United StatesEnvironmental ProtectionAgency

Air and Radiation EPA420-D-02-004October 2002

Diesel PMModel-To-MeasurementComparison

Printed on Recycled Paper

EPA420-D-02-004October 2002

Diesel PM Model-To-Measurement Comparison

Assessment and Standards DivisionOffice of Transportation and Air QualityU.S. Environmental Protection Agency

Prepared for EPA byICF Consulting

EPA Contract No. 68-C-01-164Work Assignment No. 0-5

NOTICE

This technical report does not necessarily represent final EPA decisions or positions.It is intended to present technical analysis of issues using data thatC are currently available.

The purpose in the release of such reports is to facilitate the exchange oftechnical information and to inform the public of technical developments which

may form the basis for a final EPA decision, position, or regulatory action.

Final Report

DIESEL PM MODEL-TO-MEASUREMENTCOMPARISON

EPA Contract 68-C-01-164Work Assignment No. 0-5

September 30, 2002

Prepared for:

James Richard Cook, Chad Bailey, Tesh RaoUSEPA Office of Transportation and Air Quality

2000 Traverwood DriveAnn Arbor, Michigan, 48105

Prepared by:

Jonathan Cohen, Christine Lee, Seshasai KanchiICF Consulting

101 Lucas Valley Road, Suite 230San Rafael, CA 94903

Rob KlausmeierdKC – de la Torre Klausmeier Consulting, Inc

1401 Foxtail Cove, Austin, TX 78704

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Diesel PM Model-To-Measurement ComparisonEPA Contract 68-C-01-164, Work Assignment No. 0-5

CHAPTER 1. INTRODUCTION AND SUMMARY

The purpose of this project was to compare estimated diesel particulate matter (diesel PM or DPM)concentrations based on elemental carbon (EC) and black carbon (BC) data with modeled ambientconcentrations of DPM from the 1996 National-Scale Air Toxics Assessment (NSATA).

The NSATA used DPM inventory estimates from EPA’s final rule promulgating 2007 heavy dutyengine standards. Using the ASPEN dispersion model, NSATA developed estimates of 1996 annualaverage concentrations of DPM at census tracts nationwide. The goal of this project was to evaluatethe reasonableness of DPM estimates from dispersion models for this case by comparing theNSATA DPM concentration estimates with estimates based on measured EC and BCconcentrations.

EC measurements can be obtained from PM2.5 monitoring sites that sample PM2.5 using quartzfiber media. The EC is measured using thermo-optical analysis of the carbonaceous material. Manystudies have used thermal optimal transmission (TOT), the NIOSH method developed at Sunsetlaboratories. Some studies have used thermal optical reflectance (TOR), a method developed byDesert Research Institute. In addition, some sites measure ambient BC with an Aethalometer.EPA’s Office of Air Quality Planning and Standards is reviewing the measurement of ECthrough the Speciation Trends Network, and an Agency statement on the issue is forthcoming.For now, however, existing values developed using the TOT method are being used.

All these carbon concentration measurements can be used to estimate ambient DPM by usingconversion factors based on 1) source apportionment studies, 2) source-receptor model studies, and3) studies which examine the fraction of EC in DPM.

Our analysis was carried out as a series of steps that are detailed in this report:

1. A nationwide database was compiled containing elemental carbon (EC), organic carbon(OC), and black carbon (BC) concentration measurements from PM2.5 monitoring sites fromJanuary 1994 to December 2001. The database includes daily, annual, seasonal,weekday/weekend, and overall average concentrations and other summary statistics for eachmonitoring site.

2. Using results from several source apportionment studies, multiplicative conversion factorsto estimate diesel particulate matter (DPM) from EC, OC, and BC concentrations werecompiled. Average conversion factors were compiled together with lower and upper boundvalues.

3. Based on the results of steps 1 and 2, average, minimum, and maximum estimates of theoverall average DPM at each monitoring site were computed.

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4. The monitored values of the DPM (derived in step 3) were statistically compared withmodeled values from the NSATA at the nearest census tract centroid and at the census tractcentroid with the maximum modeled value within 30 km. The goal of the comparison wasto determine if the modeled DPM concentrations in the NSATA agree reasonably well withestimates from monitored data.

Data Base

As described in Chapter 2, ICF developed a database of EC and BC measurements from PM2.5monitoring sites, consisting of EC and BC data collected in the time period from January 1, 1994to December 31, 2001. The database has been provided to EPA in a .DBF format, although ICF’sstatistical analyses were performed in SAS using a SAS database. The data sources withcurrently available data included 76 EPA PM speciation sampling sites, the Northern FrontRange Air Quality Study (NFRAQS), the Phoenix EPA PM Supersite, Interagency Monitoring ofProtected Visual Environments (IMPROVE), Clean Air Status and Trends Network(CASTNET), the California Multiple Air Toxics Exposure Study in the South Coast Air Basin(MATESii), and the 1995 Integrated Monitoring Study (IMS95). Data were also obtained fromone of EPA’s EMPACT grant recipients, Airbeat.

The EC and BC values “below the detection limit” were replaced by one half of the minimumdetection limit (MDL). Missing data were not used or substituted for, to avoid biasing theestimated standard deviations.

The database includes the following information, where available:

1. For each site measuring carbon on quartz fiber, the method by which EC and OCfractions (EC/OC) are determined

2. Latitude and longitude coordinates of the monitor3. Whether the monitor is in an urban or rural tract, based on NSATA assignments4. The minimum detection limit of the monitor and analytic method5. Monitor start and end dates6. Summary statistics of daily average EC, OC, or BC measurements for all data at the site

and also stratified by 1) year, 2) calendar quarter, 3) year and quarter, 4) weekday andweekend. The following summary statistics were obtained: mean, median, standarddeviation, geometric mean, geometric standard deviation, minimum, 10th, … 90th

percentile, maximum.7. The same set of summary statistics were obtained for ECOCX, an EC concentration value

developed from the EPA PM TOT speciation data to estimate the corresponding TORvalue, and for the various monitored DPM estimates computed by applying correctionfactors (described below) to the EC values.

8. At each PM2.5 monitoring site, the fractions of PM2.5 which are elemental carbon,organic carbon, sulfates, and nitrates.

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Conversion Factors

As described in Chapter 3, ICF developed multiplicative “conversion factors” (CFs) forestimating ambient DPM based on the ambient EC, BC or OC measurements. For each site andcarbon type (EC, OC, or BC), a low-end, most likely (“average”), and high-end CF wasassigned, as discussed below. For EC sites, the estimated ambient DPM-high equals ambient ECmultiplied by the high-end CF for EC, the estimated ambient DPM-low equals ambient ECmultiplied by the low-end CF for EC, and the estimated ambient DPM-avg equals ambient ECmultiplied by the most likely CF for EC. BC and OC conversion factors were tabulated but notapplied to the concentration data. Separate sets of conversion factors were applied to EC datacollected by the TOR or TOT method.

The CFs were developed using existing source apportionment studies. Source apportionmentstudies for the West US included the Northern Front Range Air Quality Study (Denver,Colorado), the Los Angeles Study (various analyses based on data collected in 1982 at 4 urbanSouthern California sites), and the San Joaquin Valley Study. For the East US, information fromthe recent source apportionment study for sites in the South-East US was used. We contactedseveral experts and reviewed available literature to obtain information from these studies. Inparticular, James Schauer from University of Wisconsin-Madison provided very helpfulinformation. The conversion factors were developed by dividing the reported DPM concentrationby the reported total EC or OC concentration.

Since there are several source apportionment studies, each giving different estimates of the dieselcontribution at different receptors, and since there are several diesel exhaust source profiles inthe literature, several possible CF values could be applied for each site. For the TOR conversionfactors, we developed rural CFs for rural sites and urban CFs at urban sites. We could not obtainTOR data to match by region or season, since the available data were all collected in the winterand in the West US. For the TOT conversion factors we developed separate factors by quarter forthe East US and another set of factors for the West US. We could not obtain TOT data to matchby the urban or rural classification. The minimum, average, and maximum of the possible CFswill give the low-end, most likely, and high-end CF’s for that site.

Model to Monitor Comparisons

In Chapter 4, we will describe the DPM model-to-monitor comparisons. Using the CFs toconvert the monitored EC values to estimated DPM concentrations, we compared differencesbetween the monitored and modeled DPM values. For the modeled values, the NSATApredictions for 1996 using ASPEN (and CALPUFF, for the background) were used. Wecompared the monitored value to the NSATA prediction at the nearest ASPEN receptor site(census tract centroid). We also compared the monitored value to the maximum NSATAprediction within 30 km. For the monitored value we separately analyzed the site means ofDPM-high, DPM-low, and DPM-avg, as described above. The site means were computed byaveraging all the daily averages (from January 1, 1994 to December 31, 2001). There wereinsufficient data to restrict the monitored data to the modeling year 1996.

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Separately for each model-to-monitor comparison, and for all locations, urban locations, andrural locations, we compared the modeled and monitored results using:

• Scatterplots of modeled against monitored values that include fitted regression lines• Tables summarizing the regression fits, average difference, and average percentage

difference for the modeled values against the monitored values• Tables summarizing the proportions of modeled values that were within 10%, 25%, 50%,

and 100% of the monitored values

Using the NSATA estimates of the percentage contributions of onroad and nonroad sources ateach matched receptor site, we also evaluated whether the regions dominated by either onroad ornonroad sources have better model-to-monitor comparisons. The same statistical comparisonswere applied to the subsets of sites dominated by non-road (at least 75 % of modeled DPM isnon-road) or on-road (at least 50 % of modeled DPM is on-road) emissions.

The inventory estimates from the NONROAD model have been revised since the NSATAassessment was conducted using the draft 2000 NONROAD model (also used to developinventories for EPA’s 2007 heavy duty standards). The EPA provided a single nationwidemultiplicative adjustment factor of 0.69 for the nonroad ambient DPM based on the ratio ofNONROAD year 1996 predictions from the 2002 and 2000 draft versions of the NONROADmodel . To determine if this 70 % adjustment lead to improved model-to-monitor comparisons,we applied the same statistical comparisons after adjusting the NSATA predictions using thenonroad adjustment factor (the onroad ambient DPM is unchanged).

Findings

The model-to-monitor comparisons for non-EPA TOR data (i.e. excluding the ECOCX estimatesof TOR from the EPA TOT data) were based on 15 monitoring sites. The model-to-monitorcomparisons for TOT data were based on 95 monitoring sites. The model-to-monitorcomparisons for TOR data including the EPA ECOCX values were based on 88 monitoring sites.

The regression model analyses were generally less useful because the R squared values were inmost cases less than 0.3 and the regressions tended to be over-influenced by the more extremevalues. Based on the regression results, the best model performance was for the DPM-minimummonitored value for TOT data but for the DPM-average value for TOR data. Results were verysimilar for the modeled values based on the 2000 and 2002 NONROAD model and were a littlebetter for the rural sites compared to the urban sites. For the Non-road-dominated subset of TOTsites, the regression model fitted better than the all sites regression, but the monitored valueswere significantly overpredicted. For the Non-road-dominated subset of TOR sites including theEPA ECOCX sites, the regression model fitted a bit worse than the all sites regression. Therewas not enough data to evaluate the On-road-dominated subset. The comparisons between themaximum modeled value within 30 km and the monitored values all showed that the monitoredvalue was significantly over-predicted.

A summary table of the differences between the nearest modeled values and the monitoredvalues is given on the next page. Based on the mean percentage difference and based on the

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fraction of modeled values within 100 % of the monitored value, the best model performancewas consistently for the DPM-maximum value at the nearest census tract centroid using theestimates consistent with the 2002 NONROAD model. For the non-EPA TOR, for TOT, and forthe combination of TOR data from TOR sites and from EPA TOT converted to TOR (i.e., TORand ECOCX), the mean percentage differences were 26 %, 27 %, and –12 % and the fractions ofmodeled values within 100 % of the monitored value were 73 %, 80 %, and 92 %, respectively.These results compare favorably with the results of the model to monitor comparisons for otherpollutants in the NSATA assessment. For instance, ASPEN typically agrees with monitoring datawithin 30% half the time and within a factor of 2 most of the time. The best agreement is forbenzene where the results are within a factor of two for 89 percent of the cases and within 30%59 percent of the time. The median ratio of the benzene model to monitor comparisons was 0.92.Agreement for other HAPs varies, with median ratios of model to monitor values varyingbetween 0.65 for formaldehyde to 0.17 for lead.

We can conclude that the modeled diesel PM concentrations in NSATA agree reasonably wellwith monitor values, and the agreement is better than for other pollutants evaluated, except forbenzene.

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Summary of differences between the nearest modeled concentration and the monitored values.

Fraction of ModeledValues Within

ModeledVariable1

MonitoredVariable2 N

MeanModeled

Value

MeanMonitored

Value

MeanDifference Mean

%Difference 10% 25% 50% 100%

concnear ECTOR 15 1.56 0.94 0.63 100 0.07 0.13 0.53 0.53concnear2 ECTOR 15 1.20 0.94 0.26 56 0.07 0.13 0.47 0.60concnear ECTORH 15 1.56 1.16 0.40 62 0.00 0.07 0.40 0.60concnear2 ECTORH 15 1.20 1.16 0.04 26 0.00 0.07 0.33 0.73concnear ECTORL 15 1.56 0.64 0.92 190 0.13 0.40 0.47 0.53concnear2 ECTORL 15 1.20 0.64 0.55 126 0.07 0.33 0.47 0.53concnear ECTOT 95 2.61 1.73 0.88 80 0.12 0.21 0.45 0.68concnear2 ECTOT 95 2.05 1.73 0.32 42 0.11 0.37 0.53 0.77concnear ECTOTH 95 2.61 2.10 0.52 61 0.11 0.22 0.46 0.74concnear2 ECTOTH 95 2.05 2.10 -0.05 27 0.11 0.35 0.53 0.80concnear ECTOTL 95 2.61 1.52 1.09 101 0.09 0.17 0.43 0.63concnear2 ECTOTL 95 2.05 1.52 0.52 58 0.09 0.32 0.52 0.72concnear TOR 88 2.31 1.70 0.61 47 0.10 0.30 0.59 0.78concnear2 TOR 88 1.81 1.70 0.11 15 0.17 0.30 0.59 0.85concnear TORH 88 2.31 2.23 0.08 13 0.11 0.26 0.60 0.84concnear2 TORH 88 1.81 2.23 -0.42 -12 0.08 0.22 0.52 0.92concnear TORL 88 2.31 1.19 1.12 110 0.10 0.26 0.41 0.65concnear2 TORL 88 1.81 1.19 0.62 65 0.14 0.31 0.52 0.74

Notes:1. Modeled variable:concnear Nearest modeled DPM concentration consistent with the draft 2000

NONROAD Modelconcnear2 Nearest modeled DPM concentration consistent with the draft 2002

NONROAD Model2. Monitored variable:ECTOR EC value multiplied by TOR average correction factor (missing for EC

measured using TOT).ECTORH EC value multiplied by TOR maximum correction factor (missing for EC

measured using TOT).ECTORL EC value multiplied by TOR minimum correction factor (missing for EC

measured using TOT).ECTOT EC value multiplied by TOT average correction factor (missing for EC

measured using TOR).ECTOTH EC value multiplied by TOT maximum correction factor (missing for EC

measured using TOR).ECTOTL EC value multiplied by TOR minimum correction factor (missing for EC

measured using TOR).TOR ECOCX value multiplied by TOT average correction factor for EPA data,

ECTOR for TOR data.TORH ECOCX value multiplied by TOT maximum correction factor for EPA

data, ECTOR for TOR data.TORL ECOCX value multiplied by TOR minimum correction factor for EPA

data, ECTOR for TOR data.

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CHAPTER 2. DIESEL PARTICULATE MATTER EC/OC/BC DATABASE

EPA has been provided with three DbaseIII (DBF) files, comprising the EC/OC/BC(elemental/organic/black carbon) concentration database. The files were developed by compilingand processing data from the following studies (“source”):

• AIRBEAT• CASTNET• EPA (PM Speciation data)• IMPROVE (six selected sites )1

• IMS95• MATESII• NERL (Phoenix Supersite)• NFRAQS

All concentrations are reported as µg/m3.

The dailyavg.final.dbf file contains daily average concentrations by source, site_id, and date. Thevariables are listed in Table 2-1. Note that each daily average is possibly averaged acrossmultiple time periods during the day (e.g. some black carbon data was reported every fiveminutes) and/or multiple measuring instruments at the same location (e.g. EPA daily averagedata with multiple POCs on the same date). For MDLs, we either used values reported with thedatabase or used default values obtained from the literature. Raw values below the MDL werereplaced by one half of the MDL prior to computing the daily averages (this did not happen veryoften for the EC/OC/BC concentration data). For black carbon, MDLs were not reported in thedatabases and could not be obtained from the data suppliers. According to the “AethalometerBook” (Hansen, 2000), written by the company that makes the aethalometer instrument thatmeasures BC, the black carbon MDL depends upon the filter size, air flow rate, and averagingperiod. The filter size and air flow rate were not always available. Furthermore, since we areonly interested in daily averages, the MDLs of the five-minute or hourly values do not representthe precision of the daily averages. For these reasons, we did not use MDLs for the black carbondata, effectively assuming an MDL of zero. The method variable lists all methods used for thatday (“Aethalometer” applies to all black carbon data, other methods apply to EC and OC).

EPA’s Office of Air Quality Planning and Standards is reviewing the measurement of ECthrough the Speciation Trends Network, and an Agency statement on the issue is forthcoming.For now, however, existing values developed using the TOT method are being used.

Minimum detection limits (MDLs) were obtained from the data suppliers if possible. For theEPA PM speciation data, specific MDL’s for each of the various NIOSH methods were supplied;each daily measurement had an associated measurement method. The EPA MDLs were mostlyequal to or close to 0.146 µg/m3 for EC and OC. For Airbeat, the data supplier reported ECMDLs that were either 0.059 or 0.134 µg/m3 depending on the method used. For IMPROVE and

1 The data from the two Yellowstone Park sites YELL1 and YELL2 were treated as all coming from the YELL1 site.The Yellowstone Park monitoring site was moved a short distance in 1996.

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NFRAQS, the data source contact was unable to give specific MDL values for the TOR method,because the TOR measurements of EC and OC are both sums of three components, each ofwhich has its own MDL. However, we were able to find a report by Chow and Watson (1998)that tabulated MDLs of 0.12 µg/m3 for EC and OC by TOR. We used these values for theIMPROVE and NFRAQS EC and OC data. For CASTNET, MATESII, and NERL, we wereunable to obtain specific MDL values for the NIOSH measurements, but Gary Lear (EPA)suggested a typical MDL value of 0.1 µg/m3 for EC and OC, which was used for these threestudies. For MATESII, the EC MDL was erroneously entered as 0.01 in the database; this has noaffect on the daily averages or other results since all the MATESII EC measurements were 0.47µg/m3 or greater.

