Abstract—Monitoring and preserving air quality has become one of the most essential activities in many industrial and urban areas today. The quality of air is adversely affected due to various forms of pollution caused by transportation, electricity, fuel uses etc. The deposition of harmful gases is creating a serious threat for the quality of life in smart cities. With increasing air pollution, we need to implement efficient air quality monitoring models which collect information about the concentration of air pollutants and provide assessment of air pollution in each area. Hence, air quality evaluation and prediction has become an important research area. The quality of air is affected by multi-dimensional factors including location, time, and uncertain variables. Recently, many researchers began to use the big data analytics approach due to advancements in big data applications and availability of environmental sensing networks and sensor data. The aim of this research paper is to investigate various big-data and machine learning based techniques for air quality forecasting. This paper reviews the published research results relating to air quality evaluation using methods of artificial intelligence, decision trees, deep learning etc. Furthermore, it throws light on some of the challenges and future research needs.
Index Terms—Air quality evaluation, big data analytics, data-driven air quality evaluation, and air quality prediction.
Gaganjot Kaur Kang is with the Department of Computer Engineering, San Jose State University, USA (e-mail: email@example.com).
Jerry Zeyu Gao is with San Jose State University, USA (e-mail: firstname.lastname@example.org).
Sen Chiao is with University of Taiyuan University of Technology, China.
Shengqiang Lu is with the Taiyuan University of Technology, China.
Gang Xie is with the Taiyuan University of Science and Technology, China.
Cite: Gaganjot Kaur Kang, Jerry Zeyu Gao, Sen Chiao, Shengqiang Lu, and Gang Xie, "Air Quality Prediction: Big Data and Machine Learning Approaches," International Journal of Environmental Science and Development vol. 9, no. 1, pp. 8-16, 2018.