Indoor Air

Indoor air quality (IAQ) research focuses on understanding and mitigating the health risks associated with pollutants in indoor environments. Current studies employ machine learning models, such as deep ensemble methods, random forests, and neural networks, to predict indoor pollutant concentrations, often correlating them with outdoor levels and occupant activities. This research is crucial for developing effective interventions, including improved building design and personalized recommendations, to enhance public health and create healthier indoor spaces, particularly in low-to-middle-income communities where IAQ issues are often exacerbated. The development of user-friendly tools based on these models promises to accelerate the design of more effective and sustainable indoor environments.