EAPSI: Developing high spatial resolution models of pollution for cities based on traffic volume, land use classes, and vegetation

城市是人类居住的中心，由于人类活动产生的高水平二氧化碳排放，城市已成为空气污染的中心。空气污染在一个城市内变化很大，因此这些污染物对健康的影响也是如此。已经开发了一些模型来预测空气污染物水平，这些模型使用空气污染源和汇的替代物，例如交通量、土地利用类别和植被。这些模式需要时间、费用和努力来开发，并且是城市特定的-不能从一个城市转移到另一个城市。与上海交通大学中国空气污染专家单寅教授合作，这项研究将开发空气污染模型，使这些模型能够在城市之间转移。这将使其他城市更容易开发高空间分辨率的空气污染模型，使城市能够更好地评估空气污染对健康的影响;设计更有效的空气污染缓解政策;并创建更健康的城市。本项目基于波特兰、俄勒冈州和中国上海现有的NO2（一种城市空气污染物和人为空气污染的标志物）多年地表测量数据，开发城市内（~ 250 m）尺度的NO2土地利用回归（LUR）模型。土地利用变化率模型将用于（i）推导出可能使土地利用变化率模型在城市之间转移的方法;（ii）调查城市森林在减轻空气污染方面的作用;以及（iii）将上海NO2的卫星观测从13 x 24 km限制到~ 250 m尺度，作为化学输送模型WRF的输入-这些模型之间的比较将允许创建高空间分辨率的模型，可用于受污染影响的其他城市。NSF EAPSI奖是与中国科技部合作资助的。

Cities are the centers of human habitation and have become the centers of air pollution due to high levels of carbon dioxide emissions from human activity. Air pollution varies widely within a city, and therefore so does the health impact of these pollutants. Models have been developed to predict air pollutant levels using proxies for sources and sinks of air pollution, such as traffic volume, land use classes, and vegetation. These models require time, expense, and effort to develop, and are city-specific- not transferable from one city to another. In collaboration with Professor Shan Yin, expert in air pollution in China, at the Shanghai Jiaotong University, this research will develop air pollution models that will enable transfer of such models between cities. This will make it easier for other cities to develop high spatial resolution models of their air pollution allowing cities to better assess the health impact of air pollution; design more effective air pollution mitigation policies; and create healthier cities. This project builds on existing multi-year surface measurements of NO2 (an urban air pollutant and a marker for anthropogenic air pollution) in Portland, Oregon and Shanghai, China to develop land use regression (LUR) models for NO2 at the intra-urban (~250m) scale. The LUR models will be used to (i) derive heuristics that will potentially enable the transfer of LUR models between cities; (ii) investigate the role of the urban forest in mitigating air pollution; and (iii) constrain satellite observations of NO2 for Shanghai from 13 x 24 km to the ~250m scale as input for the chemical transport model WRF-Chem. Comparisons between these models will allow the creation of high spatial resolution models that can be used in other cities affected by pollution. This NSF EAPSI award is funded in collaboration with the Chinese Ministry of Science and Technology.