Data Assimilation for Spatiotemporal Mapping for Fine Particulate Matter by Bayesian Maximum Entropy Method

• Activity Number: 16
• Type: Topic Contributed
• Date/Time: Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Hwa-Lung Yu*+
• Companies: National Taiwan University
• Keywords: Bayesian maximum entropy ; data assimilation ; particulate matter

Abstract

Numerous studies have shown that high level of fine airborne particulate matter (PM2.5) can potentially cause serious health effects to human. Though the spatial distribution of locations of PM monitoring stations and population are highly associated, the limited number of observations in space hardly characterizes the spatiotemporal distribution of PM2.5 in detail. The present study shows the estimation of spatiotemporal distribution of monthly PM2.5 over Taipei by using Bayesian Maximum Entropy method (BME) to integrate (a) the spatiotemporal dependence among PM measurements (i.e. PM10, TSP, and PM2.5), (b) landuse data including the sizes of residential, commercial and industrial areas as well as road areas of different levels, e.g. highway, (c) remote sensing data which measures the solar radiation by aerosol scattering and absorption, i.e. aerosol optical depth (AOD), and (d) the empirical relationship between the secondary information and PM2.5. The spatiotemporal estimation of PM2.5 is performed during 2005-2009 over entire Taipei city. Investigations are made to assess the contributions of secondary information to the spatiotemporal estimation of PM2.5.