Calibrating Low-Cost Sensors by a Two-Phase Learning Approach for Urban Air Quality Measurement

Urban air quality information, e.g., PM2.5 concentration, is of great importance to both the government and society. Recently, there is a growing interest in developing low-cost sensors, installed on moving vehicles, for fine-grained air quality measurement. However, low-cost mobile sensors typically suffer from low accuracy and thus need careful calibration to preserve a high measurement quality. In this paper, we propose a two-phase data calibration method consisting of a linear part and a nonlinear part. We use MLS (multiple least square) to train the linear part, and use RF (random forest) to train the nonlinear part. We propose an automatic feature selection algorithm based on AIC (Akaike information criterion) for the linear model, which helps avoid overfitting due to the inclusion of inappropriate features. We evaluate our method extensively. Results show that our method outperforms existing approaches, achieving an overall accuracy improvement of 16.4% in terms of PM2.5 levels compared with state-of-the-art approach.