# PM 2.5 

# SIP

Appendix K

CMAQ Modeling Documentation

K-1: CMAQ Model Performance and Assessment

K-2: Meterological Modeling using MM5

K-3: Processing of 2002 Anthropogenic Emissions

K-4: PM 2.5 

# Modeling using SMOKE/CMAQ{This page left blank for printing purposes}

# K-1: CMAQ Model Performance and AssessmentTSD-3



## CMAQ Model Performance and Assessment

for Base Year 2002:PM 2.5 

## Mass and Speciation

Bureau of Air Quality Analysis and Research

Division of Air Resources

New York State Department of Environmental Conservation

Albany, NY 12233

December 14, 2007Introduction

One of the required tasks for atta inment demonstration for the PM 2.5 National Ambient Air Quality Standard (NAAQS) is the evaluation and assessment of the air quality modeling system that has been utilized to predict future ai r quality over the region of interest. As part of the attainment demonstration, the MM5/SMOKE/CMAQ modeling system was applied to simulate concentration fields of PM 2.5 mass and major species for the base year 2002 emissions with the co rresponding meteorological information. The modeling databases for meteorology using MM5 (TSD-1, 2007), the emissions using SMOKE (TSD-2a, TSD-2b, 2006), and applicat ion of the SMOKE/CMAQ system (TSD2c, 2007) provide the simulated pollutant fields which are compared to measurements to establish the credibility of the simulation. In the following sections a comparison between the measured and predicted concentrations is performed and results are presented, demonstrating on an overall basis the utility of the modeling system in this application.

The results presented here serve as an illustration of some of the model evaluation and assessment, as outlined in the EPA m odeling guidance document (US EPA, 2007), performed on the Base Year 2002 CMAQ simu lation. Additional information can be made available by request from the New Yo rk State Department of Environmental
Conservation.

Summary of measured data

The ambient air quality data for annual 2002 simulation were obtained from the following sources:

 EPA A ir Q uality S ystem (AQS)

 EPA fine particulate S peciation T rends N etwork (STN)

 I nteragency M onitoring of PR Otected V isual E nvironments (IMPROVE)

 Pinnacle State Park in Addison, NY opera ted by Atmospheric Science Research Center, University at Albany, Albany, NY

 Harvard Forest, Petersham, MA operate d by Harvard University, Boston, MA

 A tmospheric I nvestigation, R egional M odeling, A nalysis and P rediction (AIRMAP) operated by University of New Hampshire, Durham, NH

 N orthE ast O zone & F ine P article S tudy (NE-OPS), led by Penn State University and other research groups in Philadelphia, PA

Measured data from sites within the Ozone Transport Region (OTR) plus the rest of Virginia were included here, which hereafte r will be referred to as the “OTR+” region.
The model-based data were obtained at the grid-cell corresponding to the monitor location and no interpolation was performed. Th e analysis examines the model response to the OTR+ region with emphasis on the New York City non-attainment area (NYC NAA), which consists of two counties in Conn ecticut (Fairfield and New Haven) and 10 counties each in New Jersey (Bergen, Essex, Hudson, Mercer, Middlesex, Monmouth, Morris, Passaic, Somerset, and Union) a nd New York (Bronx, Kings, Nassau, New York, Orange, Queens, Richmond, Rockland, Suffolk, and Westchester).Fine particulate (PM 2.5 ) mass

The 24-hour average Federal Reference Method (FRM) PM 2.5 mass data collected routinely at SLAMS/NAMS sites across the OTR+ region were extracted from AQS for 225 sites (see http://www.epa.gov/ttn/airs/air sweb/aqsweb/aqswebhome.html ). Although 39 of these locations had duplicate monitors in 2002, only data from the primary monitors are presented here. Fifty six of these FRM monitors – 45 unique locations and
11 collocated monitors – are located within the NYC NAA. Hourly PM 2.5 mass data from 62 sites across the OTR+ region were also included in this anal ysis. Twenty-eight of these hourly sites are located within the NYC NAA. Hourly PM 2.5 mass were also obtained from the AIRMAP Program ( http://airmap.unh.edu ) at Thompson Farm, NH; Pinnacle State Park ( http://www.asrc.cestm.albany.edu ); and the NE-OPS site in Philadelphia, PA ( http://lidar1.ee.psu.edu ). In addition to examining the daily averages
based on the PM 2.5 data, composite and site-specific di urnal variations ar e also presented here. Figures 1 and 2 show the locations of the FRM and hourly PM 2.5 monitors across the OTR+ region.

Fine particulate speciation

The 24-hour average PM 2.5 and fine particulate speci ation data, consisting of sulfate (SO 4 ), nitrate (NO 3 ), elemental carbon (EC), organic carbon/organic mass (OC/OM), and soil/crustal matter from Cla ss I areas across the OTR + region, collected every third day, were obtained from the IMPROVE web site ( http://vista.cira.colostate.edu/IMPROVE/Default.htm ) for 21 IMPROVE sites (which includes Dolly Sods, WV, even though this site is not in the OTR+ region. In addition to these parameters, the EPA STN ( http://www.epa.gov/ttn/amtic/speciepg.html ) also reports ammonium (NH 4 ) to AQS; data from this networ k are collected every third or sixth day. The STN data from 50 sites in the OTR+ region were used in this analysis. Organic mass is assumed to equal 1.8×OC, a nd soil/crustal matter is assumed to consist of oxides of Al, Ca, Fe, Si, and Ti. The STN OC data were first corrected by subtracting a monitor-specific constant bl ank concentration, and these ST N blank data are available
from http://www.epa.gov/airtrends/aqtrnd03/pdfs/2_chemspecofpm25.pdf . The IMPROVE OC blanks are assumed to equal ze ro. Nine of the STN sites are located within the NYC NAA. Figure 3 shows the lo cations of the STN and IMPROVE locations across the OTR+ region. In addition to thes e daily average speciati on data, a preliminary examination of diurnal variations using continuous hourly SO 4 and NO 3 data from the New York State Department of Environmental Conservation ( http://www.dec.ny.gov ) site IS52 (Bronx) is also presented here.

Evaluation of CMAQ

The following sections provide informa tion on model performance for the above referenced pollutants over the OTR+ portion of the 12-km modeling domain. The
statistical formulations that have been computed for each species are as follows: P i and O i are the individual daily average predicted and observed concentrations, respectively; Pand O are the average concentrations, respectiv ely, and N is the sample size. These
statistical measures are listed in the EPA modeling guidance document (USEPA, 2007).

Each statistic is computed two ways. First, each statistic is computed at each site over the entire year and each quarter; sec ond, each statistic is computed on each day using all available sites. Note that the July 6-9, 2002 period was excluded from this analysis since the observed PM 2.5 and OM data at many sites were greatly affected by Canadian forest fires. Additionally, thos e days on which the observed and predicted averages differed by more than a factor of 25 were not included in this analysis, since they can greatly affect some of the normalized metrics.

Observed average, in  g m -3 :

O N O i  

##  1

Predicted average, in  g m -3 (only use P i when O i is valid):

P N P i  

##  1

Correlation coefficient, R 2 : 

#  

R P PO O

PP OO i i

i i 2 2

2 2   

  

##    ( )( )

( )( )

Normalized mean error (NME), in %:

NME P O

O i i

i    

#   | | 100%

Root mean square error (RMSE), in  g m -3 :

RMSE N P O i i         

##  1 2 1 2 ( ) /

Mean fractional error (MFE), in %:

MFE N P O

P O

i i

i i  

  

##  2 100%

Mean absolute gross error (MAGE), in  g m -3 :

MAGE N P O i i   

#  1

Mean normalized gross error (MNGE), in %:MNGE N P O

O i i

i    

##  1 100%

Mean bias (MB), in  g m -3 :

MB N P O i i   

#  1 ( )

Mean normalized bias (MNB), in %:

MNB N P O

O i i

i    

#  1 100% ( )

Mean fractionalized bias (MFB), in %:

MFB N P O

P O

i i

i i  

 

  

   

##  2 100%

Normalized mean bias (NMB), in %:

NMB P O

O i i

i    

#   ( ) 100%

Daily PM 2.5 mass

Model performance statistics, based on daily average PM 2.5 levels, are presented
in this section across the OTR+ region. Figure 4 displays th e time series of observed and predicted PM 2.5 mass averaged across all FRM monitors. As stated earlier the July 6-9 period was not included in this analysis si nce many monitors across the region were substantially impacted by Canadi an wildfires. Overall, the mean bias (predicted minus observed) was about 3.8  g m -3 , ranging from -17.7  g m -3 to +24.0  g m -3 . The highest overprediction tended to occur during the cold er months, quarters 1 and 4, whereas the days on which the model tended to underpredict PM 2.5 were more likely to occur during
the summer months.

Figures 5 and 6 display the mean fracti onal error (MFE) and mean fractionalized bias (MFB), respectively, at each FRM mon itor across the OTR+ region over the entire year. Boylan and Russell (2006) propose that these two metrics are the most useful for model evaluation since they set upper bounds on e rror/bias, are not likely dominated by a few “outlier” points, and do not assume that observations are absolute truth. Boylan and Russell (2006) further point out that MF E<75% and MFB<60% define a level of accuracy close to the best a model can be e xpected to achieve in terms of acceptable model performance. The analysis shows that at 221 of the 225 FRM sites the MFE was below 75%, while at 215 sites the MFB was below 60% in the OTR + region. However, such is not the case for the NYC NAA, a regi on with high emissions density and complex meteorology. While the MFE and MFB criteria are met at each of the 10 primary FRM monitors in CT and 14 primary FRM monitors in NJ, the MFE criteria was met at 17 ofthe 21 primary FRM monitors in NY and th e MFB criteria was only met at 12 of the primary NY FRM monitors. Hence, on an overall basis the model predicts PM 2.5 mass reasonably well, although not cons istently at all locations and all days in the NYC NAA.

Tables 1 and 2 list the median and range in MFE and MFB for the OTR+ region for the entire year excluding July 6-9 that are associated with Canadian fires. The statistical estimates are based on comparing predicted with measurements from FRM, continuous hourly, STN, and IMPROVE locations . In Table 1, the MFE and MFB values are computed at each site, while in Table 2 the MFE and MFB are computed on each day using the data from all available sites. The median MFE values are about 40-50%, ranging from <20% to about 100%. The me dian MFB values are about 30%, ranging within ±100% (substantial underpredi ction to large overprediction).

