## APPENDIX IX

MATES III

FINAL REPORT

Regional Modeling Analyses

Authors

Joe Cassmassi Xinqiu Zhang

Satoru Mitsutomi Sang-Mi Lee

### MATES III Final Report

Appendix IX-1 

## Appendix IX Regional Modeling Analyses

Introduction The MATES III regional modeling analysis is presented in Chapter 4 of the main document. This Appendix provides the analyses to comp lement and support the regional modeling

demonstration. These include: characterization and validation of the me teorological input data, development of the MATES III modeling emissions inventory, discussion of the development of the boundary conditions, model performance, and risk.

Several comments received from reviewers of th e draft MATES III report were directed to the regional modeling analysis and evaluati on. The key areas addressed follow:

• The need for a direct comparison between th e regional modeling analyses generated for both MATES II and MATES III;

• The simulation performance of elemental carbon (EC 2.5 

### ) compared with observations measured during the MATES III monitoring program; and

• The adequacy of the comparison of simulated risk to risk calculated based on monitored

data at the MATES III sites.

Several additional comments suggested modifica tions to the modeling assumptions including model configuration and specific emissions allocation. These included the following suggestions:

• Modify (increase) the number of layers in the model domain;

• Evaluate alternate methodologies to cal culate vertical dispersion; and

• Review the emissions inventory, in partic ular the percentage apportionment of EC emissions released from ships.

The regional modeling analysis and evaluation presented in this re port attempts to answer the key issues and suggestions identified through the review process. Most noteworthy, this Appendix presents a newly-generated recreation of the MATES II modeling analysis that is consistent in model application, inventory development and modeli ng assumptions to the MATES III analysis described in the following sections.

For MATES III, the Comprehensive Air Quality Model with Extensions enhanced with a reactive tracer modeling capability (CAMx RTRA C, Environ, 2006) provided the dispersion modeling platform and chemistry used to simula te annual impacts of both gaseous and aerosol toxic compounds in the Basin. The version of th e RTRAC “probing tool” in CAMx used in the modeling simulations includes an ai r toxics chemistry module that is used to treat the formation

and destruction of reactiv e air toxic compounds.

### MATES III Final Report

Appendix IX-2 Modeling was conducted on a domain that encompa ssed the Basin and the coastal shipping lanes

located in the Southern Californi a Bight portions of the Basin using a grid size of two-squared kilometers. An updated version of the 2007 AQ MP emissions inventory for model year 2005, which included detailed source profiles of air toxic sources, provided mobile and stationary source input for the MATES III CAMx RTRAC simulations. An additional back-cast of the 2007 AQMP emissions inventory was generated for 1998 to project emissions for use in the new simulation covering the MATES II monitoring period.

Grid-based, hourly meteorological fields generated from the MM5 (PSU/NCAR 2004) mesoscale meteorological model using four dimens ional data assimilation, and National Weather Service model initializations for April 1998 th rough March 1999 and all days in 2005 provided the dispersion profile for the simulations.

Background

MATES III regional modeling analyses relied on the CAMx RTRAC model to simulate annual impacts of both gaseous and aerosol toxic compounds in the Basin. In the 2000 MATES II analysis, the Urban Airshed Model with TOX ( UAMTOX) chemistry was used to simulate the advection and accumulation of toxic compound emissions throughout the Basin. UAMTOX was simulated for a two-squared kilometer grid domain that overlaid the Basin. The analysis relies on the 1997-98 emissions projection from the 1997 AQMP and meteorological data fields for 1997-98 generated from objective analysis using a diagnostic wind model. These tools were

consistent with those used in both the 1997 and 2003 AQMP attainment demonstrations.

Peer review of the 2003 AQMP mo deling strongly suggested th at future AQMP attainment

demonstrations utilize more state-of-the-scien ces tools that utilize updated chemistry modules, improved dispersion algorithms, and mass consiste nt meteorological data. The recommendations were placed in action for the 2007 AQMP where th e dispersion platform moved from UAM to CAMx and the diagnostic wind meteorological model was replaced by MM5 prognostic model. CAMx coupled with MM5 input using the “one atmosphere” gaseous and particulate chemistry was used to simulate both episodic oz one and annual concentrations of PM 2.5.



### .

The original plan for MATES III was to repli cate the analysis conducted for the 1998-99 field program using the UAMTOX model and diagnostic me teorological model. The theory was to enable a true apples-to-apples comparison of the current and pr evious modeling analyses. The plan was modified prior to adoption of the 2007 AQMP to take into account the advances in annual particulate modeling that wa s conducted as part of the 2007 PM 2.5 

### attainment demonstration. Given the extensive effort in the 2007 AQMP to simulate particulates, using the peer recommended state-of-the-science art mode ling tools, it was d ecided that a better comparison linking the AQMP PM 2.5 

### projections to the base year toxics analysis would be more complementary and up-to-date. As such, the MATES III simulations were conducted using the CAMx – MM5 coupled with the RTRAC chemistry.

### MATES III Final Report

Appendix IX-3 CAMx Modeling Domain

Modeling was conducted on a domain that encompassed the South Coast Air Basin and the coastal shipping lanes located in the Southern Ca lifornia Bight portions of the Basin using a grid size of two-squared kilometers. (Figure IX -1 depicts the MATES III modeling domain. The shaded portion of the grid area represents the extension of th e domain beyond that used for MATES II). Concentrations simulated for a spec ific location in the domain consisted of ninecell distance weighted average.

Figure IX-1 MATES III Modeling Domain (Shaded area highlights the grid extens ion to the MATES II modeling domain).

Development of Meteorological Fields

The Penn State/National Center for Atmosphe ric Research Mesoscale Model 5 (MM5) was employed to produce meteorological fields for the CAMx RTRAC 2005 MATES III and 1998- 99 back-cast of the MATES II regional modeling analyses.

### MATES III Final Report

Appendix IX-4 MATES III air monitoring spanned a three-year calendar period from April 2004 through March of 2006. The regional toxic modeling analysis was conducted for data sampled during the oneyear period including January 1, through Decem ber 31, 2005. The MATES II monitoring period included April 1, 1998 through March 31, 1999.

Meteorological Outl ook of Year 2005

The beginning of year 2005 was characterized as anomalously above-average precipitation in Southern California. A pronounced split-flow configuration was evident over western North America, with one branch of the westerlies ente ring the continent over nort hern British Columbia and the other entering over the Baja Peninsula. These conditi ons were associated with a southward shift of the main jet stream and stor m track across the wester n United States. These resulted in significantly above-ave rage precipitation in Southern California, Southwest, and the western inter-mountain regions of the U.S. S outhern California experienced above-average precipitation during the period of October 2004 to May 2005. During summer months – July and August, 500 hectopascal (hPa) ge opotential heights were above- average level over the western U.S., which led to well above-average temperatur es in the area, which was situated beneath a very persistent upper-level ridge. Fall and wint er months returned close to climatology when North America generally experienced below averag e precipitation in the west and above average

rainfall in the southeastern U.S.

When comparing the meteorology between 2005 to the MATES II monitoring period of April 1998-March 1999, two issues stand out. The MA TES II period was drier than MATES III but over the course of the period experienced less stagnation. Using a statistical analysis developed for the 1997 AQMP that evaluates pollution dispersion potential based on the presence and strength of temperature inversions, 2005 was very close to average despite having greater rainfall than 1998-99. Using the same measure, 1998-199 9 was slightly above average for dispersion potential but experienced a milder winter with less storm activity. This is borne out through Basin statistics of measurable rainfall, wher e 2005 experienced a grea ter frequency of days having measurable rainfall in Downtown Los Angeles by 30% (43 versus 33 days) and total rainfall measured at USC by 182% (26.0 versus 14.1 inches). The additional rainfall may have suppressed the amount of re-entrained or fug itive dust that contributes to concentration

measurements of EC.

MM5 Numerical Model Configuration

Penn State/National Center for Atmospheric Re search Mesoscale Model 5 (MM5) was employed to produce meteorological fields for CAMx RT RAC simulations. The MM5 simulations were comprised of three nested domains of which horizontal grid distances of 18, 6, and 2 km respectively. The relative sizes and locations of each domain are given in Figure IX-2. The innermost domain spans 254 km X 164 km in east- west and north-south di rections, respectively,

which overlaps the CAMx domain by two additional rows and columns in each lateral boundary. 

### The initial guess field and lateral boundary values for the outermost domain were extracted from the operational NCEP Eta 218 grid (12km) grid analysis for the 2005 simulations and the 212 grid (40 km) analysis output for the 1998-99 simula tion. Figure IX-3 depi cts the grid specific

terrain file used in the MM5 simulations.

### MATES III Final Report

Appendix IX-5 The databases contain variables of air temper ature, geopotential height , heat flux, humidity, precipitable water, sea level pr essure, shortwave radiation, snow water equivalent, surface air temperature, surface winds, thermal infrared, upper level winds, vertical wind, and vorticity at each isobaric level of 1000, 975, 950, 925, 900, 875, 850, 800, 750, 700, 650, 600, 550, 500, 450, 400, 350, 300, 275, 250, 225, 200, 175, 150, 100, 50 hPa. (Refer to http://dss.ucar.edu/datasets/ds609.2 for further dataset information).

Four dimensional data assimilation (FDDA) was conducted by utilizing National Weather Service (NWS) twice-daily soundin g data and hourly surface m easurements taken within the

domain. Each simulation was conducted for a 6- day period with the first 24 hours of spin up period. The detailed configurati on and physical options used in th e MM5 simulation are listed in

Table IX-1.

Figure IX-2.

The relative locations and sizes of three MM5 nested domains.

### MATES III Final Report

Appendix IX-6

Table IX-1 MM5 configuration

Component 2005 1998-99

Number of grids (127 X 82) in east-west and north-south respectively (127 X 82) in east-west and northsouth respectively

Number of vertical layers 29 layers with the lowest layer being approximately at 20 m agl. 29 layers with the lowest layer being approximately at 20 m agl. Initial and boundary

values ETA 218 grid (12 km grid distance) analysis field Eta 212 grid (40 kim grid distance) analysis field

Boundary layer

scheme Blackadar Blackadar

Soil model Five-layer soil model Five-layer soil model

Cumulus parameterization Explicit Explicit

Micro physics Simple ice Simple ice

Radiation Cloud radiation Cloud radiation

Four dimensional data analysis Analysis nudging with NWS surface and upper air

measurements Analysis nudging with NWS surface and upper air

measurements



### Figure IX-3 The topography and the county boundaries of the MM5 computational domain.

### MATES III Final Report

Appendix IX-7 Meteorological Model Performance

The MM5 performance was extensively eval uated using NWS surface measurements and Environ’s METSTAT (ENVIRON, 2001) statistical software to compute mean, bias, gross error, root mean square error (RMS E), and index of agreement.

Figure IX-4 shows the time series of hourly obs erved and predicted temperature at 2 m above ground level (agl) for September 2005. The model successfully resolved overall cooling and warming trend induced by synoptic scale mo tions, while daily maximum and minimum temperatures were slightly over and underpredicted, respectively. This can be partly attr ibuted to inaccurate representation of surface characterist ics such as soil moisture content and land use

category.

Observed/Predicted Temperature

280 285 290 295 300 305 310

9/ 1 9/ 3 9/ 5 9/ 7 9/ 9 9/11 9/13 9/15 9/17 9/19 9/21 9/23 9/25 9/27 9/29K ObsTemp PrdTemp



### FIGURE IX-4 Time series of observed and predicted temper ature at 2 m above ground level for September, 2005. The data are hourly average observations of all available measurements within the domain and the corresponding predictions.

In all, the model has less than 2 degrees of bias and gross erro r and approximately 2 degrees of RMSE, which are approximately equivalent to MM5 performance for 2007 Air Quality Management Plan (AQMP) modeling case (Figure IX-5). Wind speed tu rned out to be underpredicted by less than 1 m s -1 

### . In general, all conventional surface parameters including wind speed, direction, temperature and water vapor mixing ratio showed good agreement with the observations (Figures IX-6 and IX-7).

