m. schaap , l. curier, r. kranenburg, f. boersma , h. eskes , a. segers, r. timmermans
DESCRIPTION
Exploring the Sensitivity of the OMI‐NO 2 Product to emission Changes Across Europe using a Chemistry Transport Model. M. Schaap , L. Curier, R. Kranenburg, F. Boersma , H. Eskes , A. Segers, R. Timmermans. Introduction. - PowerPoint PPT PresentationTRANSCRIPT
Exploring the Sensitivity of the OMI‐NO2 Product to emission Changes Across Europe using a Chemistry Transport Model
M. Schaap, L. Curier, R. Kranenburg, F. Boersma, H. Eskes, A. Segers, R. Timmermans
M. SchaapFossil Fuel pilot
IntroductionIn Europe, official emissions are reported are reported to EEA and UNECE/EMEP. Although many countries report high quality data, a number of caveats exist:
the inventory methodologies vary from nation to nationdata gaps exist not real-time discrepancies between eastern and western Europe
Officially reported emissions (Gg) for year: 2000
Reported in Reported in
+ 45%Nox Germany PM10
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GoalsTo contribute to the verification and improvement of the European emission inventory by synergistic use of satellite data and a chemistry transport model.To inform you on activities performed at TNO with OMI-NO2 data
Chemistry transport model LOTOS-EUROS
Lower troposphere up to 3.5 KmCBM-IV chemistryExplicit N2O5 hydrolysis
ISORROPIA-II equilibrium mod.NO2 columns calculated using
OMI averaging kernels
EnKF data assimilationParticipated in most model comparisons (GLOREAM, EURODELTA, AQMEII)Member of MACC ensemble
Labelling module that tracks source contributions for all N-containing compounds
See Kranenburg et al., 2013
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Emissions Labels
2005: MACC-2005 database2020: MACC-2005 scaled by
GAINS 2020 totals per sector
Temporal profiles per sector
SNAP sectors1 Energy industries
3 Industrial combustion
7 Road transport
8 Non-road transport
9 Agriculture
Other
Power generationHouse holdsIndustrial combustionProduction processesExtraction of fossil fuelsSolvent useRoad transportOff-road trnsportWaste incinerationAgriculture
Label definition:- 6 source sectors- 5 hours of the day
between 9 and 14- Boundary conditions
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Annual mean modelled and retrieved NO2 distribution
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Model OMI
Systematic bias of 1.1015 molec/cm2
Spatial correlation R2 = 0.91
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Comparison over EuropeEastern Europe
Western Europe Iberian Peninsula
2007 Netherlands
Power generation
Sector contributions to NO2 columns at OMI overpass
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Off Road transport
Sector contributions NO2 columns at OMI overpass
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Road transport
Sector contributions to NO2 columns at OMI overpass
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Approach:
Seasonal componentAnnual trends
[Weatherhead et al 1998]
• LOTOS-EUROS simulation with constant 2005 emissions
• Match to OMI columns using averaging kernel
• Trend estimate in the bias between OMI and LOTOS-EUROS
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Satellite derived trends in OMI-NO2 v1
NO2 trends show decreasing values of
3-6 % a year between 2005 and 2010.
Data often used as a first order estimate of the NOx emission trend
Comparison to 2011 reporting for 2009
Impact of emission scenario on NO2 columns
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Emissions NO2 Columns
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Island states
Coastal countries
Large countries
Sea areas
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Extension to 2012 using OMI-v2 and full data assimilation
Assimilation indicated a very slow decline until 2012, if any! Reason: V2.0 contains much smaller trends!
Parameter estimate using v2Trend analysis on v1
% change over 5 years
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Comparison v1 and v2 for 2005-2010
M. SchaapFossil Fuel pilotMeteo correctedAll data
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Change of Annual emissions and levels normalized to 2005
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Impact of direct NO2 percentage diesel cars
from 3 to 20%
NO2 evaluation for 1990-2009
%
Conclusions We have developed a source apportionment tool to investigate
emission sector contributions to satellite products. Source sector contributions show distinctly different spatial patterns. For land locked and large countries OMI-NO2 trends can be
translated into emission changes. Both in-situ and remote sensed NO2 levels show lower downward
trends than reported emissions. Improvements possible through update temporal variability of
emissions, natural emissions, extension of vertical extend LE. This study highlights the need for a combined use of models, a-priori
emission estimates and satellite data to verify emission trends.
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Thank you for you attention
Reference: Schaap et al., Remote Sens., 5(9), 4187-4208; Curier et al., 2014
Acknowledgement: We acknowledge the support of ESA project GlobEmission and the EC-FP7 ENERGEO project
Monday, April 11, 2011R. Lyana Curier
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Comparison to AIRBASE ground level NO2
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0
5
10
15
20
25
30
35
0 5 10 15 20 25 30 35
y = 3.0311 + 0.6743x R2= 0.40864
Mod
elle
d co
ncen
tratio
n (
g/m
3 )
Measured concentration (g/m3)
Contribution of the emissions between 09-14 to NO2 columns at OMI overpass
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