Figure 7 displays the quarterly average PM 2.5 mass at all OTR+ monitors and also
at those monitors within the NYC NAA. Note that, not surprisingly, the observed average PM 2.5 mass tends to be higher in the NYC NAA than across the entire OTR+. CMAQ tends to overpredict PM 2.5 at these locations, especially during the colder months. On the other hand, CMAQ performs well during the July-September period on a composite average basis.

PM 2.5 speciation

Composite daily average pr edicted and observed con centrations of major PM 2.5 species – SO 4 , NO 3 , NH 4 , EC, OM (defined here opera tionally as 1.8×blank-corrected organic carbon), and crustal mass (sum of oxi des of Al, Ca, Fe, Si, and Ti) – across the OTR+ region were compared in this analysis. The time series of these species at the 50
STN and 21 IMPROVE locations are shown in Figures 8-18. The data displayed are for every third day, corresponding to the nominal sampling sche dule. These two speciation
networks collect SO 4 , NO 3 , EC, OM, and crustal mass, while only the STN reports NH 4 at a number of locations. The NH 4 data from the IMPROVE network are not included here, since in the OTR+ region, only the IMPROVE sites of Dolly Sods, WV; James River Face Wilderness, VA; and Shen andoah National Park, VA reported NH 4 data in 2002. As before, the July 6-9 period was excluded.

The model exhibits good agreement for SO 4 at both the urban STN (Figure 8) and rural IMPROVE (Figure 9) sites, both in terms of absolute concentrations and the seasonal variation. During the summer months, photochemical production of SO 4 is higher than at other times of the year, which the model seem s to capture. Figures 10 and 11 suggest that the model qualitatively pr edicts the seasonal variation of NO 3 fairly well, although the observed and modeled concentratio ns during the cold season, quarters 1 and 4 (when volatilization of NO 3 is lowest), differ by several  g m -3 . The seasonal variation
in NH 4 (Figure 12) is more complicated, but appears to be consistent with SO 4 and NO 3

. During the summer, most NH 4 is tied to SO 4 (which the model tends to predict well), whereas during the winter a substantial fraction of NH 4 is tied to NO 3 (which the model appears to predict poorly). Both EC (Figur es 13 and 14) and crustal mass (Figure 17 and 18) are predominantly primary emissions, a nd the model tends to overpredict bothspecies – particularly at ur ban locations and during the colder months. Note, too, the effects of emissions from the July 4 th fireworks on crustal mass are obvious in the
observed data at both the STN and IMPROVE sites, but are not included in the model emissions inventory. The measured crustal mass on this day exceeds 4  g m -3 , while it is generally closer to ~0.5-1.0  g m -3 only.

The model appears to also have difficulty predicting the seasonal variation of OM (Figures 15 and 16). During the summer m onths a substantial fraction of OM is secondary in nature, while during the colder m onths much of the OM may be in the form of primary emissions. To illustrate this, Fi gure 19 displays the quarterly variation in observed OM, as well as the CMAQ predictions of the three components of modeled OM – anthropogenic primary organic aerosol (POA), anthropogenic secondary organic aerosol (SOA), and biogenic SOA. Note that CMAQ predicts that >80% of the modeled OM is anthropogenic POA (with a wintertim e maximum), and only predicts slight increases in the SOA fraction during the su mmer. On the other hand, the observed OM
has a summertime maximum when s econdary formation is highest.

Table 3 lists the median and range in MF E and MFB calculated at each site over the season used in this analysis. For SO 4 , which contributes ~30% to the total PM 2.5 mass, the MFE at both STN and IMPROVE lo cations is roughly 40%, and there is a general tendency to underpredict SO 4 (MFB < 0). At all 71 locations (STN and IMPROVE), the MFE and MFB criteria suggested by Boylan and Russell (2006) are met. This is not surprising, since SO 2 emissions are generally well-characterized and ambient

SO 4 levels tend to be somewhat spat ially uniform compared to other PM 2.5 species. The range in MFE and MFB tends to be lower for SO 4 compared to the other species as well. The urban-rural differences for the other species are much more evident than for SO 4

. Note that the degree of overprediction of NO 3 , NH 4 , and EC is considerably higher at the STN locations compared to IMPROVE. CMAQ tends to underpredict OM as well, although some of this discrepancy may be attrib uted to the fact that OM is operationally defined and is highly depende nt on the blank correction a nd multiplier to account for other components of OM not directly measur ed. Similarly, the predicted crustal mass tends to be much higher than that observed. This, too, is due in part to relating the CMAQ predictions of unspeciated, primary pa rticulates to the measured crustal mass which is operationally defined as the sum of major metal oxides. For these and other reasons, Boylan and Russell ( 2006) state that for the non-SO 4 or less abundant PM 2.5 species, the MFE and MFB guidelines should be relaxed.

Diurnal variations PM 2.5 , SO 4 , and NO 3

The hourly PM 2.5 mass and speciation data allow for examination of the model predictions with high temporal resolution. Figure 20 displays the composite diurnal
variation of PM 2.5 mass averaged across the 28 locations in the NYC NAA, over the entire year (with the exception of July 6-9) . For comparison, the average diurnal profiles during quarters 1 and 3 are shown in Figures 21 and 22, respectively. Quarter 1 exhibits the largest discrepancy between observed and predicted PM 2.5 , while quarter 3 exhibits the smallest discrepancy betw een observed and predicted PM 2.5 mass. Regardless of timeof year, CMAQ predicts a pronounced double pe ak, with maxima during the morning and late afternoon/early eveni ng time periods, likely corresponding to average commuting patterns. During quarter 1, there is a double pe ak in the observed diurnal profile, albeit smaller in amplitude. The double peak is not nearly as evident during quarter 3, when during the middle of the day the observed PM 2.5 levels are actually slightly higher than the predicted levels.

There were only a handful of locations in 2002 where hourly SO 4 and/or NO 3 were measured. One such location was IS52 in Bronx, NY. To illustrate how the model performed over the course of a day at this single location, Figure s 23 and 24 display the average diurnal variation of SO 4 and NO 3 , respectively. On an average basis, there is
little variation in SO 4 at this site over the day (s ee Figure 23), and the observed and predicted SO 4 levels generally are within 10-20 % of each other. The observed SO 4 levels on average do exhibit a small peak during th e summer months, but the lack of a strong diurnal indicates the regiona l nature of this pollutant. In contrast to SO 4 , there is a more pronounced diurnal variation in NO 3 at this location (see Figure 24), with a morning peak
related to NO

x emissions and a still shallow bounda ry layer, and an afternoon minimum related to a deep boundary laye r, deposition, and the thermal instability of ammonium nitrate (NH 4 NO 3 ). Similar to PM 2.5 mass, the model overpredict ion tends to be highest during the nighttime and early morning hours.

Summary

Various model evaluation statis tics are presented here for PM 2.5 mass and major species over the entire 2002 CMAQ simulation. In general, the CMAQ results were best for daily average PM 2.5 and SO 4 mass. By comparison, CMAQ does not appear to be able to reproduce the day-to-day or se asonal variation in other species like NO 3 , OM, EC, or crustal mass. The model appears to meet model performance criteria for PM 2.5 reasonably well over the OTR+ region as a whol e, but not as well if one focuses on an emissions-dense, complex urban setting such as the NYC NAA.

References

TSD-1 (2006) Meteorological Modeli ng of 2002 using Penn State/NCAR 5 th Generation Mesoscale Model (MM5)

TSD-2a (2006) Processing of 2002 Biogeni c Emissions for OTC / MANE-VU Regional

and Urban Modeling.

TSD-2b (2006) Processing of 2002 Anthropogenic Em issions: OTC Regional and Urban
12km Base Year Simulation.

TSD-2c (2006) PM 2.5 modeling using the SMOKE/CMAQ system over the Ozone Transport Region (OTR)Boylan, J. W., and Russell, A. G., 2006. PM and light extinction model performance metrics, goals, and criteria for three-di mensional air quality models. Atmospheric Environment 40, 4946-4959.

United States Environmental Protection Agen cy (USEPA), 2007. Guidance on the use of models and other analyses for demonstrating attainment of air quality goals for ozone,

PM 2.5 , and regional haze. EPA-454/B-07- 002, Research Triangle Park, NC.Table 1. The median and range in mean fractio nal error (MFE) and mean fractionalized bias (MFB) values for daily PM 2.5 mass at FRM, hourly, and speciation sites across the OTR+ region. These statistics were comput ed at each site over the entire year. Median MFE (minimum to maximum MFE) Median MFB (minimum to maximum MFB) FRM (225 sites) 42.9% (28.1 to 109%) 22.6% (-72 to 109%) Hourly (62 sites) 49.3% (17.4 to 88%) 35.3% (-41.9 to 134%)

STN (50 sites) 40.1% (28.1 to 83%) 12.3% (-27.3 to 81%) IMPROVE (21 sites) 53.1% (35.3 to 73%) 30.1% (-12.0 to 64%)

Table 2. Same as Table 1, except that the statis tics were computed on each day using all
available sites. Median MFE (minimum to maximum MFE) Median MFB (minimum to maximum MFB) FRM (225 sites) 42.5% (17.6 to 87%) 26.4% (-83 to 88%) Hourly (62 sites) 50.0% (18.6 to 99%) 35.9% (-68 to 99%)

STN (50 sites) 42.1% (17.9 to 84%) 27.0% (-80 to 75%) IMPROVE (21 sites) 48.4% (22.9 to 102%) 30.3% (-101 to 101%)

Table 3. Same as Table 1, except for daily average species concentrations at the 50 STN
and 21 IMPROVE sites. Median MFE (minimum to maximum MFE) Median MFB (minimum to maximum MFB)

STN SO 4 39.8% (27.2 to 60%) -11.1% (-40.9 to 52%)
IMPROVE SO 4 42.4% (34.5 to 60%) -22.6% (-54 to 2.2%)
STN NO 3 80% (57 to 123%) 25.3% (-76 to 118%)

IMPROVE NO 3 104% (86 to 125%) 58% (-2.4 to 122%)
STN NH 4 43.0% (27.8 to 83%) 27.8% (-13.2 to 81%)
STN EC 48.8% (29.4 to 106%) 29.5% (-43.2 to 102%)

IMPROVE EC 57% (41.3 to 87%) 7.2% (-64 to 85%)
STN OM 62% (33.7 to 106%) -34.9% (-94 to 64%)
IMPROVE OM 54% (40.1 to 83%) -4.0% (-82 to 43.8%)
STN crustal 120% (72 to 159%) 116% (56 to 159%)
IMPROVE crustal 101% (65 to 126%) 97% (36.0 to 124%)Figure 1. Locations of the FRM monitors across the OTR+ (open squares) and NYC NAA (filled squares). Forty five of the 225 monitors are within the NYC NAA.