### MATES III Final Report

Appendix IX-8 Observed/Predicted Temperature

286 288 290 292 294 296 298 300

9/ 01 9/ 03 9/ 05 9/ 07 9/ 09 9/ 11 9/ 13 9/ 15 9/ 17 9/ 19 9/ 21 9/ 23 9/ 25 9/ 27 9/29K Temprt r Mean OBS Temprt r Mean PRD

Bias/Gross Error Temperature

- 2 - 1 0 1 2 3

9/ 01 9/ 03 9/ 05 9/ 07 9/ 09 9/ 11 9/ 13 9/ 15 9/ 17 9/ 19 9/ 21 9/ 23 9/ 25 9/ 27 9/29K Temprt r Bias Temprt r Gross Error

RMSE Temperature

0 0.5

1 1.5

2 2.5

3

9/ 01 9/ 03 9/ 05 9/ 07 9/ 09 9/ 11 9/ 13 9/ 15 9/ 17 9/ 19 9/ 21 9/ 23 9/ 25 9/ 27 9/29K Temprt r RMSE Temprt r Sys RMSE Temprt r Unsys RMSE

IOA Temperature

0 0.2 0.4 0.6 0.8

1

9/ 01 9/ 03 9/ 05 9/ 07 9/ 09 9/ 11 9/ 13 9/ 15 9/ 17 9/ 19 9/ 21 9/ 23 9/ 25 9/ 27 9/29 Temprt r IOA



### Figure IX-5

Daily averaged (a) mean, (b) bias and gross error, (c) root mean square error, and (d) index of agreement for observed and predicted temperature at 2 m agl.

### MATES III Final Report

Appendix IX-9 Observed/Predicted Windspeed

0 1 2 3 4 5 6

9/ 01 9/ 03 9/ 05 9/ 07 9/ 09 9/ 11 9/ 13 9/ 15 9/ 17 9/ 19 9/ 21 9/ 23 9/ 25 9/ 27 9/ 29m/s Wind Spd Mean OBS Wind Spd Mean PRD

Bias/Gross Error Windspeed

- 2 - 1 0 1 2 3

9/ 01 9/ 03 9/ 05 9/ 07 9/ 09 9/ 11 9/ 13 9/ 15 9/ 17 9/ 19 9/ 21 9/ 23 9/ 25 9/ 27 9/ 29m/s Wind Spd Bias Wind Spd Gross Error

RMSE Windspeed

0 1 2 3 4

9/ 01 9/ 03 9/ 05 9/ 07 9/ 09 9/ 11 9/ 13 9/ 15 9/ 17 9/ 19 9/ 21 9/ 23 9/ 25 9/ 27 9/ 29m/s Wind Spd RMSE Wind Spd Sys RMSE Wind Spd Unsys RMSE

IOA Windspeed

0 0.2 0.4 0.6 0.8

1

9/ 01 9/ 03 9/ 05 9/ 07 9/ 09 9/ 11 9/ 13 9/ 15 9/ 17 9/ 19 9/ 21 9/ 23 9/ 25 9/ 27 9/ 29 Wind Spd IOA

Observed/Predicted Wind Direction

0 6 0 12 0 18 0 240 300 360

9/ 01 9/ 03 9/ 05 9/ 07 9/ 09 9/ 11 9/ 13 9/ 15 9/ 17 9/ 19 9/ 21 9/ 23 9/ 25 9/ 27 9/ 29deg Wind Dir Mean OBS Wind Dir Mean PRD

Bias/Gross Error Wind Direction

-90 -60 -30

0 3 0 6 0 9 0

9/ 01 9/ 03 9/ 05 9/ 07 9/ 09 9/ 11 9/ 13 9/ 15 9/ 17 9/ 19 9/ 21 9/ 23 9/ 25 9/ 27 9/ 29deg Wind Dir Bias Wind Dir Gross Error



### Figure IX-6 Daily averaged (a) mean, (b) bias and gross error, (c) root mean square error, and (d) index of agreement for observed and predicted wind speed. (e) Mean and (f) bias and gross error of wind direction are presented as well.

### MATES III Final Report

Appendix IX-10 Observed/Predicted Humidity

0 2 4 6 8 10 12 14

9/ 01 9/ 03 9/ 05 9/ 07 9/ 09 9/ 11 9/ 13 9/ 15 9/ 17 9/ 19 9/ 21 9/ 23 9/ 25 9/27 9/29g/kg Humdit y Mean OBS Humdit y Mean PRD

Bias/Gross Error Humidity

- 5 - 4 - 3 - 2 - 1 0 1 2 3 4 5

9/ 01 9/ 03 9/ 05 9/ 07 9/ 09 9/ 11 9/ 13 9/ 15 9/ 17 9/ 19 9/ 21 9/ 23 9/ 25 9/27 9/29g/kg Humdit y Bias Humdit y Gross Error

RMSE Humidity

0 1 2 3 4 5 6

9/ 01 9/ 03 9/ 05 9/ 07 9/ 09 9/ 11 9/ 13 9/ 15 9/ 17 9/ 19 9/ 21 9/ 23 9/ 25 9/27 9/29g/kg Humdit y RMSE Humdit y Sys RMSE Humdit y Unsys RMSE

IOA Humidity

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

9/ 01 9/ 03 9/ 05 9/ 07 9/ 09 9/ 11 9/ 13 9/ 15 9/ 17 9/ 19 9/ 21 9/ 23 9/ 25 9/27 9/29 Humdit y IOA



### Figure IX-7

Daily averaged (a) mean, (b) bias and gross error, (c) root mean square error, and (d) index of agreement for observed and predicted wind speed. (e) Mean and (f) bias and gross error of wind direction are presented as well.

### MATES III Final Report

Appendix IX-11 Wind Rose Comparison

While the METSTAT evaluation is a useful tool to assess the pe rformance of the regional MM5 simulations, it is important to examine the capab ility to recreate observe d annual local scale wind patterns. To assess the local sc ale flow, wind roses were generated from the hourly average 2005 MM5 model output for the 2 km-squared grid ce ll housing the MATES III monitoring sites and the monitoring data for those sites. An exact re plication of the measured winds was not expected in the analysis. However, comparison of the modeled and measured annual average wind roses offers a visual comparison of the fit of the si mulation to the local scale and assists in the evaluation of the CAMx RTRAC simulation performance.

Figures IX-8a through IX-8f depict the 2005 a nnual wind roses for Anaheim, Burbank, Inland Valley San Bernardino, North Long Beach, Centra l Los Angeles, and Rubidoux. (Figure IX-9 provides the 2005 MM5 generated wind roses for Compton and West Long Beach). Subtle

nuances between the simulated and observed winds are observed at all stations. In general, wind speeds are slightly higher for the MM5 simulati on. The directional freq uencies are reasonably well-captured at most sites, with an offset in the primary wind vector of less than one sector (22.5 degrees). It is important to note that the local emissions sources (particularly ground level) directly upwind of the monitoring site have a si gnificant impact to the measured concentration profile. As such, a minor one-sector difference in the simulated wind direction may impact the CAMx RTRAC performance. This is the case at Burbank, where the simulated winds are offset from the observed winds to the south by more than 45 degrees.

The Burbank monitoring station is located in th e southeast corner of the San Fernando Valley. The thermally forced sea breeze that drives in to the San Fernando Valley is channeled by the unique terrain features which cause the flow to back almost 90 degrees from a southwesterly trajectory in the coastal plain to south easterly at the monitoring station. While the MM5 simulation accomplishes the element of backi ng the wind flow in the Burbank area, the simulation is currently not capable of reversing th e flow to the degree observed. As discussed in a following section, this feature does have an impact on the CAMx RTRAC simulation performance at Burbank. (Ongoing sensitivit y analyses are current ly being conducted to evaluate the MM5 simulation sensitivity to improved local land use and soil moisture

characterization to overcome this shortfall. Th e results of these sensitivity analyses will be incorporated in future simulations).

### MATES III Final Report

Appendix IX-12 0

45

90

135

180 225 270 315

0% 5% 10% 15% 20% 25% WIND SPEED (m/sec)

>0 - 2

>2 - 5

>5 - 10

>10 - 15

>15 MATES-3 MM5: Anaheim

0

45

90

135

180 225 270 315 Anaheim: Annual

0% 5% 10% 15% 20% 25% Frequency Wind Speed (MPH)

>0 - 2.1

>2.1 - 3.6

>3.6 - 5.7

>5.7 - 8.8

>8.8



### Figure IX-8a.

2005 MM5 Simulated and Observed Annual Hour ly Averaged Wind Roses at Anaheim.

0

45

90

135

180 225 270 315

0% 5% 10% 15% 20% 25% WIND SPEED (m/sec)

>0 - 2

>2 - 5

>5 - 10

>10 - 15

>15 MATES-3 MM5: Burbank

0

45

90

135

180 225 270 315 Burbank: Annual

0% 4% 8% 12% 16% 20% Frequency Wind Speed (MPH)

>0 - 2.1

>2.1 - 3.6

>3.6 - 5.7

>5.7 - 8.8

>8.8



### Figure IX-8b.

2005 MM5 Simulated and Observed Annual Hour ly Averaged Wind Roses at Burbank.

### MATES III Final Report

Appendix IX-13 0

45

90

135

180 225 270 315

0% 10% 20% 30% 40% WIND SPEED (m/sec)

>0 - 2

>2 - 5

>5 - 10

>10 - 15

>15 MATES-3 MM5: Inland Valley S.B.

0

45

90

135

180 225 270 315 Inland Valley S.B.

0% 4% 8% 12% 16% Frequency Wind Speed (MPH)

>0 - 2.1

>2.1 - 3.6

>3.6 - 5.7

>5.7 - 8.8

>8.8



### Figure IX-8c. 2005 MM5 Simulated and Observed Annual Hourly Averaged Wind Roses at Inland Valley S.B.

0

45

90

135

180 225 270 315

0% 4% 8% 12% 16% WIND SPEED (m/sec)

>0 - 2

>2 - 5

>5 - 10

>10 - 15

>15 MATES-3 MM5: LongBeach

0

45

90

135

180 225 270 315 Long Beach: Annual

0% 5% 10% 15% 20% 25% Frequency Wind Speed (MPH)

>0 - 2.1

>2.1 - 3.6

>3.6 - 5.7

>5.7 - 8.8

>8.8



### Figure IX-8d. 2005 MM5 Simulated and Observed Annual Hourly Averaged Wind Roses at North Long

Beach.

### MATES III Final Report

Appendix IX-14 0

45

90

135

180 225 270 315

0% 5% 10% 15% 20% 25% WIND SPEED (m/sec)

>0 - 2

>2 - 5

>5 - 10

>10 - 15

>15 MATES-3 MM5: Central LA

0

45

90

135

180 225 270 315 Central LA: Annual

0% 4% 8% 12% 16% 20% Frequency Wind Speed (MPH)

>0 - 2.1

>2.1 - 3.6

>3.6 - 5.7

>5.7 - 8.8

>8.8



### Figure IX-8e.

2005 MM5 Simulated and Observed Annual Hourly Averaged Wind Roses at Central Los Angeles.

0

45

90

135

180 225 270 315

0% 10% 20% 30% 40% 50% WIND SPEED (m/sec)

>0 - 2

>2 - 5

>5 - 10

>10 - 15

>15 MATES-3 MM5: Rubidoux

0

45

90

135

180 225 270 315 Riverside: Annual

0% 5% 10% 15% 20% 25% Frequency Wind Speed (MPH)

>0 - 2.1

>2.1 - 3.6

>3.6 - 5.7

>5.7 - 8.8

>8.8



### Figure IX-8f.

2005 MM5 Simulated and Observed Annual H ourly Averaged Wind Roses at Rubidoux.

### MATES III Final Report

Appendix IX-15 0

45

90

135

180 225 270 315

0% 4% 8% 12% 16% 20% WIND SPEED (m/sec)

>0 - 2

>2 - 5

>5 - 10

>10 - 15

>15 

### MATES-3 MM5: Compton

0

45

90

135

180 225 270 315

0% 4% 8% 12% WIND SPEED (m/sec)

>0 - 2

>2 - 5

>5 - 10

>10 - 15

>15 

### MATES-3 MM5: West Long Beach

Figure IX-9. 2005 MM5 Simulated Annual Hourly Averaged Wind Roses at Compton and West Long Beach.

