Background and Objectives:
Air pollution is a global public health concern. It is responsible for a cascade of adverse health outcomes ranging from minor physiological impacts to deaths from respiratory and cardiovascular diseases. Despite these increasing threats, current surveillance ecosystems cannot monitor indoor and outdoor air pollution levels to provide early warnings for high-risk individuals. Air pollution alerts are based on ecological data collected from outdoor air quality monitoring stations, limiting the ability to monitor individual-level exposure and provide recommendations on managing the impacts of air pollution. 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 harm.

Methods:
We have partnered with UNICEF Mongolia to assess the impact of air pollution on health. Air pollution data were collected from 12 air quality monitoring stations placed across Ulaanbaatar, Mongolia. The National Center for Public Health (NCPH) provided public health data in form on ICD-10 codes for 2019 and 2020.

Results:
We tested for exposure-outcome relationship using logistic regression models. Preliminary results indicate a mean PM10 is 139.7; and PM2.5 is 71.7. Results indicate hypertension (ICD I10) to be the most prevalent disease in the population. Acute tonsilitis (J03), acute bronchitis (J20), acute nasopharyngitis (J00) round out the top 4 diseases. We did not observe any relationship (OR 1.01; 95% CI 0.9-1.04) between high exposure to air pollution with rates to hypertension. Other outcomes are still being explored.

Conclusion:
This study is a stepping-stone for using IoT based air pollution data to assess its impact on human health at a population level.