Improving Fugitive Dust Emission Inventory from Construction Sector Using UAV Images Recognition
Abstract Over rapidly developing and urbanizing countries, frequent construction activities are the primary drivers behind the substantial emissions and contributors of fugitive dust. In this study, an innovative method was developed to compile a high-resolution spatiotemporal emission inventory fro...
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| Format: | Article |
| Language: | English |
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Springer
2024-06-01
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| Series: | Aerosol and Air Quality Research |
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| Online Access: | https://doi.org/10.4209/aaqr.240112 |
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| author | Yuyao He Jicheng Jang Yun Zhu Pen-Chi Chiang Jia Xing Shuxiao Wang Bin Zhao Shicheng Long Yingzhi Yuan |
| author_facet | Yuyao He Jicheng Jang Yun Zhu Pen-Chi Chiang Jia Xing Shuxiao Wang Bin Zhao Shicheng Long Yingzhi Yuan |
| author_sort | Yuyao He |
| collection | DOAJ |
| description | Abstract Over rapidly developing and urbanizing countries, frequent construction activities are the primary drivers behind the substantial emissions and contributors of fugitive dust. In this study, an innovative method was developed to compile a high-resolution spatiotemporal emission inventory from construction sector, utilizing unmanned aerial vehicle (UAV) images. This methodology offered detailed activity level information by distinguishing various types of construction lands and equipment. Focusing on the Shunde District of Guangdong in China, the new emission inventory derived from this method highlighted that travel, topsoil excavation, and loading collectively contributed up to 90% of particulate matter (PM) emissions during the earthwork phase. Moreover, this new inventory rectified the tendency of traditional methods to underestimate PM10 emissions and overestimate PM2.5 emissions, while revealing the non-linear relationship between PM emissions and construction area. This improved PM emission inventory appeared to precisely identify major emission hotspots and enhanced performance of the Community Multi-scale Air Quality (CMAQ) model, and the correlation coefficient (R-value) is 0.08 ± 0.02 higher than that of the traditional emission inventory. Post integration of monitoring data through the Software for the Modeled Attainment Test - Community Edition (SMAT-CE), the contributions of construction dust to local PM10 and PM2.5 concentrations were estimated at 3.27 ± 0.8 µg m–3 and 1.11 ± 0.27 µg m–3, respectively, with more pronounced impacts observed in the central, northwestern, and south-central zones of the study region. This study provides valuable insight for improving construction dust and PM emission inventories, which should be benefiting the development of air pollution prevention and control strategies over this study area as well as other rapidly growing urban areas. |
| format | Article |
| id | doaj-art-ec7fa5d2e7d64a08a23b1b5b22312f45 |
| institution | Kabale University |
| issn | 1680-8584 2071-1409 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Aerosol and Air Quality Research |
| spelling | doaj-art-ec7fa5d2e7d64a08a23b1b5b22312f452025-02-09T12:24:29ZengSpringerAerosol and Air Quality Research1680-85842071-14092024-06-0124811810.4209/aaqr.240112Improving Fugitive Dust Emission Inventory from Construction Sector Using UAV Images RecognitionYuyao He0Jicheng Jang1Yun Zhu2Pen-Chi Chiang3Jia Xing4Shuxiao Wang5Bin Zhao6Shicheng Long7Yingzhi Yuan8Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega CenterGuangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega CenterGuangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega CenterGraduate Institute of Environmental Engineering, Taiwan UniversityDepartment of Civil and Environmental Engineering, University of TennesseeState Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua UniversityState Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua UniversityGuangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega CenterGuangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega CenterAbstract Over rapidly developing and urbanizing countries, frequent construction activities are the primary drivers behind the substantial emissions and contributors of fugitive dust. In this study, an innovative method was developed to compile a high-resolution spatiotemporal emission inventory from construction sector, utilizing unmanned aerial vehicle (UAV) images. This methodology offered detailed activity level information by distinguishing various types of construction lands and equipment. Focusing on the Shunde District of Guangdong in China, the new emission inventory derived from this method highlighted that travel, topsoil excavation, and loading collectively contributed up to 90% of particulate matter (PM) emissions during the earthwork phase. Moreover, this new inventory rectified the tendency of traditional methods to underestimate PM10 emissions and overestimate PM2.5 emissions, while revealing the non-linear relationship between PM emissions and construction area. This improved PM emission inventory appeared to precisely identify major emission hotspots and enhanced performance of the Community Multi-scale Air Quality (CMAQ) model, and the correlation coefficient (R-value) is 0.08 ± 0.02 higher than that of the traditional emission inventory. Post integration of monitoring data through the Software for the Modeled Attainment Test - Community Edition (SMAT-CE), the contributions of construction dust to local PM10 and PM2.5 concentrations were estimated at 3.27 ± 0.8 µg m–3 and 1.11 ± 0.27 µg m–3, respectively, with more pronounced impacts observed in the central, northwestern, and south-central zones of the study region. This study provides valuable insight for improving construction dust and PM emission inventories, which should be benefiting the development of air pollution prevention and control strategies over this study area as well as other rapidly growing urban areas.https://doi.org/10.4209/aaqr.240112Particulate matterConstruction fugitive dustUAV imageWRF-CMAQ modelDeep learning |
| spellingShingle | Yuyao He Jicheng Jang Yun Zhu Pen-Chi Chiang Jia Xing Shuxiao Wang Bin Zhao Shicheng Long Yingzhi Yuan Improving Fugitive Dust Emission Inventory from Construction Sector Using UAV Images Recognition Aerosol and Air Quality Research Particulate matter Construction fugitive dust UAV image WRF-CMAQ model Deep learning |
| title | Improving Fugitive Dust Emission Inventory from Construction Sector Using UAV Images Recognition |
| title_full | Improving Fugitive Dust Emission Inventory from Construction Sector Using UAV Images Recognition |
| title_fullStr | Improving Fugitive Dust Emission Inventory from Construction Sector Using UAV Images Recognition |
| title_full_unstemmed | Improving Fugitive Dust Emission Inventory from Construction Sector Using UAV Images Recognition |
| title_short | Improving Fugitive Dust Emission Inventory from Construction Sector Using UAV Images Recognition |
| title_sort | improving fugitive dust emission inventory from construction sector using uav images recognition |
| topic | Particulate matter Construction fugitive dust UAV image WRF-CMAQ model Deep learning |
| url | https://doi.org/10.4209/aaqr.240112 |
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