Figure 2. Locations of the hourly TEOM monito rs across the OTR+ (open squares) and NYC NAA (filled squares). Twenty-eight of the 62 monitors are within the NYC NAA.Figure 3. Locations of the speciation monitors across the OTR+ (open squares denote STN, open triangles denote IMPROVE) and the NYC NAA (filled squares denote STN).
Nine of the 50 STN monitors are within the NYC NAA. There are 21 IMPROVE monitors across the OTR+ region.

Figure 4. Time series of PM 2.5 mass based on the composite average of all 225 FRM monitors across the OTR+ region. The observe d values are denoted with the black line, the model predictions are denoted with a thick gray line and asterixes.

0 5 10 15 20 25 30 35 40 45

1/

1/

2002 1/31/ 20

02

3 / 2/

2 002 4/1/2002 5/1/2002

5/

31/20 02

6/

30/20 02

7/

30/20 02

8/

29/20 02

9/

28/20 02

10/

28

/2002 11/27/2002 12/27/2002PM2.5(g m-3)

OBS PREDFigure 5. Mean fractional error (MFE, %) at each FRM location over the entire year: blue, <30%; green, 30-45%; orange , 45-60%; red, 60-75%; pink, >75%.

Figure 6. Mean fractionalized bias (MFB, %) at each FRM location over the entire year: gray, <-45%; blue, -45 to - 15%; green, -15 to 15%; oran ge, 15-45%; red, 45-75%; pink,
>75%.Figure 7. Observed and predic ted quarterly average PM 2.5 mass at FRM locations, for the entire OTR+ region and for the NYC NAA.

0 3 6 9 12 15 18 21 24 27

Q1 Q2 Q3 Q4PM2.5(g m-3)

Obs, OTR+ Pred, OTR+ Obs, NYC Pred, NYC

Figure 8. Time series of SO 4 mass based on the composite average of all 50 STN monitors across the OTR+ region. The obser ved values are denoted with the black diamonds, the model predictions are denoted with gray squares.

0 4 8 12 16 20 24SO4(g m-3)

OBS PREDFigure 9. Time series of SO 4 mass based on the composite average of all 21 IMPROVE monitors across the OTR+ region. The obser ved values are denoted with the black diamonds, the model predictions are denoted with gray squares.

0 2 4 6 8 10 12 14 16 18SO4(g m-3)

OBS PRED

Figure 10. Time series of NO 3 mass based on the composite average of all 50 STN monitors across the OTR+ region. The obser ved values are denoted with the black diamonds, the model predictions are denoted with gray squares.

0 2 4 6 8 10 12 14 16NO3(g m-3)

OBS PREDFigure 11. Time series of NO 3 mass based on the composite average of all 21 IMPROVE monitors across the OTR+ region. The observed values are denoted with the black diamonds, the model prediction s are denoted with gray squares.

0 2 4 6 8 10 12NO3(g m-3)

OBS PRED

Figure 12. Time series of NH 4 mass based on the composite average of all 50 STN monitors across the OTR+ region. The obser ved values are denoted with the black diamonds, the model predictions are denoted with gray squares.

0 1 2 3 4 5 6 7NH4(g m-3)

OBS PREDFigure 13. Time series of EC mass based on the composite average of all 50 STN monitors across the OTR+ region. The obser ved values are denoted with the black diamonds, the model predictions are denoted with gray squares.

0 0.5

1 1.5

2 2.5

3EC (g m-3)

OBS PRED

Figure 14. Time series of EC mass based on th e composite average of all 21 IMPROVE monitors across the OTR+ region. The obser ved values are denoted with the black diamonds, the model predictions are denoted with gray squares.

0 0.2 0.4 0.6 0.8

1 1.2 1.4EC (g m-3)

OBS PREDFigure 15. Time series of OM mass based on the composite average of all 50 STN monitors across the OTR+ region. The obser ved values are denoted with the black diamonds, the model predictions are denoted with gray squares.

0 2 4 6 8 10 12 14 16OM (g m-3)

OBS PRED

Figure 16. Time series of OM mass based on the composite average of all 21 IMPROVE monitors across the OTR+ region. The obser ved values are denoted with the black diamonds, the model predictions are denoted with gray squares.

0 1 2 3 4 5 6 7 8 9 10OM (g m-3)

OBS PREDFigure 17. Time series of crustal mass based on the composite average of all 50 STN monitors across the OTR+ region. The obser ved values are denoted with the black diamonds, the model predictions are denoted with gray squares.

0 1 2 3 4 5 6 7 8crustal (g m-3)

OBS PRED

Figure 18. Time series of crustal mass base d on the composite average of all 21 IMPROVE monitors across the OTR+ region. The observed values are denoted with the black diamonds, the model prediction s are denoted with gray squares.

0 0.5

1 1.5

2 2.5

3 3.5

4 4.5

5crustal (g m-3)

OBS PREDFigure 19. Quarterly variation in observed OM and predicted components of OM – anthropogenic primary organic aerosol (POA) , anthropogenic secondary organic aerosol (SOA), and biogenic SOA – averaged over sites in the NYC NAA.

0 1 2 3 4 5 6 7 8 9 10

OBS PRED OBS PRED OBS PRED OBS PREDOMg/m3)

anthro SOA biog SOA anthro POA observed Q2 Q1

Q3 Q4

Figure 20. Composite average diurna l variation in observed (b lack line) and predicted (gray line) PM 2.5 mass over the 28 hourly monitors in the NYC NAA, entire year.

0 5 10 15 20 25 30 35

0.5 1. 5 2.5 3.5 4

. 5 5.5 6.5 7.5 8. 5 9.5 10.5 11. 5 12.5 13. 5 14. 5 15.5 16. 5 17. 5 18.5
1 9.

5 20. 5 21. 5 22.5 23. 5

Time (EST)PM2.5(g m-3)

OBS PREDFigure 21. Composite average diurna l variation in observed (b lack line) and predicted (gray line) PM 2.5 mass over the 28 hourly monitors in the NYC NAA, quarter 1.

0 5 10 15 20 25 30 35 40

0. 5 1.5 2. 5 3. 5 4 .5 5. 5 6. 5 7. 5 8.5 9. 5 10. 5 11.5 12. 5 13.5 14.5 15. 5 16.5 17.5 18. 5
1 9

.

5 20.5 21.5 22. 5 23.5

Time (EST)PM2.5(g m-3)

OBS PRED

Figure 22. Composite average diurna l variation in observed (b lack line) and predicted (gray line) PM 2.5 mass over the 28 hourly monitors in the NYC NAA, quarter 3.

0 5 10 15 20 25 30 35 40

0.5 1. 5 2.5 3.5 4

. 5 5.5 6.5 7.5 8. 5 9.5 10.5 11. 5 12.5 13. 5 14. 5 15.5 16. 5 17. 5 18.5
1 9.

5 20. 5 21. 5 22.5 23. 5

Time (EST)PM2.5(g m-3)

OBS PREDFigure 23. Average diurnal variatio n at IS52 (Bronx, NY) of SO 4 , entire year.

0 1 2 3 4 5 6 7

0. 5 1.5 2. 5 3.5 4. 5 5 .5 6.5 7. 5 8.5 9. 5 10.5 11. 5 12.5 13. 5 14.5 15. 5 16.5 17. 5 18.5 19. 5 20. 5 21.5 22. 5 23.5

Time (EST)SO4(g m-3)

OBS PRED

Figure 24. Same as Figure 23, except for NO 3

.

0 1 2 3 4 5 6

0.5 1. 5 2.5 3. 5 4.5 5
. 5 6. 5 7.5 8. 5 9.5 10. 5 11.5 12. 5 13.5 14. 5 15.5 16. 5 17.5 18. 5 19.5 20.5 21. 5 22.5 23. 5

Time (EST)NO3(g m-3)

OBS PRED

# K-2: Meterological Modeling using MM5TSD-1a



## Meteorological Modeling of 20 02 using Penn State/NCAR 5 th


## Generation Mesoscale Model (MM5)

Bureau of Air Quality Analysis and Research

Division of Air Resources

New York State Department of Environmental Conservation

Albany, NY 12233

March 19, 20062 Meteorological Modeling using Penn State/NCAR 5 th Generation Mesoscale Model (MM5)

Version 3.6 of MM5 was used to generate annual 2002 meteorology for the OTC modeling work. Prof. Dalin Zhang of the Un iversity of Maryland performed the MM5
simulations in consultation with NYSDEC st aff. The model was applied in Lambert conformal map projection and utilized MPP Ve rsion developed for clusters. The two-way nested domain consisted of coarse (36k m) and fine (12km) mesh corresponding to 149x129 and 175x175 grids, respectively, in this application (see Figure 1).

The Lambert projection used in this work followed the Regional Planning Organization (RPO) national domain setup with the center at (40 o N, 97 o W) and parallels at 33 o N and

45 o N. Map projection parameters in referen ce to the projection center point are as follows: Southwest corner for the 36 km grid is at (-2664km, -2304km) and the northeast corner at (2664km, 2304km). In the case of the 12km grid, the southwest corner is at (252km, -900km) and the northeast corner at (2 340km, 1188km). In th e vertical direction, the terrain following σ -coordinate system was used with the pressure at each σ -level
determined from a reference state that is estimated using the hydrostatic equation from a given sea-level pressure and temperature with a standard lapse rate. There are 30 unevenly spaced σ levels, giving 29 vertical layers, with higher resolution within the planetary boundary layer (PBL). The σ levels are:

1.0000, 0.9974, 0.9940, 0.8980, 0.9820, 0.9720, 0.9590, 0.9430, 0.9230, 0.8990,

0.8710, 0.8390, 0.8030, 0.7630, 0.7180, 0.6680, 0.6180, 0.5680, 0.5180, 0.4680,

0.3680, 0.3180, 0.2680, 0.2180, 0.1680, 0.1230, 0.0800, 0.0400, 0.0000

The surface layer was set at about 10m, the level at which surface winds were typically observed, and the model top was set at 50hP a with a radiative top boundary condition. The time steps for the 36km and 12km domains were 75 and 25 seconds, respectively.

