Improving regional air quality model results at the city scale : results from the EC4MACS project

INERIS : Bertrand Bessagnet, Etienne Terrenoire, Augustin Colette, Laurent Létinois, Laure Malherbe , Laurence Rouïl, Frédéric Tognet, Anthony Ung

RSE/INERIS : Guido Pirovano

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CityDelta background

EC4MACS « urban modelling » component : better account for the urban dimension in the integrated assessment modelling

What? : concentration increment (or decrement) due to the city itself Why? : to correct coarse resolution model used in integrated assessment

How? :

• Can be defined as δ=Chigh- Clow

• In the former CityDelta exercice : with a set of CTM results over 7 cities in Europe that lead to a single formula for all European cities

• CHIMERE high resolution (7 km) simulation over a large part of Europe [ECMWF data + WRF ; EMEP emissions]

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Improving emissions

« residential emissions » (SNAP2) reallocated with population density (+ wood burning share urban vs rural with french data)

« Crops » landuse proxy for Agricultural sector « built-up » landuse proxy for the other anthropogenic sectors « roadmap » proxy for road traffic emissions (in progress)

PPM2.5 emission beforePPM2.5 emission after

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Inluence of vertical resolution

Three simulations performed with CHIMERE over the Paris area• C8 : Reference run with 8 levels (first at 40 m) up to 500

Hpa

• C20 : Simulation with 20 levels (first at 40 m) up to 500 Hpa

• C9 : Simulation with 9 levels (first at 10 m) up to 500 Hpa

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Inluence of vertical resolution

Coll. L. Menut LMD/IPSL-CNRS

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Improving horizontal resolution – why 7 km resolution?

For secondary pollutants like O3, 12 km seems an optimal resolution (Valari and Menut , 2008)

From the POMI exercize , no gain from 6km to 3 km (even for PM)

Computing time…(increase of grid cell number and decrease of time step)

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The simulation domains A high resolution run is performed

over the grey domain (7 km)

A high resolution run is performed over the grey domain

For each small cell (i,j) :

the high res. conc :

the coarse res. Conc. :

the averaged concentration :

(i,j)

COARSE (50 km)

NEST (7km) 300 x 400 grid points!

DELTA assumed to be :

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Simulation results Two simulations performed for the year 2006 :

• A simulation with only primary particulate matter and low level sources (SNAP 2, 7 and partly 3) PPM run

• A full chemistry run

Delta PPM2.5 species

2006

µg.m-3

1 2 3 4 5 6 7 8 9 10 11 120

1

2

3

4

5

6

Wien

Month

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[PPM based delta] versus [full PM based delta]

PPM run: Delta PPM2.5 species FULLCHEM run: Delta PM2.5 species µg.m-3 µg.m-3

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Model underestimations

Usually we have an underestimate of PM

• SOA formation (background issue)

• Wildfires (60% of the total PM10 emissions in Europe! including a part of Russia - AQMEII project) (background issue)

• Domestic wood burning in wintertime • Road traffic resuspension• Resuspension from soil erosion (background issue)

• Emission vertical profiles• Meteorology (kz calculation, wet deposition)

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PM2.5 Jan 2006 – using EMEP vertical profile for SNAP 2 emissions

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PM2.5 Jan 2006 – putting all SNAP 2 in the first CHIMERE layer

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Impact of emision injection heights of city deltas

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Conclusion

Downscaling method of EMEP emission dataset improved for our high resolution

High resolution run was performed over Europe to compute citydeltas

Improvment of CHIMERE runs at all resolutions (high and low)

Define a strategy to use « deltas » in integrated assessment model

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Extra slides

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About validation…

Pratically, it is not possible to validate a « delta »

δ=Chigh- Clow ; Chigh and Clow are comparable with measurements, but δ ??

What is the order of magnitude of PM2.5 deltas? With measurements in 2009, we roughly estimate the delta =1.6 µg.m-3 versus 0.94 µg.m-3 found in our work (for 2006).

Validation on PM2.5 for the “full chemistry run”

City

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Box model approximation

Reminder : a coefficient K is defined by city as δ=K.Q

Possible implementation of a box model by city to introduce a sensitivity to meteorological parameter

Box model increment :

Xcity= diameter of the city (m)

Xbckg= charateristic length of the background (m) (EMEP grid compliant)

Scity= surface of the city (m²)

Sbckg= surface of the low resolution cell (m2)

Q= city emissions (kg/s)

h= ABL height

U= Wind speed at 10m (m/s)

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CityDelta background – are observations useful to compute the citydelta?

Main goal is to correct a coarse EMEP simulation

• We can consider : δideal=Creal – Clow then, δideal corrects the model behavior and the lack of sources (using optimal interpolation methods)

• Then , δideal= δknown physics and emissions + δmissing sources & processes

• And , δideal=K.Q + δmissing sources & processes

• We must correct only what we know, implementing observations in the methodology introduces a bias difficult to handle in GAINS calculations

Computed in this work

What to do with this term? Nothing!

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Background O3 (AVERAGE)

Background O3 (low resol)

ppb

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AQMEII project

Area Point Fire TotalCO 33 250 900 5 656 101 39 982 886 (50.7%) 78 889 887

NOx 12 192 815 4 537 769 1 233 454 (6.9%) 17 964 038

NMVOC 10 682 173 1 323 089 552 780 (4.4%) 12 558 042

SO2 4 329 097 9 565 896 170 122 (1.2%) 14 065 115

PM10 2 578 181 1 109 540 5 396 873 (59.4%) 9 084 594

NH3 5 115 214 130 631 595 396 (10.2%) 5 841 241

Domain-wide yearly emissions [tons/y]