DRAFT REPORT
MCCM PARAMETRIC STUDIES:
Estimation of the NOx, HC and PM10 emission reductions required to
produce a 10% reduction in the Ozone and PM10 surface concentrations
and compliance with the MCMA air quality standards,
with reference to the 2010 MCMA Emission Inventory.
By
Grupo de Modelación de la Comisión Ambiental Metropolitana
Jalapa No. 15, Col Roma, México, D.F., MEXICO
Alejandro Salcido
Instituto de Investigaciones Eléctricas
Francisco Hernández Ortega
Secretaría del Medio Ambiente del D.F.
José Manuel González Gómez
Secretaría del Medio Ambiente del D.F.
Rodolfo Iniestra Gómez
José Andrés Aguilar Gómez
Instituto Nacional de Ecología
Secretaría de Ecología del Estado de México
Enero, 2002
D:\81921350.doc
MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
CONTENTS
1. INTRODUCTION
3
2. OBJECTIVES
4
3. METHODOLOGY
5
3.1. Model updating
5
3.2. Meteorological conditions
5
3.3. Reference emission conditions
5
3.4. Strategy followed for Ozone reductions
5
3.5. Strategy followed for PM10 reductions
7
4. RESULTS
8
4.1. Emission source affectation procedures
8
4.2. Modeling results
14
4.3. Emission reductions for a 10% reduction in Ozone and PM10
35
4.4. Compliance with the MCMA Air Quality Standards
37
5. CONCLUSIONS
41
6. ACNOWLEDGEMENTS
42
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MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
1. INTRODUCTION
Nowadays, air quality modeling is one of the main tools available for
environmental assessment and to support the policy makers in all the
environmental issues. This is one of the reasons why the Metropolitan
Environmental Commission (Comisión Ambiental Metropolitana, CAM)
has integrated a modeling team (the CAM Modeling Team) to support
the formulation of the Air Quality Improvement Program 2001-2010
(PROAIRE III) for the Mexico City Metropolitan Area (MCMA). For this
purpose, the CAM Modeling Team was trained by IFU (Fraunhofer
Institut für Atmosphärische Umweltforschung) on the application of the
Multiscale Climate Chemistry Model (MCCM) in air quality studies.
For the PROAIRE III, in a close collaboration with IFU, the CAM Modeling
Team used the MCCM model to simulate the 1998 and 2010 base line
scenarios, among others. In both cases, the meteorological conditions
were those ones that were prevailing in the period May 3-11, 1998. The
simulation of the 2010 base line scenario was performed by assuming
that the 1998 MCMA emission inventory could be projected to the year
2010 under certain emission increasing tendencies. In Table 1, the main
results for the ozone and PM10 surface concentrations that were
obtained from the MCCM simulations of the 1998 and 2010 scenarios
are presented.
Table 1
Ozone (ppb)
(Daylight Hours)
PM10 (g/m3)
(All Hours)
Year
MIN
MAX
AVG
MFV
MIN
MAX
AVG
MFV
1998
0.172
387.1
150.0
153.0
0.115
473.9
12.48
7.22
2010
0.019
416.7
160.9
172.9
0.116
660.2
15.97
3.42
The figures shown in this table were obtained as follows. For ozone, the
minimum (MIN), maximum (MAX), average (AVG) and most frequent
(MFV) values were found by considering the respective values in all the
cells (excepting the grid border) of the higher spatial resolution MCCM
domain (D3) and all the daylight hours of the simulation period. For
PM10, these values were found as before, but all hours (day and night)
of the simulation period were considered. In both scenarios, 1998 and
2010, the critical day for ozone (i.e. the day of the simulation period
when the maximum concentration was observed) was May 9 (21:00 z),
and the critical day for PM10 was May 4 (13:00 z). As a reference, it is
worth of mention that the MCMA air quality standards for ozone and
PM10 are 110 ppb (1-hr average) and 150 g/m3 (24-hrs average),
respectively.
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MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
As it is observed in Table 1 for the simulation period here considered,
although the PM10 maximum values are higher than the standard value,
the average value, in the worse case, is only the 10% of the standard.
For ozone, however, both concentrations, the maximum and the
average, are higher than the ozone standard: 3.52 times in 1998 and
3.79 times in 2010, in the case of the maximum concentrations; and
1.36 times in 1998 and 1.46 times in 2010, in the case of the average
concentrations. The most frequent values found for ozone concentration
for the 1998 and 2010 scenarios are also higher than the air quality
standard.
All these observations suggested to perform a further MCCM simulation
study in order to investigate the NOx, HC and PM10 emission conditions
that may produce a 10% reduction in the ozone and PM10
concentrations, as well as their compliance with the respective air
quality standards.
In order to find out those emission conditions, the CAM modeling team
suggested that performing a set of MCCM parametric runs it could be
possible to find graphic approximations for the ozone and PM10 surface
concentrations as functions of the NOx, HC and PM10 emissions. In this
way, it could be possible to identify the emission conditions for the
ozone and PM10 desired reductions.
In this report, the results of the parametric studies carried out by the
CAM modeling team are presented and discussed. As a reference, in the
following two sections, the objectives of the studies and the main
aspects of the methodology proposed and followed by the CAM modeling
team to perform the MCCM parametric runs are described.
2. OBJECTIVES
1. Estimation of the NOx and HC emission reductions that, according to
MCCM, will produce a 10% reduction in the maximum ozone
concentration, relative to the respective value found for the 2010
base line scenario.
2. Estimation of the NOx and HC emission reductions that, according to
MCCM, will produce a maximum ozone concentration in compliance
with the ozone air quality standard.
3. Estimation of the PM10 emission reduction that, according to MCCM,
will produce a 10% reduction in the maximum PM10 concentration,
relative to the respective value found for the 2010 base line scenario.
4. Estimation of the PM10 emission reduction that, according to MCCM,
will produce a maximum PM10 concentration in compliance with the
PM10 air quality standard.
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MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
3. METHODOLOGY
In order to avoid a strictly trial and error procedure, the strategy (or
working methodology) proposed and followed by the CAM modeling
team was as comes next:
3.1. Model updating
As it was recommended by IFU, the most recent version of the MCCM
model (received by the CAM modeling team by September 18 th, 2001)
was installed, compiled and tested in the workstations Aguila and Edison
that the Mexico City Environmental Ministry made available for the
parametric studies.
3.2. Meteorological Conditions
For the purposes of the MCCM parametric studies, it was assumed valid
the same meteorological conditions that were prevailing in the period
May 3-11, 1998. This meteorology was used for all the reduction
scenarios considered in this study.
3.3. Reference Emission Conditions
The simulation of the 2010 base line scenario was performed by
assuming that the 1998 MCMA emission inventory could be projected to
the year 2010 under certain emission increasing tendencies. This
projection was carried out by the Emission Inventories Group of the
Mexico City Environmental Ministry. The 2010 Emission Inventory was
used as reference in preparing the reduction scenarios here considered.
3.4. Strategy followed for ozone reductions
3.4.1. A maximum incremental reactivity (MIR) was assigned to each
one of the species involved in the HC emissions, such as they are
considered in the emission inventory projected for the 2010 base
line scenario. [W. P. L. Carter, 1994. Journal of the Air and Waste
Management Association, Vol. 44, pp. 881-899]
3.4.2. It was assumed that none of the NOx and HC emission reductions
could exceed the 40% in searching the 10% reduction in ozone.
This assumption was supported by previous simulation results
which produced an ozone reduction around the 5% when, in the
2010 emission inventory, it was imposed a reduction of the
mobile source emissions of 20% in NOx and 10% in HC.
However, it was pointed out that further reductions would be
probably necessary to find out the compliance with the ozone
standard. It was assumed also that the NOx and HC reductions
might be considered independently.
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MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
3.4.3. The NOx emission reduction was applied to each particular source
(particular vehicles, taxis, industrial sources, etc.) according to
the following formula:
NOx (new)  NOx (2010)  (1  r )
where r is the emission reduction fraction defined as
r  1
New NOx Total Emission
2010 NOx Total Emission
3.4.4. Although the HC emission reductions could be applied to each
one of the emission sources according to a similar reduction
equation, the CAM modeling team suggested that the formula
HC (new)  HC (2010)  w  r  HCT
could be more convenient in order to privilege the sources with
high emissions of the most reactive HC species. In this formula,
HCT is the total emission of HC (including all sources), r is the
emission reduction fraction, and w is a weight factor defined in
terms of the HC emission profile of the source and the maximum
incremental reactivity values (MIRE) of the HC species emitted by
this particular source:
w
RE S
RE T
with
RE T   RE S
S
RE S  HCS   pSE  MIRE
E
Here, pSE is the emission fraction (in weight) of the species E as
emitted by the source S, and HCS is the total HC emission by the
source S. As in the NOx reduction case, the HC emission
reduction fraction r was defined as
r  1
New HC Total Emission
2010 HC Total Emission
As it will be discussed later, this procedure demanded an
emission reduction fraction less than 0.25, and some corrections
were necessary to consider larger HC reductions.
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MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
3.4.5. Once concluded the MCCM runs, an interpolation procedure was
used to identify, graphically, the ozone iso-concentration curves
of interest (in particular, that one that corresponds to the ozone
reduction of 10%) in the NOx-HC variable space.
3.5. Strategy followed for PM10 reductions
Taking into account that the CAM modeling team has not any
documentation about the chemical processes included in MCCM to model
PM10 production, it was assumed that:

