Modular Air Quality Calibration and Forecasting Method for Low-Cost Sensor Nodes

Abstract

The climatic challenges are rising across the globe in general and in worst hit under-developed countries in particular. The need for accurate measurements and forecasting of pollutants with low-cost deployment is more pertinent today than ever before. Low-cost air quality monitoring sensors are prone to erroneous measurements, frequent downtimes, and uncertain operational conditions. Such a situation demands a prudent approach to ensure an effective and flexible calibration scheme. We propose a modular air quality calibration, and forecasting (MAQ-CaF) methodology, that side-steps the challenges of unreliability through its modular machine learning-based design which leverages the potential of IoT framework. It stores the calibrated data both locally and remotely with an added feature of future predictions. Our specially designed validation process and the discussion of the results help to establish the proposed solution’s applicability and flexibility. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {CO}, \text {SO}_{{2}}, \text {NO}_{{2}}, \text {O}_{{3}}, \text {PM}_{{1.0}}, \text {PM}_{{2.5}}~ \text {and}~ \text {PM}_{{10}}$ </tex-math></inline-formula> were calibrated and monitored with reasonable accuracy. Such an attempt is a step toward addressing climate change’s global challenge through appropriate monitoring and air quality tracking across a wider geographical region via affordable monitoring.