TY - JOUR
T1 - Where to place methane monitoring sites in China to better assist carbon management
AU - Zhang, Xiaorui
AU - Zhou, Chenhong
AU - Zhang, Yuzhong
AU - Lu, Xiao
AU - Xiao, Xiang
AU - Wang, Fan
AU - Song, Jun
AU - Guo, Yike
AU - Leung, Kenneth K. M.
AU - Cao, Junji
AU - Gao, Meng
N1 - Funding Information:
This study was supported by grants from Research Grants Council of the Hong Kong Special Administrative Region, China (project no. HKBU22201820 and HKBU12202021) and National Natural Science Foundation of China (No. 42005084).
Publisher Copyright:
© 2023, The Author(s).
PY - 2023/4/21
Y1 - 2023/4/21
N2 - Methane (CH4) is the second most potent greenhouse gas (GHG), and China emerges as the largest anthropogenic CH4 emitter by country. Current limited CH4 monitoring systems in China are unfortunately inadequate to support carbon management. Here we use the Weather Research and Forecasting model (WRF) coupled with a GHG module and satellite constrained emissions to simulate the spatiotemporal distribution of CH4 over East Asia in 2017. Model evaluations using both satellite retrievals and ground-based observations indicate reliable performance. We further inter-compare four proper orthogonal decomposition (POD)-based sensor placement algorithms and find they are able to capture main spatial features of surface CH4 under an oversampled condition. The QR pivot algorithm exhibits superiority in capturing high CH4, and it offers the best reconstruction with both high efficiency and accuracy. Areas with high CH4 concentrations and intense anthropogenic activities remain underrepresented by current CH4 sampling studies, leading to notable reconstruction error over central and eastern China. Optimal planning of 160 sensors guided by the QR pivot algorithm can yield reasonable reconstruction performance and costs of site construction. Our results can provide valuable references for future planning of CH4 monitoring sites.
AB - Methane (CH4) is the second most potent greenhouse gas (GHG), and China emerges as the largest anthropogenic CH4 emitter by country. Current limited CH4 monitoring systems in China are unfortunately inadequate to support carbon management. Here we use the Weather Research and Forecasting model (WRF) coupled with a GHG module and satellite constrained emissions to simulate the spatiotemporal distribution of CH4 over East Asia in 2017. Model evaluations using both satellite retrievals and ground-based observations indicate reliable performance. We further inter-compare four proper orthogonal decomposition (POD)-based sensor placement algorithms and find they are able to capture main spatial features of surface CH4 under an oversampled condition. The QR pivot algorithm exhibits superiority in capturing high CH4, and it offers the best reconstruction with both high efficiency and accuracy. Areas with high CH4 concentrations and intense anthropogenic activities remain underrepresented by current CH4 sampling studies, leading to notable reconstruction error over central and eastern China. Optimal planning of 160 sensors guided by the QR pivot algorithm can yield reasonable reconstruction performance and costs of site construction. Our results can provide valuable references for future planning of CH4 monitoring sites.
UR - http://www.scopus.com/inward/record.url?scp=85154043577&partnerID=8YFLogxK
U2 - 10.1038/s41612-023-00359-6
DO - 10.1038/s41612-023-00359-6
M3 - Journal article
SN - 2397-3722
VL - 6
JO - npj Climate and Atmospheric Science
JF - npj Climate and Atmospheric Science
M1 - 32
ER -