Only the PM10 emissions (by the industrial and mobile sources, as
described in the 2010 emission inventory) are responsible of the
atmospheric PM10 concentrations predicted by MCCM.

PM10 emission reductions produce no other effects than reductions
in the atmospheric PM10 concentration, as predicted by the model.
Under these hypotheses, the strategy followed for PM10 reduction was
as comes next:
3.5.1. The same MCCM runs programmed for ozone reduction (see
above) were used to find out the desired PM10 concentration
reductions.
3.5.2. The PM10 emission reductions were applied to each one of the
individual industrial and mobile sources according to the following
formula:
PM 10(new)  PM 10(2010)  (1  r )
where r is the PM10 emission reduction fraction, defined in a
similar way as before. A maximum PM10 emission reduction of
60% (r = 0.60) was considered.
3.5.3. Once concluded the MCCM runs, a linear best fitting procedure
was applied to find the PM10 concentration as function of the
PM10 emissions. This “empirical” relation was used to identify the
PM10 emission conditions that could produce the 10% reduction
in PM10 and its compliance with the respective air quality
standard.
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MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
4. RESULTS
This section describes in some detail the particularities of the emission
source affectation procedures and the results obtained from the MCCM
parametric runs.
4.1. Emission source affectation procedures
In practice, 25 emission reduction scenarios were considered in this
study, which were defined by the NOx, HC and PM10 emission reduction
fractions described in Table 1.
Table 1. Emission Reduction Fractions
Scenario
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Emission Reduction Fractions
NOx
HC
PM10
0.000
0.000
0.000
0.100
0.023
0.030
0.200
0.023
0.060
0.300
0.023
0.090
0.400
0.023
0.120
0.100
0.046
0.150
0.200
0.046
0.180
0.300
0.046
0.210
0.400
0.046
0.240
0.100
0.069
0.270
0.200
0.069
0.300
0.300
0.069
0.330
0.400
0.069
0.360
0.100
0.092
0.390
0.200
0.092
0.420
0.300
0.092
0.450
0.400
0.092
0.480
0.600
0.139
0.570
0.800
0.185
0.600
0.100
0.200
0.150
0.300
0.200
0.210
0.200
0.300
0.300
0.400
0.300
0.360
0.100
0.400
0.390
0.300
0.400
0.450
0.400
0.400
0.480
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MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
Each one of the lines in Table 1 represents one MCCM simulation run.
The first line corresponds to the 2010 base line scenario (no emission
reductions). As a reference, in Table 2 it is shown the area, mobile and
point sources total emissions of NOx, HC and PM10, as they were
projected to the year 2010.
As it was explained in the methodology section, the emission reduction
fractions indicated in Table 1 (rNOx, rHC and rPM10) were defined as
r = 1 – (New Total Emission / 2010 Total Emission)
The maximum emission reduction fractions used in the parametric runs
were: rNOx = 0.8 (80% reduction in NOx. Scenario 18), rHC = 0.4 (40%
reduction in HC. Scenarios 23-25) and rPM10 = 0.6 (60% reduction in
PM10. Scenario 18). This means that the minimum NOx, HC and PM10
total emissions used in defining the parametric scenarios were: 20% of
the NOx, 60% of the HC, and 40% of the PM10 total emissions that
were considered in the 2010 reference scenario.
As an example, it is shown in Table 3 a detailed description of the
emission reductions (expressed in Ton/Year) that were used in the
scenario 16 (see Table 1). In this case, the emission reduction fractions
were rNOx = 0.4 (40% reduction in NOx), rHC = 0.092 (9.2% reduction in
HC) and rPM10 = 0.48 (48% reduction in PM10). As a consequence, the
total emissions in this scenario were HC=507,153.79, NOx=163,525.36,
and PM10=6,891.57, all expressed in Ton/Year.
As it was also detailed in the methodology presentation, the NOx and
PM10 emission reductions were applied to each particular source
accordingly to the formulas
NOx(new)  NOx(2010)  (1  rNOx )
PM10(new)  PM10(2010)  (1  rPM 10 )
In this sense, for each one of these pollutants, all sources (area, line
and point sources) were affected equally (i.e. with exactly the same
reduction percent), such as it can be inferred from Table 3 in the
particular case of the scenario 16. This source affectation procedure was
extended up to the temporal and spatial splitting of the emissions, such
as it is required in the emission input-files of the MCCM (Smiatek’s)
emission preprocessors.
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Grupo de Modelación de la Comisión Ambiental Metropolitana
Table 2. 2010 Emission Inventory: Area, Line and Point sources.
Source Type
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
LINE
LINE
LINE
LINE
LINE
LINE
LINE
LINE
LINE
LINE
LINE
LINE
POINT
POINT
POINT
POINT
POINT
POINT
POINT
POINT
POINT
POINT
POINT
Giro ID
GA01
GA02
GA03
GA04
GA05
GA06
GA07
GA08
GA09
GA10
GA11
GA12
GA13
GA15
GA16
GA19
GA20
GA21
GA23
GA24
GA25
GM01
GM02
GM03
GM04
GM05
GM06
GM07
GM08
GM09
GM10
GM11
GM12
GP01
GP02
GP03
GP04
GP05
GP06
GP07
GP08
GP09
GP10
GP11
Emission Source Name
Artes gráficas
Consumo de solventes
Lavado en seco
Limpieza de superficies
Pintura automotriz
Pintura de tránsito
Recubrimiento de superficies arquitectónicas
Recubrimiento de superficies industriales
Panaderías
Fugas de GLP en uso doméstico
Distribución de GLP
Distribución y venta de gasolina
Almacenamiento masivo de gasolina
Operación de aeronaves
Recarga de aeronaves
Combustión comercial/institucional
Combustión habitacional
Combustión en hospitales
Tratamiento de aguas residuales
Aplicación de asfalto
Rellenos sanitarios
Autos particulares
Taxis
Combis
Microbuses
Pick up
Camiones de carga a gasolina
Motocicletas
Vehículos a diesel < 3 toneladas
Tractocamiones a diesel
Autobuses a diesel
Vehículos a diesel > 3 toneladas
Camiones de carga a gas LP
Generación de energía eléctrica
Productos de vida media
Productos metálicos
Madera y derivados
Productos de vida larga
Industria química
Industria de consumo alimenticio
Mineral no metálica
Productos de impresión
Industria del vestido
Combustión industrial/institucional
TOTAL EMISSIONS
TOTAL EMISSIONS AS PROJECTED TO
THE YEAR 2010
(Ton/año)
HC
NOx
PM10
7,494.11
85,810.37
11,253.87
33,760.67
2,435.39
899.57
25,479.60
23,981.92
2,913.07
56,078.62
22,957.03
712.20
158.00
448.86
1,736.00
5.37
158.41
3,439.02
915.92
290.91
7,222.70
230.42
2.94
73.35
231.01
14,920.29
115,230.55
66,307.00
1,278.00
13,209.95
10,346.00
202.00
926.00
442.00
5.00
13,851.95
6,676.00
41.00
35,072.86
27,034.00
261.00
26,637.90
21,810.00
120.00
4,741.98
215.00
22.00
238.99
214.00
190.00
10,816.56
32,334.00
2,839.00
5,065.80
15,300.00
1,543.00
13,122.47
39,440.00
3,652.00
212.84
308.00
16.00
75.43
14,670.04
212.95
1,302.77
549.05
32.03
3,850.11
7,604.88
179.08
886.62
1,358.78
170.61
3,391.90
3,404.53
128.49
13,540.10
1,908.09
240.95
572.86
904.56
438.04
1,310.50
7,502.89
355.12
4,630.37
179.80
8.12
70.05
1,556.12
165.31
1.03
79.80
6.98
558,825.15
10
272,542.26
13,253.02
MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
Table 3. HC, NOx and PM10 reductions imposed in the scenario 16.
Source Type
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
LINE
LINE
LINE
LINE
LINE
LINE
LINE
LINE
LINE
LINE
LINE
LINE
POINT
POINT
POINT
POINT
POINT
POINT
POINT
POINT
POINT
POINT
POINT
Giro ID
GA01
GA02
GA03
GA04
GA05
GA06
GA07
GA08
GA09
GA10
GA11
GA12
GA13
GA15
GA16
GA19
GA20
