Institute of Future Cities (IOFC), The Chinese University of Hong Kong, Hong Kong, China.
Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China.
Int J Environ Res Public Health. 2021 Dec 29;19(1):321. doi: 10.3390/ijerph19010321.
Poor air quality has been a major urban environmental issue in large high-density cities all over the world, and particularly in Asia, where the multiscale complex of pollution dispersal creates a high-level spatial variability of exposure level. Investigating such multiscale complexity and fine-scale spatial variability is challenging. In this study, we aim to tackle the challenge by focusing on PM (particulate matter with an aerodynamic diameter less than 2.5 µm,) which is one of the most concerning air pollutants. We use the widely adopted land use regression (LUR) modeling technique as the fundamental method to integrate air quality data, satellite data, meteorological data, and spatial data from multiple sources. Unlike most LUR and Aerosol Optical Depth (AOD)-PM studies, the modeling process was conducted independently at city and neighborhood scales. Correspondingly, predictor variables at the two scales were treated separately. At the city scale, the model developed in the present study obtains better prediction performance in the AOD-PM relationship when compared with previous studies (R2¯ from 0.72 to 0.80). At the neighborhood scale, point-based building morphological indices and road network centrality metrics were found to be fit-for-purpose indicators of PM spatial estimation. The resultant PM map was produced by combining the models from the two scales, which offers a geospatial estimation of small-scale intraurban variability.
空气质量差一直是世界上许多高密度大城市面临的主要城市环境问题，尤其是在亚洲，污染扩散的多尺度复杂结构造成了暴露水平的高度空间变异性。研究这种多尺度复杂性和细尺度空间变异性具有挑战性。在这项研究中，我们旨在通过聚焦于 PM（空气动力学直径小于 2.5 µm 的颗粒物）来应对这一挑战，PM 是最令人关注的空气污染物之一。我们采用广泛应用的土地利用回归（LUR）建模技术作为整合空气质量数据、卫星数据、气象数据和来自多个来源的空间数据的基本方法。与大多数 LUR 和气溶胶光学深度（AOD）-PM 研究不同，该模型的构建过程分别在城市和社区尺度上独立进行。相应地，两个尺度上的预测变量也分别处理。在城市尺度上，与之前的研究相比（AOD-PM 关系的 R2¯从 0.72 提高到 0.80），本研究中开发的模型在 AOD-PM 关系中的预测性能更好。在社区尺度上，发现基于点的建筑形态指数和道路网络中心度指标适合 PM 空间估计。通过结合两个尺度的模型生成 PM 地图，提供了城市内部小尺度空间变异性的地理空间估计。
