Assessing and Predicting Air Pollution in Asia: A Regional and Temporal Study (2018-2023)
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866909561069240320 |
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| author | Rahman, Anika Khatun, Mst. Taskia |
| author_facet | Rahman, Anika Khatun, Mst. Taskia |
| contents | This study analyzes and predicts air pollution in Asia, focusing on PM 2.5 levels from 2018 to 2023 across five regions: Central, East, South, Southeast, and West Asia. South Asia emerged as the most polluted region, with Bangladesh, India, and Pakistan consistently having the highest PM 2.5 levels and death rates, especially in Nepal, Pakistan, and India. East Asia showed the lowest pollution levels. K-means clustering categorized countries into high, moderate, and low pollution groups. The ARIMA model effectively predicted 2023 PM 2.5 levels (MAE: 3.99, MSE: 33.80, RMSE: 5.81, R: 0.86). The findings emphasize the need for targeted interventions to address severe pollution and health risks in South Asia. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_15590 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Assessing and Predicting Air Pollution in Asia: A Regional and Temporal Study (2018-2023) Rahman, Anika Khatun, Mst. Taskia Machine Learning Applications This study analyzes and predicts air pollution in Asia, focusing on PM 2.5 levels from 2018 to 2023 across five regions: Central, East, South, Southeast, and West Asia. South Asia emerged as the most polluted region, with Bangladesh, India, and Pakistan consistently having the highest PM 2.5 levels and death rates, especially in Nepal, Pakistan, and India. East Asia showed the lowest pollution levels. K-means clustering categorized countries into high, moderate, and low pollution groups. The ARIMA model effectively predicted 2023 PM 2.5 levels (MAE: 3.99, MSE: 33.80, RMSE: 5.81, R: 0.86). The findings emphasize the need for targeted interventions to address severe pollution and health risks in South Asia. |
| title | Assessing and Predicting Air Pollution in Asia: A Regional and Temporal Study (2018-2023) |
| topic | Machine Learning Applications |
| url | https://arxiv.org/abs/2501.15590 |