Land-use regression modelling of intra-urban air pollution variation in China: current status and future needs

He, Baihuiqian; Heal, Mathew R.; Reis, Stefan ORCID: https://orcid.org/0000-0003-2428-8320. 2018 Land-use regression modelling of intra-urban air pollution variation in China: current status and future needs. Atmosphere, 9 (4), 134. 19, pp. 10.3390/atmos9040134

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Abstract/Summary

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 for discussion in this paper. We highlight that LUR modelling in China is currently constrained by a scarcity of input data, especially air pollution monitoring data. There is an 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 the potential to enhance derivation of urban pollution maps.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.3390/atmos9040134
UKCEH and CEH Sections/Science Areas:	Atmospheric Chemistry and Effects (Science Area 2017-)
ISSN:	2073-4433
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	NO2, PM2.5, PM10, air quality, LUR models
NORA Subject Terms:	Ecology and Environment Atmospheric Sciences
Date made live:	12 Apr 2018 10:12 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/519816

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