The quality of air is closely linked with the life quality of humans, plantations, and wildlife. It needs to be monitored and preserved continuously. Transportations, industries, construction sites, generators, fireworks, and waste burning have a major percentage in degrading the air quality. These sources are required to be used in a safe and controlled manner. Using traditional laboratory analysis or installing bulk and expensive models every few miles is no longer efficient. Smart devices are needed for collecting and analyzing air data. The quality of air depends on various factors, including location, traffic, and time. Recent researches are using machine learning algorithms, big data technologies, and the Internet of Things to propose a stable and efficient model for the stated purpose. This review paper focuses on studying and compiling recent research in this field and emphasizes the Data sources, Monitoring, and Forecasting models. The main objective of this paper is to provide the astuteness of the researches happening to improve the various aspects of air polluting models. Further, it casts light on the various research issues and challenges also.

翻译：空气质量与人类、植物和野生动物的生活质量息息相关，因此需要不断监测和保护。运输、工业、建筑工地、发电机、烟花和垃圾燃烧等活动在降低空气质量方面起到重要作用，因此需要以安全和可控的方式进行使用。传统的实验室分析或每隔几英里安装大型昂贵的模型已不再有效。需要使用智能设备来收集和分析空气数据。空气质量取决于各种因素，包括位置、交通和时间等。最近的研究正在使用机器学习算法、大数据技术和物联网来提出稳定有效的模型以达到监测和保护空气质量的目的。本综述论文侧重于研究和汇编该领域的最新研究，并强调数据来源、监测和预测模型等关键要素。本文的主要目的是提供关于改进空气污染模型的研究进展，以及各种研究问题和挑战的启示。