Two-Dimensional Wavelet Decompositions on Irregularly Spaced Grids with Application to PM2.5 in New England

• Activity Number: 314
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics and the Environment
• Author(s): Joseph Antonelli* and Brent Coull
• Companies: Harvard University and Harvard University
• Keywords: Wavelets ; Spatial statistics ; Air pollution ; Multiresolution analysis

Abstract

Fine particulate matter (PM2.5) is composed of many sources of pollution, each potentially varying at different spatial scales. An unanswered question in the air pollution literature is at which spatial scales PM2.5 is most harmful. Identifying whether harmful effects are driven by local, finely varying pollution or from regional, slowly varying sources is important to future air pollution regulations. We propose a two-dimensional wavelet decomposition that alleviates restrictive assumptions about the spatial surface required for standard Wavelet decompositions. Using this method we can decompose the surface of PM2.5 to identify which sources of pollution are driving adverse health outcomes. We apply our method to a study of birth weights in Massachusetts and find that both local and regional sources of pollution negatively impact birth weight, though the effect is greater for regional sources.