open air análise
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Openair Software
Openair is a set of innovative data analysis tools forthe air pollution community. It is intended to provide
free, open-source software that allows users to bettervisualise, analyse and interpret their data
The main objectives of Openair are:
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To develop innovative analysis tools for air pollution
measurement data and dispersion modelling output;
To provide those tools to the wider air pollution community;
To seek the involvement of other researchers nationally and
internationally.
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Openair project
Originally led by ITS at Leeds
Is a NERC Funded knowledge exchangeprogramme for the air quality community
Principal investigator is Dr David Carslaw, now at
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ngs o ege on on All the tools have been developed within the R
Software Package
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R Environment
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Openair R Environment
First step is formatting data before importing intopackage
Openair is rather fussy about data formats
Requires that missing data is formatted consistently
For those unfamiliar with command line
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programming and languages interface can befrustrating at first;
Commands are case sensitive and not intuitive at
first
Still provides a powerful and extremely useful toolfor air quality scientists
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Data usually imported through a csv file
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Openair Data Import
Date must be in required format
dd/mm/yyyy HH:MM although more expert userscan use functions that allow more flexible dataimport
Although the software can handle multiple missing
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,
Some data preparation is therefore helpful beforeimporting into package
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Summary of data summaryPlot function
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Some Familiar Data Analyses
Wind and Pollution Roses
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But Openairs strengths lie in its lessfamiliar functions
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How do you use this to find out more?
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With Temporal and DirectionalData you can find out so muchmore!
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timeVariation function reveals
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Examine the Directional Influence
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polarPlot function
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Now combine temporal anddirectional data a cross between adiurnal plot and a pollution rose!
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polarAnnulus functionQueuing traffic at
peak hours
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Some functions amusing can begood for a general view of the data
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calendarPlot function
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trendLevel function
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linearRelation function
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Is a particular source responsiblefor a problem?
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First the raw data
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Examination of the temporal variations
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Examination of the Directional Influences
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Combine the two
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Use of Openair for assessment ofmodel performance
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Assessment of Model Performance
Our normal approach to model validation is simplyto compare the predicted and measured values
But we want to know that the model is predictingthe correct value for the right reasons
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oes e mo e represen w a s o serve n eenvironment
Openair can help examine this.
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Example (a real one!)
We have a monitoring station near to a large road;
The local authority also has a background site in a
good location, not unduly influenced by the localroad network within a few km
We have ood traffic data
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Setting up a simple ADMS-Roads model using thelocal weather and background data and the trafficinformation provides us with a predicted NO2 value
within 2% (with no model correction) At this point we could walk away with a job well
done!
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Measured data
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Modelled Data
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Comparing Measured and Modelled Data
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Conclusions
Openair provides a valuable set of new data analysistools aimed at the air quality community
These tools allow us to examine data in much moredetail and using new graphical techniques providesa substantially great insight into both monitoring
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Its functions can be readily applied for theassessment of model performance and can improveour understanding of the modelling process
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Conclusions
Openair provides a valuable set of new data analysistools aimed at the air quality community
These tools allow us to examine data in much moredetail and using new graphical techniques providesa substantially great insight into both monitoring
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Its functions can be readily applied for theassessment of model performance and can improveour understanding of the modelling process
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Conclusions
Command line approach makes the use of Openairsomewhat more difficult that a typical Windowspackage
Interface is not intuitive, you need to have themanual next to you to remember the commands
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Commands are complex and difficult to remember E.G. smoothTrend(mydata, pollutant = "o3",
deseason = TRUE, type = "wd", simulate = TRUE)
BUT! The software provides a great set of tools thatcan open your eyes to what your data says!
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