Quantile-Based Bayesian Maximum Entropy Approach for Spatiotemporal Air Quality Modeling

• Activity Number: 289
• Type: Topic Contributed
• Date/Time: Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Physical and Engineering Sciences
• Author(s): Hwa-Lung Yu*+ and Yi-Jen Lien
• Companies: National Taiwan University and National Taiwan University
• Keywords: Bayesian maximum entropy ; quantile ; spatiotemporal

Abstract

The complex short-term physical and chemical mechanisms among the ambient pollutants result in high heterogeneity in not only the averaged pollution levels, but also the intraday variance levels of the daily observations of ambient concentration across space and time. This characteristic decreases the estimation performance of common techniques. This study proposes a novel quantile-based Bayesian maximum entropy (QBME) method to account for the nonstationary and nonhomogeneous characteristics of ambient air pollution dynamics. The QBME method characterizes the spatiotemporal dependence among the ambient air quality levels based on their location-specific quantiles and accounts for spatiotemporal variations using a local weighted smoothing technique, and allow to consider the data uncertainty. This study presents the spatiotemporal modeling of daily CO and PM10 concentrations across Taiwan from 1998 to 2009 using the QBME method. Results show that the QBME method can effectively improve estimation accuracy in terms of lower mean absolute errors and standard deviations over space and time, especially for pollutants with strong nonhomogeneous variances across space.