MAQ-CaF: A Modular Air Quality Calibration and Forecasting method for cross-sensitive pollutants

TL;DR

This paper introduces MAQ-CaF, a modular machine learning-based calibration and forecasting system for low-cost air quality sensors, enhancing accuracy and reliability in pollutant measurement and prediction across diverse regions.

Contribution

The paper presents a novel modular calibration and forecasting framework that improves the reliability of low-cost air quality sensors using IoT and machine learning techniques.

Findings

Calibrated pollutants include CO, SO2, NO2, O3, PM1.0, PM2.5, PM10 with reasonable accuracy.

The system demonstrates flexibility and applicability across different geographical regions.

The approach enhances air quality monitoring for climate change mitigation.

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 MAQ-CaF, a modular air quality calibration, and forecasting 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 helps to establish the proposed solution's applicability and flexibility without compromising accuracy. CO, SO2, NO2, O3, PM1.0, PM2.5 and PM10 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.