Abstract

Abstract. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for the MODerate Resolution Imaging Spectroradiometer (MODIS), which provides aerosol optical depth (AOD) at 1 km resolution. The relationship between MAIAC AOD and PM2.5 as measured by 84 EPA ground monitoring stations in the entire New England and the Harvard super site during 2002–2008 was investigated and also compared to the AOD–PM2.5 relationship using conventional MODIS 10 km AOD retrieval from Aqua platform (MYD04) for the same days and locations. The correlations for MYD04 and for MAIAC are r = 0.62 and 0.65, respectively, suggesting that AOD is a reasonable proxy for PM2.5 ground concentrations. The slightly higher correlation coefficient (r) for MAIAC can be related to its finer resolution resulting in better correspondence between AOD and EPA monitoring sites. Regardless of resolution, AOD–PM2.5 relationship varies daily, and under certain conditions it can be negative (due to several factors such as an EPA site location (proximity to road) and the lack of information about the aerosol vertical profile). By investigating MAIAC AOD data, we found a substantial increase, by 50–70% in the number of collocated AOD–PM2.5 pairs, as compared to MYD04, suggesting that MAIAC AOD data are more capable in capturing spatial patterns of PM2.5. Importantly, the performance of MAIAC AOD retrievals is slightly degraded but remains reliable under partly cloudy conditions when MYD04 data are not available, and it can be used to increase significantly the number of days for PM2.5 spatial pattern prediction based on satellite observations.

Highlights

  • Exposure to particulate matter (PM) with aerodynamic diameter ≤ 2.5 μm (PM2.5) causes a variety of adverse health effects in humans

  • There are several main findings from this analysis: (1) a direct comparison between coarse MYD04 10 km aerosol optical depth (AOD) and high-resolution Multi-Angle Implementation of Atmospheric Correction (MAIAC) 1 km AOD for all collocated AOD– PM2.5 pairs for the same days and locations showed that both retrievals provide reasonable and similar correlations; (2) both retrievals indicate clear temporal variation in the association between AOD and PM2.5; (3) considering both clear and partly cloudy days, MAIAC provides on average a factor of 1.77 more retrievals at 85 Environmental Protection Agency (EPA) monitoring sites

  • The increase in data coverage has the potential to capture more days with greater spatial variability in PM2.5 as compared to 10 km MYD04, which should improve usefulness of AOD data to fill in the spatial pattern of PM2.5 for cells without monitoring stations; (4) analysis of MAIAC AOD– PM2.5 collocated pairs for cloudy days when MYD04 provided no retrievals showed that while there is some reduction in MAIAC data quality, the total correlation coefficient and distribution of its daily values are relatively similar to their clear sky counterparts

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Summary

Introduction

Exposure to particulate matter (PM) with aerodynamic diameter ≤ 2.5 μm (PM2.5) causes a variety of adverse health effects in humans. Chudnovsky et al.: A critical assessment of high-resolution aerosol optical depth retrievals information such as meteorological data (Pelletier et al, 2007; Emili et al, 2010; Wu et al, 2012; Kanabkaew, 2013) and boundary layer height (Engel-Cox et al, 2006; Tsai et al, 2011; Estellés et al, 2012), and employs light detection and ranging (lidar) instruments to capture the vertical aerosol distribution at specific locations (Schaap et al, 2009; Barnaba et al, 2010) These recently developed approaches use various statistical methods, from linear regression models (Wang and Christopher, 2003; Tian and Chen, 2010) to more complex multiple regression and neural network techniques (Gupta and Christopher, 2009a, b), generalized additive mixed models (GAMs) (Liu et al, 2009; Paciorek et al, 2008; Paciorek and Liu, 2012), and mixed effects models (Lee et al, 2011; Chudnovsky et al, 2012). We explored the quality of MAIAC retrieval on days when MYD04 was not available, by examining the PM2.5–AOD relationship on a daily basis

Material and methods
Satellite data
Data processing and analyses
Direct comparison between MYD04 and MAIAC retrievals
High-resolution retrievals: the entire data set
MAIAC data quality when MYD04 is not available
Findings
Concluding remarks
Full Text
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