The important model physics options used for this MM5 simulation include:

 Kain-Fritsch (1993) conv ective scheme for both 36- and 12-km domains

 Explicit moisture scheme (without the mixed phase) containing prognostic equations for cloud water (ice) and ra inwater (snow) (Dudhia 1989; Zhang 1989)

 Modified version of the Blackadar pl anetary boundary layer (PBL) scheme (Zhang and Anthes 1982; Zhang and Zheng 2004)

 Simple radiative cooling scheme (Grell et al. 1994)

 Multi-layer soil model to predict land surface temperatures using the surface energy budget equation (Dudhia 1996)

Note that the Blackadar PBL scheme has been modified in order to correct the phase shift of surface wind speed and temperature diurnal cycle, following a study that compared five different PBL schemes: the Gayno-Seaman TKE scheme (Shafran et al. 2000), Burk-3 Thompson (1989), Blackadar (Zhang and Anthes 1982), MRF (Hong and Pan 1996), and Mellor-Yamada-Jajic (M ellor and Yamada 1974; Jajic 1990, 1994). The details of the study can be found at Zhang and Zheng (2004).

Nudging Processes

The MM5 provides options for nudging observations for each domain during the model integration process (Stauffer and Seaman, 1990; Stauffer et al. 1991). The Eta analyses of upper-air winds, temperature and water-vapor mixing ratio as well as their associated surface fields were used for nudging every 6 hours, and the Eta surface wind fields blended with surface wind observations were used to nudge every 3 hours. While only the surface winds were nudged, their influences could extend into the PBL as well (see Stauffer et al. 1991). Based on UMD’s prior experience in numerical experiments, the following nudging
coefficients have been used:

 Upper-air wind fields: 5. 0E-4s -1 for Domain 1 (36km), and 2. 5E-4s -1 for Domain 2 (12km);

 Upper-air temperature fields: 1.0E-5s -1 for both Domains;

 Surface winds: 5. 0s -1 E-4s -1 for Domain 1, and 2.5E-4s -1 for Domain 2; and

 Surface temperature and moisture: not nudged due to instability consideration.

ASSESSMENT

This assessment covers the period of May through September 2002.

National Weather Service (NWS) and CAST Net data – Surface temperature, Wind Speed, and Humidity

NWS (TDL) and CASTNet ( www.epa.gov/castnet/ ) surface measurements of temperature, wind speed, and humidity (note there were no humidity measurements for CASTNet) were used to compare with th e MM5 outputs. The evaluation was performed with METSTAT program devel oped by Environ Corporation ( www.camx.com/files/metstat.15feb05.tar.gz) When comparing to NWS data, the METSTAT interpolates the first layer MM5 (at 10m height) temperature and humidity data to a height of 2m, the level that corresponds to the NWS measurement of these parameters. However, no such interpolation was made for wind speed and direction. In the case of CASTNet surface measurements, no such changes were needed as CASTNet data were reported at a height of 10m. In this analysis, no exclusion was made for calm conditions. The reported calm wind s (zero wind speed measured) were treated as is in
this evaluation effort. The METSTAT cal culated standard stat istical measures – average, bias, error and index of agreem ent between the measured and predicted
parameters.

Figure 2 displays the temperature and wi nd speed comparison of MM5 and measured data from NWS and CASTNet networks for August 2002. MM5 performance for both in magnitude and diurnal timing, temperature can be considered to be quite good for both NWS and CASTNet data, while MM5 underpre dicted NWS and overpredicted CASTNet4 daytime wind speed, respectively. It should be pointed out that there are differences in how the meteorological information is collect ed and reported by the two networks as well
as in MM5. The CASTNet measurements are based on hourly averaged wind speed while NWS reports 2min average at 10min before the hour, whereas MM5 predictions are reflective of the last time-step of the hour of computation. Interest ingly, MM5 appears to track quite well the nighttime minimum wind sp eed for both networks. In the case of humidity (not shown), MM5 tracks the NW S observed humidity trend well, but MM5 missed the observed semi-diurnal cycles. Comp arisons for the five months including bias and root mean square error from both NWS and CASTNet are available on request from

NYSDEC.

The above assessment is based on domain-wide averages to provide an overall response
of the model over the five months. Another wa y of assessing the model is to examine the degree of correlation between the measured a nd predicted parameters. Figures 3a and 3b displays such a comparison for wind speed and temperature, respectively, for the NWS hourly data covering the period of May th rough September 2002. For the NWS data, the correlations are in the range from 0.7 to 0.8 for wind speed, above 0.96 for temperature, and in the range of 0.8 to 0.9 for humidity. CASTNet data (not shown) also exhibit
similar correlation. These correlations indica te that MM5 simulati on has captured both the diurnal and synoptic scale variations. Deta iled plots of this comparison are available on request from NYSDEC.

Vertical Profiler – Winds

The Wind-Profiler network measurements along the U. S. East Coast ( www.madisfsl.org/cap) were used to evaluate the vertical profiles from MM5. There are twelve wind-profiler measurement stations from whic h data were available for comparison. For convenience of comparison, the wind-profiler measurements were interpolated to the MM5 vertical levels. The approach used was simple interpolation between two adjacent wind-profiler layers to the MM5 vertical level, and was limited to that reported by the profiler measurement. The focus of the comparison was to assess if MM5 was able to capture the measured vertical structure, and for this we used the observed Low Level Jet (LLJ) as an indicator. The comparison was performed for June, July and August 2002. In general it is found that MM5 cap tures the profiler measured vertical wind field structure reasonably well. Figure 4 disp lays an example of the MM5 and wind profiler comparison for the August 2002 episode at Richmond, VA and Concord, NH. MM5 predicted weaker LLJ winds compared to those based on the wind-profiler measurements. The detailed plots of this comparison are availa ble on request from NYSDEC.

Cloud Cover – Satellite cloud image

Cloud information derived from satellite image data ( www.atmos.umd.edu/~srb/gcip/webgcip.htm )were used to assess the MM5 prediction
of cloud cover. The 0.5 o by 0.5 o resolution of the satellite data were interpolated into the 12km MM5 grid for comparison. The MM5 to tal cloud fraction was estimated by MCIP based on the MM5’s low cloud, middle cloud an d high cloud predictions. In general,5 MM5 captured the satellite cloud pattern we ll but underestimates the satellite cloud fraction (see Figure 5 as an exam ple). Part of problem may due to the coarse resolution of
the satellite cloud data.

Precipitation comparison

The monthly total observed prec ipitation data were constructed from 1/8-degree daily precipitation analysis data ( http://data.eol.ucar.edu/codiac/dss/id=21.093 produced by Climate Prediction Center, based on 7,000- 8,000 hourly/6-hourly gauge reports and radar). The MM5 monthly total precipitati on was estimated from the MM5 predicted
convective and non-convective ra infall and summed up for each month. In general, MM5 captured the observed spatial patterns in Ma y and September, but no so well for June, July and August (See Figure 6), perhaps refl ective of the summer time convective rain activities not captured by MM5. Detailed plots of this comparison are available on request from NYSDEC.

Calm Conditions

Calm conditions are defined as observed wi nd speed of zero knots and wind direction as

0 o . It would be useful to assess how MM5 performs under observed calm conditions, because of potential pollutant buildup that could occur under such conditions. Table 1 lists the summary of the percentage of calm condition at each hour for the August 2002
from the NWS data within the 12km domain. It is apparent from the Table that the calm conditions occur primarily during the night and early morning hours, from 23Z (7 p.m. EDT) to 15Z (11 a.m. EDT) with a peak at 10Z (6 a.m. EDT). To assess MM5 performance, the observed and MM5 predicte d wind speeds were divided into calm and non-calm according to observed wind speed. Figure 7 displays such a comparison of the MM5 predicted wind speed to the observ ed wind speed under the calm and non-calm conditions for the month of August 2002. For the “calm” group, the average wind speed for MM5 varies from 1 m/s during the ni ght and early morning hours and over 1.5 m/s during the day. MM5 is over-predicting dur ing observed calm wind conditions. There are local minima every 3 hours, due to the surface observed wind speed nudging in MM5. In contrast under the non-calm conditions, MM5 underpredicts by about 0.5 m/s for all
hours with noticeable local maximum ha ppening at the nudging hours. The MM5 nudging process would pull pr edictions toward the measured data, while the underprediction of MM5 for the non-calm conditions may due to the adopted PBL
scheme in this simulation.

Summary

In this study, we performed an assessment of the MM5 simulation to real-world data,
both at the surface level as well as in the vertical. Wh ile there are no specific recommended procedures identified for this assessment, similar approaches have been used elsewhere (Dolwick 2005, Baker 2004, and Johnson 2004). Traditionally, the NWS surface measurements are used for such a comparison. Since NWS data had been used through nudging processes in developing the MM5 simulation, the comparisons should6 not be far removed from each other. In th is study, we extended the evaluation by using
CASTNet measurements that were not used in the MM5 simulations. Thus comparison with CASTNet data provides for an indepe ndent assessment and should complement the comparison with NWS data. We also compared the MM5 results with the wind profiler
data and cloud data derived from satellite images to diagnose if the MM5 simulation is yielding the right type of dynamics in the vertic al. The analyses shows that in general, the performance of the MM5 is reasonable both at the surface and in the vertical, thereby providing confidence in the use of th ese data in the CMAQ simulations.

References

Baker, K. 2004: www.ladco.org/tech/phot o/photochemical.html

Burk, S. D. and W. T. Thompson, 1989: A vert ically nested region al numerical weather prediction model with second -order closure physics. Mon. Wea. Rev., 117, 2305–2324.

Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiments using a mesoscale two-dimensio nal model. J. Atmos. Sci., 46, 3077–3107.

Dudhia, J., 1996: A multi-layer soil te mperature model for MM5. Preprints, 6 th Annual MM5 Users Workshop, Boulder, CO.

Dolwick, P. 2005: http://cleanairinfo.com /modelingworkshop/presentations/MPE_Dolwick.pdf

Grell, G. A., J. Dudhia , and D. R. Stauffer 1994: A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR Tech.Note NCAR/TN-398 1 STR, 122 pp.

Hong, S.-H., and H.-L. Pan, 199 6: Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon. Wea. Rev., 124, 2322–2339.

Jajic, Z. I., 1990: The step -mountain coordinate: Physical package. Mon. Wea. Rev., 118, 1429-1443.

Jajic, Z. I., 1994: The step-mountain Eta c oordinate model: Further development of the convection, viscous sublayer a nd turbulent closure schemes. Mon. Wea. Rev ., 122, 927-
945.

Johnson, M. 2004: www.ladco.org/tech/phot o/photochemical.html

Kain, J.S., and J.M. Fritsch, 1993: Convec tive parameterization for mesoscale models:
The Kain-Fritsch scheme. Cumulus Para meterization. Meteor. Monogr., 46, Amer. Meteor. Soc., 165-170.

