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Spatial mapping of greenhouse gases using a UAV monitoring platform over a megacity in China.
Han, Tianran; Xie, Conghui; Yang, Yanrong; Zhang, Yuheng; Huang, Yufei; Liu, Yayong; Chen, Keyu; Sun, Haijiong; Zhou, Jietao; Liu, Chang; Guo, Junfei; Wu, Zhijun; Li, Shao-Meng.
Afiliación
  • Han T; State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, PR China.
  • Xie C; State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, PR China.
  • Yang Y; State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, PR China.
  • Zhang Y; State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, PR China.
  • Huang Y; State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, PR China.
  • Liu Y; State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, PR China.
  • Chen K; State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, PR China.
  • Sun H; State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, PR China.
  • Zhou J; State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, PR China.
  • Liu C; State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, PR China.
  • Guo J; Beijing Wisdominc Technology Co., Ltd., Beijing 100070, PR China.
  • Wu Z; State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, PR China.
  • Li SM; State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, PR China. Electronic address: shaomeng.li@pku.edu.cn.
Sci Total Environ ; 951: 175428, 2024 Nov 15.
Article en En | MEDLINE | ID: mdl-39128527
ABSTRACT
Urban environments are recognized as main anthropogenic contributors to greenhouse gas (GHG) emissions, characterized by unevenly distributed emission sources over the urban environments. However, spatial GHG distributions in urban regions are typically obtained through monitoring at only a limited number of locations, or through model studies, which can lead to incomplete insights into the heterogeneity in the spatial distribution of GHGs. To address such information gap and to evaluate the spatial representation of a planned GHG monitoring network, a custom-developed atmospheric sampler was deployed on a UAV platform in this study to map the CO2 and CH4 mixing ratios in the atmosphere over Zhengzhou in central China, a megacity of nearly 13 million people. The aerial survey was conducted along the main roads at an altitude of 150 m above ground, covering a total distance of 170 km from the city center to the suburbs. The spatial distributions of CO2 and CH4 mixing ratios in Zhengzhou exhibited distinct heterogeneities, with average mixing ratios of CO2 and CH4 at 439.2 ± 10.8 ppm and 2.12 ± 0.04 ppm, respectively. A spatial autocorrelation analysis was performed on the measured GHG mixing ratios across the city, revealing a spatial correlation range of approximately 2 km for both CO2 and CH4 in the urban area. Such a spatial autocorrelation distance suggests that the urban GHG monitoring network designed for emission inversion purposes need to have a spatial resolution of 4 km to characterize the spatial heterogeneities in the GHGs. This UAV-based measurement approach demonstrates its capability to monitor GHG mixing ratios across urban landscapes, providing valuable insights for GHG monitoring network design.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sci Total Environ Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sci Total Environ Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos