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Air pollution is one of the world’s biggest problems, and it causes respiratory problems, lung diseases, and cardiovascular issues and can contribute to mental health issues and aggravate existing health conditions. It can cause depletion to planetary health equally. Therefore, reducing and making
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With the accurate method of forecasting air pollution, it becomes easier to manage and mitigate the risks of air pollution and ensure a safe level of pollutant concentration in the region. It also helps assess risks to the environment and the
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Urban air quality models require a very fine computational mesh, requiring the use of high-resolution mesoscale weather models; in spite of this, the quality of numerical weather guidance is the main uncertainty in air quality forecasts.
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Baklanov, Alexander; Rasmussen, Alix; Fay, Barbara; Berge, Erik; Finardi, Sandro (September 2002). "Potential and
Shortcomings of Numerical Weather Prediction Models in Providing Meteorological Data for Urban Air Pollution Forecasting".
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A model of pollutant emission. This can include traffic, industry, and pollen. The cycles of pollutant emission range from daily to weekly (for human commute) and yearly (for pollen and coal-burning). Non-periodic sources such as
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Countries and cities are given forecasts by state and local government organizations, as well as private companies like Airly, AirVisual, Aerostate, Ambee, BreezoMeter, PlumeLabs, and DRAXIS that provide air pollution forecasts.
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observations to provide insights, analysis, and forecasts from global to street-level air pollution. It also takes into consideration local factors like traffic, regional weather patterns, or emissions in the atmosphere.
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caused by poor air quality standards. Accurate forecasting can also lead to ease in planning day-to-day activities, avoiding locations with high alert areas, and implementing effective pollution control measures.
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Pollen forecasting not only matters for forecast of PM concentration, but also for allergies. There are a range of ways to do so, some taking into account the predicted weather.
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Suanno, Chiara; Aloisi, Iris; Fernández-González, Delia; Del Duca, Stefano (September 2021). "Pollen forecasting and its relevance in pollen allergen avoidance".
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can prevent surface air from rising, trapping pollutants near the surface, which makes accurate forecasts of such events crucial for air quality modeling.
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The forecast temporally resolution is usually daily or hourly and the spatial resolution can change from block resolution to dozens of km resolution.
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Kolehmainen, M; Martikainen, H; Ruuskanen, J (1 January 2001). "Neural networks and periodic components used in air quality forecasting".
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Advanced approaches in air quality forecasting combine historical data with data generated via on-ground sensors and
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techniques such as neural networks, regressions, and random forests to achieve high accuracy in this part.
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An input of the forecasted weather during the period of prediction, to predict any pollutant's movement.
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Governments can utilize air quality forecasts to implement effective pollution control measures.
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Kumar, Rajesh; Peuch, Vincent-Henri; Crawford, James H.; Brasseur, Guy (September 2018).
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By knowing the air quality forecast one can decide how to act, e.g. due to air pollution
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to predict what happens next. A typical algorithm uses the following components:
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is the application of science and technology to predict the composition of the
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619:"An Artificial Intelligence Framework to Forecast Air Quality"
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for a given location and time. An algorithm prediction of the
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Atmosphere, ocean, and climate dynamics: an introductory text
605:"Air Pollution Maps: Users Love Them, Your Brand Needs Them"
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Find the cleanest route for driving, walking or cycling.
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Most forecasts of air quality cover two to five days.
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Deciding whether to leave the windows open or closed.
228:velocity of movement through the atmosphere, their
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544:. Amsterdam: Elsevier Academic Press. pp.
335:"Five steps to improve air-quality forecasts"
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593:. Greater London Authority. 14 July 2017.
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