DeepAir: deep learning and satellite imagery to estimate high-resolution PM2.5 at scale
Air pollution, specifically PM _2.5 , has become a significant global concern owing to its detrimental impacts on public health. Even so, the high-resolution monitoring of air pollution is still a challenge on a global scale. To cope with this, machine learning (ML) techniques have been utilized to...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-2153/adb67a |
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| author | Wenxuan Guo Zhaoping Hu Ling Jin Yanyan Xu Marta C Gonzalez |
| author_facet | Wenxuan Guo Zhaoping Hu Ling Jin Yanyan Xu Marta C Gonzalez |
| author_sort | Wenxuan Guo |
| collection | DOAJ |
| description | Air pollution, specifically PM _2.5 , has become a significant global concern owing to its detrimental impacts on public health. Even so, the high-resolution monitoring of air pollution is still a challenge on a global scale. To cope with this, machine learning (ML) techniques have been utilized to infer the concentration of air pollutants at a fine scale. In this study, we propose DeepAir , a learning framework for estimating PM _2.5 concentrations at a fine scale with sparsely distributed observations. DeepAir integrates a pre-trained convolutional neural network with the LightGBM method. This framework estimates the PM _2.5 concentration of a given patch, utilizing a synergy of geographical information, meteorological conditions, and satellite observations. We select California as the focal region and train the model with data from 2014 to 2017 provided by 130 PM _2.5 observation stations in the state. Upon training, the model can be applied to estimate the daily PM _2.5 concentrations at 1 km resolution across California. Our methodology meticulously incorporates meteorological variables, with a particular emphasis on wildfire propagation, and contemplates the complex interplay of various features. To ascertain the efficacy of our model, we employ the 10-fold cross-validation technique, which confirms that our model surpasses traditional ML and standalone deep learning methods. |
| format | Article |
| id | doaj-art-a4ba3cdcb2964edb8d3bfb3e670440f8 |
| institution | DOAJ |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-a4ba3cdcb2964edb8d3bfb3e670440f82025-08-20T03:00:03ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016101505710.1088/2632-2153/adb67aDeepAir: deep learning and satellite imagery to estimate high-resolution PM2.5 at scaleWenxuan Guo0https://orcid.org/0000-0001-6336-3819Zhaoping Hu1Ling Jin2Yanyan Xu3https://orcid.org/0000-0001-5429-3177Marta C Gonzalez4MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University , Shanghai 200240, People’s Republic of ChinaMoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University , Shanghai 200240, People’s Republic of ChinaEnergy Technologies Area , Lawrence Berkeley National Laboratory, Berkeley, CA 94720, United States of AmericaMoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University , Shanghai 200240, People’s Republic of China; Energy Technologies Area , Lawrence Berkeley National Laboratory, Berkeley, CA 94720, United States of America; Department of City and Regional Planning, University of California , Berkeley, CA 94720, United States of AmericaEnergy Technologies Area , Lawrence Berkeley National Laboratory, Berkeley, CA 94720, United States of America; Department of City and Regional Planning, University of California , Berkeley, CA 94720, United States of America; Department of Civil and Environmental Engineering, University of California , Berkeley, CA 94720, United States of AmericaAir pollution, specifically PM _2.5 , has become a significant global concern owing to its detrimental impacts on public health. Even so, the high-resolution monitoring of air pollution is still a challenge on a global scale. To cope with this, machine learning (ML) techniques have been utilized to infer the concentration of air pollutants at a fine scale. In this study, we propose DeepAir , a learning framework for estimating PM _2.5 concentrations at a fine scale with sparsely distributed observations. DeepAir integrates a pre-trained convolutional neural network with the LightGBM method. This framework estimates the PM _2.5 concentration of a given patch, utilizing a synergy of geographical information, meteorological conditions, and satellite observations. We select California as the focal region and train the model with data from 2014 to 2017 provided by 130 PM _2.5 observation stations in the state. Upon training, the model can be applied to estimate the daily PM _2.5 concentrations at 1 km resolution across California. Our methodology meticulously incorporates meteorological variables, with a particular emphasis on wildfire propagation, and contemplates the complex interplay of various features. To ascertain the efficacy of our model, we employ the 10-fold cross-validation technique, which confirms that our model surpasses traditional ML and standalone deep learning methods.https://doi.org/10.1088/2632-2153/adb67ahigh resolution PM2.5spatial interpolationsatellite imagerydeep learning |
| spellingShingle | Wenxuan Guo Zhaoping Hu Ling Jin Yanyan Xu Marta C Gonzalez DeepAir: deep learning and satellite imagery to estimate high-resolution PM2.5 at scale Machine Learning: Science and Technology high resolution PM2.5 spatial interpolation satellite imagery deep learning |
| title | DeepAir: deep learning and satellite imagery to estimate high-resolution PM2.5 at scale |
| title_full | DeepAir: deep learning and satellite imagery to estimate high-resolution PM2.5 at scale |
| title_fullStr | DeepAir: deep learning and satellite imagery to estimate high-resolution PM2.5 at scale |
| title_full_unstemmed | DeepAir: deep learning and satellite imagery to estimate high-resolution PM2.5 at scale |
| title_short | DeepAir: deep learning and satellite imagery to estimate high-resolution PM2.5 at scale |
| title_sort | deepair deep learning and satellite imagery to estimate high resolution pm2 5 at scale |
| topic | high resolution PM2.5 spatial interpolation satellite imagery deep learning |
| url | https://doi.org/10.1088/2632-2153/adb67a |
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