GA21
GA23
GA24
GA25
GM01
GM02
GM03
GM04
GM05
GM06
GM07
GM08
GM09
GM10
GM11
GM12
GP01
GP02
GP03
GP04
GP05
GP06
GP07
GP08
GP09
GP10
GP11
Emission Source
Artes gráficas
Consumo de solventes
Lavado en seco
Limpieza de superficies
Pintura automotriz
Pintura de tránsito
Recubrimiento de superficies arquitectónicas
Recubrimiento de superficies industriales
Panaderías
Fugas de GLP en uso doméstico
Distribución de GLP
Distribución y venta de gasolina
Almacenamiento masivo de gasolina
Operación de aeronaves
Recarga de aeronaves
Combustión comercial/institucional
Combustión habitacional
Combustión en hospitales
Tratamiento de aguas residuales
Aplicación de asfalto
Rellenos sanitarios
Autos particulares
Taxis
Combis
Microbuses
Pick up
Camiones de carga a gasolina
Motocicletas
Vehículos a diesel < 3 toneladas
Tractocamiones a diesel
Autobuses a diesel
Vehículos a diesel > 3 toneladas
Camiones de carga a gas LP
Generación de energía eléctrica
Productos de vida media
Productos metálicos
Madera y derivados
Productos de vida larga
Industria química
Industria de consumo alimenticio
Mineral no metálica
Productos de impresión
Industria del vestido
Combustión industrial/institucional
SCENARIO: 16
Emission Reductions (Ton/Year)
HC
NOx
PM10
53.77
0.00
0.00
2,253.20
0.00
0.00
56.79
0.00
0.00
446.65
0.00
0.00
7.68
0.00
0.00
0.27
0.00
0.00
234.56
0.00
0.00
296.26
0.00
0.00
3.43
0.00
0.00
850.93
0.00
0.00
142.60
0.00
0.00
0.26
0.00
0.00
0.01
0.00
0.00
0.21
694.40
0.00
0.00
0.00
0.00
0.01
1,375.61
439.64
0.06
2,889.08
110.60
0.00
0.00
0.00
0.00
0.00
0.00
0.06
0.00
0.00
377.76
0.00
0.00
25,217.44
26,522.80
613.44
2,890.91
4,138.40
96.96
202.65
176.80
2.40
3,031.41
2,670.40
19.68
7,675.46
10,813.60
125.28
5,829.53
8,724.00
57.60
1,037.75
86.00
10.56
7.10
85.60
91.20
321.38
12,933.60
1,362.72
150.51
6,120.00
740.64
389.89
15,776.00
1,752.96
0.02
123.20
7.68
0.00
5,868.02
102.22
4.45
219.62
15.37
15.99
3,041.95
85.96
0.73
543.51
81.89
5.97
1,361.81
61.68
149.79
763.24
115.66
0.13
361.82
210.26
0.73
3,001.16
170.46
15.00
71.92
3.90
0.00
622.45
79.35
0.00
31.92
3.35
TOTAL REDUCTIONS
51,671.36
11
109,016.90
6,361.45
MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
In the case of the HC emissions, the reductions were applied to each
particular source using the formula
HC (new)  HC (2010)  w  rHC  HCT
Here, the effect of the HC species reactivity is taken into account
through the weight factor w (see the methodology section), privileging
the affectation to those sources that emit the most reactive HC species.
In practice, this source affectation procedure worked out quite well for
relative small emission reduction fractions (rHC < 0.25). However, some
corrections were necessary for larger emission reduction fractions in
order to avoid the non-physical HC emission values that may appear
when the affectation formula, for a particular source, demands a HC
reduction in excess. Although this problem could be solved by imposing
a limiting constraint on the affectation formula and a secondary
redistribution of the remaining reduction, in considering emission
reduction fractions larger than 0.25, it was preferred to apply an
affectation formula similar to the one used for NOx and PM10. This
resulted in a hybrid source affectation procedure for HC emissions.
HC (2010)  w  rHC  HCT
HC (new)  
HC (2010)  (1  rHC )
if rHC  0.25
if rHC  0.25
The emission reductions shown in Table 3 are an example where
rHC<0.25. On the other side, in Table 4, it is shown a detailed
description of the emission reductions used to prepare the scenario 25,
where the emission reduction fractions were rNOx = 0.4, rHC = 0.4 and
rPM10 = 0.48. In this case, the total emissions were HC = 335,295.09,
NOx = 163,525.36, and PM10 = 6,891.57, all expressed in Ton/Year.
Finally, it is convenient to highlight that the hybrid character of the
affectation formula is quite irrelevant for the purposes of this work. As it
can be inferred from the last equation, for a given rHC (< 0.25), both
affectation procedures produce the same total HC reductions (i.e. the
same result when adding the respective HC reductions of all sources).
So, the effects of the hybrid character of the affectation procedure will
be reflected chiefly in the spatial distribution of the ozone concentration,
which will be considered here only marginally.
12
MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
Table 4. HC, NOx and PM10 reductions imposed in the scenario 25.
Source Type
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
AREA
LINE
LINE
LINE
LINE
LINE
LINE
LINE
LINE
LINE
LINE
LINE
LINE
POINT
POINT
POINT
POINT
POINT
POINT
POINT
POINT
POINT
POINT
POINT
Giro ID
GA01
GA02
GA03
GA04
GA05
GA06
GA07
GA08
GA09
GA10
GA11
GA12
GA13
GA15
GA16
GA19
GA20
GA21
GA23
GA24
GA25
GM01
GM02
GM03
GM04
GM05
GM06
GM07
GM08
GM09
GM10
GM11
GM12
GP01
GP02
GP03
GP04
GP05
GP06
GP07
GP08
GP09
GP10
GP11
Emission Source
Artes gráficas
Consumo de solventes
Lavado en seco
Limpieza de superficies
Pintura automotriz
Pintura de tránsito
Recubrimiento de superficies arquitectónicas
Recubrimiento de superficies industriales
Panaderías
Fugas de GLP en uso doméstico
Distribución de GLP
Distribución y venta de gasolina
Almacenamiento masivo de gasolina
Operación de aeronaves
Recarga de aeronaves
Combustión comercial/institucional
Combustión habitacional
Combustión en hospitales
Tratamiento de aguas residuales
Aplicación de asfalto
Rellenos sanitarios
Autos particulares
Taxis
Combis
Microbuses
Pick up
Camiones de carga a gasolina
Motocicletas
Vehículos a diesel < 3 toneladas
Tractocamiones a diesel
Autobuses a diesel
Vehículos a diesel > 3 toneladas
Camiones de carga a gas LP
Generación de energía eléctrica
Productos de vida media
Productos metálicos
Madera y derivados
Productos de vida larga
Industria química
Industria de consumo alimenticio
Mineral no metálica
Productos de impresión
Industria del vestido
Combustión industrial/institucional
TOTAL REDUCTIONS
SCENARIO: 25
Emission Reductions (Ton/Year)
HC
NOx
PM10
2,997.64
0.00
0.00
34,324.15
0.00
0.00
4,501.55
0.00
0.00
13,504.27
0.00
0.00
974.16
0.00
0.00
359.83
0.00
0.00
10,191.84
0.00
0.00
9,592.77
0.00
0.00
1,165.23
0.00
0.00
22,431.45
0.00
0.00
9,182.81
0.00
0.00
284.88
0.00
0.00
63.20
0.00
0.00
179.55
694.40
0.00
2.15
0.00
0.00
63.37
1,375.61
439.64
116.36
2,889.08
110.60
1.17
0.00
0.00
29.34
0.00
0.00
92.40
0.00
0.00
5,968.12
0.00
0.00
46,092.22
26,522.80
613.44
5,283.98
4,138.40
96.96
370.40
176.80
2.40
5,540.78
2,670.40
19.68
14,029.14
10,813.60
125.28
10,655.16
8,724.00
57.60
1,896.79
86.00
10.56
95.60
85.60
91.20
4,326.62
12,933.60
1,362.72
2,026.32
6,120.00
740.64
5,248.99
15,776.00
1,752.96
85.14
123.20
7.68
30.17
5,868.02
102.22
521.11
219.62
15.37
1,540.04
3,041.95
85.96
354.65
543.51
81.89
1,356.76
1,361.81
61.68
5,416.04
763.24
115.66
229.14
361.82
210.26
524.20
3,001.16
170.46
1,852.15
71.92
3.90
28.02
622.45
79.35
0.41
31.92
3.35
223,530.06
13
109,016.90
6,361.45
MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
4.2. Modeling Results
Although the simulation period was from May 3 (12:00 z) to May 11
(12:00 z), the analysis of the MCCM outputs for Ozone and PM10 was
carried out only within the period (from now on referred as the analysis
period) from May 4 (00:00 z) to May 10 (23:00 z). This was done in
order to reduce the effect of the initial conditions on the analysis results.
The main software tools used in this study to analyze the MCCM output
files were:

