  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.
