Current Research Projects

1. Ensemble Air Quality Forecast (In collaboration with NASA, NOAA, EPA, the U.S. Forest Service, Naval Research Lab, ECCC, and Kaiser Permanente)

We develop and evaluate an ensemble-based air quality forecasting system that integrates multiple models to improve predictions of air pollution events. This approach enhances forecast accuracy by reducing uncertainties associated with individual models and provides probabilistic air quality assessments for decision-makers. Our ensemble system supports air quality management, emergency response, and public health initiatives.

2. Subseasonal to Seasonal Wildfire Emission Forecast (In collaboration with NOAA GSL, George Mason University)

Wildfires significantly impact air quality and climate, yet predicting their emissions remains a challenge. We develop subseasonal-to-seasonal (S2S) wildfire emission forecasting frameworks that incorporate climate variability, fuel conditions, and fire behavior models to improve early warnings of wildfire-driven air pollution events.

3. Oil and Gas Industry Air Quality Impacts (In collaboration with George Mason University, University of North Texas)

Oil and gas extraction activities emit large quantities of pollutants, including volatile organic compounds (VOCs), nitrogen oxides (NOx), and methane (CH4). Our research quantifies the impact of these emissions on regional air quality, ozone formation, and human health, using chemical transport models and satellite data.

4. High-Resolution Regional Air Quality Modeling (In collaboration with George Mason University, NOAA ARL)

We employ high-resolution regional air quality models to simulate pollutant transport and chemical processes at fine spatial scales. These models are critical for understanding urban air pollution dynamics, exposure risks, and localized emission impacts, helping inform policy and mitigation strategies.

5. Fire Plume Height Estimation (In collaboration with NOAA ARL)

Fire plume height plays a crucial role in determining how wildfire emissions disperse in the atmosphere and impact air quality. We use remote sensing, in situ observations, and model simulations to improve the representation of fire plume injection heights in atmospheric models, enhancing our ability to predict smoke transport and air pollution episodes.

6. Connecting Satellite Data with GDP (In collaboration with World Bank)

Satellite remote sensing provides a unique opportunity to assess air quality trends and their socioeconomic implications. Our research explores the relationship between satellite-derived air pollution indicators (such as NO2 and PM2.5) and GDP across different countries and economic sectors. This work helps reveal disparities in air pollution exposure and its economic drivers.