Spatiotemporal Analysis and Machine Learning-Based Profiling of Atmospheric Pollutants Using Sentinel-5P TROPOMI Satellite Data at Tier-Two Administrative Units in Bangladesh.
Air pollution poses a critical environmental and public health threat in Bangladesh, yet a comprehensive, multi pollutant analysis at a high administrative resolution has been lacking. This study bridges this gap by leveraging the high-resolution capabilities of Sentinel-5P TROPOMI satellite data an...
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| Formato: | Online |
| Idioma: | inglés |
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Journal of Science
2026
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| Acceso en liña: | http://journals.juniv.edu/index.php/jos/article/view/425 |
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| _version_ | 1873958352048881664 |
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| author | Bipro Acharjee Monim Abdullah Mahmudul Hasan Hritwik Roy Toqi Tahmid Fahim Mohammad Mizanur Rahman |
| author_facet | Bipro Acharjee Monim Abdullah Mahmudul Hasan Hritwik Roy Toqi Tahmid Fahim Mohammad Mizanur Rahman |
| author_sort | Bipro Acharjee |
| collection | OJS |
| description | Air pollution poses a critical environmental and public health threat in Bangladesh, yet a comprehensive, multi pollutant analysis at a high administrative resolution has been lacking. This study bridges this gap by leveraging the high-resolution capabilities of Sentinel-5P TROPOMI satellite data and an unsupervised machine learning framework to conduct a district-level assessment of five key atmospheric pollutants—SO₂, NO₂, O₃, HCHO, and CH₄—from 2020 to 2024. Annual mean concentrations were extracted for all 64 districts using Google Earth Engine, followed by rigorous spatiotemporal trend analysis using the Mann-Kendall test. The core novelty of this research lies in the application of K-means clustering to classify districts based on their holistic, multi-annual pollutant profiles. The analysis revealed distinct national trends: a significant increase in O₃ (54 districts) and CH₄ (61 districts), a volatile trend for SO₂, and a notable decrease in NO₂ in 21 urban districts. Critically, the K-means algorithm (k=5) segmented the country into five discrete pollution clusters, ranked from best to worst: 1) Cleanest (16 districts, e.g., coastal and hilly regions), 2) Moderate (7 districts, e.g., northeastern regions), 3) High O₃ & CH₄ (16 northern agricultural districts), 4) High Multi-Pollutant (23 southwestern and central industrial districts), and 5) Extreme Urban/Industrial (4 districts: Dhaka, Gazipur, Munshiganj, Narayanganj). This final cluster exhibited an extreme concentration of NO₂ and CH₄, over 50% higher than the national average. The findings demonstrate that Bangladesh's air pollution landscape is not monolithic but a mosaic of distinct regional typologies, each driven by different dominant sources—from vehicular and industrial combustion in urban centers to agricultural emissions in the north. This study provides a novel, data-driven framework for "pollution zonation," moving beyond one-size-fits-all policies towards targeted, cluster-specific mitigation strategies. The methodology establishes a replicable model for evidence-based air quality management in data-scarce regions globally. |
| format | Online |
| id | ojs-425 |
| institution | My University |
| language | eng |
| publishDate | 2026 |
| publisher | Journal of Science |
| record_format | ojs |
| spelling | ojs-4252026-08-18T05:22:45Z Spatiotemporal Analysis and Machine Learning-Based Profiling of Atmospheric Pollutants Using Sentinel-5P TROPOMI Satellite Data at Tier-Two Administrative Units in Bangladesh. Bipro Acharjee Monim Abdullah Mahmudul Hasan Hritwik Roy Toqi Tahmid Fahim Mohammad Mizanur Rahman Air pollution analysis, K-means clustering, Multi-pollutant assessment, District-level air quality mapping, Google Earth Engine (GEE) Air pollution poses a critical environmental and public health threat in Bangladesh, yet a comprehensive, multi pollutant analysis at a high administrative resolution has been lacking. This study bridges this gap by leveraging the high-resolution capabilities of Sentinel-5P TROPOMI satellite data