DRAFT REPORT MCCM PARAMETRIC STUDIES

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

C:\Users\Alejandro Salcido\Downloads\PStudies.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

2


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. 3


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. 4


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. 5


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 NOxTotal Emission 2010 NOxTotal 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

RES RET

with RET   RES S

RES  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.

6


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:

PM10(new)  PM10(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.

7


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

8


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.

9


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


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


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 if rHC  0.25 HC (new)   if rHC  0.25  HC (2010 )  (1  rHC )

The emission reductions shown in Table 3 are an example where rHC 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


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


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


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


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


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


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


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


(a)

HC Reduction Fraction

0.4

0.3

0.2

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0 0

0.1

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0.8

NOx Reduction Fraction

(b)


0.4

0.3

0.2

0.1

0 0

0.1

0.2

0.3

0.4

0.5


Figure 3 Maximum Values of the Ozone Surface Concentration (ppm). Interpolation Method: (a) Kriging, (b) Polynomial Regression.

21


(a)


0.4

0.3

0.2

0.1

0 0

0.1

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(b)


0.4

0.3

0.2

0.1

0 0

0.1

0.2

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0.4

0.5


Figure 4 Reduction Fractions of the Maximum Value of Ozone Surface Concentration. Interpolation Method: (a) Kriging, (b) Polynomial Regression.

22


(a)


0.4

0.3

0.2

0.1

0 0

0.1

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0.8


(b)


0.4

0.3

0.2

0.1

0 0

0.1

0.2

0.3

0.4

0.5


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


(a)


0.4

0.3

0.2

0.1

0 0

0.1

0.2

0.3

0.4

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(b)


0.4

0.3

0.2

0.1

0 0

0.1

0.2

0.3

0.4

0.5


Figure 6 Average Values of the Ozone Surface Concentration (ppm). Case: Sunlight hours. Interpolation Method: (a) Kriging, (b) Polynomial Regression.

24


(a)


0.4

0.3

0.2

0.1

0 0

0.1

0.2

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0.4

0.5

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0.8


(b)


0.4

0.3

0.2

0.1

0 0

0.1

0.2

0.3

0.4

0.5


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


(a)


0.4

0.3

0.2

0.1

0 0

0.1

0.2

0.3

0.4

0.5

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0.7

0.8


(b)


0.4

0.3

0.2

0.1

0 0

0.1

0.2

0.3

0.4

0.5


Figure 8 Reduction Fractions of the Average Value of Ozone Surface Concentration. Case: Sunlight hours. Interpolation Method: (a) Kriging, (b) Polynomial Regression.

26


(a)


0.4

0.3

0.2

0.1

0 0

0.1

0.2

0.3

0.4

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0.8


(b)


0.4

0.3

0.2

0.1

0 0

0.1

0.2

0.3

0.4

0.5


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


(a)


0.4

0.3

0.2

0.1

0 0

0.1

0.2

0.3

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(b)


0.4

0.3

0.2

0.1

0 0

0.1

0.2

0.3

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0.5


Figure 10 Most Frequent Values of the Ozone Surface Concentration (ppm). Case: Sunlight hours. Interpolation Method: (a) Kriging, (b) Polynomial Regression.

28


(a)


0.4

0.3

0.2

0.1

0 0

0.1

0.2

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(b)


0.4

0.3

0.2

0.1

0 0

0.1

0.2

0.3

0.4

0.5


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


(a)


0.4

0.3

0.2

0.1

0 0

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(b)


0.4

0.3

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0.1

0 0

0.1

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0.3

0.4

0.5


Figure 12 Reduction Fractions of the Most Frequent Value of Ozone Surface Concentration. Case: Sunlight hours. Interpolation Method: (a) Kriging, (b) Polynomial Regression.

30


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


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


Figure 14 Reduction Fraction of the Maximum Value of PM10 Surface Concentration. Case: All Hours Included (Day and Night).

32


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


Figure 15 Average Value of PM10 Surface Concentration [ug/m3] Case: All Hours Included (Day and Night).

33

0.6


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


Figure 16 Reduction Fraction of the Average Value of PM10 Surface Concentration. Case: All Hours Included (Day and Night).

34


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%. 35


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

36


(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 

0.504 (r  0.1) NOx

with (0.66 < rNOx < 1).

37


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


1

1

0.9

0.9

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0

0 0

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0.8

0.9


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

1


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.

39


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

0

0.2

0.4

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0.8

1

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


Figure 18 Maximum value of PM10 surface concentration as function of the PM10 emission reduction fraction. Additional Hypothesis: PM10Max = 0 when rPM10 = 1.

40

1


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.

41


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

DRAFT REPORT MCCM PARAMETRIC STUDIES

DRAFT REPORT MCCM PARAMETRIC STUDIES

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