Introduction Progress and examples Future developments The Open-Source Air Pollution Project Community tools for analysing air pollution data David Carslaw Institute for Transport Studies, University of Leeds 14 May 2008 Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
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IntroductionProgress and examples
Future developments
The Open-Source Air Pollution ProjectCommunity tools for analysing air pollution data
David Carslaw
Institute for Transport Studies, University of Leeds
14 May 2008
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
Summary
Background to the project
Progress and examples
Future directions and developments
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
The benefits of openness
Transparency should be at the heart of environmentalregulation
Those affected by environmental decisions should be able toscrutinise the tools that lead to those decisions
Many examples from the USA
Open-source software
All source code made available, free and can be modified byanyoneNo longer in the realm of enthusiasts e.g. Linux, MySQL, R
Both promote participation and ownership
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
The benefits of openness
Transparency should be at the heart of environmentalregulation
Those affected by environmental decisions should be able toscrutinise the tools that lead to those decisionsMany examples from the USA
Open-source software
All source code made available, free and can be modified byanyoneNo longer in the realm of enthusiasts e.g. Linux, MySQL, R
Both promote participation and ownership
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
The benefits of openness
Transparency should be at the heart of environmentalregulation
Those affected by environmental decisions should be able toscrutinise the tools that lead to those decisionsMany examples from the USA
Open-source software
All source code made available, free and can be modified byanyoneNo longer in the realm of enthusiasts e.g. Linux, MySQL, R
Both promote participation and ownership
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
The benefits of openness
Transparency should be at the heart of environmentalregulation
Those affected by environmental decisions should be able toscrutinise the tools that lead to those decisionsMany examples from the USA
Open-source software
All source code made available, free and can be modified byanyone
No longer in the realm of enthusiasts e.g. Linux, MySQL, R
Both promote participation and ownership
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
The benefits of openness
Transparency should be at the heart of environmentalregulation
Those affected by environmental decisions should be able toscrutinise the tools that lead to those decisionsMany examples from the USA
Open-source software
All source code made available, free and can be modified byanyoneNo longer in the realm of enthusiasts e.g. Linux, MySQL, R
Both promote participation and ownership
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
The benefits of openness
Transparency should be at the heart of environmentalregulation
Those affected by environmental decisions should be able toscrutinise the tools that lead to those decisionsMany examples from the USA
Open-source software
All source code made available, free and can be modified byanyoneNo longer in the realm of enthusiasts e.g. Linux, MySQL, R
Both promote participation and ownership
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
The benefits of openness
Transparency should be at the heart of environmentalregulation
Those affected by environmental decisions should be able toscrutinise the tools that lead to those decisionsMany examples from the USA
Open-source software
All source code made available, free and can be modified byanyoneNo longer in the realm of enthusiasts e.g. Linux, MySQL, R
Both promote participation and ownership
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
Interesting aside – climate change and the “hockey stick”
Controversy over IPCC “hockeystick” temperature grapha
Statistical methods shown to beflawed (McIntyre andMcKitrick)
Code and data made available –some in R
aMann, M.E. et al. (1998). Global-scaletemperature patterns and climate forcingover the past six centuries. Nature, Vol.392, pp. 779787.
