  Cost-effective sensors are capable of real-time capturing a variety of air
quality-related modalities from different pollutant concentrations to
indoor/outdoor humidity and temperature. Machine learning (ML) models are
capable of performing air-quality "ahead-of-time" approximations. Undoubtedly,
accurate indoor air quality approximation significantly helps provide a healthy
indoor environment, optimize associated energy consumption, and offer human
comfort. However, it is crucial to design an ML architecture to capture the
domain knowledge, so-called problem physics. In this study, we propose six
novel physics-based ML models for accurate indoor pollutant concentration
approximations. The proposed models include an adroit combination of
state-space concepts in physics, Gated Recurrent Units, and Decomposition
techniques. The proposed models were illustrated using data collected from five
offices in a commercial building in California. The proposed models are shown
to be less complex, computationally more efficient, and more accurate than
similar state-of-the-art transformer-based models. The superiority of the
proposed models is due to their relatively light architecture (computational
efficiency) and, more importantly, their ability to capture the underlying
highly nonlinear patterns embedded in the often contaminated sensor-collected
indoor air quality temporal data.
