Next Article in Journal
Socioeconomic Differences in the Effectiveness of the Removal of the “Light” Descriptor on Cigarette Packs: Findings from the International Tobacco Control (ITC) Thailand Survey
Next Article in Special Issue
Evaluating and Mapping of Spatial Air Ion Quality Patterns in a Residential Garden Using a Geostatistic Method
Previous Article in Journal
Bystander Exposure to Ultra-Low-Volume Insecticide Applications Used for Adult Mosquito Management
Previous Article in Special Issue
Spatial Pattern Analysis of Heavy Metals in Beijing Agricultural Soils Based on Spatial Autocorrelation Statistics
Article Menu

Export Article

Open AccessArticle
Int. J. Environ. Res. Public Health 2011, 8(6), 2153-2169; doi:10.3390/ijerph8062153

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

Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan
Department of Design for Sustainable Environment, Ming Dao University, 369 Wen-Hua Rd., Peetow, Chang-Hua 52345, Taiwan
Author to whom correspondence should be addressed.
Received: 7 March 2011 / Revised: 26 May 2011 / Accepted: 7 June 2011 / Published: 14 June 2011
(This article belongs to the Special Issue Geostatistics in Environmental Pollution and Risk Assessment)
View Full-Text   |   Download PDF [1121 KB, uploaded 19 June 2014]   |  


Fine airborne particulate matter (PM2.5) has adverse effects on human health. Assessing the long-term effects of PM2.5 exposure on human health and ecology is often limited by a lack of reliable PM2.5 measurements. In Taipei, PM2.5 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 PM2.5 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 PM2.5 levels in the Taipei area (Taiwan) from 2005–2007. View Full-Text
Keywords: Bayesian maximum entropy; landuse regression; particulate matter Bayesian maximum entropy; landuse regression; particulate matter

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Yu, H.-L.; Wang, C.-H.; Liu, M.-C.; Kuo, Y.-M. Estimation of Fine Particulate Matter in Taipei Using Landuse Regression and Bayesian Maximum Entropy Methods. Int. J. Environ. Res. Public Health 2011, 8, 2153-2169.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Int. J. Environ. Res. Public Health EISSN 1660-4601 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top