Prediction of daily fine particulate matter concentrations using aerosol optical depth retrievals from the Geostationary Operational Environmental Satellite (GOES)

• Published in: Journal of the Air & Waste Management Association (1995) Vol. 62; no. 9; pp. 1022 - 1031
• Main Authors: Chudnovsky, Alexandra A., Lee, Hyung Joo, Kostinski, Alex, Kotlov, Tanya, Koutrakis, Petros
• Format: Journal Article
• Language: English
• Published: Pittsburgh, PA Taylor & Francis Group 01.09.2012; Air & Waste Management Association; Taylor & Francis Ltd
• Subjects: Aerosols; Aerosols - analysis; Applied sciences; Argon oxygen decarburizing; Atmospheric aerosols; Atmospheric pollution; Epidemiology; Exact sciences and technology; Mathematical models; Meteorological satellites; Meteorology; Models, Theoretical; Optical analysis; Optical properties; Outdoor air quality; Particulate matter; Particulate Matter - analysis; Pollution; Reflectivity; Remote sensing; Remote Sensing Technology; Retrieval; Satellites; Studies; United States; United States Environmental Protection Agency; United States; Concentration measurement; Surveillance; Optical properties; Prediction; Aerosols; Air pollution; Air quality; Visibility; Optical thickness; Modeling
• ISSN: 1096-2247; 2162-2906
• DOI: 10.1080/10962247.2012.695321

Although ground-level PM 2.5 (particulate matter with aerodynamic diameter <2.5 μm) monitoring sites provide accurate measurements, their spatial coverage within a given region is limited and thus often insufficient for exposure and epidemiological studies. Satellite data expand spatial coverage, enhancing our ability to estimate location- and/or subject-specific exposures to PM 2.5 . In this study, the authors apply a mixed-effects model approach to aerosol optical depth (AOD) retrievals from the Geostationary Operational Environmental Satellite (GOES) to predict PM 2.5 concentrations within the New England area of the United States. With this approach, it is possible to control for the inherent day-to-day variability in the AOD-PM 2.5 relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles, and ground surface reflectance. The model-predicted PM 2.5 mass concentration are highly correlated with the actual observations, R 2 = 0.92. Therefore, adjustment for the daily variability in AOD-PM 2.5 relationship allows obtaining spatially resolved PM 2.5 concentration data that can be of great value to future exposure assessment and epidemiological studies.