Vertical Dispersion

The initial CAMx RTRAC simulations were conducte d using a vertical stru cture of eight layers, the CMAQ vertical stability para meterization, and a minimum value for vertical diffusivity (KV)

of 1.0 m 2 

### /sec. In the development phase of the me teorological data sets, the MM5 output was subjected to a variety of comb inations of mixing schemes (CMAQ and the O’Brien 70 [OB70], O’Brien, 1970) using varying default minimum valu es of vertical diffusivity. The analysis evaluated CAMx RTRAC performance for EC 2.5 

### for both an eight and 16 vertical layer modeling structure. (Note: the MM5 simu lations were conducted using a 29 layer vertical structure).

CAMx RTRAC EC 2.5 

### simulation performance was nearly identic al for both layer structures using the CMAQ dispersion option us ing a minimum KV of 1.0 m 2 

### /sec. The OB70 scheme resulted in overprediction at key sites (both layer structures) as did the CMAQ scheme us ing 16 layers and a

minimum KV of 0.1 m 2 

### /sec. All of the combinations, rega rdless of layer structure or minimum KV, resulted in overprediction at Long Beach and West Long Beach and underprediction to varying degrees at Rubidoux and Inland Valley San Bernardino. Since there was little difference between CMAQ dispersion option using a minimum KV of 1.0 m 2 

### /sec for either the eight or 16 layer structures, the eight-la yer structure using the CMAQ scheme and a minimum KV of 1.0

m 2 

### /sec was selected to reduce computational demand.

Comments received from reviewers after release of the draft MATES III analysis focused on the underprediction of EC 2.5 

### in the eastern portion of the Basin. Additional sensitivity simulations using ENVIRON’s KV-Patch software (Environ , 2006) were conducted to attempt to improve the simulation performance. Briefly, KV-Patch was used to assign a minimum value of KV (0.1

### MATES III Final Report

Appendix IX-16 m 2 

### /sec) to individual grid cells based on land use sp ecification. The results of the application are

summarized in Table IX-2 where the simulation performance is presented as a ratio of the

observed EC 2.5 

### concentration measurement at each monitoring site.

The eight-layer simulation using the KV-Patch option and a minimum 0.1 m 2 

### /sec value of KV labeled as interim improved the model perfor mance in the east Basin while only having a nominal impact on the performance in the coas tal zone. The 16-layer simulation using a 0.1

m 2 

### /sec value of KV also resulted in improve d performance in the east Basin but at the degradation of performance in the coastal plain. As a result of th e additional sensitivity analyses, the vertical dispersion profile used in the fi nal MATES III CAMx RTRAC simulations relied on an eight-layer structure using the CMAQ scheme overlaid with the KV-Patch option set at 0.1

m 2 

### /sec value of KV.

MATES III Modeling Emissions

An updated version of the 2007 AQMP emissi ons inventory for model year 2005, which included detailed source profiles of AB2588 air toxic sources, pr ovided mobile and stationary source input for the MATES III CAMx RTRAC simulations. Mobile source emissions were adjusted for time-of-day and day-of-week trav el patterns based on CalTrans weigh-in-motion data profiles. Table IX-3 lists the January w eekday daily diesel emissions projected for 2005 and back-cast for 1998. (A comprehensive br eakdown of the planning VOC, NOx, CO, SO 2 

### and particulate emissions for 2005 used in the MATES III simulation is provided in Chapter 3 and Appendix XIII). Table IX-3 also includes th e MATES II TSP diesel emissions for 1998 for comparison.

A comparison of the MATES III (2007 AQMP) 2005 projection of the weekday PM 2.5 

### diesel

emissions shows a 4.8% reduction in emissions from the back-cast for 1998. The most significant area of diesel particulate matter emi ssions growth occurs in the shipping categories associated with goods movement. MATES III back-casts of the weekday 1998 TSP diesel inventory using the 2007 AQMP inventory were almost 21% higher th an the corresponding

MATES II values.

Figures IX-10a through IX-10x provides the grid based w eekday modeling emissions for selected toxic pollutant and pr ecursor emissions categories.

MATES III vs. MATES II: Key Emissions Modeling Assumptions

Three changes to emissions data preparation were implemented in the MATES III modeling. First, emissions from vessels in the shipping lanes were assumed emitted into the first two vertical modeling layers to better estimate plum e rise from the hot stack emissions. Combined stack heights and plume rise for typical oceango ing (deep draft) vessels extend above 36 and below 73 meters (WRAP, 2007). MATES II held sh ipping emissions in the first vertical UAM layer.MATES IIIFinal ReportAppendix IX-16TABLE IX-2Simulated Annual Average EC2.5Using Selected Vertical Dispersion Profiles and Layer StructuresDispersion SchemeCMAQO’Brien 70Option/Location Observed Draft Interim TestTest Test Test Test Test TestLayer Structure Surface 8 8 16 16 16 8 8 16 16Minimum KV (m2/sec) N/A1.0 0.11.0 0.1 0.1 0.1 0.10.1 0.1KV-Patch Used N/A No Yes No No Yes Yes No No YesAnaheim 1.001.031.071.011.57 1.101.211.771.661.24Burbank 1.000.530.540.540.730.570.610.780.810.63Compton 1.001.121.151.101.76 1.181.291.971.811.32Inland Valley S.B. 1.00 0.72 0.88 0.73 1.12 0.88 1.00 1.28 1.17 0.98Huntington Park 1.00 0.94 0.96 0.92 1.44 0.98 1.07 1.62 1.47 1.08North Long Beach 1.00 1.42 1.47 1.41 2.13 1.51 1.63 2.38 2.17 1.67Central Los Angeles 1.00 1.09 1.11 1.06 1.59 1.12 1.26 1.83 1.69 1.25Pico Rivera1.00 0.76 0.85 0.77 1.21 0.89 0.95 1.33 1.28 0.97Rubidoux 1.00 0.600.840.640.98 0.890.931.001.020.94West Long Beach 1.00 1.22 1.26 1.17 1.61 1.26 1.38 1.82 1.60 1.36

### MATES III Final Report

Appendix IX-17 Table IX-3

January Weekday MATES III Diesel/EC Modeling Emissions (TPD)

Compound MATES III MATES II

2005 1998 (Back-cast) 1998

PM 2.5 

### TSP PM 2.5 

### TSP PM 2.5 

### TSP

Total Diesel 26.19 28.47 27.42 29.81 N/A 23.56

EC 14.12 18.9 14.68 19.89 N/A 25.87

D P M

On-road 9.52 10.35 10.81 11.35 N/A 11.95

Off-road 11.02 11.97 12.29 11.36 N/A 8.08

Ships 4.28 4.65 2.75 2.99 N/A 2.59

Trains 0.86 0.94 0.79 0.86 N/A 0.53

Stationary 0.51 0.55 0.78 0.85 N/A 0.41

FIGURE IX-10a Weekday average emissions pattern for Total Diesel 

### PM 2.5.

### MATES III Final Report

Appendix IX-18 FIGURE IX-10b Weekday average emissions pattern for Elemental Carbon.

FIGURE IX-10c Weekday average emissions pattern for On-Road Diesel 

### PM 2.5.

### MATES III Final Report

Appendix IX-19 FIGURE IX-10d Weekday average emissions pattern for Off-Road Diesel 

### PM 2.5.

FIGURE IX-10e Weekday average emissions pattern Diesel 

### PM 2.5 from Ships.

### MATES III Final Report

Appendix IX-20 FIGURE IX-10f Weekday average emissions pattern Diesel 

### PM 2.5 from Trains.

FIGURE IX-10g Weekday average emissions pattern Diesel 

### PM 2.5 from Stationary Sources.

### MATES III Final Report

Appendix IX-21 FIGURE IX-10h Weekday average VOC emissions pattern.

FIGURE IX-10i Weekday average NOx emissions pattern.

### MATES III Final Report

Appendix IX-22 FIGURE IX-10j Weekday average CO emissions pattern.

FIGURE IX-10k Weekday average emissions pattern for Acetaldehyde.

### MATES III Final Report

Appendix IX-23 FIGURE IX-10l Weekday average Arsenic emissions pattern.

FIGURE IX-10m Weekday average Benzene emissions pattern.

### MATES III Final Report

Appendix IX-24 FIGURE IX-10n Weekday average 1,3 Butadiene emissions pattern.

FIGURE IX-10o Weekday average Cadmium 

### PM 2.5 emissions pattern.

### MATES III Final Report

Appendix IX-25 FIGURE IX-10p Weekday average Chromium 

### PM 2.5 emissions pattern.

FIGURE IX-10q Weekday average Hexavalent Chromium 

### PM 2.5 emissions pattern.

### MATES III Final Report

Appendix IX-26 FIGURE IX-10r Weekday average Lead PM 2.5 emissions pattern.

FIGURE IX-10s Weekday average Methylene Chloride emissions pattern.

### MATES III Final Report

Appendix IX-27 FIGURE IX-10t Weekday average Naphthalene emissions pattern.

FIGURE IX-10u Weekday average Nickel 

### PM 2.5 emissions pattern.

### MATES III Final Report

Appendix IX-28 FIGURE IX-10v Weekday average Nickel emissions pattern.

FIGURE IX-10w Weekday average Perchloroethylene emissions pattern.

### MATES III Final Report

Appendix IX-29 FIGURE IX-10x Weekday average Trichloroethylene emissions pattern.



### Table IX-4 summarizes the impact of using th e revised emissions prof ile where CAMx RTRAC simulation overprediction of EC 2.5 

### at Long Beach, West Long Beach and Compton was lessened without adversely impacting performance at th e remaining sites. The total diesel mass emissions from this source category we re not impacted by the revision.

The third modification impacted the distribution of truck movement throughout the Basin. At the time of MATES II, no heavy-duty truck move ment profile was available to characterize the

truck distribution and travel on fr eeways, arterials, and major stre ets. Truck travel was assigned the travel model characteristics designated for light-duty passenger vehicle travel. MATES III directly incorporated the out put of the heavy-duty truck demand model to provide a more

realistic characterization of wee kday travel. Weekend travel was assigned the same routes but at substantially lowered demand.

A brief assessment of the changes made to th e modeling emissions from MATES II to MATES III showed that for diesel 97% of the grids exhi bited net changes of 10 kg/ day or less. The maximum change in grid level emissions rang ed from -81 kg/day to 120 kg/day. (A positive

number indicates an increase in emissions from the MATES II inventory to MATES III). Overall, the shift in the emissions pattern from MATES II to MATES III reflects relatively small

increments of emissions increase or decrease. Refinements to travel patterns and shipping result in more clearly defined offshore shipping routes and enhanced dies el emissions along Interstate 5

and 710. Reductions in diesel emissions are noted in sout hwestern San Bernardino and

northwestern Riverside Counties.

### MATES III Final Report

Appendix IX-30

TABLE IX-4 Simulated Impact of Revised Marine EC 2.5 

### Emissions Profile

Location Observed Initial* Interim* Final*

Anaheim 1.00 1.03 1.07 0.94

Burbank 1.00 0.53 0.54 0.50

Compton 1.00 1.12 1.15 1.04

Inland Valley San Bernardino 1.00 0.72 0.88 0.84

Huntington Park 1.00 0.94 0.96 0.91

North Long Beach 1.00 1.42 1.47 1.26

Central Los Angeles 1.00 1.09 1.11 1.06

Pico Rivera 1.00 0.76 0.85 0.80

Rubidoux 1.00 0.60 0.84 0.80

West Long Beach 1.00 1.22 1.26 1.04

* Ratio of Simulated to Observed EC 2.5 

### at Measurement Site

Boundary and Initial Conditions

The MATES III boundaries differed significantly from those used in MATES II. Overall, the concentrations were lower; and, unlike MATES II, the boundary conditions were not uniform. The boundaries along the eastern and western porti ons of the modeling domain were sectioned into thirds, and the north and s outh boundaries were apportioned into fifths. Each section of the four boundaries was assigned a unique value. Ta ble IX-5 provides the boundary assignments.

