Air Pollution Monitoring in Urban Areas Using Low Cost IoT Devices: ML based Calibration and Mobile Sensing

Abstract

The combination of Internet of Things (IoT) and Machine Learning (ML) technologies has the potential to completely transform air pollution monitoring and management. Low-cost air pollution measurement sensors have grown in popularity in recent years because they enable a low-cost solution to measure air quality in real time. However, these sensors frequently have accuracy concerns and must be calibrated to ensure that their results are valid. This thesis mainly focuses on calibrating low-cost sensors using ML models and the detection of pollutant hot spots in urban areas using IoT devices on a mobile platform. In mobile monitoring of air pollution, IoT-enabled sensors assembled onto automobiles, and portable devices offer real-time data collecting when traveling between areas. This dynamic technique provides useful insights into real-world pollution fluctuations, allowing the identification of pollution sources and trends along traffic routes. The study highlights the importance of calibrating the IoT devices in mobile setting, even when the devices are calibrated in laboratory settings. Real-time ML algorithms can be used to calibrate sensor data based on location, weather, and additional relevant data. In this thesis, a methodology is proposed for detecting emission spikes of PM2.5, CO, and NO2 in polluted urban environments employing portable low-cost sensors. Identification of harmful pollutant concentrations is achieved using two different IoT device types (MegaSense One and Prana Air) mounted on a mobile platform. Reliable identification of the PM2.5, CO, and NO2 emission spikes can be attained by driving through the city on different days. IoT device measurement errors were corrected by ML based calibration against a reference instrument co-located on a mobile platform. ML regression models like simple linear regression (LR), multivariate linear regression (MLR), polynomial regression (PR), support vector regression (SVR), decision tree regression (DT), random forest regression (RF) were applied to calibrate the devices.Among these models RF was the most suitable technique to reduce the variability between the IoT devices due to heterogeneity in the mobile sensing datasets. The spatial variability of PM2.5, CO, and NO2 harmful emission spikes at a resolution of 50 m were identified, but their intensity changes on a daily basis according to meteorological conditions. The data from the PM2.5, CO, and NO2 emission spikes at points of interests that disturb traffic flows clearly show the need for public education about when it is hazardous for persons with respiratory conditions to be outside, as well as when it is unsafe for young children and the elderly to be outside for extended periods of time. This detection strategy is adaptable to any mobile platform used by individuals traveling by foot, bicycle or drones in any metropolis.