Quantile-based Bayesian maximum entropy approach for spatiotemporal modeling of ambient air quality levels
Journal
Environmental Science and Technology
Journal Volume
47
Journal Issue
3
Pages
1416-1424
Date Issued
2013
Author(s)
Wang, Chih-Hsin
Abstract
Understanding the daily changes in ambient air quality concentrations is important to the assessing human exposure and environmental health. However, the fine temporal scales (e.g., hourly) involved in this assessment often lead to high variability in air quality concentrations. This is because of the complex short-term physical and chemical mechanisms among the pollutants. Consequently, high heterogeneity is usually present 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. The epistemic framework of the QBME method can allow researchers to further consider the uncertainty of space-time observations. 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. In addition, the epistemic framework can allow researchers to assimilate the site-specific secondary information where the observations are absent because of the common preferential sampling issues of environmental data. The proposed QBME method provides a practical and powerful framework for the spatiotemporal modeling of ambient pollutants. ? 2012 American Chemical Society.
SDGs
Other Subjects
Ambient air pollution; Ambient air quality; Ambient concentrations; Bayesian maximum entropies; Chemical mechanism; Daily change; Environmental data; Environmental health; Estimation performance; High heterogeneity; High variability; Human exposures; Mean absolute error; Non-homogeneous; Nonstationary; PM10 concentration; Pollution level; Site-specific; Smoothing techniques; Space and time; Spatio-temporal variation; Spatiotemporal modeling; Standard deviation; Temporal scale; Air quality; Particles (particulate matter); Pollution; Maximum entropy methods; accuracy assessment; air quality; ambient air; assessment method; atmospheric pollution; Bayesian analysis; carbon monoxide; concentration (composition); data set; entropy; heterogeneity; homogeneity; numerical model; physicochemical property; pollution exposure; spatiotemporal analysis; accuracy; air pollutant; air pollution; air quality; ambient air; analytical error; article; Bayes theorem; conceptual framework; entropy; environmental exposure; quantile based Bayesian maximum entropy; spatiotemporal analysis; Taiwan; Air; Air Pollution; Bayes Theorem; Carbon Monoxide; Entropy; Environmental Monitoring; Models, Theoretical; Particle Size; Particulate Matter; Reproducibility of Results; Seasons; Taiwan
Type
journal article