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

机译:中国城市内部空气污染变化的土地利用回归建模:现状和未来需求

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摘要

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.
机译:中国的快速城市化导致空气质量问题实质性不利,特别是对于No2和颗粒物(PM)。目前正在应用土地使用回归(LUR)模型来模拟中国城市地区高空间分辨率的污染物浓度。然而,中国城市地区与欧洲和北美的城市区不同,例如就人口密度,城市形态和污染物排放密度而言,是为了评估当前在中国的LUR研究,以突出当前的挑战并确定未来的需求。 NO2和PM2.5 / PM10(具有空气动力学直径<2.5μm和<10μm)的24个最近的LUR模型的细节被列表并审查作为本文讨论的基础。我们强调,中国的LUR建模目前受到输入数据的稀缺性,特别是空气污染监测数据。迫切需要提供质量保证的测量数据的可访问档案,以及用于城市排放的更高空间分辨率代理数据,特别是在与流量相关的变量方面。中国城市景观的迅速发展的性质使得维持最新的土地使用和城市形态学数据集是一个挑战。我们还突出了中国LUR模型的重要性,以受到适当的验证统计数据。 LUR与便携式监视器数据,遥感和分散建模集成有可能增强城市污染地图的推导。

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