and an unsupervised machine learning framework to conduct a district-level assessment of five key atmospheric pollutants—SO₂, NO₂, O₃, HCHO, and CH₄—from 2020 to 2024. Annual mean concentrations were extracted for all 64 districts using Google Earth Engine, followed by rigorous spatiotemporal trend analysis using the Mann-Kendall test. The core novelty of this research lies in the application of K-means clustering to classify districts based on their holistic, multi-annual pollutant profiles. The analysis revealed distinct national trends: a significant increase in O₃ (54 districts) and CH₄ (61 districts), a volatile trend for SO₂, and a notable decrease in NO₂ in 21 urban districts. Critically, the K-means algorithm (k=5) segmented the country into five discrete pollution clusters, ranked from best to worst: 1) Cleanest (16 districts, e.g., coastal and hilly regions), 2) Moderate (7 districts, e.g., northeastern regions), 3) High O₃ & CH₄ (16 northern agricultural districts), 4) High Multi-Pollutant (23 southwestern and central industrial districts), and 5) Extreme Urban/Industrial (4 districts: Dhaka, Gazipur, Munshiganj, Narayanganj). This final cluster exhibited an extreme concentration of NO₂ and CH₄, over 50% higher than the national average. The findings demonstrate that Bangladesh's air pollution landscape is not monolithic but a mosaic of distinct regional typologies, each driven by different dominant sources—from vehicular and industrial combustion in urban centers to agricultural emissions in the north. This study provides a novel, data-driven framework for "pollution zonation," moving beyond one-size-fits-all policies towards targeted, cluster-specific mitigation strategies. The methodology establishes a replicable model for evidence-based air quality management in data-scarce regions globally. Journal of Science 2026-08-11 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Conference Proceedings Submission application/pdf http://journals.juniv.edu/index.php/jos/article/view/425 10.59185/jos.v46i1.425 Journal of Science; Vol. 46 No. 1 (2026): Jahangirnagar University Journal of Science; 1-28 10.59185/jos.v46i1 eng http://journals.juniv.edu/index.php/jos/article/view/425/344 Copyright (c) 2026 Journal of Science |
| spellingShingle | Air pollution analysis, K-means clustering, Multi-pollutant assessment, District-level air quality mapping, Google Earth Engine (GEE) Bipro Acharjee Monim Abdullah Mahmudul Hasan Hritwik Roy Toqi Tahmid Fahim Mohammad Mizanur Rahman Spatiotemporal Analysis and Machine Learning-Based Profiling of Atmospheric Pollutants Using Sentinel-5P TROPOMI Satellite Data at Tier-Two Administrative Units in Bangladesh. |
| title | Spatiotemporal Analysis and Machine Learning-Based Profiling of Atmospheric Pollutants Using Sentinel-5P TROPOMI Satellite Data at Tier-Two Administrative Units in Bangladesh. |
| title_full | Spatiotemporal Analysis and Machine Learning-Based Profiling of Atmospheric Pollutants Using Sentinel-5P TROPOMI Satellite Data at Tier-Two Administrative Units in Bangladesh. |
| title_fullStr | Spatiotemporal Analysis and Machine Learning-Based Profiling of Atmospheric Pollutants Using Sentinel-5P TROPOMI Satellite Data at Tier-Two Administrative Units in Bangladesh. |
| title_full_unstemmed | Spatiotemporal Analysis and Machine Learning-Based Profiling of Atmospheric Pollutants Using Sentinel-5P TROPOMI Satellite Data at Tier-Two Administrative Units in Bangladesh. |
| title_short | Spatiotemporal Analysis and Machine Learning-Based Profiling of Atmospheric Pollutants Using Sentinel-5P TROPOMI Satellite Data at Tier-Two Administrative Units in Bangladesh. |
| title_sort | spatiotemporal analysis and machine learning based profiling of atmospheric pollutants using sentinel 5p tropomi satellite data at tier two administrative units in bangladesh |
| topic | Air pollution analysis, K-means clustering, Multi-pollutant assessment, District-level air quality mapping, Google Earth Engine (GEE) |
| topic_facet | Air pollution analysis, K-means clustering, Multi-pollutant assessment, District-level air quality mapping, Google Earth Engine (GEE) |
| url | http://journals.juniv.edu/index.php/jos/article/view/425 |
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