Air quality models (AQMs) are useful for studying various types of air pollutions and provide the possibility to reveal the contributors of air pollutants. Existing AQMs have been used in many scenarios having a variety of goals, e.g., focusing on some study areas and specific spatial units. Previous AQM reviews typically cover one of the forming elements of AQMs. In this review, we identify the role and relevance of every component for building AQMs, including (1) the existing techniques for building AQMs, (2) how the availability of the various types of datasets affects the performance, and (3) common validation methods. We present recommendations for building an AQM depending on the goal and the available datasets, pointing out their limitations and potentials. Based on more than 40 works on air quality, we concluded that the main utilized methods in air pollution estimation are land-use regression (LUR), machine learning, and hybrid methods. In addition, when incorporating LUR methods with traffic variables, it gives promising results; however, when using kriging or inverse distance weighting techniques, the monitoring stations measurements of air pollution data are enough to have good results. We aim to provide a short manual for people who want to build an AQM given the constraints at hands such as the availability of datasets and technical/computing resources.

中文翻译：

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空气质量模型综述

空气质量模型（AQM）可用于研究各种类型的空气污染，并提供揭示空气污染物贡献者的可能性。现有的AQM已用于具有多种目标的许多场景中，例如，侧重于某些研究领域和特定的空间单位。先前的AQM审核通常涵盖AQM的构成要素之一。在本文中，我们确定了构建AQM的每个组件的作用和相关性，包括（1）构建AQM的现有技术，（2）各种类型的数据集的可用性如何影响性能以及（3）通用验证方法。我们提出了根据目标和可用数据集构建AQM的建议，指出了其局限性和潜力。根据40多项有关空气质量的作品，我们得出的结论是，空气污染评估中主要使用的方法是土地利用回归（LUR），机器学习和混合方法。此外，将LUR方法与交通量变量结合使用时，它会产生可喜的结果。但是，当使用克里金法或反距离加权技术时，监测站对空气污染数据的测量足以产生良好的结果。我们的目标是为那些想要建立AQM的人提供一本简短的手册，因为他们面临着诸如数据集的可用性和技术/计算资源之类的限制。监测站对空气污染数据的测量足以产生良好的结果。我们的目标是为那些想要建立AQM的人提供一本简短的手册，因为他们面临着诸如数据集的可用性和技术/计算资源之类的限制。监测站对空气污染数据的测量足以产生良好的结果。我们的目标是为那些想要建立AQM的人提供一本简短的手册，因为他们面临着诸如数据集的可用性和技术/计算资源之类的限制。