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| DOI | 10.1016/j.envpol.2024.123463 |
| Decoding seasonal variability of air pollutants with climate factors: A geostatistical approach using multimodal regression models for informed climate change mitigation | |
| 发表日期 | 2024 |
| ISSN | 0269-7491 |
| EISSN | 1873-6424 |
| 起始页码 | 345 |
| 卷号 | 345 |
| 英文摘要 | In response to changes in climatic patterns, a profound comprehension of air pollutants (AP) variability is vital for enhancing climate models and facilitating informed decision-making in nations susceptible to climate change. Earlier research primarily depended on limited models, potentially neglecting intricate relationships and not fully encapsulating associations. This study, in contrast, probed the spatiotemporal variability of airborne particles (CO, CH4, SO2, and NO2) under varying climatic conditions within a climate-sensitive nation, utilizing multiple regression models. Spatial and seasonal AP data were acquired via the Google Earth Engine platform, which indicated elevated AP concentrations in primarily urban areas. Remarkably, the average airborne particle levels were lower in 2020 than in 2019, though they escalated during winter. The study employed linear regression, Pearson's correlation (PC), Spearman rank correlation models, and Geographically Weighted Regression (GWR) models to probe the relationship between pollutant variability and climatic elements such as rainfall, temperature, and humidity. Across all seasons, APs showed a negative correlation with rainfall while displaying positive correlations with temperature and humidity. The GWR and PC models produced the most reliable results from all the models employed, with the GWR model superseding the rest. Moreover, heightened aerosol levels were detected within a rainfall range of 600 mm/season, a temperature range of 25-30 degrees C, and humidity levels of 75 %-85 %. Overall, this study emphasizes the growing levels of APs in correlation with meteorological changes. By adopting a comprehensive approach and considering multiple factors, this research provides a more sophisticated understanding of the relationship between AP variability and climatic shifts. |
| 英文关键词 | Geographically weighted regression; Air pollutants; Seasonal variability analysis; Google Earth Engine |
| 语种 | 英语 |
| WOS研究方向 | Environmental Sciences & Ecology |
| WOS类目 | Environmental Sciences |
| WOS记录号 | WOS:001181938200001 |
| 来源期刊 | ENVIRONMENTAL POLLUTION
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/289031 |
| 作者单位 | Khulna University of Engineering & Technology (KUET); State University System of Florida; Florida State University; University of Texas System; University of Texas Austin; King Khalid University; Imam Abdulrahman Bin Faisal University; King Khalid University; University of Texas System; University of Texas Austin |
| 推荐引用方式 GB/T 7714 | . Decoding seasonal variability of air pollutants with climate factors: A geostatistical approach using multimodal regression models for informed climate change mitigation[J],2024,345. |
| APA | (2024).Decoding seasonal variability of air pollutants with climate factors: A geostatistical approach using multimodal regression models for informed climate change mitigation.ENVIRONMENTAL POLLUTION,345. |
| MLA | "Decoding seasonal variability of air pollutants with climate factors: A geostatistical approach using multimodal regression models for informed climate change mitigation".ENVIRONMENTAL POLLUTION 345(2024). |
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