Air pollution forecasting
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Air pollution forecasting is the application of science and technology to predict the composition of the air pollution in the atmosphere for a given location and time. Mainstream pollution prediction algorithms tend to utilize air quality index or PM2.5 concentration to indicate pollution level.
The forecast may give the pollutant's concentration or the air quality index.
Countries and cities are given forecasts by state and local government organizations, as well as private companies like Airly, AirVisual, Aerostate, BreezoMeter, PlumeLabs, and DRAXIS that provide air pollution forecasts.
Techniques
- Air pollution forecasting can be done by coupling weather forecasting systems with chemical transport model and atmospheric dispersion modeling.
- Recent studies have incorporated machine learning techniques such as neural networks, regressions, and random forests to achieve high accuracy.[1]
The forecast takes into account local emission sources (like nearby traffic or industry) and remote sources (e.g. dust that is carried by air parcels and follows the wind direction).
The forecast temporally resolution is usually daily or hourly and the spatial resolution can change from block resolution to dozens of km resolution.
Most forecasts of air quality cover two to five days.[2]
Motivation
- By knowing the air quality forecast one can decide how to act, e.g. due to air pollution health effects, one can prepare ahead of time and choose the best time to do an outdoor activity.
- Deciding whether to put on a skin care ointment.[3]
- Find the cleanest route for cars.[4]
- Deciding whether to leave the windows open or closed.[5]
- Governments can utilize air quality forecasts to implement effective pollution control measures.[6]
References
- ^ Kolehmainen, M; Martikainen, H; Ruuskanen, J (1 January 2001). "Neural networks and periodic components used in air quality forecasting". Atmospheric Environment. 35 (5): 815–825. Bibcode:2001AtmEn..35..815K. doi:10.1016/S1352-2310(00)00385-X.
- ^ Kumar, Rajesh; Peuch, Vincent-Henri; Crawford, James H.; Brasseur, Guy (September 2018). "Five steps to improve air-quality forecasts". Nature. 561 (7721): 27–29. Bibcode:2018Natur.561...27K. doi:10.1038/d41586-018-06150-5. PMID 30181644.
- ^ "Dermalogica & BreezoMeter partner to educate on pollution's skin effects". Retrieved 31 May 2018.
- ^ "Clean Air Route Finder". Greater London Authority. 14 July 2017.
- ^ "Air Pollution Maps: Users Love Them, Your Brand Needs Them".
- ^ "An Artificial Intelligence Framework to Forecast Air Quality".
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