Jon Lin. Banner Photo: A Google Street Car loaded with air quality instrumentation. Credit: Logan Mitchell
In the ensuing months John Lin, professor of atmospheric sciences at the U, developed a new modeling approach that used modeled wind patterns and statistical analysis to trace pollution back to its source location to a scale previously missed by coarser scale monitoring projects that have traditionally characterized air quality averaged over an entire urban airshed.
In a U- and Environmental Defense Fund (EDF)-led study that was published in the October 2023 issue of the journal Atmospheric Environment, the results are in.
“With mobile vehicles, you can literally send them anywhere that they could drive to map out pollution, including sources that are off the road that previous monitoring missed,” said Lin, who also serves as associate director of the Wilkes Center for Climate Science & Policy. “I think the roving sentinel idea would be quite doable for a lot of cities.”
The researchers loaded the vehicles with air quality instrumentation and directed drivers to trawl through neighborhoods street by street, taking one air sample per second to create a massive dataset of air pollutant concentrations in the Salt Lake Valley from May 2019 to March 2020. The observations yielded the highest-resolution map yet of pollution hotspots at fine scales—the data captured variability within 200 meters or about two football fields.
“The big takeaway is that there is a lot of spatial variability of air pollution from one end of a block to another. There can be big differences in what people are breathing, and that scale is not captured by the typical regulatory monitors and the policy that the U.S. EPA uses to control air pollution,” said Tammy Thompson, senior air quality scientist for EDF and co-author of the study.