Since some site-days had more than one measured EC or OC concentration, a daily average canbe treated as being below the MDL if any of the measurements for that day and carbon specieswere below the MDL. Using this definition, across the entire dataset, 9.9 % of the 13,993 ECdaily averages were below the MDL and 3.3 % of the 13,804 OC daily averages were below theMDL.

In addition to the carbon species EC, OC, and BC, the daily average file also includes dailyaverage concentrations for the various estimated DPM concentrations defined as follows:

ECOCX The EPA EC and OC data were collected using the NIOSH (SunsetLaboratory) method of thermal optical transmission (TOT). EPA alsocomputed a value ECOCX intended to approximate the equivalent ECconcentration based on thermal optical reflectance (TOR, as developedand applied by Desert Research Institute). This value is missing for non-EPA data.

ECTOR EC value multiplied by TOR average correction factor (missing for ECmeasured using TOT).

ECTORH EC value multiplied by TOR maximum correction factor (missing for ECmeasured using TOT).

ECTORL EC value multiplied by TOR minimum correction factor (missing for ECmeasured using TOT).

ECTOT EC value multiplied by TOT average correction factor (missing for ECmeasured using TOR).

ECTOTH EC value multiplied by TOT maximum correction factor (missing for ECmeasured using TOR).

ECTOTL EC value multiplied by TOR minimum correction factor (missing for ECmeasured using TOR).

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EPATOR ECOCX value multiplied by TOR average correction factor (missing fornon-EPA data).

EPATORH ECOCX value multiplied by TOR maximum correction factor (missing fornon-EPA data).

EPATORL ECOCX value multiplied by TOR minimum correction factor (missing fornon-EPA data).

In the concentration summary file, EC, OC, BC, and the above DPM estimates are all referred toas “SPECIES”.

The TOR and TOT (NIOSH) minimum, maximum, and average conversion factors are reportedin Table 3-2 of Chapter 3 “Diesel Particulate Matter Conversion Factors.” The converted valuesestimate the diesel particulate matter (DPM) concentration. The applicable conversion factorsdepend upon the measurement method (TOR or TOT), whether the location is in the East orWest US, whether the site is urban or rural, and the calendar quarter. For this calculation, siteswith (signed) longitude less than −92° were treated as being in the West US. The Mississippiriver roughly lies along the −92° longitude line. Sites were defined as urban or rural based on theNSATA assignment for the nearest census tract centroid; this assignment is given by the variableurbannear in the site summary file.

The sitesummary.final.dbf file contains summary information about the individual sites(identified by the source and site_id variables). The variables are listed in Table 2-2. Informationincludes: the city or county; state; latitude and longitude; first and last measurement dates forEC, BC, or OC (within the time frame starting January 1, 1994); method; ratios of EC, OC,sulfate and nitrate to PM2.5; location, distance, location type (urban or rural) and modeledconcentration for the nearest modeled diesel PM2.5 concentration from NATA; location andmodeled concentration for the maximum modeled diesel PM2.5 concentration from NATAwithin 30 km, if any; and the dominant source. Note that the maximum modeled value within 30km is missing if there are no census tract centroids within 30 km. The method variable lists allmethods used for that site (“Aethalometer” applies to all black carbon data, other methods applyto EC and OC).

The first set of analyses used the 1996 NSATA model predictions consistent with the year 2000draft of the NONROAD model. The second set of analyses used 1996 NSATA model predictionsconsistent with the year 2002 draft of the NONROAD model: The earlier model’s ambient andbackground non-road components were both multiplied by 0.69 for every census tract. (since the1996 national modeled NONROAD DPM was reduced by 31 % for the 2002 draft model). Thevalues of the nearest modeled concentration and of the location and modeled value for themaximum modeled concentration within 30 km are each given separately for each version of theNONROAD model. The dominant source is defined for the 2000 NONROAD model only. If thetotal on-road modeled DPM is 50 % or greater of the total modeled DPM, the dominant source is“On-road.” If the total on-road modeled DPM is less than 25 % of the total modeled DPM, thedominant source is “Non-road.” Otherwise there is no dominant source.

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The concsummary.final.dbf file contains summary statistics for the daily average concentrationdata by source and site, species (BC, EC, OC, ECOCX, ECTOR, ECTORL, ECTORH, ECTOT,ECTOTH, ECTOTL, EPATOR, EPATORL, EPATORH), year, calendar quarter, andweekday/weekend. The variables are listed in Table 2-3. Possible values of year are 1994, 1995,1996, 1997, 1998, 1999, 2000, 2001, and “All” (all years combined). Possible values of quarterare 1, 2, 3, 4, and “All” (all quarters, i.e., the entire year or years). Possible values of dayofweekare “Weekday” (Monday to Friday), “Weekend” (Saturday or Sunday), and “All.” For example,overall averages for a site are obtained by considering year = quarter = dayofweek = “All.”Summary statistics are available by year (including “All”) and/or by quarter (including “All”).For the weekend/weekday split, separate weekend and weekday summary statistics are reportedby year (including “All”) or by quarter (including “All”) but not for specific year and quartercombinations.

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Table 2-1. Daily Average File (dailyavg.dbf)Variable DescriptionDate DateSource Data source (study)Site_id Site identifierCitycounty City or CountyState StateEC Elemental carbon daily averageOC Organic carbon daily averageBC Black carbon daily averageECOCX Estimated EC by TOR (EPA data)ECTOR Average estimated DPM (TOR data)ECTORH Maximum estimated DPM (TOR data)ECTORL Minimum estimated DPM (TOR data)ECTOT Average estimated DPM (TOT data)ECTOTH Maximum estimated DPM (TOT data)ECTOTL Minimum estimated DPM (TOT data)EPATOR Average estimated DPM (EPA ECOCX data)EPATORH Maximum estimated DPM (EPA ECOCX data)EPATORL Minimum estimated DPM (EPA ECOCX data)MinECMDL Minimum EC MDL for dateMaxECMDL Maximum EC MDL for dateMinOCMDL Minimum OC MDL for dateMaxOCMDL Maximum OC MDL for dateSulfate Sulfate daily averageNitrate Nitrate daily averagePM25 PM2.5 daily averageLatitude Latitude (degrees and fractions of a degree)Longitude Longitude (degrees and fractions of a degree)Method List of all measurement methods used on date,

separated by semicolons.

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Table 2-2. Site Summary File (sitesummary.dbf)Variable DescriptionSource Data source (study)

Site_id Site identifierCitycounty City or CountyState StateLatitude Latitude (degrees and fractions of a

degree)Longitude Longitude (degrees and fractions of

a degree)MinECDate First date with non-missing ECMaxECDate Last date with non-missing ECMinBCDate First date with non-missing BCMaxBCDate Last date with non-missing BECMinOCDate First date with non-missing OCMaxOCDate Last date with non-missing OCMinECMDL Minimum EC MDL for siteMaxECMDL Maximum EC MDL for siteMinOCMDL Minimum OC MDL for siteMaxOCMDL Maximum OC MDL for siteEC_PM25 Mean EC divided by mean PM2.5

(for days when both were reported)OC_PM25 Mean OC divided by mean PM2.5

(for days when both were reported)Sulf_PM25 Mean sulfate divided by mean

PM2.5 (for days when both werereported)

Nitr_PM25 Mean nitrate divided by mean PM2.5

(for days when both were reported)Method List of all measurement methods

used at site, separated bysemicolons

Fipsmax FIPS code for census tract centroidwith maximum modeled DPMwithin 30 km (2000 NONROADmodel)

Tractmax Tract ID code for census tractcentroid with maximum modeledDPM within 30 km (2000NONROAD model)

Dist Distance (km) to nearest censustract centroid

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Table 2-2. Site Summary File (sitesummary.dbf)Variable DescriptionMaxconc Maximum modeled DPM within 30

km (2000 NONROAD model)Concnear Modeled DPM at nearest census

tract centroid (2000 NONROADmodel)

Fipsnear FIPS code at nearest census tractcentroid

Tractnear Tract ID code at nearest census tractcentroid

Urbannear NSATA location type (U = urban,R = rural) at nearest census tractcentroid

Fipsmax2 FIPS code for census tract centroidwith maximum modeled DPMwithin 30 km (2002 NONROADmodel)

Tractmax2 Tract ID code for census tractcentroid with maximum modeledDPM within 30 km (2002NONROAD model)

Maxconc2 Maximum modeled DPM within 30km(2002 NONROAD model)

Concnear2 Modeled DPM at nearest censustract centroid (2002 NONROADmodel)

Dominant Dominant source: “Non-road,”“On-Road,” or “ ” (blank).Consistent with the 2000NONROAD model.

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Table 2-3. Concentration Summary File (concsummary.dbf)Variable DescriptionSource Data source (study)

Site_id Site identifierYear Calendar year or “All”Quarter Calendar quarter or “All”DayofWeek “Weekday,” “Weekend,” or “All”Species EC, OC, BC, ECOCX, ECTOR,

ECTORL, ECTORH, ECTOT,ECTOTH, ECTOTL, EPATOR,EPATORL, or EPATORH

N Number of daysMean Arithmetic meanMedian MedianGeommean Geometric meanStddev Standard deviationMinimum MinimumMaximum MaximumPerc10 10th percentilePerc20 20th percentilePerc30 30th percentilePerc40 40th percentilePerc50 50th percentilePerc60 60th percentilePerc70 70th percentilePerc80 80th percentilePerc90 90th percentile

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CHAPTER 3. DIESEL PARTICULATE MATTER CONVERSION FACTORS

This part of the effort was primarily carried out by the consulting firm dKC. We compiled“conversion factors” (CFs) for estimating ambient diesel particulate matter (DPM) based onelemental carbon (EC), organic carbon and PM2.5 measurements. We attempted to collectinformation on CFs for black carbon (BC), but found none in the literature. See below for adiscussion of the recommended treatment of the black carbon data. The CFs were collected fromexisting source apportionment and source-receptor model studies.

We received assistance from the following researchers to identify data sources: James Schauer(University of Wisconsin), Philip Hopke (Clarkston University), Alan Gertler (Desert ResearchInstitute), and Steve Cadle (GM Research). Overall, we identified and compiled data on CFsfrom the following sources:

1. Zheng, Cass, Schauer et al (2002).Apportionments of PM2.5 (mass) and organic carbon in PM2.5 for 8 SE US sites:4 urban, 3 rural and 1 suburban. Season: All 4 individually. Dr. Schauer alsoprovided an elemental carbon breakdown by season (but not by site).

2. Ramadan, Song, and Hopke (2000).Apportionments of PM (mass) in Phoenix, AZ. Season: Annual Average.

3. Schauer et al (1996).Apportionments of primary fine organic aerosol and fine particulate massconcentrations for 4 urban sites in Southern California. Season: Annual Average.

4. Schauer and Cass (2000).Apportionments of primary fine organic aerosol and fine particulate massconcentrations for 3 sites in the Central Valley of California: 2 urban and 1 rural.Season: Winter

5. Watson, Fujita, Chow, Zielinska et al (1998).NFRAQS. This was the most comprehensive and current analysis of sources ofambient PM. Two techniques were used to apportion ambient PM: ConventionalCMB and Extended Species CMB. Extended Species CMB breaks down gasolinevehicle emissions into 3 categories: cold start, hot transient, and high PM emitter(e.g. a vehicle with visible smoke). PM was apportioned into total carbon, organiccarbon, elemental carbon and PM2.5. A total of 9 sites were evaluated: 3 urban,4 rural, one suburban, and one to characterize regional transport. Two sites wereused for the Extended Species CMB analysis: one rural and one urban. For theExtended Species CMB analysis, a temporal apportionment was done. Season:Winter

6. Air Improvement Resources (1997).Summary and analysis of available data on contribution of gasoline poweredvehicles to ambient levels of fine particulate matter. Most of the data was covered

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in Reference #3. Included projections of sources of fine carbonaceous PM. for 4Southern California sites. Season: Annual Average.

7. Cass (1997).Summary and analysis of available data on contribution of motor vehicles toambient levels of fine particulate matter. Most of the data was covered inReference #3. Included projections of sources of elemental carbon for 3 SouthernCalifornia sites. Season: Annual Average.

The numbering of these source apportionment study references is arbitrary but is retained herefor consistency with the attached Excel spreadsheet.

Dr. Schauer noted that several important source apportionment studies are currently underway,and results should start being available in the next 6 months.

We compiled data on conversion factors (CFs) into an Excel spreadsheet provided to EPA. Thespreadsheet contains a page for CFs from each reference. The following information wascompiled for each data point:

• Year data were collected

• Site evaluated

• State data were collected

• Type of site: urban, rural, suburban (in some cases more specific types were used,e.g. rural down valley).

• Season data were collected

• Ambient Measurement Technique: Thermal optical transmission (TOT, alsoreferred to as the NIOSH method, as developed by Sunset laboratories) or thermaloptical reflectance (TOR, as developed and applied by Desert Research Institute,which was also used for the IMPROVE database and the Northern Front Rangestudy).

• PM measurement parameter (organic carbon, elemental carbon, PM2.5)

• Concentration apportioned to diesel powered engines

• Concentration apportioned to gasoline engine exhaust. (One of the data sources,Northern Front Range Air Quality Study, had a breakdown of gasoline poweredvehicle emissions into LDHV Cold Start, LDGV hot stabilized (warmed-up vehicleemissions, and LDGV high PM emitter)

• Total concentration

ICF Consulting 18

• % of PM from diesel, for each measurement parameter This value was calculatedfor each site (in some cases, by season) as the ratio of the CMB estimated dieselfine particulate matter to the site average total fine particulate matterconcentration. Both values were either reported in the source apportionment studyreport or were obtained directly from the researcher.

• Multiplicative conversion factor to convert total organic carbon (OC) or totalelemental carbon (EC) concentration to diesel PM2.5 concentration. Conversionfactors (CFs) were calculated by dividing the diesel PM2.5 concentration reportedby the study by the total organic carbon or elemental carbon concentrationreported by the study.

• Z factor: % of diesel PM2.5 that is OC or EC. This was calculated by dividing thereported % of OC or EC that is from diesels by the CF calculated above. The % ofdiesel PM2.5 that is OC or EC varied significantly for data based on the twomeasurement techniques: TOR or NIOSH.

The Z factor equals the OC or EC diesel PM2.5 concentration divided by the total diesel PM2.5

concentration. This factor can be compared with the measured OC or EC fraction in theassociated diesel PM2.5 source profile. For most of the studies, the diesel source profiles were noteasily obtained. For reference 5 (NFRAQS), the calculated Z factor for EC is compared with thesource profile Z factor in the attached spreadsheet. The calculated and source profile Z factorswere quite close except for the Chatfield and Highlands sites. For consistency with the treatmentof other studies, for all the NFRAQS sites (including Chatfield and Highlands) we used themultiplicative conversion factor defined above and did not correct for any differences betweenthe calculated and source profile Z factors.

The originally proposed approach for this project was to compute the conversion factors as thepercentages of diesel in PM2.5 OC or EC (from CMB) divided by the source profile percentage ofOC or EC in diesel PM2.5 (i.e., divided by the source profile Z factor). This alternative approachgives almost the same results, as can be shown in the reference 5 worksheets, which demonstratethat the two methods give almost the same conversion factors for the NFRAQS sites, except forthe Chatfield and Highlands sites. The method used here does not require the CMB study toprovide a source apportionment of OC or EC (just the source apportionment for PM2.5) and doesnot need the diesel source profile.

The spreadsheet also contains sheets that compile available data on the following:

• Organic Carbon (OC) conversion factors (conversion factors to convert total OCto diesel PM2.5 concentration).

• Elemental Carbon (EC) conversion factors (conversion factors to convert total ECto diesel PM2.5 concentration).

• Fraction of fine particulate mass attributed to diesels.

ICF Consulting 19

Table 3-1 presents the minimum, maximum, and average diesel fraction of PM2.5 as a functionof:

• Urban or rural• Season• East or West US

The reported minimum, maximum, and average values in Table 3-1 are the minima, maxima, andarithmetic means of the “% of PM from diesel” values across all sites (and seasons, whereapplicable) in the given site subset.

Table 3-2 presents the minimum, maximum, and average EC conversion factors as a function of:

• Measurement technique• East or West US• Season• Urban or rural

The reported minimum, maximum, and average values in Table 3-2 are the minima, maxima, andarithmetic means of the EC conversion factors across all sites (and seasons, where applicable) inthe given site subset. For the NIOSH (same as TOT) data collected in the East, the minimum,maximum, and average conversion factors are all equal. This is because these values were basedonly on the Zheng, Cass, Schauer, et al (2002) study. For this project, Dr, Schauer provided ECsummary data from this study averaged over sites, by season. Hence only one value is availablefor NIOSH data for each season in the Eastern US.

Table 3-3 presents the minimum, maximum, and average OC conversion factors as a function of:

• Urban or rural• Season• East or West US

The reported minimum, maximum, and average values in Table 3-3 are the minima, maxima, andarithmetic means of the OC conversion factors across all sites (and seasons, where applicable) inthe given site subset.

Black Carbon

Black carbon is measured on an “aethalometer,” a measuring instrument developed by MageeScientific. The following summary is taken from the “Aethalometer Book,” by Hansen (2000).

“The Aethalometer is an instrument that provides a real-time readout of the concentrationof ‘Black’ or ‘Elemental’ carbon aerosol particles. (BC or EC). These particles (“soot”)are emitted from all types of combustion, most notably from diesel exhaust. ‘BC’ isdefined by blackness, an optical measurement. The Aethalometer uses an optical

ICF Consulting 20

measurement, and gives a continuous readout. The ‘EC’ definition is more common. It isbased on a thermal-chemical measurement, an analysis of material collected on a filtersample for several hours and then sent to a laboratory. Research at Harvard showed thatthe Aethalometer BC measurement is directly related and equivalent to the filter-basedEC measurement. In fact, an option in the software allows it to read out in EC units.”

More details are given in the full document (Hansen, 2000) and in various references, includingAllen et al (1999), Chow et al (1993), Hansen and Mc Murry (1990), and Liousse et al (1991).

On this basis, and because none of the source apportionment studies that we found used blackcarbon measurements, we recommend using the same conversion factors to convert BC and ECconcentrations to diesel PM2.5.

Recommendations

The final columns in Tables 3-2 and 3-3 give our recommendations for which minimum,maximum, and average EC and OC conversion factors should be applied to the database. ForBC, the available data are more limited and we did not find any source apportionment studiesbased on BC measurements. OC is not as useful a surrogate for diesel PM as EC because dieselPM source profiles tend to contain much more EC than OC, on average, and because the dieselfraction in EC is typically estimated to be much higher than the diesel fraction in OC. Forexample, the NFRAQS study (Watson, Fujita, Chow, Zielinska, et al, 1998), determined that ECcontains about 60 % diesel and OC contains about 8 % diesel. Therefore, EPA determined thatonly EC data be used for the model to monitor comparisons.

