Air Quality Forecasting - How accurate can it be?

Posted on March 23rd 2015
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STRONG LAPSE CONDITION (LOOPING)

WEAK LAPSE CONDITION (CONING)

INVERSION CONDITION (FANNING)

Examples of Atmospheric Stability (attribution)
In weather prediction, forecast models are used to predict future states of the atmosphere, based on how the climate system evolves with time from an initial state. While the forecast models are quite complex (and do require strong scientific and engineering capabilities), the science of analyzing those forecast models, and verifying their accuracy, by comparing actual empirical observations to predicted values, is quite straightforward.

For the domain of Air Quality, just like for weather prediction, it is possible to define models used to predict the future set of atmospheric pollution. There are actually plenty of such models, often referred as Atmospheric Dispersion Modeling. And just like weather prediction, the same concept of accuracy analysis can be applied to Atmospheric pollution predictions. This article is the first of a series on Air Quality forecasting.

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PM2.5 air pollution forecast is already available on the World Air Quality Index project for Asia, Europe as well as the whole world. But the data used to feed the forecast models is mostly based on satellite observation (see this article) rather than terrestrial stations readings. Using satellite data has the advantage of being able to cover any part of the globe, including oceans, provided there are no cloud. But, on the other hand, satellite data is also inherently less accurate, and only available twice a day, compared to 24 times (every hour) for terrestrial observations. Considering the dynamics of Air Pollution in Asia, having only two readings a day might introduced a significant true forecast error in the prediction, following Rosanne Cole's classification[2]:
An observed forecast error may contain data errors of two kinds: (1) measurement errors in the data used to construct the forecast and (2) measurement error in the realized value. Data errors of the first kind will be a component of the true forecast error
Error of type 2 are related to the dispersion model used for the forecast. Since different models are used for the different countries and continents (this is currently the case for the World Air Quality Index project), the accuracy analysis needs to be done for each of the model. So, to start with, this article will focus on the model used for the Asian continent. In later post, we will extend the analysis to more continents.

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Back to the initial question about the forecast accuracy, one last item to be considered in the analysis in the how far in advance the forecast in computer. The less in the advance, the more accurate the model is likely to be. So, just to start with, the following analysis graphs are based on "day +1" forecast (e.g. forecast for the next day, or if you are a Tuesday, then the forecast is for the Wednesday).

There are several ways of representing the accuracy, the most obvious one being a simple number representing percentage of forecast matching the actual observation. But because we do believe that graphic visualization are much more powerful than numbers, we prefer to present the superposed forecast/observation matching for several cities in Asia. The squares at the top are the empirical observations and the one at the bottom the foretasted values.


By checking all the graphs below, one can notice quite disappointing results for Guangzhou, Chengdu and South Korea... to the extent that the model in use for Asia could almost be disqualified for public usage. This is something that we will be writing in the second post of this series on Air Quality Forecasting.


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Forecast advance:


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Some interesting links for those interested to read about about forecast accuracy:


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  • Nitrogen Dioxyde (NO2) in our atmosphere
  • Ozone AQI Scale update
  • Kriging Interpolation

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    À propos de la qualité de l'air et des mesures de la pollution atmosphérique :

    À propos des niveaux de qualité de l'air

    IQANiveau de pollution de l'air Impact sur la santé
    0 - 50BonLa qualité de l'air est jugée satisfaisante, et la pollution de l'air pose peu ou pas de risque.
    51 -100ModéréLa qualité de l'air est acceptable. Cependant, pour certains polluants, il peut y avoir un risque sur la santé pour un très petit nombre de personnes inhabituellement sensibles à la pollution atmosphérique.
    101-150Mauvais pour les groupes sensiblesLa qualité de l'air est acceptable; Cependant, pour certains polluants, il peut y avoir un problème de santé modérée pour un très petit nombre de personnes qui sont particulièrement sensibles à la pollution de l'air.
    151-200MauvaisTout le monde peut commencer à ressentir des effets sur la santé; les membres des groupes sensibles peuvent ressentir des effets de santé plus graves.
    201-300Très mauvaisAvertissements de santé de conditions d'urgence. Toute la population est plus susceptible d'être affecté.
    300+DangereuxAlerte de santé: tout le monde peut ressentir des effets de santé plus graves.

    Pour en savoir plus sur la qualité de l'air, consultez le sujet Qualité de l'air sur Wikipedia ou le guide AirNow de la qualité de l'air et de votre santé .

    Pour des conseils de santé très utiles du Dr. Richard Saint Cyr, consultez le blog myhealthbeijing.com.


    Notice d'utilisation: Toutes les données sur la qualité de l'air ne sont pas validées au moment de la publication et, pour des raisons d'assurance de la qualité, ces données peuvent être modifiées à tout moment et sans préavis. Le projet Indice de la qualité de l'air dans le monde a exercé toutes les compétences et l'attention réalisables dans la compilation du contenu de ces informations. Indice de la qualité de l’air dans le monde , l’équipe de projet ou ses agents ne peuvent être tenus responsables, contractuellement, judiciairement ou autrement, de toute perte, blessure ou préjudice résultant directement ou indirectement de la fourniture de ces données.



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