multi-objective analysis to control ozone exposure

DEA - Università degli Studi di Brescia

Multi-objective analysis to control ozone exposure

C. Carnevale, G. Finzi, E. Pisoni, M. VoltaDipartimento di Elettronica per l’Automazione

Università degli Studi di Brescia, Italy


Research aim

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

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

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


Problem formulation: objective 1 the Air Quality Indicator (AQI)

)])1()(),1()((40[min)min(, 1

,,,

ji

D

d

Vs

sji

Ns

sjiji rdVrdNAOTAQI

)d(V),d(N sj,i

sj,i

daily cell NOx and VOC emissions in the reference case for CORINAIR sector s;

111,...,sVs

Ns )r,r( decision variable set: CORINAIR sector

precursor emission reductions;


Problem formulation : objective 2 the emission reduction cost (C)

))r(c)r(V)r(c)r(N(min)Cmin( Vs

Vs

Vs

sNs

s

Ns

Ns

s

1111

1

)r(c),r(c Vs

Vs

Ns

Ns

unit costs related respectively to NOx and VOC emission reduction;

111,...,sVs

Ns )r,r( decision variable set: CORINAIR sector

precursor emission reductions;


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


AQI model identification

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

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


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


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– the run of such a simulation takes about 12 days of CPU time– simulation period: 1999 april to september

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

O3 precursor CLE % MFR %

NOx -29.79 -44.50

VOC -38.16 -58.74


Source-receptor models (NN)

• Elman NN architecture:– Nodes of input layer: 2– Nodes of output layer: 1– Nodes of hidden layer: 8

• One neural network for each group of 2x2 (10x10 km2) domain cells

• Input data: daily NOx and VOC emissions

• Target data: cell AOT40 daily values computed by the GAMES system

IW

g

FW

OW

b

+ AF1

AF2+[MxQ]

[Mx1]

[LxM]

[Lx1]

Delay

1

vn

an

an-1

1

[MxM]

f(vn)



• Identification and validation dataset:– 3 TCAM seasonal simulations

• Base Case;• Current LEgislation;• Most Feasible Reduction.

• Validation dataset (126 values):– Third week of each month.

• Identification dataset (423 values):– Remaining patterns



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

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

NBIAS

=0.97


Cost functions

• Cost curves used are estimated on the basis of RAINS-IIASA database (http://www.iiasa.ac.at)

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

• Decision variables = emission reduction for sectors:– VOC: 2, 3, 4 ,5, 6, 7, 8, 9– NOx: 2, 3, 4, 7, 8


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:


Optimization problem solution

• Weighted Sum Method

• Constraints1. Maximum Feasible Reductions

2. Technologies reducing both precursors

))(C)()(AQI(min

1

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11

0 0.39 0.33 0.80 0 0 0.28 0.25 0 0 0

0 0.68 0.60 0.32 0.33 0.27 0.47 0.67 0.06 0 0

VsR

NsR


Results

• Pareto boundaries

1,5E+07

1,7E+07

1,9E+07

2,1E+07

2,3E+07

2,5E+07

2,7E+07

2,9E+07

3,1E+07

0,E+00 8,E+04 2,E+05 2,E+05 3,E+05 4,E+05

Cost reduction (Keuro)

AO

T r

ed

uc

tio

n (

pp

m)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Cost reduction (% max)

AO

T4

0 r

ed

uc

tio

n (

% m

ax

)

Utopia


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

AOT reduction (% max)

VOC

em

issi

on re

duct

ion

(%)

S2S4S5S6S7S8S9

Results

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

AOT reduction (% max)

NO

x em

issi

on r

educ

tion

(%)

S2S3S4S7S8

VOC reductions NOx reductions


Results

VOC emissions

0

50000

100000

150000

200000

250000

300000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

AOT40 reduction (% max)

VO

C e

mis

sion

s (t

on/y

ear)

2456789

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

AOT40 reduction (% max)

NO

X e

mis

sion

s (t

on/y

ear)

23478

NOx emissions


Conclusions

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

– The procedure implements Elman neural 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 ozone exposure (60% of the maximum air quality improvement) can be attained with a small fraction of the emission reduction technology costs (about 12%)


Current activities

– Uncertainty analysis:• Source-receptor models• Cost curves• VOC/NOx reduction functions for transport sectors

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

– source-receptor models for SOMO35, AOT60, max8h, mean PM10 and PM2.5 concentrations;

– PM10 and PM2.5 precursor (NOx, VOC, primary PM10, NH3, SO2) cost curves;

– PM10 and PM2.5 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.


References

• Finzi, G., Guariso, G., 1992. Optimal air pollution control strategies: a case study. Ecological Modelling 64, 221–239.

• Barazzetta, S., Corani, G., Guariso, G., 2002. A neural emission-receptor model for ozone reduction planning. In: Proc. iEMSs 2002.

• Volta, M. 2003. Neuro-fuzzy models for air quality planing. The case study of ozone in Northern Italy. European Control Conference.

• Guariso, G., Pirovano, G., Volta, M., 2004. Multi-objective analysis of ground level ozone concentration control. Journal of Environmental Management 71, 25–33.

• Carnevale C., Finzi G., Pisoni E., Volta M., 2006. Identification of source-receptor models for secondary tropospheric pollution control. 14th IFAC Symposium on System Identification. 29-31 march, 2006 (pp. 762-767). IFAC Ed., CDROM published by Causal Productions.

• M Carnevale C., Finzi G., Pisoni E., Volta M., 2006. Multi-objective analysis to control ozone exposure, 28th ITM-NATO.


Constraints

• Maximum feasible reductions allowed by technologies for macrosector s:

• Technologies reducing both NOx and VOC emissions

Vs

Vs

Ns

Ns

Rr

Rr

0

0


Optimization problem solution

NOx reduction NOx reduction]

VO

C r

edu

ctio

n

VO

C r

edu

ctio

n

macrosector 7 macrosector 8

Constraints (2): technologies reducing both precursors


scenario A

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Cost reduction (% max)

AO

T4

0 r

ed

uc

tio

n (

% m

ax

)

A

Utopia


basecase emission scenario

s45,0%

s55,5%

s629,6%

s746,5%

s11,6%

s21,8% s3

0,0%

s89,0%

s90,2%

s100,0%

s110,7%

VOC emissions

s415,8%

s50,0%

s60,0%

s735,2%

s120,8%

s24,0%

s37,4%

s814,8%

s91,7%

s100,0%

s110,4%

NOx emissions


AOT40 scenarios

basecase Scenario A

source-receptor model simulations

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

55000

60000

65000

5 1 0 1 5 2 0 2 5 3 0

5

1 0

1 5

2 0

2 5

3 0

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

5 1 0 1 5 2 0 2 5 3 0

5

1 0

1 5

2 0

2 5

3 0

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

ppb*h


scenario A: emissions

-60%

-50%

-40%

-30%

-20%

-10%

0%

s2 s3 s4 s5 s6 s7 s8 s9

NOx

VOC1

2

1

2

3 control priorities

-140000

-120000

-100000

-80000

-60000

-40000

-20000

0

s2 s3 s4 s5 s6 s7 s8 s9

NOx

VOC1

21

23

emission reductions(ton/year)

multi-objective analysis to control ozone exposure

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