Copyright: © the author(s), publisher and licensee Technoscience Academy. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited International Journal of Scientific Research in Computer Science, Engineering and Information Technology ISSN : 2456-3307 (www.ijsrcseit.com) doi : https://doi.org/10.32628/CSEIT217331 222 Air Pollution Evaluation by Combining Stationary, Smart Mobile Pollution Monitoring and Data-Driven Modelling A. Shifa* 1 , Dr. S. Rathi 2 * 1 ME Student, Department of Computer Science and Engineering, Government College of Technology, Coimbatore, Tamil Nadu, India 2 Professor, Department of Computer Science and Engineering, Government College of Technology, Coimbatore, Tamil Nadu, India Article Info Volume 7, Issue 3 Page Number : 222-227 Publication Issue : May-June-2021 Article History Accepted : 15 May 2021 Published : 22 May 2021 ABSTRACT Air pollution has become a major issue in large cities because increasing traffic, industrialization and it becomes more difficult to manage due to its hazardous effects on the human health and many air pollution-triggering factors. This paper puts forth a machine learning approach to evaluate the accuracy and potential of such mobile generated information for prediction of air pollution. Temperature, wind, humidity play a vital role in influencing the pollution dispersion and accumulation, majorly influencing the prediction of pollution levels. Thus, this paper includes the atmospheric condition information registered throughout the study period in order to understand the influence of these factors on air pollution monitoring. Data driven modelling is an efficient way of extracting valuable information from generated data sets, however it is less efficient when the data is incomplete or contains inaccuracies. This modelling approach has true potential for real time operations because it can detect non-linear spatial relationships between sensing units and could aggregate results for regional investigation. Neural networks comparatively showed good capability in air quality prediction than support vector regression. Keywords : Air Pollution, AQI value, Neural Networks, Support Vector Regression, R2 value I. INTRODUCTION Addressing air pollution problems in growing urban cities has become a serious downside due to ever- increasing traffic in densely inhabited urban areas, extended industrialization, high-energy consumption, skimpy resources for monitoring and various issues in shaping custom-made policies. The challenge of managing air pollution becomes tougher because of its dangerous effects on public health and the multitude of air pollution triggering factors. Therefore, numerous studies in recent years are concentrating on evaluating the impact of bad air quality on citizens. This is done by moving away from traditional monitoring stations which are normally placed in high altitude locations across