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
Project aims
To build a set of free, open-source tools for the analysis of airpollution data
Use highly developed open-source statistical software ‘R’
To make it easier to analyse data and to gain insights from itExploit the enormous (and growing) amount of air pollutiondata availableProgressively include advanced approaches generally not widelyavailable
Outputs
An R ‘package’ dedicated to air pollution analysisA web site to act as a central resourceComprehensive documentation
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
Project aims
To build a set of free, open-source tools for the analysis of airpollution data
Use highly developed open-source statistical software ‘R’To make it easier to analyse data and to gain insights from it
Exploit the enormous (and growing) amount of air pollutiondata availableProgressively include advanced approaches generally not widelyavailable
Outputs
An R ‘package’ dedicated to air pollution analysisA web site to act as a central resourceComprehensive documentation
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
Project aims
To build a set of free, open-source tools for the analysis of airpollution data
Use highly developed open-source statistical software ‘R’To make it easier to analyse data and to gain insights from itExploit the enormous (and growing) amount of air pollutiondata available
Progressively include advanced approaches generally not widelyavailable
Outputs
An R ‘package’ dedicated to air pollution analysisA web site to act as a central resourceComprehensive documentation
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
Project aims
To build a set of free, open-source tools for the analysis of airpollution data
Use highly developed open-source statistical software ‘R’To make it easier to analyse data and to gain insights from itExploit the enormous (and growing) amount of air pollutiondata availableProgressively include advanced approaches generally not widelyavailable
Outputs
An R ‘package’ dedicated to air pollution analysisA web site to act as a central resourceComprehensive documentation
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
Project aims
To build a set of free, open-source tools for the analysis of airpollution data
Use highly developed open-source statistical software ‘R’To make it easier to analyse data and to gain insights from itExploit the enormous (and growing) amount of air pollutiondata availableProgressively include advanced approaches generally not widelyavailable
Outputs
An R ‘package’ dedicated to air pollution analysisA web site to act as a central resourceComprehensive documentation
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
Project aims
To build a set of free, open-source tools for the analysis of airpollution data
Use highly developed open-source statistical software ‘R’To make it easier to analyse data and to gain insights from itExploit the enormous (and growing) amount of air pollutiondata availableProgressively include advanced approaches generally not widelyavailable
Outputs
An R ‘package’ dedicated to air pollution analysis
A web site to act as a central resourceComprehensive documentation
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
Project aims
To build a set of free, open-source tools for the analysis of airpollution data
Use highly developed open-source statistical software ‘R’To make it easier to analyse data and to gain insights from itExploit the enormous (and growing) amount of air pollutiondata availableProgressively include advanced approaches generally not widelyavailable
Outputs
An R ‘package’ dedicated to air pollution analysisA web site to act as a central resource
Comprehensive documentation
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
Project aims
To build a set of free, open-source tools for the analysis of airpollution data
Use highly developed open-source statistical software ‘R’To make it easier to analyse data and to gain insights from itExploit the enormous (and growing) amount of air pollutiondata availableProgressively include advanced approaches generally not widelyavailable
Outputs
An R ‘package’ dedicated to air pollution analysisA web site to act as a central resourceComprehensive documentation
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
Project aims
To build a set of free, open-source tools for the analysis of airpollution data
Use highly developed open-source statistical software ‘R’To make it easier to analyse data and to gain insights from itExploit the enormous (and growing) amount of air pollutiondata availableProgressively include advanced approaches generally not widelyavailable
Outputs
An R ‘package’ dedicated to air pollution analysisA web site to act as a central resourceComprehensive documentation
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
Progress – case studies and user involvement
Sefton Council
Support as part of their ‘Beacon’ status
Help inform continuing air quality management activities
North Lincolnshire/AEA
Scunthorpe steel worksHighly complex mixture of sourcesMany non-road traffic sources
Defra/AEA
Developments to enhance informatics value of AURN data
NERC Knowledge Transfer bid
Would provide significant fundingWidely supported
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
Progress – case studies and user involvement
Sefton Council
Support as part of their ‘Beacon’ statusHelp inform continuing air quality management activities
North Lincolnshire/AEA
Scunthorpe steel worksHighly complex mixture of sourcesMany non-road traffic sources
Defra/AEA
Developments to enhance informatics value of AURN data
NERC Knowledge Transfer bid
Would provide significant fundingWidely supported
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
Progress – case studies and user involvement
Sefton Council
Support as part of their ‘Beacon’ statusHelp inform continuing air quality management activities
North Lincolnshire/AEA
Scunthorpe steel worksHighly complex mixture of sourcesMany non-road traffic sources
Defra/AEA
Developments to enhance informatics value of AURN data
NERC Knowledge Transfer bid
Would provide significant fundingWidely supported
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
Progress – case studies and user involvement
