Development and Application of Automatic Spatialtemporal Estimation Method (A Case Study of Spatiotemporal Distribution of Particulate Matter in Taipei)
Date Issued
2010
Date
2010
Author(s)
Wang, Chih-Hsin
Abstract
Mary geostatistics approached assume the homogeneity and stationarity of the data process. However, the assumption is not valid for most if environment processes of interest (e.g. spatiotemporal distribution of PM). This study developed an automatic scheme to discompose a nonstationary and nonhomogeneous process into a deterministic trend and a random process which can be characterized by the stationary and homogeneous S/T covariance model. Kernel smoothing method and particle swarm optimization method as well as Nelder-Mead simplex method were applied for trend modeling of parameter estimation, respectively. By the proposed scheme, the spatiotemporal bandwidths as well as the covariance parameters are estimated iteratively in order to account for the goodness-of-fit of trend and covariance modeling as well as the complexity of nested covariance model and S/T correlation among the dataset.
Numerous studies have shown that fine airborne particulate matter particles (PM2.5) are more dangerous to human health than coarse particles, e.g. PM10. The assessment of the impacts to human health or ecological effects by long-term PM2.5 exposure is often limited by lack of PM2.5 measurements. In Taipei, PM2.5 was not systematically observed until August, 2005. Taipei is the largest metropolitan area in Taiwan, where a variety of industrial and traffic emissions are continuously generated and distributed across space and time. PM-related data, i.e., PM10 and Total Suspended Particles (TSP) are independently systematically collected by different central and local government institutes. In this study, the retrospective prediction of spatiotemporal distribution of monthly PM2.5 over Taipei will be performed by using Bayesian Maximum Entropy method (BME) to integrate (a) the spatiotemporal dependence among PM measurements (i.e. PM10, TSP, and PM2.5), (b) the site-specific information of PM measurements which can be certain or uncertain information, and (c) empirical evidence about the PM2.5/PM10 and PM10/TSP ratios. The performance assessment of the retrospective prediction for the spatiotemporal distribution of PM2.5 was performed over space and time during 2003-2004 by comparing the posterior pdf of PM2.5 with the observations.
By the proposed scheme, the optimal parameters of trend and covariance models are obtained from PM10 dataset. The retrospective predictions of PM2.5 provide reasonable results in which the relative errors in 2003 at Sinjhuang and 2004 at TWEPA stations are 10.6% and 10.3%, respectively. High values of PM2.5 concentration and the ratios of PM2.5/PM10 and PM2.5/TSP are shown in the areas of Datong district, south of Jungshan district, Jungieng district and Sinjhuang.
Subjects
particulates matter
BME
PSO
NM
covariance fitting
SDGs
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