Development and application of an adjoint version for a multi-phase air quality model

多相空气质量模型伴随版本的开发与应用

• 批准号: 355474-2008
• 负责人: Hakami, Amir
• 金额: $ 1.38万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=482704
• 关键词: Development; application; adjoint; version; multi

Mathematical models play an increasingly significant role in shaping our understanding of air pollution, and in identifying possible solutions for reducing it to acceptable levels. Scientists, regulators, and policymakers use air quality models to gain insight and make informed decisions about the quality of air and ways that it can be impacted by human activities. An important method that is often used for such purposes is sensitivity analysis. Sensitivity analysis tools can provide answers to the main question with respect to air pollution: how air quality will be affected by changes in emissions? Sophisticated mathematical tools have been developed to calculate sensitivity information in air quality models. Some of these methods are source-based and provide detailed sensitivity information about emission sources. Others are receptor-based and are, therefore, deemed to be ideal for addressing policy questions. In this project, the goal is to develop one group of such receptor-based tools, referred to as an adjoint, for a widely used air quality model. It is intended to use the developed adjoint model for various applications that include: 1) improved air quality forecasting, 2) developing better estimation of emission inventories that are used in the models, 3) design of optimal pollution control strategies for reaching air quality objectives and cost-benefit analysis, and 4) quantitative evaluation of inter-provincial, trans-border, and intercontinental transport of pollution.

数学模型在塑造我们对空气污染的理解以及确定将其减少到可接受水平的可能解决方案方面发挥着越来越重要的作用。科学家，监管机构和政策制定者使用空气质量模型来获得洞察力，并就空气质量及其受人类活动影响的方式做出明智的决策。经常用于这种目的的一种重要方法是敏感性分析。敏感性分析工具可以回答与空气污染有关的主要问题：排放量的变化将如何影响空气质量？已经开发出复杂的数学工具来计算空气质量模型中的敏感性信息。其中一些方法是基于排放源的，提供了关于排放源的详细敏感性信息。另一些是以接受国为基础的，因此被认为是解决政策问题的理想选择。在这个项目中，目标是开发一组这样的受体为基础的工具，被称为伴随，广泛使用的空气质量模式。它的目的是使用开发的伴随模式的各种应用，包括：1）改善空气质量预测，2）开发更好的估计模型中使用的排放清单，3）设计最佳的污染控制策略，以达到空气质量目标和成本效益分析，以及4）定量评估省际，跨境和洲际污染运输。