Sefton Council
Support as part of their ‘Beacon’ statusHelp inform continuing air quality management activities
North Lincolnshire/AEA
Scunthorpe steel works
Highly complex mixture of sourcesMany non-road traffic sources
Defra/AEA
Developments to enhance informatics value of AURN data
NERC Knowledge Transfer bid
Would provide significant fundingWidely supported
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
Progress – case studies and user involvement
Sefton Council
Support as part of their ‘Beacon’ statusHelp inform continuing air quality management activities
North Lincolnshire/AEA
Scunthorpe steel worksHighly complex mixture of sources
Many non-road traffic sources
Defra/AEA
Developments to enhance informatics value of AURN data
NERC Knowledge Transfer bid
Would provide significant fundingWidely supported
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
Progress – case studies and user involvement
Sefton Council
Support as part of their ‘Beacon’ statusHelp inform continuing air quality management activities
North Lincolnshire/AEA
Scunthorpe steel worksHighly complex mixture of sourcesMany non-road traffic sources
Defra/AEA
Developments to enhance informatics value of AURN data
NERC Knowledge Transfer bid
Would provide significant fundingWidely supported
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
Progress – case studies and user involvement
Sefton Council
Support as part of their ‘Beacon’ statusHelp inform continuing air quality management activities
North Lincolnshire/AEA
Scunthorpe steel worksHighly complex mixture of sourcesMany non-road traffic sources
Defra/AEA
Developments to enhance informatics value of AURN data
NERC Knowledge Transfer bid
Would provide significant fundingWidely supported
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
Progress – case studies and user involvement
Sefton Council
Support as part of their ‘Beacon’ statusHelp inform continuing air quality management activities
North Lincolnshire/AEA
Scunthorpe steel worksHighly complex mixture of sourcesMany non-road traffic sources
Defra/AEA
Developments to enhance informatics value of AURN data
NERC Knowledge Transfer bid
Would provide significant fundingWidely supported
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
Progress – case studies and user involvement
Sefton Council
Support as part of their ‘Beacon’ statusHelp inform continuing air quality management activities
North Lincolnshire/AEA
Scunthorpe steel worksHighly complex mixture of sourcesMany non-road traffic sources
Defra/AEA
Developments to enhance informatics value of AURN data
NERC Knowledge Transfer bid
Would provide significant fundingWidely supported
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
Progress – case studies and user involvement
Sefton Council
Support as part of their ‘Beacon’ statusHelp inform continuing air quality management activities
North Lincolnshire/AEA
Scunthorpe steel worksHighly complex mixture of sourcesMany non-road traffic sources
Defra/AEA
Developments to enhance informatics value of AURN data
NERC Knowledge Transfer bid
Would provide significant funding
Widely supported
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
Progress – case studies and user involvement
Sefton Council
Support as part of their ‘Beacon’ statusHelp inform continuing air quality management activities
North Lincolnshire/AEA
Scunthorpe steel worksHighly complex mixture of sourcesMany non-road traffic sources
Defra/AEA
Developments to enhance informatics value of AURN data
NERC Knowledge Transfer bid
Would provide significant fundingWidely supported
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
Progress – case studies and user involvement
Sefton Council
Support as part of their ‘Beacon’ statusHelp inform continuing air quality management activities
North Lincolnshire/AEA
Scunthorpe steel worksHighly complex mixture of sourcesMany non-road traffic sources
Defra/AEA
Developments to enhance informatics value of AURN data
NERC Knowledge Transfer bid
Would provide significant fundingWidely supported
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
What effort is required to use these tools?
The project aims to overcome barriers to analysis
Lack of time, money, specialist software, know-how or all four!
Not required to learn R
Learning R can be hard workMake things as simple as possible for the user
Example of code required to make a polar plot
polar.plot(mydata, pollutant = "so2")
Some examples of current capabilities to follow . . .
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
What effort is required to use these tools?
The project aims to overcome barriers to analysis
Lack of time, money, specialist software, know-how or all four!
Not required to learn R
Learning R can be hard workMake things as simple as possible for the user
Example of code required to make a polar plot
polar.plot(mydata, pollutant = "so2")
Some examples of current capabilities to follow . . .
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
What effort is required to use these tools?
The project aims to overcome barriers to analysis
Lack of time, money, specialist software, know-how or all four!
Not required to learn R
Learning R can be hard work
Make things as simple as possible for the user
Example of code required to make a polar plot
polar.plot(mydata, pollutant = "so2")
Some examples of current capabilities to follow . . .
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
What effort is required to use these tools?
The project aims to overcome barriers to analysis
Lack of time, money, specialist software, know-how or all four!
Not required to learn R
Learning R can be hard workMake things as simple as possible for the user
Example of code required to make a polar plot
polar.plot(mydata, pollutant = "so2")
Some examples of current capabilities to follow . . .
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
What effort is required to use these tools?
The project aims to overcome barriers to analysis
Lack of time, money, specialist software, know-how or all four!
Not required to learn R
Learning R can be hard workMake things as simple as possible for the user
Example of code required to make a polar plot
polar.plot(mydata, pollutant = "so2")
Some examples of current capabilities to follow . . .