The western and southern boundaries were scaled to show a diminishing concentration as the southwest corner of the modeling domain wa s approached. The ove rland boundaries residing over populated areas or grid cells in major tr ansportation corridors were assigned higher boundary concentrations compared w ith those cells over water or ove r mountains or desert areas.

The majority of the values of the boundary and initial conditions were extracted from the 2005

annual PM 2.5 

### simulations used for the 2007 AQMP compliance demonstration. Boundary conditions for EC and diesel particulate were ge nerated from model simulations using the larger SCOS97 modeling grid and a clean boundary assump tion. (The MATES III grid is a subset of the SCOS97 modeling grid which encompasses 550 km in the east-west dir ection and 370 km in the north-south direction).

### MATES III Final Report

Appendix IX-31

TABLE IX-5 Boundary Conditions for Gaseous Compounds (PPM): North and East Boundaries

Compound North East

N1 N2 N3 N4 N5 E1 E2 E3

NO 0.00017 0.00022 0.0002 0.00022 0.0002 0.00016 0.00016 0.00016

NO2 0.0028 0.0038 0.0038 0.00431 0.00395 0.00298 0.00297 0.00298

O3 0.05 0.05 0.049 0.05 0.05 0.05 0.05 0.05

OLE 0.0007 0.00082 0.00106 0.00115 0.0012 0.001393 0.001393 0.001393

PAR 0.019 0.021 0.023 0.023 0.0239 0.02571 0.02571 0.02571

TOL 0.00023 0.0003 0.00033 0.00032 0.00036 0.00038 0.00038 0.00038

XYL 0.00009 0.000116 0.00011 0.00009 0.000109 0.000112 0.000112 0.000112

FORM 0.002 0.0021 0.0021 0.00203 0.00201 0.00238 0.00238 0.00238

ALD2 0.001 0.0012 0.00132 0.00136 0.0014 0.00163 0.00163 0.00163

ETH 0.00041 0.000542 0.000636 0.0006 0.00061 0.0006 0.0006 0.0006

CRES 0.000007 0.000007 0.000007 0.000007 0.000007 0.000009 0.000009 0.000009

MGLY 0.000001 0.000001 0.000001 0.000001 0.000001 0.000001 0.000001 0.000001

OPEN 0.000002 0.000002 0.000002 0.000002 0.000002 0.000003 0.000003 0.000003

PNA 0.00001 0.00001 0.000012 0.000011 0.000011 0.000008 0.000008 0.000008

NXOY 0.00007 0.00008 0.000095 0.000103 0.000115 0.00007 0.00007 0.00007

PAN 0.00059 0.00058 0.000565 0.00054 0.00054 0.0005 0.0005 0.0005

CO 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

HONO 0.000002 0.000003 0.000003 0.000003 0.000003 0.000004 0.000004 0.000004

H2O2 0.0018 0.0017 0.00165 0.0016 0.00165 0.00187 0.00187 0.00187

MEOH 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001

ETOH 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001

ISOP 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001

BENZ 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002

BUTA 0.0000037 0.0000037 0.0000037 0.0000037 0.0000037 0.0000037 0.0000037 0.0000037

PACET 0.0001 0.00012 0.000132 0.000136 0.00014 0.000163 0.000163 0.000163

HCHO 0.0002 0.00021 0.00021 0.000203 0.000201 0.000238 0.000238 0.000238

SACET 0.00045 0.00054 0.0006 0.00061 0.00063 0.000735 0.000735 0.000735

SFORM 0.0009 0.00095 0.00095 0.00092 0.000945 0.00107 0.00107 0.00107

PDIC 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001

MCHL 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001

PERC 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001

TCE 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001

NAPH 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001

### MATES III Final Report

Appendix IX-32 TABLE IX-5 (Continued) Boundary Conditions for Gaseous Compounds (PPM): West and South Boundaries

Compound West South

W1 W2 W3 S1 S2 S3 S4 S5

NO 0.000128 0.00035 0.000325 0.00014 0.00028 0.00039 0.000585 0.00052

NO2 0.00103 0.00311 0.00317 0.00115 0.002 0.00426 0.0077 0.00664

O3 0.044 0.044 0.047 0.045 0.046 0.045 0.044 0.047

OLE 0.000069 0.000155 0.000389 0.00007 0.0001 0.000365 0.000928 0.001248

PAR 0.0108 0.0182 0.0204 0.011 0.015 0.0221 0.03485 0.0349

TOL 0.000096 0.000183 0.000213 0.00012 0.00017 0.00038 0.000855 0.0008

XYL 0.000031 0.000073 0.000081 0.00004 0.00007 0.000165 0.000381 0.000352

FORM 0.000637 0.00098 0.00167 0.00061 0.00073 0.00105 0.00173 0.00222

ALD2 0.000242 0.000422 0.000687 0.00024 0.0003 0.00058 0.00114 0.00139

ETH 0.000099 0.000175 0.000343 0.0001 0.00015 0.000349 0.000658 0.000827

CRES 0.000004 0.000006 0.000006 0.000004 0.000006 0.00001 0.000019 0.000019

MGLY 0.000001 0.000001 0.000001 0.000001 0.000001 0.000001 0.000001 0.000001

OPEN 0.000001 0.0000015 0.000001 0.000001 0.000002 0.000003 0.000005 0.000005

PNA 0.000004 0.000006 0.000009 0.000004 0.000004 0.000008 0.000013 0.000016

NXOY 0.00005 0.00006 0.000048 0.00005 0.0001 0.000125 0.000134 0.000112

PAN 0.0003 0.000413 0.00051 0.0003 0.00034 0.000427 0.000565 0.000657

CO 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

HONO 0.000002 0.000005 0.000004 0.000002 0.000003 0.000005 0.000007 0.000006

H2O2 0.00114 0.00127 0.00163 0.0011 0.0012 0.0013 0.00145 0.00168

MEOH 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001

ETOH 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001

ISOP 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001

BENZ 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002

BUTA 0.0000037 0.0000037 0.0000037 0.0000037 0.0000037 0.0000037 0.0000037 0.0000037

PACET 0.0000242 0.000042 0.0000687 0.000024 0.00003 0.000058 0.000114 0.000139

HCHO 0.0000637 0.000098 0.000167 0.000063 0.000073 0.000105 0.000173 0.000222

SACET 0.000109 0.00019 0.000618 0.000108 0.000135 0.00026 0.000501 0.000625

SFORM 0.000285 0.00044 0.0015 0.000275 0.00032 0.000475 0.00078 0.001

PDIC 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001

MCHL 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001

PERC 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001

TCE 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001

NAPH 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001

### MATES III Final Report

Appendix IX-33 TABLE IX-5 (Continued) Boundary Conditions for Particulate Compounds ( μ g/m3): North and East Boundaries

Compound North (Fine) East (Fine)

N1 N2 N3 N4 N5 E1 E2 E3

AR 0.00004 0.0001 0.000144 0.0004 0.0004 0.00034 0.0002 0.000192

CD 0.000013 0.00003 0.0000425 0.000125 0.000125 0.000108 0.0000625 0.00006

CR 0.0001 0.00025 0.00034 0.001 0.001 0.00085 0.0005 0.00048

CR6 0.00001 0.00002 0.00002 0.00008 0.0001 0.00007 0.000045 0.000045

DPMa 0.078 0.117 0.085 0.3 0.29 0.104 0.075 0.084

DPMb 0.029 0.068 0.093 0.222 0.188 0.15 0.108 0.088

DPMc 0.0017 0.0041 0.005 0.0127 0.0123 0.0103 0.0055 0.00475

DPMd 0.0014 0.0034 0.0044 0.035 0.037 0.0075 0.0084 0.0123

DPMe 0.0088 0.01 0.011 0.0075 0.0055 0.005 0.0037 0.00246

DSL 0.119 0.2 0.2 0.58 0.503 0.278 0.201 0.192

EC 0.059 0.11 0.128 0.3 0.283 0.154 0.124 0.125

NI 0.000056 0.00014 0.00019 0.00056 0.00056 0.000476 0.00028 0.000269

OC 0.011 0.22 0.255 0.61 0.66 0.336 0.257 0.25

PB 0.00017 0.000425 0.00056 0.0017 0.0017 0.001445 0.00085 0.000817

North (Coarse) East (Coarse)

N1 N2 N3 N4 N5 E1 E2 E3

ARC 0.00005 0.00012 0.00022 0.0004 0.0004 0.000276 0.00016 0.000152

CDC 0.000014 0.00004 0.0000688 0.000125 0.000125 0.000086 0.00005 0.000048

CR6C 0.000001 0.000001 0.000001 0.00001 0.00001 0.000005 0.000005 0.000005

CRC 0.00012 0.0003 0.00055 0.001 0.001 0.00069 0.0004 0.00038

DPMaC 0.005 0.0072 0.005 0.017 0.016 0.0045 0.003 0.0034

DPMbC 0.0013 0.0027 0.006 0.0084 0.006 0.00625 0.00429 0.003

DPMcC 0.00003 0.00007 0.00011 0.00017 0.00016 0.000235 0.00011 0.00014

DPMdC 0.00006 0.00013 0.0005 0.0028 0.0023 0.00033 0.00037 0.0006

DPMeC 0.0005 0.00041 0.0004 0.00022 0.00018 0.00025 0.00016 0.0001

DSLC 0.008 0.01 0.0124 0.028 0.025 0.0116 0.008 0.0072

ECC 0.011 0.023 0.034 0.072 0.071 0.036 0.029 0.0319

NIC 0.000067 0.00017 0.00031 0.00056 0.00056 0.000386 0.000216 0.000213

OCC 0.07 0.15 0.25 0.36 0.29 0.261 0.154 0.135

PBC 0.0002 0.00051 0.00093 0.0017 0.0017 0.00117 0.00068 0.000646

### MATES III Final Report

Appendix IX-34 TABLE IX-5 (Continued) Boundary Conditions for Particulate Compounds ( μ g/m3): West and South Boundaries

West (Fine) South (Fine)

W1 W2 W3 S1 S2 S3 S4 S5

AR 0.000002 0.000022 0.000032 0.000004 0.00014 0.00016 0.00044 0.00056

CD 6.3E-07 6.88E-06 0.00001 0.0000013 0.000043 0.00005 0.0001375 0.0002

CR 0.000005 0.000055 0.00008 0.00001 0.00034 0.0004 0.0011 0.0016

CR6 0.0000005 0.00001 0.00001 0.000001 0.00006 0.00007 0.00017 0.0002

DPMa 0.00016 0.00494 0.0078 0.00025 0.0013 0.003 0.096 0.143

DPMb 0.00034 0.0048 0.012 0.00055 0.0025 0.005 0.097 0.165

DPMc 0.015 0.0211 0.02 0.0245 0.0185 0.0216 0.025 0.0166

DPMd 0.000125 0.00125 0.00142 0.00014 0.00032 0.0005 0.00327 0.006

DPMe 0.0001 0.0015 0.0058 0.00013 0.00036 0.00058 0.0031 0.0055

DSL 0.0155 0.033 0.047 0.0255 0.023 0.031 0.219 0.335

EC 0.006 0.043 0.05 0.0097 0.019 0.028 0.134 0.196

NI 0.0000028 0.0000308 0.000045 0.0000056 0.00019 0.000226 0.000616 0.000896

OC 0.011 0.026 0.043 0.018 0.0175 0.025 0.223 0.377

PB 0.0000085 0.0000935 0.000126 0.000017 0.000578 0.00068 0.00187 0.00272

West (Coarse) South (Coarse)

W1 W2 W3 S1 S2 S3 S4 S5

ARC 0.0000006 0.000006 0.000026 0.0000004 0.000002 0.000008 0.00016 0.00028

CDC 0.0000002 1.88E-06 0.000008 1.3E-07 0.0000006 0.0000025 0.00005 0.0001

CR6C 1E-08 0.0000001 0.0000005 0.000001 0.000001 0.000001 0.000001 0.000005

CRC 0.0000015 0.000015 0.000065 0.000001 0.000005 0.00002 0.0004 0.0008

DPMaC 0.00001 0.00039 0.00055 0.000015 0.00007 0.00017 0.0065 0.0086

DPMbC 0.00002 0.00034 0.00082 0.00003 0.00013 0.00027 0.0065 0.0094

DPMcC 0.001 0.0015 0.00139 0.0015 0.00096 0.0011 0.00125 0.00063

DPMdC 0.00001 0.00009 0.0001 0.00001 0.00002 0.00003 0.00021 0.00032

DPMeC 0.000005 0.0001 0.00042 0.00001 0.00002 0.00003 0.0002 0.00018

DSLC 0.001 0.0024 0.0032 0.0015 0.0012 0.0016 0.0146 0.019

ECC 0.00036 0.0036 0.0065 0.0005 0.0011 0.0022 0.036 0.055

NIC 8.4E-07 0.0000084 0.000036 5.6E-07 0.0000028 0.0000112 0.000224 0.000448

OCC 0.0014 0.011 0.035 0.0019 0.0041 0.0079 0.236 0.392

PBC 2.55E-06 0.0000255 0.00011 0.0000017 0.0000085 0.000034 0.00068 0.00136

### MATES III Final Report

Appendix IX-35 Modeling Results

The performance of the CAMx regional modeling simulation for the 2005 emissions and meteorology is summarized through model perfor mance statistics and gr aphically through time series displays and bivariate pl ots of key projected pollutant c oncentrations. Summarized in Table IX-6 are the toxic components observed an d simulated concentrations and the prediction accuracy (PA) measured as the percentage di fference between the mean annual observed and

simulated concentrations.

