Quantitative Analysis for Application Specific Calibration Approaches for Low-Cost Sensors for Air Quality Monitoring

Abstract

Air quality monitoring (AQM) at fine and granular levels helps in implementing better air pollution control and mitigation plans. Low-cost sensing is a relatively new paradigm for AQM at high spatial and temporal resolutions. However, the data obtained in this technique is less reliable due to various error sources involved. Calibration is a promising approach to counteract error sources and to improve the accuracy of the data obtained from low-cost sensors (LCS). However, fitting a calibration model that works for every application is cumbersome since each application is associated with varying sources of error. Hence, we focus on finding a better calibration model for a given application. Our approach is application-centric. We classify LCS applications into static and mobile, based on sensor deployment in the field. Then, we sub-categorize both stationary and mobile applications further to narrow down to understand each case. Next, we analyze the possible error sources in each application and the corresponding techniques to counteract them. Then, we map applications to the calibration models via possible error sources and assign weights/scores to each parameter based on our analysis. Our quantitative analysis determines scores for each calibration model which results in finding the best suited model for a given set of conditions and applications based on the highest score. We verify our analysis with real time data obtained from LCS deployed in Chennai city, India.