CAREER: Timely Estimation of Nitrogen Oxides Emissions for Improved Monitoring and Simulation of Atmospheric Chemical Processes

职业：及时估算氮氧化物排放，以改进大气化学过程的监测和模拟

• 批准号: 2338758
• 负责人: Kang Sun
• 金额: $ 64.36万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-05-01 至 2029-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2338758&HistoricalAwards=false
• 关键词: CAREER; Timely; Estimation; Nitrogen; Oxides

This CAREER project has the goal of quantifying the emission sources of nitrogen oxides using new-generation satellite observations and applying an observation-based framework to other atmospheric species that are crucial for air quality and climate change. The framework developed through this project will harness a large volume of observational data from new-generation satellites and transform the data into advanced atmospheric chemistry knowledge that will help to monitor, simulate, and regulate air pollution and greenhouse gases.This project is focused on achieving a more efficient method for estimating pollutant emissions from satellite observations using the directional derivative approach (DDA). This research has following objectives: (1) Assess the accuracy and improve the theoretical understanding of the observation-based framework using well-defined atmospheric model simulations, (2) Validate and calibrate satellite-based nitrogen oxide (NOx) emissions by ground-truth observations, and (3) Streamline satellite-based NOx emissions for community applications and extend the emission estimation to species beyond NOx. An additional objective is to promote engagement in science and enhance awareness of air pollution and climate change through integrated education, outreach, and research.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.

这一职业项目的目标是利用新一代卫星观测量化氮氧化物的排放源，并将以观测为基础的框架应用于对空气质量和气候变化至关重要的其他大气物种。通过该项目开发的框架将利用来自新一代卫星的大量观测数据，并将这些数据转化为高级大气化学知识，有助于监测、模拟和调节空气污染和温室气体。该项目的重点是利用方向导数法(DDA)实现一种更有效的卫星观测污染物排放估计方法。这项研究的目标如下：(1)利用定义明确的大气模式模拟评估基于观测的框架的准确性并提高其理论理解；(2)通过地面实况观测验证和校准基于卫星的氮氧化物(NOx)排放；以及(3)简化基于卫星的NOx排放以用于社区应用，并将排放估计扩展到NOx以外的物种。另一个目标是通过综合教育、外展和研究促进科学参与并提高人们对空气污染和气候变化的认识。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。