alone (Figure 2(b)).

As shown in Figures 3(a) and (b), more sufficient number of valid-AOD days at each monitoring station was generally obtained with both Terra and Aqua, compared with their individual AODs. We also observed that integrated AODs with Terra and Aqua provide more sufficient observations with valid monthly-average AODs across all ground monitoring stations, as depicted in Figures 3(c) and (d).

Overall, we found that AOD measurements were significantly associated with PM_2.5, concentrations in all states except Colorado, although the strength of this association varied substantially by state, and the association was generally stronger in eastern states than in western states (Table 1 and Figure 4). State-level regression coefficients for AOD ranged from 1.93 in Colorado to 46.0 in Nebraska.

We also found a significantly greater correlation between AOD measurements and PM_2.5 concentrations during the summer and fall than during the winter and spring in all states except Montana, Nebraska, and Wyoming. The reason we found no significant seasonal effects in these three states was largely because of a lack of data for the winter and spring: Montana had only 6 records for the spring and winter, Nebraska had only 4, and Wyoming had only 2.