IoT-based AQI monitoring: Evaluation of low-cost PM sensors and AQI Estimation using AQSense

Abstract

Air pollution poses a threat to the lives of all living beings. Government authorities generally monitor pollution levels using a high-grade setup. The high-grade instruments are expensive and also require space for setup. A few low-cost sensors have been introduced for monitoring air quality. But, those sensors also have a few shortcomings. This thesis mainly focuses on the performance evaluation of low-cost PM sensors and an image processing-based technique to estimate air quality. Firstly, the performance of three new and popular low-cost particulate matter (PM) sensors, namely SDS011, Prana Air, and SPS30, for measuring PM2.5 and PM10 levels is evaluated against a standard reference Aeroqual Series-500. The test setup was exposed to PM concentrations ranging from 30 µg/cm3 to 600 µg/cm3 . The results were based on 1 min, 15 min, 30 min, and 1 hr average readings. The experiments were carried out in indoor as well as outdoor environments. The comparative evaluation was performed before and after calibration. The performance of these sensors is evaluated in terms of coefficient of determination (R2 ), coefficient of variation (Cv), and root mean square error (RMSE). A real-time Air Quality Index (AQI) estimation technique using images and weather sensors on Indian roads is also presented. A mixture of image features, i.e., traffic density, visibility, and sensor features, i.e., temperature and humidity, were used to predict the AQI. Object detection and localizationbased Deep Learning (DL) method and image processing techniques were used to extract image features. At the same time, an ML model was trained on those features to estimate the AQI. For this experiment, a dataset containing 5048 images was collected over four months from September-December 2021 using AQSense device that was developed in International Institute of Information Technology (IIIT), Hyderabad, and co-located AQI values across different seasons were collected by driving on the roads of Hyderabad city in India. The experimental results report an overall accuracy of 82% for AQI prediction. A few challenges faced during the measurement campaign are also discussed.