Optimal Planning of Air Quality-Monitoring Sites for Better Depiction of PM_2.5 Pollution across China.

ABSTRACT: A myriad of studies have attempted to use ground-level observations to obtain gap-free spatiotemporal variations of PM_2.5, 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 limit

SUBMITTER: Zhou C 

PROVIDER: S-EPMC10125350 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature