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，由于人类活动排放的二氧化碳水平较高，城市已成为空气污染的中心。一个城市的空气污染差异很大，因此这些污染物对健康的影响也很大。利用交通流量、土地利用类别和植被等空气污染源和汇的替代指标，已经开发出预测空气污染物水平的模型。开发这些模型需要时间、费用和精力，而且是特定于城市的--不能从一个城市转移到另一个城市。这项研究将与上海交通大学中国的空气污染专家单印教授合作，开发空气污染模型，使此类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。该项目基于波特兰、俄勒冈和上海已有的多年地面NO2(一种城市空气污染物和人为空气污染的标志)的测量，中国在城市内部(~250米)尺度上建立了NO2的土地利用回归(LUR)模型。LUR模型将被用于(I)推导可能使LUR模型在城市之间转移的启发式方法；(Ii)调查城市森林在缓解空气污染中的作用；以及(Iii)将卫星对上海NO2的观测限制在13x24公里到~250m尺度作为化学传输模型WRF-Chem的输入。这些模型之间的比较将允许创建高空间分辨率模型，这些模型可以用于其他受污染影响的城市。该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.