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Clustering the Concentrations of PM10 and O3: Application of Spatiotemporal Model–Based Clustering
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.
Clustering the Concentrations of PM10 and O3: Application of Spatiotemporal Model–Based Clustering
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.
Clustering the Concentrations of PM10 and O3: Application of Spatiotemporal Model–Based Clustering
Environ Model Assess
Saeipourdizaj, Parisa (author) / Musavi, Saeed (author) / Gholampour, Akbar (author) / Sarbakhsh, Parvin (author)
Environmental Modeling & Assessment ; 27 ; 45-54
2022-02-01
10 pages
Article (Journal)
Electronic Resource
English