Impact of particles on photolysis rates and Results Impact on - - PowerPoint PPT Presentation

Introduction Model description Results

Impact on photolysis rates Impact on concentrations Impact on air quality monitoring

Conclusions

Impact of particles on photolysis rates and air quality

Elsa Real a and Karine Sartelet a 3 juin 2010

a CEREA (joint laboratory ENPC - EDF R&D) Université Paris-Est, Marne la Vallée, France

Introduction Model description Results

Impact on photolysis rates Impact on concentrations Impact on air quality monitoring

Conclusions

How aerosols impact photolysis rates and gas concentrations ?

Φ

= solar flux

J(λ)=∫ σgaz (λ) * Φ (λ) σgaz = cross section

Optical Properties and Optical Depth:

• aerosol numbers
• size distribution
• chemical composition

Clear sky conditions With clouds and aerosols

photolysis rates

Introduction Model description Results

Impact on photolysis rates Impact on concentrations Impact on air quality monitoring

Conclusions

How aerosols impact photolysis rates and gas concentrations ?

Φ

= solar flux

J(λ)=∫ σgaz (λ) * Φ (λ) σgaz = cross section

Optical Properties and Optical Depth:

• aerosol numbers
• size distribution
• chemical composition

Clear sky conditions With clouds and aerosols

photolysis rates

Introduction Model description Results

Impact on photolysis rates Impact on concentrations Impact on air quality monitoring

Conclusions

How aerosols impact photolysis rates and gas concentrations ?

Φ

= solar flux

J(λ)=∫ σgaz (λ) * Φ (λ) σgaz = cross section

Optical Properties and Optical Depth:

• aerosol numbers
• size distribution
• chemical composition

Clear sky conditions With clouds and aerosols

photolysis rates

Main photolysis reactions Ozone production NO2 + hν

JNO2

− − − → NO + O(3P) O(3P) + O2 + M − − − → O3 + M Ozone loss and OH production O3 + hν

JO3

− − → O1D + O2 O2 + H2O − − − → 2 OH

Introduction Model description Results

Impact on photolysis rates Impact on concentrations Impact on air quality monitoring

Conclusions

Coupling a regional CTM with a photolysis scheme

Polyphemus Polyphemus Polair3D Polair3D

Aerosols

concentrations, size, chemical composition

FAST-J FAST-J

photolysis rate scheme

Photolysis rates

Radiative model

Gases

Regional CTM

Chemical scheme :RACM, Aerosol scheme : SIREAM (16 aerosol species)

Introduction Model description Results

Impact on photolysis rates Impact on concentrations Impact on air quality monitoring

Conclusions

Coupling a regional CTM with a photolysis scheme

Polyphemus Polyphemus Polair3D Polair3D

Aerosols

concentrations, size, chemical composition

OPAC OPAC (refractive index database) + Mie code Mie code Optical properties and depth (ω, Q, φ)

FAST-J FAST-J

photolysis rate scheme

Photolysis rates

Radiative model

(each hour of the simulation) Gases

Regional CTM

Chemical scheme :RACM, Aerosol scheme : SIREAM (16 aerosol species)

Introduction Model description Results

Impact on photolysis rates Impact on concentrations Impact on air quality monitoring

Conclusions

Setting of the simulations

Run : 2 simulations, with and without taking into account photolysis rate modifications by aerosols R-AERO and R-NOAERO Domain : Europe (0.5˚×0.5˚) and 13 vertical levels up to 10 km. Meteorological data : ECMWF (0.36˚×0.36˚). Boundary conditions : MOZART for gas and GOCART for aerosols Time : 1 summer month (15 July-15 August) of 2001

Introduction Model description Results

Impact on photolysis rates Impact on concentrations Impact on air quality monitoring

Conclusions

Description of the aerosols simulation

FIG.: Monthly mean tropospheric AOD FIG.: AOD vertical profile averaged over Europe.

Contributions of individual species to the total AOD are shown.

Introduction Model description Results

Impact on photolysis rates Impact on concentrations Impact on air quality monitoring

Conclusions

Description of the aerosols simulation

FIG.: Monthly mean tropospheric AOD FIG.: AOD vertical profile averaged over Europe.

Contributions of individual species to the total AOD are shown.

AOD comparaison with AERONET data Mean correlation RMSE NMB NME Measurement 0.2 Simulation 0.3 78% 0.2 20% 39% Acording to these statistics AOD simulated by the model are realistic.

