1. TFMM trend analysis: use of AirBase. Preliminary results
Alberto González Ortiz – EEA
Augustin Colette, Maxime Beuchamp, Laure Malherbe – ETC/ACM (INERIS)
Frank de Leeuw – ETC/ACM (RIVM)
Krakow, 6 May 2015
16th TFMM meeting

2. Outline Quality-check and selection of stations: Parametric modelling
Temporal detection of discontinuities
Spatial kriging
Trend analysis:
Selected background stations
All stations in AirBase
Different ways of presenting results
Conclusions 2

3. 1. Quality-check
Testing three complementary approaches to check the quality of background station AirBase data for the purpose of trend analyses
Visual inspection to detect the difference with a parametric model
Automatic statistical tests:
Temporal: looking for discontinuities of records at individual stations
Spatial: using kriging to discard outliers
ETC/ACM Working paper 3

4. TEMPORAL DETECTION OF DISCONTINUITIES
1. Quality-check: temporal
TEMPORAL DETECTION OF DISCONTINUITIES
Methodology
Report ETC/ACM TP 2011/8
Adaptive filters to locate local maxima in the variance
Main parameters:
Width of the smoothing window
Threshold parameter
Based on de-seasonalised data
Result
Efficient in detecting discontinuities
But approach based on local maxima detects a jump in any time series
=> not practical for trend analysis purpose
Examples for NO2 4

5. SPATIAL KRIGING: METHODOLOGY
1. Quality-check: spatial
SPATIAL KRIGING: METHODOLOGY
Using kriging to estimate value + confidence interval at each station/day.
Check whether the observed value fits in the confidence interval.
If not: discard the data.
Check for temporal completeness of the record.
NO2 daily mean and kriging estimates on 12 June 2006.
Circles=valid observations; Triangles=outliers. The observed value is given with the colour inside the pictogram, while the kriging value is given in the contour of the pictogram 5

6. SPATIAL KRIGING: RESULTS
1. Quality-check: spatial
SPATIAL KRIGING: RESULTS
Nr of airbase station providing data and complying to the 75/75 criteria
Before applying the geostatistical filter
After applying the geostatistical filter 6

7. 2. Trend analysis Using the TFMM methodology: Two sets of stations:
The background stations selected by kriging
The whole set of stations in AirBase
Time periods: / /
Data completeness: 75% of data coverage and 75% of valid years/seasons (+ valid data in all seasons for the selected set)
Statistical analysis :
Mann-Kendall and Sen-Theil slope on the basis of annual series (based on daily means for all pollutants, and on daily max 8h for O3).
Also trends by season (as DJF, MAM, JJA, SON).
For the selected background stations: PM10, NO2, ozone
For the whole set: also PM2.5 7

8. 2. Trend analysis: PM10 (bkgd)
Key results :
Decrease throughout Europe
Downward trend more pronounced in summer. 8

9. 2. Trend analysis: PM10 (whole set)
Key results :
Decrease throughout Europe
Downward trend more pronounced in summer 9

10. 2. Trend analysis: PM10 (whole set)
Key results :
Decrease throughout Europe
Downward trend more pronounced in summer
Largest trends in traffic and other sites 10

11. 2. Trend analysis: PM2.5 (whole set)
Key results :
20-50 % of significant trends, mostly decreasing (e.g., year: total 58  significant 25  decreasing 20)
The average slope is negative, for year and all seasons
Larger average slope in summer, smaller in winter 11

12. 2. Trend analysis: NO2 Key results Robust decrease over Europe
Using a consistent set of stations, the decrease is larger over first decade
Downward trend less pronounced in winter (specially at traffic stations in the whole set) 12

13. 2. Trend analysis: O3 (daily max of 8h means)
Key results (background selection)
Robust decrease in summer, especially in the second period
Larger decrease in second period (opposed to NO2)
Increase in urban areas
For the whole set (average slope): for all periods, decrease only in summer. In the second period, decrease except in winter.
Urban increase mainly in winter. 13

14. 3. Conclusions Outlier detection:
Airbase is a precious repository of air quality information.
Temporal consistency test requires screening.
Any feedback of the TFMM about outlier detection methodologies?
Preliminary trends results:
Substantial downward trend in PM10, NO2 and PM2.5.
Also for O3 over (except in winter).
Downward trend:
Less pronounced in winter for NO2 and PM10.
More pronounced in summer for O3.
Larger decrease over the first period for NO2, smaller for O3 (not necessarily the same sites).
Larger decrease of PM10 in traffic; increase of O3 in urban sites. 14

15. Many thanks for your attention!! 15