dea - università degli studi di brescia multi-objective optimization to select effective pm10...

DEA - Università degli Studi di Brescia

Multi-objective optimization to select effective PM10 control policies in

Northern ItalyC. Carnevale, E. Pisoni, M. VoltaDipartimento di Elettronica per l’Automazione

Università degli Studi di Brescia, Italy


Methodology: research aim

To develop a secondary pollution control plan:• Multi-objective optimization:

– Objective 1: Air Quality Index (AQI)– Objective 2: Internal Costs (C)

• for a mesoscale domain– Milan CityDelta domain (Northern Italy)


Methodology: multi-objective problem

Jmin

emission reduction costs

PM exposure index (meanPM)

set of the feseable solutions

decision variables: reduction of the precursor emissions

)( CJ


ji

D

d

djiDJI , 1

,11

spdjiE

,,,

daily (d) cell (i,j) precursor (p) emissions for CORINAIR sectors (s) for the basecase scenario

PpSs

sp , decision variable set: precursor (p) reduction for

CORINAIR sector (s)

spdji

dji E ,,

,,

domain yearly mean PM exposure (g/m3):

source-receptor models

PMNHSOxNOVOCP ,3,2,,

111, SSS

PM precursors

CORINAIR sectors (s)

Methodology: Obj 1 - the Air Quality Index: ()


Methodology: Obj 2 - emission reduction Costs (C)

sp

spspspsp cEC,

,,,,

spspc ,, unit cost curve for precursor (p) and CORINAIR sector (s)

Cost curves used are estimated on the basis of RAINS-IIASA database

An emission reduction cost curve has been assessed for each CORINAIR sector.


Case study:domain

300x300km2

400 450 500 550 600 650

4900

4950

5000

5050

5100

5150

TORI NO

MI LANO

GENOVA

TRENTO

VERONA

PI ACENZA

MODENA

BRESCI A

VARESE

BERGAMO

SONDRI O

PARMA

NOVARA

ALESSANDRI A

0

200

400

600

800

1000

1200

1400

1600

1800

2000

2200

2400

2600

2800

3000

(m )

Milan domain


Case study: Obj 1: AQI

• Pollutant concentration are computed by 3D deterministic chemical transport multiphase modelling system – Time consuming

• Identification of source-receptor models (Neuro-fuzzy Networks), describing the nonlinear relation between decision variables (emission reduction) and air quality objective, processing the simulations of TCAM


Case study: Obj 1 - GAMES

Continental modeloutput

Land useTopography

DiagnosticModel Output

LocalMeasurements

RAMS-CALMETMeterological

Model

TCAM

Initial andBoundary condition

Pre-processors

3D concentration fields

POEM-PMEmission

Model

Radiosounding

Emissioninventories

Emission Fields

3D meteo fields

VOC, PM speciation

Profiles

TemporalProfiles

SystemEvaluation

Tool

IC, BC


Case study: Obj 1 - TCAM model

• gas phase chemical mechanisms: SAPRC90, SAPRC97, COCOH97, CBIV

• 21 aerosol chemical species• 10 Size classes

– Size varying during the simulation– Fixed-Moving approach

• processes involved:– Condensation/Evaporation– Nucleation– Aqueous Chemistry

Shell

Core


Case study: Obj 1 - TCAM simulations

• base case simulation:– 300 x 300 km2, 60 x 60 cells, cell resolution: 5x5 km2 – 11 vertical layers– emission and meteorological fields: JRC (CityDelta Project)– initial and boundary conditions: EMEP– simulation takes several days of CPU time– simulation period: year 1999

• alternative scenario:– CLE: current legislation– MFR: most feasible reduction

emission scenario

precursor Base case

[ton/year]

CLE

[%]

MFR

[%]

NOx 466 803 -29.79 -44.50

VOC 718 087 -38.16 -58.74

SOx 714 796 -77.49 -90.64

NH3 172 389 0.51 -35.12

PM10 176 726 -39.65 -77.19


Case study: Obj 1 - SR models

• 4-layer NF architecture– Number of MF for input: 2– Number of rules: 25=32– Nodes of hidden layer: 8

