Atmosphere Monitoring

A flexible tool to assess mitigation scenarios in air quality forecasts

Augustin Colette, Ineris

CAMS_ACTAir Control Toolbox

AtmosphereMonitoring • Concept

– A flexible web-based tool to assess interactively, in the every-day air quality forecast, the benefit expected from a mitigation scenario

• Methodology

– Train everyday a new non-linear surrogate model on the basis of a few scenarios computed with the Chimere Chemistry Transport Model

S c o p e

AtmosphereMonitoring • Regional Air Quality Forecasts

– Ensemble of 7 (+2) CTMs coveringEurope at about 10km resolutionup to D+3

• CAMS Policy Service– Ineris, Met Norway, Nilu, TNO

– Emission scenario development

– Interim Assessment Report

– City/Country allocation service• Sensitivity simulation: EMEP

• Source apportionment: LOTOS

– CAMS_ACT: Chimere

C o p e r n i c u s A t m o s p h e r e M o n i t o r i n g S e r v i c e

EURAD

EMEP

CHIMERE

L-EUROS

MATCH

MOCAGE

SILAM

AtmosphereMonitoring

C A M S _ A C T

AtmosphereMonitoring

B u i l d i n g u p o n t h e C A M S g r e e n s c e n a r i o s

• 3 days forecasts of emission reduction scenarios at 0.25 deg

• Allows assessing the impact of the main air pollution drivers for various types of situations

• Illustrate the benefit of up-coming legislation

• 10 scenarios currently available

– 2 References

– 30% reduction for 4 activity sectors (Industrial, Residential Heating, Traffic, Agriculture) + 1 joint Traffic & Agriculture

– NEC2020, NEC2030, MTFR

AtmosphereMonitoring

• Surrogate Model

– Fit a simple polynomial (1st, 2nd or 3rd order + interactions)

– Training data set: limited set of Chimere scenarios

– At each grid point

– For each forecast day (mean for PM10, max for O3)

• Development

– Explore several combinations of emission reductions

• IND, RES, TRA, AGR

• 10, 30, 60, 90, 100%

• + Interactions

– Developed for 3 types of episodes:

• Winter (201612-201701)

• Spring (201503)

• Summer (201706)

S u r r o g a t e m o d e l m e t h o d o l o g y

Note: ConcenAGRtion reduction expressed as >0

AtmosphereMonitoring

S u r r o g a t e m o d e l m e t h o d o l o g y

Note: Concentration reduction expressed as >0

O3 daily max, Paris, 20170620PM10, Paris, 20161201

AtmosphereMonitoring

0 REF

-10% AGR10

-30% AGR30

-60% AGR60

-90% AGR90

-100% AGR100

N u m e r i c a l E x p e r i m e n t a l P l a n : 1 D

AG

RIC

ULT

UR

E1st order polynomial 1D combinations :• Fitted on (REF, AGR10), tested on (AGR30, AGR60, AGR90, AGR100)• Fitted on (REF, AGR30), tested on (AGR10, AGR60, AGR90, AGR100)• Fitted on (REF, AGR60), tested on (AGR10, AGR30, AGR90, AGR100)• Fitted on (REF, AGR90), tested on (AGR10, AGR30, AGR60, AGR100)• Fitted on (REF, AGR100), tested on (AGR10, AGR30, AGR60, AGR90)

2nd order polynomial 1D combinations :• Fitted on (REF, AGR10, AGR30), tested on (AGR60, AGR90, AGR100)• Fitted on (REF, AGR10, AGR60), tested on (AGR30, AGR90, AGR100)• Etc…

3rd order polynomial 1D combinations :• Fitted on (REF, AGR10, AGR30, AGR60), tested on (AGR90, AGR100)• Fitted on (REF, AGR10, AGR60, AGR90), tested on (AGR30, AGR100)• Etc…

+ Same 1D combinations for TRA, IND, RES

AtmosphereMonitoring • PM10 sensitivity to AGR model during a springtime episode (March 2015) in Paris

