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

• Published in: Journal of the Air & Waste Management Association (1995) Vol. 59; no. 7; pp. 865 - 881
• Main Authors: Al-Hamdan, Mohammad Z., Crosson, William L., Limaye, Ashutosh S., Rickman, Douglas L., Quattrochi, Dale A., Estes, Maurice G., Qualters, Judith R., Sinclair, Amber H., Tolsma, Dennis D., Adeniyi, Kafayat A., Niskar, Amanda Sue
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis Group 01.07.2009; Air and Waste Management Association; Taylor & Francis Ltd
• Subjects: Air pollution; Air quality; Air quality monitoring stations; Environmental aspects; Environmental monitoring; Environmental Monitoring - methods; Health surveillance; Health Surveys; Measurement; Methods; Outdoor air quality; Particle Size; Particulate Matter - analysis; Public health; Regression Analysis; Studies; Time Factors; United States
• ISSN: 1096-2247; 2162-2906
• DOI: 10.3155/1047-3289.59.7.865

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 μm (PM 2.5 ) for the purpose of inte grating 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 although the IDW technique can introduce some numerical artifacts that could be due to its interpolating nature, which assumes that the maxima and minima can occur only at the observation points, the daily IDW PM 2.5 surfaces had smaller errors in general, with respect to observations, than those of the B-Spline surfaces. Finally, the methods discussed in this paper establish a foundation for environmental public health linkage and association studies for which determining the concentrations of an environmental hazard such as PM 2.5 with high accuracy is critical.