3. Forecasting of Air Pollution via a Low-Cost IoT-Based Monitoring System

Air pollution causes a number of pulmonary and cardiovascular diseases. Recording of air pollution via real-time low-cost IoT-based monitoring systems and its subsequent forecasting are likely to help timely warn people about prevailing air pollution across a large number of sites. In this paper, we propose and compare a real-time low-cost IoT-based air pollution monitoring system against an existing, accurate, and expensive industry-grade system. Furthermore, we undertake the task of predicting the accurate values of the industry-grade system from values recorded by the low-cost system. For forecasting, a Vector Autoregressive (VAR) model, a Vector Autoregressive Moving Average (VARMA) model, a Seasonal Autoregressive Integrated Moving Average with Exogenous variable (SARIMAX) model, and a weighted ensemble model of VAR, VARMA, and SARIMAX models were trained and tested on particular matter data. Data for forecasting were collected from the low-cost monitoring system and the industry-grade system over a period of time. Results revealed that the low-cost monitoring system predicted the values of the industry-grade system accurately. Furthermore, the ensemble model performed the best among all models in forecasting of accurate particular matter values of the industry-grade system by using the output of the low-cost system. We highlight the implication of using low-cost systems for monitoring of air pollution.