The program MMView, developed by A. Salcido (IIE/SMA-GDF) in
collaboration with the CAM modeling team. This is an MS-Windows
application developed to extract the modeling data (time series,
vertical profiles, fields, etc.) directly from the MCCM output files. See
Figure 1.

The program MCCMAna, developed by A. Salcido (IIE/SMA-GDF) in
collaboration with the CAM modeling team. This is an MS-Windows
application developed to perform the statistical analysis of the time
series of the MCCM field variables, as organized in data files by the
MMView program.

The Grapher and Surfer software applications developed by Golden
Software, Inc.
Figure 1. Main window of the MMView program.
14
MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
The MCCM outputs are organized in binary files with a very special but
practical format inherited from MM5. The data extracting procedure is,
in principle, very simple, but it can not be done without a practical
software application able to organize the data properly, as the user
requires it. Although IFU provided to the CAM modeling team with a
very simple tool to do this task, in practice, it is only useful on the
workstation UNIX environment. The program MMView was developed
by the CAM modeling team to overcome this lacking when using PC’s
(running MS-Windows as operating system) to analyze the MCCM
outputs in routine air quality studies. Similar reasons led the CAM
modeling team to the development of the MCCMAna program (name
coming from MCCM Data Analysis).
Using the MMView program, for all the MCCM D3-cells in the level K=24,
it was extracted the time series (hourly surface concentrations during
the simulation period) of the field variables O3, PM10 and SULF (Ozone,
PM10, and Sulfate chemically formed). The data files produced by
MMView were processed using the program MCCMAna to find out the
Minimum, Maximum, Average, and Most Frequent values, as it is
described in the next paragraphs. The spatial domain that was
considered for the data analysis (from now on referred as the analysis
domain) was all the MCCM D3 domain excepting the border cells.
Ozone:
For ozone, two cases were considered: In the first one, the analysis was
performed taking into account all the hours (day and night) of the
analysis period (May 4-10) above indicated. In the second one, the
analysis took into account only the sunlight hours (14:00 z to 23:00 z)
of the analysis period, for which the photochemical processes take
place. In this sense, the Minimum, Maximum, Average, and Most
Frequent values here reported were defined as follows:
O3Min: This is the smallest value of the ozone surface concentration
that was found among all the hourly concentration values in all the cells
of the analysis domain.
O3Max: This is the largest value of the ozone surface concentration that
was found among all the hourly concentration values in all the cells of
the analysis domain.
O3Avg: This is the average value of the ozone surface concentration
calculated over all the hourly concentration values in all the cells of the
analysis domain.
O3MFV: This is the ozone surface concentration value occurring with
the highest frequency among all the hourly concentration values in all
the cells of the analysis domain.
15
MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
It must be observed that, excepting for the minimum, two values were
reported for each one of these parameters: One for the case of all
hours, and other one for the case of the sunlight hours of the analysis
period. For obvious reasons, the minimum value was reported only for
the case of the sunlight hours.
PM10:
For PM10, as Dr. Forkel indicated it in the documentation lines of the
readd.f program code, the values of the PM10 and SULF field variables
were added first cell by cell, to obtain the total concentration of PM10
for each cell:
Total PM10 = PM10 + SULF*1000*3.7 [in g/m3]
So, the chemically formed sulfate was included in the PM10 surface
concentrations that were considered for the analysis. However, it is
worth of mention that the sulfate contribution to PM10 was found very
small in all the cases, affecting, in general, only the decimal figures of
the PM10 values. The analysis of PM10 was performed taking into
account only all the hours (day and night) of the analysis period. The
Maximum, Average, and Most Frequent values here reported for PM10
were defined similarly as in the ozone case, but only the case of all
hours of the analysis period was considered.
In figures 1-28, it is shown a set of plots with the results of the data
analysis. This set of plots includes:

The frequency distribution of the Ozone surface concentration for the
2010 base line scenario. It was made taking into account the ozone
hourly concentration values in the entire analysis domain, for the
cases: (a) sunlight hours, and (b) all hours (day and night), of the
analysis period. (Figs. 1a and 1b.)

The frequency distribution of the PM10 surface concentration for the
2010 base line scenario. It was made taking into account the PM10
concentration values in the entire analysis domain for all hours (day
and night) of the analysis period. (Fig. 2)

The Maximum Value of the Ozone Surface Concentration as function
of the NOx and HC emission reduction fractions. (Interpolation
Methods: Kriging and Polynomial Regression. Figs. 3a and 3b.) These
plots are the same for both cases: all hours and sunlight hours.

The Reduction Fraction of the Maximum Value of the Ozone Surface
Concentration as function of the NOx and HC emission reduction
fractions.
(Interpolation
Methods:
Kriging
and
Polynomial
16
MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
Regression. Figs. 4a and 4b.) These plots are the same for both
cases: all hours and sunlight hours.

The Average Value of the Ozone Surface Concentration as function of
the NOx and HC emission reduction fractions. (Interpolation
Methods: Kriging and Polynomial Regression.) Case: All Hours: Figs.
5a and 5b. Case: Sunlight Hours: Figs. 6a and 6b.

The Reduction Fraction of the Average Value of the Ozone Surface
Concentration as function of the NOx and HC emission reduction
fractions.
(Interpolation
Methods:
Kriging
and
Polynomial
Regression.) Case: All Hours: Figs. 7a and 7b. Case: Sunlight Hours:
Figs. 8a and 8b.

The Most Frequent Value of the Ozone Surface Concentration as
function of the NOx and HC emission reduction fractions.
(Interpolation Methods: Kriging and Polynomial Regression.) Case:
All Hours: Figs. 9a and 9b. Case: Sunlight Hours: Figs. 10a and 10b.

The Reduction Fraction of the Most Frequent Value of the Ozone
Surface Concentration as function of the NOx and HC emission
reduction fractions. (Interpolation Methods: Kriging and Polynomial
Regression.) Case: All Hours: Figs. 11a and 11b. Case: Sunlight
Hours: Figs. 12a and 12b.

