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密切相关，需要持续监测与保护。交通运输、工业、建筑工地、发电机、烟花燃放和垃圾焚烧是导致空气质量下降的主要因素。这些污染源必须以安全可控的方式使用。传统的实验室分析方法或每隔几英里安装昂贵的笨重监测设备已不再高效，亟需智能设备来收集和分析空气数据。空气质量受地理位置、交通流量和时间等多种因素影响。近年来的研究利用机器学习算法、大数据技术和物联网，为上述目标构建稳定高效的模型。本篇综述论文重点研究并汇编了该领域的最新成果，强调数据源、监测与预测模型。本文的主要目标是阐明当前研究在改善空气污染模型各方面所取得的进展，并进一步揭示研究领域的各类问题与挑战。