Regression Models to Predict Air Pollution from Affordable Data Collections

Air quality monitoring is key in assuring public health. However, the necessary equipment to accurately measure the criteria pollutants is expensive. Since the countries with more serious problems of air pollution are the less wealthy, this study proposes an affordable method based on machine learning to estimate the concentration of PM2.5. The capital city of Ecuador is used as case study. Several regression models are built from features of different levels of affordability. The first result shows that cheap data collection based on web traffic monitoring enables us to create a model that fairly correlates traffic density with air pollution. Building multiple models according to the hourly occurrence of the pollution peaks seems to increase the accuracy of the estimation, especially in the morning hours. The second result shows that adding meteorological factors allows for a significant improvement of the prediction of PM2.5 concentrations. Nevertheless, the last finding demonstrates that the best predictive model should be based on a hybrid source of data that includes trace gases. Since the sensors to monitor such gases are costly, the last part of the chapter gives some recommendations to get an accurate prediction from models that consider no more than two trace gases