Analysis of Air Quality Estimation based on Air Pollutants Parameters

  • Tejaswini Rajendra Patil
  • Dr. Siddhivinayak Kulkarni
Keywords: Air Quality Prediction, Deep Learning, Neural Networks, meteorological factors, PM values.


Air quality is the degree that tells us how pure or polluted the air is. It is important to know air quality in our surrounding as it negatively impacts human health and environment. Modernization and industrialization have given birth to air pollution which has become hidden killer. Making cities more respirable starts by analyzing and seizing the air pollution data. Long-established air quality prediction model gave less accurate and unsatisfactory result. There is a need of reliable data-analytics based solutions which will optimally predict air quality thereby enhancing our quality life. We evaluated various studies in this domain and summated the important researches done. This work will give future directions to the upcoming researchers.


[1]Athira Va, Geetha Pb, Vinayakumar Rab, Soman K P*, “DeepAirNet: Applying Recurrent Networks for Air Quality Prediction”, Elsevier 2018, Procedia Computer Science 132(2018) 1394-1403
[2] Songgang Zhao, Xingyuan Yuan, Da Xiao, Jianyuan Zhang, Zhouyuan Li. (2018) "AirNet: a machine learning dataset for air quality forecasting"
[3] XiamenYi, Junbo Zhang Zhaoyuan Wang, Tianrui Li Yu Zheng “Deep Distributed Fusion Network for Air Quality Prediction”, KDD2018, August 1923,2018, London, United Kingdom.
[4]Yue Shan Chang, Kuan-Ming Lin, Yi-Ting Tsai, Yu-Ren Zeng, Cheng-Xiang Hung, “Big data platform for air quality analysis and prediction”, IEEE 2018 27th Wireless and Optical Communication Conference (WOCC),30 April-1 May 2018, Hualien, Taiwan.
[5] NidhiSharma, ShwetaTaneja, VaishaliSagar, Arshita Bhatt, “Forecasting air pollution load in Delhi using data analysis tools”, Elsevier2018, International Conference on Computational Intelligence and Data Science, Page no.1077–1085.
[6] Key Gu, June Qiao,,Weisi Lin , “Recurrent Air Quality Predictor Based on Meteorology- and Pollution-Related Factors”,IEEE 2018, Volume: 14 , Issue: 9, Pages. 3946 - 3955
[7] Xiang Li, LingPeng, XiaojingYao, ShaolongCui, YuanHu, ChengzengYou, Tianhechi, “Long short-term memory neural network for air pollutant concentration predictions: Method development and evalution”, Elsevier2017, Environmental Pollution 231(2017),997-1004.
[8]Ping-Wei Soh, Jia-Wei Chang, Jen-Wei Huang, “Adaptive Deep Learning-Based Air Quality Prediction Model using the most Relevant Spatial-Temporal Relations”, IEEE 2018, Pages 38186 - 38199.
[9] Jianzhou Wang, XiaoboZhang, ZhenhaiGuo, Haiyan Lu, “Developing an earlywarning system for air quality prediction and assessment of cities in China”, Elsevier 2017 Expert System with Applications 84(2017)102-116.
[10] ChaoZhang, JunchiYan, ChangshengLi, XiaoguangRui, LiangLiu, Rongfang Bie, “On Estimating Air Pollution from Photos Using Convolutional Neural Network”, ACM2016, Amesterdam, Netherland.
[11]Nadjet Djebbri, Mounira_Rouainia, “Artificial Neural Networks Based Air Pollution Monitoring in Industrial Sites”, IEEE ICET2017, Antalya, Turkey.
How to Cite
Patil, T., & Kulkarni, D. S. (2018). Analysis of Air Quality Estimation based on Air Pollutants Parameters. Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, 4(II). Retrieved from

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.