https://scholars.lib.ntu.edu.tw/handle/123456789/637361
標題: | Unmasking air quality: A novel image-based approach to align public perception with pollution levels | 作者: | Lin, Tzu Chi Wang, Shih Ya Kung, Zhi Ying Su, Yi Han PEI-TE CHIUEH TA-CHIH HSIAO |
關鍵字: | Air quality | Image feature extraction | Image-based data | Particulate Matter (PM) | Perceived visibility | 公開日期: | 1-十一月-2023 | 卷: | 181 | 來源出版物: | Environment International | 摘要: | In the quest to reconcile public perception of air pollution with scientific measurements, our study introduced a pioneering method involving a gradient boost-regression tree model integrating PM2.5 concentration, visibility, and image-based data. Traditional stationary monitoring often falls short of accurately capturing public air quality perceptions, prompting the need for alternative strategies. Leveraging an extensive dataset of over 20,000 public visibility perception evaluations and over 8,000 stationary images, our models effectively quantify diverse air quality perceptions. The predictive prowess of our models was validated by strong performance metrics for perceived visibility (R = 0.98, RMSE = 0.19), all-day PM2.5 concentrations (R: 0.77–0.78, RMSE: 8.31–9.40), and Central Weather Bureau visibility records (R = 0.82, RMSE = 9.00). Interestingly, image contrast and light intensity hold greater importance than scenery clarity in the visibility perception model. However, clarity is prioritized in PM2.5 and Central Weather Bureau models. Our research also unveiled spatial limitations in stationary monitoring and outlined the variations in predictive image features between near and far stations. Crucially, all models benefit from the characterization of atmospheric light sources through defogging techniques. The image-based insights highlight the disparity between public perception of air pollution and current policy implementation. In other words, policymakers should shift from solely emphasizing the reduction of PM2.5 levels to also incorporating the public's perception of visibility into their strategies. Our findings have broad implications for air quality evaluation, image mining in specific areas, and formulating air quality management strategies that account for public perception. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/637361 | ISSN: | 01604120 | DOI: | 10.1016/j.envint.2023.108289 |
顯示於: | 環境工程學研究所 |
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