Retrospective prediction of intraurban spatiotemporal distribution of PM2.5 in Taipei
Journal
Atmospheric Environment
Journal Volume
44
Journal Issue
25
Pages
3053-3065
Date Issued
2010
Author(s)
Chih-Hsin W.
Abstract
Numerous studies have shown that fine airborne particulate matter particles (PM2.5) are more dangerous to human health than coarse particles, e.g. PM10. The assessment of the impacts to human health or ecological effects by long-term PM2.5 exposure is often limited by lack of PM2.5 measurements. In Taipei, PM2.5 was not systematically observed until August, 2005. Taipei is the largest metropolitan area in Taiwan, where a variety of industrial and traffic emissions are continuously generated and distributed across space and time. PM-related data, i.e., PM10 and Total Suspended Particles (TSP) are independently systematically collected by different central and local government institutes. In this study, the retrospective prediction of spatiotemporal distribution of monthly PM2.5 over Taipei will be performed by using Bayesian Maximum Entropy method (BME) to integrate (a) the spatiotemporal dependence among PM measurements (i.e. PM10, TSP, and PM2.5), (b) the site-specific information of PM measurements which can be certain or uncertain information, and (c) empirical evidence about the PM2.5/PM10 and PM10/TSP ratios. The performance assessment of the retrospective prediction for the spatiotemporal distribution of PM2.5 was performed over space and time during 2003-2004 by comparing the posterior pdf of PM2.5 with the observations. Results show that the incorporation of PM10 and TSP observations by BME method can effectively improve the spatiotemporal PM2.5 estimation in the sense of lower mean and standard deviation of estimation errors. Moreover, the spatiotemporal retrospective prediction with PM2.5/PM10 and PM2.5/TSP ratios can provide good estimations of the range of PM2.5 levels over space and time during 2003-2004 in Taipei. ? 2010 Elsevier Ltd.
Subjects
BME; PM2.5; Retrospective prediction; Spatiotemporal modeling
SDGs
Other Subjects
5-level; Airborne particulate matters; Bayesian maximum entropy methods; Coarse particles; Ecological effect; Empirical evidence; Estimation errors; Human health; Local government; Metropolitan area; Performance assessment; PM10 and PM2.5; Site-specific information; Space and time; Spatiotemporal distributions; Spatiotemporal modeling; Standard deviation; Total suspended particles; Traffic emissions; Uncertain informations; Estimation; Industrial emissions; Particles (particulate matter); Forecasting; Bayesian analysis; health impact; health risk; industrial emission; maximum entropy analysis; particulate matter; public health; spatiotemporal analysis; traffic emission; article; particle size; particulate matter; precipitation; prediction; priority journal; retrospective study; space; Taiwan; temperature; time; velocity; wind; winter; Taipei; Taiwan
Type
journal article