ArticleEnvironmental monitoring and assessment2020
Modeling the spatio-temporal dynamics of air pollution index based on spatial Markov chain model.
Article in Environmental monitoring and assessment, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 37 citations in OpenAlex.
- Efficient Likelihood-Based Temporal Changepoint Detection in Spatio-Temporal Processes.Statistics and computing · 2025Article
- Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions of obesity trends.BMC research notes · 2023Article
- Temporal-Spatial Evolution and Trend Prediction of the Supply Efficiency of Primary Medical Health Service-An Empirical Study Based on Central and Western Regions of China.International journal of environmental research and public health · 2023Article
- Research Orientation and Development of Social Psychology's Concept of Justice in the Era of Cloud Computing.Frontiers in psychology · 2022Article
- Robust empirical Bayes approach for Markov chain modeling of air pollution index.Journal of environmental health science & engineering · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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 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.
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