Using Data to Clear the Air

New mathematical models can help predict outdoor pollutants’ impact on air quality.

In early June 2023, haze from record-setting wildfires in Canada traveled down the east coast of the United States. Major U.S. cities, including New York and Philadelphia, took turns atop the list of worst air quality in the world. A distinct smell of smoke permeated the air, first outside and then indoors. Suddenly, AQI, or air quality index, shifted from a dismissed statistic at the bottom most weather apps to a number that everyone knew off the top of their heads. And while people scrambled to replace their home air filters and dig their N95 masks out of storage, the work of researchers like Shannon Capps, PhD, took on new meaning.

Capps, an associate professor of civil, architectural and environmental engineering, is head of the Atmospheric Modeling Group in Drexel’s College of Engineering. The group works to find new and more accurate ways to both measure and to determine the effects of different pollutants on air quality and its subsequent impacts on ecosystems and human health.

Recently, Capps led the development of a novel air quality model that can calculate the effects of changes in pollutant emissions more accurately. The new method builds on the Community Multiscale Air Quality (CMAQ) model, a sophisticated three-dimensional chemical transport model developed by the EPA and used to design policies to improve air quality. The group implemented a technique called the hyperdual-step method, which uses hyperdual numbers a type of generalized complex number–to allow efficient and exact calculations of first-and second-order sensitivities, calling it CMAQ- hyd. The approach is like having the usual numbers in the model carry a backpack that holds information about where they’ve come from or what influenced them.

"For decades, CMAQ has been the most important tool for assessing benefits to human health and public welfare of costly strategies to reduce air pollution, which are highly nonlinear relationships,” Capps explained. “With improving air quality, very accurate estimations of the changes in benefits in response to ever smaller changes in pollutant emissions are needed. Existing computational tools that provide similar insights from CMAQ are either expensive to calculate or to maintain. They also approximate the answer to varying degrees. The hyperdual-step method is easy to maintain and ensures that the answers are as accurate as a computer can calculate.”

To evaluate CMAQ-hyd, Capps' team compared it to existing methods for calculating both first-and second-order sensitivities, which together show how much a change in one emission impacts air quality and cross-sensitivities, which reveal non-linear impacts, like when reducing two pollutants together differs from reducing each alone. The novel method provided the expected results.

The CMAQ-hyd method, according to Capps’ findings, eliminates truncation and cancellation errors, while needing only a single model run versus multiple iterations. This improves accuracy and saves computational expense. Because of these advantages, Capps believes that CMAQ-hyd will serve local and federal environmental decisionmakers by providing indispensable insights for developing emissions control policies.

“Distinguishing the impacts of intricate interactions of pollutants produced by people and plants is one way CMAQ-hyd is shown to add value,” said Capps. “Jiachen Liu, one of the doctoral researchers in my group, showed how reactions of oxides of nitrogen, a typical pollutant from internal combustion engines in cars, and volatile organic compounds produced by trees produced small amounts of particulate matter across the Southeast U.S. in the summertime. These contributions are small enough that they would be hard to represent accurately with CMAQ alone, but the precision and accuracy of CMAQ-hyd allowed us to probe how controlling pollutant emissions from cars, especially in summer, might have added benefits of reducing exposure to particles, which harm human health.”

In addition to her work modeling outdoor air quality, Capps collaborates with other researchers to understand how pollutants transform when they move indoors. Recently, in a project led by Michael Waring, PhD,professor and department head of civil, architectural and environmental engineering, Capps and additional colleagues combined two existing models — an aerosol thermodynamic model called ISORROPIA and an indoor air quality model called IMAGES — to track changes in outdoor inorganic aerosols as they become indoor air pollutants.

ISORROPIA predicts gas and particle phase partitioning of inorganic pollutants like sulfate, nitrate and ammonium. By incorporating it into IMAGES, the group could simulate how temperature, humidity and ventilation impact indoor distribution of these pollutants. This allowed them to reproduce indoor concentrations under varying conditions more realistically.

“Because residents of developed countries spend most of their time indoors, the most likely place they will experience air pollution is inside,” Capps explained. “The fact that the smoke from the wildfires pervaded indoors, even with windows closed, underlines the importance of understanding how outdoor pollution can travel and even transform indoors, even in the presence of home air filters.”

With climate change exacerbating air quality issues worldwide, the need for advanced modeling techniques like those pioneered by Capps becomes increasingly urgent. As Capps continues applying this kind of modeling in innovative ways, she hopes to provide data that can help government leaders craft effective emissions reduction policies and protect human health.

“We want to use this modeling to inform smart decisions about air quality management and strategic placement of sensors to accurately capture pollution levels,” Capps said. “The end goal is providing information to drive positive change and ensure the air we breathe, both outdoors and in, is safe.”