Data assimilation for urban air quality simulation Vivien Mallet 1 , - - PowerPoint PPT Presentation

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Sep 26, 2023 22 likes •223 views

Data assimilation for urban air quality simulation Vivien Mallet 1 , 2 1 Inria 2 CEREA, joint ENPC - EDF R&D laboratory, universit Paris-Est With contributions by Raphal Prillat 1 , 2 , Anne Tilloy 1 , 2 , SME Numtech, association

SLIDE 1

Data assimilation for urban air quality simulation

Vivien Mallet1,2

1Inria 2CEREA, joint ENPC - EDF R&D laboratory, université Paris-Est

With contributions by Raphaël Périllat1,2, Anne Tilloy1,2, SME Numtech, association Airparif BIS 2014, CNAM, June 2014

V. Mallet (Inria & CEREA)

Data assimilation at urban scale June 2014 1 / 20

SLIDE 2

Road network for trafic modeling

Example of "Votre Air" (part of Paris), in collaboration with Airparif and Numtech

594000 596000 598000 600000 602000 604000 1000 2000 3000 4000 5000 6000 7000 +2.426e6

V. Mallet (Inria & CEREA)

Data assimilation at urban scale June 2014 2 / 20

SLIDE 3

Output points for the air quality model

Example of "Votre Air" (part of Paris), in collaboration with Airparif and Numtech

594000 596000 598000 600000 602000 604000 1000 2000 3000 4000 5000 6000 7000 +2.426e6

594600 594800 595000 595200 595400 595600 200 400 600 800 1000 +2.427e6

V. Mallet (Inria & CEREA)

Data assimilation at urban scale June 2014 3 / 20

SLIDE 4

Simulated air quality

Example of "Votre Air" (part of Paris), in collaboration with Airparif and Numtech

Map of [NO2] (µg m−3) simulated for 26 June 2011 at 07:00

V. Mallet (Inria & CEREA)

Data assimilation at urban scale June 2014 4 / 20

SLIDE 5

Air quality after data assimilation

Example of "Votre Air" (part of Paris), in collaboration with Airparif and Numtech

Map of [NO2] (µg m−3) analyzed for 26 June 2011 at 07:00

V. Mallet (Inria & CEREA)

Data assimilation at urban scale June 2014 5 / 20

SLIDE 6

Data assimilation over Paris

Example of "Votre Air" (part of Paris), in collaboration with Airparif and Numtech

Map of [NO2] (µg m−3), simulated Map of [NO2] (µg m−3), analyzed 26 June 2011 at 07:00

V. Mallet (Inria & CEREA)

Data assimilation at urban scale June 2014 6 / 20

SLIDE 7

Data assimilation over Paris

Example of "Votre Air" (part of Paris), in collaboration with Airparif and Numtech

Map of [NO2] (µg m−3), simulated Map of [NO2] (µg m−3), analyzed 24 June 2011 at 09:00

V. Mallet (Inria & CEREA)

Data assimilation at urban scale June 2014 7 / 20

SLIDE 8

Data assimilation over Paris

Example of "Votre Air" (part of Paris), in collaboration with Airparif and Numtech

Map of [NO2] (µg m−3), simulated Map of [NO2] (µg m−3), analyzed 26 June 2011 at 15:00

V. Mallet (Inria & CEREA)

Data assimilation at urban scale June 2014 8 / 20

SLIDE 9

Data assimilation over Paris

Example of "Votre Air" (part of Paris), in collaboration with Airparif and Numtech

Map of [NO2] (µg m−3), simulated Map of [NO2] (µg m−3), analyzed 29 June 2011 at 19:00

V. Mallet (Inria & CEREA)

Data assimilation at urban scale June 2014 9 / 20

SLIDE 10

Data assimilation over Paris

Example of "Votre Air" (part of Paris), in collaboration with Airparif and Numtech

Map of [NO2] (µg m−3), simulated Map of [NO2] (µg m−3), analyzed 30 June 2011 at 08:00 Currently installed or being installed in at least 12 cities in France, by the SME Numtech.

V. Mallet (Inria & CEREA)

Data assimilation at urban scale June 2014 10 / 20

SLIDE 11

Mobile application: air quality index

Example of "Votre Air" (part of Paris), in collaboration with Airparif and Numtech

V. Mallet (Inria & CEREA)

Data assimilation at urban scale June 2014 11 / 20

SLIDE 12

Underlying computations: quick introduction

Best linear unbiased estimator, the so-called BLUE

The model computes the vector c whose error is assumed to have

Zero mean Variance B

The observation vector o has an error with

Zero mean Variance R No correlation with the error on c

The observation operator H is introduced so that o is comparable with Hc The linear estimator without bias and with minimum error variance trace is c∗ = c + K(o − Hc) K = BH⊤ HBH⊤ + R

−1

Note: this is the correction stage of a Kalman filter.

Cf. the data assimilation library Verdandi,

http://verdandi.gforge.inria.fr/

V. Mallet (Inria & CEREA)

Data assimilation at urban scale June 2014 12 / 20

SLIDE 13

Over the eastern part of Paris and for particulate matter

Map of [PM10] (µg m−3), simulated Map of [PM10] (µg m−3), analyzed 1st September 2012 at 18:00 This approach was applied to NO2, PM2.5, PM10 and black carbon. It was also used for Île-de-France (i.e., Paris region).

V. Mallet (Inria & CEREA)

Data assimilation at urban scale June 2014 13 / 20

SLIDE 14

Toward network design

Variance (µg2m−6) of the a posteriori error on [NO2]

V. Mallet (Inria & CEREA)

Data assimilation at urban scale June 2014 14 / 20

SLIDE 15

Toward network design

Variance (µg2m−6) of the a posteriori error on [NO2]

V. Mallet (Inria & CEREA)

Data assimilation at urban scale June 2014 15 / 20

SLIDE 16

Toward network design

Variance (µg2m−6) of the a posteriori error on [NO2]

V. Mallet (Inria & CEREA)

Data assimilation at urban scale June 2014 16 / 20

SLIDE 17

Toward network design

Variance (µg2m−6) of the a posteriori error on [NO2]

V. Mallet (Inria & CEREA)

Data assimilation at urban scale June 2014 17 / 20

SLIDE 18

Toward an application to noise pollution

Daily average noise map, computed by the city of San Francisco

Cf. Sara Hachem’s talk—A Middleware Solution for Democratizing Urban

Data

V. Mallet (Inria & CEREA)

Data assimilation at urban scale June 2014 18 / 20

SLIDE 19

Management system for a smart city: available tools

Modeling

High-dimensional and complex numerical models for analysis, real-time simulation and forecasting Fast surrogate models derived from dimension reduction and statistical emulation

Observations

Fixed networks with few high-quality sensors Toward larger networks with lower-quality sensors Toward crowd-sourced, possibly mobile, sensing

Mathematical methods

Data assimilation, machine learning, statistics, . . .

V. Mallet (Inria & CEREA)

Data assimilation at urban scale June 2014 19 / 20

SLIDE 20

Management system for a smart city: objectives and tools

Objectives Tools Best evaluation of past, current Data assimilation and future states of the city Best estimate of forcings Inverse modeling Screening scenarios, impact studies Model reduction Risk management Uncertainty quantification Probabilistic forecasts

V. Mallet (Inria & CEREA)

Data assimilation at urban scale June 2014 20 / 20