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Ozone is an atmospheric constituent that affects not only human health but also vegetation, especially annual crops. Thus, ozone can impact land and water use. Detecting increases and declines in ozone and other constituents within a certain area or timeframe is hard. Why? The ozone signal is swamped by natural variations or cycles. Now, researchers developed a method to optimize the air quality signal detection capability over much of the continental United States. Their method uses 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.
Surface - Ozone - Data - United - States
Working with simulated and observed surface ozone data within the United States 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...
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