For EC, as shown in Table 3-2, available CF data based on the NIOSH (TOT) method in the Eastmainly allows a breakdown by season. There is not enough seasonal data to stratify by locationtype. The seasonal stratification in the East is based only on reference 1, which had data forJanuary, April, July, August, and October only. Thus for the data in the East, the seasonalstratification is equivalent to a quarterly stratification: Winter = Quarter 1, Spring = Quarter 2,Summer = Quarter 3, Fall = Quarter 4. For the East US, we recommend using the EC conversionfactors for each daily mean according to the calendar quarter (equivalently, the season). For theWest US, the available data were collected at urban sites in Los Angeles and the season was notreported. Thus we suggest applying the same factors for all EC data collected in the West,regardless of location type. For observations based on the TOR method, we suggest thatconversion factors be based on location type (urban or rural), regardless of season. This is due toan absence of TOR data from non-Winter observations. For BC, the approximate equivalencebetween EC and BC suggests using the same conversion factors as for EC.

For OC, separate conversion factors for TOR and TOT data were not computed, although theywould be preferred due to the wide differences in the two measurement methods. For OC, asshown in Table 3-3, the data in the East is stratified by Urban or Rural location and by season,but the data in the West is only available for the winter season. The seasonal stratification in theEast is based only on reference 1, which had data for January, April, July, August, and Octoberonly. Thus for the data in the East, the seasonal stratification is equivalent to a quarterlystratification: Winter = Quarter 1, Spring = Quarter 2, Summer = Quarter 3, Fall = Quarter 4. For

ICF Consulting 21

the East US, we recommend using the OC conversion factors for each daily mean according tothe calendar quarter (equivalently, the season), and whether the site is urban or rural. For theWest US at rural sites, only winter data are available for OC conversion factors, but at rural sitesin the winter, the CF distributions for East and West are quite similar (the factors in the West area little lower). On this basis, assuming the same applies to all seasons, we recommend applyingthe OC CF’s for the seasonal/quarterly totals to the daily averages at West US rural sites in thefirst three quarters. For West US rural sites in the winter, i.e., Quarter 1, we recommend usingthe corresponding OC CF distribution. For urban sites, the winter CF distributions are verydifferent between the East and West sites—the averages differ by a factor of about two—so thisapproach is not recommended. Instead, for West US urban sites, we recommend using the“Urban All” OC CF distribution since the uncertainty range is conservatively wide, the mean isclose to the mean for West US urban sites, and the minimum is the same as the minimum forWest US urban sites in the winter.

ICF Consulting 22

Table 3-1Summary of Percent of Fine PM Apportioned to Diesels

% Contribution From DieselsLocationtype

Season EastorWest Minimum* Maximum* Average*

Fall East 8.9% 10.9% 9.8%Spring East 11.4% 15.2% 12.9%Summer East 7.5% 10.9% 8.7%Winter East 10.0% 13.5% 12.1%

West 2.7% 11.4% 5.9%

Rural

Winter Total 2.7% 13.5% 7.8%RuralTotal 2.7% 15.2% 9.1%

All West 12.7% 35.7% 21.2%Fall East 7.5% 32.0% 20.7%Spring East 10.1% 29.9% 19.8%Summer East 9.6% 25.5% 14.8%Winter East 17.0% 24.1% 21.2%

West 5.3% 12.7% 9.4%

Urban

Winter Total 5.3% 24.1% 13.3%UrbanTotal 5.3% 35.7% 16.6%GrandTotal 2.7% 35.7% 13.9%

Notes:* Minimum, maximum, or average value across all sites of the % contribution from diesel, whichis defined as the ratio of the CMB estimate of diesel PM2.5 divided by the total PM2.5

ICF

Con

sult

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23

Tab

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ICF

Con

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24

Tab

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ICF Consulting 25

CHAPTER 4. MODEL-TO-MONITOR COMPARISONS

Using the CFs to convert the monitored EC values to estimated DPM concentrations, wecompared differences between the monitored and modeled DPM values. For the modeled values,the NSATA predictions for 1996 using ASPEN (and CALPUFF, for the background) were used.We compared the monitored value to the NSATA prediction at the nearest ASPEN receptor site(census tract centroid). These comparisons were made for the original NSATA model predictionsconsistent with the draft 2000 NONROAD model (“concnear”) and for the revised NSATAmodel predictions consistent with the draft 2002 NONROAD model (“concnear2”) with non-road model predictions reduced to 69 % of their original value. We also compared the monitoredvalue to the maximum NSATA prediction within 30 km. These comparisons were also made forthe original NSATA model predictions consistent with the draft 2000 NONROAD model(“maxconc”) and for the revised NSATA model predictions consistent with the draft 2002NONROAD model (“maxconc2”)

For the monitored value we separately analyzed the site means of DPM-high, DPM-low, andDPM-avg, as described above. The site means were computed by averaging all the dailyaverages (from January 1, 1994 onward), since there were insufficient data to restrict themonitored data to the modeling year 1996. The first set of comparisons used EC data collectedby the TOR method only (average, minimum, and maximum DPM values ECTOR, ECTORL,and ECTORH respectively). The second set of comparisons used EC data collected by the TOT(NIOSH) method only (average, minimum, and maximum DPM values ECTOT, ECTOTL, andECTOTH respectively). The third set of comparisons combined the EC data collected using theTOR method with the EPA ECOCX value (based on estimated TOR values). This set of average,minimum, and maximum DPM values are denoted by TOR, TORL, and TORH, respectively.

Each of these model-to-monitor comparisons were applied to the subsets of all locations, urbanlocations, and rural locations. Additionally, but only for the predictions consistent with the draft2000 NONROAD model, we considered the subsets of sites with modeled DPM dominated byNon-road (at least 75 % non-road) or by On-road (at least 50 % on-road).

We compared the modeled and monitored results using:

• Scatterplots of modeled against monitored values that include fitted regression lines• Tables summarizing the regression fits, average difference, and average percentage

difference for the modeled values against the monitored values• Tables summarizing the proportions of modeled values that were within 10%, 25%, 50%,

and 100% of the monitored values

Table 4-1 summarizes each of the regressions. The modeled values are regressed against theaverage, maximum, and minimum “DPM monitored” values for each given data subset. The“DPM monitored” value is the EC value multiplied by the applicable conversion factor toconvert it to the estimated DPM. Scatterplots are shown for a few representative cases. In eachscatterplot, the fitted regression lines of the modeled values against the DPM monitored values.Each plot shows the modeled values plotted against the average, maximum, and minimum DPMmonitored values for a given data subset. The three regression lines are shown together with the

ICF Consulting 26

Y=X line. The Y=X line has zero intercept and slope 1 and represents the ideal case wheremodeled and monitored values agree precisely. For easier comparison, the regressions have beennumbered in Table 4-1, and those numbers are included in the Figure footnotes.

In some cases, all three regression lines intersect and have the same intercept (but differentslopes). This occurs in cases where the same set of three conversion factor values apply at eachmonitored value in the data subset. In some cases there are some data points where all threeDPM monitored values are identical, or almost identical. This is attributable to the fact that forNIOSH data in the East, the minimum, maximum, and average estimated CFs were identical foreach season, and the seasonal values were almost identical, as shown in Table 3-2.

Table 4-1 presents the regression lines in tabular format. For each case is presented: theintercept, its standard error, the slope, its standard error, and the R squared goodness-of-fitstatistic. Ideally the intercept should be close to zero and the slope should be close to 1. Rsquared values close to 1 indicate a good fit of the simple linear model and values close to zeroindicate a poor fit.

Tables 4-2 to 4-4, respectively, show the monitored DPM values and nearest modeled value(consistent with the draft 2000 NONROAD model) for the TOR sites, for the TOT sites, and forthe combination of the TOR sites and the EPA sites with the ECOCX data. These data were usedfor the “concnear” regressions (except that the TOR outlier value mentioned below was excludedfrom the regressions).

Table 4-5 presents the mean modeled and monitored values, the mean difference, the meanpercentage difference, and the fractions of modeled values within 10 %, 25 %, 50 %, or 100 % ofthe monitored values. Ideally the mean differences and mean percentage differences should beclose to zero and the fractions of modeled values within a small percentage of the modeledvalues should be close to one.

Review of preliminary scatterplots showed clearly that for the TOR data, there was one site withan extremely high modeled value that was greater than 12 ug/m3 compared to monitored DPMvalues close to 1 ug/m3. Other TOR modeled to monitor ratios were much lower. This obviousoutlier value from the IMPROVE Washington DC site (WASH1) was removed from all theseanalyses although it was retained in the database.

Results

TOR Regressions excluding EPA data

The TOR comparisons excluding the EPA ECOCX data are based on only 15 monitoring sitesand therefore are relatively less representative. As shown in Figure 4-1 (regressions 1, 19, and37), for all sites combined, the regression line of the nearest modeled concentration against theDPM average is closest to the Y=X line but, as shown in Table 1, regression 1, the R squaredvalue is 0.22, indicating a poor regression fit. The model tends to overpredict as shown by thefact that more of the values are above the Y=X line. The same analysis for rural sites only(regressions 2. 20, and 38) shows a better fit for the regression but a greater tendency to

ICF Consulting 27

overpredict, with slopes close to 2. There are too few urban TOR locations to properly evaluatethis subset. There are also too few Non-road dominated locations (and zero on-road dominatedlocations) to compare the modeled and monitored data. Comparing Figure 4-2 to Figure 4-1, theregression model using the revised NONROAD model appears to fit slightly worse but thenumerical differences are relatively small for most locations. The comparisons between themaximum modeled value within 30 km and the monitored values show that the monitored valueis significantly over-predicted (e.g., see Figure 4-3).

TOT Regressions

The TOT comparisons are primarily based on the EPA PM speciation data producing a total of95 monitored values for each of the DPM-minimum, DPM-maximum, and DPM-average. Figure4 uses all these data. The best results are for the DPM-minimum case, with a R squared value of0.17, an intercept of 1.52 and a slope of 0.72. Figure 4-4 shows that there are some monitoredvalues that are significantly over-predicted and some monitored values that are significantlyunder-predicted. The results for the urban and rural subsets are similar although the modelperformance appears to be a little better for the rural sites. For the Non-road dominated sites, theregression model fits better than the all sites regression, but the monitored values aresignificantly overpredicted (the slope is 1.82 for the DPM-average, regression 58). There are notenough data points to properly evaluate the subset of On-road dominated sites. ComparingFigure 4-4 to Figure 4-5, the regression model using the revised NONROAD model appears to fitslightly better but the numerical differences are relatively small for most locations; the DPM-minimum predictions give the best model performance. Similarly to TOR, the comparisonsbetween the maximum modeled value within 30 km and the monitored values show that themonitored value is significantly over-predicted.

TOR Regressions including EPA data

The TOR comparisons including the EPA ECOCX data are based on 88 monitoring sites. Of thethree monitored DPM values, the best model performance is obtained for the DPM-averagevalue, regression 127, with a slope of 0.91 and an intercept of 0.77 although the R squared valueis only 0.12. (Figure 4-6). The regression results for the rural and urban subsets are very similar,but the regression fit is a little better for the rural subset. For the 18 Non-road dominated sites,the model performance is a bit worse than the performance for all sites. There are insufficientlymany sites to evaluate the subset of on-road dominated sites. The numerical differences betweenthe 2000 and 2002 models are relatively small for most locations and the model performance isvery similar. (Using the revised model the R squared values are slightly higher, the intercept iscloser to zero, but the slope is further from 1). The comparisons between the maximum modeledvalue within 30 km and the monitored values show that the monitored value is significantly over-predicted.

Differences and percentage differences

The following table extracted from Table 4-5 summarizes the differences and percentagedifferences between the nearest modeled value and the monitored values. The ECTOR outliervalue for the IMPROVE Washington DC site is excluded from these calculations.

ICF Consulting 28

Summary of differences between the nearest modeled concentration and the monitored values.

Fraction of ModeledValues Within

ModeledVariable1

MonitoredVariable2 N

MeanModeled

Value

MeanMonitored

Value

MeanDifference Mean

%Difference 10% 25% 50% 100%

concnear ECTOR 15 1.56 0.94 0.63 100 0.07 0.13 0.53 0.53concnear2 ECTOR 15 1.20 0.94 0.26 56 0.07 0.13 0.47 0.60concnear ECTORH 15 1.56 1.16 0.40 62 0.00 0.07 0.40 0.60concnear2 ECTORH 15 1.20 1.16 0.04 26 0.00 0.07 0.33 0.73concnear ECTORL 15 1.56 0.64 0.92 190 0.13 0.40 0.47 0.53concnear2 ECTORL 15 1.20 0.64 0.55 126 0.07 0.33 0.47 0.53concnear ECTOT 95 2.61 1.73 0.88 80 0.12 0.21 0.45 0.68concnear2 ECTOT 95 2.05 1.73 0.32 42 0.11 0.37 0.53 0.77concnear ECTOTH 95 2.61 2.10 0.52 61 0.11 0.22 0.46 0.74concnear2 ECTOTH 95 2.05 2.10 -0.05 27 0.11 0.35 0.53 0.80concnear ECTOTL 95 2.61 1.52 1.09 101 0.09 0.17 0.43 0.63concnear2 ECTOTL 95 2.05 1.52 0.52 58 0.09 0.32 0.52 0.72concnear TOR 88 2.31 1.70 0.61 47 0.10 0.30 0.59 0.78concnear2 TOR 88 1.81 1.70 0.11 15 0.17 0.30 0.59 0.85concnear TORH 88 2.31 2.23 0.08 13 0.11 0.26 0.60 0.84concnear2 TORH 88 1.81 2.23 -0.42 -12 0.08 0.22 0.52 0.92concnear TORL 88 2.31 1.19 1.12 110 0.10 0.26 0.41 0.65concnear2 TORL 88 1.81 1.19 0.62 65 0.14 0.31 0.52 0.74

Notes:3. Modeled variable:concnear Nearest modeled DPM concentration consistent with the draft 2000

NONROAD Modelconcnear2 Nearest modeled DPM concentration consistent with the draft 2002

NONROAD Model4. Monitored variable:ECTOR EC value multiplied by TOR average correction factor (missing for EC

measured using TOT).ECTORH EC value multiplied by TOR maximum correction factor (missing for EC

measured using TOT).ECTORL EC value multiplied by TOR minimum correction factor (missing for EC

measured using TOT).ECTOT EC value multiplied by TOT average correction factor (missing for EC

measured using TOR).ECTOTH EC value multiplied by TOT maximum correction factor (missing for EC

measured using TOR).ECTOTL EC value multiplied by TOR minimum correction factor (missing for EC

measured using TOR).TOR ECOCX value multiplied by TOT average correction factor for EPA data,

ECTOR for TOR data.TORH ECOCX value multiplied by TOT maximum correction factor for EPA

data, ECTOR for TOR data..TORL ECOCX value multiplied by TOR minimum correction factor for EPA

data, ECTOR for TOR data.

ICF Consulting 29

Tables 4-2 to 4-4 show the monitored and modeled (nearest tract, DPM modeled valuesconsistent with the draft 2000 NONROAD model) DPM values. In most cases the modeledvalues are within at most a factor of 2 of the monitored values.

Table 4-5 summarizes the differences and percentage differences between all the sets of modeledand monitored values. The ECTOR outlier value for the IMPROVE Washington DC site isexcluded from these calculations.

For the TOR comparisons excluding the EPA ECOCX data, the best model performance basedon the mean percentage difference and based on the fraction of modeled values within 100 % ofthe monitored value is for the DPM-maximum value consistent with the 2002 NONROADmodel. For all 15 sites the mean percentage difference is 26 % and the fraction of modeledvalues within 100 % of the monitored value is 73 %. This fraction would have been 69 %including the outlier. For the 10 rural sites the mean percentage difference is 20 % and thefraction of modeled values within 100 % of the monitored value is 70 %. For the 5 urban sitesthe mean percentage difference is 39 % and the fraction of modeled values within 100 % of themonitored value is 80 %. Interestingly, this finding is different to the regression analyses whichfound the model performance to be slightly worse with the modeled value consistent with therevised NONROAD model and better using the DPM-average monitored values.

For the TOT comparisons, the best model performance based on the mean percentage differenceand based on the fraction of modeled values within 100 % of the monitored value is also for theDPM-maximum value consistent with the 2002 NONROAD model. (The results for the subset ofOn-road dominated sites are even better, but are based on only 6 monitors) For all 95 sites themean percentage difference is 27 % and the fraction of modeled values within 100 % of themonitored value is 80 %. For the 30 rural sites the mean percentage difference is 16 % and thefraction of modeled values within 100 % of the monitored value is 80 %. For the 65 urban sitesthe mean percentage difference is 32 % and the fraction of modeled values within 100 % of themonitored value is 80 %.

For the TOR comparisons including the EPA ECOCX data, the best model performance basedon the mean percentage difference and based on the fraction of modeled values within 100 % ofthe monitored value is also for the DPM-maximum value using the 2002 NONROAD model. Forall 88 sites the mean percentage difference is -12 % and the fraction of modeled values within100 % of the monitored value is 92 %. For the 30 rural sites the mean percentage difference is 6% and the fraction of modeled values within 100 % of the monitored value is 87 %. For the 58urban sites the mean percentage difference is -14 % and the fraction of modeled values within100 % of the monitored value is 95 %.

Discussion

The model performance evaluation based on the regression models leads to different conclusionsthan the model performance evaluation based on the differences. One primary reason is that theregression analyses are more influenced by the extreme values. For most purposes the analysis ofthe differences is more useful, especially since all of the regression models fitted relativelypoorly (except for those with only 2 data points). The best model performance based on the mean

ICF Consulting 30

percentage difference and based on the fraction of modeled values within 100 % of themonitored value is for the DPM-maximum value consistent with the 2002 NONROAD model.The corresponding fractions of modeled values within 100 % of the monitored value are 73 %for all TOR sites excluding the EPA ECOCX data, 80 % for all TOT sites, and 92 % for all TORsites including the EPA ECOCX data. As discussed in Chapter 1, this performance comparesfavorably with the model to monitor results for the other pollutants assessed in the NSATA,except for benzene.

ICF

Con

sult

ing

31

Tab

le 4

-1. R

egre

ssio

n m

odel

s fo

r m

odel

ed a

gain

st m

onito

red

DP

M c

once

ntra

tions

.

Nu

mb

erM

od

eled

Var

iab

le1M

on

ito

red

Var

iab

le2S

ub

set3

Lo

cati

on

NIn

terc

ept

Inte

rcep

tS

tan

dar

d E

rro

rS

lop

eS

lop

eS

tan

dar

d E

rro

rR S

qu

ared

1co

ncne

arE

CT

OR

All

All

150.

800.

490.

810.

420.

222

conc

near

EC

TO

RA

llR

ural

100.

040.

611.

570.

590.

473

conc

near

EC

TO

RA

llU

rban

51.

780.

740.

070.

550.

014

conc

near

EC

TO

RN

on-r

oad

All

41.

992.

220.

391.

200.

055

conc

near

EC

TO

RN

on-r

oad

Rur

al2

-2.3

7.

3.20

.1.

006

conc

near

EC

TO

RN

on-r

oad

Urb

an2

3.71

.-0

.64

.1.

007

conc

near

2E

CT

OR

All

All

150.

