School of Civil and Environmental Engineering

Ph.D. Thesis Defense Announcement

Effects of Prescribed Burning on Air Quality in the Southeastern U.S. and Implications for Public Health Studies

By

Ran Huang

Advisors:

Dr. Armistead G. Russell (CEE) & Dr. M. Talat Odman (CEE)

Committee Members:

Dr. James A. Mulholland (CEE), Dr. Yongtao Hu (CEE), Dr. Jennifer Kaiser (CEE), Dr. Rodney J. Weber (EAS)

Date & Time: Tuesday, March 12, 1:00pm

Location: ES&T 3229

Biomass burning is an important global source of gases and aerosols, e.g., carbon monoxide, carbon dioxide, PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 μm) and black carbon. These products, generally referred to as "smoke" can reduce visibility and have adverse health effect. Prescribed burning, a type of biomass burning, is a land management practice used in the U.S. to reduce wildfire risk and maintain healthy ecosystems. This dissertation is a presentation of research quantifying the impact of prescribed burning on air quality and human health in the southeastern U.S., the most active prescribed burning area in the U.S. Considering the potential impacts, the estimation of prescribed burning emissions is crucial. However, current satellite-derived products have limitations in estimating the burned areas of small fires and still need improvements. Another need is to split the combined prescribed fire impact derived from chemical transport models (CTMs) into individual fire impacts. A novel source apportionment method (Dispersive Apportionment of Source Impacts) has been developed for this by using concentration fields derived from dispersion modeling. Individual burn impacts obtained in this manner could help local land and air quality managers decide which burns should be allowed or restricted based on their impacts on air quality and public health in areas of concern. The feasibility of applying low-cost PM sensors for the detection of fire impacts has been evaluated. It was found that low-cost PM sensors can provide spatial information that is missed by a sparse regulatory monitoring network and, in combination with CTM simulations, they can be used in preparing high accuracy exposure fields needed for health assessments. Data fusion is a method that integrates observations from sensors/monitors with simulations from CTM to better estimate ground-level air pollutant concentrations. The method has been applied in North Carolina from 2006 to 2008 to support the health analysis of coronary heart disease patients by developing spatiotemporal exposure fields. It has also been utilized to generate exposure fields to smoke from prescribed fire. These fields have been input to a health impact function for asthma-related Emergency Room Visits to find the health impact due to the prescribed fire in Georgia during burning season from 2015 to 2018. The spatial and temporal variations of health impacts from prescribed burning illustrate the importance of distinguishing seasons and areas when studying the relationship between exposure to pollutants from prescribed fire and its health effects. Overall, the methods and results presented in this dissertation improve the understanding of the impact of prescribed burning on air quality and human health. The data generated would also benefit future health epidemiological studies. The work presented could be useful to scientists and policy makers interested in prescribed fire and air quality, and inspire further research.