Rapid urbanization in China is leading to substantial adverse air quality issues, particularly for NO 2 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 NO 2 and PM 2.5 /PM 10 (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.
Background: Nitrogen dioxide (NO 2 ) poses substantial public health risks in large cities globally. Concentrations of NO 2 shows high spatial variation, yet intra-urban measurements of NO 2 in Chinese cities are sparse. The size of Chinese cities and shortage of some datasets is challenging for high spatial resolution modelling. The aim here was to combine advantages of dispersion and land-use regression (LUR) modelling to simulate population exposure to NO 2 at high spatial resolution for health burden calculations, in the example megacity of Guangzhou. Methods: Ambient concentrations of NO 2 simulated by the ADMS-Urban dispersion model at 83 'virtual' monitoring sites, selected to span both the range of NO 2 concentration and weighting by population density, were used to develop a LUR model of 2017 annual-mean NO 2 across Guangzhou at 25 m×25 m spatial resolution. Results: The LUR model was validated against both the 83 virtual sites (adj R 2 : 0.96, RMSE: 5.48 μg m −3 ; LOOCV R 2 : 0.96, RMSE: 5.64 μg m −3 ) and, independently, against available observations (n=11, R 2: : 0.63, RMSE: 18.0 μg m −3 ). The modelled population-weighted long-term average concentration of NO 2 across Guangzhou was 52.5 μg m −3 , which contributes an estimated 7270 (6960−7620) attributable deaths. Reducing concentrations in exceedance of the China air quality standard/WHO air quality guideline of 40 μg m −3 would reduce NO 2 -attributable deaths by 1900 (1820-1980). Conclusions: We demonstrate a general hybrid modelling method that can be employed in other cities in China to model ambient NO 2 concentration at high spatial resolution for health burden estimation and epidemiological study. By running the dispersion model with alternative mitigation policies, new LUR models can be constructed to quantify policy effectiveness on NO 2 population health burden.
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