Abstract
Satellite-retrieved aerosol optical properties have been extensively used to estimate ground-level fine particulate matter (PM2.5) concentrations in support of air pollution health effects research and air quality assessment at the urban to global scales. However, a large proportion, ~70%, of satellite observations of aerosols are missing as a result of cloud-cover, surface brightness, and snow-cover. The resulting PM2.5 estimates could therefore be biased due to this non-random data missingness. Cloud-cover in particular has the potential to impact ground-level PM2.5 concentrations through complex chemical and physical processes. We developed a series of statistical models using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product at 1 km resolution with information from the MODIS cloud product and meteorological information to investigate the extent to which cloud parameters and associated meteorological conditions impact ground-level aerosols at two urban sites in the US: Atlanta and San Francisco. We find that changes in temperature, wind speed, relative humidity, planetary boundary layer height, convective available potential energy, precipitation, cloud effective radius, cloud optical depth, and cloud emissivity are associated with changes in PM2.5 concentration and composition, and the changes differ by overpass time and cloud phase as well as between the San Francisco and Atlanta sites. A case-study at the San Francisco site confirmed that accounting for cloud-cover and associated meteorological conditions could substantially alter the spatial distribution of monthly ground-level PM2.5 concentrations.
Highlights
Satellite observations of aerosol optical properties, such as the aerosol optical depth (AOD), are increasingly being used to infer spatial and temporal patterns of fine-mode particulate matter, PM2.5, for health studies [1]
The current study aims to advance our understanding of whether satellite-retrieved cloud properties are associated with changes in ground-level PM2.5 concentration and composition, and the extent to which cloud properties are associated with these changes
We examine the empirical relationship between cloud properties and the meteorological conditions associated with cloud presence and ground-level concentrations of
Summary
Satellite observations of aerosol optical properties, such as the aerosol optical depth (AOD), are increasingly being used to infer spatial and temporal patterns of fine-mode particulate matter, PM2.5 , for health studies [1]. A large proportion of satellite observations are missing (estimated at ~70% in the 10 km AOD products), as a result of cloud-cover, snow-cover, and surface brightness [2,3]. Previous work to address this gap-filling problem has largely assumed that the observed aerosols are comparable to aerosols that could not be observed [4,5]. Contradicting this assumption, global and US-centric studies have estimated that missing satellite observations result in an underestimation of true PM2.5 concentrations, by an average of 20% in the US [6,7].
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