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CAREER: Leveraging Complex Variables to Refine Estimates of Air Pollution Emissions and their Impacts under Uncertainty

职业：利用复杂变量来完善对空气污染排放及其在不确定性下的影响的估计

基本信息

批准号：
1944669
负责人：
Shannon Capps
金额：
$ 50.93万
依托单位：
Drexel University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-01 至 2025-03-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1944669&HistoricalAwards=false
关键词：
CAREER Leveraging Complex Variables Refine

项目摘要

Air pollution is one of the top ten health risks for Americans. We need to understand which actions result in the most pollution to effectively reduce these risks. This CAREER project will develop a new computational approach to understanding these relationships using satellite data. This technique holds great promise to provide results more rapidly, efficiently, and economically than existing techniques. The public will be able to access these estimates through an interactive website at science museums and in urban classrooms. The second aim of this project is to use these computer-based tools to improve estimates of where air pollution moves to better identify sources. Finally, this work aims to develop improved air pollution forecasts across the United States. Successful completion of this research will benefit the public by providing tools to identify pollution sources to help clean the air. The public will also benefit from rapid and accurate forecasts and warnings of pollution to protect populations vulnerable to the health effects of air pollution. The researcher will engage with regulators and other stakeholders to improve forecasts and partner with schools to teach students about the science of air pollution. Together, these efforts will increase the scientific literacy of the Nation. The goal of this CAREER project is to augment GEOS-Chem and the Community Multiscale Air Quality model to efficiently elucidate the relationship between pollutant emissions and atmospheric concentrations. By augmenting two of the most widely used atmospheric chemical transport models, the results will be available to environmental decisionmakers for use in designing emission control strategies. Satellite-based atmospheric composition observations will be assimilated with a novel ensemble-based four-dimensional variational framework. Observations of ammonia from the Cross-track Infrared Sensor will be used to refine the ammonia emissions estimates to allow comparison of this novel approach to existing techniques that have previously been used to refine ammonia emissions. The utility of the assimilation framework for improving air quality forecasting will be evaluated for particulate matter and ozone for which observations will be produced in approximately real-time from NASA’s geo-stationary satellite-based Tropospheric Emissions: Monitoring of Pollution (TEMPO) sensor. Successful completion of this research will benefit the scientific community long-term through the development of an easily extensible approach to sensitivity analysis in two widely used atmospheric chemistry models. As such, it has great potential to provide faster and more accurate predictions than presently used techniques using satellite-based observations of atmospheric composition. These results will provide tools for policy decision makers who are tasked to identify emission strategies to meet the National Ambient Air Quality Standards (NAAQS).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

空气污染是美国人十大健康风险之一。我们需要了解哪些行为会导致最严重的污染，以有效降低这些风险。这个CAREER项目将开发一种新的计算方法，利用卫星数据来理解这些关系。这种技术具有很大的希望，以提供更快，更有效，更经济的结果比现有的技术。公众将能够通过科学博物馆和城市教室的互动网站获得这些估计。该项目的第二个目标是利用这些基于计算机的工具来改善对空气污染移动的估计，以更好地确定污染源。最后，这项工作旨在改进美国各地的空气污染预报。这项研究的成功完成将通过提供识别污染源的工具来帮助清洁空气，从而使公众受益。公众还将受益于快速准确的污染预报和警告，以保护易受空气污染健康影响的人群。研究人员将与监管机构和其他利益相关者合作，改善预测，并与学校合作，向学生传授空气污染科学。这些努力将共同提高国家的科学素养。该CAREER项目的目标是增强GEOS-Chem和社区多尺度空气质量模型，以有效阐明污染物排放与大气浓度之间的关系。通过扩充两个最广泛使用的大气化学迁移模型，环境决策者可利用这些结果制定排放控制战略。卫星上的大气成分观测将与一个新的基于集合的四维变分框架同化。将使用跨轨道红外传感器对氨的观测来改进氨排放量估计，以便将这种新方法与以前用于改进氨排放量的现有技术进行比较。将评价同化框架对改进颗粒物和臭氧空气质量预报的效用，美国航天局的地球静止卫星对流层排放：污染监测传感器将对颗粒物和臭氧进行近似实时的观测。这项研究的成功完成将有利于科学界的长期发展，通过一个易于扩展的方法，在两个广泛使用的大气化学模型的敏感性分析。因此，与目前使用的利用卫星观测大气成分的技术相比，它具有提供更快和更准确预测的巨大潜力。这些结果将为负责确定排放策略以满足国家环境空气质量标准（NAAQS）的政策决策者提供工具。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。