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 (firstname.lastname@example.org) or Ronald Prinn (email@example.com)
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]
SC-23.1 Climate and Environmental Sciences Division, BER
BER supports basic research and scientific user facilities to advance DOE missions in energy and environment. More about BER
Mar 22, 2019
Improving Projections of Future Hydropower Changes in the Western United States
Integrated modeling system with a new, process-based hydropower module accounts for both electric [more...]
Mar 15, 2019
The River Runs Over, Around, and Through It: Accounting for Intensive Water Resource Management in a Semiarid Watershed
Integrated hydrological modeling of the Yakima River Basin. The Science Increasin [more...]
Feb 27, 2019
Regional Responses to Water Scarcity: Agriculture or Power?
Increases in water demand lead to different responses in different regions. The Science  [more...]
Feb 14, 2019
A Decade of CO2 Enrichment Stimulates Wood Growth by 30%
Synthesis of four long-term, DOE supported, CO2 enrichment experiments show that young te [more...]
Feb 13, 2019
When It Comes to the Circadian Clock, Proteins Can Have Their Own Rhythm
The most in-depth proteome study of its kind shows rhythmic RNA is not essential for metabolic prote [more...]
List all highlights (possible long download time)