Particle Pollution Estimation from Images Using Convolutional Neural Network and Weather Features

Airborne particulate matter with a diameter less than 2.5 micrometers (PM2.5) is one of the most harmful air pollutants, because PM2.5 can be inhaled into human body and cause serious health problems by transmitting hazardous chemicals deeply into lung and bloodstream. A reliable, easily accessible, and low-cost PM2.5 monitoring system can greatly help people raise public awareness of PM2.5 and reduce health hazards of air pollution. In this paper, we combine image and weather information to estimate PM2.5 indices of outdoor images using deep learning and support vector regression (SVR) techniques. The proposed method first uses a convolutional neural network (CNN) to predict the PM2.5 index based on image information, and then the PM2.5 predicted by CNN and two weather features, humidity and wind speed, are combined to yield final estimated PM2.5 index using a created SVR model. We assessed our method using two datasets collected from Shanghai City and Beijing City in China and experimental results demonstrated the effectiveness of the proposed method for PM2.5 estimation.