Introduction Model description Results

Impact on photolysis rates Impact on concentrations Impact on air quality monitoring

Conclusions

Impact on photolysis rates

FIG.: JNO2 monthly mean ground relative

differences between R-AERO and R-NOAERO

FIG.: Vertical profiles of relative difference between

R-AERO and R-NOAERO for JNO2 and JO3

Strong decrease of photolysis rates are spatially correlated with strong OD Larger impact at the ground

Introduction Model description Results

Impact on photolysis rates Impact on concentrations Impact on air quality monitoring

Conclusions

Impact on gas concentrations

FIG.: Vertical profiles of relative difference between O3, NO, NO2 and OH concentrations simulated with

R-AERO and R-NOAERO

Main photolysis reactions NO2 + hν

JNO2

− − − → NO + O(3P) O(3P) + O2 + M − − − → O3 + M O3 + hν

JO3

− − → O1D + O2 O2 + H2O − − − → 2 OH

Introduction Model description Results

Impact on photolysis rates Impact on concentrations Impact on air quality monitoring

Conclusions

Impact on gas concentrations

FIG.: Vertical profiles of relative difference between O3, NO, NO2 and OH concentrations simulated with

R-AERO and R-NOAERO

Main photolysis reactions for OH O3 + hν

JO3

− − → O1D + O2 O2 + H2O − − − → 2 OH

Introduction Model description Results

Impact on photolysis rates Impact on concentrations Impact on air quality monitoring

Conclusions

Impact on gas concentrations

FIG.: Vertical profiles of relative difference between O3, NO, NO2 and OH concentrations simulated with

R-AERO and R-NOAERO

Main photolysis reactions for NO NO2 + hν

JNO2

− − − → NO +O(3P) .

Introduction Model description Results

Impact on photolysis rates Impact on concentrations Impact on air quality monitoring

Conclusions

Impact on gas concentrations

FIG.: Vertical profiles of relative difference between O3, NO, NO2 and OH concentrations simulated with

R-AERO and R-NOAERO

Main photolysis reactions for NO2 NO2 +hν

JNO2

− − − → NO + O(3P) .

Introduction Model description Results

Impact on photolysis rates Impact on concentrations Impact on air quality monitoring

Conclusions

Impact on gas concentrations

FIG.: Vertical profiles of relative difference between O3, NO, NO2 and OH concentrations simulated with

R-AERO and R-NOAERO

Main photolysis reactions for O3 NO2 + hν

JNO2

− − − → NO + O(3P) O(3P) + O2 + M − − − → O3 +M O3 +hν

JO3

− − → O1D + O2 O2 + H2O − − − → 2 OH

Introduction Model description Results

Impact on photolysis rates Impact on concentrations Impact on air quality monitoring

Conclusions

Impact on ground gas concentrations

OH ground concentrations

Mean reductions of 12% with peak up to 30% ⇒ reduction of the global oxydizing

• capacity. For example isoprene

concentrations increase by around 10 %

O3 ground concentrations

O3 decrease is maximum where O3 production strongly dominates O3 loss. O3 mean decrease is about 4% with peak at 8%.

Introduction Model description Results

Impact on photolysis rates Impact on concentrations Impact on air quality monitoring

Conclusions

Impact on ground aerosols concentrations

Secondary aerosol formation is modified by the change on oxydant (OH, O3, HO2) concentrations Ground concentrations of SO4 and secondary organic are reduced by 3-4 %. Small changes on spatially averaged PM10 ground concentrations but reduction by up to 8% in regions where AOD and secondary aerosol formation are important

Introduction Model description Results

Impact on photolysis rates Impact on concentrations Impact on air quality monitoring

Conclusions

Comparaison with EMEP data

Mean correlation RMSE NMB NME Measurement 74.7 O3 R-NOAERO 84.6 53.9% 28.5 19% 33.4% R-AERO 81.9 54.4% 27.5 15% 32.1% Measurement 4.8 NO2 R-NOAERO 4.1 32.2% 2.5 10% 62.7% R-AERO 4.2 32.9% 2.5 13% 63.9% Measurement 18.7 PM10 R-NOAERO 10.9 71.5% 9.9 37% 42.1% R-AERO 10.9 72.1% 9.8 36% 42%

Statistics are slightly better when including photolysis rates modification by aerosols but differences are small.

Introduction Model description Results

Impact on photolysis rates Impact on concentrations Impact on air quality monitoring

Conclusions

Which consequences on pollutants thresholds ?

FIG.: Number of O3 threshold exceedances non simulated with R-AERO

Including photolysis rates modifications by aerosols leads to a decrease by a factor 2 of the exceedances of the European alert O3 threshold (O3 > 240 µg.m−3) and the information threshold (O3 > 180 µg.m−3).

Introduction Model description Results

Impact on photolysis rates Impact on concentrations Impact on air quality monitoring

Conclusions

Conclusions

FAST-JX photolysis scheme

Polair3D regional CTM

1 hour

aerosol concentrations and size distribution photolysis rates

Dust aerosols have the strongest impact on photolysis rates followed by sulphate and nitrate Larger impact are simulated on OH and NO concentrations (∼-12% in the boundary layer) Mean impact on PM10 is small but local impact is important where strong secondary aerosol formation is simulated Systematic reduction of ground O3 peaks O3 European threshol exceedances are divided by 2 when including aerosol impact on photolysis rates