Estimation of Fine Particulate Matter in Taipei Using Landuse Regression and Bayesian Maximum Entropy Methods

Hwa-Lung Yu, Chih-Hsih Wang, Ming-Che Liu and Yi-Ming Kuo
Additional contact information
Hwa-Lung Yu: Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan
Chih-Hsih Wang: Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan
Ming-Che Liu: Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan
Yi-Ming Kuo: Department of Design for Sustainable Environment, Ming Dao University, 369 Wen-Hua Rd., Peetow, Chang-Hua 52345, Taiwan

IJERPH, 2011, vol. 8, issue 6, 1-17

Abstract: Fine airborne particulate matter (PM 2.5 ) has adverse effects on human health. Assessing the long-term effects of PM 2.5 exposure on human health and ecology is often limited by a lack of reliable PM 2.5 measurements. In Taipei, PM 2.5 levels were not systematically measured until August, 2005. Due to the popularity of geographic information systems (GIS), the landuse regression method has been widely used in the spatial estimation of PM concentrations. This method accounts for the potential contributing factors of the local environment, such as traffic volume. Geostatistical methods, on other hand, account for the spatiotemporal dependence among the observations of ambient pollutants. This study assesses the performance of the landuse regression model for the spatiotemporal estimation of PM 2.5 in the Taipei area. Specifically, this study integrates the landuse regression model with the geostatistical approach within the framework of the Bayesian maximum entropy (BME) method. The resulting epistemic framework can assimilate knowledge bases including: (a) empirical-based spatial trends of PM concentration based on landuse regression, (b) the spatio-temporal dependence among PM observation information, and (c) site-specific PM observations. The proposed approach performs the spatiotemporal estimation of PM 2.5 levels in the Taipei area (Taiwan) from 2005–2007.

Keywords: Bayesian maximum entropy; landuse regression; particulate matter (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2011
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1660-4601/8/6/2153/pdf (application/pdf)
https://www.mdpi.com/1660-4601/8/6/2153/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:8:y:2011:i:6:p:2153-2169:d:12750

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().