Air Quality Forecasting Models
Real-time and Historical Analalysis

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Air Quality Forecasting - also refered as 'Atmospheric Dispersion Modeling' (wikipedia) is the art of simulating how air pollutants (eg PM2.5 or Ozone) disperse is the ambient atmosphere. The result of the simulation gives the ambient concentration for each air pollutants, from which the Air Quality Index can be calculated.

There are dozens of dispertion models, and many universities and institutions arround the world are running one or several of them for different regions (eg Europe, Continental US, Asia ...). Below is the a non-exaustive and selected list of few models which are beeing used for providing Air Quality forecast on the "World Air Quality Index" project.

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Help is needed for keeping the world-wide Air Quality forecast model inventory up-to-date.

  • Do you know any model not listed below but for which gridded data is available?
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  • Atmospheric Models

    You can click on any of the forecast map to see the real-time forecast animation, as well as the model efficiency analysis.

    SPRINTARS Asia
    Spectral Radiation-Transport Model for Aerosol Species
    SPRINTARS East Asia
    Spectral Radiation-Transport Model for Aerosol Species
    MPIM China
    Max Planck Institut fur Meteorologie
    SILAM Asia
    System for Integrated modeLling of Atmospheric coMposition
    CAMS Asia / Panda
    Copernicus Atmosphere Monitoring Service NRT
    CAMS Europe
    Copernicus Atmosphere Monitoring Service NRT
    EMEP Europe
    European Monitoring and Evaluation Program
    CAMS World
    Copernicus Atmosphere Monitoring Service NRT
    SILAM World
    System for Integrated modeLling of Atmospheric coMposition
    NGAC
    NOAA NEMS GFS Aerosol Component
    GFS
    Global Forecast System
    NCEP EMP AQM
    Air Quality Model (AQM) Product


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    Please note that all above analysis are done on the World Air Quality Index (WAQI) project own budget.
    We did not receive any subsidies from any of the intuitions publishing Air Quality forecast models.

    Detailled City Air Quality Forecast


    Detailled Air Quality Forecast is available for more than 600 cities:
    Or just pick any of those cities: Beijing, Shanghai, Chengdu, Shenyang, Shenzhen, Guangzhou, Qingdao, Xian, Tianjin, Saitama, Kyoto, Osaka, Seoul, Busan, Bogotá, Delhi, Jakarta, Ulaanbaatar, Hanoi, Chennai, Kolkata, Mumbai, Hyderabad, Santiago, Lima, Sao Paulo, quito, Singapore, Kuala Lumpur, Ipoh, Perai, Miri, New York, Seattle, Chicago, Boston, Atlanta.

    Disclaimer

    This forecasting model, and all AQFS which it is based on, are research products intended to provide information related to Air Quality forecast. All reasonable measures have been taken to ensure its quality and accuracy. However:
    • We do not make warranty, express or implied, nor assume any legal liability or responsibility for the accuracy, correctness, completeness of the information.
    • We do not assume any legal liability or responsibility for any damage or loss that may directly or indirectly result from any information contained on this website or any actions taken as a result of the content of this website;
    • We may change, delete, add to, or otherwise amend information contained on this website without notice

    For more information about the underlying concepts of Air Quality forecasting (or Atmospheric Dispertion Modeling), check the article on a visual study of wind impact of PM2.5 concentration.

    Air Quality Forecastign Model Sources

    The above prediction is based on composite meta model, computed using several Air Quality Forecasting Systems (AQFS): The above map is based on the PM2.5 surface level modeling, and colors are following the US EPA AQI standard.

    About the Air Quality and Pollution Measurement:

    About the Air Quality Levels

    AQIAir Pollution LevelHealth ImplicationsCautionary Statement (for PM2.5)
    0 - 50 Good Air quality is considered satisfactory, and air pollution poses little or no risk None
    51 -100 Moderate Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people who are unusually sensitive to air pollution. Active children and adults, and people with respiratory disease, such as asthma, should limit prolonged outdoor exertion.
    101-150 Unhealthy for Sensitive Groups Members of sensitive groups may experience health effects. The general public is not likely to be affected. Active children and adults, and people with respiratory disease, such as asthma, should limit prolonged outdoor exertion.
    151-200 Unhealthy Everyone may begin to experience health effects; members of sensitive groups may experience more serious health effects Active children and adults, and people with respiratory disease, such as asthma, should avoid prolonged outdoor exertion; everyone else, especially children, should limit prolonged outdoor exertion
    201-300 Very Unhealthy Health warnings of emergency conditions. The entire population is more likely to be affected. Active children and adults, and people with respiratory disease, such as asthma, should avoid all outdoor exertion; everyone else, especially children, should limit outdoor exertion.
    300+ Hazardous Health alert: everyone may experience more serious health effects Everyone should avoid all outdoor exertion

    To know more about Air Quality and Pollution, check the wikipedia Air Quality topic or the airnow guide to Air Quality and Your Health.

    For very useful health advices of Beijing Doctor Richard Saint Cyr MD, check www.myhealthbeijing.com blog.


    Usage Notice: All the Air Quality data are unvalidated at the time of publication, and due to quality assurance these data may be amended, without notice, at any time. The World Air Quality Index project has exercised all reasonable skill and care in compiling the contents of this information and under no circumstances will the World Air Quality Index project team or its agents be liable in contract, tort or otherwise for any loss, injury or damage arising directly or indirectly from the supply of this data.



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