670.

360.

560.

310.

208

conc

near

2E

CT

OR

All

Rur

al10

0.14

0.45

1.07

0.43

0.44

9co

ncne

ar2

EC

TO

RA

llU

rban

51.

390.

560.

040.

410.

0010

max

conc

EC

TO

RA

llA

ll14

3.40

1.33

1.41

1.12

0.12

11m

axco

ncE

CT

OR

All

Rur

al9

2.24

1.95

2.16

1.79

0.17

12m

axco

ncE

CT

OR

All

Urb

an5

4.91

2.16

0.75

1.60

0.07

13m

axco

ncE

CT

OR

Non

-roa

dA

ll4

4.45

2.07

1.32

1.12

0.41

14m

axco

ncE

CT

OR

Non

-roa

dR

ural

27.

60.

0.00

..

15m

axco

ncE

CT

OR

Non

-roa

dU

rban

22.

23.

1.98

.1.

0016

max

conc

2E

CT

OR

All

All

142.

490.

940.

980.

790.

1117

max

conc

2E

CT

OR

All

Rur

al9

1.66

1.38

1.50

1.26

0.17

18m

axco

nc2

EC

TO

RA

llU

rban

53.

571.

490.

511.

100.

0719

conc

near

EC

TO

RH

All

All

150.

890.

480.

580.

330.

1920

conc

near

EC

TO

RH

All

Rur

al10

0.04

0.61

1.37

0.51

0.47

21co

ncne

arE

CT

OR

HA

llU

rban

51.

780.

740.

050.

390.

0122

conc

near

EC

TO

RH

Non

-roa

dA

ll4

2.28

2.12

0.17

0.89

0.02

23co

ncne

arE

CT

OR

HN

on-r

oad

Rur

al2

-2.3

7.

2.80

.1.

0024

conc

near

EC

TO

RH

Non

-roa

dU

rban

23.

71.

-0.4

6.

1.00

25co

ncne

ar2

EC

TO

RH

All

All

150.

730.

350.

400.

240.

1826

conc

near

2E

CT

OR

HA

llR

ural

100.

140.

450.

940.

380.

4427

conc

near

2E

CT

OR

HA

llU

rban

51.

390.

560.

030.

300.

0028

max

conc

EC

TO

RH

All

All

143.

451.

271.

100.

840.

1229

max

conc

EC

TO

RH

All

Rur

al9

2.24

1.95

1.89

1.56

0.17

30m

axco

ncE

CT

OR

HA

llU

rban

54.

912.

160.

541.

150.

0731

max

conc

EC

TO

RH

Non

-roa

dA

ll4

4.90

2.12

0.82

0.89

0.30

32m

axco

ncE

CT

OR

HN

on-r

oad

Rur

al2

7.60

.0.

00.

.33

max

conc

EC

TO

RH

Non

-roa

dU

rban

22.

23.

1.42

.1.

00

ICF

Con

sult

ing

32

Tab

le 4

-1. R

egre

ssio

n m

odel

s fo

r m

odel

ed a

gain

st m

onito

red

DP

M c

once

ntra

tions

.

Nu

mb

erM

od

eled

Var

iab

le1M

on

ito

red

Var

iab

le2S

ub

set3

Lo

cati

on

NIn

terc

ept

Inte

rcep

tS

tan

dar

d E

rro

rS

lop

eS

lop

eS

tan

dar

d E

rro

rR S

qu

ared

34m

axco

nc2

EC

TO

RH

All

All

142.

520.

890.

770.

590.

1235

max

conc

2E

CT

OR

HA

llR

ural

91.

661.

381.

311.

100.

1736

max

conc

2E

CT

OR

HA

llU

rban

53.

571.

490.

370.

790.

0737

conc

near

EC

TO

RL

All

All

150.

820.

491.

150.

610.

2138

conc

near

EC

TO

RL

All

Rur

al10

0.04

0.61

2.33

0.87

0.47

39co

ncne

arE

CT

OR

LA

llU

rban

51.

780.

740.

100.

770.

0140

conc

near

EC

TO

RL

Non

-roa

dA

ll4

2.07

2.20

0.49

1.71

0.04

41co

ncne

arE

CT

OR

LN

on-r

oad

Rur

al2

-2.3

7.

4.75

.1.

0042

conc

near

EC

TO

RL

Non

-roa

dU

rban

23.

71.

-0.9

0.

1.00

43co

ncne

ar2

EC

TO

RL

All

All

150.

690.

350.

790.

450.

1944

conc

near

2E

CT

OR

LA

llR

ural

100.

140.

451.

590.

640.

4445

conc

near

2E

CT

OR

LA

llU

rban

51.

390.

560.

060.

580.

0046

max

conc

EC

TO

RL

All

All

143.

401.

322.

051.

600.

1247

max

conc

EC

TO

RL

All

Rur

al9

2.24

1.95

3.20

2.65

0.17

48m

axco

ncE

CT

OR

LA

llU

rban

54.

912.

161.

052.

250.

0749

max

conc

EC

TO

RL

Non

-roa

dA

ll4

4.57

2.09

1.80

1.63

0.38

50m

axco

ncE

CT

OR

LN

on-r

oad

Rur

al2

7.60

.0.

00.

.51

max

conc

EC

TO

RL

Non

-roa

dU

rban

22.

23.

2.79

.1.

0052

max

conc

2E

CT

OR

LA

llA

ll14

2.49

0.93

1.42

1.12

0.12

53m

axco

nc2

EC

TO

RL

All

Rur

al9

1.66

1.38

2.23

1.87

0.17

54m

axco

nc2

EC

TO

RL

All

Urb

an5

3.57

1.49

0.72

1.56

0.07

55co

ncne

arE

CT

OT

All

All

951.

740.

320.

510.

140.

1256

conc

near

EC

TO

TA

llR

ural

301.

260.

430.

540.

230.

1657

conc

near

EC

TO

TA

llU

rban

652.

050.

430.

450.

180.

0958

conc

near

EC

TO

TN

on-r

oad

All

191.

601.

311.

820.

590.

3659

conc

near

EC

TO

TN

on-r

oad

Rur

al7

0.69

2.18

2.16

1.38

0.33

60co

ncne

arE

CT

OT

Non

-roa

dU

rban

122.

311.

981.

610.

790.

2961

conc

near

EC

TO

TO

n-ro

adA

ll6

1.48

0.45

0.21

0.18

0.25

62co

ncne

arE

CT

OT

On-

road

Rur

al2

1.43

.0.

47.

1.00

63co

ncne

arE

CT

OT

On-

road

Urb

an4

1.24

0.74

0.25

0.26

0.32

64co

ncne

ar2

EC

TO

TA

llA

ll95

1.37

0.23

0.39

0.10

0.13

65co

ncne

ar2

EC

TO

TA

llR

ural

300.

960.

310.

430.

170.

1966

conc

near

2E

CT

OT

All

Urb

an65

1.63

0.31

0.34

0.13

0.10

ICF

Con

sult

ing

33

Tab

le 4

-1. R

egre

ssio

n m

odel

s fo

r m

odel

ed a

gain

st m

onito

red

DP

M c

once

ntra

tions

.

Nu

mb

erM

od

eled

Var

iab

le1M

on

ito

red

Var

iab

le2S

ub

set3

Lo

cati

on

NIn

terc

ept

Inte

rcep

tS

tan

dar

d E

rro

rS

lop

eS

lop

eS

tan

dar

d E

rro

rR S

qu

ared

67m

axco

ncE

CT

OT

All

All

954.

674.

847.

912.

150.

1368

max

conc

EC

TO

TA

llR

ural

30-0

.35

2.22

5.49

1.22

0.42

69m

axco

ncE

CT

OT

All

Urb

an65

8.85

7.13

7.73

2.95

0.10

70m

axco

ncE

CT

OT

Non

-roa

dA

ll19

-4.2

426

.43

27.1

111

.98

0.23

71m

axco

ncE

CT

OT

Non

-roa

dR

ural

7-4

.39

7.71

9.05

4.87

0.41

72m

axco

ncE

CT

OT

Non

-roa

dU

rban

1227

.73

40.3

919

.83

16.1

70.

1373

max

conc

EC

TO

TO

n-ro

adA

ll6

3.39

1.46

0.74

0.59

0.27

8374

max

conc

EC

TO

TO

n-ro

adR

ural

22.

11.

2.06

.1.

0000

75m

axco

ncE

CT

OT

On-

road

Urb

an4

3.17

2.51

0.75

0.88

0.26

6076

max

conc

2E

CT

OT

All

All

953.

453.

375.

521.

500.

1271

77m

axco

nc2

EC

TO

TA

llR

ural

30-0

.09

1.54

3.88

0.84

0.43

1778

max

conc

2E

CT

OT

All

Urb

an65

6.37

4.96

5.38

2.05

0.09

8179

conc

near

EC

TO

TH

All

All

952.

010.

300.

290.

100.

0792

80co

ncne

arE

CT

OT

HA

llR

ural

301.

480.

400.

300.

160.

1058

81co

ncne

arE

CT

OT

HA

llU

rban

652.

320.

400.

260.

130.

0598

82co

ncne

arE

CT

OT

HN

on-r

oad

All

192.

071.

361.

350.

530.

2792

83co

ncne

arE

CT

OT

HN

on-r

oad

Rur

al7

2.64

2.85

0.65

1.42

0.03

9884

conc

near

EC

TO

TH

Non

-roa

dU

rban

122.

651.

811.

300.

630.

2986

85co

ncne

arE

CT

OT

HO

n-ro

adA

ll6

1.56

0.44

0.12

0.12

0.20

0486

conc

near

EC

TO

TH

On-

road

Rur

al2

1.59

.0.

26.

1.00

0087

conc

near

EC

TO

TH

On-

road

Urb

an4

1.36

0.74

0.15

0.18

0.25

3788

conc

near

2E

CT

OT

HA

llA

ll95

1.57

0.22

0.23

0.07

0.09

0389

conc

near

2E

CT

OT

HA

llR

ural

301.

140.

290.

240.

120.

1300

90co

ncne

ar2

EC

TO

TH

All

Urb

an65

1.83

0.29

0.20

0.09

0.06

6691

max

conc

EC

TO

TH

All

All

959.

114.

554.

411.

560.

0793

92m

axco

ncE

CT

OT

HA

llR

ural

301.

062.

113.

540.

840.

3864

93m

axco

ncE

CT

OT

HA

llU

rban

6514

.11

6.62

4.15

2.14

0.05

6499

max

conc

EC

TO

TH

Non

-roa

dA

ll19

19.3

428

.55

12.9

411

.05

0.07

4695

max

conc

EC

TO

TH

Non

-roa

dR

ural

71.

7510

.41

3.80

5.19

0.09

7096

max

conc

EC

TO

TH

Non

-roa

dU

rban

1253

.90

39.1

47.

3113

.65

0.02

7997

max

conc

EC

TO

TH

On-

road

All

63.

661.

410.

440.

400.

2261

98m

axco

ncE

CT

OT

HO

n-ro

adR

ural

22.

80.

1.14

.1.

0000

99m

axco

ncE

CT

OT

HO

n-ro

adU

rban

43.

532.

480.

430.

610.

2002

ICF

Con

sult

ing

34

Tab

le 4

-1. R

egre

ssio

n m

odel

s fo

r m

odel

ed a

gain

st m

onito

red

DP

M c

once

ntra

tions

.

Nu

mb

erM

od

eled

Var

iab

le1M

on

ito

red

Var

iab

le2S

ub

set3

Lo

cati

on

NIn

terc

ept

Inte

rcep

tS

tan

dar

d E

rro

rS

lop

eS

lop

eS

tan

dar

d E

rro

rR S

qu

ared

100

max

conc

2E

CT

OT

HA

llA

ll95

6.54

3.17

3.08

1.09

0.07

9610

1m

axco

nc2

EC

TO

TH

All

Rur

al30

0.90

1.46

2.50

0.58

0.39

5210

2m

axco

nc2

EC

TO

TH

All

Urb

an65

10.0

34.

612.

891.

490.

0563

103

conc

near

EC

TO

TL

All

All

951.

520.

330.

720.

170.

1573

104

conc

near

EC

TO

TL

All

Rur

al30

1.01

0.44

0.85

0.30

0.22

2710

5co

ncne

arE

CT

OT

LA

llU

rban

651.

850.

450.

620.

220.

1171

106

conc

near

EC

TO

TL

Non

-roa

dA

ll19

1.92

1.23

1.83

0.61

0.34

8510

7co

ncne

arE

CT

OT

LN

on-r

oad

Rur

al7

0.59

1.50

2.65

1.09

0.54

0010

8co

ncne

arE

CT

OT

LN

on-r

oad

Urb

an12

2.74

2.03

1.52

0.87

0.23

3310

9co

ncne

arE

CT

OT

LO

n-ro

adA

ll6

1.39

0.47

0.33

0.25

0.30

6111

0co

ncne

arE

CT

OT

LO

n-ro

adR

ural

21.

14.

0.86

.1.

0000

111

conc

near

EC

TO

TL

On-

road

Urb

an4

1.11

0.74

0.40

0.34

0.40

9511

2co

ncne

ar2

EC

TO

TL

All

All

951.

210.

240.

550.

130.

1719

113

conc

near

2E

CT

OT

LA

llR

ural

300.

770.

320.

670.

210.

2580

114

conc

near

2E

CT

OT

LA

llU

rban

651.

480.

330.

470.

160.

1250

115

max

conc

EC

TO

TL

All

All

951.

024.

9911

.38

2.61

0.16

9611

6m

axco

ncE

CT

OT

LA

llR

ural

30-1

.52

2.37

7.43

1.60

0.43

5011

7m

axco

ncE

CT

OT

LA

llU

rban

654.

547.

3811

.25

3.55

0.13

7311

8m

axco

ncE

CT

OT

LN

on-r

oad

All

19-1

0.13

23.0

533

.26

11.3

40.

3360

119

max

conc

EC

TO

TL

Non

-roa

dR

ural

7-3

.85

5.39

10.3

63.

920.

5827

120

max

conc

EC

TO

TL

Non

-roa

dU

rban

1214

.93

37.6

427

.43

16.1

40.

2242

121

max

conc

EC

TO

TL

On-

road

All

63.

061.

491.

150.

800.

3415

122

max

conc

EC

TO

TL

On-

road

Rur

al2

0.83

.3.

76.

1.00

0012

3m

axco

ncE

CT

OT

LO

n-ro

adU

rban

42.

732.

511.

201.

160.

3499

124

max

conc

2E

CT

OT

LA

llA

ll95

0.91

3.47

7.94

1.82

0.17

0112

5m

axco

nc2

EC

TO

TL

All

Rur

al30

-0.9

31.

645.

251.

110.

4462

126

max

conc

2E

CT

OT

LA

llU

rban

653.

375.

147.

832.

470.

1372

127

conc

near

TO

RA

llA

ll88

0.77

0.48

0.91

0.27

0.11

7712

8co

ncne

arT

OR

All

Rur

al30

0.34

0.66

1.06

0.37

0.22

8612

9co

ncne

arT

OR

All

Urb

an58

1.26

0.73

0.68

0.40

0.04

8313

0co

ncne

arT

OR

Non

-roa

dA

ll18

2.04

1.65

1.06

0.83

0.09

2013

1co

ncne

arT

OR

Non

-roa

dR

ural

90.

602.

481.

571.

240.

1861

132

conc

near

TO

RN

on-r

oad

Urb

an9

2.93

2.39

0.82

1.22

0.06

06

ICF

Con

sult

ing

35

Tab

le 4

-1. R

egre

ssio

n m

odel

s fo

r m

odel

ed a

gain

st m

onito

red

DP

M c

once

ntra

tions

.

Nu

mb

erM

od

eled

Var

iab

le1M

on

ito

red

Var

iab

le2S

ub

set3

Lo

cati

on

NIn

terc

ept

Inte

rcep

tS

tan

dar

d E

rro

rS

lop

eS

lop

eS

tan

dar

d E

rro

rR S

qu

ared

133

conc

near

TO

RO

n-ro

adA

ll5

1.82

1.67

-0.0

30.

840.

0006

134

conc

near

TO

RO

n-ro

adR

ural

21.

20.

0.40

.1.

0000

135

conc

near

TO

RO

n-ro

adU

rban

38.

231.

20-3

.52

0.63

0.96

8813

6co

ncne

ar2

TO

RA

llA

ll88

0.63

0.36

0.69

0.20

0.12

2813

7co

ncne

ar2

TO

RA

llR

ural

300.

290.

470.

800.

260.

2455

138

conc

near

2T

OR

All

Urb

an58

1.02

0.55

0.52

0.30

0.04

9713

9m

axco

ncT

OR

All

All

87-3

.38

9.21

10.4

65.

060.

0478

140

max

conc

TO

RA

llR

ural

290.

282.

853.

581.

570.

1616

141

max

conc

TO

RA

llU

rban

58-9

.75

15.6

816

.33

8.62

0.06

0314

2m

axco

ncT

OR

Non

-roa

dA

ll18

-0.9

538

.73

20.7

419

.53

0.06

5914

3m

axco

ncT

OR

Non

-roa

dR

ural

90.

938.

624.

034.

300.

1118

144

max

conc

TO

RN

on-r

oad

Urb

an9

-0.1

658

.79

36.9

429

.99

0.17

8114

5m

axco

ncT

OR

On-

road

All

54.

405.

46-0

.02

2.74

0.00

0014

6m

axco

ncT

OR

On-

road

Rur

al2

1.10

.1.

75.

1.00

0014

7m

axco

ncT

OR

On-

road

Urb

an3

26.7

93.

66-1

1.99

1.93

0.97

4814

8m

axco

nc2

TO

RA

llA

ll87

-2.2

76.

417.

353.

530.

0486

149

max

conc

2T

OR

All

Rur

al29

0.20

1.99

2.60

1.09

0.17

2915

0m

axco

nc2

TO

RA

llU

rban

58-6

.62

10.9

211

.39

6.00

0.06

0415

1co

ncne

arT

OR

HA

llA

ll88

0.79

0.48

0.68

0.20

0.11

8315

2co

ncne

arT

OR

HA

llR

ural

300.

340.

660.

920.

320.

2286

153

conc

near

TO

RH

All

Urb

an58

1.26

0.73

0.49

0.29

0.04

8315

4co

ncne

arT

OR

HN

on-r

oad

All

181.

941.

570.

880.

620.

1103

155

conc

near

TO

RH

Non

-roa

dR

ural

90.

602.

481.

371.

080.

1861

156

conc

near

TO

RH

Non

-roa

dU

rban

92.

932.

390.

590.

880.

0606

157

conc

near

TO

RH

On-

road

All

52.

541.

71-0

.31

0.67

0.06

6715

8co

ncne

arT

OR

HO

n-ro

adR

ural

21.

20.

0.35

.1.

0000

159

conc

near

TO

RH

On-

road

Urb

an3

8.23

1.20

-2.5

30.

450.

