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大量二氧化碳排放，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。已经开发了一些模型，利用空气污染源和汇（如交通量、土地利用类别和植被）的代用物来预测空气污染水平。这些模式需要时间、费用和精力来开发，并且是城市特有的，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的中国空气污染专家单银教授合作，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其空气污染的高空间分辨率模型，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；创造更健康的城市。本项目基于俄勒冈州波特兰市和中国上海现有的多年NO2（一种城市空气污染物和人为空气污染标志）地表测量数据，开发城市内（~250m）尺度NO2的土地利用回归（LUR）模型。LUR模型将用于(i)推导可能使城市之间的LUR模型迁移成为可能的启发式方法；（ii）调查城市森林在缓解空气污染方面的作用；（iii）将上海NO2的卫星观测值从13 × 24 km限制到~250m尺度，作为化学输运模式WRF-Chem的输入。这些模型之间的比较将有助于创建高空间分辨率的模型，可用于其他受污染影响的城市。该奖项由国家科学基金会与中国科技部共同资助。

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.