Mellor, G. L., and T. Yama da, 1974: A hierarchy of tu rbulence closure models for planetary boundary layers. J. Atmos. Sci., 31, 1791–1806.7 Shafran, P.C., N.L. Se aman, and G. A. Gayno, 2000: Evaluation of numerical predictions of boundary layer structure during the Lake Mi chigan ozone study. J. Appl. Meteor., 39, 412-426.

Stauffer, D. R., N. L. Seaman and F. S. Binkowski 1991: Use of four-dimensional data
assimilation in a limited-area mesoscale m odel. Part II: Effects of data assimilation within the planetary boundary layer. Mon. W ea. Rev., 119, 734-754.

Stauffer, D. R. and N. L. Seaman, 1990: Us e of four-dimensional data assimilation in a
limited-area mesoscale model. Part I: Experiments with synoptic-scale data. Mon. Wea. Rev., 118, 1250-1277.

Zhang, D.-L., 1989: The effe ct of parameterized i ce microphysics on the
simulation of vortex circul ation with a mesoscale hydrost atic model. Tellus, 41A, 132-

147.

Zhang, D.-L, and R. A. Anthes, 1982: A hi gh-resolution model of the planetary boundary layer-sensit ivity tests and comparisons w ith SESAME-79 data. J. Appl. Meteor., 21, 1594–1609.

Zhang, D.-L, and W.-Z. Zheng, 2004: Diurnal cycles of surface wi nds and temperatures as simulated by five boundary -layer parameteriza tions. J. Appl. Me teor., 43, 157-169.8 Table 1 Measured calm and non-calm occurrences over the modeling domain during August 2002 based on NWS data

Hour #NonCalm #Calm #Total % Calm

00Z 18209 3924 22133 17.7

01Z 16531 6026 22557 26.7

02Z 15604 6929 22533 30.8

03Z 14983 7245 22228 32.6

04Z 14309 7540 21849 34.5

05z 14073 7735 21808 35.5

06Z 13934 7949 21883 36.3

07Z 13792 8040 21832 36.8

08Z 13542 8273 21815 37.9

09Z 13542 8385 21927 38.2

10Z 13708 8591 22299 38.5

11Z 14139 8693 22832 38.1

12Z 15297 7690 22987 33.5

13Z 17336 5192 22528 23

14Z 18522 3439 21961 15.7

15Z 18755 2617 21372 12.2

16Z 19169 2015 21184 9.5

17Z 19555 1617 21172 7.6

18Z 19982 1430 21412 6.7

19Z 20149 1389 21538 6.4

20Z 20565 1288 21853 5.9

21Z 20518 1383 21901 6.3

22Z 20672 1556 22228 7

23Z 20231 2292 22523 10.29 Figure 1: OTC MM5 modeling domain with areal extent of 12km and 36km grids10 Figure 2: Temperature and Wind speed comparisons for August 2002. In each case the upper panel corresponds to comparison betw een MM5 and NWS data and the lower panel between MM5 and CASTNet data. O b se rve d /P re d icte d T e m p e ra tu re

275 280 285 290 295 300 3058/ 18/ 28/ 38/ 48/ 58/ 68/ 78/ 88/ 98/108/118/128/138/148/158/168/178/188/198/208/218/228/238/248/258/268/278/288/298/308/31K O b sTe m p P rdTem p

O b se rve d /P re d icte d T e m p e ra tu re

275 280 285 290 295 300 3058/ 18/ 28/ 38/ 48/ 58/ 68/ 78/ 88/ 98/108/118/128/138/148/158/168/178/188/198/208/218/228/238/248/258/268/278/288/298/308/31K O b sTe m p P rdTem p

Observed/Predicted Windspeed

1 2 3 4 5 6 78/ 18/ 28/ 38/ 48/ 58/ 68/ 78/ 88/ 98/108/118/128/138/148/158/168/178/188/198/208/218/228/238/248/258/268/278/288/298/308/31m/s ObsWndSpd PrdWndSpd

Observed/Predicted Windspeed

1 2 3 4 5 6 78/ 18/ 28/ 38/ 48/ 58/ 68/ 78/ 88/ 98/108/118/128/138/148/158/168/178/188/198/208/218/228/238/248/258/268/278/288/298/308/31m/s ObsWndSpd PrdWndSpd11 Figure 3a: Spatial correlation estimates between MM5 and NWS data for wind speed from May to September 200212 Figure 3b: Spatial distribution of co rrelation coefficients for Temperature be tween MM5 and NWS data from May to September 2002.13 Richmond, VA

Concord, NH

Figure 4: MM5 and Wind profiler comparison for August 6 to 17, 2002 at Richmond, VA and Concord, NH. The upper and lower panes at each station are for MM5 and profiler, respectively. The abcissa represents da y and the ordinate the height (m).14 Figure 5: MM5 and Satellite cloud im ages for August 14, 2002 at 0700 EST15 Figure 6: MM5 predicted and measured precip itation over the domain for the month of August 200216 0 0.5

1 1.5

2 2.5

3 3.5

4 4.5

00Z 03Z 06Z 09Z 12Z 15Z 18Z 21Z

TIMEWIND SPEED (m/s)

TDLAvgNC
MM5AvgNC
MM5AvgC

Figure 7: Comparison of averaged wind speed between MM5 and observed under calm (C) and non-calm (NC) conditions.

# K-3: Processing of 2002 Anthropogenic EmissionsTSD-2b



## Processing of 2002 Anth ropogenic Emissions: OTC Regional and Urban 12km Base Year Simulation

Bureau of Air Quality Analysis and Research
Division of Air Resources New York State Department of Environmental Conservation Albany, NY 12233

March 19, 20072 Overview

All emissions processing for the re vised 2002 OTC regional and urban 12 km base case simulations was performed with SMOKE2.1 compiled on a Red Hat 9.0 Linux operating system with the Portland group fo rtran compiler version 5.1. The emissions processing was performed on a month-by-m onth and RPO-by-RPO basis, i.e. SMOKE processing was performed for each month fo r each of the RPOs (MANE-VU, VISTAS, CENRAP, MRPO) individually as well as for Canada. For each month/RPO combination, a separate SMOKE ASSIGNS file was created, and the length of the episode in each of
these ASSIGNS files was set to the entire m onth. Also, as discussed in Section 3, there was no difference between “episode-averag e” temperatures and “monthly-average” temperatures for the Mobile6 simulations that used the option of te mperature averaging.

This document is structured as follows: A listing of all emission inventories is given in Section 2, organized by RPO and source category. Section 3 discusses the Mobile6 processing approach employed for the different RPOs, while Section 4 describes the processing of biogenic emissions w ith BEIS3.12. Finally, Sections 5 through7 describe the temporal allocation, speciati on, and spatial allocation of the emissions inventories, respectively.

1. Emission Inventories

1.1 MANE-VU

Version 3 of the MANE_VU inventory wa s utilized to generate CMAQ-ready emissions. This emissions inventory data we re obtained from the MANEVU archive in April 2006.

1.1.1 Area Sources

 Files: MANEVU_AREA_SMOKE_INPUT_ ANNUAL_SUMMERDAY_040606.txt and MANEVU_AREA_SMOKE_INP UT_ANNUAL_WINTERDAY_040606.txt prepared by PECHAN, downloaded from ftp.marama.org (username mane-vu, password exchange)

 Fugitive dust correction: This was applied as county-specific correction factors
for SCC’s listed at http://www.epa.gov/ttn/chief/e mch/invent/index.html#dust ; the correction factor file gcntl.xportfrac. txt was obtained from EPA’s CAIR NODA ftp site http://www.airmodelingftp.com (password protected).; this adjustment was performed using the SMOKE programs cntlmat and grwinven to generate an adjusted IDA inventory file used for subsequent SMOKE processing

1.1.2 Nonroad Sources

 File: MANEVU_NRD2002_SMOKE_030306 prepared by PECHAN;
downloaded from ftp.marama.org (username mane-vu, password exchange)3 1.1.3 Mobile Sources

 VMT/Speed: MANEVU_2002_mbinv_02022006_addCT.txt prepared by PECHAN and NESCAUM; downloaded from http://bronze.nescaum .org/Private/junghun/MANEVU/onroad_ver3_update/MANEVU_V3_update.tar

1.1.4 Point Sources

 Files: MANEVU_Point_SMOKE_INPUT_ ANNUAL_SUMMERDAY_041006.txt and MANEVU_Point_SMOKE_INPUT _ANNUAL_WINTERDAY_041006.txt prepared by PECHAN were downloaded from ftp.marama.org (username manevu, password exchange)

 Fugitive dust correction: This was applied as county-specific correction factors
for SCC’s listed at http://www.epa.gov/ttn/chief/e mch/invent/index.html#dust ; the correction factor file gcntl.xportfrac. txt was obtained from EPA’s CAIR NODA ftp site http://www.airmodelingftp.com (password protected).; this adjustment was performed using the SMOKE programs cntlmat and grwinven to generate an adjusted IDA inventory file used for subsequent SMOKE processing

 Corrected the omission of 2,100 tons/year VOC emissions from several point sources in NJ. NJDEP provided updated IDA files on June 30 that were used for modeling.

1.2 CENRAP

The inventory data were obtained from the CENRAP ftp site in March 2006 and reflect version BaseB of the CENRAP inventory.

1.2.1 Area Sources

 Files:

o CENRAP_AREA_SMOKE_INPUT_ANN_STATES_081705.txt

o CENRAP_AREA_MISC_SMOKE _INPUT_ANN_STATE_071905.txt

o CENRAP_AREA_BURNING_SMOKE_INPUT_ANN_TX_ NELI_071905.txt

o CENRAP_AREA_MISC_SMOKE _INPUT_NH3_MONTH_{MMM} _072805.txt where {MMM} is JAN, FEB, ... DEC

o CENRAP_AREA_SMOKE_INPUT_NH3_MONTH_{MMM} _071905.txt where {MMM} is JAN, FEB, ... DEC

 Fugitive dust correction: This was applied as county-specific correction factors
for SCC’s listed at http://www.epa.gov/ttn/chief/e mch/invent/index.html#dust ; the correction factor file gcntl.xportfrac. txt was obtained from EPA’s CAIR NODA ftp site http://www.airmodelingftp.com (password protected).; this adjustment was performed using the SMOKE programs cntlmat and grwinven to generate an adjusted IDA inventory file used for subsequent SMOKE processing

 Note about area and nonroad source SMOKE processing for the CENRAP region: All area source inventories (both annual and month-specific) were processed in4 one step through SMOKE. SMK_AVEDAY_ YN was set to N, so seasonal profiles were used to apportion the annua l inventories numbers by month. This setting was also used for the nonroad pr ocessing performed in a separate step. This was necessary since the month-specifi c files had zero in their ‘average-day’
column and the annual tota l column reflects the “monthly emissions as annual totals” as per header line. Therefore, seasonal profiles are used to apportion both the annual and month-specific f iles. As described below, we utilized the temporal profiles and cross-reference files gene rated by CENRAP. However, we did not verify that this approach indeed lead s to the intended monthly allocation of
ammonia and nonroad emissions.

