Abstract
PM2.5 is a major air pollutant which has a high probability to cause many serious cardiopulmonary diseases, such as asthma, lung cancer, trachea cancer, bronchus cancer, etc. Up to 2014, a World Health Organization (WHO) air quality model confirmed that 92% of the population in the world lived in areas where air quality levels exceeded WHO limits (i.e., 10 µg/m3). This indicates that PM2.5 is still one of the most serious world-wide problems, and monitoring PM2.5 concentrations is extremely necessary. In this paper, we proposed a easy and flexible spatial-temporal Gaussian mixture model to analyze annual average PM2.5 concentrations. Because of the bimodal distribution of PM2.5 concentrations, we decided for a two- component Gaussian mixture model with county-year-level spatial-temporal random effects. A Markov Chain Monte Carlo (MCMC) algorithm is used to estimating model parameters.
Highlights
Fine particles with a diameter of 2.5 μm or less (PM2.5) is a major air pollutant which has a high probability to cause many serious cardiopulmonary diseases, such as asthma, lung cancer, trachea cancer, bronchus cancer, etc. (Monn & Becker, 1999; Cohen et al, 2005)
Much effort has been put into lowering PM2.5 concentration, up to 2014, a World Health Organization (WHO) air quality model confirmed that 92% of the population in the world still lived in areas where air quality levels exceeded WHO limits (i.e., 10 μg/m3)
Pérez et al (2000) compared the predictions produced by multilayer neural networks, linear regression and persistence based on the data of PM2.5 in Santiago, Chile
Summary
Fine particles with a diameter of 2.5 μm or less (PM2.5) is a major air pollutant which has a high probability to cause many serious cardiopulmonary diseases, such as asthma, lung cancer, trachea cancer, bronchus cancer, etc. (Monn & Becker, 1999; Cohen et al, 2005). Brown et al (1994) developed and applied a multivariate approach to the spatial interpolation for analyzing air pollutant in southern Ontario, Canada. Pérez et al (2000) compared the predictions produced by multilayer neural networks, linear regression and persistence based on the data of PM2.5 in Santiago, Chile. They found that the neural network gives the best results. Wang & Fang (2016) analyzed PM2.5 in Bohai rime, Chine, with a spatial-temporal model. They set the parameters of covariates as functions of spatial coordinates. A Markov Chain Monte Carlo (MCMC) algorithm for model parameter estimation was implemented in Winbugs 1.4.3 (http://www.mrc-bsu.cam.ac.uk/software/bugs/the-bugs- project-winbugs/)
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