Modeling advance enables more efficient and precise estimates of trends in ozone and other pollutants within selected geographical regions and timeframes.
Tropospheric ozone is an atmospheric pollutant that affects not only human health but also vegetation, especially annual crops, and can thus impact land and water use, with links to the broader energy-water-land nexus. A key challenge in detecting increases and declines in concentrations of ozone and other surface air pollutants within a particular geographical region or timeframe is that the magnitude of such trends can be smaller than that of underlying natural variations or cycles in chemical, meteorological and climatological conditions. Now researchers at the MIT Joint Program on the Science and Policy of Global Change have 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.
The new air quality signal detection method could improve researchers’ understanding of and ability to track air quality trends. It may be applied not only to surface ozone data but also to a wide range of modeled or observational data.
Working with simulated and observed surface ozone data within the U.S. covering a 25-year period, the researchers analyzed how the magnitude of the variability of the data due to meteorology depended on the spatial (kilometers) or temporal (years) scale over which the data were averaged. As they homed in on the extent of the region and timeframe needed to obtain a clear signal of air quality change within the data set, they effectively determined the risk of getting an insufficiently representative sample when averaging the data over too small a region or timeframe. As expected, they found that averaging over a greater area and timeframe, which reduces the “noise” from natural variability, will boost signal detection accuracy. The researchers’ most salient finding was that over much of the continental U.S., they could achieve the most sensitive signal detection capability by strategically combining specific spatial and temporal averaging scales. In other words, they developed a way to systematically identify a data set’s “sweet spot”—the number of kilometers and years over which to average the data so as to detect the signal most efficiently. For the hardest-to-detect signals, they recommended averaging the data over 10-15 years and over an area extending up to several hundred kilometers.
BER PM Contact
Noelle Selin (email@example.com) or Ronald Prinn (firstname.lastname@example.org)
MIT Joint Program on the Science and Policy of Global Change
The study was funded by the U.S. Department of Energy (DOE) Office of Science under the grant DE-FG02-94ER61937 and other government, industry and foundation sponsors of the MIT Joint Program.
Brown-Steiner, B., N. E. Selin, R.G. Prinn, E. Monier, S. Tilmes, L. Emmons, and F. Garcia-Menendez. “Maximizing ozone signals among chemical, meteorological, and climatological variability.” Atmospheric Chemistry and Physics 18, 8373-8388 (2018). [DOI: 10.5194/acp-18-8373-2018]
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