CGRER researchers develop air pollution prediction system for Santiago, Chile

Santiago, Chile. Photo via

Quicker predictions will help officials protect public health

In the Southern Hemisphere, the months of April, May and June mark the transition from summer to winter and usher in masses of stagnant air that often give rise to urban air pollution.

That’s why a study conducted by CGRER researchers — and published in the May issue of the journal Atmospheric Environment — that describes a system to predict periods of high air pollution is attracting attention in Santiago, Chile, a city of nearly 6 million people.

The study was led by civil and environmental engineering doctoral student Pablo Saide at

Pablo Saide

CGRER under the guidance of UI professor and CGRER co-director Gregory Carmichael and Scott Spak, CGRER postdoctoral researcher. They collaborated with UI alumnus Marcelo Mena, currently at Universidad Andres Bello, and other researchers from Universidad de Chile and the U.S. National Oceanic and Atmospheric Administration.

Currently, the local government declares a Santiago air pollution episode based upon air quality forecasts conducted 24 hours in advance. Then, by law, it can temporarily restrict activities, such as manufacturing and the use of private cars, in an effort to protect public health by reducing high levels of smog. However, the researchers found that by waiting until an air pollution episode is imminent, officials often act too late to prevent it.

In fact, the researchers found that actions have to be permanent or at least have to be taken one or two days before an episode in order to work.

According to Saide, the new system accurately predicts air quality in Santiago and it does so with greater lead time. He and his colleagues used CGRER computer model simulations to forecast urban air pollution up to three days in advance, and then compared the results to those of a local observational network.

“We found that, in general, contribution to episode concentrations is dominated by emissions from the days before,” Saide noted. “Thus, since emissions contribution from the same day is little, a decrease in these emissions is not going to impact concentrations that generate episodes.”

In brief, the study concluded that 24-hour forecasts are too little, too late, and that there is a need for the 48-hour forecasts the system can provide.

The forecasting system itself works by accurately measuring levels of carbon monoxide, which behaves like particulate matter (PM), and using it to simulate PM. In this way, an accurate estimate of PM is made without having to conduct more difficult aerosol chemistry modeling.

At present, the prediction system is being transferred to the Chilean Meteorological Office, where it will be run operationally and evaluated during this year’s winter air pollution season.

“We plan to continue to work with the Chilean authorities to help them improve their air quality forecasting skill and their use of these forecasts to develop more effective emission mitigation strategies,” said Carmichael.

The formal title of the journal paper is “Forecasting urban PM10 and PM2.5 pollution episodes in very stable nocturnal conditions and complex terrain using WRF-Chem CO tracer model.”

Saide is supported by a Fulbright scholarship, and the research was supported in part by a grant from the National Science Foundation.

Read more about CGRER air quality research: 

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