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
ContentsOpennessBackground
What effort is required to use these tools?
The project aims to overcome barriers to analysis
Lack of time, money, specialist software, know-how or all four!
Not required to learn R
Learning R can be hard workMake things as simple as possible for the user
Example of code required to make a polar plot
polar.plot(mydata, pollutant = "so2")
Some examples of current capabilities to follow . . .
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
Source detectionDisplaying lots of informationTrends
Diurnal variation in concentrations
Diurnal and day of weekvariations can provideclues as to the source
Function diurnal.errorproduces three plots
Uncertainty bands canhelp determine whetherone source is differentfrom another
day of week
conc
20
30
40
50
60
Sun Mon Tue Wed Thu Fri Sat
hour of day
conc
20
25
30
35
40
45
50
0 5 10 15 20 25
day of week
conc
35
40
Sun Mon Tue Wed Thu Fri Sat
●
●
●●
●
●
●
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
Source detectionDisplaying lots of informationTrends
Bivariate polar plots
Useful for sourcedetection
Methods have beenextended to ‘model’surface concentrations
Can usefully be combinedwith other methods
Sulphur dioxide at Marylebone Rd
W
S
N
E
1
2
3
4
5
6
7
8
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
Source detectionDisplaying lots of informationTrends
Concentrations by wind direction, year and month
month
win
d di
rect
ion
(°°)
100
200
300
Jan Apr Jul Oct
1998
Jan Apr Jul Oct
1999
Jan Apr Jul Oct
2000
Jan Apr Jul Oct
2001
Jan Apr Jul Oct
2002
Jan Apr Jul Oct
2003
Jan Apr Jul Oct
2004
Jan Apr Jul Oct
2005
0
20
40
60
80
100
120
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
Source detectionDisplaying lots of informationTrends
Maximum hourly ozone concentrations by hour of day, yearand month
month
hour
0
5
10
15
20
Jan Apr Jul Oct
1998
Jan Apr Jul Oct
1999
Jan Apr Jul Oct
2000
Jan Apr Jul Oct
2001
Jan Apr Jul Oct
2002
Jan Apr Jul Oct
2003
Jan Apr Jul Oct
2004
Jan Apr Jul Oct
2005
10
20
30
40
50
60
70
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
Source detectionDisplaying lots of informationTrends
Diurnal and day of week variation – PMcoarse
hour of day
coar
se p
artic
les
10
20
30
1998M
onda
y
0 5 10 15 20
1999
Mon
day
2000
Mon
day
0 5 10 15 20
2001
Mon
day
2002
Mon
day
0 5 10 15 20
2003
Mon
day
2004
Mon
day
0 5 10 15 20
2005
Mon
day
1998
Tue
sday
1999
Tue
sday
2000
Tue
sday
2001
Tue
sday
2002
Tue
sday
2003
Tue
sday
2004
Tue
sday
10
20
30
2005
Tue
sday
10
20
30
1998
Wed
nesd
ay
1999
Wed
nesd
ay
2000
Wed
nesd
ay
2001
Wed
nesd
ay
2002
Wed
nesd
ay
2003
Wed
nesd
ay
2004
Wed
nesd
ay
2005
Wed
nesd
ay
1998
Thu
rsda
y
1999
Thu
rsda
y
2000
Thu
rsda
y
2001
Thu
rsda
y
2002
Thu
rsda
y
2003
Thu
rsda
y
2004
Thu
rsda
y
10
20
30
2005
Thu
rsda
y
10
20
30
1998
Frid
ay
1999
Frid
ay
2000
Frid
ay
2001
Frid
ay
2002
Frid
ay
2003
Frid
ay
2004
Frid
ay
2005
Frid
ay
1998
Sat
urda
y
1999
Sat
urda
y
2000
Sat
urda
y
2001
Sat
urda
y
2002S
atur
day
2003
Sat
urda
y
2004
Sat
urda
y
10
20
30
2005
Sat
urda
y
10
20
30
0 5 10 15 20
1998
Sun
day
1999
Sun
day
0 5 10 15 20
2000
Sun
day
2001
Sun
day
0 5 10 15 20
2002
Sun
day
2003S
unda
y
0 5 10 15 20
2004
Sun
day
2005
Sun
day
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
Source detectionDisplaying lots of informationTrends
Flexible treatment of trends – ozone at Marylebone Road
Trend with smooth fit and95 % confidence intervals
Trend with smooth fit and95 % confidence intervals– deseasonalise first
Consider “no trend”hypothesis throughbootstrap resampling
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
Source detectionDisplaying lots of informationTrends
Flexible treatment of trends – ozone at Marylebone Road
Trend with smooth fit and95 % confidence intervals
Trend with smooth fit and95 % confidence intervals– deseasonalise first
Consider “no trend”hypothesis throughbootstrap resampling
1998 2000 2002 2004 2006 2008
510
15date
conc
entr
atio
n (p
pb)
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
Source detectionDisplaying lots of informationTrends
Flexible treatment of trends – ozone at Marylebone Road
Trend with smooth fit and95 % confidence intervals
Trend with smooth fit and95 % confidence intervals– deseasonalise first
Consider “no trend”hypothesis throughbootstrap resampling