1.2.2 Nonroad Sources

 Files:

o CENRAP_NONROAD_SMOKE _INPUT_ANN_071305.txt

o CENRAP_NONROAD_SMOKE_I NPUT_MONTH_{MMM}_071305.txt where {MMM} is JAN, FEB, ... DEC

1.2.3 Mobile Sources

 VMT/Speed files:

o mbinv02_vmt_cenrap_ce.ida

o mbinv02_vmt_cenrap_no.ida

o mbinv02_vmt_cenrap_so.ida

o mbinv02_vmt_cenrap_we.ida

1.2.4 Point Sources

 File: CENRAP_POINT_SMOKE_INPUT_ANNUAL_DAILY_072505.txt

 Fugitive dust correction: This was applied as county-specific correction factors
for SCC’s listed at http://www.epa.gov/ttn/chief/e mch/invent/index.html#dust ; the correction factor file gcntl.xportfrac. txt was obtained from EPA’s CAIR NODA ftp site http://www.airmodelingftp.com (password protected).; this adjustment was performed using the SMOKE programs cntlmat and grwinven to generate an adjusted IDA inventory file used for subsequent SMOKE processing.

1.3 VISTAS

All VISTAS emission files were obtained fr om the Alpine Geophysics ftp site. They
reflect version BaseG of the VISTAS invent ory with the exception of fire emissions
which reflect BaseF and BaseD. These file s were downloaded between February and August, 2006.

1.3.1 Area Sources

 Files:5 o arinv_vistas_2002g_2453922_w_pmfac.txt

o ida_ar_fire_2002_vistaonly_basef.ida

 Note: the header lines of these files i ndicate that the fugitive dust correction was already applied, so no further correction was performed.

1.3.2 Nonroad Sources

 Files:

o nrinv_vistas_2002g_2453908.txt

o marinv_vistas_2002g_2453972.txt

1.3.3 Mobile Sources

 VMT/Speed file: mbinv_vistas_02g_vmt_12jun06.txt

1.3.4 Point Sources

 Files:

o Annual:

 egu_ptinv_vistas_2002 typ_baseg_2453909.txt

 negu_ptinv_vistas_2002typ_baseg_2453909.txt

 ptinv_fires_{MM}_typ.vistas.ida where {MM} is 01, 02, 03, etc. depending on the month; these annual point fire files were generated as part of the VIST AS BaseD inventory and were obtained in January 2005

o Hour-specific:

 pthour_2002typ_baseg_{MMM}_28jun2006.ems where {MMM} is jan, feb, mar, etc.

 pthour_fires_{MM}_typ.vistas.ida where {MM} is 01, 02, 03, etc. depending on the month; these hourly point fire files were generated as part of the VIST AS BaseD inventory and were obtained in January 2005

 Note: No fugitive dust correction was performed for these files.

1.4 MRPO

MRPO emissions for SMOKE modeling were generated by Alpine Geophysics through a
contract from MARAMA to convert the MRPO BaseK inventor y from NIF to IDA format. The files were downloaded from the MARAMA ftp site ftp.marama.org (username mane-vu, password exchange) between April and June 2006.

1.4.1 Area Sources

 Files:

o Annual:

 arinv_mar_mrpok_2002_27apr2006.txt

 arinv_other_mrpok_2002_20jun2006.txt

o Month-specific:6  arinv_nh3_2002_mrpok_{mmm}_3may2006.txt where {mmm} is jan, feb, etc.

 dustinv_2002_mrpok_{mmm}_23may2006.t xt where {mmm} is jan, feb, etc.

 Fugitive dust correction: This correction was performed only to the arinv_other_mrpok_2002_20jun2006.txt file using county-specific correction
factors for SCC’s listed at http://www.epa.gov/ttn/chief/emch/invent/index.html#dust ; the correction factor file gcntl.xportfrac.txt was obtaine d from EPA’s CAIR NODA ftp site http://www.airmodelingftp.com (password protected).; this adjustment was performed using the SMOKE programs cn tlmat and grwinven to generate an adjusted IDA inventory file used for subsequent SMOKE processing.

 Note about area source SMOKE processi ng: SMOKE processing was performed separately for the annual and month-sp ecific files. For the annual inventory processing, SMK_AVEDAY_YN was set to N, so seasonal profiles were used to apportion the annual inventories numb ers by month. For the month-specific inventory processing, this variable was set to Y so that no seasonal profiles would be applied and the inventory numbers in th e ‘average day’ column would be used. To save a SMOKE processing ste p, the annual “marine” inventory “arinv_mar_mrpok_2002_27apr2006.txt” was pro cessed together with the annual “other area source” inventory “arinv_other_mrpok_2002_20jun2006.txt” even though it technically is part of the nonroad inventory.

1.4.2 Nonroad Sources

 Files: nrinv_2002_mrpok_{mmm}_3may2006.txt where {mmm} is jan, feb, etc.

1.4.3 Mobile Sources

 VMT/Speed file: mbinv_mrpo_02f_vmt_02may06.txt

1.4.4 Point Sources

 Files: ptinv_egu_negu_2002_mrpok_1may2006.txt

 Fugitive dust correction: This correction was performed only to the arinv_other_mrpok_2002_20jun2006.txt file using county-specific correction
factors for SCC’s listed at http://www.epa.gov/ttn/chief/emch/invent/index.html#dust ; the correction factor file gcntl.xportfrac.txt was obtaine d from EPA’s CAIR NODA ftp site http://www.airmodelingftp.com (password protected).; this adjustment was performed using the SMOKE programs cn tlmat and grwinven to generate an adjusted IDA inventory file used for subsequent SMOKE processing.7 1.5 Canada

1.5.1 Area Sources

 File: AS2000_SMOKEready.txt obtained from ftp://ftp.epa.gov/EmisInventory/canada_2000inventory

 Fugitive dust correction: We applied “d ivide-by-four” correction for SCC’s listed

at http://www.epa.gov/ttn/chief/emch/invent/index.html#dust ; this adjustment wasperformed outside SMOKE with in-house Fortran programs. No county/provincespecific correction factors were available for Canada

1.5.2 Nonroad Sources

 File: NONROAD2000_SMOKEready.txt obtained from ftp://ftp.epa.gov/EmisInventory/canada_2000inventory

1.5.3 Mobile Sources

 File: MOBILE2000_SMOKEready.txt obtained from ftp://ftp.epa.gov/EmisInventory/canada_2000inventory

 Fugitive dust correction: applied “divid e-by-four” correction for SCC’s listed at http://www.epa.gov/ttn/chief/emch/invent/index.html#dust ; this adjustment was performed outside of SMOKE with in-house Fortran programs. No county/province-specific correction fa ctors were available for Canada.

1.5.4 Point Sources

There has long been difficulty in obtai ning an up-to-date Canadian criteria emissions inventory for point sources. This is due largely to confidentiality rights afforded to Canadian facilities. Thus far, the most recent invent ory of Canadian point
sources is rooted in the 1985 NAPAP data and is close to two decades old. Because there are a number of high emitting indus trial facilities in southern Canada it is of particular importance to have a reasonably accurate i nventory of these sources especially when modeling air quality over the Northeast and Midw est United States. Toward this end, an
effort was made to obtain more recent Canadian point source data and incorporate it into an inventory database, which could then be used for the 2002 OTC air quality modeling.

Perhaps the most accurate and publicly accessible source of Canadian pollutant
data is now available from the National Po llutant Release Inventory (NPRI) database.
This database contains 268 substances. Faci lities that manufacture, process or otherwise
use one of these substances and that meet re porting thresholds are required to report these
emissions to Environment Canada on an annua l basis. The NPRI data are available at
Environment Canada’s website and can be found at the link http://www.ec.gc.ca/pdb/npri/npri_home_e.cfm . The page hosts an on-line search engine where one can locate emissions by pollutant or location. In addition, the entire database is
available for download as an MS Access or Excel file. The NPRI database contains8 numerous pages with a rather comprehensive lis t of information. Detailed information is available about each facility, including lo cation, activity and annual emissions. In addition, facilities having stacks with a height of 50 meters or more are required to report stack parameters.

Unfortunately, one of the limitations of th e NPRI database for modeling purposes is that the data are only available at the fac ility level. Emissions models require process level information, so in order to use this data , a few generalizations had to be made. Each facility has a Standard Industrial Classificat ion (SIC) code associated with it; however, emissions models require Source Classifica tion Codes (SCC’s). SCC’s are of critical importance as the emissions models use thes e codes for assignment of temporal and speciation profiles. SIC codes describe the ge neral activity of a facility while SCC codes describe specific processes taking place at each facility. While no direct relationship exists between these two codes, a general al beit subjective associa tion can be made.

For the purposes of creating a model-ready inventory file it was necessary to obtain the whole NPRI database. After merging all the necessary components from the NPRI database required in the SMOKE inventory file, the SIC code from each facility was examined and assigned an SCC code. In most cases, only a SCC3 level code was assigned with confidence. While this is admittedly a less than desirable process, it does
allow for the use of the most recent emissions from the NPRI database to be used in modeling. Furthermore, having some level of SCC associated with these emissions will ensure that they will be assigned a temporal and speciation profile by the model, other than the default. Once the model-ready inve ntory file was devel oped, it was processed through SMOKE.

