Methods for Characterizing Fine Particulate Matter Using Ground Observations and Remotely Sensed Data: Potential Use for Environmental Public Health Surveillance

Abstract

This study describes and demonstrates different techniques for surface fitting daily environmental hazards data of particulate matter with aerodynamic diameter less than or equal to 2.5 m (PM 2.5 ) for the purpose of integrating respiratory health and environmental data for the Centers for Disease Control and Prevention (CDC) pilot study of Health and Environment Linked for Information Exchange (HELIX)-Atlanta. It presents a methodology for estimating daily spatial surfaces of ground-level PM 2.5 concentrations using the B-Spline and inverse distance weighting (IDW) surface-fitting techniques, leveraging National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectrometer (MODIS) data to complement U.S. Environmental Protection Agency (EPA) ground observation data. The study used measurements of ambient PM 2.5 from the EPA database for the year 2003 as well as PM 2.5 estimates derived from NASA's satellite data. Hazard data have been processed to derive the surrogate PM 2.5 exposure estimates. This paper shows that merging MODIS remote sensing data with surface observations of PM 2.5 not only provides a more complete daily representation of PM 2.5 than either dataset alone would allow, but it also reduces the errors in the PM 2.5 -estimated surfaces. The results of this study also show that IMPLICATIONSThe described method of estimating concentrations of PM 2.5 by merging NASA MODIS remote sensing data with surface observations provides a more complete daily representation of PM 2.5 than either dataset alone, and it reduces the errors in the PM 2.5 -estimated surfaces with respect to observations. These new data products have the potential to serve as a tool for environmental public health surveillance to monitor trends and as an early warning system for prevention of human exposure to potential hazards. Such continuous spatial surfaces of environmental hazards as those developed in this study would enable linking environmental hazards to health outcomes on the grid-aggregated level as well as the individual level at the geographic locations of patients' residences.

Exploring Land Use and Land Cover Effects on Air Quality in Central Alabama Using GIS and Remote Sensing

“…This is helpful for tracking pollution levels for use in assuring compliance with national standards, but it does not allow us to gain an accurate regional perspective on PM 2.5 . Studies in recent years [27][28][29][30] have shown that a combination of ground PM 2.5 measurements and satellite retrieved AOT can provide a better spatial assessment than ground observations alone at regional to global scales.…”

A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth

“…(1) Model predictability: MLR was commonly used in early studies [17,20,21,[24][25][26][39][40][41]46,47,49,50,54,75], whereas MEM and CTM gradually became the dominant methods and replaced MLR after 2010. However, GWR has developed at a slower pace with a limited number of studies to data, and had moderate performance [32,74,125,126].…”

“…More recently, in order to improve model performance, some studies have explored covariate factors in the MLR model under different conditions [17,20,21,[24][25][26][39][40][41]46,47,49,50,54,75]. A few covariate factors, such as relative humidity and height of the boundary layer, were regarded as significant enough to affect and even invert the relationships between AOD and PM 2.5 .…”

Remote Sensing of Particulate Pollution from Space: Have We Reached the Promised Land?

“…The need for greater collaboration of air quality and space scientists is evident in an article published in the July issue of the journal. 57 AlHamdan et al 57 provide an interesting and useful analysis of relationships between surface air quality and spacebased satellite AOD to estimate human exposure. They obtain mostly urban PM data from EPA's Air Quality System (AQS), 58,59 but they neglect the potentially more useful PM 2.5 and chemical speciation data from the nonurban Interagency Monitoring of Protected Visual Environments (IMPROVE) 47,60 and the Southeastern Aerosol Research and Characterization (SEARCH) [61][62][63] networks.…”