Research Projects

Google Scholar Profile

Wildland fires and air quality

Wildfires and prescribed burns can significantly impact air quality across large areas. We are using air quality models and data analyses to investigate the contributions of fires to air pollution and associated health impacts. One current research project is aimed at developing an Integrated Prescribed Fire Information System for the Southeastern US. Along with collaborators at the US Forest Service and Georgia Tech, our goal is to create a sustainable system that unifies fire records and can be used to address questions about the effects of prescribed burning on smoke exposure and air quality in the region. In another ongoing study, we are developing a modeling framework to estimate potential health impacts associated with wildfire smoke exposure. The modeling tools included will be used to quantify adverse impacts of smoke exposure from the 2016 Southeast wildfires on North Carolina public health. Past work has focused on the uncertainties associated with fire-related air quality forecasts and developing tools to improve model performance. Across these research efforts, we aim to provide useful guidance to environmental and land managers about the impacts of fires and address research needs in fire-related simulations.

Data layers of the Prescribed Fire and Air Quality Integrated Information System

Climate impacts on air quality and health

Climate change and air pollution are closely connected. Many conventional air pollutants are co-emitted by CO2 sources. Additionally, climate change can lead to atmospheric conditions that will degrade air quality and aggravate health effects. In this research, we are evaluating the effects of climate change and climate policy on U.S. air quality and public health using integrated economic, climate and atmospheric chemistry projections. Our work explores the main drivers of uncertainty in climate simulations and how this uncertainty propagates to projections of future air quality. The ultimate goal is to design modeling strategies that better capture the full range of uncertainty associated with climate simulations and can be used to provide more insightful impact assessments. We are closely working with collaborators at UC Davis and the University of Waterloo on this research.

Variability in projections of climate change impacts on ozone pollution.  

Power systems, air quality, and public health

Emissions associated with electricity generation are an important source of air pollution and adverse health effects. In this research, computational modeling is being used to explore the connections between power systems, air quality, and public health. In collaboration with economics and energy systems researchers at NC State, we are developing a coupled framework of power systems models, air pollution models, and economic evaluation of human health impacts. This framework will allow us to investigate if operational strategies for dispatching and grid-connected energy storage can yield cost-effective reductions in the human health impacts associated with power sector emissions.

Electricity generating units in the Texas Interconnection.

Air quality modeling in Latin America

Air pollution is a major contributor to global mortality and disease. Millions of people in large Latin American cities are exposed to especially high air pollutant concentrations.  We are working with collaborators from Universidad de La Salle in Colombia to model emissions and air quality in the city of Bogota. In this project, we will evaluate the impacts of different energy policy and emissions scenarios on the city’s air pollution. To pursue this goal, we are using regional-scale photochemical modeling to simulate this challenging urban atmosphere. The research is supported by Ecopetrol, the largest petroleum company in Colombia.

Atmosphere over Bogota, Colombia. 

Computational methods in air quality modeling

We are applying computational and numerical methods to develop new tools that can be used to simulate the interactions between environmental and human systems. Towards this end, we are setting up a modeling system that builds on the high-performance computing resources at NC State. Techniques being explored include adaptive grid modeling, reduced-form modeling, and machine learning approaches. We will use these to investigate different applications, including dynamic grid refinement in global chemical transport models, population-driven adaptive grid modeling for health impacts, reduced-form models for wildfire health and economic impacts, and machine learning algorithms for air quality forecasting.

Fixed- and adaptive-grid simulations of a fine particulate matter (PM2.5) plume.