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:

Click here to see all the FAQ entries
  • AQI Scale: What do the colors and numbers mean?
  • Using Statistical Distances for Real-time Sensor Networks Validation
  • Nitrogen Dioxyde (NO2) in our atmosphere
  • About the Air Quality and Pollution Measurement:

    About the Air Quality Levels

    AQIAir Pollution LevelHealth ImplicationsCautionary Statement (for PM2.5)
    0 - 50GoodAir quality is considered satisfactory, and air pollution poses little or no riskNone
    51 -100ModerateAir 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.
    101-150Unhealthy for Sensitive GroupsMembers 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.
    151-200UnhealthyEveryone may begin to experience health effects; members of sensitive groups may experience more serious health effectsActive 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
    201-300Very UnhealthyHealth 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+HazardousHealth alert: everyone may experience more serious health effectsEveryone 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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