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

数学模型在塑造我们对空气污染的理解方面发挥着越来越重要的作用，并在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深入了解空气质量，并就人类活动对空气质量的影响方式做出明智的决定。敏感性分析是一种重要的分析方法。敏感性分析工具可以回答有关空气污染的主要问题：排放的变化如何影响空气质量？复杂的数学工具已经开发出来计算空气质量模型的敏感性信息。其中一些方法是基于源的，并提供有关发射源的详细灵敏度信息。其他的则是基于受体的，因此被认为是解决政策问题的理想方法。在这个项目中，目标是开发一组这样的基于受体的工具，称为伴随物，用于广泛使用的空气质量模型。它旨在将开发的附带模型用于各种应用，包括：1)改进空气质量预测，2)开发模型中使用的排放清单的更好估计，3)设计最佳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)省际，跨境和洲际污染运输的定量评估。

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.