2005 CAMx RTRAC Simulation

Simulated annual average EC 2.5 

### was the compound used to assess overall model performance for the 2005 MATES III period at the eight sites having a full year’s sampling. The analysis used annual average EC 2.5 

### model performance to provide c onsistency with the 2007 AQMP annual average PM 2.5 

### attainment demonstration modeling assessment. While the 2007 EC 2.5 

### AQMP modeling was conducted on a coarser grid (5 kilo meter squared), it was expected that the summary performance of the CAMx RTRAC 200 5 MATES III simulation should be consistent,

but not identical.

In general, EC 2.5 

### performs well in the simulation. The EC 2.5 

### performance is comparable to that observed in the 2007 AQMP annual PM 2.5 

### attainment demonstration. Performance for several of the minor toxic components varies. This in pa rt can be attributed to very low measured concentrations nearing levels of detection, uncertainties in th e emissions inventory and model performance in recreating dispersi on patterns. Adding to the uncerta inty is the nine-cell distance weighted averaging to recreate a meas urement made at a discreet location.

EPA guidance (U.S. EPA, 2006) recommends ev aluating gaseous and particulate modeling performance using measures of prediction bias and error. PA goals of ±20% for ozone and ±30% for individual components of PM 2.5 

### or PM 10 

### have been used to assess simulation performance in previous modeli ng attainment demonstrations.

Table IX-7 provides the CAMx performance for EC 2.5 

### at the eight MATES III monitoring sites that have complete monitoring records for 2005. Three of the eight sites (Burbank, Inland Valley San Bernardino, and Rubidoux) unde rpredict the annual average EC 2.5 

### concentration. The greatest tendency for overprediction is at North Long Beach. The mean error of the simulated versus measured concentrations ranges from 0.54 μg/m 3 

### to 1.11 μg/m 3 

### . PA at seven of the eight MATES III sites meets the particul ate goal with only Burbank exhibiting a large degree (50%) of underprediction of the annual average concentration. Of the remaining sites, Inland Valley San Bernardino, and Rubidoux ar e underpredicted by 19 and 22%, respectively and Long Beach is overpredicted by 22%. All othe r sites PA falls within ±10% of observations.

Table IX-8 provides the CAMx RTRAC perfor mance for Benzene at the eight MATES III monitoring sites that have complete monitoring records for 2005. Benzene model performance is

included in the evaluation because of the confid ence in the benzene measurement data based on the long-term monitoring conducted in the Basin and throughout California. With the exceptions of Burbank and Compton, the remaining sites ac hieve a PA of less than 20%. The simulation bias is mixed and is generally less than 0.1 ppb (w ith the exceptions of Burbank and Compton).

### MATES III Final Report

Appendix IX-36 The time series fit of the simulated EC 2.5 

### concentrations to measurements for each station is depicted in Figures IX-11a th rough IX-11h and in the cumulative eight-site combined bivariate plot shown in Figure IX-12. The time series depiction of the measured and simulated EC 2.5 

### echo

the statistical evaluation wher eby concentrations are underpre dicted throughout the year at Burbank and Riverside, with the greatest margin occurring in the fall and early winter periods. Similarly, EC 2.5 

### at Inland Valley San Bernardino is underpre dicted in the second half of the year. The time series for the other sites show a gene ral tendency to nominally overpredict in the summer but capture the trend in the fall and winter.

In Figure IX-12, the EC 2.5 

### predictions for the eight sites combined show an overall tendency towards underprediction. However a large percent of the predictions ar e within 30% of the

measured concentrations.

1998-99 CAMx RTRAC Simulation

Table IX-9 and Table IX-10 provide a comp arison of the 2005 and 1998-99 model performance

for EC 2.5 

### and benzene, respectively. The tables provi de comparisons of the performance of the

mean annual CAMx RTRAC simulated EC 2.5 

### and benzene and the annua l average concentrations based on the monitoring data. Performance sole ly for days when observations were conducted

was not calculated for the 1998-99 simulation. List ed in each table are PA, mean error, and

absolute error. The 1998-99 CAMx RTRAC eval uation includes simulation performance at Huntington Park and Pico Rivera in the assessment.

The 1998-99 CAMx RTRAC model simu lations were developed usi ng the same procedures and methodologies as the 2005 simulation. Over all, the 1998-99 simulated annual average concentrations tended to underpredict observed mean concentrations.

As presented in Table IX-8, the EC 2.5 

### 1998-99 simulation depicts a systematic bias towards underprediction. In addition, prediction accuracy degrades compared to the 2005 simulation with only three of eight stations meeting th e ±30% goal. The ratio of the 1998-99 average absolute error to the average observed concentr ation was over 36% compared to a similar ratio for 2005 valued at 17%. (Note: the mix of sta tions is different for the two periods).

The 1998-99 simulation performance for benzene, (d epicted in Table IX-9), is also degraded compared to the 2005 simulations, however, to a lesser degree than for EC 2.5 

### . While the average

bias for the 1998-99 simulation trends towards unde rprediction, several coas tal metropolitan sites are well-simulated. The poorest performing site for PA and bias in both simulations 1998-99 and 2005 is Compton. The ratio of the 1998-99 aver age absolute error to the average observed concentration was 29% compared to a si milar ratio for 2005, valued at 17%.

Discussion of Simulation Uncertainties

It is difficult to assign incremental degrees of uncertainty to the model simulations. Both simulation periods required emissions projec tions from the 2002 base-year inventory. A significant amount of effort was taken in the development of the 2002 to 2005 emissions projections, particularly for the mobile source inventory as a co mponent in the development of the 2007 AQMP. The 1998-99 back-cast of the emissions data used the same projection

### MATES III Final Report

Appendix IX-37 protocol as the forward projection from 2002 to 2005. The simulation used the 1998 reported point source emissions and mobile sour ce emissions based on EMFAC 2007.

There are several areas where incr emental uncertainty in the back-cast of the emissions can take place. Traffic patterns, spatial vehicle age dist ributions and marine vessel activities all changed from 1998 to 2005. Weigh-in-motion (WIM) traffic count data has been used in the 2003 and 2007 AQMPs to help better define weekday and week end traffic patterns. The WIM data used in the analysis is more contemporary with the 2002-2005 time frames and was not changed in the development of the 1998-99 diurnal traffic patterns. Also, the greater number of older, higher emitting cars and their distribution throughout the Basi n may have added to the uncertainty in the 1998 mobile source emissions estimate. Finally, de tailed shipping surveys, including port calls and route assignment for oceangoing vessels a nd marine craft, were conducted for the development of the 2003 and 2007 AQMPs. Uncerta inties arise in the back-casts of the 1998 activities contributing to the estimation of these emissions.

As previously discussed, a general assessment of the meteorological profiles of the two monitoring periods suggests that the dispersion potentials were comparable but not identical. The meteorological input fields generated from th e MM5 simulations followed the same protocol with the exceptions of the grid resolution of the analysis ini tialization simulations and the day specific data used in the FDDA analysis. Finally, both simulations use the same initial and boundary conditions.

Simulation Evaluation Averaged Over the Monitoring Network

For this comparison, the monitored data for six sta tions are combined to pr ovide an estimate of average basin-wide conditions for the two sampling periods: 2005 and 1998-99. Table IX-11 summarizes the network average measured and predicte d pollutant concentrations at the six sites. Two stations in 2005, Huntington Park and Pico Rivera, did not have complete measurement records for the full 12 months and were excluded from the analysis. Similarly, complete measurements for Compton and West Long B each were not available for 1998-99. CAMx RTRAC simulated pollutant concentr ations for the six stations that have complete data for the two measurement periods were calculated from th e grid data using the distance weighted ninecell average. No direct measurements of PM 2.5 

### diesel were available for comparison to simulate annual average concentrations. However, estimates of diesel concentrations based on Chemical Mass Balance (CMB) analysis using ambient m easured elemental carbon concentrations are

discussed later. Measured concentrations of na phthalene were available for Central Los Angeles

and Riverside. Each of the four counties is represented by at leas t one station. The six stations’ average measured and simulated concentrations pr ovide an estimate of th e regional profile but with a bias towards impacts to the coastal comm unities in the heavily transited areas of the Basin. Moreover, the assessment provides a direct comparison for model performance

evaluation.

In general, 2005 model simulated particulate EC 2.5 

### , EC 10 

### , hexavalent chromium and PM 2.5 

### nickel average annual toxic compound concentrations comp ared well with the measured annual average values. The majority of gaseous components were well-simulated with the sole exception of acetaldehyde which is underpredicted. Arsenic a nd TSP lead exhibit the greatest tendency for

### MATES III Final Report

Appendix IX-38 overprediction. Cadmium, and PM 2.5 

### lead concentrations tend to be underpredicted. In general, the concentrations of the gaseous compounds are closely recreated.

For 1998-99, there exists a gene ral tendency for underprediction. Hexavalent chromium and nickel average annual toxic compound concentrati ons are exceptions that are closely matched to

observations. Aside from the uncertainties as sociated with the modeling analyses, some uncertainty in prediction accuracy is introduced into the analysis through the measurement and analysis programs. The 1998-99 data samples were measured and analyzed by different agencies (AQMD and ARB) and their laborat ories. In addition, to the s ubstitution of one-half level of

detection for data measured below the detection lim it also adds to the uncertainty in the analysis.

