Characterization of Subgrid-Scale Variability in Particulate Matter with Respect to Satellite Aerosol Observations

Abstract

Recent use of satellite observations of aerosol optical depth (AOD) to characterize surface concentrations of particulate matter (PM) air pollution has proven extremely valuable in estimating exposures for health effects studies. While the spatial resolutions of satellite data provide far better coverage than existing fixed site surface monitoring stations, they are not able to capture atmospheric processes such as dilution of primary pollutants that vary at small spatial scales. As a result, small-scale variability due to highly localized sources such as traffic may be poorly represented, which in turn may lead to exposure measurement error in epidemiological analyses. Using a fixed spatial grid representing 4.4 km Multiangle Imaging SpectroRadiometer (MISR) aerosol observations, we examined the spatial variability in fine and coarse mode PM (PM 2.5 and PM 2.5-10 respectively) measured at ground monitors from a unique spatially-dense sampling campaign in Southern California. We found that while the variance in measured PM 2.5 differed seasonally (warm 6.82 µg 2 /m 6 and cool 24.5 µg 2 /m 6 ) across the study region, the average subgrid (<4.4 km) variance did not (warm 2.03 µg 2 /m 6 and cool 2.43 µg 2 /m 6 ) and was significantly smaller. On the other hand, ground monitor PM 2.5-10 concentrations showed large variance in warm (18.6 µg 2 /m 6 ) and cool (20.6 µg 2 /m 6 ) seasons, as well as seasonal differences in subgrid variance (warm 8.90 µg 2 /m 6 and cool 3.28 µg 2 /m 6 ). Geostatistical analysis of the semivariance as a function of distance indicated that variability in measured PM 2.5 and PM 2.5-10 concentrations was relatively constant for spatial scales of one to five kilometers, but there was evidence of small-scale (~500 m) variability in PM 2.5-10 concentrations in the cool season. The lack of small-scale spatial variability in the warm season was likely due to large photochemical contributions to regional PM 2.5 , and greater regional contributions to PM 2.5-10 from windblown dust. In contrast, in the cool season there tends to be greater localized concentrations from primary traffic sources due to stronger nocturnal inversions and delayed morning winds reducing dilution that contribute to greater spatial heterogeneity. Overall, these results suggest that regional contributions tend to dominate PM 2.5 , and spatial resolutions of satellite observations including the 4.4 km MISR and 3 km MODIS aerosol products aptly capture relevant spatial variability. Coarse PM 2.5-10 can have seasonally dependent localized contributions, leading to small-scale variability below current satellite aerosol product resolutions.

Using Multi-Angle Imaging SpectroRadiometer Aerosol Mixture Properties for Air Quality Assessment in Mongolia

“…While the machine learning methods presented here did not reach this level of performance for predicting PM 10 , our observed ability to predict PM 2.5 with far fewer ground monitors and no adjustments for meteorology is encouraging. The poor performance in predicting PM 10 is likely in part due to its spatial variability, which can be on the order of less than 1 km for coarse particles [26]. Due to data availability, we matched MISR observations with ground monitoring concentrations that were within 10 km, likely too large a spatial scale to detect associations with PM 10 .…”

Introducing the 4.4 km spatial resolution Multi-Angle Imaging SpectroRadiometer (MISR) aerosol product

“…4. Prototype versions of the 4.4 km MISR product have already been used extensively over parts of southern and central California, and the current V23 product has been used over Mongolia to estimate PM with diameter less than 2.5 µm (PM 2.5 ), less than 10 µm (PM 10 ), and speciated PM 2.5 concentrations (Franklin et al, 2017(Franklin et al, , 2018bMeng et al, 2018). The 4.4 km MISR AOD product was proven, through cross-validation against surface monitoring stations, to capture PM 2.5 variably on a 4.4 km scale and to separate PM 2.5 and PM 10 size modes (Franklin et al, 2018a).…”

Introducing the 4.4 km Spatial Resolution MISR Aerosol Product

“…Prototype versions of the 4.4 km MISR product have already been used extensively over parts of southern and central California and the current V23 product has been used over Mongolia to estimate PM2.5, PM10, and speciated PM2.5 concentrations (Franklin et al, 2017(Franklin et al, , 2018bMeng et al, 2018). The 4.4 km MISR AOD product was proved, through cross-validation against surface monitoring stations, to capture PM2.5 variably on a 4.4 km scale, and to separate PM2.5 and PM10 size modes (Franklin et al, 2018a). Ongoing studies are extending the MISR 4.4 km product application for predicting spatially resolved PM types to other highly polluted regions.…”