Land-Use Regression Modelling of Intra-Urban Air Pollution Variation in China: Current Status and Future Needs

Baihuiqian He, Mathew R, Heal, Stefan Reis

Research output: Contribution to journalArticlepeer-review


Rapid urbanization in China is leading to substantial adverse air quality issues, particularly for NO2 and particulate matter (PM). Land-use regression (LUR) models are now being applied to simulate pollutant concentrations with high spatial resolution in Chinese urban areas. However, Chinese urban areas differ from those in Europe and North America, for example in respect of population density, urban morphology and pollutant emissions densities, so it is timely to assess current LUR studies in China to highlight current challenges and identify future needs. Details of twenty-four recent LUR models for NO2 and PM2.5/PM10 (particles with aerodynamic diameters <2.5 µm and <10 µm) are tabulated and reviewed as the basis of discussion in this paper. We highlight that LUR modelling in China is currently constrained by scarcity of input data, especially air pollution monitoring data. There is urgent need for accessible archives of quality-assured measurement data, and for higher spatial resolution proxy data for urban emissions, particularly in respect of traffic-related variables. The rapidly evolving nature of the Chinese urban landscape makes maintaining up-to-date land-use and urban morphology datasets a challenge. We also highlight the importance for Chinese LUR models to be subject to appropriate validation statistics. Integration of LUR with portable monitor data, remote sensing, and dispersion modelling has potential to enhance derivation of urban pollution maps.
Original languageEnglish
Article number134
Number of pages19
Publication statusPublished - 3 Apr 2018


  • Land-use regression
  • China
  • Air Pollution
  • NO2
  • PM2.5
  • PM10
  • Modelling

Fingerprint Dive into the research topics of 'Land-Use Regression Modelling of Intra-Urban Air Pollution Variation in China: Current Status and Future Needs'. Together they form a unique fingerprint.

Cite this