Air pollution, acknowledged as the paramount environmental risk to health by the World Health Organization (WHO), presents a substantial and intricate global public health challenge. This challenge emanates from the emission of toxic particles and gases, inducing severe health and developmental adversities while concurrently serving as a notable driver of climate change. Despite the escalating threats, contemporary surveillance ecosystems encounter limitations in effectively monitoring both indoor and outdoor air pollution levels, particularly in delivering timely alerts for individuals at heightened risk.Existing air pollution alert systems presently rely on ecological data derived from outdoor air quality monitoring stations. However, this methodology constrains the capacity to monitor individual-level exposure and provide personalized recommendations for mitigation or adaptation. The integration of machine learning (ML) emerges as a transformative solution, facilitating advanced projections, monitoring, modeling, and assessment of air quality. Leveraging sensor data, ML empowers informed, evidence-based decision-making, thereby presenting a substantial opportunity for innovation and enhancement in the realm of air pollution management.

Air pollution is a global public health concern. It is responsible for a cascade of adverse health outcomes. However, quantifying the effects and impacts of air pollution is complicated. The advancement of IoT and big data technologies can now allow public health officials and researchers to monitor air pollution levels and take appropriate and rapid actions to mitigate the harms. We propose the development of an agnostic ecosystem that collects big data and from various sensors, analyzes and predicts harm using AI and deep learning.