Spatiotemporal Analysis of PM2.5 Exposure in Taipei (Taiwan) by Integrating PM10 and TSP Observations

Abstract

Many studies have shown a significant association between human exposure to Particulate Matter (PM) and population health effects (premature mortality, respiratory and cardiovascular diseases, emergency room visits, and systemic inflammation). Fine PM particles (PM2.5) are believed to be more dangerous than PM10 because fine particles are easier inhaled and accumulated deeply into human lungs. Taipei is the largest city in Taiwan, where a variety of industrial and traffic emissions are continuously generated across space and time. Thus, it is crucial for health agencies to improve their understanding of spatiotemporal PM2.5 exposure of people living in Taipei city. The Bayesian Maximum Entropy (BME) theory of spatiotemporal statistics and science-based mapping provides valuable information about population exposure to PM2.5 pollution in Taipei. PM-related data (PM10, PM2.5 and Total Suspended Particles, TSP) are collected by different central and local government institutes. BME analysis introduces concepts and techniques that have a number of important features (theoretical and computational): several kinds of site-specific data and core knowledge bases are integrated that are associated with different physical scales; a variety of uncertainty sources are taken into account; non-linear, in general, PM estimators are used at unobserved locations; non-Gaussian laws are automatically incorporated; and a complete characterization of the dynamic PM distribution is obtained in terms of the probability density function at each space-time point rather than a single PM value. These BME advantages have considerable consequences as far as health risk analysis is concerned. Detailed space-time PM2.5 maps account for (i) composite space-time dependence structure of PM values, (ii) hard and soft datasets available about PM2.5, PM10 and TSP, and (iii) empirical evidence about the PM2.5/PM10 and PM10/TSP ratios. PM measures are investigated, including the fraction of fine particles that vary considerably across space-time. BME analysis properly identifies and quantifies factors that influence the spatiotemporal patterns of these measures, such as weather conditions and land use (e.g., the PM distributions in highly-developed commercial or industrial areas generally have higher fine particle fractions). Information generated by rigorous BME analysis and mapping across space-time constitutes valuable input to health management and decision-making under conditions of uncertainty.