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

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

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

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

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

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

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: 1990-2012 / 1990-2001 / 2002-2012 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

2. Trend analysis: PM10 (bkgd) Key results 2002-2012: Decrease throughout Europe Downward trend more pronounced in summer. 8

2. Trend analysis: PM10 (whole set) Key results 2002-2012: Decrease throughout Europe Downward trend more pronounced in summer 9

2. Trend analysis: PM10 (whole set) Key results 2002-2012: Decrease throughout Europe Downward trend more pronounced in summer Largest trends in traffic and other sites 10

2. Trend analysis: PM2.5 (whole set) Key results 2002-2012: 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

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

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

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 2002-2012 (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

Many thanks for your attention!! alberto.gonzalez@eea.europa.eu 15