• The 2nd degree polynomial fit on 30 and 100% reductions is optimal

E v a l u a t i o n – 1 D P M 1 0 m o d e l f o r A G R

PM10 concentration (not on scale)

x

x + x2

x + x2 + x3

AtmosphereMonitoring • Non-linear terms for PM10 related to agriculture during a springtime episode (March 2015) for Paris

• The 2nd degree polynomial fit on 30 and 100% reductions is optimal

E v a l u a t i o n – 1 D P M 1 0 m o d e l f o r A G R

AtmosphereMonitoring

mod sce AGR IND RH TRA

x 10 3.933 1.133 0.130 0.339

x 30 3.123 0.894 0.082 0.236

x 60 1.983 0.573 0.047 0.137

x 90 1.310 0.382 0.032 0.092

x 100 1.463 0.471 0.036 0.106

x+x2 10 30 3.540 0.698 0.284 0.407

x+x2 10 60 2.097 0.329 0.066 0.117

x+x2 10 90 0.833 0.137 0.027 0.046

x+x2 10 100 0.856 0.147 0.027 0.047

x+x2 30 60 1.917 0.285 0.055 0.096

x+x2 30 90 0.663 0.104 0.018 0.031

x+x2 30 100 0.646 0.105 0.017 0.030

x+x2 60 90 0.697 0.101 0.017 0.032

x+x2 60 100 0.634 0.099 0.015 0.028

x+x2 90 100 1.563 0.254 0.029 0.058

x+x2+x3 10 30 60 3.437 0.634 0.315 0.428

x+x2+x3 10 30 90 0.867 0.140 0.054 0.076

x+x2+x3 10 30 100 0.736 0.122 0.049 0.068

x+x2+x3 10 60 90 0.596 0.081 0.022 0.034

x+x2+x3 10 60 100 0.407 0.057 0.017 0.026

x+x2+x3 10 90 100 0.739 0.099 0.024 0.038

x+x2+x3 30 60 90 0.560 0.074 0.020 0.032

x+x2+x3 30 60 100 0.360 0.049 0.015 0.022

x+x2+x3 30 90 100 0.514 0.068 0.017 0.027

x+x2+x3 60 90 100 1.150 0.143 0.029 0.051

S e l e c t i o n o f 1 D P M 1 0 m o d e l , a l l s e c t o r s

Relative error (in %)

Averaged for 201503, 201612, 201701

1D models selected: • AGR+AGR2 fitted on AGR60, AGR100• IND+IND2 fitted on IND60, IND100• RH fitted on RH90• TRA+TRA2 fitted on TRA60, TRA100

AtmosphereMonitoring

0 -10% -20% -30% -50% -60% -90% -100%

0 REF AGR10 AGR30 AGR60 AGR90 AGR100

-10% TRA10

-20% TRA20AGR50

-30% TRA30 TRA30AGR60

-50%

-60% TRA60 TRA60AGR30

-90% TRA90

-100% TRA100 TRA100AGR100

N u m e r i c a l E x p e r i m e n t a l P l a n : 2 D

AGRICULTURE

TRA

FFIC

+ 1D combinations for IND, RES + 2D interactions: AGRRES, INDRES, TRARES, AGRIND, TRAIND+ 4D interactions: INDRESTRAAGR (100%)

47 scenarios285 combinations

AtmosphereMonitoring • Testing the need to include interaction terms

– 2D Model

• With interactions: T+T2+A+A2+TA

• Without interactions: T+T2+A+A2

• Trained on 30/60% : TRA30AGR60, TRA60AGR30

• Tested on 20/50%: TRA20AGR50

• Results for PM10– Relative error (in %)

– Averaged for 201503, 201612, 201701

• Interaction terms required

– Substantial improvement for AI, TA (+TI?)

