Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
J Environ Sci (China). 2023 Mar;125:513-523. doi: 10.1016/j.jes.2022.02.041. Epub 2022 Mar 7.
Traditional air quality data have a spatial resolution of 1 km or above, making it challenging to resolve detailed air pollution exposure in complex urban areas. Combining urban morphology, dynamic traffic emission, regional and local meteorology, physicochemical transformations in air quality models using big data fusion technology, an ultra-fine resolution modeling system was developed to provide air quality data down to street level. Based on one-year ultra-fine resolution data, this study investigated the effects of pollution heterogeneity on the individual and population exposure to particulate matter (PM and PM), nitrogen dioxide (NO), and ozone (O) in Hong Kong, one of the most densely populated and urbanized cities. Sharp fine-scale variabilities in air pollution were revealed within individual city blocks. Using traditional 1 km average to represent individual exposure resulted in a positively skewed deviation of up to 200% for high-end exposure individuals. Citizens were disproportionally affected by air pollution, with annual pollutant concentrations varied by factors of 2 to 5 among 452 District Council Constituency Areas (DCCAs) in Hong Kong, indicating great environmental inequities among the population. Unfavorable city planning resulted in a positive spatial coincidence between pollution and population, which increased public exposure to air pollutants by as large as 46% among districts in Hong Kong. Our results highlight the importance of ultra-fine pollutant data in quantifying the heterogeneity in pollution exposure in the dense urban area and the critical role of smart urban planning in reducing exposure inequities.
传统的空气质量数据具有 1 公里或以上的空间分辨率，因此难以解决复杂城市地区的详细空气污染暴露问题。本研究结合城市形态学、动态交通排放、区域和本地气象学、空气质量模型中的物理化学转化，利用大数据融合技术，开发了一个超精细分辨率建模系统，以提供可达到街道级别的空气质量数据。基于一年的超精细分辨率数据，本研究调查了污染异质性对香港个体和人群暴露于颗粒物（PM 和 PM）、二氧化氮（NO）和臭氧（O）的影响，香港是人口最密集和城市化程度最高的城市之一。在个别城市街区内揭示了空气污染的精细尺度变化。使用传统的 1 公里平均水平来代表个体暴露，会导致高暴露个体的正向偏态偏差高达 200%。市民不成比例地受到空气污染的影响，香港 452 个区议会选区（DCCAs）之间的年度污染物浓度差异高达 2 到 5 倍，表明人口之间存在巨大的环境不平等。不利的城市规划导致污染和人口之间存在正空间巧合，这使香港各地区的公众暴露于空气污染物的风险增加了 46%。我们的研究结果强调了在量化密集城市地区污染暴露的异质性方面，超精细污染物数据的重要性，以及智能城市规划在减少暴露不平等方面的关键作用。