9688

160

conc

near

2T

OR

HA

llA

ll88

0.63

0.36

0.53

0.15

0.12

7216

1co

ncne

ar2

TO

RH

All

Rur

al30

0.29

0.47

0.70

0.23

0.24

5516

2co

ncne

ar2

TO

RH

All

Urb

an58

1.02

0.55

0.37

0.22

0.04

9716

3m

axco

ncT

OR

HA

llA

ll87

-7.1

98.

949.

673.

740.

0728

164

max

conc

TO

RH

All

Rur

al29

0.28

2.85

3.13

1.37

0.16

1616

5m

axco

ncT

OR

HA

llU

rban

58-9

.75

15.6

811

.73

6.19

0.06

03

ICF

Con

sult

ing

36

Tab

le 4

-1. R

egre

ssio

n m

odel

s fo

r m

odel

ed a

gain

st m

onito

red

DP

M c

once

ntra

tions

.

Nu

mb

erM

od

eled

Var

iab

le1M

on

ito

red

Var

iab

le2S

ub

set3

Lo

cati

on

NIn

terc

ept

Inte

rcep

tS

tan

dar

d E

rro

rS

lop

eS

lop

eS

tan

dar

d E

rro

rR S

qu

ared

166

max

conc

TO

RH

Non

-roa

dA

ll18

-20.

8835

.28

24.8

614

.00

0.16

4816

7m

axco

ncT

OR

HN

on-r

oad

Rur

al9

0.93

8.62

3.52

3.75

0.11

1816

8m

axco

ncT

OR

HN

on-r

oad

Urb

an9

-0.1

658

.79

26.5

521

.56

0.17

8116

9m

axco

ncT

OR

HO

n-ro

adA

ll5

5.68

5.73

-0.5

22.

240.

0179

170

max

conc

TO

RH

On-

road

Rur

al2

1.10

.1.

53.

1.00

0017

1m

axco

ncT

OR

HO

n-ro

adU

rban

326

.79

3.66

-8.6

21.

390.

9748

172

max

conc

2T

OR

HA

llA

ll87

-4.9

16.

236.

782.

610.

0737

173

max

conc

2T

OR

HA

llR

ural

290.

201.

992.

270.

960.

1729

174

max

conc

2T

OR

HA

llU

rban

58-6

.62

10.9

28.

184.

310.

0604

175

conc

near

TO

RL

All

All

880.

750.

481.

310.

380.

1194

176

conc

near

TO

RL

All

Rur

al30

0.34

0.66

1.57

0.54

0.22

8617

7co

ncne

arT

OR

LA

llU

rban

581.

260.

730.

950.

560.

0483

178

conc

near

TO

RL

Non

-roa

dA

ll18

1.98

1.64

1.57

1.19

0.09

8217

9co

ncne

arT

OR

LN

on-r

oad

Rur

al9

0.60

2.48

2.32

1.83

0.18

6118

0co

ncne

arT

OR

LN

on-r

oad

Urb

an9

2.93

2.39

1.15

1.72

0.06

0618

1co

ncne

arT

OR

LO

n-ro

adA

ll5

2.01

1.73

-0.1

91.

250.

0073

182

conc

near

TO

RL

On-

road

Rur

al2

1.20

.0.

59.

1.00

0018

3co

ncne

arT

OR

LO

n-ro

adU

rban

38.

231.

20-4

.96

0.89

0.96

8818

4co

ncne

ar2

TO

RL

All

All

880.

620.

361.

010.

290.

1256

185

conc

near

2T

OR

LA

llR

ural

300.

290.

471.

180.

390.

2455

186

conc

near

2T

OR

LA

llU

rban

581.

020.

550.

730.

430.

0497

187

max

conc

TO

RL

All

All

87-4

.70

9.19

16.0

87.

240.

0549

188

max

conc

TO

RL

All

Rur

al29

0.28

2.85

5.30

2.32

0.16

1618

9m

axco

ncT

OR

LA

llU

rban

58-9

.75

15.6

822

.99

12.1

30.

0603

190

max

conc

TO

RL

Non

-roa

dA

ll18

-7.2

038

.11

34.8

327

.75

0.08

9619

1m

axco

ncT

OR

LN

on-r

oad

Rur

al9

0.93

8.62

5.98

6.37

0.11

1819

2m

axco

ncT

OR

LN

on-r

oad

Urb

an9

-0.1

658

.79

52.0

142

.24

0.17

8119

3m

axco

ncT

OR

LO

n-ro

adA

ll5

4.73

5.66

-0.2

74.

090.

0014

194

max

conc

TO

RL

On-

road

Rur

al2

1.10

.2.

59.

1.00

0019

5m

axco

ncT

OR

LO

n-ro

adU

rban

326

.79

3.66

-16.

882.

720.

9748

196

max

conc

2T

OR

LA

llA

ll87

-3.1

96.

4011

.29

5.04

0.05

5719

7m

axco

nc2

TO

RL

All

Rur

al29

0.20

1.99

3.86

1.62

0.17

2919

8m

axco

nc2

TO

RL

All

Urb

an58

-6.6

210

.92

16.0

48.

450.

0604

ICF

Con

sult

ing

37

Not

es f

or T

able

4-1

:1.

M

odel

ed v

aria

ble:

conc

near

Nea

rest

mod

eled

DPM

con

cent

ratio

n co

nsis

tent

with

the

draf

t 200

0 N

ON

RO

AD

Mod

elco

ncne

ar2

Nea

rest

mod

eled

DPM

con

cent

ratio

n co

nsis

tent

with

the

draf

t 200

2 N

ON

RO

AD

Mod

elm

axco

ncN

earb

y (w

ithin

30

km)

max

imum

mod

eled

DPM

con

cent

ratio

n co

nsis

tent

with

the

draf

t 200

0 N

ON

RO

AD

Mod

elm

axco

nc2

Nea

rby

(with

in 3

0 km

) m

axim

um m

odel

ed D

PM c

once

ntra

tion

cons

iste

nt w

ith th

e dr

aft 2

002

NO

NR

OA

DM

odel

2.

Mon

itore

d va

riab

le:

EC

TO

RE

C v

alue

mul

tipl

ied

by T

OR

ave

rage

cor

rect

ion

fact

or (

mis

sing

for

EC

mea

sure

d us

ing

TO

T).

EC

TO

RH

EC

val

ue m

ultip

lied

by T

OR

max

imum

cor

rect

ion

fact

or (

mis

sing

for

EC

mea

sure

d us

ing

TO

T).

EC

TO

RL

EC

val

ue m

ultip

lied

by T

OR

min

imum

cor

rect

ion

fact

or (

mis

sing

for

EC

mea

sure

d us

ing

TO

T).

EC

TO

TE

C v

alue

mul

tipl

ied

by T

OT

ave

rage

cor

rect

ion

fact

or (

mis

sing

for

EC

mea

sure

d us

ing

TO

R).

EC

TO

TH

EC

val

ue m

ultip

lied

by T

OT

max

imum

cor

rect

ion

fact

or (

mis

sing

for

EC

mea

sure

d us

ing

TO

R).

EC

TO

TL

EC

val

ue m

ultip

lied

by T

OR

min

imum

cor

rect

ion

fact

or (

mis

sing

for

EC

mea

sure

d us

ing

TO

R).

TO

RE

CO

CX

val

ue m

ultip

lied

by T

OT

ave

rage

cor

rect

ion

fact

or f

or E

PA d

ata,

EC

TO

R f

or T

OR

dat

a.T

OR

HE

CO

CX

val

ue m

ultip

lied

by T

OT

max

imum

cor

rect

ion

fact

or f

or E

PA d

ata,

EC

TO

R f

or T

OR

dat

a..

TO

RL

EC

OC

X v

alue

mul

tiplie

d by

TO

R m

inim

um c

orre

ctio

n fa

ctor

for

EPA

dat

a, E

CT

OR

for

TO

R d

ata.

3.

Subs

et:

All

All

sit

esN

on-r

oad

Site

s do

min

ated

by

Non

-roa

d so

urce

(at

leas

t 75

% o

f m

odel

ed D

PM c

onsi

sten

t with

the

draf

t 200

0 N

ON

RO

AD

Mod

el)

On-

road

Site

s do

min

ated

by

On-

road

sou

rce

(at l

east

50

% o

f m

odel

ed D

PM c

onsi

sten

t with

the

draf

t 200

0 N

ON

RO

AD

Mod

el)

ICF

Con

sult

ing

38

Tab

le 4

-2. N

eare

st m

odel

ed c

once

ntra

tion

and

EC

TO

R, E

CT

OR

L, a

nd E

CT

OR

H m

onito

red

valu

es fo

r ea

ch s

ite.

Cit

y / C

ou

nty

1S

tate

1N

eare

stM

od

eled

Co

nce

ntr

atio

n2

Urb

an o

rR

ura

lS

ub

set3

EC

TO

R4

EC

TO

RL

4E

CT

OR

H4

Oc

ea

nvi

lleN

J2.

032

R0.

4357

550.

2940

030.

4987

56Se

att

leW

A3.

2264

UN

on

-ro

ad

0.75

129

0.53

3525

1.04

5273

She

na

nd

oa

h N

atio

na

lP

ark

VA

0.96

12R

0.30

8284

0.20

7999

0.35

2855

Lake

Ta

ho

eN

V0.

6111

R0.

9424

380.

6358

621.

0786

94W

ash

ing

ton

DC

DC

12.2

63R

No

n-

roa

d1.

0127

30.

6832

881.

1591

49

Ye

llow

sto

ne

Na

tion

al

Pa

rkW

Y0.

0548

R0.

1066

370.

0719

480.

1220

55

De

nve

rC

O0.

7626

RN

on

-ro

ad

0.97

8226

0.66

0008

1.11

9656

De

nve

rC

O1.

9723

UN

on

-ro

ad

2.71

1257

1.92

5386

3.77

2184

De

nve

rC

O1.

5939

U0.

4525

860.

3214

020.

6296

85D

en

ver

CO

0.73

04R

1.33

0274

0.89

7534

1.52

2603

De

nve

rC

O0.

7953

U0.

8205

710.

5827

241.

1416

64D

en

ver

CO

2.59

18R

0.81

5005

0.54

9883

0.93

2837

De

nve

rC

O1.

6963

U0.

5937

280.

4216

330.

8260

56D

en

ver

CO

1.39

13R

1.06

5238

0.71

8715

1.21

9248

De

nve

rC

O0.

4174

R0.

5609

070.

3784

430.

6420

02D

en

ver

CO

4.58

09R

No

n-

roa

d2.

1697

91.

4639

552.

4834

95

Not

es f

or T

able

4-2

:1.

C

ity

/ cou

nty

and

Stat

e m

ay a

ppea

r m

ultip

le ti

mes

if th

ere

are

seve

ral d

iffe

rent

mon

itori

ng s

ites

at th

at g

ener

al lo

catio

n.2.

M

odel

ed v

aria

ble

= “

conc

near

”N

eare

st m

odel

ed D

PM c

once

ntra

tion

cons

iste

nt w

ith th

e dr

aft 2

000

NO

NR

OA

D M

odel

3.

Subs

et:

Non

-roa

dSi

tes

dom

inat

ed b

y N

on-r

oad

sour

ce (

at le

ast 7

5 %

of

mod

eled

DPM

con

sist

ent w

ith th

e dr

aft 2

000

NO

NR

OA

DM

odel

)O

n-ro

adSi

tes

dom

inat

ed b

y O

n-ro

ad s

ourc

e (a

t lea

st 5

0 %

of

mod

eled

DPM

con

sist

ent w

ith th

e dr

aft 2

000

NO

NR

OA

DM

odel

)4.

M

onito

red

vari

able

:

ICF

Con

sult

ing

39

EC

TO

RE

C v

alue

mul

tipl

ied

by T

OR

ave

rage

cor

rect

ion

fact

or (

mis

sing

for

EC

mea

sure

d us

ing

TO

T).

EC

TO

RH

EC

val

ue m

ultip

lied

by T

OR

max

imum

cor

rect

ion

fact

or (

mis

sing

for

EC

mea

sure

d us

ing

TO

T).

EC

TO

RL

EC

val

ue m

ultip

lied

by T

OR

min

imum

cor

rect

ion

fact

or (

mis

sing

for

EC

mea

sure

d us

ing

TO

T).

5.

Out

lier

valu

e no

t use

d fo

r st

atis

tical

com

pari

sons

ICF

Con

sult

ing

40

Tab

le 4

-3. N

eare

st m

odel

ed c

once

ntra

tion

and

TO

T, T

OT

L, a

nd T

OT

H m

onito

red

valu

es fo

r ea

ch s

ite.

Cit

y / C

ou

nty

1S

tate

1N

eare

stM

od

eled

Co

nce

ntr

atio

n2

Urb

an o

rR

ura

lS

ub

set3

TO

T4

TO

TL

4T

OT

H4

Ch

ico

pe

eM

A1.

7019

U0.

6837

160.

6837

160.

6837

16H

arr

iso

nM

A4.

0016

UN

on

-ro

ad

1.88

9732

1.88

9732

1.88

9732

Ad

am

sP

A1.

501

R0.

8383

760.

8383

760.

8383

76C

ha

mp

aig

nIL

1.20

5R

0.73

6247

0.73

6247

0.73

6247

Trig

gK

Y0.

8134

R0.

6744

210.

6744

210.

6744

21To

mp

kin

sN

Y0.

7298

R0.

5104

660.

5104

660.

5104

66W

ash

ing

ton

IN0.

8839

R0.

7095

420.

7095

420.

7095

42M

erc

er

PA

0.94

14R

0.98

0116

0.98

0116

0.98

0116

No

ble

OH

0.97

18R

1.20

8977

1.20

8977

1.20

8977

Win

n P

aris

hLA

0.35

88R

0.44

4273

0.32

9798

0.64

5967

Jeff

ers

on

Ala

ba

ma

1.99

58U

3.01

4976

3.01

4976

3.01

4976

Ma

rico

pa

Ariz

on

a1.

5009

U1.

3527

11.

0041

591.

9668

24Fr

esn

oC

alif

orn

ia0.

6956

UO

n-r

oa

d1.

4865

941.

1035

462.

1614

9K

ern

Ca

lifo

rnia

0.71

67U

1.72

0329

1.27

7054

2.50

1338

Riv

ers

ide

Ca

lifo

rnia

2.31

67R

2.19

6692

1.63

0673

3.19

3963

Sac

ram

en

toC

alif

orn

ia1.

2622

U1.

1891

640.

8827

541.

7290

3Sa

n D

ieg

oC

alif

orn

ia1.

6952

UO

n-r

oa

d1.

3420

480.

9962

441.

9513

21Sa

nta

Cla

raC

alif

orn

ia2.

5023

U1.

7174

21.

2748

952.

4971

08V

en

tura

Ca

lifo

rnia

2.25

41R

On

-ro

ad

1.74

411.

2947

2.53

59A

da

ms

Co

lora

do

1.74

3R

No

n-

roa

d1.

9298

11.

4325

582.

8059

2

Ke

nt

De

law

are

2.30

66U

0.95

8036

0.95

8036

0.95

8036

Ne

w C

ast

leD

ela

wa

re3.

903

U1.

9802

881.

9802

881.

9802

88D

istric

t o

f C

olu

mb

iaD

istric

t o

fC

olu

mb

ia3.

3894

U1.

6544

081.

6544

081.

6544

08

Da

de

Flo

rida

2.47

64U

4.23

7155

4.23

7155

4.23

7155

Hill

sbo

rou

gh

Flo

rida

1.44

1U

1.04

8591

1.04

8591

1.04

8591

De

Ka

lbG

eo

rgia

3.31

68R

2.37

7515

2.37

7515

2.37

7515

Co

ok

Illin

ois

10.2

885

UN

on

-ro

ad

2.23

2918

2.23

2918

2.23

2918

Co

ok

Illin

ois

2.24

96U

1.82

7492

1.82

7492

1.82

7492

ICF

Con

sult

ing

41

Tab

le 4

-3. N

eare

st m

odel

ed c

once

ntra

tion

and

TO

T, T

OT

L, a

nd T

OT

H m

onito

red

valu

es fo

r ea

ch s

ite.

Cit

y / C

ou

nty

1S

tate

1N

eare

stM

od

eled

Co

nce

ntr

atio

n2

Urb

an o

rR

ura

lS

ub

set3

TO

T4

TO

TL

4T

OT

H4

Co

ok

Illin

ois

2.34

21U

1.43

8204

1.43

8204

1.43

8204

Ma

rion

Ind

ian

a2.

3757

U1.

3648

531.

3648

531.

3648

53Li

nn

Iow

a1.

0643

R0.

7156

30.

7156

30.

7156

3P

olk

Iow

a1.

0352

R0.

5885

650.

4369

10.

8557

67Sc

ott

Iow

a1.

4764

U0.

7646

040.

7646

040.

7646

04W

yan

do

tte

Ka

nsa

s1.

7453

U1.

1027

30.

8185

911.

6033

57Ea

st B

ato

n R

ou

ge

Pa

rish

Lou

isia

na

6.74

31R

No

n-

roa

d1.

4202

141.

4202

141.

4202

14

Balti

mo

reM

ary

lan

d2.

9017

U1.

5484

651.

5484

651.

5484

65Ba

ltim

ore

Ma

ryla

nd

3.75

13U

1.10

4987

1.10

4987

1.10

4987

Ha

mp

de

nM

ass

ac

hu

sett

s1.

7019

U0.

5865

250.

5865

250.

5865

25Su

ffo

lkM

ass

ac

hu

sett

s4.

6792

U1.

3472

311.

3472

311.

3472

31O

akl

an

dM

ich

iga

n1.

9572

U1.

6752

11.

6752

11.

6752

1W

ayn

eM

ich

iga

n2.

0105

U1.

5521

971.

5521

971.

5521

97W

ayn

eM

ich

iga

n2.

0734

U1.

2488

261.

2488

261.

2488

26H

en

ne

pin

Min

ne

sota

2.24

69U

0.49

3075

0.36

6025

0.71

6925

He

nn

ep

inM

inn

eso

ta2.

3247

U0.

8561

370.

6355

371.

2448

13H

arr

iso

nM

issi

ssip

pi

0.98

87U

0.94

3544

0.94

3544

0.94

3544

Jeff

ers

on

Mis

sou

ri1.

2994

R1.

2184

581.

2184

581.

2184

58St

. Lo

uis

Mis

sou

ri2.

1799

U1.

5822

431.

5822

431.

5822

43M

isso

ula

Mo

nta

na

0.51

05U

0.73

4287

0.54

5084

1.06

7644

Do

ug

las

Ne

bra

ska

1.35

8U

0.67

3697

0.50

0106

0.97

9547

Wa

sho

eN

eva

da

3.98

17R

No

n-

roa

d1.

6047

141.

1912

292.

3332

34

Ca

md

en

Ne

w J

ers

ey

4.18

74U

1.40

286

1.40

286

1.40

286

Mid

dle

sex

Ne

w J

ers

ey

3.38

77U

1.40

4459

1.40

4459

1.40

4459

Mo

rris

Ne

w J

ers

ey

2.83

48R

0.82

8597

0.82

8597

0.82

8597

Un

ion

Ne

w J

ers

ey

5.29

35U

No

n-

roa

d4.