Simulation Estimated Spatial Concentration Fields

Figures IX-13a through IX-13u depict the CA Mx projected annual average concentration

distributions of selected toxic co mpounds as well as the impacts of five emissions categories of diesel particulates in the Basin. In general, the distribution of diesel particulates follows the major arterials. However, localized hot spot s with annual average co ncentrations to 4.8 μg/m 3


### are observed in the Central Los Angeles area and 8.5 μg/m 3 

### at the Ports of Los Angeles and North Long Beach. Figures IX-13h and IX-13i provide the distributions of benzene and 1,3- butadiene, respectively, whereby the toxic co mpounds are almost uniformly distributed throughout the Basin (reflecting patterns of light-duty vehicle fuel consumption). The formaldehyde profile (Figure IX-13j) depicts hi gher concentrations in the heavily-traveled western and central Basin, with a dditional hot spots in the downwi nd areas of the Basin that are impacted by higher levels of ozone forma tion (Santa Clarita and Crestline).MATES IIIFinal ReportAppendix IX-39Table IX-62005 Station Observed and CAMx SimulatedMATES III Annual Average ConcentrationsCompoundUnits Anaheim Burbank ComptonInland Valley SanBernardinoObs Model PA Obs Model PA Obs Model PA Obs Model PA1,3 Butadiene ppb 0.090.0990.150.07560.190.15190.070.0711Acetaldehyde ppb 1.281.04191.941.02471.571.04341.901.1042As (TSP)ηg/m30.441.913330.741.491020.663.414170.752.94294As (2.5)ηg/m30.500.76520.520.5120.462.143630.541.15115Benzene ppb 0.440.50150.710.47340.800.57290.490.4411Cd (TSP)ηg/m31.580.59631.490.38741.420.98311.671.3221Cd (2.5)ηg/m31.630.32801.300.21841.700.71581.730.8750Cr6 (TSP)ηg/m30.160.1720.180.11350.310.19380.200.41104EC10μg/m31.601.82132.351.35421.842.34272.402.304EC2.5μg/m31.411.3542.041.03501.761.8872.181.7719Formaldehyde ppb 2.962.9703.842.85263.143.1813.703.0817Methylene Chloride ppb 0.240.36540.340.27200.290.39330.180.182Naphthalene ppbNi (TSP)ηg/m33.933.13203.752.05456.027.97323.7211.87219Ni (2.5)ηg/m34.311.71603.721.12704.505.87303.507.89126Pb (TSP )ηg/m33.068.801883.125.49763.068.141663.1112.71308Pb (2.5)ηg/m33.571.96454.661.17755.842.40598.676.1629p-Dichlorobenzene ppb 0.030.091740.040.07950.070.11620.030.0596Perchloroethylene ppb 0.060.10600.100.1010.120.13110.050.0734Trichloroethylene ppb 0.030.03170.050.02530.030.06870.030.0444MATES IIIFinal ReportAppendix IX-40Table IX-6 (Continued)2005 Station Observed and CAMx SimulatedMATES III Annual Average ConcentrationsCompound UnitsHuntington Park(Less than 12 Months)North Long Beach Central Los AngelesPico Rivera(Less than 12 Months)Obs Model PA Obs Model PA Obs Model PA Obs Model PA1,3 Butadiene ppb 0.21 0.25210.100.1020.120.1350.140.133Acetaldehyde ppb 1.58 1.13281.261.11121.781.31261.731.2726As (2.5)ηg/m31.46 10.676290.652.212410.663.954961.034.04294As (TSP)ηg/m31.27 8.795930.510.90770.511.521971.342.1459Benzene ppb 0.83 0.62250.500.57130.590.69160.700.665Cd (2.5)ηg/m31.46 1.04281.650.88461.430.82431.380.7943Cd (TSP)ηg/m32.18 0.67691.270.65491.390.47661.210.4563Cr6 (TSP)ηg/m30.23 0.38620.180.1840.180.26420.170.2437EC10μg/m32.54 3.03191.812.25242.052.67302.632.775EC2.5μg/m32.28 2.2901.401.71211.932.0462.332.0313Formaldehyde ppb 3.70 3.6903.503.3644.234.1033.543.705Methylene Chloride ppb 0.39 0.51310.620.34450.380.58530.310.3717Naphthalene ppb205.33149.5127Ni (2.5))ηg/m35.54 6.59196.648.77324.874.9114.934.4110Ni (TSP)ηg/m32.59 4.11594.155.83404.332.99313.342.5125Pb (2.5 )ηg/m33.14 13.073163.267.671353.0410.092323.169.66206Pb (TSP)ηg/m37.74 5.48294.321.87574.672.14546.022.3761p-Dichlorobenzene ppb 0.08 0.13680.030.091840.040.122340.040.09150Perchloroethylene ppb 0.11 0.16480.050.101100.060.131250.070.1156Trichloroethylene ppb 0.04 0.06670.030.05880.030.0300.040.0329MATES IIIFinal ReportAppendix IX-41Table IX-6 (Continued)2005 Station Observed and CAMx SimulatedMATES III Annual Average ConcentrationsCompoundUnits Rubidoux West Long BeachObs Model PA Obs Model PA1,3 Butadiene Ppb 0.09 0.0950.120.0922Acetaldehyde Ppb 1.67 1.09351.411.0823As (2.5)ηg/m30.82 1.961390.712.33227As (TSP)ηg/m30.45 0.56260.470.99113Benzene Ppb 0.44 0.4420.530.6014Cd (2.5)ηg/m31.54 0.57631.461.329Cd (TSP)ηg/m31.46 0.32781.471.0827Cr6 (TSP)ηg/m30.39 0.14650.260.1734EC10μg/m32.06 1.74152.302.5712EC2.5μg/m31.69 1.32222.072.143Formaldehyde Ppb 3.44 3.01133.343.253Methylene Chloride Ppb 0.33 0.21370.200.2943Naphthalene Ppb 151.73 81.4246188.6896.0849Ni (2.5))ηg/m33.73 3.311110.5517.3665Ni (TSP)ηg/m33.57 2.17397.4510.9647Pb (2.5 )ηg/m33.12 7.261323.177.17126Pb (TSP)ηg/m36.31 1.78724.502.3647p-Dichlorobenzene Ppb 0.04 0.06580.030.10191Perchloroethylene Ppb 0.03 0.081300.050.09100Trichloroethylene Ppb 0.02 0.03380.040.040MATES IIIFinal ReportAppendix IX-42Table IX-72005 Simulation Performance Statistics for PM2.5Elemental CarbonLocationObserved(μg/m3)SamplesPredicted(μg/m3)PAMeanBias(μg/m3)MeanError(μg/m3)NormalizedMean Bias(μg/m3)NormalizedMean Error(μg/m3)Anaheim 1.41 120 1.35 4 -0.06 0.54 0.39 0.61Burbank 2.04 119 1.03 50 -1.02 1.11 -0.31 0.48Compton 1.76 115 1.88 7 0.12 0.61 0.39 0.52Inland Valley S.B. 2.18 117 1.77 19 -0.41 0.91 0.09 0.56North Long Beach 1.40 107 1.71 21 0.30 0.61 0.55 0.65Central L.A. 1.93 117 2.04 6 0.11 0.76 0.39 0.58Rubidoux 1.69 119 1.32 22 -0.38 0.74 0.09 0.58West Long Beach 2.07 114 2.14 3 0.07 0.79 0.33 0.53Table IX-82005 Simulation Performance Statistics for BenzeneLocationObserved(μg/m3)SamplesPredicted(μg/m3)PAMeanBias(μg/m3)MeanError(μg/m3)NormalizedMean Bias(μg/m3)NormalizedMean Error(μg/m3)Anaheim 0.439 115 0.504 15 0.0640.213 0.597 0.735Burbank 0.709 121 0.466 34 -0.2430.344 -0.072 0.428Compton 0.796 117 0.567 29 -0.2290.388 0.105 0.498Inland Valley S.B. 0.492 117 0.438 11 -0.054 0.169 0.092 0.398North Long Beach 0.504 118 0.571 13 0.066 0.213 0.485 0.621Central L.A.0.588 119 0.685 16 0.097 0.249 0.469 0.614Rubidoux 0.435 119 0.442 2 0.007 0.16 0.267 0.464West Long Beach 0.525 119 0.598 14 0.073 0.214 0.603 0.725MATES IIIFinal ReportAppendix IX-43Table IX-9Comparative Simulation Performance Statistics for EC1998-99 2005LocationObservedDays(μg/m3)ModeledAllDays(μg/m3) PABias(μg/m3)AbsoluteError(μg/m3)ObservedDays(μg/m3)ModeledAllDays(μg/m3) PABias(μg/m3)AbsoluteError(μg/m3)Anaheim 2.30 2.12 8 -0.18 0.18 1.41 1.35 4 -0.06 0.06Burbank 3.19 1.59 50 -1.6 1.6 2.04 1.05 49 -0.99 0.99Compton 1.76 1.87 6 0.11 0.11Inland Valley S.B. 3.10 1.84 41 -1.26 1.26 2.18 1.82 17 -0.36 0.36Huntington Park 4.35 2.73 37 -1.62 1.62North Long Beach 2.54 2.41 5 -0.13 0.13 1.4 1.82 30 0.42 0.42Central L.A. 3.53 2.74 22 -0.79 0.79 1.93 2.08 8 0.15 0.15Pico Rivera 4.35 2.14 51 -2.21 2.21Rubidoux 3.39 1.39 59 -2.00 2.00 1.69 1.36 20 -0.33 0.33West Long Beach 2.07 2.16 4 0.09 0.09Average 3.34 2.12 34 -1.22 1.22 1.81 1.69 17 -0.12 0.31MATES IIIFinal ReportAppendix IX-44Table IX-10Comparative Simulation Performance Statistics for Benzene1998-99 2005LocationObservedDays(ppb)ModeledAllDays(ppb) PABias(ppb)AbsoluteError(ppb)ObservedDays(ppb)ModeledAllDays(ppb) PABias(ppb)AbsoluteError(ppb)Anaheim 1.05 0.81 23 -0.24 0.24 0.44 0.51 15 0.07 0.07Burbank 1.26 0.52 59 -0.74 0.74 0.71 0.47 33 -0.24 0.24Compton 1.8 0.84 53 -0.96 0.96 0.8 0.57 29 -0.23 0.23Inland Valley S.B. 0.74 0.56 24 -0.18 0.18 0.49 0.45 9 -0.04 0.04Huntington Park 1.65 1.05 36 -0.6 0.6 0.63North Long Beach 0.83 0.85 2 0.02 0.02 0.5 0.57 15 0.07 0.07Central L.A. 1.01 1.07 6 0.06 0.06 0.59 0.69 17 0.10 0.10Pico Rivera0.89 0.88 1 -0.01 0.01 0.60Rubidoux 0.87 0.52 40 -0.35 0.35 0.44 0.45 2 0.01 0.01West Long Beach 1.27 1.15 9 -0.12 0.12 0.53 0.60 12 0.07 0.07Average 1.14 0.83 25 -0.31 0.33 0.56 0.55 17 -0.02 0.10

### MATES III Final Report

Appendix IX-45

Figure IX-11a

EC 2.5 Time Series: Simulated Vs. Measured at Anaheim.

Figure IX-11b

EC 2.5 Time Series: Simulated Vs. Measured at Burbank.

Figure IX-11c

EC 2.5 Time Series: Simulated Vs. Measured at Compton. 

### Anaheim EC 2.5

0 2 4 6 8 10

2 26 50 74 98 122 146 170 194 218 242 266 290 314 338 362 [ug/m3]

Observed Predicted



### Burbank EC 2.5

0 2 4 6 8 10

2 26 50 74 98 122 146 170 194 218 242 266 290 314 338 362 [ug/m3]

Observed Predicted



### Compton EC 2.5

0 2 4 6 8 10

2 26 50 74 98 122 146 170 194 218 242 266 290 314 338 362 [ug/m3]

Observed Predicted

### MATES III Final Report

Appendix IX-46

Figure IX-11d

EC 2.5 Time Series: Simulated Vs. Measured at Inland Valley San Bernardino.

Figure IX-11e

EC 2.5 Time Series: Simulated Vs. Measured at North Long Beach.

Figure IX-11f

EC 2.5 Time Series: Simulated Vs. Measured at Central Los Angeles. 

### Inland Valley S.B. EC 2.5



### 0 2 4 6 8 10

2 26 50 74 98 122 146 170 194 218 242 266 290 314 338 362[ug/m3]

Observed Predicted



### North Long Beach EC 2.5



### 0 2 4 6 8 10

2 26 50 74 98 122 146 170 194 218 242 266 290 314 338 362[ug/m3]

Observed Predicted



### Central Los Angeles. EC 2.5



### 0 2 4 6 8 10

2 26 50 74 98 122 146 170 194 218 242 266 290 314 338 362[ug/m3]

Observed Predicted

### MATES III Final Report

Appendix IX-47

Figure IX-11g

EC 2.5 Time Series: Simulated Vs. Measured at Rubidoux.

Figure IX-11h

EC 2.5 Time Series: Simulated Vs. Measured at West Long Beach. 