The Maximum Value of the PM10 Surface Concentration as function
of the PM10 emission reduction fraction. (Fig. 13)

The Reduction Fraction of the Maximum Value of the PM10 Surface
Concentration as function of the PM10 emission reduction fraction.
(Fig 14)

The Average Value of the PM10 Surface Concentration as function of
the PM10 emission reduction fraction. (Fig. 15)

The Reduction Fraction of the Average Value of the PM10 Surface
Concentration as function of the PM10 emission reduction fraction.
(Fig 16)
In these plots, when it is the case, the reduction fraction of the
maximum, average and most frequent values of the ozone and PM10
surface concentrations was defined similarly as it was indicated
previously for the emissions:
r = 1 – (Actual Concentration Value / 2010 Concentration Value)
17
MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
3
Frequency [%]
2
1
0
0
0.1
0.2
0.3
0.4
Ozone Surface Concentration [ppm]
Figure 1a
Frequency distribution of the Ozone surface concentration for the 2010 base
line scenario. Data: Entire analysis domain, sunlight hours of the analysis
period.
18
0.5
MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
16
Frequency [%]
12
8
4
0
0.001
0.01
0.1
Ozone Surface Concentration [ppm]
Figure 1b
Frequency distribution of the Ozone surface concentration for the 2010 base
line scenario. Data: Entire analysis domain, all hours (day and night) of the
analysis period.
19
1
MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
60
Frequency [%]
40
20
0
1
10
100
PM10 Surface Concentration [ug/m3]
Figure 2
Frequency distribution of the PM10 surface concentration for the 2010 base
line scenario. Data: Entire analysis domain, all hours (day and night) of the
analysis period.
20
1000
MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
(a)
HC Reduction Fraction
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.6
0.7
0.8
NOx Reduction Fraction
(b)
HC Reduction Fraction
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
NOx Reduction Fraction
Figure 3
Maximum Values of the Ozone Surface Concentration (ppm).
Interpolation Method: (a) Kriging, (b) Polynomial Regression.
21
MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
(a)
HC Reduction Fraction
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.6
0.7
0.8
NOx Reduction Fraction
(b)
HC Reduction Fraction
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
NOx Reduction Fraction
Figure 4
Reduction Fractions of the Maximum Value of Ozone Surface Concentration.
Interpolation Method: (a) Kriging, (b) Polynomial Regression.
22
MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
(a)
HC Reduction Fraction
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.6
0.7
0.8
NOx Reduction Fraction
(b)
HC Reduction Fraction
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
NOx Reduction Fraction
Figure 5
Average Values of the Ozone Surface Concentration (ppm).
Case: All hours included (day and night).
Interpolation Method: (a) Kriging, (b) Polynomial Regression.
23
MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
(a)
HC Reduction Fraction
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.6
0.7
0.8
NOx Reduction Fraction
(b)
HC Reduction Fraction
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
NOx Reduction Fraction
Figure 6
Average Values of the Ozone Surface Concentration (ppm).
Case: Sunlight hours.
Interpolation Method: (a) Kriging, (b) Polynomial Regression.
24
MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
(a)
HC Reduction Fraction
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.6
0.7
0.8
NOx Reduction Fraction
(b)
HC Reduction Fraction
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
NOx Reduction Fraction
Figure 7
Reduction Fractions of the Average Value of Ozone Surface Concentration.
Case: All hours included (day and night).
Interpolation Method: (a) Kriging, (b) Polynomial Regression.
25
MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
(a)
HC Reduction Fraction
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.6
0.7
0.8
NOx Reduction Fraction
(b)
HC Reduction Fraction
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
NOx Reduction Fraction
Figure 8
Reduction Fractions of the Average Value of Ozone Surface Concentration.
Case: Sunlight hours.
Interpolation Method: (a) Kriging, (b) Polynomial Regression.
26
MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
(a)
HC Reduction Fraction
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.6
0.7
0.8
NOx Reduction Fraction
(b)
HC Reduction Fraction
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
NOx Reduction Fraction
Figure 9
Most Frequent Values of the Ozone Surface Concentration (ppm).
Case: All hours included (day and night).
Interpolation Method: (a) Kriging, (b) Polynomial Regression.
27
MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
(a)
HC Reduction Fraction
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.6
0.7
0.8
NOx Reduction Fraction
(b)
HC Reduction Fraction
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
NOx Reduction Fraction
Figure 10
Most Frequent Values of the Ozone Surface Concentration (ppm).
Case: Sunlight hours.
Interpolation Method: (a) Kriging, (b) Polynomial Regression.
28
MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
(a)
HC Reduction Fraction
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.6
0.7
0.8
NOx Reduction Fraction
(b)
HC Reduction Fraction
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
NOx Reduction Fraction
Figure 11
Reduction Fractions of the Most Frequent Value of Ozone Surface
Concentration. Case: All hours included (day and night).
Interpolation Method: (a) Kriging, (b) Polynomial Regression.
29
MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
(a)
HC Reduction Fraction
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.6
0.7
0.8
NOx Reduction Fraction
(b)
HC Reduction Fraction
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
NOx Reduction Fraction
Figure 12
Reduction Fractions of the Most Frequent Value of Ozone Surface
Concentration. Case: Sunlight hours.
Interpolation Method: (a) Kriging, (b) Polynomial Regression.
30
MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
Maximum Value of PM10 Surface Concentration [ug/m3]
as a Function of the PM10 Emission Reduction Fraction.
Analysis Period: May 4-10. Hours: All.
Interpolation Method: Linear Regression
Maximum Value of PM10 Surface Concentration [ug/m3]
700
Linear Fit Results
Equation Y = -631.227402 * X + 600.8642091
Number of data points used = 12
Average X = 0.305
Average Y = 408.34
Residual sum of squares = 4960.07
Regression sum of squares = 181334
Coef of determination, R-squared = 0.973375
Residual mean square, sigma-hat-sq'd = 496.007
600
500
400
300
200
0
0.2
0.4
PM10 Emission Reduction Fraction
Figure 13
Maximum Value of PM10 Surface Concentration [ug/m3].