1983

294.

1983

294.

1983

29

Bro

nx

Ne

w Y

ork

8.00

06U

No

n-

roa

d1.

7750

31.

7750

31.

7750

3

Bro

nx

Ne

w Y

ork

7.14

04U

No

n-

roa

d2.

6067

242.

6067

242.

6067

24

Bro

nx

Ne

w Y

ork

8.59

15U

No

n-

2.39

4239

2.39

4239

2.39

4239

ICF

Con

sult

ing

42

Tab

le 4

-3. N

eare

st m

odel

ed c

once

ntra

tion

and

TO

T, T

OT

L, a

nd T

OT

H m

onito

red

valu

es fo

r ea

ch s

ite.

Cit

y / C

ou

nty

1S

tate

1N

eare

stM

od

eled

Co

nce

ntr

atio

n2

Urb

an o

rR

ura

lS

ub

set3

TO

T4

TO

TL

4T

OT

H4

roa

dEs

sex

Ne

w Y

ork

0.22

18R

0.38

3065

0.38

3065

0.38

3065

Mo

nro

eN

ew

Yo

rk1.

7872

RO

n-r

oa

d0.

7513

360.

7513

360.

7513

36Q

ue

en

sN

ew

Yo

rk6.

6119

UN

on

-ro

ad

1.62

4842

1.62

4842

1.62

4842

Ste

ub

en

Ne

w Y

ork

0.59

36R

0.44

4674

0.44

4674

0.44

4674

Me

ckl

en

bu

rgN

ort

h C

aro

lina

2.27

06U

1.35

2154

1.35

2154

1.35

2154

Burle

igh

No

rth

Da

kota

0.53

87U

No

n-

roa

d0.

3444

820.

2557

20.

5008

72

Ca

ssN

ort

h D

ako

ta0.

6603

RN

on

-ro

ad

0.59

1956

0.43

9428

0.86

0697

Cu

yah

og

aO

hio

6.95

79R

No

n-

roa

d2.

4340

122.

4340

122.

4340

12

Tuls

aO

kla

ho

ma

1.48

89U

0.76

7198

0.56

9515

1.11

5497

Mu

ltno

ma

hO

reg

on

2.08

5U

1.14

655

0.85

112

1.66

707

Ad

am

sP

en

nsy

lva

nia

1.50

1R

0.57

1485

0.57

1485

0.57

1485

Alle

gh

en

yP

en

nsy

lva

nia

2.77

74U

1.83

2849

1.83

2849

1.83

2849

Alle

gh

en

yP

en

nsy

lva

nia

1.96

99U

1.36

5899

1.36

5899

1.36

5899

Ph

ilad

elp

hia

Pe

nn

sylv

an

ia4.

9986

U1.

7690

761.

7690

761.

7690

76W

ash

ing

ton

Pe

nn

sylv

an

ia1.

1656

R0.

8565

760.

8565

760.

8565

76W

est

mo

rela

nd

Pe

nn

sylv

an

ia1.

6285

U1.

5885

811.

5885

811.

5885

81C

ha

rlest

on

Sou

th C

aro

lina

0.89

3U

0.85

3549

0.85

3549

0.85

3549

She

lby

Ten

ne

sse

e3.

0845

UN

on

-ro

ad

2.99

937

2.99

937

2.99

937

Da

llas

Texa

s3.

3398

RN

on

-ro

ad

0.84

3332

0.62

6032

1.22

6195

El P

aso

Texa

s0.

8152

U0.

8507

710.

6315

541.

2370

1H

arr

isTe

xas

2.39

03U

0.48

2679

0.35

8308

0.70

1809

Salt

Lake

Uta

h1.

5764

U1.

2924

070.

9593

941.

8791

44U

tah

Uta

h0.

606

R0.

8868

360.

6583

261.

2894

49C

hitt

en

de

nV

erm

on

t1.

855

U0.

7999

920.

7999

920.

7999

92R

ich

mo

nd

Virg

inia

1.54

32U

1.14

5547

1.14

5547

1.14

5547

Kin

gW

ash

ing

ton

3.65

13R

No

n-

roa

d1.

4930

961.

1083

722.

1709

44

ICF

Con

sult

ing

43

Tab

le 4

-3. N

eare

st m

odel

ed c

once

ntra

tion

and

TO

T, T

OT

L, a

nd T

OT

H m

onito

red

valu

es fo

r ea

ch s

ite.

Cit

y / C

ou

nty

1S

tate

1N

eare

stM

od

eled

Co

nce

ntr

atio

n2

Urb

an o

rR

ura

lS

ub

set3

TO

T4

TO

TL

4T

OT

H4

Kin

gW

ash

ing

ton

3.22

64U

No

n-

roa

d1.

0087

210.

7488

051.

4666

68

Milw

au

kee

Wis

co

nsi

n2.

3524

UO

n-r

oa

d1.

3064

851.

3064

851.

3064

85P

ue

rto

Ric

o1.

1811

U5.

0364

145.

0364

145.

0364

14A

na

he

imC

A3.

5344

U3.

7534

112.

7862

745.

4574

13Bu

rba

nk

CA

2.60

58U

On

-ro

ad

5.17

8159

3.84

3909

7.52

8979

Fon

tan

aC

A2.

0988

R5.

0476

553.

7470

327.

3392

29H

un

ting

ton

Pa

rkC

A4.

6059

U7.

3902

855.

4860

410

.745

38C

en

tra

l LA

CA

3.05

31R

5.75

4731

4.27

1917

8.36

7309

Lon

g B

ea

ch

CA

11.7

44U

No

n-

roa

d4.

1345

513.

0692

076.

0115

87

Pic

o R

ive

raC

A2.

4915

U7.

0975

025.

2686

9810

.319

68R

ub

ido

ux

CA

2.55

44U

5.59

7411

4.15

5133

8.13

8567

Ph

oe

nix

AZ

2.56

82U

No

n-

roa

d1.

7796

641.

3211

2.58

761

Not

es f

or T

able

4-3

1.

Cit

y / c

ount

y an

d St

ate

may

app

ear

mul

tiple

tim

es if

ther

e ar

e se

vera

l dif

fere

nt m

onito

ring

site

s at

that

gen

eral

loca

tion.

2.

Mod

eled

var

iabl

e =

“co

ncne

ar”

Nea

rest

mod

eled

DPM

con

cent

ratio

n co

nsis

tent

with

the

draf

t 200

0 N

ON

RO

AD

Mod

el3.

Su

bset

:N

on-r

oad

Site

s do

min

ated

by

Non

-roa

d so

urce

(at

leas

t 75

% o

f m

odel

ed D

PM c

onsi

sten

t with

the

draf

t 200

0 N

ON

RO

AD

Mod

el)

On-

road

Site

s do

min

ated

by

On-

road

sou

rce

(at l

east

50

% o

f m

odel

ed D

PM c

onsi

sten

t with

the

draf

t 200

0 N

ON

RO

AD

Mod

el)

4.

Mon

itore

d va

riab

le:

EC

TO

TE

C v

alue

mul

tipl

ied

by T

OT

ave

rage

cor

rect

ion

fact

or (

mis

sing

for

EC

mea

sure

d us

ing

TO

R).

EC

TO

TH

EC

val

ue m

ultip

lied

by T

OT

max

imum

cor

rect

ion

fact

or (

mis

sing

for

EC

mea

sure

d us

ing

TO

R).

EC

TO

TL

EC

val

ue m

ultip

lied

by T

OR

min

imum

cor

rect

ion

fact

or (

mis

sing

for

EC

mea

sure

d us

ing

TO

R).

5.

Out

lier

valu

e no

t use

d fo

r st

atis

tical

com

pari

sons

ICF

Con

sult

ing

44

Tab

le 4

-4. N

eare

st m

odel

ed c

once

ntra

tion

and

TO

R, T

OR

L, a

nd T

OR

H m

onito

red

valu

es fo

r ea

ch s

ite.

Cit

y / C

ou

nty

1S

tate

1N

eare

stM

od

eled

Co

nce

ntr

atio

n2

Urb

an o

rR

ura

lS

ub

set3

TO

R4

TO

RL

4T

OR

H4

Jeffe

rson

Ala

bam

a1.

9958

U2.

8728

32.

0401

263.

9969

81M

aric

opa

Ariz

ona

1.50

09U

1.65

6433

1.17

6308

2.30

4603

Fre

sno

Cal

iforn

ia0.

6956

UO

n-ro

ad2.

1124

931.

5001

762.

9391

2K

ern

Cal

iforn

ia0.

7167

U2.

2417

411.

5919

613.

1189

44R

iver

side

Cal

iforn

ia2.

3167

R3.

2206

932.

1729

983.

6863

36S

acra

men

toC

alifo

rnia

1.26

22U

1.80

5432

1.28

2118

2.51

1905

San

Die

goC

alifo

rnia

1.69

52U

On-

road

1.90

3438

1.35

1717

2.64

8261

San

ta C

lara

Cal

iforn

ia2.

5023

U1.

8496

251.

3135

022.

5733

92V

entu

raC

alifo

rnia

2.25

41R

On-

road

2.62

6535

1.77

212

3.00

6275

Ada

ms

Col

orad

o1.

743

RN

on-r

oad

2.50

8408

1.69

242

2.87

1069

Ken

tD

elaw

are

2.30

66U

1.82

3765

1.29

5138

2.53

7412

New

Cas

tleD

elaw

are

3.90

3U

2.25

5988

1.60

2079

3.13

8766

Dis

tric

t of C

olum

bia

Dis

tric

t of C

olum

bia

3.38

94U

1.98

1439

1.40

7109

2.75

6785

Dad

eF

lorid

a2.

4764

U2.

2731

141.

6142

413.

1625

94H

illsb

orou

ghF

lorid

a1.

441

U1.

5642

981.

1108

782.

1764

14D

eKal

bG

eorg

ia3.

3168

R2.

8126

881.

8977

173.

2193

41C

ook

Illin

ois

2.24

96U

1.83

4895

1.30

3041

2.55

2898

Coo

kIll

inoi

s2.

3421

U1.

2646

690.

8980

981.

7595

4M

ario

nIn

dian

a2.

3757

U1.

8532

1.31

6041

2.57

8366

Linn

Iow

a1.

0643

R1.

3312

750.

8982

11.

5237

48P

olk

Iow

a1.

0352

R1.

5766

181.

0637

421.

8045

63S

cott

Iow

a1.

4764

U1.

2535

910.

8902

311.

7441

27W

yand

otte

Kan

sas

1.74

53U

2.23

7625

1.58

9038

3.11

3217

Eas

t Bat

on R

ouge

Par

ish

Loui

sian

a6.

7431

RN

on-r

oad

1.65

8044

1.11

868

1.89

7761

Bal

timor

eM

aryl

and

2.90

17U

1.88

6666

1.33

9806

2.62

4927

Bal

timor

eM

aryl

and

3.75

13U

1.90

302

1.35

142

2.64

768

Ham

pden

Mas

sach

uset

ts1.

7019

U0.

8989

090.

6383

561.

2506

56S

uffo

lkM

assa

chus

etts

4.67

92U

1.87

3434

1.33

041

2.60

6518

Oak

land

Mic

higa

n1.

9572

U1.

8078

171.

2838

122.

5152

24W

ayne

Mic

higa

n2.

0105

U1.

7921

321.

2726

732.

4934

01W

ayne

Mic

higa

n2.

0734

U2.

0179

591.

4330

432.

8075

95H

enne

pin

Min

neso

ta2.

3247

U1.

6034

131.

1386

552.

2308

35

ICF

Con

sult

ing

45

Tab

le 4

-4. N

eare

st m

odel

ed c

once

ntra

tion

and

TO

R, T

OR

L, a

nd T

OR

H m

onito

red

valu

es fo

r ea

ch s

ite.

Cit

y / C

ou

nty

1S

tate

1N

eare

stM

od

eled

Co

nce

ntr

atio

n2

Urb

an o

rR

ura

lS

ub

set3

TO

R4

TO

RL

4T

OR

H4

Har

rison

Mis

siss

ippi

0.98

87U

1.57

3738

1.11

7582

2.18

9548

Jeffe

rson

Mis

sour

i1.

2994

R2.

2740

061.

5342

692.

6027

78S

t. Lo

uis

Mis

sour

i2.

1799

U1.

8688

361.

3271

442.

6001

19M

isso

ula

Mon

tana

0.51

05U

1.68

2974

1.19

5156

2.34

1529

Dou

glas

Neb

rask

a1.

358

U1.

4315

21.

0165

871.

9916

8W

asho

eN

evad

a3.

9817

RN

on-r

oad

2.80

9452

1.89

5534

3.21

5638

Cam

den

New

Jer

sey

4.18

74U

1.91

7837

1.36

1942

2.66

8295

Mid

dles

exN

ew J

erse

y3.

3877

U1.

7338

851.

2313

12.

4123

62M

orris

New

Jer

sey

2.83

48R

1.79

8739

1.21

3607

2.05

8798

Uni

onN

ew J

erse

y5.

2935

UN

on-r

oad

2.86

6781

2.03

583

3.98

8565

Bro

nxN

ew Y

ork

7.14

04U

Non

-roa

d2.

2124

771.

5711

793.

0782

28B

ronx

New

Yor

k8.

5915

UN

on-r

oad

1.83

2909

1.30

1631

2.55

0134

Ess

exN

ew Y

ork

0.22

18R

1.10

2289

0.74

3713

1.26

1656

Mon

roe

New

Yor

k1.

7872

RO

n-ro

ad1.

4592

650.

9845

641.

6702

43Q

ueen

sN

ew Y

ork

6.61

19U

Non

-roa

d1.

5821

661.

1235

672.

2012

75S

teub

enN

ew Y

ork

0.59

36R

1.22

3072

0.82

5205

1.39

9902

Mec

klen

burg

Nor

th C

arol

ina

2.27

06U

1.98

954

1.41

2862

2.76

8056

Bur

leig

hN

orth

Dak

ota

0.53

87U

Non

-roa

d0.

9671

50.

6868

171.

3456

Cas

sN

orth

Dak

ota

0.66

03R

Non

-roa

d1.

5129

521.

0207

871.

7316

92C

uyah

oga

Ohi

o6.

9579

RN

on-r

oad

2.63

7039

1.77

9207

3.01

8298

Tul

saO

klah

oma

1.48

89U

1.85

8217

1.31

9603

2.58

5345

Mul

tnom

ahO

rego

n2.

085

U1.

8004

1.27

8545

2.50

4904

Ada

ms

Pen

nsyl

vani

a1.

501

R1.

9873

281.

3408

482.

2746

53A

llegh

eny

Pen

nsyl

vani

a2.

7774

U2.

1119

181.

4997

682.

9383

2A

llegh

eny

Pen

nsyl

vani

a1.

9699

U1.

9462

731.

3821

362.

7078

58P

hila

delp

hia

Pen

nsyl

vani

a4.

9986

U1.

8391

061.

3060

322.

5587

56W

ashi

ngto

nP

enns

ylva

nia

1.16

56R

2.08

5923

1.40

737

2.38

7502

Wes

tmor

elan

dP

enns

ylva

nia

1.62

85U

1.96

3036

1.39

404

2.73

118

Cha

rlest

onS

outh

Car

olin

a0.

893

U1.

6143

351.

1464

122.

2460

32S

helb

yT

enne

ssee

3.08

45U

Non

-roa

d2.

4157

631.

7155

423.

3610

61D

alla

sT

exas

3.33

98R

Non

-roa

d1.

4419

50.

9728

821.

6504

25E

l Pas

oT

exas

0.81

52U

1.03

7519

0.73

6789

1.44

3505

Har

risT

exas

2.39

03U

0.88

649

0.62

9536

1.23

3377

ICF

Con

sult

ing

46

Tab

le 4

-4. N

eare

st m

odel

ed c

once

ntra

tion

and

TO

R, T

OR

L, a

nd T

OR

H m

onito

red

valu

es fo

r ea

ch s

ite.

Cit

y / C

ou

nty

1S

tate

1N

eare

stM

od

eled

Co

nce

ntr

atio

n2

Urb

an o

rR

ura

lS

ub

set3

TO

R4

TO

RL

4T

OR

H4

Sal

t Lak

eU

tah

1.57

64U

1.67

9742

1.19

286

2.33

7033

Uta

hU

tah

0.60

6R

1.79

0894

1.20

8314

2.04

9819

Chi

ttend

enV

erm

ont

1.85

5U

1.39

7072

0.99

2123

1.94

3752

Ric

hmon

dV

irgin

ia1.

5432

U2.

0931

341.

4864

292.

9121

87K

ing

Was

hing

ton

3.65

13R

Non

-roa

d1.

5180

211.

0242

071.

7374

94K

ing

Was

hing

ton

3.22

64U

Non

-roa

d0.

9589

050.

6809

621.

3341

29M

ilwau

kee

Wis

cons

in2.

3524

UO

n-ro

ad1.

6469

251.

1695

552.

2913

74P

uert

o R

ico

1.18

11U

2.45

4399

1.74

2979

3.41

4816

Oce

anvi

lleN

J2.

032

R0.

4357

550.

2940

030.

4987

56S

eattl

eW

A3.

2264

UN

on-r

oad

0.75

129

0.53

3525

1.04

5273

She

nand

oah

Nat

iona

l Par

kV

A0.

9612

R0.

3082

840.

2079

990.

3528

55La

ke T

ahoe

NV

0.61

11R

0.94

2438

0.63

5862

1.07

8694

Was

hing

ton

DC

DC

12.2

635

RN

on-r

oad

1.01

273

0.68

3288

1.15

9149

Yel

low

ston

e N

atio

nal P

ark

WY

0.05

48R

0.10

6637

0.07

1948

0.12

2055

Den

ver

CO

0.76

26R

Non

-roa

d0.

9782

260.

6600

081.

1196

56D

enve

rC

O1.

9723

UN

on-r

oad

2.71

1257

1.92

5386

3.77

2184

Den

ver

CO

1.59

39U

0.45

2586

0.32

1402

0.62

9685

Den

ver

CO

0.73

04R

1.33

0274

0.89

7534

1.52

2603

Den

ver

CO

0.79

53U

0.82

0571

0.58

2724

1.14

1664

Den

ver

CO

2.59

18R

0.81

5005

0.54

9883

0.93

2837

Den

ver

CO

1.69

63U

0.59

3728

0.42

1633

0.82

6056

Den

ver

CO

1.39

13R

1.06

5238

0.71

8715

1.21

9248

Den

ver

CO

0.41

74R

0.56

0907

0.37

8443

0.64

2002

Den

ver

CO

4.58

09R

Non

-roa

d2.

1697

91.

4639

552.

4834

95

Not

es f

or T

able

4-4

:1.

C

ity

/ cou

nty

and

Stat

e m

ay a

ppea

r m

ultip

le ti

mes

if th

ere

are

seve

ral d

iffe

rent

mon

itori

ng s

ites

at th

at g

ener

al lo

catio

n.2.

