UNC researchers study impact of traffic-related particulate matter upon premature mortality

March 20, 2017

Researchers in the UNC Institute for the Environment and UNC Gillings School of Global Public Health led a study which found that previous research may have underestimated premature mortality due to particulate matter from traffic emissions.

The study, published online Feb. 28 in Risk Analysis, utilizes a novel modeling technique and is the first to quantify the potential error in estimating on-road particulate matter mortality in previous models.

Traffic-related air pollutants can have an adverse impact upon people’s health, including decreased lung function, coronary heart disease, asthma, thrombosis and tuberculosis. From a public health perspective, it is therefore important to understand the burden of disease due to exposure to such pollutants.

Traditionally, the disease burden related to air pollutant exposure is estimated by combining chemical-transport air-quality models and health impact functions. The models incorporate emission data and current knowledge about physical and chemical processes in the atmosphere to predict pollutant concentrations. The estimated concentrations then are used to estimate the resultant mortality or morbidity, based on health functions.

Dr. William Vizuete

Dr. William Vizuete

Study leaders Sarav Arunachalam, PhD, research associate professor at the Institute for the Environment, and William Vizuete, PhD, associate professor of environmental sciences and engineering at the Gillings School, used a new hybrid modeling approach to estimate disease burden due to on-road particulate matter. Their data were collected in the Piedmont region of North Carolina.

This hybrid approach helped researchers to increase spatial resolution and allowed a better characterization of particulate matter concentrations due to traffic-related emissions. The researchers hypothesize that the more finely resolved concentration field from the hybrid approach results in a higher burden of disease estimate for particulate matter – primarily due to on-road emissions.

The hybrid modeling approach estimated 24 percent more on-road particulate-matter-related premature mortality than did the Community Multiscale Air Quality (CMAQ) model alone. The major difference arose from the primary on-road particulate matter, which the hybrid approach estimated as resulting in 2.5 times more primary on-road particulate-matter-related premature mortality than did the CMAQ model. This was due to predicted exposure hotspots near roadways that coincide with high-population areas.

Results show that 72 percent of primary on-road particulate-matter-related premature mortality occurs within 1,000 meters from roadways, where 50 percent of the total population resides. This highlights the importance of characterizing near-road primary particulate matter and suggests that previous studies may have underestimated premature mortality due to particulate matter from traffic-related emissions.

“We knew that traffic-related pollutants have a sharp gradient in the immediate vicinity of major roads, and we wanted to quantify how much spatial resolution mattered when estimating health risk due to traffic-related pollutants,” said Arunachalam. “By combining multiple models, we were able to get the best of both worlds.”

The approach used in this study can be expanded to a national scale to evaluate the total impact from on-road particulate matter upon nationwide health risks and to provide insights for policymakers for emissions control strategies.

The study is the first to quantify the potential error in estimating on-road particulate-matter-related mortality due to resolution of air-quality models in a large spatial domain. The researchers demonstrated the possibility for prior studies to have underestimated on-road particulate-matter-related premature mortality, especially the primary particulate matter, which is a key component in the near-road environment.

“Millions of people live near major roadways,” Vizuete said, “and this model allows us to predict, more accurately and efficiently, where the high concentrations of emitted pollutants occur.”

Co-authors of the study include Marc Serre, PhD, associate professor of environmental sciences and engineering (ESE) at the Gillings School; Shih Ying Chang, PhD, and Lakshmi PradeepaVennam, PhD, both recent ESE graduates; Mohammad Omary, PhD, from UNC’s Institute for the Environment; and Vlad Isakov, PhD, and Michael Breen, PhD, research physical scientists at the U.S. Environmental Protection Agency.


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Gillings School of Global Public Health contact: David Pesci, director of communications, (919) 962-2600 or dpesci@unc.edu