Interpolation of real-time ozone measurements in Europe
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Interpolation of real-time ozone Interpolation of real-time ozone measurements in Europemeasurements in Europe
Results of a feasibility exercise for the Neighbourhood project
Bill [email protected]
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ObjectivesObjectives
• Review of available interpolation method and operational constraints
• Tests on archive data• Tests on real-time data
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Review of Methods and ConstraintsReview of Methods and Constraints
• What methods can be used• What is the most feasible for EEA• Numbers and types of stations:
• What is the minimum density of stations needed?
• How many stations per country does this represent?
• Do we need to make a distinction between rural and urban stations?
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Tests on Archive DataTests on Archive Data
• Datasets (from AirBase)• 12th August 2003: greatest number of stations exceeding the alert
threshold• 1st April 2003: contrasting, spring concentrations
• Methods tested• Simple – nearest neighbour• Interpolation – IDW, Kriging
• Station selection• All• Random• 3 densities: 1 station per 100x100km / 200x200km / 300x300km• Exclusion of urban and roadside concentrations
• Validation• Internal (Jack-knife) and External (test dataset) RMSE
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Results (Simple Interpolation)Results (Simple Interpolation)
Number of StationsRMSE (ug/m3)
Cell Size: 50km102 55.73157 49.75261 48.10465 45.58
Number of Stations
RMSE (ug/m3)
Cell size (km)
10 25 50102 41.78 41.39 38.96
157 40.40 39.72 36.74261 37.61 35.75 34.01
465 36.93 35.09 33.59
Nearest Neighbour
IDW
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Results (Kriging Interpolation)Results (Kriging Interpolation)
DAY TIME DENSITY SELECTIONNumber of stations
Cross-validation
RMSE ug/m3
External Valdiation
RMSE ug/m301 April 2 PM all valid points all 1491 16.7 01 April 2 PM 100k no urban no roadside 260 18.2 17.501 April 2 PM 100k all 311 20.1 17.501 April 2 PM 300k all 37 20.4 20.201 April 2 PM 200k no urban no roadside 67 20.8 20.201 April 2 AM 300k no urban no roadside 31 22.1 24.701 April 2 AM 100k all 303 22.1 21.9
12 August 2 PM 200k all 133 22.4 28.801 April 2 PM 300k no urban no roadside 34 23.0 18.801 April 2 PM 200k all 76 23.1 18.501 April 2 AM 100k no urban no roadside 250 23.2 23.001 April 2 AM 200k no urban no roadside 124 23.6 24.001 April 2 AM 300k all 34 23.7 26.301 April 2 AM 200k all 132 24.0 23.1
12 August 2 PM 100k no urban no roadside 264 24.6 25.812 August 2 PM 200k no urban no roadside 124 26.0 28.212 August 2 PM 100k all 314 26.4 25.112 August 2 AM 200k all 129 31.3 36.312 August 2 AM 300k all 26 31.6 40.312 August 2 PM 300k all 37 31.9 34.012 August 2 AM 100k all 309 33.6 33.212 August 2 AM 200k no urban no roadside 121 36.1 37.912 August 2 PM 300k no urban no roadside 33 37.2 31.412 August 2 AM 300k no urban no roadside 32 37.2 38.212 August 2 AM 100k no urban no roadside 257 38.5 32.7
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Interpolation Errors
10.0
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40.0
0 50 100 150 200 250 300 350 400 450 500
Number of Points
RM
SE
01 April 2 AM
01 April 2 PM
12 August 2 AM
12 August 2 PM
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Nearest Neighbour
50km grid
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IDW
10km grid
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Methods and Constraints – Findings Methods and Constraints – Findings #1#1
• Accuracy levels can be improved from 50 ug/m3 to 30 ug/m3 (and better) by selection of improved interpolation methods• Kriging techniques delivered highest accuracy
results
• Station number and density has less impact on accuracy than the time of year / day:• Optimum station spacing of 200km
• No consistent pattern from the different type of station included
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Methods and Constraints – Findings Methods and Constraints – Findings #2#2
• Geostatistical Analyst methods not available in automation routines• No “de-trending” functions
• Spatial Analyst methods are available in automation routines
• Kriging proven as the most accurate from the tests
• Choice of automatic vs. manual determination of variogram parameters
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Real-time data objectivesReal-time data objectives
• How much real-time data exists?• Is real-time interpolation for ozone
feasible and practical?• What are the accuracy levels?• Automated vs. manual determination of
variogram parameters?
