International Congress on the EU-LIFE-ENVIRONMENT Project KAPA GS

Transcription

1 Fine! Dust-Free International Congress on the EU-LIFE-ENVIRONMENT Project KAPA GS Air Quality Assessment of PM, the results of the AIR4EU Project Klagenfurt, Austria, 29, 30 March 2007 Peter Builtjes

2 I) Air4EU II) Some highlights III) AIR4EU and PM 2

3 I) AIR4EU Air quality assessment for Europe from local to continental scale AIR4EU is a EU 6 th FP Project in support of the CAFÉ-programme till

4 Research Partners: TNO, NILU, Aristotle Univ. Thess., Univ. Stuttgart, Univ. Hertfordshire, Univ. Aveiro User Partners: Paris, Rome, London, Prague, Athens, Rotterdam, Oslo, Berlin 4

5 Scope of AIR4EU To provide recommendations on integrated AQ-assessment for all relevant temporal and spatial scales Integrated means: Combining monitoring and modelling in order to reduce uncertainties and to achieve a more complete picture Focus: PM, but also NO 2 and O 3 Focus is accurate and reliable Assessment, not Evaluation of abatement strategies Recommendations splitted in Basic Requirements and Best Practice and Science Recommendations 5

6 II) Some highlights Combining modelling and monitoring Mapping tool 6

7 Air quality assessment Measurements Modelling To be combined using data assimilation 7

8 Passive and active approaches Passive approach: Modelled fields are synthesized with measurements without a feedback to the model state Synthesis is performed as a finishing touch using the model output Examples: Kriging, Optimum interpolation Active approach: Modelled fields are synthesized with measurements with a feedback to the model state The system uses and updates the causal relations within the modelling system Assimilation is performed on the fly Examples: Kalman filtering, 3D-VAR 8

9 The process Gather the measurement data Perform quality control on the data Uncertainty assessment, incl. spatial representativity If needed, determine model uncertainty Set up the system and perform experiment Analysis of the results including validation 9

10 Passive data assimilation: Synthesizing in-situ observations, satellite data and modelled fields Purpose: To generate regional PM10 map over Europe for 2003 Methods: Geo-statistical methods Input: AIRBASE PM10, LOTOS-EUROS PM2.5, MODIS AOD PM10 measurements (µg/m 3 ) PM2.5 LOTOS-EUROS (µg/m 3 ) AOT (-) Jan van de Kasteele et al. 10

11 PM10 maps based on different conbinatins of observations, modelled fields and satelite data measurements only meas. and LOTOS-EUROS Combination of these information sources gives an added value Validation: meas. and AOT meas., LOTOS-EUROS and AOT RMSE Model AOD Model&AOD Measurements in combination with: 11

12 Goals to achieve 12

13 Air quality mapping for Europe 13

14 III) AIR4EU and PM One of the key questions of the cities: The determination of the PM10 regional background concentrations Which part of the observed PM10 at a hot spot, at streetlevel is due to: local emissions urban scale emissions regional scale emissions 14

15 Determination of PM10 regional background, in principle on an hour-by-hour basis By regional background/rural observations By modelling By a combination of modelling and observations 15

16 By observations Basic Requirement: Two PM10 monitoring sites upwind and downwind of the city Best Practice Recommendation: Three PM10 monitoring sites in triangle form around the city Science Recommendation: Speciated PM observations, Sulphate, Nitrate, Ammonia, BC, OC, etc. Remark: Do not scale observations to produce PM10 Regional Background for 2010/

17 By modelling Largest problem: All Regional Scale Chemical Transport Models underestimate PM10 concentrations Possible explanation: Missing sources: Windblown Dust, Resuspension, Agriculture Missing processes: Biogenic and Anthropogenic Secondary Organic Aerosols Correct description vertical exchange 17

18 Validation for PM10 60 PM10 Model vs Airbase metingen 50 Underestimation : 40-60% Model Bias correction : 50% Meting 18

19 PM10 % data obs.mean mod.mean residue RMSE corr. sigma ratio EMEP_v LOTOS_v REM-CALGRID_v DEHM_v CHIMERE_v MATCH_v MODELS

20 0,8 correlation PM10; EMEP and range of other models EMEP correlation PM10 0,6 0,4 0,2 stations 0 S125 S014 S039 S110 S016 S026 S126 S023 S012 S015 S019 S058 Correlation coefficient of PM10; EMEP model and the range of correlation coefficients of the other models; year:

21 Recommendation is not to use the models as they are to determine the Regional Background of PM10 Combination of modelling and observations: Basic Requirement: Using Optimal Interpolation or Kriging Best Practice Recommendation: Ensemble Kalman Filtering/4-D var Science Recommendation: Improve PM 10 modelling Example: LOTOS-EUROS with Ensemble Kalman Filter for PM10 21

22 PM10 fields for March-April,

23 Validation plot First results give a mixed picture Small innovations point at too low model uncertainty in combination with high measurement uncertainty, in this case noise on rainout is needed PM is complex, due to large measurement uncertainties Meas Model Assim Model*

24 Question Question: For a city user, Where do I get the regional background of PM10? Concept: Making the combined modelling and monitoring data for PM10 (and NO 2, O 3 ) available to the end-users Structure: EEA? GEMS? Under discussion 24

25 Thank you for your attention For more information: 25