### Rubidoux EC 2.5



### 0 2 4 6 8 10

2 26 50 74 98 122 146 170 194 218 242 266 290 314 338 362[ug/m3]

Observed Predicted



### West Long Beach EC 2.5

0 2 4 6 8 10

2 26 50 74 98 122 146 170 194 218 242 266 290 314 338 362[ug/m3]

Observed Predicted

### MATES III Final Report

Appendix IX-48 Scatter Plot of Predicted vs. Observed Concentrations

EC

0 1 2 3 4 5 6 7 8 9 10

0 1 2 3 4 5 6 7 8 9 10

Observed [ug/m3]Predicted [ug/m3]



### Figure IX-12

EC 2.5 

### Bivariate Scatter Plot Simula ted Vs. Measured All Stations.

### MATES III Final Report

Appendix IX-49

Table IX-11 Toxic Compounds Simulated and Measured:

Six-Station Annual Aver age Concentrations 2005 MATES III and 1998-99 CAMx RTRAC Analyses

2005 MATES III 1998-99 MATES II (CAMx RTRAC Simulation)

Toxic Compound Units Measured

Annual Average Simulated

Annual Average Measured

Annual Average Simulated Annual Average

EC 2.5 μ g/m 3 1.78 1.58 N/A N/A

EC 10 μ g/m 3 2.04 2.05 3.01 2.03

Cr6 (TSP) η g/m 3 0.22 0.21 0.18 0.17

As (2.5) η g/m 3 0.5 0.92 N/A N/A

As (TSP) η g/m 3 0.68 2.46 1.79 3.00

Cd (2.5) η g/m 3 1.46 0.49 N/A N/A

Cd (TSP) η g/m 3 1.56 0.78 6.57 1.00

Ni (2.5)) η g/m 3 3.93 3.65 N/A N/A

Ni (TSP) η g/m 3 4.44 5.82 7.51 6.83

Pb (2.5 ) η g/m 3 5.41 2.60 N/A N/A

Pb (TSP) η g/m 3 10.64 8.68 22.72 10.00
Benzene Ppb 0.53 0.52 0.97 0.75
Perchloroethylene Ppb 0.06 0.09 0.27 0.18
p-Dichlorobenzene Ppb 0.03 0.08 0.12 0.06
Methylene Chloride Ppb 0.35 0.32 0.70 0.54
Trichloroethylene Ppb 0.03 0.03 0.10 0.05
1,3Butadiene Ppb 0.1 0.09 0.29 0.13
Formaldehyde Ppb 3.61 3.26 4.00 3.75
Acetaldehyde Ppb 1.64 1.12 1.81 1.26
Naphthalene Ppb 0.02* 0.01 N/A 0.02 

### * Two station average

### MATES III Final Report

Appendix IX-50 FIGURE IX-13a CAMx simulated 2005 annual average Diesel 

### PM 2.5

.

FIGURE IX-13b CAMx simulated 2005 annual average Elemental Carbon 

### PM 2.5

.

### MATES III Final Report

Appendix IX-51 FIGURE IX-13c CAMx simulated 2005 annual average On-Road Diesel 

### PM 2.5

.

FIGURE IX-13d CAMx simulated 2005 annual average Off-Road Diesel 

### PM 2.5

.

### MATES III Final Report

Appendix IX-52 FIGURE IX-13e CAMx simulated 2005 annual average Diesel from Ships 

### PM 2.5.

FIGURE IX-13f CAMx simulated 2005 annual average Diesel from Trains 

### PM 2.5

.

### MATES III Final Report

Appendix IX-53 FIGURE IX-13g CAMx simulated 2005 annual average Diesel from Stationary Sources 

### PM 2.5

.

FIGURE IX-13h CAMx simulated 2005 annual average Benzene.

### MATES III Final Report

Appendix IX-54 FIGURE IX-13i CAMx simulated 2005 annual average 1,3 Butadiene.

FIGURE IX-13j CAMx simulated 2005 annual average 

### for Total Formaldehyde.

### MATES III Final Report

Appendix IX-55 FIGURE IX-13k CAMx simulated 2005 annual average Acetaldehyde.

FIGURE IX-13l CAMx simulated 2005 annual average Arsenic 

### PM 2.5

.

### MATES III Final Report

Appendix IX-56 FIGURE IX-13m CAMx simulated 2005 annual average Cadmium 

### PM 2.5

.

FIGURE IX-13n CAMx simulated 2005 annual average Chromium 

### PM 2.5

.

### MATES III Final Report

Appendix IX-57 FIGURE IX-13o CAMx simulated 2005 annual average Lead 

### PM 2.5

.

FIGURE IX-13p CAMx simulated 2005 annual average Methylene Chloride.

### MATES III Final Report

Appendix IX-58 FIGURE IX-13q CAMx simulated 2005 annual average Naphthalene.

FIGURE IX-13r CAMx simulated 2005 annual average Nickel 

### PM 2.5

.

### MATES III Final Report

Appendix IX-59 FIGURE IX-13s CAMx simulated 2005 annual average p-Dichlorobenzene.

FIGURE IX-13t CAMx simulated 2005 annual average Perchloroethylene.

### MATES III Final Report

Appendix IX-60 FIGURE IX-13u CAMx simulated 2005 annual average Trichloroethylene.



### Estimation of Risk

Figure IX-14 depicts the distri bution of risk estimated from the predicted annual average concentrations of the key toxic compounds. (Figur e IX-14 is presented tw ice, first in shaded black and white, then in color). Risk is calculated for each grid cell as follows:

Risk i,j 

### = Σ Concentration i,j,k 

### X Risk Factor i,j,k



### Where i,j 

### is the grid cell (easting, north ing) and k is the toxic compound.

The grid cell having the maximum simulated ri sk of 3,693 was located in the Ports of Los Angeles and Long Beach. More spec ifically, the grids having the top 25 estimated risk values in

2005 were located in cells around th e ports area. The cell having the highest risk outside of the port area occurred in South Los Angeles as part of a cluster of grids that extended from Central Los Angeles to the southeast, following Interstate 5. Other elevated areas included the eastern Basin near the communities of Colton, Inland Va lley San Bernardino, and San Bernardino. As with the MATES II analysis, areas projected to have higher risk followed transportation corridors, including freeways and railways.

Figure IX-15 provides the CAMx RTRAC simulated risk for the 1998-99 period (using back-cast 1998 emissions and 1998-99 MM5 generated meteorologi cal data fields). Figure IX-16 depicts the 1998-99 to 2005 change in risk estimated from the CAMx RTRAC simulations. The greatest increase in risk occurred in the port area. Over all, air toxics risk impr oves to varying levels in

### MATES III Final Report

Appendix IX-61 most of the Basin with the exceptions of the area s directly downwind of the Ports and those areas heavily impacted by activities a ssociated with goods movement. Risk increases of more than 800 in a million between the two periods were not ed in the immediate areas encompassing the

ports.

The 2005 Basin average population weighted risk summed for th e toxic components yielded a

lifetime estimated cancer risk of 853 in a million. (The Basin average risk included all populated over-land cells that reside within the Basin por tion of the modeling domain). The MATES III Basin population weighted average risk is appr oximately 87% of the MATES II Basin average risk (981 per million) determined from the UAM TOX modeling analysis. However, when Basin population weighted average risk is recalcul ated for the 1998-99 MATES II period based on the CAMx RTRAC simulations the comparable Basi n average risk is 931 per million. A direct comparison of Basin population weighted risk ca lculated using the CAMx RTRAC simulations shows an 8% improvement between 1998-99 and 2005. The 8% reduction in Basin risk can be attributed to several factors most notably change s in quantity and spatial allocation of emissions

between 1998 and 2005. While weat her profile between the two monitoring periods varied, no appreciable difference was observed in th e meteorological dispersion potential.

Figures IX-17a through IX-17f depict risk asso ciated with diesel a nd its specific emissions categories. Figure I-17 provides th e Basin risk excluding the contribution of diesel particulates. (Again, Figures IX-16a through IX-1 7 are presented twice, first in shaded black and white, then in color). On and off-road diesel impact s are spread throughout the Basin following the transportation corridors and off-road facilities such as the intermodal transfer sites. The shipping impacts are concentrated in the vicinity of the Ports of Los Angeles and Long Beach and the adjacent downwind communities. The shipping la nes to the northwest and southeast following the coastline and Asia-bound are clearly depicted. Risk impacts from rail travel/transport and stationary sources range from over 200 to 500 in one million respectively.

Regional risk from nondiesel sour ces (Figure IX-18) is also un iformly distributed throughout the Basin, with values typically ranging from 100 to 300 in one million. Several elevated grid cells are apparent with risk estimated upwards of 400 in one million in the coastal plain encompassing Los Angeles International Airport and the heavily industr ialized areas of south of Downtown Los Angeles. Selected elevated grid cells are also ev ident in the east Basin with localized risk values of up to 1,000 in one million.MATES IIIFinal ReportAppendix IX-62FIGURE IX-142005 MATES III CAMx RTRAC Simulated Air Toxic Risk.MATES IIIFinal ReportAppendix IX-63FIGURE IX-14 (Repeated)2005 MATES III CAMx RTRAC Simulated Air Toxic Risk.MATES IIIFinal ReportAppendix IX-64FIGURE IX-151998-99 CAMx RTRAC Simulated Air Toxic Risk.MATES IIIFinal ReportAppendix IX-65FIGURE IX-15 (Repeated)1998-99 CAMx RTRAC Simulated Air Toxic Risk.MATES IIIFinal ReportAppendix IX-66FIGURE IX-16Change in CAMx RTRAC simulated risk from the 1998-99 to 2005(using back-cast 1998 emissions and 1998-99 MM5generated meteorological data fields).MATES IIIFinal ReportAppendix IX-67FIGURE IX-17aMATES III Risk from Diesel.MATES IIIFinal ReportAppendix IX-68FIGURE IX-17a (Repeated)MATES III Risk from Diesel.MATES IIIFinal ReportAppendix IX-69FIGURE IX-17bMATES III Simulated Risk from On-Road Diesel.MATES IIIFinal ReportAppendix IX-70FIGURE IX-17b (Repeated)MATES III Simulated Risk from On-Road Diesel.MATES IIIFinal ReportAppendix IX-71FIGURE IX-17cMATES III Simulated Risk from Off-road Diesel (including rail yards but excluding trains and ships).MATES IIIFinal ReportAppendix IX-72FIGURE IX-17c (Repeated)MATES III Simulated Risk from Off-road Diesel (including rail yards but excluding trains and ships).MATES IIIFinal ReportAppendix IX-73FIGURE IX-17dMATES III Simulated Risk from Ship Diesel.MATES IIIFinal ReportAppendix IX-74FIGURE IX-17d (Repeated)MATES III Simulated Risk from Ship Diesel.MATES IIIFinal ReportAppendix IX-75FIGURE IX-17eMATES III Simulated Risk from Trains (Excluding Rail Yards).MATES IIIFinal ReportAppendix IX-76FIGURE IX-17e (Repeated)MATES III Simulated Risk from Trains (Excluding Rail Yards).MATES IIIFinal ReportAppendix IX-77FIGURE IX-17fMATES III Simulated Risk from Stationary Diesel.MATES IIIFinal ReportAppendix IX-78FIGURE IX-17f (Repeated)MATES III Simulated Risk from Stationary Diesel.MATES IIIFinal ReportAppendix IX-79FIGURE IX-18MATES III Simulated Risk No-Diesel.MATES IIIFinal ReportAppendix IX-80FIGURE IX-18 (Repeated)MATES III Simulated Risk No-Diesel.

### MATES III Final Report

Appendix IX-81 Figure IX-19 provides a focused 2005 estimated air t oxics risk in the Ports area. Table IX-12 provides a summary risk estimated for the Basin, for the Ports area, and for the Basin excluding the Ports area. For this assessment, the Port s area includes the populated cells roughly bounded by the Interstate 405 to the north, San Pedro to the west, Balboa Harbor to the east and Pt. Fermin to the south. The 2005 average population we ighted air toxics risk in the Ports area (as defined above) was 1,415 in one million. The Basi n average population weight ed air toxics risk, excluding the grid cells in the Ports area, valued 816 in one million. (It is important to note that the downwind impacts resulting from Port area ac tivities are reflected in the air toxics risk estimates for the grid cells categorized as “Bas in excluding Ports”). A similar calculation based

on the CAMx RTRAC simulations for 1998-99 indicated that the Ports area air toxics risk was 1,208; and the Basin, minus the Ports area, was 912 in one million. Overall, the Ports area experienced an approximate 17% increase in ris k, while the average popula tion weighted risk in

other areas of the Basin decreased by about 11%.

