The ability to separate out a distinct signal from ambient noise in reams of scientific data is critical to detecting a meaningful trend or turning point in the data. That’s especially true when it comes to identifying signals of improving or declining air quality trends, whose magnitude can be smaller than that of underlying natural variations or cycles in chemical, meteorological and climatological conditions. This discrepancy makes it challenging to track how concentrations of surface air pollutants such as ozone change as a result of policies or other causes within a particular geographical region or timeframe.

A case in point is any attempt to estimate whether there has been any change in summertime mean ozone concentration over the Northeastern U.S., which can vary from place to place as well as from year to year. To obtain a reliable estimate in the most computationally efficient manner, one would need to know the minimum geographical area required to capture the full range of localized ozone concentrations, as well as the minimum number of years to sample to rule out short-term natural variability of atmospheric conditions—such as an abnormally hot summer or El Niño year—that may skew the numbers.

Now a team of researchers from the MIT Joint Program on the Science and Policy of Global Change and collaborating institutions has developed a method to optimize air quality signal detection capability over much of the continental U.S. by applying a strategic combination of spatial and temporal averaging scales. Presented in a study in the journal Atmospheric Chemistry and Physics, the method could improve researchers’ and policymakers’ understanding of air quality trends and their ability to evaluate the efficacy of existing and proposed emissions-reduction policies.