2. Mobile6 Processing

2.1 MANE-VU

2.1.1 Mobile6 input files

 Month-specific input files were prep ared by PECHAN and NESCAUM and were downloaded from http://bronze.ne scaum.org/Private/junghun/MANEVU/onroad_ver3_update/MANEVU_V3_update.tar

 Added the line “REBUILD EFFECTS :0.10” to each file before the
SCENARIO record to override the Mob ile6 default setting of 0.9 (90%) for the “chip reflash” effectiveness

2.1.2 SMOKE/Mobile6 auxiliary files

 SMOKE/Mobile6 auxiliary files were pr epared by PECHAN and NESCAUM and were downloaded from http://bronz e.nescaum.org/Private/junghun/MANEVU/onroad_ver3_update/MANEVU_V3_update.tar9 2.1.3 Temperature averaging

 Following the setting in the MANEVU_2002_mvref.txt files, the following procedures were used by SMOKE for tem poral and spatial temperature averaging
in the calculation of emission factors:

o Spatial averaging: temperatures were averaged over all counties that share a common reference county (i.e. Mobile6 input file)

o Temporal averaging for May – Se ptember emissions processing: no temporal averaging was used, i.e. day- specific temperatures were used to calculate emission factors for each day.

o Temporal averaging for non-summer -months emissions processing: Temporal averaging over the duration of the episode (i.e. the entire month, see introduction) was used, i.e. monthl y average temperatures were used
to calculate the emission factors.

2.2 CENRAP

2.2.1 Mobile6 input files

 Mobile6 input files for the CENRAP regi on for January and July were contained in the files central_M6_{MMM}.zip, north_M6_{MMM}.zip, south_M6_{MMM}.zip, west_M6_{MMM}.zip where {MMM} is either jan or jul. July input files were used for Ap ril – September processing, while January input files were used for the remaining months

 All files were downloaded from th e CENRAP ftp site in March 2006.

2.2.2 SMOKE/Mobile6 auxiliary files

 SMOKE/Mobile6 auxiliary files were cont ained in the files central_M6_RD.zip, north_M6_RD.zip, south_M6_RD.zip, and west_M6_RD.zip. The SMOKE MCREF, MVREF, and MCODES files were contained in the file MOBILESMOKE_Inputs.zip. The MCREF and MVREF files were combined for the different regions (“centra l”, “east”, “west”, “north”)

 All files were downloaded from th e CENRAP ftp site in March 2006.

2.2.3 Temperature averaging

 The following procedures were used by SMOKE for temporal and spatial temperature averaging in the calculati on of emission factor s according to the setting in the mvref files:

o Spatial averaging: no spatial aver aging of temperatures, i.e. the temperatures for the reference county is used to calculate emission factors
for all counties that share this refe rence county (i.e. M obile6 input file)

o Temporal averaging: Temporal averag ing over the duration of the episode (i.e. the entire month, see introductio n) was used, i.e. monthly average temperatures were used to calculate the emission factors.10 2.3 VISTAS

2.3.1 Mobile6 input files

 Month-specific Mobile6 input files were obtained from the Alpine Geophysics ftp site in July 2006. They reflect vers ion BaseG of the VISTAS inventory.

2.3.2 SMOKE/Mobile6 auxiliary files

 SMOKE/Mobile6 auxiliary files utilized were obtained from the Alpine Geophysics ftp site in July 2006. They re flect version BaseG of the VISTAS inventory.

2.3.3 Temperature averaging

 The following procedures were used by SMOKE for the temporal and spatial temperature averaging in the calculati on of emission factor s according to the setting in the mvref_baseg.36k.ag.txt file:

o Spatial averaging: temperatures aver aged over all counties that share a common reference county (i.e. Mobile6 input file)

o Temporal averaging: Temporal averag ing over the duration of the episode (i.e. the entire month, see introductio n) was used, i.e. monthly average temperatures were used to calculate the emission factors.

2.4 MRPO

2.4.1 Mobile6 input files

 Month-specific Mobile6 input files for SMOKE modeling were generated by Alpine Geophysics through a contract from MARAMA. They are based on version BaseK of the MRPO inventory. The files were downloaded from the MARAMA ftp site ftp.marama.org (username mane-vu, password exchange) in May 2006.

2.4.2 SMOKE/Mobile6 auxiliary files

 SMOKE/Mobile6 auxiliary files for SMOKE modeling were gene rated by Alpine Geophysics through a contract from MA RAMA. They are based on version BaseK of the MRPO inventory. The file s were downloaded from the MARAMA ftp site ftp.marama.org (username mane-vu, password exchange) in May 2006.

2.4.3 Temperature averaging

 The following procedures were used by SMOKE for the temporal and spatial temperature averaging in the calculati on of emission factor s according to the setting in the mvreg_mrpo_basek.txt file:

o Spatial averaging: temperatures aver aged over all counties that share a common reference county (i.e. Mobile6 input file)11 o Temporal averaging: Temporal averag ing over the duration of the episode (i.e. the entire month, see introductio n) was used, i.e. monthly average temperatures were used to calculate the emission factors.

3. Biogenic Emission Processing

Hourly gridded biogenic emissions for the 12 km and 36 km modeling domains were calculated by BEIS3.12 through SMOKE, using MCIP-processed MM5 fields for temperature (“TA”, layer-1 temperature), so lar radiation (“RGRND” ), surface pressure (“PRES”), and precipitation (“RN” and “RC”). A ‘seasonal switch’ file was generated by the SMOKE utility metscan to determine whether winter or summer emission factors should be used for any given grid cell on a ny given day. Winter emission factors are used from January 1 st through the date of the last frost a nd again from the data of the first frost in fall through December 31 st . Summer emission factors are used for the time period in between. This calculation is perfor med separately for each grid cell.

4. Temporal Allocation

4.1 MANE-VU

4.1.1 Area and nonroad sources

 Generated as part of the MANE-VU version 1 inventory

 amptpro.m3.us+can.manevu.030205.txt

 amptref.m3.manevu.012405.txt

 downloaded from ftp.marama.org (username mane-vu, password exchange) in January 2005

4.1.2 Mobile sources

 MANEVU_2002_mtpro_02022006_addCT.txt

 MANEVU_2002_mtref_02022006_addCT.txt

 prepared by PECHAN and NESCAUM and downloaded from http://bronze.nescaum .org/Private/junghun/MANEVU/onroad_ver3_update/MANEVU_V3_update.tar

4.1.3 Point Sources

 Based on the same files as for the M ANE-VU area and nonroad temporal files listed above, but added the CEM-based 2002 st ate-specific temporal profiles and
cross-references for EGU sources for th e MANE-VU states that were generated by VISTAS for their BaseD modeli ng and obtained in February 2005.

 No CEM-based hour-specific EGU emissions were utilized

4.2 CENRAP

The following temporal profiles and cross-reference files were used:12  Area and nonroad sources:

o amptpro.m3.us+can.cenrap.010605_incl_nrd.txt

o amptref.m3.cenrap.010605_add_nh3_and_nrd.txt

 Mobile sources:

o mtpro.cenrap.v3.txt

o mtref.cenrap.v3.txt

 Point sources:

o ptpro.{QQ}.cenrap_egus_cem.00-03avg.121205.txt where {QQ} is Q1 for January/February/March, Q2 for April/May/June, etc.

o ptref.{QQ}.cenrap_egus_cem.00-03avg.121205.txt where {QQ} is Q1 for January/February/March, Q2 for April/May/June, etc.

 All files were downloaded from th e CENRAP ftp site in March 2006.

4.3 VISTAS

The following month-specific temporal profil es and cross-reference files were used:

 Area and nonroad sources:

o atpro_vistas_basef_15jul05.txt

o atref_vistas_basef_15jul05.txt

 Mobile sources:

o mtpro_vistas_basef_04jul05.txt

o mtref_us_can_vistas_basef_04jul05.txt

 Point sources:

o ptpro_typ_{MMM}_vistasg_28jun2006.txt where {MMM} is jan, feb,

mar, etc.

o ptref_typ_vistas_baseg_28jun2006.txt

 These files were obtained from the Al pine Geophysics ftp site. They reflect version BaseG of the VISTAS inventory fo r the point source allocation files and version BaseF for the area, nonroad, and mobile source allocation files. These files were downloaded between February and July, 2006.

4.4 MRPO

The following month-specific temporal profiles and cross-reference files were used for all source categories:

 amptpro_typ_us_can_{MMM}_vistas_27nov04.txt where {MMM} is jan, feb,

mar, etc.

 amptref_2002_us_can_vistas_17dec04.txt

 These files were obtained from VISTAS in January 2005 and reflect their BaseD modeling. No updated temporal profiles or cross-reference files were developed for use with the MRPO BaseK inventory.

4.5 Canada

For Canada, the SMOKE2.1 default temporal profiles and cross-reference files (amptpro.m3.us+can.txt and amptref.m3.us+can.txt) were utilized.13 5. Speciation

The same speciation profiles (gspro.c maq.cb4p25.txt) and cross-references (gsref.cmaq.cb4p25.txt) were utilized for all re gions and all source ca tegories. Different
versions of these files were obtained (SMOKE2.1 default, EPA-CAIR modeling, VISTAS, CENRAP and MANE-VU) and compar ed. After comparing the creation dates and header lines of these files, it was determined that the EPA-CAIR and MANE-VU files had the most recent updates, and conseque ntly the final speciation profile and crossreference files used for all regions and source categories was based on the EPA-CAIR
files with the addition of MANE-VU specific updates.

6. Spatial Allocation

6.1 U.S.

The spatial surrogates for the 12km domain we re extracted from the national grid 12km U.S. gridding surrogates pos ted at EPA’s website at http://www.epa.gov/ttn/chief/ emch/spatial/newsurrogate.html The gridding cross-references were also obtained from this website, but for the processing of MANE-VU area source emi ssions, MANE-VU specific cross-reference entries posted on the MARAMA ftp site were added.

6.2 Canada

The spatial surrogates for Canadian emissions for the 12km domain were extracted from the national grid 12km Canadian gridding surrogates posted at EPA’s website at http://www.epa.gov/ttn/chief/ emch/spatial/newsurrogate.html The gridding cross-references were also obtained from this website.

Reference:

Pechan: (2006) Technical Support do cument for 2002 MANE-VU SIP Modeling inventories, version 3. Prepared by E. H. Pechan & Associates, Inc. 3622 Lyckan Parkway, Suite 2005, Durham, NC 27707.

# K-4: PM 2.5 

# Modeling using SMOKE/CMAQTSD-2c



## PM 2.5 

## modeling using the SMOKE/CMAQ system over the Ozone Transport Region (OTR)

Bureau of Air Quality Analysis and Research

Division of Air Resources

New York State Department of Environmental Conservation

Albany, NY 12233

February 1, 20062 Air Quality Modeling Domain

The modeling domain utilized in this applicati on represented a sub-set of the inter-RPO’s continental modeling domain th at covered the entire 48-stat e region with emphasis on the Ozone Transport Region. The OTC modeli ng domain at 12km horizontal mesh is displayed in Figure 1 is part of the 36km c ontinental domain that is designed to provide boundary conditions (BCs). The particulars of the two modeling domains are:

The 36km domain covered the continental US by a 149 by 129 mesh in the east-west and north-south directions, respectively. Th e domain is based on Lambert Conformal Projection with the center at (97oW 40oN) a nd parallels at 33oN and 45oN. As evident from Figure 1, the 12km domain utilized in this analysis covers most areas of the eastern US and has 172 by 172 mesh in the horizontal. Both domains utilize 22 layers in the vertical extending to about 16km with 16 layers placed within the lower 3km.

