Adaptive Air Quality Sensing using Machine Learning

Air pollution is a worldwide problem having impacts on both local and global scales. According to the World Health Organization (WHO), air pollution causes 7 million deaths every year, with 4.2 million attributed to exposure to outdoor air pollution. Compared to the reference methods defined in the Air Quality Directive, the use of low-cost air quality sensors for monitoring ambient air pollution would reduce air pollution monitoring costs and would also allow larger spatial coverage especially in remote areas where monitoring with traditional facilities is uneasy. Theses multi-sensors were either calibrated against standard gas mixtures or using artificial neural network under field conditions. The later method resulted in mixed results either satisfactory for short periods or generally weak for longer data series. This project mainly focuses on the adaptive calibration of the low-cost sensors using the trained machine learning model. The performances of several field calibration methods for low-cost sensors, including linear/multi linear regression and supervised learning techniques will be compared. A cluster of ozone, nitrogen dioxide, nitrogen monoxide, carbon monoxide and carbon dioxide sensors will be operated. Subsequently, the accuracy of the predicted values will be evaluated for about a period. These predicted values are fed through the training model and the model will run accordingly and it will adapt all the error situations. This will be useful to the automobile industry in the current situation for smoke emission control and also for many refinery industries in which low-cost sensors can be used with the following modelling for the higher accuracy. This would reduce the cost and also yields more accuracy beyond the varying situation. Keywords: 1) Low-Cost Sensors (LCS) 2) Internet of Things (IoT)