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Clustering the Concentrations of PM10 and O3: Application of Spatiotemporal Model–Based Clustering

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A Correction to this article was published on 10 December 2021

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Abstract

Air pollution data are large-scale datasets that can be analyzed in low scales by clustering to recognize the pattern of pollution and have simpler and more comprehensible interpretations. So, this study aims to cluster the days of the year 2017 according to the hourly O3 and PM10 amounts collected from four stations of Tabriz by using spatiotemporal mixture model–based clustering (STMC). Besides, mixture model–based clustering with temporal dimension (TMC) and mixture model–based clustering without considering spatiotemporal dimensions (MC) were utilized to compare with STMC. To evaluate the efficiency of these three models, and obtain the optimal number of clusters in each model, BIC and ICL criteria were used. According to BIC and ICL, STMC outperforms TMC and MC. Three clusters for O3 and four clusters for PM10 were selected as the optimal number of clusters to fit STMC models. Regarding PM10, the average concentration was the highest in cluster 4. Regarding O3, all summer days were in cluster 3, and the average concentration of this cluster was the highest. Cluster 2 had the lowest concentration with a high difference from clusters 1 and 3, and its average temperature was the lowest. Autumn days make up about 84% of this cluster. The clustering of polluted and clean days into separate groups and observing the effect of meteorological factors on the amount of concentration in each cluster clearly prove the efficiency of the model. Results of STMC showed that the efficiency of clustering in air pollution data increases by considering both spatiotemporal dimensions.

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Data Availability

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported by the Tabriz University of Medical Sciences. The Health and Environment Research Center provided financial support.

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Authors and Affiliations

  1. Health and Environment Research Center, Department of Statistics and Epidemiology, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran

    Parisa Saeipourdizaj, Saeed Musavi & Parvin Sarbakhsh

  2. Health and Environment Research Center, Department of Environmental Health Engineering, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran

    Akbar Gholampour

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  1. Parisa Saeipourdizaj
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  2. Saeed Musavi
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  4. Parvin Sarbakhsh
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Contributions

Parisa Saeipourdizaj (first author): formulation and evaluation of overarching research goals and aims; setting the data in software package format; application of statistical, computational, and other formal techniques to analyze; application of available software codes; preparation (drafting, reviewing, translating, and revising the paper), and presentation of the manuscript. Saeed Musavi: statistical analysis, manuscript preparation and reviewing the paper. Akbar Gholampour: reviewing the paper. Parvin Sarbakhsh (corresponding author): formulation and evaluation of overarching research goals and aims; statistical analysis; preparation (drafting, reviewing, translating, and revising the paper), and presentation of the manuscript. All the authors have read and approved the final manuscript.

Corresponding author

Correspondence to Parvin Sarbakhsh.

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This article has been extracted from the thesis submitted for MSc degree in Biostatistics which has been approved by the ethics committee of Tabriz University of Medical Sciences (Ethic number: IR.TBZMED.REC.1398.352).

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The authors declare no competing interests.

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Saeipourdizaj, P., Musavi, S., Gholampour, A. et al. Clustering the Concentrations of PM10 and O3: Application of Spatiotemporal Model–Based Clustering. Environ Model Assess 27, 45–54 (2022). https://doi.org/10.1007/s10666-021-09802-5

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