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
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)
DEA - Università degli Studi di Brescia
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;
DEA - Università degli Studi di Brescia
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;
DEA - Università degli Studi di Brescia
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
DEA - Università degli Studi di Brescia
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
DEA - Università degli Studi di Brescia
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
DEA - Università degli Studi di Brescia
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
DEA - Università degli Studi di Brescia
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)
DEA - Università degli Studi di Brescia
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
DEA - Università degli Studi di Brescia
Source-receptor models (NN)
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
DEA - Università degli Studi di Brescia
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
DEA - Università degli Studi di Brescia
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:
DEA - Università degli Studi di Brescia
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
DEA - Università degli Studi di Brescia
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
DEA - Università degli Studi di Brescia
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
DEA - Università degli Studi di Brescia
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
DEA - Università degli Studi di Brescia
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%)
DEA - Università degli Studi di Brescia
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
DEA - Università degli Studi di Brescia
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.
DEA - Università degli Studi di Brescia
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.
DEA - Università degli Studi di Brescia
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
DEA - Università degli Studi di Brescia
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
DEA - Università degli Studi di Brescia
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
DEA - Università degli Studi di Brescia
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
DEA - Università degli Studi di Brescia
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
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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
DEA - Università degli Studi di Brescia
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)