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The climatic challenges are rising across the globe in general and in worsthit under-developed countries in particular. The need for accurate measurementsand forecasting of pollutants with low-cost deployment is more pertinent todaythan ever before. Low-cost air quality monitoring sensors are prone toerroneous measurements, frequent downtimes, and uncertain operationalconditions. Such a situation demands a prudent approach to ensure an effectiveand flexible calibration scheme. We propose MAQ-CaF, a modular air qualitycalibration, and forecasting methodology, that side-steps the challenges ofunreliability through its modular machine learning-based design which leveragesthe potential of IoT framework. It stores the calibrated data both locally andremotely with an added feature of future predictions. Our specially designedvalidation process helps to establish the proposed solution's applicability andflexibility without compromising accuracy. CO, SO2, NO2, O3, PM1.0, PM2.5 andPM10 were calibrated and monitored with reasonable accuracy. Such an attempt isa step toward addressing climate change's global challenge through appropriatemonitoring and air quality tracking across a wider geographical region viaaffordable monitoring.

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