Pollution Parameter Analysis and Predictive Model Building

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, industries, 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 our project is to implement various big-data and machine learning based techniques for air quality forecasting. For this, the front end is designed using HTML and CSS, back end is designed using PHP and MySQL is used for the data storage. The algorithm implementation and Predictive Model building is done using Python.