  In recent years, indoor air pollution has posed a significant threat to our
society, claiming over 3.2 million lives annually. Developing nations, such as
India, are most affected since lack of knowledge, inadequate regulation, and
outdoor air pollution lead to severe daily exposure to pollutants. However,
only a limited number of studies have attempted to understand how indoor air
pollution affects developing countries like India. To address this gap, we
present spatiotemporal measurements of air quality from 30 indoor sites over
six months during summer and winter seasons. The sites are geographically
located across four regions of type: rural, suburban, and urban, covering the
typical low to middle-income population in India. The dataset contains various
types of indoor environments (e.g., studio apartments, classrooms, research
laboratories, food canteens, and residential households), and can provide the
basis for data-driven learning model research aimed at coping with unique
pollution patterns in developing countries. This unique dataset demands
advanced data cleaning and imputation techniques for handling missing data due
to power failure or network outages during data collection. Furthermore,
through a simple speech-to-text application, we provide real-time indoor
activity labels annotated by occupants. Therefore, environmentalists and ML
enthusiasts can utilize this dataset to understand the complex patterns of the
pollutants under different indoor activities, identify recurring sources of
pollution, forecast exposure, improve floor plans and room structures of modern
indoor designs, develop pollution-aware recommender systems, etc.
