Optimal Planning of Air Quality-Monitoring Sites for Better Depiction of PM2.5Pollution across China

Chenhong Zhou, Meng Gao*, Jianjun Li, Kaixu Bai, Xiao Tang, Xiao Lu, Cheng Liu, Zifa Wang, Yike Guo*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

4 Citations (Scopus)


A myriad of studies have attempted to use ground-level observations to obtain gap-free spatiotemporal variations of PM2.5, in support of air quality management and impact studies. Statistical methods (machine learning, etc.) or numerical methods by combining chemical transport modeling and observations with data assimilation techniques have been typically applied, yet the significance of site placement has not been well recognized. In this study, we apply five proper orthogonal decomposition (POD)-based sensor placement algorithms to identify optimal site locations and systematically evaluate their reconstruction ability. We demonstrate that the QR pivot is relatively more reliable in deciding optimal monitoring site locations. When the number of planned sites (sensors) is limited, using a lower number of modes would yield lower estimation errors. However, the dimension of POD modes has little impact on reconstruction quality when sufficient sensors are available. The locations of sites guided by the QR pivot algorithm are mainly located in regions where PM2.5 pollution is severe. We compare reconstructed PM2.5 pollution based on QR pivot-guided sites and existing China National Environmental Monitoring Center (CNEMC) sites and find that the QR pivot-guided sites are superior to existing sites with respect to reconstruction accuracy. The current planning of monitoring stations is likely to miss sources of pollution in less-populated regions, while our QR pivot-guided sites are planned based on the severity of PM2.5 pollution. This planning methodology has additional potentials in chemical data assimilation studies as duplicate information from current CNEMC-concentrated stations is not likely to boost performance.

Original languageEnglish
Pages (from-to)314-323
Number of pages10
JournalACS Environmental Au
Issue number4
Early online date10 Mar 2022
Publication statusPublished - 20 Jul 2022

Scopus Subject Areas

  • Environmental Science (miscellaneous)
  • Environmental Engineering
  • Water Science and Technology

User-Defined Keywords

  • aerosol data assimilation
  • ground-level PMestimation
  • PMpollution
  • sensor placement


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