Study: Modeling can reduce data required for accurate, on-road air pollution analysis

Land use regression models can help produce high-quality maps, even with limited data

October 24, 2018
Anne Marie Borrego, (202-5)72-3508,

An article published today in the journal Environmental Science and Technology demonstrates that it is possible to create reliable maps that show changes in air quality with limited amounts of mobile monitoring data, when that information is fed into a statistical model. As interest in mobile air pollution measurement grows Environmental Defense Fund is testing new air quality measurement techniques that are both accurate and cost-effective.

The new EDF study, which utilizes data collected for an earlier project of air quality in Oakland, CA, was led by scientists from the Department of Civil, Architectural & Environmental Engineering at the University of Texas at Austin. The research team – which also included EDF scientists and experts from the United States, Canada, and the Netherlands – combined the data collected for the earlier study and incorporated it into a statistical model to determine just how much collected data was required to create an accurate picture of air quality in Oakland.

The authors found that driving about 30 percent of the roads in the city a minimum of four randomly sampled times was sufficient to predict the key patterns of how air pollution varied across the cities. They also found that highly accurate estimates of a city’s air pollution patterns could be made even without the use of a model if every road was sampled on ten or more occasions.

“Mobile monitoring is a powerful technique for creating high-resolution air pollution maps, this study shows us that we can produce reliable maps with less data than previously believed, “said Steven Hamburg, EDF’s Chief Scientist. “By reducing the amount of data required, organizations can scale their efforts more cost-effectively.”

If possible to make repeated measurements, the authors determined that data-only maps (those created without the use of a model) are a simpler, more accurate approach. “Our results point to a path towards efficiently developing high-resolution air pollution maps for cities around the world,” said Joshua Apte, Assistant Professor at University of Texas, who led the study.

For the original study, the project team used two Google Street View mapping cars equipped with Aclima’s mobile platform of fast-response air pollution instruments. Engineers from UT Austin created daily driving plans, capturing data from three neighborhoods in Oakland at various times of the day, week and year. They ultimately drove more than 12,000 miles in order to sample air pollution on each road in these neighborhoods an average of 30 times between May 2015 and 2017.

EDF is continuing to test a variety of stationary and mobile air quality monitoring techniques in Northern California, Texas and London.

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