Case: All Hours Included (Day and Night).
31
0.6
MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
Reduction Fraction of the Maximum Value of PM10 Surface Concentration
as a Function of the PM10 Emission Reduction Fraction.
Reduction Fraction of the Maximum Value of PM10 Surface ConcentrationPM10
Analysis Period: May 4-10. Hours: All.
Interpolation Method: Linear Regression
Fit Results
0.8
Fit 2: Through origin
Equation Y = 1.165435561 * X
Number of data points used = 12
Average X = 0.305
Average Y = 0.381484
Residual sum of squares = 0.039447
Coef of determination, R-squared = 0.981853
Residual mean square, sigma-hat-sq'd = 0.00358609
0.6
0.4
0.2
0
0
0.2
0.4
0.6
PM10 Emission Reduction Fraction
Figure 14
Reduction Fraction of the Maximum Value of PM10 Surface Concentration.
Case: All Hours Included (Day and Night).
32
MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
Average Value of PM10 Surface Concentration [ug/m3]
as a Function of the PM10 Emission Reduction Fraction.
Analysis Period: May 4-10. Hours: All.
Interpolation Method: Linear Regression
16
Average Value of PM10 Surface Concentration [ug/m3]
Fit Results
Fit 1: Linear
Equation Y = -10.44397846 * X + 14.04461849
Number of data points used = 12
Average X = 0.305
Average Y = 10.8592
Residual sum of squares = 5.2424
Regression sum of squares = 49.6408
Coef of determination, R-squared = 0.904481
Residual mean square, sigma-hat-sq'd = 0.52424
14
12
10
8
0
0.2
0.4
PM10 Emission Reduction Fraction
Figure 15
Average Value of PM10 Surface Concentration [ug/m3]
Case: All Hours Included (Day and Night).
33
0.6
MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
Reduction Fraction of the Average Value of PM10 Surface Concentration
as a Function of the PM10 Emission Reduction Fraction.
Analysis Period: May 4-10. Hours: All.
Interpolation Method: Linear Regression
Reduction Fraction of the Average Value of PM10 Surface Concentration
0.5
0.4
0.3
Fit Results
0.2
Fit 2: Through origin
Equation Y = 0.9351154084 * X
Number of data points used = 12
Average X = 0.305
Average Y = 0.320188
Residual sum of squares = 0.0712378
Coef of determination, R-squared = 0.950712
Residual mean square, sigma-hat-sq'd = 0.00647617
0.1
0
0
0.2
0.4
0.6
PM10 Emission Reduction Fraction
Figure 16
Reduction Fraction of the Average Value of PM10 Surface Concentration.
Case: All Hours Included (Day and Night).
34
MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
4.3. Emission reductions for a 10% reduction in Ozone and PM10
Using the plots included in the previous section, it is relatively easy to
find out the NOx, HC and PM10 emission reductions required to produce
a 10% reduction in Ozone and PM10 surface concentrations. This,
however, can be done in several non-equivalent ways: It is not the
same, for example, to look for a 10% reduction in the ozone maximum
concentration than in the average concentration or in the most frequent
concentration. The results will be quite different in general, and also its
interpretation. So, the selection of the proper parameter is very
dependent on what is what one wants to measure, evaluate or highlight.
From the point of view of air quality assessment, the average and most
frequent concentrations are quite important, and the estimation of the
10% reduction in Ozone and PM10 will be expressed, in this work, in
terms of them.
4.3.1. The 10% Reduction in Ozone
In the case of ozone, as it can be observed in Figure 1a, if we focus on
the sunlight hours of the simulation period, the frequency distribution of
the surface concentration values is approximately normal (gaussian),
and, consequently, the average and the most frequent values will be
quite similar. This is not the case if all hours (day and night) are taken
into account for the analysis, as it is shown in Figure 1b.
Case 1: Average Concentration. All Hours.
In this case, using Figure 7, it is easy to identify that a 10% reduction in
the average value of ozone surface concentration will be obtained for all
those NOx and HC reduction fractions defined (approximately) for the
following equation:
rNOx  0.33  0.075  rHC
with 0 < rHC < 0.4. Moreover, as it can be observed in Figures 4 and 11,
in this case the maximum and most frequent values of the ozone
surface concentration will have reductions larger than 10%.
Case 2: Average Concentration. Sunlight Hours.
Now, using Figure 8, it is observed that a 10% reduction in the average
value of ozone surface concentration will be obtained for all those NOx
and HC reduction fractions that obey (approximately) the equation:
rNOx  0.29  0.15  rHC
with 0 < rHC < 0.4. As it is observed in Figures 4 and 12, the maximum
and most frequent values of the ozone surface concentration will have,
again, reductions larger than 10%.
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MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
Case 3: Most Frequent Concentration. All Hours.
In this case, due to the fact that the frequency distribution includes the
nocturnal hours, the most frequent values of the ozone surface
concentration will be very small (around 1 ppb), as it is shown in Figure
9. In this case, of course, the most frequent concentration is not a good
enough parameter for ozone assessment.
Case 4: Most Frequent Concentration. Sunlight Hours.
Using Figure 12, it can be seen that the 10% reduction in the most
frequent value of ozone surface concentration will be obtained for all the
NOx and HC reduction fractions that satisfy the equation:
rNOx  0.1  0.2  rHC
with 0 < rHC < 0.4. In this case, however, as it can be observed in
Figures 4 and 8, the reductions of the maximum and average ozone
concentrations will be (approximately) 7% and 5 %, respectively.
4.3.2. The 10% Reduction in PM10
The PM10 surface concentrations presented in this report include, as it
was already mentioned in section 4.2, the contribution of the sulfate
chemically formed. This means that the PM10 concentrations estimated
by MCCM depend not only on the PM10 emissions, but also on other
chemical species that could be emitted to the atmosphere. However, on
one hand, due to the lack of the model documentation, it is not
sufficiently clear for the CAM modeling team whether the NOx and/or HC
emissions play a relevant role in the aerosol formation processes
(particularly for PM10) that MCCM includes on its formulation. On the
other hand, as it has been observed in the MCCM outputs, the
contribution of sulfate chemically formed to the PM10 concentrations is
very small. These last observations led the CAM modeling team in
assuming that, for the purpose of the parametric studies, the PM10
concentrations estimated by MCCM could be represented as a function
depending only on the PM10 emissions.
This hypothesis has been widely confirmed by the modeling results
