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- Base Module Air Pollution Module

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What's the sense of a library for meteorological data, when everybody knows that meteo data should best be measured locally? You will quickly understand the sensibility to store copies with several classification settings for different model approaches, different treatment of calms, different approaches to fill data lacks; maybe you need also selections of data filtered by daytime or by seasons and so on. That's why we call it library. If you often work  in the same area, you can also store those library elements globally to use them in further projects.

However, much more it is a mighty tool!It helps to analyze data, to find data lacks, to control if data fit to your project area, to deal with calms and circulating winds, to adjust raw data and to derive information like stability classes from several measured parameters, using different stability class approaches.

Whenever your data have any insufficiency, this library assists you with several types of diagrams to visualize what your problem is and to communicate it to your customer in order to get better data or to show the risk of using bad data. The possibility to match wind roses and pollutant roses with site maps and georeferenced aerial photos is helpful to clear up the last doubts according to your concern.

If your data are ok, you can spice up your report with those diagrams to make your work transparent and comprehensible:

All diagrams can be exported to your report as scalable Enhanced Meta Files (*.EMF) via clipboard. Wind roses can also be stored as geo referenced bitmap with user defined size in meters to include them in maps, 3d terrain views or aerial photos to analyze, if the measurement is influenced by obstacles and if it reproduces terrain effects as expected.

A wind rose placed in an aerial photo upon the source location can sometimes replace a whole expert study if , e.g. a wind rose placed on a chicken farm, shows immediately, that there will be no annoyance in the nearby housing area which is located outside of any critical wind flow direction.

Background pollution

Background measurement is mostly made in locations representative for purpose of a monitoring of changes. Often everybody knows that the measured values are too high to support sensibly a dispersion calculation with a background value - but how can one seriously estimate, how much it is too high?

If measurement is based on hourly values, you can load the background concentration data together with the meteorological data to the Meteorological Station Library to prepare a detailed analysis. But what can be done if the measured background value is too high for your calculation area? Here also the Library 'Meteorological Station' has answers:

Pollutant rose diagrams show the mean concentration for each wind direction and give an idea, which directions can be representative and which are too much influenced by local sources - especially when you apply the pollutant roses geo referenced to aerial photographs.

Other diagrams display day histograms of the mean concentrations for selected wind direction sectors. They can be created for one or two pollutants at a time, to show e.g. how the relation between NO and NO2 varies during the day. Also mean and percentile wind speeds are displayed to avoid misinterpretations. Using additionally the filters of the Meteorological Station Library to regard seasonal variations, you have a mighty tool to explain, why you can't accept the background value and how much you can decrease it sensibly for your project.

Emission Time Histograms

Hourly time histograms are useful for several purposes. They can be used to define daily emissions over 24 hours as well as to define seasonal emission variations or, for AUSTAL2000, to define time variable buoyant plume rise.

The concept is made to support as well simple emission cycles as complex cycles with annual variations. The year can be divided to freely chosen periods between 1 and 365 days. To each period you assign a week histogram and to each week histogram you assign 1-7 day histograms. You can also add factors for weekdays and for each defined period to simplify the procedure. Assigning such a year histogram to a source, the defined emission mass distribution is normalized and adjusted to the individual average emission of each source object. If emissions have to be defined non-cyclic, e.g. like wind erosion sources, you can alternatively enter a list with values for each hour.

Thinking about emission time histograms please regard that the use of emission histograms is not always sensible and will sometimes automatically replaced by a constant mean emission value: E.g. if you enter the emission time histogram together with classified meteo data, there is no hourly meteo information to be combined with emissions. Models like GRAL calculate dispersion basically with an averaged concentration and adjust the results afterwards, in a post processing, by assigning histograms as hourly distribution pattern to whole source groups.

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