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

Abstract

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.

Assessing NO2 Concentration and Model Uncertainty with High Spatiotemporal Resolution across the Contiguous United States Using Ensemble Model Averaging

“…NO 2 models with fine spatial resolution or/and temporal resolution were often constrained to a small study area, usually at the city level, 13,19,31,33,38−43 while studies extending over a larger area had either relative low temporal resolution 16,18,22,30 (e.g., national models only available at the annual level) or low spatial resolution. 23 Second, most existing studies relied on a single model and a single fitting algorithm to estimate NO 2 , even though recent studies suggest that a hybrid model is better at integrating monitoring data, land-cover regression, remote sensing data, and dispersion data 44 and could potentially improve model performance. 23 Therefore, in this study, we integrated multiple types of predictor variables and multiple types of machine learners into an ensemble model to estimate NO 2 with high spatial resolution (1 km × 1 km), high temporal resolution (daily), and large spatial coverage (the contiguous United States) from 2000 to 2016.…”

A hybrid model approach for estimating health burden from NO2 in megacities in China: a case study in Guangzhou

“…There are of course uncertainties, as for any modelling approach. Even though we have sought to play to the strengths of the available data for Guangzhou, data accessibility and quality are generally an issue for China (He et al 2018). For example, detailed industrial emissions as point sources for the dispersion model are not accessible in Guangzhou.…”

“…A dispersion model strives to accurately simulate the fundamental physical-chemical processes in the atmosphere from emission source to all selected receptor locations, whilst a LUR model establishes significant predictor variables for concentrations at known locations and uses the statistical relationship to estimate concentrations at all other locations. However, modelling NO 2 concentrations in China is challenging as the detailed input data required for modelling that exist for Western cities are much more limited for Chinese cities (He et al 2018). Chinese cities are large in both population number and geographical area compared with cities in Europe and North America (United Nations 2017), and are also characterised by high population density: an average of 5000 per km 2 in urban areas with a population >500 000, which is almost double that for EU cities (2900 per km 2 ) and triple that for North American cities (1600 per km 2 ) (Demographia 2019).…”

Exposure to particulate matter and life loss from traveling by four different common commuting modes

“…Cyclists experienced the most severe pollution among the four common traffic modes, and higher exposure concentrations were found when they crossed the "street canyons" identified in the satellite remote sensing map. (Several related studies have stressed the importance of land-use types on pollution diffusion [54]).…”