As a sensitivity analysis, simulations were ge nerated to examine the hypothesis “what would the Basin toxics risk profile in 2005 be if no-growth occurred in the goods movement sector from 1998?” To attempt to answer this question, hea vy-duty truck transport, shipping, port and rail operation activity levels associated with goods move ment were held at 1998 levels. The impacts of fleet turnover and control measure implemen tation were allowed to go forward through 2005 to develop the hypothetical emission s inventory. The results of the sensitivity test indicated that the Ports area, Basin, and Basin excluding the Port s areas would experience lower air toxic risk levels by 6.2%, 14.8%, and 15.4% respectively.

Figures IX-20 through IX-23 provide close up depi ctions of air toxics risk to Central Los Angeles, Mira Loma/Colton, Central Orange Count y and West Los Angeles areas, respectively.

Figure IX-19

2005 Ports area MATES III Simulated Air Toxic Risk.

### MATES III Final Report

Appendix IX-82

Table IX-12

Basin and Port Area Po pulation Weighted Risk

MATES III MATES II*

Region 2005 Population Average

Risk (Per Million) 1998 Population Average

Risk (Per Million) Average Percentage Change in

Risk

Basin 15,662,620 853 14,404,993 931 -8

Ports Area 959,761 1,415 911,834 1207 17 Basin Excluding Ports Area 14,702,859 816 13,493,159 912 -11

* CAMx RTRAC Simulations

County Risk Assessment

Table IX-13 provides the county breakdown of air toxics risk to the a ffected population for 2005 MATES III and the 1998-99 MATES II CAMx RTRAC simulations. As presented in the spatial distribution, Los Angeles County bears the greate st average risk at 951 per one million person population. Orange County has th e second highest number of projected risk at 781 per one million person population. Risk in the Eastern Ba sin is lower. The estimated risk for San Bernardino is 712 per million, and Riverside wa s estimated to have the lowest population weighted risk at 485. It shoul d be noted that these are county-wide averages and individual communities could have higher risks than the average if they are near emissions sources, such as rail yards or intermodal facilities.

Comparison of the county-wide population weighted air toxics risk shows that the greatest reduction occurred in Los Angeles County. Reduc tions in emissions from mobile sources including benzene, 1,3 butadiene, and diesel particulate have contri buted to the improved county-wide risk. A similar profile is eviden t in Orange County. Despite across-the-board improvements in measured toxic air quality from MATES II (with th e sole exception of hexavalent chromium at Rubidoux), population gr owth in the Eastern Basin and associated

increases in mobile source emissions have resu lted in a nominal increase in population-weighted risk for Riverside County. Similarly, San Bernardino County risk levels improved only marginally.

### MATES III Final Report

Appendix IX-83

Table IX-13 County-Wide Population Weighted Air Toxic Risk

MATES III MATES II

CAMx RTRAC

Simulations Region

2005 Population Average

Risk (Per Million) 1998 Population Average

Risk (Per Million) Average Percentage Change in

Risk

Los Angeles 9,887,127 951 9,305,726 1,047 -9 Orange 2,764,620 781 2,579,794 833 -6 Riverside 1,548,031 485 1,249,554 478 2 San Bernardino 1,462,842 712 1,269,919 725 -2
SCAB 15,662,620 853 14,404,993 931 -8

Figure IX-20 2005 Central Los Angeles MATES III Simulated Air Toxic Risk.

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Appendix IX-84

Figure IX-21

2005 Mira Loma/Colton MATES III Simulated Air Toxic Risk.

Figure IX-22 2005 Central Orange County MATES III Simulated Air Toxic Risk.

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Appendix IX-85

Figure IX-23 2005 West Los Angeles MATES III Simulated Air Toxic Risk.

Risk from Key Compounds

Table IX-14 provides the Basin average breakdown of risk associated with each of the key compounds simulated in the analysis. Diesel pa rticulate ranked highest as the toxic compound contributing to the overall risk to the population. The next three highest contributors included benzene, 1,3 butadiene, and hexavalent chromium. Formaldehyde, acetaldehyde and arsenic each contribute approximately 1% of the ri sk while the remaining compounds combined

accounted for less than 3% of the total.

Network Risk Evaluation

Table IX-15 provides the CAMx RTRA C simulated risk at each of the eight stations for the three main toxic compounds and the remaining aggregat e based on the regional modeling. Risk is calculated using the predicted concentrations of each toxic component for the specific monitoring station location (based on a nine-cell weighted average concentration). The summary also provides the comparison between simulated av erage risk for the eight stations combined and the average risk calculated using the annual toxic compound m easurements and the estimated

diesel concentrations at those sites.

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Appendix IX-86

Table IX-14

2005 MATES III Risk from Simulate d Individual Toxic Compounds

Compound Risk Factor ( μ g/m 3 ) Highest Grid

Cell

Concentration Population Weighted

Annual Average

Concentration Units Population Weighted

Risk (per million) Percent

Contribution

Diesel 3.00E-04 11.70 2.35 μ g/m 3 703.76 82.5

Benzene 2.90E-05 1.15 0.48 ppb 44.53 5.2

1,3 Butadiene 1.70E-04 2.32 0.081 ppb 30.45 3.6

Hexavalent Chromium 1.50E-01 0.003 0.00016 μ g/m 3 23.41 2.7 Primary Formaldehyde 6.00E-06 4.89 1.60 ppb 11.78 1.4 Secondary Formaldehyde 6.00E-06 1.60 1.30 ppb 9.61 1.1

Arsenic 3.30E-03 0.022 0.0024 μ g/m 3 7.97 0.9

p-Dichlorobenzene 1.10E-05 0.208 0.076 ppb 5.01 0.6 Secondary Acetaldehyde 2.70E-06 0.766 0.67 ppb 3.25 0.4

Perchloroethylene 5.90E-06 0.370 0.92 ppb 3.67 0.4

Napthalene 3.40E-05 0.046 0.017 ppb 3.10 0.4

Cadmium 4.20E-03 0.009 0.00054 μ g/m 3 2.28 0.2

Primary Acetaldehyde 2.70E-06 0.917 0.35 ppb 1.72 0.2

Methylene Chloride 1.00E-06 1.062 0.29 ppb 1.02 0.1

Nickel 2.60E-04 0.298 0.0035 μ g/m 3 0.90 0.1

Trichloroethylene 2.00E-06 0.340 0.029 ppb 0.31 < 0.1

Lead 1.20E-05 0.104 0.0075 μ g/m 3 0.09 <0.1



### The highest simulated risk was estimated for West Long Beach followed by Los Angeles, North Long Beach and Compton. The lowest modeled ri sk was simulated at Burbank. As previously discussed, simulation performance at Burbank s howed a tendency for underprediction, and this feature appears to be translated to the risk calculation.

The nondiesel portion of the simulated risk can be directly compared to risk calculated from the toxic compound measurements. Figure IX-24 presen ts a comparison of the model simulated and

measurement estimated nondiesel risk at each monitoring site as well as the eight-station average. Simulated nondiesel risk is within 30% of measurements at all stations with the sole exception of Burbank. In general, there a ppears to be no geographical bias in model

### MATES III Final Report

Appendix IX-87 performance and the simulated eight-station average ri sk is essentially equal to the risk estimated

from the measurements.

Simulated total risk includes the contribution of diesel particulates; and, taken as an eight-station average, the modeled risk is 1,166 in a million. The eight-station average simulated risk is approximately 6% lower than the risk cal culated from the measured toxic compound concentrations, and the estimates of diesel concentrations using the emissions based factor (1.95) applied to the EC 2.5 

### average concentration. When the mode l simulated risk is compared to the measurement calculated risk including the range of CMB estimated diesel concentrations, the eight-station average risk was nominally less th an the lower projection of the range based on measurement data. The eight-station simulated risk based on the CAMx RTRAC analyses was approximately 10% lower than th e average of the CMB estimated diesel risk based on the two source profiles.

Table IX-15 Comparison of Network Averaged CAMX RTRAC 2005 Modeled Risk to Measured Risk at the Eight MATES III Sites

2005 MATES III CAMX RTRAC Simulation

Location Benzene 1,3

Butadiene Others Diesel Total

Anaheim 47 31 75 900 1,054

Burbank 44 25 64 613 746 Compton 52 54 94 950 1,150 Inland Valley San Bernardino 41 25 121 734 922 North Long Beach 53 36 84 1,282 1,455 Central Los Angeles 64 47 115 1,256 1,482

Rubidoux 42 33 70 700 845 West Long Beach 55 30 86 1,501 1,672 Simulated 8-Station Average 50 35 89 992 1,166 8-Station MATES III Average Measured (EC 2.5 

### * 1.95 for Diesel) 53 34 83 1,070 1,240

8-Station Average Measured (with range of CMB Diesel risk ) 53 34 83 1,004– 1,120 1,174 – 1,290 8-Station Average Measured (average of CMB Diesel risk ) 53 34 83 1,062 1,232

### MATES III Final Report

Appendix IX-88 0 50 100 150 200 250 300

Anaheim Burbank Compton Inland
Valley

S.B. North Long

Beach Los Angeles Rubidoux West Long

Beach 8-Station
AverageRisk Per Million

Observed Estimated



### Figure IX-24 2005 MATES III Simulated Vs. Measured Compounds NonDiesel Air Toxics Risk.

Evaluation

The population weighted average Basin air toxic risk (853 per million) simulated using CAMx RTRAC for the 2005 MATES III period was estimated to be 8% lower than that estimated for 1998-99 (931 in a million) when the same modeling platforms and year-specific meteorology are evaluated. This is loosely compared to a 13% reduction in average population weighted risk estimated for the 1998-99 MATES II analysis (981 in a million) using the UAMTOX modeling platform. The areas of the Basin having maximum risk continued to be the Ports of Los Angeles and Long Beach with a secondary maximum occurri ng in an area starting in South Los Angeles and extending towards south eastern Los Angeles.

The average simulated Basin risk for the 2005 MATES III data is 8% lower than the comparable average risk estimated for the 1998 MATES II an alysis. Using the 2007 AQMP inventory backcast methodology, the percentage reduction in diesel mass emissions from 1998 to 2005 is approximately 5%. However, emissions reducti ons of benzene (36%), 1,3-butadiene (31%), arsenic (20%) and hexavalent ch romium (85%) contribute greatly to the overall reduction in 2005 simulated risk. A general assessment of th e observed meteorological profile suggests that the two monitoring periods were comparable in dispersion potential.

### MATES III Final Report

Appendix IX-89 References

ENVIRON, Inc., 2008, CAMx User’s Guide Version 4.5 . ENVIRON. Novato, CA 94945

ENVIRON, Inc., 2005, “METSTAT software for MM5 version 3 (02/11/05),” ENVIRON. Novato, CA 94945, http://www.camx.com/down/support.php

ENVIRON, Inc., 2006, ”KVPATCH software for CAMx,” ENVIRON. Novato, CA 94945, http://www.camx.com/down/support.php

J.J. O’Brien, 1970, A note on the vertical st ru cture of the eddy exchange coefficient in the planetary boundary layer. J. Atmos. Sci., 27, 1213-1215

PSU/NCAR Mesoscale Model (MM5) 2004, http ://www.mmm.ucar.edu/mm5/mm5-home.html

WRAP, 2007, Western Regional Air Partnership, Technical Support System, Emissions Method, Offshore Emissions, http://vista.cira.colostate.edu/

U.S. EPA, 2006,” Guidance on Use of Modele d and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM 2.5 

### and Regional Haze NAAQS,” U.S. EPA, Office of Air Quality Planning and Standards, Emissions, Monitoring, and Analysis Division, Air Quality Modeling Group, Research Triang le Park, North Carolina, September, 2006