Optimization of Land Use Regression Modelling of PM2.5Spatial Variations in Different Seasons across China

Jun Chai, Jun Song*, Le Zhang, Bing Guo*, Yawen Xu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Fine particulate matter (PM2.5), one of the main components of haze, is of wide concern for its potential negative health effects. In order to further improve ambient air quality, it is essential to conclude the spatial variability of pollutants by investigating air pollution exposure. We divide China into two parts, north and south, and use a Land Use Regression (LUR) model to extract data including meteorological data, land use factors, and AOD retrievals, and use the machine learning algorithm to optimize the model to achieve predictions of the spatial distribution of near-surface PM2.5 mass concentrations in southern and northern China. We evaluated the seasonal consistency of the models in southern and northern China, and in northern China, we found a better fit with better seasonal consistency for the heating season and annual average model, while in southern China, we did not find a more fitted seasonal phase. The study illustrates that it is feasible to simulate the spatial distribution of PM2.5 mass concentration in large-scale areas based on the LUR model, and the seasonal consistency of the LUR model has been done to some extent.

Original languageEnglish
Article number3659254
Number of pages9
JournalJournal of Sensors
Publication statusPublished - 28 Sept 2022

Scopus Subject Areas

  • Control and Systems Engineering
  • Instrumentation
  • Electrical and Electronic Engineering


Dive into the research topics of 'Optimization of Land Use Regression Modelling of PM2.5Spatial Variations in Different Seasons across China'. Together they form a unique fingerprint.

Cite this