Photochemical Modeling -- CMAQ

The CMAQ (version 4.5.1) with CB4 chemistry, aerosol module for PM 2.5 and RADM cloud scheme was utilized in this study. P hotochemical modeling was performed with the CCTM software that is part of the CMAQ modeling package. Version 4.5.1 of this modeling software was obtained fr om the CMAS modeling center at http://www.cmascenter.org . The following module options were used in compiling the
CCTM executable:

 Horizontal advection: yamo

 Vertical advection: yamo

 Horizontal diffusion: multiscale

 Vertical diffusion: eddy

 Plume-in-Grid: non operational

 Gas phase chemical mechanism: CB-4

 Chemical solver: EBI

 Aerosol module: aero3

 Process analysis: non operational

The following computational choices were made during compilation:

 Compiler version: PGI 6.0

 Fortran compiler flags:-Mfixed -Mexte nd -Bstatic -O2 -module ${MODLOC} -I.

 C compiler flags : -v -O2 -I${MPICH}/include

 IOAPI library: version 3.0

 NETCDF library: version 3.6.0

 Parallel processing library version: mpich 1.2.6

 Static compilation on 32-bit system3 The following choices were made for running the executable:

 Number of processors: 8

 Domain decomposition for parallel processing: 4 columns, 2 rows

 Number of species written to the laye r-1 hourly-average co ncentration output (ACONC) file: 39 (O3, NO, CO, NO2, HNO3, N2O5, HONO, PNA, PAN, NTR, NH3, SO2, FORM, ALD2, PAR, OLE, ETH, TOL, XYL, ISOP, ASO4I, ASO4J, ANO3I, ANO3J, ANH4I, ANH4J, AORG AI, AORGAJ, AORGPAI, AORGPAJ, AORGBI, AORGBJ, AECI, AECJ, A25I , A25J, ACORS, ASEAS, ASOIL)

 Each daily simulation was performed for 24 hours starting at 05:00 GMT (00:00 EST)

The following postprocessing steps were perfor med using utility t ools from the “ioapi” software package obtained from http://www.baronams.com/products/ioapi/AA.html#tools :

 Extract and combine the following species for each hour for the first 16 model layers from the full 3-D instantaneous concentration output file: O3, CO, NO, NO2, NOY_1 ( =NO + NO2 + PAN + HNO3 ), NOY_2 ( =NO + NO2 + PAN +
HNO3 + HONO + N2O5 + NO3 + PNA + NTR ), HOX ( =OH + HO2 ), VOC ( =2*ALD2 + 2*ETH + FORM + 5*ISOP + 2*OLE + PAR + 7*TOL + 8*XYL ), ISOP, PM2.5 ( =ASO4I + ASO4J + ANO3I + ANO3J + ANH4I + ANH4J +
AORGAI + AORGAJ + 1.167*AORGPAI + 1.167*AORGPAJ + AORGBI +
AORGBJ + AECI + AECJ + A25I + A25J ), PM_SULF ( =ASO4I + ASO4J ), PM_NITR ( =ANO3I + ANO3J ), PM_AMM ( =ANH4I + ANH4J ), PM_ORG_SA ( =AORGAI + AORGAJ ), PM_ORG_PA ( =1.167*AORGPAI +
1.167*AORGPAJ ), PM_ORG_SB( =AORGBI + AORGBJ ), PM_ORG_TOT ( =AORGAI + AORGAJ + 1.167*AORGPAI + 1.167*AORGPAJ + AORGBI +

AORGBJ ), PM_EC ( =AECI + AECJ ), PM_OTH ( =A25I + A25J ), PM_COARS ( =ACORS + ASEAS + ASOIL ), SO2, HNO3, NH3, H2O2

 Extract all species for all model laye rs for the last hour of each daily instantaneous concentration output file to enable “hot” restarts of modeling
simulations

 Create daily files of hourly running-average 8-hr ozone concentrations with time stamps assigned to the first hour of the averaging interval

The following files are archived on LTO2 comput er tapes (each tape holds approximately 200 Gb of data) for each day:

 Aerosol/visibility file

 Layer-1 hourly-average concentration output file (contains 39 species)

 Dry deposition file

 Wet deposition file

 Extracted 16-layer species file

 Restart file (last hour of full 3-D instantaneous concentration file)

 Hourly 8-hr concentration file4 Photolysis Rates

One of the inputs to CMAQ is the photolysis ra tes. In this study, photolysis rate lookup tables were generated for each day of 2002 with the JPROC software that is part of the CMAQ modeling package. This software was obtained from the CMAS modeling center

at http://www.cmascenter.org . Rather than using climatolog ical ozone column data, daily
ozone column measurements from the NASA Earthprobe TOMS instrument were
downloaded from ftp://toms.gsfc.nasa.gov/pub/eptoms/data/ozone/Y2002/ and used as input to the JPROC processor. It should be noted that TOMS data were missing for the time period from August 3 – 11, 2002. The missing period was filled as follows-- TOMS data file for August 2 was used as JPROC input for August 3 rd through August 7 th , and the TOMS data file for August 12 th was used as JPROC input for August 8 th through August 11 th

.

Boundary Conditions (BCs)

The boundary conditions for the 12km grid were extracted from the 36km CMAQ
simulation. The 36km simulation utilized bound ary conditions that were based on a oneway nest approach to GEOS-CHEM global model outputs (Moon and Byun 2004, Baker 2005). As stated above, the intent of th e 36km CMAQ simulation was to provide the
BCs for the 12km model that would be more reflective of the emissions and meteorology
rather than to use either cl ean or arbitrary pollutant fiel ds. Also, in this study the CMAQ simulations utilized a 15-day ramp-up peri od, thereby minimizing the propagation of the boundary fields into the areas of concern. A report on the setup and application of the 36km CMAQ and the extraction of th e BCs is available from NYSDEC.

Meteorological data

The meteorological data for this study wa s based on MM5 modeling (see Meteorological Modeling, 2007). The MM5 fields are then processed by MCIP version 3.0, a utility available as part of the CCTM softwa re from CMAS Modeling Center (see http://www.cmascenter.org ) to provide CMAQ model-ready inputs.

Emissions

The emissions data for 2002 were generate d by individual states within the OTR and were assembled and processed through the Mid Atlantic Northeast Visibility Union (MANE-VU), a Regional Planning Organizati on (RPO). These emissions were then processed by NYSDEC using SMOKE processo r to provide CMAQ compatible inputs (Anthro-Emissions 2006). The 2002 emissi ons for the non-OTR areas within the modeling domain were obtained from the co rresponding RPOs and were processed using SMOKE, in a manner similar to that of the OT R.emissions. Details of this processing are outlined in the report (Pechan 2007), and the hourly biogenic emissions (Bio-Emissions, 2006)5 CMAQ simulations

CMAQ simulations were performed using the one-way nesting approach in which we perform the continental CMAQ simulation at 36 km grid spacing. For this simulation we
utilized clean initial conditi ons with boundary conditions extracted from the simulation of GEOS-CHEM global chemical model. The interface program used in this application was developed by University of Huston (Moon and Byun 2004), which was applied to obtain hourly 36km boundary concentrations fr om GEOS-CHEM outputs. The CMAQ 36km simulation was initiated from December 15, 2001 with the first 15 days as spin up period and terminated on December 31, 2002. The simu lation utilized the 2002 emissions data
available from the RPOs and 2002 MM5 me teorological fields developed by the University of Maryland (TSD-1a). The hour ly boundary fields for the 12km CMAQ domain were obtained by application of BC ON program to the 3-D concentration fields generated by the 36km CMAQ simulation.

The 12km simulations for both base and fu ture year were assigned the boundary conditions based on the 36km CMAQ simulation and clean initial conditions. The annual simulation was parsed out betw een different member states or their contractors of the OTR, so as to expedite the process of completing the simulation in a limited time. The approach used is as follows: The annual si mulation was parsed out into five parts and each modeling center identified below initiated and completed the simulation, extracted the outputs which were then combined to provide the annual simulation. There was considerable exchange of information in th e setup and execution of the modeling system between the centers using benchmark runs to ensure consistency and uniformity between the centers. The process was followed both for the base year 2002 and for the future year 2009. Details on CMAQ setup and run sc ripts are available from NYSDEC.

Modeling Center Simulation period Analysis period

MDE/UMD Dec 15, 2001 to Feb 28, 2002 Jan 01, 2002 to Feb 28, 2002 NJDEP/Rutgers Feb 15, 2002 to May 14, 2002 Mar 01, 2002 to May 14, 2002 NYSDEC May 01, 2002 to Sep 30, 2002 May 15, 2002 to Sep 30, 2002 VA DEQ Sep 15, 2002 to Oct 30, 2002 Oct 01, 2002 to Oct 30, 2002 NESCAUM Oct 15, 2002 to Dec 31, 2002 Nov 01, 2002 to Dec 31, 2002

References

Baker, K.: (2005) http://www.ladco.org/tech /photo/present/ozone.pdf

Moon, N. and D. Byun: (2004) A simple user’s guide for “geos2cmaq”code: Linking CMAQ with GEOS-CHEM. Version 1.0. Instit ute for Multidimensional Air quality Studies (IMAQS), University of Houston, Houston TX.6 Meteorological Modeling: (2007) Mete orological Modeling of 2002 using Penn
State/NCAR 5 th Generation Mesoscale Model (MM5). TSD-1a

Pechan: (2006) Technical Support do cument for 2002 MANE-VU SIP Modeling inventories, version 3. Prepared by E. H. Pechan & Associates, Inc. 3622 Lyckan Parkway, Suite 2005, Durham, NC 27707.

Bio-Emissions: (2006) Processing of Biogenic Emissions for OTC/MANE-VU Modeling. TSD-1b

Anthro-Emissions: (2006) Emission Proce ssing for the Revised 2002 OTC Regional and
Urban 12 km Base Case Simulations. TSD-1c

Figure 1 Display of 36- and 12km air quality modeling domains.