1. Bayesian Monte Carlo analysis applied to a regional scale transport chemistry model
Deguillaume L., Beekmann M., Menut L., Derognat C.
Improving emission uncertainties
Characterizing ozone production and chemical regimes
Over the Ile-de-France region
13/10/ Gloream

2. Context  Photochemical air pollution
CHEMISTRY
NOx limited
VOC limited
TRANSPORT
EMISSIONS

3. Airparif Context  Modelling those processes
 Development of a chemistry transport model CHIMERE
Ile-de-France region
(IPSL/ INERIS/LISA)
Model domain : 150×150km
Horizontal resolution : 6×6km grid
Vertical resolution : 8 layers in hybrid pressure coordinates
Chemical mechanism : reduced Melchior
Meteorology: ECMWF
Anthropogenic emissions: EMEP, ARIA, AIRPARIF
Biogenic emissions: Simpson et al. (1999)
Airparif

4. Objectives
(1) Uncertainties in emissions is always rather large and difficult to estimate
 Uncertainties on activity factor, emission factor spatial distribution, temporal variability...
(2) Evaluation  difficult since emitted pollutants undergo chemical transformation and are tranported away from sources
Problems
Observation : Simulated concentrations are very sensitive to emissions
Inverse modelling of emissions from observations (ground based and satellite)
Objectives  Improving emission uncertainties with a Bayesian approach
Application to semi-climatologic (summers ) period for generalization

5. Methods : inverse modelling
To verify and improve available estimates of atmospheric pollutants emissions
Alternative to bottom-up construction of emission cadastres
To improve performance of atmospheric models, especially in diagnostic studies
To develop a general observation-based methodology for estimating parameters of the atmosphere that cannot be observed directly
Adjoint model
Kalman filter
Bayesian Monte Carlo analysis

6. Principle of the Bayesian Monte Carlo analysis
A priori uncertainties
in emissions
A priori uncertainties of
input parameters
« Model uncertainties »
Monte Carlo
simulations
A priori concentrations
without constraints
Weighting by
observations
A posteriori
distributions of
emissions
Uncertainties
in observations
 Correction on a priori distributions of emissions
(also on other perturbed input parameters)
(-) Single correction factor over the whole grid domain and time period of the simulations
(+) Information on the value but also uncertainty associated to the emissions

7. Mathematical formulation
P(O|Yk)
For each kth Monte Carlo
simulation, the agreement function:
« Probability to observe a vector of observations O given that the model output Yk is the true value for the kth Monte Carlo simulation »
Hypothesis:  Observations present a normally distributed errors ε
 N independent observations Oj
Each simulation is weighted by P(O|Yk)  Cost function
 A posteriori probability density function vs. a priori one
Results ?
 Cumulative probability density function (CPDFs)
(probability that a given model prediction Xk stays below the limit X)

8. Perturbed model input parameters
A priori uncertainties of
input parameters
« Model uncertainties »
Parameters
1 σ Uncertainty
Emissions
Anthropogenic VOCs
+ 40
Anthropogenic NOx
Biogenic VOCs
+ 50
Rate constants
NO + O3
+ 10
NO2 + OH
NO + HO2
NO + RO2
+ 30
HO2 + HO2
RO2 + HO2
RH + OH
CH3COO2 + NO
+ 20
CH3COO2 + NO2
PAN + M
Photolysis frequencies and radiation
Actinic fluxes
+ 10
J(O3  2 OH)
+ 30
J(NO2  NO + O3)
+ 20
J(CH2O  CO + 2 HO2)
+ 40
J(CH3COCO  …)
+ 50
Meteorological parameters
Zonal wind speed
+ 1
Meridional wind speed
Mixing layer height
Temperature
+ 1.5
Relative humidity
Vertical mixing coefficient
Others
Deposition velocity
+ 25
 Log-normal distribution
 Uncertainty ranges  uncertainty assessment studies and expert judgements

9. Measurement constraints
observations
(1) Urban NO and O3
NO  direct forcing for NOx emissions
O3  information on ozone precursor emissions (VOC, NOx)
PARIS
(2) Rural O3 buildup
The two daily maximal [O3]  O3 plume
The 3 lowest [O3]  O3 background
For simulations and observations: +
(1) Days where the maximal [O3] is observed and simulated at the same or neighbouring station
(2) [O3 max] - [O3 back] > 10 ppb for simulations and observations
AIRPARIF NETWORK

10. Results – Cumulative probability
Simulated urban NO, O3 and O3 production in the plume- summers
Total
constraints
Red  Total constraints 2σ 2σ 2σ 2σ
Uncertainties are reduced by a factor :
3.2
2.4
1.7
Blue  without constraints
Deguillaume et al., in press, JGR, 2006

11. Results – Probability density functions
Emissions of anthropogenic NOx and VOC – cumulative summers 1998 and 1999
Total
constraints
Red lines  a posteriori distribution
Blue histogram  a priori distribution with 40% 1σ uncertainty
 1σ uncertainty : 22 % for NOx , 31% for VOC emissions
 NOx emissions remain nearly unchanged - VOC emissions are enhanced (+16%)
Better fit the observations in 1999 vs (in 1998, the constraints do not act in a similar way)

12. Other region ?  Marseille area (ESCOMPTE, AIRMAIX network)
Conclusion & perspectives
Bayesian Monte Carlo uncertainty analysis
Semi-climatologic period
Ile-de-France region
 A posteriori PDF  NOx emission  unchanged average
 reduced standard deviation (20% vs. 40%)
 VOC emission  enhancement (+16%)
 reduced standard deviation (30% vs. 40%)
 Uncertainty in the simulated urban NO, urban O3 and O3 production in the plume are strongly reduced
indirect constraints on VOC emissions (urban O3 and O3 production)  lower reduction
 Adjustements in the other model input parameters (vertical mixing coefficient, 1998)
 Better fit in 1999 than in 1998 because constraints in 1998 do not act in a similar way
Other region ?  Marseille area (ESCOMPTE, AIRMAIX network)
Satellite measurements

13. Perspectives…
Better understand the buildup of pollution episodes around Paris region
Objectives  Characterizing ozone production and chemical regimes
Application to semi-climatologic (summers ) period for generalization
Methodology
2 approaches
Direct simulation
Bayesian Monte Carlo approach
Constraints by observations
 Emission reduced scenario (-30% NOx and -30% VOC emissions)
Analysis of the chemical regime over the Ile de France region

14. (2) Characterizing ozone production and chemical regimes
Preliminary results ...
Daily maximum of O3 averaged over summers 1998 and 1999
Reference
NOx -30%
VOC -30%
Anticyclone sur l’ocean  mouvement aiguille montre  vent vers le sud