Yuyao He1, Jicheng Jang1, Yun Zhu This email address is being protected from spambots. You need JavaScript enabled to view it.1, Pen-Chi Chiang2,3, Jia Xing4, Shuxiao Wang5, Bin Zhao5, Shicheng Long1, Yingzhi Yuan1 

1 Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
2 Graduate Institute of Environmental Engineering, Taiwan University, Taipei 10673, Taiwan
3 Carbon Cycle Research Center, National Taiwan University, Taipei 10672, Taiwan
4 Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN, 37996, USA
5 State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China


Received: April 27, 2024
Revised: June 5, 2024
Accepted: June 5, 2024

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.240112  


Cite this article:

He, Y., Jang, J., Zhu, Y., Chiang, P.C., Xing, J., Wang, S., Zhao, B., Long, S., Yuan, Y. (2024). Improving Fugitive Dust Emission Inventory from Construction Sector Using UAV Images Recognition. Aerosol Air Qual. Res. 24, 240112. https://doi.org/10.4209/aaqr.240112


HIGHLIGHTS

  • Utilizes drone images and deep learning for enhanced dust emission inventory.
  • Reveals non-linear relationship between particulate matter (PM) emission and area.
  • Identifies major PM emission hotspots in urban construction zones.
  • Significantly improves WRF-CMAQ model accuracy with new inventory data.
  • Analyzes construction dust's specific contributions to air particulates.
 

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.


Keywords: Particulate matter, Construction fugitive dust, UAV image, WRF-CMAQ model, Deep learning




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