M

odel

ed v

aria

ble

= “

conc

near

”N

eare

st m

odel

ed D

PM c

once

ntra

tion

cons

iste

nt w

ith th

e dr

aft 2

000

NO

NR

OA

D M

odel

3.

Subs

et:

Non

-roa

dSi

tes

dom

inat

ed b

y N

on-r

oad

sour

ce (

at le

ast 7

5 %

of

mod

eled

DPM

con

sist

ent w

ith th

e dr

aft 2

000

NO

NR

OA

DM

odel

)O

n-ro

adSi

tes

dom

inat

ed b

y O

n-ro

ad s

ourc

e (a

t lea

st 5

0 %

of

mod

eled

DPM

con

sist

ent w

ith th

e dr

aft 2

000

NO

NR

OA

DM

odel

)

ICF

Con

sult

ing

47

4.

Mon

itore

d va

riab

le:

TO

RE

CO

CX

val

ue m

ultip

lied

by T

OT

ave

rage

cor

rect

ion

fact

or f

or E

PA d

ata,

EC

TO

R f

or T

OR

dat

a.T

OR

HE

CO

CX

val

ue m

ultip

lied

by T

OT

max

imum

cor

rect

ion

fact

or f

or E

PA d

ata,

EC

TO

R f

or T

OR

dat

a..

TO

RL

EC

OC

X v

alue

mul

tiplie

d by

TO

R m

inim

um c

orre

ctio

n fa

ctor

for

EPA

dat

a, E

CT

OR

for

TO

R d

ata.

5.

Out

lier

valu

e no

t use

d fo

r st

atis

tical

com

pari

sons

ICF

Con

sult

ing

48

Tab

le 4

-5. S

umm

ary

of d

iffer

ence

s an

d pe

rcen

tage

diff

eren

ces

betw

een

mod

eled

aga

inst

mon

itore

d D

PM

con

cent

ratio

ns.

Fra

ctio

n o

f M

od

eled

Val

ues

Wit

hin

Mo

del

ed V

aria

ble

1M

on

ito

red

Var

iab

le2S

ub

set3

Lo

cati

on

N

Mea

nM

od

eled

Val

ue

Mea

nM

on

ito

red

Val

ue

Mea

nD

iffe

ren

ceM

ean

% D

iffe

ren

ce10

%25

%50

%10

0%co

ncne

arE

CT

OR

All

All

151.

560.

940.

6310

00.

070.

130.

530.

53co

ncne

arE

CT

OR

All

Rur

al10

1.41

0.87

0.54

760.

000.

100.

600.

60co

ncne

arE

CT

OR

All

Urb

an5

1.86

1.07

0.79

147

0.20

0.20

0.40

0.40

conc

near

EC

TO

RN

on-r

oad

All

42.

641.

650.

9898

0.00

0.25

0.50

0.50

conc

near

EC

TO

RN

on-r

oad

Rur

al2

2.67

1.57

1.10

450.

000.

500.

500.

50co

ncne

arE

CT

OR

Non

-roa

dU

rban

22.

601.

730.

8715

10.

000.

000.

500.

50co

ncne

ar2

EC

TO

RA

llA

ll15

1.20

0.94

0.26

560.

070.

130.

470.

60co

ncne

ar2

EC

TO

RA

llR

ural

101.

070.

870.

2038

0.10

0.10

0.50

0.70

conc

near

2E

CT

OR

All

Urb

an5

1.44

1.07

0.37

930.

000.

200.

400.

40m

axco

ncE

CT

OR

All

All

144.

801.

003.

8151

60.

070.

070.

070.

21m

axco

ncE

CT

OR

All

Rur

al9

4.30

0.96

3.35

416

0.11

0.11

0.11

0.33

max

conc

EC

TO

RA

llU

rban

55.

711.

074.

6469

60.

000.

000.

000.

00m

axco

ncE

CT

OR

Non

-roa

dA

ll4

6.63

1.65

4.98

376

0.00

0.00

0.00

0.00

max

conc

EC

TO

RN

on-r

oad

Rur

al2

7.60

1.57

6.03

464

0.00

0.00

0.00

0.00

max

conc

EC

TO

RN

on-r

oad

Urb

an2

5.66

1.73

3.93

288

0.00

0.00

0.00

0.00

max

conc

2E

CT

OR

All

All

143.

461.

002.

4734

60.

000.

070.

210.

36m

axco

nc2

EC

TO

RA

llR

ural

93.

100.

962.

1527

50.

000.

110.

330.

33m

axco

nc2

EC

TO

RA

llU

rban

54.

111.

073.

0547

30.

000.

000.

000.

40co

ncne

arE

CT

OR

HA

llA

ll15

1.56

1.16

0.40

620.

000.

070.

400.

60co

ncne

arE

CT

OR

HA

llR

ural

101.

411.

000.

4254

0.00

0.10

0.40

0.70

conc

near

EC

TO

RH

All

Urb

an5

1.86

1.48

0.37

780.

000.

000.

400.

40co

ncne

arE

CT

OR

HN

on-r

oad

All

42.

642.

110.

5353

0.00

0.00

0.50

0.75

conc

near

EC

TO

RH

Non

-roa

dR

ural

22.

671.

800.

8726

0.00

0.00

0.50

1.00

conc

near

EC

TO

RH

Non

-roa

dU

rban

22.

602.

410.

1980

0.00

0.00

0.50

0.50

conc

near

2E

CT

OR

HA

llA

ll15

1.20

1.16

0.04

260.

000.

070.

330.

73co

ncne

ar2

EC

TO

RH

All

Rur

al10

1.07

1.00

0.08

200.

000.

100.

400.

70co

ncne

ar2

EC

TO

RH

All

Urb

an5

1.44

1.48

-0.0

439

0.00

0.00

0.20

0.80

max

conc

EC

TO

RH

All

All

144.

801.

233.

5739

40.

000.

070.

140.

29m

axco

ncE

CT

OR

HA

llR

ural

94.

301.

093.

2135

10.

000.

110.

220.

33m

axco

ncE

CT

OR

HA

llU

rban

55.

711.

484.

2247

20.

000.

000.

000.

20m

axco

ncE

CT

OR

HN

on-r

oad

All

46.

632.

114.

5328

60.

000.

000.

000.

00

ICF

Con

sult

ing

49

Tab

le 4

-5. S

umm

ary

of d

iffer

ence

s an

d pe

rcen

tage

diff

eren

ces

betw

een

mod

eled

aga

inst

mon

itore

d D

PM

con

cent

ratio

ns.

Fra

ctio

n o

f M

od

eled

Val

ues

Wit

hin

Mo

del

ed V

aria

ble

1M

on

ito

red

Var

iab

le2S

ub

set3

Lo

cati

on

N

Mea

nM

od

eled

Val

ue

Mea

nM

on

ito

red

Val

ue

Mea

nD

iffe

ren

ceM

ean

% D

iffe

ren

ce10

%25

%50

%10

0%m

axco

ncE

CT

OR

HN

on-r

oad

Rur

al2

7.60

1.80

5.80

392

0.00

0.00

0.00

0.00

max

conc

EC

TO

RH

Non

-roa

dU

rban

25.

662.

413.

2517

90.

000.

000.

000.

00m

axco

nc2

EC

TO

RH

All

All

143.

461.

232.

2325

80.

070.

140.

360.

36m

axco

nc2

EC

TO

RH

All

Rur

al9

3.10

1.09

2.01

228

0.11

0.22

0.33

0.33

max

conc

2E

CT

OR

HA

llU

rban

54.

111.

482.

6331

20.

000.

000.

400.

40co

ncne

arE

CT

OR

LA

llA

ll15

1.56

0.64

0.92

190

0.13

0.40

0.47

0.53

conc

near

EC

TO

RL

All

Rur

al10

1.41

0.59

0.83

161

0.10

0.50

0.50

0.60

conc

near

EC

TO

RL

All

Urb

an5

1.86

0.76

1.10

248

0.20

0.20

0.40

0.40

conc

near

EC

TO

RL

Non

-roa

dA

ll4

2.64

1.15

1.49

184

0.25

0.50

0.50

0.50

conc

near

EC

TO

RL

Non

-roa

dR

ural

22.

671.

061.

6111

40.

000.

500.

500.

50co

ncne

arE

CT

OR

LN

on-r

oad

Urb

an2

2.60

1.23

1.37

254

0.50

0.50

0.50

0.50

conc

near

2E

CT

OR

LA

llA

ll15

1.20

0.64

0.55

126

0.07

0.33

0.47

0.53

conc

near

2E

CT

OR

LA

llR

ural

101.

070.

590.

4910

40.

000.

300.

500.

60co

ncne

ar2

EC

TO

RL

All

Urb

an5

1.44

0.76

0.68

171

0.20

0.40

0.40

0.40

max

conc

EC

TO

RL

All

All

144.

800.

694.

1279

20.

000.

000.

070.

07m

axco

ncE

CT

OR

LA

llR

ural

94.

300.

653.

6666

50.

000.

000.

110.

11m

axco

ncE

CT

OR

LA

llU

rban

55.

710.

764.

9510

210.

000.

000.

000.

00m

axco

ncE

CT

OR

LN

on-r

oad

All

46.

631.

155.

4859

10.

000.

000.

000.

00m

axco

ncE

CT

OR

LN

on-r

oad

Rur

al2

7.60

1.06

6.54

735

0.00

0.00

0.00

0.00

max

conc

EC

TO

RL

Non

-roa

dU

rban

25.

661.

234.

4344

60.

000.

000.

000.

00m

axco

nc2

EC

TO

RL

All

All

143.

460.

692.

7854

50.

070.

070.

070.

14m

axco

nc2

EC

TO

RL

All

Rur

al9

3.10

0.65

2.46

456

0.11

0.11

0.11

0.22

max

conc

2E

CT

OR

LA

llU

rban

54.

110.

763.

3570

70.

000.

000.

000.

00co

ncne

arE

CT

OT

All

All

952.

611.

730.

8880

0.12

0.21

0.45

0.68

conc

near

EC

TO

TA

llR

ural

301.

991.

360.

6366

0.13

0.30

0.60

0.73

conc

near

EC

TO

TA

llU

rban

652.

901.

901.

0086

0.11

0.17

0.38

0.66

conc

near

EC

TO

TN

on-r

oad

All

195.

171.

963.

2017

10.

110.

160.

260.

32co

ncne

arE

CT

OT

Non

-roa

dR

ural

73.

871.

472.

3916

40.

140.

290.

290.

29co

ncne

arE

CT

OT

Non

-roa

dU

rban

125.

922.

253.

6817

50.

080.

080.

250.

33co

ncne

arE

CT

OT

On-

road

All

61.

901.

97-0

.07

280.

000.

000.

500.

83co

ncne

arE

CT

OT

On-

road

Rur

al2

2.02

1.25

0.77

840.

000.

000.

500.

50co

ncne

arE

CT

OT

On-

road

Urb

an4

1.84

2.33

-0.4

91

0.00

0.00

0.50

1.00

ICF

Con

sult

ing

50

Tab

le 4

-5. S

umm

ary

of d

iffer

ence

s an

d pe

rcen

tage

diff

eren

ces

betw

een

mod

eled

aga

inst

mon

itore

d D

PM

con

cent

ratio

ns.

Fra

ctio

n o

f M

od

eled

Val

ues

Wit

hin

Mo

del

ed V

aria

ble

1M

on

ito

red

Var

iab

le2S

ub

set3

Lo

cati

on

N

Mea

nM

od

eled

Val

ue

Mea

nM

on

ito

red

Val

ue

Mea

nD

iffe

ren

ceM

ean

% D

iffe

ren

ce10

%25

%50

%10

0%co

ncne

ar2

EC

TO

TA

llA

ll95

2.05

1.73

0.32

420.

110.

370.

530.

77co

ncne

ar2

EC

TO

TA

llR

ural

301.

551.

360.

1930

0.10

0.40

0.63

0.80

conc

near

2E

CT

OT

All

Urb

an65

2.28

1.90

0.37

470.

110.

350.

480.

75m

axco

ncE

CT

OT

All

All

9518

.36

1.73

16.6

391

80.

020.

040.

080.

12m

axco

ncE

CT

OT

All

Rur

al30

7.11

1.36

5.76

423

0.03

0.03

0.10

0.13

max

conc

EC

TO

TA

llU

rban

6523

.55

1.90

21.6

511

460.

020.

050.

080.

11m

axco

ncE

CT

OT

Non

-roa

dA

ll19

48.9

81.

9647

.02

2142

0.00

0.00

0.00

0.05

max

conc

EC

TO

TN

on-r

oad

Rur

al7

8.95

1.47

7.48

489

0.00

0.00

0.00

0.14

max

conc

EC

TO

TN

on-r

oad

Urb

an12

72.3

42.

2570

.09

3106

0.00

0.00

0.00

0.00

max

conc

EC

TO

TO

n-ro

adA

ll6

4.84

1.97

2.87

214

0.00

0.17

0.33

0.33

max

conc

EC

TO

TO

n-ro

adR

ural

24.

681.

253.

4330

70.

000.

000.

000.

00m

axco

ncE

CT

OT

On-

road

Urb

an4

4.92

2.33

2.59

168

0.00

0.25

0.50

0.50

max

conc

2E

CT

OT

All

All

9512

.99

1.73

11.2

662

60.

020.

090.

120.

21m

axco

nc2

EC

TO

TA

llR

ural

305.

171.

363.

8128

40.

000.

130.

130.

30m

axco

nc2

EC

TO

TA

llU

rban

6516

.60

1.90

14.7

078

30.

030.

080.

110.

17co

ncne

arE

CT

OT

HA

llA

ll95

2.61

2.10

0.52

610.

110.

220.

460.

74co

ncne

arE

CT

OT

HA

llR

ural

301.

991.

710.

2848

0.07

0.27

0.57

0.80

conc

near

EC

TO

TH

All

Urb

an65

2.90

2.28

0.63

670.

120.

200.

420.

71co

ncne

arE

CT

OT

HN

on-r

oad

All

195.

172.

292.

8813

80.

160.

210.

320.

47co

ncne

arE

CT

OT

HN

on-r

oad

Rur

al7

3.87

1.89

1.98

116

0.00

0.14

0.29

0.57

conc

near

EC

TO

TH

Non

-roa

dU

rban

125.

922.

523.

4015

10.

250.

250.

330.

42co

ncne

arE

CT

OT

HO

n-ro

adA

ll6

1.90

2.71

-0.8

110

0.00

0.33

0.33

0.83

conc

near

EC

TO

TH

On-

road

Rur

al2

2.02

1.64

0.38

630.

000.

500.

500.

50co

ncne

arE

CT

OT

HO

n-ro

adU

rban

41.

843.

24-1

.40

-17

0.00

0.25

0.25

1.00

conc

near

2E

CT

OT

HA

llA

ll95

2.05

2.10

-0.0

527

0.11

0.35

0.53

0.80

conc

near

2E

CT

OT

HA

llR

ural

301.

551.

71-0

.16

160.

100.

400.

600.

80co

ncne

ar2

EC

TO

TH

All

Urb

an65

2.28

2.28

0.00

320.

110.

320.

490.

80m

axco

ncE

CT

OT

HA

llA

ll95

18.3

62.

1016

.26

832

0.03

0.05

0.12

0.19

max

conc

EC

TO

TH

All

Rur

al30

7.11

1.71

5.41

369

0.00

0.03

0.17

0.27

max

conc

EC

TO

TH

All

Urb

an65

23.5

52.

2821

.27

1046

0.05

0.06

0.09

0.15

max

conc

EC

TO

TH

Non

-roa

dA

ll19

48.9

82.

2946

.69

2051

0.00

0.05

0.05

0.16

max

conc

EC

TO

TH

Non

-roa

dR

ural

78.

951.

897.

0637

60.

000.

140.

140.

43

ICF

Con

sult

ing

51

Tab

le 4

-5. S

umm

ary

of d

iffer

ence

s an

d pe

rcen

tage

diff

eren

ces

betw

een

mod

eled

aga

inst

mon

itore

d D

PM

con

cent

ratio

ns.

Fra

ctio

n o

f M

od

eled

Val

ues

Wit

hin

Mo

del

ed V

aria

ble

1M

on

ito

red

Var

iab

le2S

ub

set3

Lo

cati

on

N

Mea

nM

od

eled

Val

ue

Mea

nM

on

ito

red

Val

ue

Mea

nD

iffe

ren

ceM

ean

% D

iffe

ren

ce10

%25

%50

%10

0%m

axco

ncE

CT

OT

HN

on-r

oad

Urb

an12

72.3

42.

5269

.81

3027

0.00

0.00

0.00

0.00

max

conc

EC

TO

TH

On-

road

All

64.

842.

712.

1316

90.

170.

170.

330.

33m

axco

ncE

CT

OT

HO

n-ro

adR

ural

24.

681.

643.

0325

60.

000.

000.

000.

00m

axco

ncE

CT

OT

HO

n-ro

adU

rban

44.

923.

241.

6812

50.

250.

250.

500.

50m

axco

nc2

EC

TO

TH

All

All

9512

.99

2.10

10.9

056

40.

020.

080.

180.

27m

axco

nc2

EC

TO

TH

All

Rur

al30

5.17

1.71

3.46

245

0.03

0.17

0.27

0.37

max

conc

2E

CT

OT

HA

llU

rban

6516

.60

2.28

14.3

371

20.

020.

050.

140.

23co

ncne

arE

CT

OT

LA

llA

ll95

2.61

1.52

1.09

101

0.09

0.17

0.43

0.63

conc

near

EC

TO

TL

All

Rur

al30

1.99

1.16

0.83

860.

130.

270.

570.

70co

ncne

arE

CT

OT

LA

llU

rban

652.

901.

691.

2110

80.

080.

120.

370.

60co

ncne

arE

CT

OT

LN

on-r

oad

All

195.

171.

783.

3920

70.

050.

110.

160.

26co

ncne

arE

CT

OT

LN

on-r

oad

Rur

al7

3.87

1.24

2.63

219

0.00

0.14

0.14

0.29

conc

near

EC

TO

TL

Non

-roa

dU

rban

125.

922.

093.

8320

10.

080.

080.

170.

25co

ncne

arE

CT

OT

LO

n-ro

adA

ll6

1.90

1.55

0.35

490.

000.

000.

330.

83co

ncne

arE

CT

OT

LO

n-ro

adR

ural

22.

021.

021.

0010

60.

000.

000.

000.

50co

ncne

arE

CT

OT

LO

n-ro

adU

rban

41.

841.

810.

0220

0.00

0.00

0.50

1.00

conc

near

2E

CT

OT

LA

llA

ll95

2.05

1.52

0.52

580.

090.

320.

520.

72co

ncne

ar2

EC

TO

TL

All

Rur

al30

1.55

1.16

0.39

460.

100.

430.

630.

73co

ncne

ar2

EC

TO

TL

All

Urb

an65

2.28

1.69

0.59

640.

090.

260.

460.

71m

axco

ncE

CT

OT

LA

llA

ll95

18.3

61.