shown in Figures 14 and 16. Although a not clear behavior was found for
very small reduction fractions, the linear fitting (through origin) of the
reduction fraction of PM10 concentration (Maximum and Average) as
function of the reduction fraction of the PM10 emission resulted with a
slope very close to unit.
As it is observed in Figure 16, the linear fitting (through origin) of the
reduction fraction of the average value of PM10 surface concentration
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MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
(PM10-CRF) as a function of the PM10 emission reduction fraction
(PM10-ERF) gives the following relation:
PM10-CRF = 0.94 (PM10-ERF)
This means that the 10% reduction in the average PM10 surface
concentration will be obtained with a PM10 emission reduction fraction
approximately equal to 0.1.
4.4. Compliance with the MCMA Air Quality Standards
In order to estimate the emission conditions required for the compliance
with the MCMA air quality standards, it was necessary to prepare two
additional plots. One for the Maximum Value of the Ozone Surface
Concentration as function of the NOx and HC emission reduction
fractions (Fig. 17), and other one for he Maximum Value of the PM10
Surface Concentration as function of the PM10 emission reduction
fraction (Fig. 18). In these additional plots, the hypothetical result that
the maximum values of the Ozone and PM10 surface concentrations will
be equal to zero when the emission reduction fractions will be equal to
one, was artificially included. The reason was the small number of
parametric runs that it was possible to carry out within the time period
that the CAM modeling team had available for this work.
4.4.1. Compliance with the Ozone air quality standard
In Figure 17, it is shown the maximum value of ozone surface
concentration (O3Max) as a function of the NOx and HC emission
reduction fractions. The plot in this figure includes the hypothetical
result that O3Max = 0 when rNOx = 1 and rHC = 1.
If one agrees with the results shown in Figure 17, it can be expected
that the maximum value of ozone surface concentration will be smaller
that 0.11 ppm (the ozone air quality standard) when the NOx and HC
emission reduction fractions will be larger than 0.81, simultaneously. It
is clear that other values of the NOx and HC reduction fractions are
possible for this purpose. In fact, a maximum ozone concentration equal
to 0.11 ppm will be found for all the NOx and HC reduction fractions
satisfying the fitting equation
rHC  0.1 
with (0.66 < rNOx < 1).
0.504
(r
 0.1)
NOx
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MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
Maximum Value of Ozone Surface Concentration [ppm]
as a Function of the NOx and HC Reduction Fractions.
Analysis Period: May 4-10. Hours: All.
Interpolation Method: Kriging.
Including the Hypotetical Point: O3Max = 0 for NOx-RF = 1 and HC-RF = 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
HC Reduction Fraction
1
1
0.9
0.9
0.8
0.8
0.7
0.7
0.6
0.6
0.5
0.5
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
NOx Reduction Fraction
Figure 17
Maximum value of Ozone surface concentration
as function of the NOx and HC emission reduction fractions.
Additional Hypothesis: O3Max = 0 when rNOx = 1 and rHC = 1.
38
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MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
It is worth of mention that, as Dr. Forkel (IFU) pointed out it (private
communication); some ozone will always be present, even if we have no
NOx and HC emissions. In fact, a study with a global model made by
Roelofs et al. [1997, Monthly Weather Review, 102, 23389-23401]
indicates that the background value in pre-industrial times could be
something like 20-25 ppb, so some value in this order could be taken for
zero emissions. This will significantly affect the position of the 0.11 ppm
“isopleth” in Figure 17. In fact, if it is assumed a maximum ozone
concentration equal to 40 ppb (assuming an average value of 20 ppb)
under no emission conditions, a maximum ozone concentration equal to
0.11 ppm will be found for all the NOx and HC reduction fractions
satisfying the fitting equation
rHC  0.522 
0.113
(r
 0.522)
NOx
with (0.76 < rNOx < 1).
4.4.2. Compliance with the PM10 air quality standard
In Figure 18, it is plotted the maximum value of PM10 surface
concentration (PM10Max) as a function of the PM10 emission reduction
fraction (PM10-RF). The plot in this figure includes the hypothetical
result that
PM10Max = 0 when PM10-RF = 1
In this case, if one agrees with Figure 18, it is observed that the
maximum value of PM10 will be equal to (or smaller than) 150 g/m3
(the PM10 air quality standard) when the PM10 reduction fraction is
equal to (or larger than) 0.75.
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MCCM Parametric Studies
Grupo de Modelación de la Comisión Ambiental Metropolitana
Maximum Value of PM10 Surface Concentration [ug/m3]
as a Function of the PM10 Emission Reduction Fraction.
Analysis Period: May 4-10. Hours: All.
Interpolation Method: Linear Regression
Including the Hypothetical Point: PM10Max = 0 for PM10-ERF = 1
1
0.8
0.6
0.4
0.2
0
800
800
750
Maximum Value of PM10 Surface Concentration [ug/m3]
700
650
600
600
550
500
450
400
400
350
300
250
200
200
PM10 Standard
150
100
50
0
0
0
0.05 0.1 0.15
0.2
0.25 0.3 0.35
0.4
0.45 0.5 0.55
0.6
0.65 0.7 0.75
0.8
0.85 0.9 0.95
PM10 Emission Reduction Fraction
Figure 18
Maximum value of PM10 surface concentration
as function of the PM10 emission reduction fraction.
Additional Hypothesis: PM10Max = 0 when rPM10 = 1.
40
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MCCM Parametric Studies
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5. CONCLUSIONS
Parametric modeling studies can be a very practical and interesting tool
in air quality assessment to evaluate the impact of emission abatement
strategies. In this work, the Multiscale Climate Chemitry Model (MCCM),
developed at the Fraunhofer Institute for Atmospheric Environmental
Research (IFU), was applied to study the behavior of the ozone and
PM10 surface concentrations as functions of the NOx, HC and PM10
emission conditions. The results of these parametric studies proved to
be useful, in particular, to estimate the NOx and HC emission reductions
required to obtain a 10% reduction in the ozone surface concentration
and compliance with the respective MCMA air quality standard. The
modeling results were useful also to estimate the PM10 emission
reductions that will produce a 10% reduction in the PM10 surface
concentration and its compliance with the air quality standard. These
studies took as reference the MCCM modeling results previously
obtained for the 2010 base line scenario. In this sense, the most
relevant results of the parametric studies were:

