Air Quality Monitoring System: Predict Concentrations of Pollutants

Abstract

In this work, the system provides clear visualizations of air pollutants details (NO₂, PM₂₅, P M₁₀, SO₂, CO and O₃) in our environment and list outs the diseases that may affect living beings by considering the concentration levels of each pollutant. In addition to that our system also recommends some overcome measures to reduce levels of poisonous air pollutants which are gradually increasing. So that Government can monitor pollutants concentrations over a period of time and take control measures to make the world pollution-free. It also prognosticates hourly concentrations of air-pollutants by training the model with historical data and calculate AQI values by using respective concentration levels. We prognosticate air-pollutant concentrations by using some machine learning concepts, which yields an efficient model to predict the values by training the model with a large amount of historical data. There are many models proposed by many others to predict air-quality by applying simple standard Regression models using both linear and non-linear. Here we consider it to be multi-task learning and applied various regularization routines to list out the efficient model which can forecast concentration levels of pollutants.