5216

.84

1014

0.02

0.02

0.05

0.11

max

conc

EC

TO

TL

All

Rur

al30

7.11

1.16

5.95

484

0.00

0.00

0.10

0.10

max

conc

EC

TO

TL

All

Urb

an65

23.5

51.

6921

.86

1258

0.03

0.03

0.03

0.11

max

conc

EC

TO

TL

Non

-roa

dA

ll19

48.9

81.

7847

.21

2244

0.00

0.00

0.00

0.00

max

conc

EC

TO

TL

Non

-roa

dR

ural

78.

951.

247.

7161

50.

000.

000.

000.

00m

axco

ncE

CT

OT

LN

on-r

oad

Urb

an12

72.3

42.

0970

.25

3194

0.00

0.00

0.00

0.00

max

conc

EC

TO

TL

On-

road

All

64.

841.

553.

2926

50.

170.

170.

170.

33m

axco

ncE

CT

OT

LO

n-ro

adR

ural

24.

681.

023.

6536

30.

000.

000.

000.

00m

axco

ncE

CT

OT

LO

n-ro

adU

rban

44.

921.

813.

1021

60.

250.

250.

250.

50m

axco

nc2

EC

TO

TL

All

All

9512

.99

1.52

11.4

769

40.

000.

070.

110.

16m

axco

nc2

EC

TO

TL

All

Rur

al30

5.17

1.16

4.01

329

0.00

0.10

0.10

0.17

max

conc

2E

CT

OT

LA

llU

rban

6516

.60

1.69

14.9

186

30.

000.

060.

110.

15co

ncne

arT

OR

All

All

882.

311.

700.

6147

0.10

0.30

0.59

0.78

ICF

Con

sult

ing

52

Tab

le 4

-5. S

umm

ary

of d

iffer

ence

s an

d pe

rcen

tage

diff

eren

ces

betw

een

mod

eled

aga

inst

mon

itore

d D

PM

con

cent

ratio

ns.

Fra

ctio

n o

f M

od

eled

Val

ues

Wit

hin

Mo

del

ed V

aria

ble

1M

on

ito

red

Var

iab

le2S

ub

set3

Lo

cati

on

N

Mea

nM

od

eled

Val

ue

Mea

nM

on

ito

red

Val

ue

Mea

nD

iffe

ren

ceM

ean

% D

iffe

ren

ce10

%25

%50

%10

0%co

ncne

arT

OR

All

Rur

al30

2.04

1.60

0.44

380.

000.

200.

570.

73co

ncne

arT

OR

All

Urb

an58

2.44

1.75

0.70

510.

160.

340.

600.

81co

ncne

arT

OR

Non

-roa

dA

ll18

4.01

1.86

2.14

128

0.00

0.06

0.33

0.44

conc

near

TO

RN

on-r

oad

Rur

al9

3.60

1.91

1.69

870.

000.

110.

330.

44co

ncne

arT

OR

Non

-roa

dU

rban

94.

411.

812.

6016

80.

000.

000.

330.

44co

ncne

arT

OR

On-

road

All

51.

761.

95-0

.19

-50.

000.

600.

801.

00co

ncne

arT

OR

On-

road

Rur

al2

2.02

2.04

-0.0

24

0.00

1.00

1.00

1.00

conc

near

TO

RO

n-ro

adU

rban

31.

581.

89-0

.31

-12

0.00

0.33

0.67

1.00

conc

near

2T

OR

All

All

881.

811.

700.

1115

0.17

0.30

0.59

0.85

conc

near

2T

OR

All

Rur

al30

1.57

1.60

-0.0

37

0.13

0.17

0.50

0.87

conc

near

2T

OR

All

Urb

an58

1.93

1.75

0.19

190.

190.

360.

640.

84m

axco

ncT

OR

All

All

8714

.57

1.72

12.8

572

10.

050.

070.

170.

25m

axco

ncT

OR

All

Rur

al29

6.20

1.65

4.55

296

0.07

0.10

0.24

0.38

max

conc

TO

RA

llU

rban

5818

.75

1.75

17.0

193

30.

030.

050.

140.

19m

axco

ncT

OR

Non

-roa

dA

ll18

37.6

91.

8635

.83

1802

0.00

0.00

0.11

0.11

max

conc

TO

RN

on-r

oad

Rur

al9

8.65

1.91

6.74

365

0.00

0.00

0.22

0.22

max

conc

TO

RN

on-r

oad

Urb

an9

66.7

31.

8164

.92

3239

0.00

0.00

0.00

0.00

max

conc

TO

RO

n-ro

adA

ll5

4.36

1.95

2.41

134

0.00

0.00

0.20

0.20

max

conc

TO

RO

n-ro

adR

ural

24.

682.

042.

6313

40.

000.

000.

000.

00m

axco

ncT

OR

On-

road

Urb

an3

4.16

1.89

2.27

135

0.00

0.00

0.33

0.33

max

conc

2T

OR

All

All

8710

.34

1.72

8.63

484

0.06

0.16

0.23

0.40

max

conc

2T

OR

All

Rur

al29

4.51

1.65

2.85

189

0.07

0.28

0.38

0.55

max

conc

2T

OR

All

Urb

an58

13.2

61.

7511

.51

632

0.05

0.10

0.16

0.33

conc

near

TO

RH

All

All

882.

312.

230.

0813

0.11

0.26

0.60

0.84

conc

near

TO

RH

All

Rur

al30

2.04

1.83

0.21

210.

070.

130.

470.

77co

ncne

arT

OR

HA

llU

rban

582.

442.

430.

018

0.14

0.33

0.67

0.88

conc

near

TO

RH

Non

-roa

dA

ll18

4.01

2.36

1.65

780.

060.

110.

330.

50co

ncne

arT

OR

HN

on-r

oad

Rur

al9

3.60

2.19

1.41

640.

000.

110.

330.

56co

ncne

arT

OR

HN

on-r

oad

Urb

an9

4.41

2.52

1.89

930.

110.

110.

330.

44co

ncne

arT

OR

HO

n-ro

adA

ll5

1.76

2.51

-0.7

5-2

60.

400.

400.

801.

00co

ncne

arT

OR

HO

n-ro

adR

ural

22.

022.

34-0

.32

-90.

500.

501.

001.

00co

ncne

arT

OR

HO

n-ro

adU

rban

31.

582.

63-1

.05

-37

0.33

0.33

0.67

1.00

ICF

Con

sult

ing

53

Tab

le 4

-5. S

umm

ary

of d

iffer

ence

s an

d pe

rcen

tage

diff

eren

ces

betw

een

mod

eled

aga

inst

mon

itore

d D

PM

con

cent

ratio

ns.

Fra

ctio

n o

f M

od

eled

Val

ues

Wit

hin

Mo

del

ed V

aria

ble

1M

on

ito

red

Var

iab

le2S

ub

set3

Lo

cati

on

N

Mea

nM

od

eled

Val

ue

Mea

nM

on

ito

red

Val

ue

Mea

nD

iffe

ren

ceM

ean

% D

iffe

ren

ce10

%25

%50

%10

0%co

ncne

ar2

TO

RH

All

All

881.

812.

23-0

.42

-12

0.08

0.22

0.52

0.92

conc

near

2T

OR

HA

llR

ural

301.

571.

83-0

.27

-60.

070.

170.

400.

87co

ncne

ar2

TO

RH

All

Urb

an58

1.93

2.43

-0.5

0-1

40.

090.

240.

590.

95m

axco

ncT

OR

HA

llA

ll87

14.5

72.

2512

.32

510

0.06

0.14

0.22

0.36

max

conc

TO

RH

All

Rur

al29

6.20

1.89

4.31

246

0.07

0.17

0.34

0.45

max

conc

TO

RH

All

Urb

an58

18.7

52.

4316

.32

643

0.05

0.12

0.16

0.31

max

conc

TO

RH

Non

-roa

dA

ll18

37.6

92.

3635

.33

1303

0.00

0.06

0.11

0.17

max

conc

TO

RH

Non

-roa

dR

ural

98.

652.

196.

4630

60.

000.

110.

220.

22m

axco

ncT

OR

HN

on-r

oad

Urb

an9

66.7

32.

5264

.21

2300

0.00

0.00

0.00

0.11

max

conc

TO

RH

On-

road

All

54.

362.

511.

8583

0.00

0.00

0.00

0.60

max

conc

TO

RH

On-

road

Rur

al2

4.68

2.34

2.34

104

0.00

0.00

0.00

0.50

max

conc

TO

RH

On-

road

Urb

an3

4.16

2.63

1.53

690.

000.

000.

000.

67m

axco

nc2

TO

RH

All

All

8710

.34

2.25

8.09

335

0.08

0.18

0.32

0.51

max

conc

2T

OR

HA

llR

ural

294.

511.

892.

6115

20.

170.

280.

410.

62m

axco

nc2

TO

RH

All

Urb

an58

13.2

62.

4310

.83

426

0.03

0.14

0.28

0.45

conc

near

TO

RL

All

All

882.

311.

191.

1211

00.

100.

260.

410.

65co

ncne

arT

OR

LA

llR

ural

302.

041.

080.

9610

50.

130.

400.

530.

67co

ncne

arT

OR

LA

llU

rban

582.

441.

241.

2011

20.

090.

190.

340.

64co

ncne

arT

OR

LN

on-r

oad

All

184.

011.

292.

7222

80.

110.

220.

280.

33co

ncne

arT

OR

LN

on-r

oad

Rur

al9

3.60

1.29

2.31

178

0.11

0.22

0.33

0.33

conc

near

TO

RL

Non

-roa

dU

rban

94.

411.

293.

1227

80.

110.

220.

220.

33co

ncne

arT

OR

LO

n-ro

adA

ll5

1.76

1.36

0.40

360.

000.

000.

400.

80co

ncne

arT

OR

LO

n-ro

adR

ural

22.

021.

380.

6454

0.00

0.00

0.50

1.00

conc

near

TO

RL

On-

road

Urb

an3

1.58

1.34

0.24

240.

000.

000.

330.

67co

ncne

ar2

TO

RL

All

All

881.

811.

190.

6265

0.14

0.31

0.52

0.74

conc

near

2T

OR

LA

llR

ural

301.

571.

080.

4959

0.10

0.30

0.50

0.73

conc

near

2T

OR

LA

llU

rban

581.

931.

240.

6968

0.16

0.31

0.53

0.74

max

conc

TO

RL

All

All

8714

.57

1.20

13.3

710

660.

010.

030.

070.

13m

axco

ncT

OR

LA

llR

ural

296.

201.

125.

0848

70.

030.

030.

070.

14m

axco

ncT

OR

LA

llU

rban

5818

.75

1.24

17.5

113

550.

000.

030.

070.

12m

axco

ncT

OR

LN

on-r

oad

All

1837

.69

1.29

36.4

025

960.

060.

060.

060.

06m

axco

ncT

OR

LN

on-r

oad

Rur

al9

8.65

1.29

7.36

589

0.11

0.11

0.11

0.11

ICF

Con

sult

ing

54

Tab

le 4

-5. S

umm

ary

of d

iffer

ence

s an

d pe

rcen

tage

diff

eren

ces

betw

een

mod

eled

aga

inst

mon

itore

d D

PM

con

cent

ratio

ns.

Fra

ctio

n o

f M

od

eled

Val

ues

Wit

hin

Mo

del

ed V

aria

ble

1M

on

ito

red

Var

iab

le2S

ub

set3

Lo

cati

on

N

Mea

nM

od

eled

Val

ue

Mea

nM

on

ito

red

Val

ue

Mea

nD

iffe

ren

ceM

ean

% D

iffe

ren

ce10

%25

%50

%10

0%m

axco

ncT

OR

LN

on-r

oad

Urb

an9

66.7

31.

2965

.44

4602

0.00

0.00

0.00

0.00

max

conc

TO

RL

On-

road

All

54.

361.

363.

0123

70.

000.

200.

200.

20m

axco

ncT

OR

LO

n-ro

adR

ural

24.

681.

383.

3024

60.

000.

000.

000.

00m

axco

ncT

OR

LO

n-ro

adU

rban

34.

161.

342.

8123

10.

000.

330.

330.

33m

axco

nc2

TO

RL

All

All

8710

.34

1.20

9.14

730

0.02

0.03

0.11

0.24

max

conc

2T

OR

LA

llR

ural

294.

511.

123.

3932

80.

030.

030.

140.

34m

axco

nc2

TO

RL

All

Urb

an58

13.2

61.

2412

.02

931

0.02

0.03

0.10

0.19

Not

es o

n ne

xt p

age.

ICF

Con

sult

ing

55

Not

es f

or T

able

4-5

:

5.

Mod

eled

var

iabl

e:co

ncne

arN

eare

st m

odel

ed D

PM c

once

ntra

tion

cons

iste

nt w

ith th

e dr

aft 2

000

NO

NR

OA

D M

odel

conc

near

2N

eare

st m

odel

ed D

PM c

once

ntra

tion

cons

iste

nt w

ith th

e dr

aft 2

002

NO

NR

OA

D M

odel

max

conc

Nea

rby

(with

in 3

0 km

) m

axim

um m

odel

ed D

PM c

once

ntra

tion

cons

iste

nt w

ith th

e dr

aft 2

000

NO

NR

OA

DM

odel

max

conc

2N

earb

y (w

ithin

30

km)

max

imum

mod

eled

DPM

con

cent

ratio

n co

nsis

tent

with

the

draf

t 200

2 N

ON

RO

AD

Mod

el

6.

Mon

itore

d va

riab

le:

EC

TO

RE

C v

alue

mul

tipl

ied

by T

OR

ave

rage

cor

rect

ion

fact

or (

mis

sing

for

EC

mea

sure

d us

ing

TO

T).

EC

TO

RH

EC

val

ue m

ultip

lied

by T

OR

max

imum

cor

rect

ion

fact

or (

mis

sing

for

EC

mea

sure

d us

ing

TO

T).

EC

TO

RL

EC

val

ue m

ultip

lied

by T

OR

min

imum

cor

rect

ion

fact

or (

mis

sing

for

EC

mea

sure

d us

ing

TO

T).

EC

TO

TE

C v

alue

mul

tipl

ied

by T

OT

ave

rage

cor

rect

ion

fact

or (

mis

sing

for

EC

mea

sure

d us

ing

TO

R).

EC

TO

TH

EC

val

ue m

ultip

lied

by T

OT

max

imum

cor

rect

ion

fact

or (

mis

sing

for

EC

mea

sure

d us

ing

TO

R).

EC

TO

TL

EC

val

ue m

ultip

lied

by T

OR

min

imum

cor

rect

ion

fact

or (

mis

sing

for

EC

mea

sure

d us

ing

TO

R).

TO

RE

CO

CX

val

ue m

ultip

lied

by T

OT

ave

rage

cor

rect

ion

fact

or f

or E

PA d

ata,

EC

TO

R f

or T

OR

dat

a.T

OR

HE

CO

CX

val

ue m

ultip

lied

by T

OT

max

imum

cor

rect

ion

fact

or f

or E

PA d

ata,

EC

TO

R f

or T

OR

dat

a..

TO

RL

EC

OC

X v

alue

mul

tiplie

d by

TO

R m

inim

um c

orre

ctio

n fa

ctor

for

EPA

dat

a, E

CT

OR

for

TO

R d

ata.

7.

Subs

et:

All

All

sit

esN

on-r

oad

Site

s do

min

ated

by

Non

-roa

d so

urce

(at

leas

t 75

% o

f m

odel

ed D

PM c

onsi

sten

t with

the

draf

t 200

0 N

ON

RO

AD

Mod

elO

n-ro

adSi

tes

dom

inat

ed b

y O

n-ro

ad s

ourc

e (a

t lea

st 5

0 %

of

mod

eled

DPM

con

sist

ent w

ith th

e dr

aft 2

000

NO

NR

OA

DM

odel

ICF

Con

sult

ing

56

ICF

Con

sult

ing

57

ICF

Con

sult

ing

58

ICF

Con

sult

ing

59

ICF

Con

sult

ing

60

ICF

Con

sult

ing

61

ICF Consulting 62

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Allen, G. A., Lawrence, J., and Koutrakis, P. (1999). Field Validation of a Semi-ContinuousMethod for Aerosol Black Carbon (Aethalometer) and temporal Patterns of Summertime HourlyBlack Carbon Measurements in Southwestern P. A. Atmospheric Environment. 33 (5), pp. 817-823.

Cass, G. R. (1997). Contribution of Vehicle Emissions to Ambient Carbonaceous ParticulateMatter: A Review and Synthesis of the Available Data in the South Coast Air Basin. CRC ProjectA-18.

Chow, J. C. and Watson, J. G. (1998). Guideline on Speciated Particulate Monitoring. DesertResearch Institute.

Chow, J. C., Watson, J. G., Pritchett, L. C., Pierson, W. R., Frazier, C. A., and Purcell, R. G.(1993). The DRI Thermal/Optical Reflectance Carbon Analysis System: Description, Evaluation,and Applications in U.S. Air Quality Studies. Atmospheric Environment. Vol 27A, No. 8, pp.1185 –1201.

Hansen, A. D. A. (2000). The Aethalometer. Magee Scientific Company, Berkeley, California,USA.

Hansen, A. D. A, and McMurry, P. H. (1990). An intercomparison of measurements of aerosolelemental carbon during the 1986 carbonaceous species method comparison study. J. Air &Waste Manage. Assoc. 40, pp. 394-395.

Liousse, C., Cashier, H., and Jennings, S. G. (1993). Optical and Thermal Measurements ofBlack Carbon Aerosol Content in Different Environments: Variation of the Specific AttenuationCross Section, Sigma (σ).Atmospheric Environment. Vol 27A, No. 8, pp. 1203 –1211.

Ramadan, Z., Song, X-H, and Hopke, P. K. (2000). Identification of Sources of Phoenix Aerosolby Positive Matrix Factorization. J. Air & Waste Manage. Assoc. 50, pp. 1308-1320.

Schauer, J. J., and Cass, G. R. (2000). Source Apportionment of Wintertime Gas-Phase andParticle Phase Air Pollutants Using Organic Compounds as Tracers. Environmenal Science andTechnology. Vol 34, No. 9, pp. 1821 –1832.

Schauer, J. J., Rogge, W. F., Hildemann, L. M., Mazurek, M. A., Cass, G. R., and Simoneit, B.R. T. (1996). Source Apportionment of Airborne PM Using Organic Compounds as Tracers.Atmospheric Environment. Vol 30, No. 22, pp. 3837 –3855.

ICF Consulting 63

Watson, J. G., Fujita, E., Chow, J. G., Zielinska, B., Richards, L. W., Neff, W., and Dietrich, D.(1998). Northern Front Range Air Quality Study Final Report. Desert Research Institute. 6580-685-8750.1F2.

Zheng, M., Cass, G. R., Schauer, J. J., and Edgerton, E. S. (2002). Source Apportionment ofPM2.5 in the Southeastern United States Using Solvent-Extractable Organic Compounds asTracers. Environmental Science and Technology. To appear.