A 10% reduction in the average ozone surface concentration (for
sunlight hours) will be obtained for all the NOx and HC emission
reduction fractions defined by the fitting equation
rNOx  0.1  0.2  rHC
with 0 < rHC < 0.4. These NOx and HC emission reductions will
produce reductions larger than 10% in the maximum and most
frequent values of the ozone surface concentration.

The maximum ozone surface concentration will be equal to 0.11 ppm
(the ozone air quality standard) all the NOx and HC reduction
fractions satisfying the fitting equation
rHC  0.1 
0.504
(r
 0.1)
NOx
with (0.66 < rNOx < 1). In particular, values of the NOx and HC
emission reduction fractions larger than 0.81 will produce a
maximum ozone concentration smaller than 0.11 ppm.

A 10% reduction in the average PM10 surface concentration will be
obtained with a PM10 emission reduction fraction approximately
equal to 0.1.

The maximum value of the PM10 surface concentration will be equal
to (or smaller than) 150 g/m3 (the PM10 air quality standard) when
the PM10 reduction fraction is equal to (or larger than) 0.75.
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MCCM Parametric Studies
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6. ACKNOWLEDGEMENTS
The authors acknowledge the support of the Mexico City Environmental
Minister, Dr. Claudia Sheinbaum Pardo, for all the facilities made
available in preparing this work. The authors thank also to Dr. Angel
Fierros Palacios, Director of the Alternative Energies Division of the
Mexican Electrical Research Institute (Instituto de Investigaciones
Eléctricas, IIE), for facilitating the participation of one of them (AS) in
this work.
42