Few-Shot Calibration of Low-Cost Air Pollution (PM$_{2.5}$) Sensors Using Meta Learning

Abstract

Low-cost particulate matter sensors are transforming air quality monitoring because they have greater mobility as compared to reference monitors. Calibration of these low-cost sensors requires training data from codeployed reference monitors. Machine learning-based calibration gives better performance than conventional techniques, but requires a large amount of training data from the sensor, to be calibrated, codeployed with a reference monitor. In this letter, we propose novel transfer learning methods for quick calibration of sensors with minimal co-deployment with reference monitors. Transfer learning utilizes a large amount of data from other sensors along with a limited amount of data from the target sensor. Our experimentation finds the proposed model-agnostic-meta learning-based transfer learning method to be significantly more effective over other competitive baselines, reducing the calibration errors by 32% and 15% relative to the raw observations and the best baseline, respectively.