The potential impact of satellite-retrieved cloud parameters on ground-level PM<inf>2.5</inf>mass and composition

• Type of Material: Article
• Authors: Jessica H. Belle, Emory University; Howard Chang, Emory University; Yujie Wang, NASA Goddard Space Flight Center; Xuefei Hu, Emory University; Alexei Lyapustin, NASA Goddard Space Flight Center; Yang Liu, Emory University
• Language: English
• Date: 2017-10-18
• Publisher: MDPI
• Publication Version: Final Published Version
• Copyright Statement: © 2017 by the authors. Licensee MDPI, Basel, Switzerland.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.3390/ijerph14101244
• Title of Journal or Parent Work: International Journal of Environmental Research and Public Health
• ISSN: 1661-7827
• Volume: 14
• Issue: 10
• Start Page: 1244
• End Page: 1244
• Grant/Funding Information: The work of Jessica H. Belle and Yang Liu is partially supported by the NASA Applied Sciences Program (Grant # NNX14AG01G and NNX16AQ28G, PI: Liu).
• Keywords: PM2.5; RUC/RAP; cloud properties; MAIAC AOD; non-random missingness
• Research Categories: Environmental Sciences; Biology, Bioinformatics; Biology, Biostatistics

Abstract

Satellite-retrieved aerosol optical properties have been extensively used to estimate ground-level fine particulate matter (PM 2.5 ) 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 PM 2.5 estimates could therefore be biased due to this non-random data missingness. Cloud-cover in particular has the potential to impact ground-level PM 2.5 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 clou d 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 PM 2.5 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 PM 2.5 concentrations.