Multi-objective analysis to control ozone
exposure
C. Carnevale, G. Finzi, E. Pisoni, M. Volta
Dipartimento di Elettronica per l’Automazione
Università degli Studi di Brescia, Italy
DEA - Università degli Studi di Brescia
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)
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Problem formulation: objective 1
the Air Quality Indicator (AQI)
min( AQI )  min
D
  [ AOT 40 i , j (Nis, j (d )  (1  rsN ),Vi s, j (d )  (1  rsV ))]
 i , jd 1
Nis, j ( d ),Vi s, j ( d )
daily cell NOx and VOC emissions in the
reference case for CORINAIR sector s;
( rsN , rsV )s 1,...,11 decision variable set: CORINAIR sector
precursor emission reductions;
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Problem formulation : objective 2
the emission reduction cost (C)
11
min( C )  min  ( N s  (1  rsN )  csN ( rsN )  V s  (1  rsV )  csV ( rsV ))

s 1
csN ( rsN ), csV ( rsV )
unit costs related respectively to NOx and
VOC emission reduction;
( rsN , rsV )s 1,...,11 decision variable set: CORINAIR sector
precursor emission reductions;
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Study domain
300x300km2
5150
SONDRIO
TRENTO
5100
(m)
3000
VARESE
2800
BERGAMO
2600
5050
BRESCIA
NOVARA
2400
VERONA
MILANO
2200
2000
5000
1800
TORINO
1600
PIACENZA
1400
ALESSANDRIA
1200
PARMA
1000
4950
MODENA
800
600
400
GENOVA
Milan domain
200
4900
0
400
450
500
550
600
650
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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
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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
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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
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Source-receptor models (NN)
• Elman NN architecture:
– Nodes of input layer: 2
– Nodes of output layer: 1
– Nodes of hidden layer: 8
an-1
• One neural network for each
group of 2x2 (10x10 km2)
domain cells
vn
• Input data: daily NOx and
VOC emissions
FW
IW
+
Delay
[MxM]
AF1
an
[MxQ]
1
b
[Mx1]
OW
[LxM]
1
f(vn)
+
AF2
g
[Lx1]
• Target data: cell AOT40 daily
values computed by the
GAMES system
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Source-receptor models (NN)
• 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
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Source-receptor models (NN)
NBIAS
r=0.97
5150
0.25
SONDRIO
TRENTO
5100
0.2
VARESE
0.15
BERGAMO
5050
0.1
BRESCIA
NOVARA
VERONA
MILANO
0.05
0
5000
PIACENZA
-0.05
ALESSANDRIA
PARMA
4950
-0.1
MODENA
-0.15
GENOVA
4900
400
450
500
550
600
650
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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
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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.
2500
NOx, sector 3:
unit cost (K€)
2000
y = 11419x2 - 182,13x + 380,88
1500
1000
500
0
0%
10%
20%
30%
40%
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Optimization problem solution
•
Weighted Sum Method
min(   AQI(  )  (1   )  C (  ))

•
Constraints
1. Maximum Feasible Reductions
RsN
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
RsV
2. Technologies reducing both precursors
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Results
• Pareto boundaries
100%
2,9E+07
90%
AOT40 reduction (% max)
AOT reduction (ppm)
Utopia
3,1E+07
2,7E+07
2,5E+07
2,3E+07
2,1E+07
1,9E+07
1,7E+07
1,5E+07
0,E+00
80%
70%
60%
50%
40%
30%
20%
10%
0%
8,E+04
2,E+05
2,E+05
Cost reduction (Keuro)
3,E+05
4,E+05
0%
10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Cost reduction (% max)
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Results
NOx reductions
VOC reductions
100%
S2
S4
S5
S6
S7
S8
S9
90%
80%
70%
60%
50%
40%
NOx emission reduction (%)
VOC emission reduction (%)
100%
30%
20%
10%
90%
80%
70%
60%
50%
S2
S3
S4
S7
S8
40%
30%
20%
10%
0%
0%
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)
AOT reduction (% max)
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Results
NOx emissions
VOC emissions
180000
2
4
5
6
7
8
9
250000
200000
150000
100000
50000
0
NOX emissions (ton/year)
VOC emissions (ton/year)
300000
160000
140000
2
3
4
7
8
120000
100000
80000
60000
40000
20000
0
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100
%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100
%
AOT40 reduction (% max)
AOT40 reduction (% max)
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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%)
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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
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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.
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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. 2931 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.
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Constraints
• Maximum feasible reductions allowed by
technologies for macrosector s:
0  rsN  RsN
0  rsV  RsV
• Technologies reducing both NOx and VOC
emissions
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Optimization problem solution
Constraints (2): technologies reducing both precursors
macrosector 8
VOC reduction
VOC reduction
macrosector 7
NOx reduction
NOx reduction]
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scenario A
Utopia
100%
AOT40 reduction (% max)
90%
80%
70%
60%
A
50%
40%
30%
20%
10%
0%
0%
10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Cost reduction (% max)
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basecase emission scenario
NOx emissions
VOC emissions
s10
0,0%
s9
0,2%
s8
9,0%
s10
0,0%
s1
s11
1,6%
0,7%
s2
1,8%
s3
0,0%
s11
0,4%
s9
1,7%
s4
5,0%
s5
5,5%
s1
20,8%
s8
14,8%
s2
4,0%
s6
29,6%
s3
7,4%
s7
35,2%
s4
15,8%
s7
46,5%
s5
0,0%
s6
0,0%
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AOT40 scenarios
source-receptor model simulations
basecase
Scenario A
30
30
ppb*h
65000
5150
60000
25
5150
25
55000
SONDRIO
TRENTO
5100
50000
SONDRIO
TRENTO
5100
20
20
45000
VARESE
VARESE
BERGAMO
BERGAMO
5050
40000
BRESCIA
NOVARA
15
VERONA
MILANO
35000
5050
BRESCIA
NOVARA
15
VERONA
MILANO
30000
5000
5000
PIACENZA
10
25000
MODENA
5
GENOVA
4950
10000
5
10
500
MODENA
GENOVA
4900
450
PARMA
15000
5000
5
ALESSANDRIA
20000
PARMA
4950
400
PIACENZA
10
ALESSANDRIA
15
550
20
600
25
650
30
0
4900
400
5
450
10
500
15
550
20
600
25
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650
30
scenario A: emissions
s2
s3
s4
s5
s6
s7
s8
s9
0%
-10%
2
-20%
-30%
-40%
3
2
NOx
-50%
-60%
control priorities
1
1
VOC
s2
s3
s4
s5
s6
s7
s8
s9
0
3
-20000
-40000
emission reductions
(ton/year)
-60000
2 2
1
-80000
-100000
NOx
-120000
-140000
1
VOC
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