New Algorithm Increases Accuracy of Air-Pollution Predictions
Scientists from the Argonne National Laboratory in the US, in collaboration with scientists from the University of North Carolina and Bristol University in the UK, have created a new air pollution computer model based on new algorithms that can generate more reliable forecasts based on observational data.
The model was made to predict carbon monoxide, but its underlying principles and innovations could also be used to work with CO2 and other greenhouse gases.
Meeting Air-Quality Targets
The reason why they started by working on carbon monoxide is because of air pollution regulations:
When air-quality monitors and environmental regulators inspect the pollution levels of certain cities, the difference of one or two parts per million in the concentration of pollutants like ozone and carbon monoxide can mean the difference between achieving a target and having to implement additional costly provisions to get failing areas back on track.
This model can help predict pollution hot spots and so help better direct resources and efforts toward these areas.
Because it is important to have probabilistic information about the future that is as reliable as possible, this new tool could help steer public policy and research.
Data assimilation may also boost researchers' ability to project likely climate scenarios for the "near-term decadal scale"—approximately 10 to 20 years—which would help public officials assess the consequences of their decisions that concern climate change
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More on Argonne's Air Pollution COmputer Model
::New Argonne algorithm increases accuracy of air-pollution predictions
::New Algorithm Increases Accuracy of Air Pollution Predictions