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
Source detectionDisplaying lots of informationTrends
Flexible treatment of trends – ozone at Marylebone Road
Trend with smooth fit and95 % confidence intervals
Trend with smooth fit and95 % confidence intervals– deseasonalise first
Consider “no trend”hypothesis throughbootstrap resampling
1998 2000 2002 2004 2006 2008
46
810
1214
date
conc
entr
atio
n (p
pb)
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
Source detectionDisplaying lots of informationTrends
Flexible treatment of trends – ozone at Marylebone Road
Trend with smooth fit and95 % confidence intervals
Trend with smooth fit and95 % confidence intervals– deseasonalise first
Consider “no trend”hypothesis throughbootstrap resampling
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
Source detectionDisplaying lots of informationTrends
Flexible treatment of trends – ozone at Marylebone Road
Trend with smooth fit and95 % confidence intervals
Trend with smooth fit and95 % confidence intervals– deseasonalise first
Consider “no trend”hypothesis throughbootstrap resampling
1998 2000 2002 2004 2006 2008
46
810
1214
date
conc
entr
atio
n (p
pb)
MeasurementsNo trend hypothesis
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
Documentation supportConcluding remarks
Documentation
Documentation has beenstarted
Part 1: Introduction tousing R to analysemonitoring dataPart II: Dedicatedfunctions to analysemonitoring data
Longer term aims
Develop a Frameworkfor analysing dataCase studies spanninga range ofcontemporary problems
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
Documentation supportConcluding remarks
Longer-term aims
The big picture
Change the way we do things
Environmental models?Work that is truly reproducible
Wider issues
Developing countriesActively seek participation of researchers elsewhere in the world
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
Documentation supportConcluding remarks
Longer-term aims
The big picture
Change the way we do thingsEnvironmental models?
Work that is truly reproducible
Wider issues
Developing countriesActively seek participation of researchers elsewhere in the world
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
Documentation supportConcluding remarks
Longer-term aims
The big picture
Change the way we do thingsEnvironmental models?Work that is truly reproducible
Wider issues
Developing countriesActively seek participation of researchers elsewhere in the world
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
Documentation supportConcluding remarks
Longer-term aims
The big picture
Change the way we do thingsEnvironmental models?Work that is truly reproducible
Wider issues
Developing countriesActively seek participation of researchers elsewhere in the world
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
Documentation supportConcluding remarks
Longer-term aims
The big picture
Change the way we do thingsEnvironmental models?Work that is truly reproducible
Wider issues
Developing countries
Actively seek participation of researchers elsewhere in the world
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
Documentation supportConcluding remarks
Longer-term aims
The big picture
Change the way we do thingsEnvironmental models?Work that is truly reproducible
Wider issues
Developing countriesActively seek participation of researchers elsewhere in the world
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
Documentation supportConcluding remarks
Longer-term aims
The big picture
Change the way we do thingsEnvironmental models?Work that is truly reproducible
Wider issues
Developing countriesActively seek participation of researchers elsewhere in the world
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
Documentation supportConcluding remarks
Longer-term aims
The big picture
Change the way we do thingsEnvironmental models?Work that is truly reproducible
Wider issues
Developing countriesActively seek participation of researchers elsewhere in the world
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
IntroductionProgress and examples
Future developments
Documentation supportConcluding remarks
Thank you for your attention!
OPEN sourc
e
Air Pollution Forecasting Seminar The Open-Source Air Pollution Project
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