CyberSEES: Type 2: Collaborative Research: Connecting Next-generation Air Pollution Exposure Measurements to Environmentally Sustainable Communities

在世界上许多人口稠密地区，环境暴露于地面空气污染与不利的健康影响有关。然而，将空气污染暴露与可持续社区联系起来的进展受到限制，因为暴露的直接观察有限，而且用于决策的区域和全球空气质量模型粗糙。因此，现有模型既不能解决准确评估暴露所必需的污染物浓度或人口分布的变异性尺度，也不能提供政策所需的概率不确定性界限类型。该项目旨在吸收综合网络信息，用于空气质量管理。它通过以下调查推动了网络环境研究的跨学科领域：(1)网络尺度数据分析，利用和提炼有价值的网络信息来支持社区尺度的空气污染建模；(2)微环境定向传感，以精确、按需和原位传感能力增强社区尺度研究；(3)利用包含网络、传感和模型输出的高维数据，解决复杂的、时空变化的问题，进行可扩展的暴露建模和分析。网络环境研究的进步有可能改善政府在环境可持续社区发展方面的政策制定、法规和个人选择。该项目的成果可广泛应用于能源、交通和医疗保健等领域。该项目拓宽了信息技术、环境科学与工程的垂直研究和教育整合，面向研究生和本科生。通过实地试验，该项目提供了独特的现实教育和研究机会，以吸引来自代表性不足的群体和行业专业人士的学生。

Ambient exposure to ground-level air pollution is linked to adverse health effects in many populated areas of the world. However, advances in relating air pollution exposure to sustainable communities are hindered by limited direct observations of exposure and the coarseness of regional and global air quality models used for decision making. As a result, existing models do not resolve the scales of variability in either pollutant concentrations or population distributions necessary to accurately assess exposure nor provide the type of probabilistic uncertainty bounds required for policy.This project aims to assimilate comprehensive cyber information for use in air quality management. It advances the interdisciplinary field of cyber-environmental research through investigation into (1) cyber-scale data analysis to harness and distill valuable cyber information to support community-scale air pollution modeling; (2) micro-environment targeted sensing to augment community-scale studies with accurate, on-demand, and in situ sensing capabilities; and (3) scalable exposure modeling and analysis by solving a complex, spatiotemporally varying problem with high-dimensional data containing cyber, sensing, and model outputs.Advances in cyber-environmental research have the potential to improve government policy making, regulations, and personal choices with regard to environmentally sustainable community development. Results of this project can apply across a wide spectrum of sectors including energy, transportation, and healthcare. This project broadens vertical research and education integration across information technologies and environmental science and engineering, to both graduate and undergraduate students. Through in-field trials, this project offers unique real-world education and research opportunities to attract students from underrepresented groups and industrial professionals.