airaware
AirAware is a real-time machine learning pipeline that forecasts campus air quality at Lewis University — predicting pollution levels before they occur and surfacing actionable insights for campus health and operations decisions.
context
Lewis University sits in Romeoville, IL, in the Chicago metropolitan area — one of the most air-quality-variable regions in the midwest, affected by industrial output, traffic corridors, and seasonal atmospheric inversions. traditional air quality monitoring is reactive: it tells you what the air is now, not what it's going to be in an hour. AirAware changes that.
the pipeline
sensor data is ingested from campus-deployed air quality monitors measuring PM2.5, NO2, O3, and CO2. features are engineered from weather data — wind speed, temperature, humidity, precipitation — and temporal features like time-of-day and day-of-week. an ML model (XGBoost ensemble and LSTM for time-series forecasting) is trained on historical sensor readings. forecasts update the dashboard in real time.
what it enables
predictive alerts for campus administration when air quality is projected to exceed health thresholds. a data foundation for research publications. a framework extensible to other campuses or municipal monitoring networks. the long-term goal is a system that makes environmental health data as accessible and actionable as a weather forecast.