• Input data: daily NOx,VOC, PM10, NH3, SOx emissions (CDII)

• Target data: daily PM10 concentration computed by the GAMES system (CDII)


Case study: Obj 1 - SR models

• Identification of a neural network for each group of 6x6 TCAM grid cells

400 450 500 550 600 650

4900

4950

5000

5050

5100

5150

MI LANO

GENOVA

TRENTO

VERONA

PI ACENZA

MODENA

BRESCI A

VARESE

BERGAMO

SONDRI O

PARMA

NOVARA

ALESSANDRI A

0

200

400

600

800

1000

1200

1400

1600

1800

2000

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Case study: Obj 1 - SR models validation

BIAS Scatter Plot

400 450 500 550 600 650

4950

5000

5050

5100

5150

MI LANO

TRENTO

VERONA

PI ACENZA

MODENA

BRESCI A

VARESE

BERGAMO

SONDRI O

PARMA

NOVARA

ALESSANDRI A

-0 .20

-0.15

-0.10

-0.05

-0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0 5 10 15 20 25 30 35 40 45 500

5

10

15

20

25

30

35

40

45

50

PM10 simulated by TCAM 3d model

PM

10 s

imul

ated

by

NF

sys

tem

y=2x

y=x

y=0.5x


Case study: Obj 2 - Cost functions

• Fitting the costs of the available technologies:

– considering 2nd order polynomial functions– with the constraint of estimating a monotonically increasing

and convex function.

y = 11419x2 - 182,13x + 380,88

0

500

1000

1500

2000

2500

0% 10% 20% 30% 40%

un

it c

os

t (K

€)

NOx, sector 3:


Case study: optimization problem solution

• Weighted Sum Method

• Constraints1. Maximum Feasible Reductions:

2. Technologies reducing both precursors

))()1()((min

C

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11

0.00 0.54 0.00 0.19 0.49 0.33 0.47 0.61 0.06 0.00 0.00

0.31 0.22 0.46 0.29 0.00 0.00 0.29 0.25 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.35 0.00

0.00 0.59 0.09 0.40 0.00 0.00 0.41 0.39 0.69 0.00 0.00

0.00 0.14 0.08 0.75 0.00 0.00 0.18 0.14 0.00 0.00 0.00

sNOxR ,

sVOCR ,

spsp R ,,0

sNHR ,3

sPMR ,

sSOR ,2


Case study: Results (pareto boundary)

Optimisation performed only on the 50% cells with highest mean PM concentration


Case study: Results (VOC)

road transport (7), resid. combustion plants (2)

road transport (7)


Case study: Results (NOx)

industrial combustion (3), public power plants (1), production processes (4)

road transport (7), public power plants (1), production processes (4)


Case study: Results (PM)

waste treatment (9), production processes (4), other mobile sources (8)

road transport (7), production processes (4), other mobile sources (8)


Case study: Results (SO2)

production processes (4), road transport (7), other mobile sources (8)

production processes (4),


Conclusions

– A procedure to formulate a multi-objective analysis to control PM exposure has been presented;

– The procedure implements neuro-fuzzy networks tuned by the outputs of a deterministic 3D modelling system;

– The methodology has been applied over Milan CityDelta domain (Northern Italy): a strong reduction of 70% of air qualiy index can be attained with only 15% of maximum costs


Current activities

– Uncertainty analysis:• cost curves• NOx/VOC and NOx/PM reduction functions for transport sectors• sensitivity of source-receptor models to NH3 emission reduction

– CityDeltaIII simulations to extend source-receptor model calibration and validation sets;

– source-receptor models for mean PM10 and PM2.5 concentrations: spatial resolution 10x10km2;

– PM2.5 two-objective optimization– Ozone and PM10 two-objective optimization


Thanks to…

• This research has been partially supported by MIUR (Italian Ministry of University and Research).

• The authors are grateful to the CityDelta community.

• The work has been developed in the frame of NoE ACCENT (T&TP, Atmospheric sustainability).

dea - università degli studi di brescia multi-objective optimization to select effective pm10...

Documents

dea universit degli

brescia case study

brescia methodology

milan domain slide

bc slide

precursor emissions

simulations of tcam

emission reduction cost