CAREER: Ground-truthing Ozone and Particulate Matter Sensitivities to Emissions Trends

职业：臭氧和颗粒物对排放趋势的真实敏感性

• 批准号: 0847386
• 负责人: Daniel Cohan
• 金额: $ 49.78万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0847386&HistoricalAwards=false
• 关键词: CAREER; Ground; truthing; Ozone; Particulate

The research will consider recent abatement efforts in the United States as a real-world experiment in emissions perturbations, developing innovative techniques to ground-truth pollutant sensitivities to emissions trends. An iterative inverse modeling method with four-dimensional data assimilation will be extended and applied to quantify long-term emissions trends and to evaluate ozone and particulate matter (PM) responsiveness to those trends. The inverse modeling will identify leading contributors to uncertainty in sensitivities and investigate potential adjustments to improve the accuracy of photochemical modeling. Scientific investigations and environmental decision-making rely on environmental models to accurately simulate not only pollutant concentrations but also their sensitivities to emissions. Simulating sensitivities is especially challenging for air pollutants such as ozone and PM that exhibit nonlinear and highly variable responses to multiple precursor emissions. Whereas models are routinely evaluated against observations for pollutant concentrations, much less is known about the accuracy of pollutant sensitivities that cannot be directly observed.Broader impacts will include an education plan that will engage a significant number of fifth-grade students in hands-on, inquiry-based learning about meteorology and air pollution. Students will develop hypotheses and conduct hands-on measurements to investigate how weather and pollutant conditions vary on their school campuses. Lesson plans will be developed and implemented at a pilot school and then used to train teachers from across the Houston region to teach scientific environmental inquiry at their own campuses. The principal investigator will also engage undergraduate and graduate students in scientific learning and research through vertically-integrated research experiences and the development of new curriculum and colloquia.

该研究将把美国最近的减排努力视为排放扰动的真实实验，开发创新技术，以确定污染物对排放趋势的敏感性。 一个迭代的逆模拟方法与四维数据同化将被扩展和应用到量化长期的排放趋势，并评估臭氧和颗粒物（PM）对这些趋势的反应。 逆建模将确定敏感性不确定性的主要贡献者，并研究潜在的调整，以提高光化学建模的准确性。 科学调查和环境决策依赖于环境模型，不仅要准确模拟污染物浓度，还要准确模拟其对排放的敏感性。 模拟敏感性对于臭氧和PM等空气污染物来说尤其具有挑战性，这些污染物对多种前体排放表现出非线性和高度可变的响应。尽管模型通常是根据污染物浓度的观测结果进行评估的，但对无法直接观测到的污染物敏感性的准确性所知甚少，更广泛的影响将包括一项教育计划，该计划将吸引大量五年级学生参与关于气象学和空气污染的实践和基于探究的学习。 学生将提出假设并进行实际测量，以调查校园内天气和污染物状况的变化情况。 课程计划将在一所试点学校制定和实施，然后用于培训来自休斯顿地区的教师，在他们自己的校园里教授科学环境调查。 首席研究员还将通过垂直整合的研究经验以及新课程和座谈会的开发，让本科生和研究生参与科学学习和研究。