Modeling the spatio-temporal dynamics of air pollution index based on spatial Markov chain model
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Modeling the spatio-temporal dynamics of air pollution index based on spatial Markov chain model Yousif Alyousifi & Kamarulzaman Ibrahim & Wei Kang & Wan Zawiah Wan Zin
Received: 12 June 2020 / Accepted: 5 October 2020 # Springer Nature Switzerland AG 2020
Abstract An environmental problem which is of concern across the globe nowadays is air pollution. The extent of air pollution is often studied based on data on the observed level of air pollution. Although the analysis of air pollution data that is available in the literature is numerous, studies on the dynamics of air pollution with the allowance for spatial interaction effects through the use of the Markov chain model are very limited. Accordingly, this study aims to explore the potential impact of spatial dependence over time and space on the distribution of air pollution based on the spatial Markov chain (SMC) model using the longitudinal air pollution index (API) data. This SMC model is pertinent to be applied since the daily data of API from 2012 to 2014 that have been gathered from 37 different air quality stations in Peninsular Malaysia is found to exhibit the property of spatial autocorrelation. Based on the spatial transition probability matrices found from the SMC model, specific characteristics of air pollution are
Y. Alyousifi : K. Ibrahim : W. Z. W. Zin Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia Y. Alyousifi (*) School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia e-mail: [email protected] W. Kang Center for Geospatial Sciences, University of California, Riverside, CA, USA
studied in the regional context. These characteristics are the long-run proportion and the mean first passage time for each state of air pollution. It is found that the probability for a particular station’s state to remain good is 0.814 if its neighbors are in a good state of air pollution and 0.7082 if its neighbors are in a moderate state. For a particular station having neighbors in a good state of air pollution, the proportion of time for it to continue being in a good state is 0.6. This proportion reduces to 0.4, 0.01, and 0 for the cell of moderate, unhealthy, and very unhealthy states, respectively. In addition, there exists a significant spatial dependence of API, indicating that air pollution for a particular station is dependent on the states of the neighboring stations. Keywords Air pollution assessment . Spatial Markov chain . Spatial dependence . Spatial autocorrelation . Spatial distribution . Moran’s I statistic . Peninsular Malaysia Abbreviations API Air pollution index C Geary’s C test Chi-2 Chi-squared CO Carbon monoxide DMC Discrete Markov chain DOE Department of Environment FDI Foreign Direct Investment I Moran’s I statistic LR Likelihood ratio Max Maximum value MFPT Mean first passage time Min Minimum value
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MLE NO2 O3 PM10 SD SMC SO2 WHO GTM GTPM STM STPM GSSP
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