– Interactions negligible for AR, IR, TR

E v a l u a t i o n – 2 D P M 1 0 m o d e l s

AI AR IR TA TI TR0.355 0.102 0.051 0.244 0.069 0.039x+x2+y+y2+xy0.355 0.102 0.051 0.244 0.069 0.039x+x2+y+y2+xy0.878 0.286 0.158 0.651 0.271 0.090x+x2+y+y21.025 0.289 0.147 0.704 0.283 0.091x+x2+y+y2

AtmosphereMonitoring • Selecting the optimal training scenario for interaction terms

– 2D Model structure: T+T2+A+A2+TA

– Trained on TRA20AGR50, tested on TRA30AGR60, TRA60AGR30, TRA100AGR100

– Trained on TRA30AGR60, tested on TRA60AGR30, TRA20AGR50, TRA100AGR100

– Trained on TRA60AGR30, tested on TRA30AGR60, TRA20AGR50, TRA100AGR100

– Trained on TRA100AGR100, tested on TRA30AGR60, TRA60AGR30, TRA20AGR50

• Results for PM10– Relative error (in %)

– Averaged for 201503, 201612, 201701

• 2D models selected:

– AGRxIND fitted on AGR30IND60

– TRAxAGR fitted on TRA100AGR100

E v a l u a t i o n – 2 D P M 1 0 m o d e l s

AI AR IR TA TI TR1.003 0.941 0.128 0.535 0.067 0.05720 500.638 0.558 0.072 0.614 0.074 0.02830 600.884 0.781 0.095 0.519 0.060 0.03160 300.996 0.832 0.127 0.353 0.128 0.054100 100

AtmosphereMonitoring • Selected structure

– 4-dimensional IND, RES, TRA, AGR– 1st or 2nd order degree polynomial models– 1st order interaction terms– Closure with 4D interactions

• Training scenarios– AGR 60%, AGR 100%– IND 60%, IND 100%– RES 90%– TRA 60%, TRA 100%

– TRA 30% & IND 60%– TRA 100% & AGR100%– AGR 30% & IND 60%– IND100% & RES100% & TRA100% AGR 100%

C o n c l u s i o n o n S u r r o g a t e M o d e l

𝑅𝐸𝐹 = 𝐴𝐺𝑅 + 𝐴𝐺𝑅2 + 𝐼𝑁𝐷 + 𝐼𝑁𝐷2 + 𝑅𝐸𝑆 + 𝑇𝑅𝐴 + 𝑇𝑅𝐴2 +𝐴𝐺𝑅 × 𝐼𝑁𝐷 + 𝑇𝑅𝐴 × 𝐴𝐺𝑅 + 𝑇𝑅𝐴 × 𝐼𝑁𝐷 +𝐼𝑁𝐷 × 𝑅𝐸𝑆 × 𝑇𝑅𝐴 × 𝐴𝐺𝑅

AtmosphereMonitoring

U s e c a s e s

• PM10– March 2015

– Decembre 2016

• Ozone

– June 2017

AtmosphereMonitoring

2 0 1 6 1 2 0 1 : P M 1 0

Reference

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2 0 1 5 0 3 1 8 : P M 1 0

Reference

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2 0 1 7 0 6 2 1 : O 3

Reference

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I n t e r a c t i o n s : P M 1 0 : A g r i c u l t u r e & T r a f f i c

• Very different sensibility for two typologies of episodes

– March 2015: mainly agriculture, very small sensibility to Traffic, but important interactions

– December 2016: smaller importance of interactions, and larger role of Traffic

Concentration (in µg/m3) reductionwhen TRA/AGR are reduced from 0 to 100%

AtmosphereMonitoring • Development

– Interactive web tool

– Trained on a limited set of Chimere scenarios

– Accounts for non-linearities, interactions & long range Transport

– Target errors• < 0.1µg/m3

• ~0.5%

• Pre-Operational– Implement in forecast mode

– Open online Summer 2018

S u m m a r y & O u t l o o k