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MethodsMethods
• Manual data “scraping” from existing AQ sites• Existing OzoneWeb stations
• Interpolation• Spatial Analyst within ArcGIS• Ordinary Kriging
• Semi-variogram: Spherical, self-optimising nugget and sill
• Lag-size: 50,000m• Search Radius: variable, 12 nearest neighbours
• 10km grid
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14th July
Countries: 7
Valid Stations: 801
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2nd August
Countries: 9
valid Stations: 921
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1st September
Countries: 14
valid Stations: 985 (Airbase only)
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14th July
Countries: 7
Valid Stations: 801
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2nd August
Countries: 9
valid Stations: 921
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1st September
Countries: 14
valid Stations: 985 (Airbase only)
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Semivariograms and Semivariograms and Accuracy – 17Accuracy – 17thth July July
14 July 2005 0900hrs (RMSE=126.28)
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14 July 2005 1000hrs (RMSE=306.26)
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14 July 2005 1100hrs (RMSE=252.72)
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14 July 2005 1200hrs (RMSE=54.20)
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14 July 2005 1300hrs (RMSE=376.18)
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14 July 2005 1400hrs (RMSE=440.36)
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14 July 2005 1500hrs (RMSE=469.57)
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14 July 2005 1600hrs (RMSE=613.18)
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14 July 2005 1700hrs (RMSE=503.50)
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14 July 2005 1800hrs (RMSE=520.29)
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DAY TIME Sites
In Sites Tested
RMSE (ug/m3)
14 July 8 AM 198 44 32.4114 July 9 AM 446 92 25.6414 July 10 AM 498 107 23.6314 July 11 AM 528 109 22.8214 July 12 PM 597 116 21.1314 July 1 PM 637 125 15.4414 July 2 PM 648 128 15.9414 July 3 PM 596 112 16.7114 July 4 PM 620 122 17.4214 July 5 PM 618 121 16.1514 July 6 PM 629 122 17.62
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Semivariograms and Semivariograms and Accuracy – 2Accuracy – 2ndnd August August
2 August 2005 0900hrs (RMSE=59.04)
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2 August 2005 1000hrs (RMSE=136.56)
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2 August 2005 1100hrs (RMSE=530.53)
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2 August 2005 1200hrs (RMSE=682.71)
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2 August 2005 1300hrs (RMSE=109.21)
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2 August 2005 1400hrs (RMSE=783.28)
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2 August 2005 1500hrs (RMSE=1297.29)
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2 August 2005 1600hrs (RMSE=1314.76)
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2 August 2005 1700hrs (RMSE=1227.25)
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2 August 2005 1800hrs (RMSE=188.93)
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DAY TIMESites
InSites
Tested RMSE
(ug/m3)02 Aug 8 AM 297 49 23.9802 Aug 9 AM 656 111 19.5102 Aug 10 AM 706 118 16.9502 Aug 11 AM 750 119 14.6402 Aug 12 PM 761 122 11.0202 Aug 1 PM 605 98 9.4302 Aug 2 PM 747 123 11.7502 Aug 3 PM 760 119 11.5602 Aug 4 PM 793 123 12.2802 Aug 5 PM 787 121 12.5902 Aug 6 PM 335 52 12.61
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1st September Tests1st September Tests
• Earlier tests and results discussed with ETC – suggestions for further analysis:
• Stratify by type of station (NB all “background” sites)• Urban• Rural• Suburban
• Run tests within the class of station, and between the classes of stations
• e.g. Rural stations for interpolation map, test the accuracy at the urban stations
• 10 Daylight Hours• Test #1: Average of the RMSE• Test #2: Sum of the Maximum Residuals
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Test #1: Average RMSE (ug/mTest #1: Average RMSE (ug/m33))
• The least accurate: rural interpolation – urban test site • These can be mitigated for by including suburban sites
in additional to the rural sites. • This does however slightly decrease the accuracy for
the rural sites themselves.
Tested SitesInterpolated Sites ALL RURAL SUBURBAN URBAN
ALL 16.3 16.4 17.1 16.5
RURAL - 15.7 20.5 23.0
RURAL & SUBURBAN - 18.3 17.6 16.0
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Test #2: Sum of Max. Residuals (ug/mTest #2: Sum of Max. Residuals (ug/m33))
Tested Sites
Interpolated Sites ALL RURAL SUBURBAN URBAN
ALL 477 386 419 377
RURAL - 442 530 559
RURAL & SUBURBAN - 552 517 468
• Same pattern as the RMSE results
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Overall FindingsOverall Findings
• Interpolation of real-time Ozone concentration data is feasible
• Even from a relatively small number of monitoring stations,
• Results of acceptable accuracy can readily be generated using the standard interpolation techniques found within the ESRI software selected for the Neighbourhood Project
• Next steps:• Improved accuracy through meteorological
parameters• Increased resolution through differential interpolation
for station groups