# COLORADO STATE

IMPLEMENTATION PLAN FOR

REGIONAL HAZE

TECHNICAL SUPPORT DOCUMENT

Douglas Pass and Ripple Creek Pass Sites Analyses

Prepared by

The Colorado Department of Pu blic Health and Environment

Air Pollution Control Division

4300 Cherry Creek Drive South

Denver, Colorado 80246-1530

(303) 692-3100

June 2007

# 2 

# Douglas Pass and Ripple Creek Pass Sites Analyses

Introduction. The State of Colorado was involved in a project to monitor acid

deposition, visibility, and mete orological parameters in nor thwest Colorado. The project

is important to Colorado for defining existi ng conditions and estimating potential effects

on Colorado’s Class I areas from current a nd future air pollution sources. The project

had two parts: 1) Shell E xploration and Producti on Company consulted and collaborated

with the State, federal la nd managers, and the U.S. Geological Survey regarding a

monitoring site near the Flat Tops Wilderness Area at Ripple Creek Pass (elevation 9575

ft.). The site began operating approximately December 1, 2002 and is still in operation.

The site is funded entirely by Shell. 2) The Colorado Governor’s Office of Energy

Management and Conservation (OEMC) in co llaboration with the Colorado Department

of Public Health and Environment/Air Pollution Control Division (CDPHE/APCD)

awarded a contract through a co mpetitive bidding process to Air Sciences, Inc. to build

and operate a monitoring site approximately 75 miles due west of the Ripple Creek site.

The OEMC funded the Douglas Pass site a nd CDPHE/APCD provided technical support

to the OEMC. The site was funded to be in operation approximately 24 months.

The Douglas Pass site (elevation 8200 ft.) consisted of an IMPROVE II particulate

monitor and a basic set of meteorological m easurements. The site began operating in the

fall of 2003 and was shut-down in January 2006. The purpose of this short-term site was

to measure regional haze/visibility events as they enter Colorado from the west to better

understand regional haze in Colorado’s Class I areas. A secondary purpose of the site

(related to the first part of th e overall project as discussed in the above paragraph) was to

provide upwind information in relation to the Ripple Creek site for the period the 2 sites

were operating at the same time. This is of interest to the State of Colorado in the context

of better understanding air quality at and upw ind of the Flat Tops Wilderness Area and

potential oil shale development.

The monitoring programs at both Douglas Pass and Ripple Creek Pass consisted of

IMPROVE samplers and basic meteorological parameters. Both sites were operated by

Air Sciences, Inc. The data are considered to be of similar qualit y as federally operated

IMPROVE sites. The same protoc ols and equipment were utilized.

Douglas Pass is within 25 m iles of the Utah/Colorado border. The site was intentionally

selected to be close to the border in order to measure haze as it entered Colorado. The

following analyses explore:

• Correlations: relationships between this site and other monitoring sites in

Colorado;

• Back Trajectories: where dirty air that was monitored at these sites came from;

and

• PMF: possible sources of dirty air at the sites.

The analysis is conducted within a gene ral hypothesis/framework stated below:

Colorado is generally a downwind state in relation to other states in the

# 3 

# West. Haze from upwind states enters Colorado and is a significant

contributor to visibility impairment as measured at Colorado monitoring

sites. While modeling exercises have been conducted to examine the issue of

what source categories, regions and st ates are contributing to haze at

Colorado Class I areas, this project was intended to be monitoring-based.

Douglas Pass measurements represent haze entering Colorado and it can be

utilized to quantify the influence of ou t-of-state haze on Colorado Class I

areas.

Correlations. Paired Pearson product-moment corre lations and significance levels were

computed for data collected 2003-2005. Pairs of correlations will have more or less data

in common depending on when sites ope rated during this period, missing data,

operational problems, etc.

Aerosol Extinction, Worst Days . The worst 20% aerosol extin ction days were determined

for Ripple Creek Pass. A squared Pears on correlation between Ripple Creek Pass and

Douglas Pass will represent the amount of va riation at Ripple Creek Pass (downwind)

that can be associated with variation in m easurements at Douglas Pass (upwind). Table I

indicates a high correlation of .969 betw een the two sites. This is an r 2 

# of .94 or 94%.

Table 1

Worst Day Aerosol Extinction 2003-2005

Douglas

Pass Ripple Creek

Pass

Pearson Correlation 1 .969(**)

Sig. (2-tailed) .000 Douglas Pass

N 47 47

Pearson Correlation .969(**) 1

Sig. (2-tailed) .000 Ripple Creek Pass

N 47 47

** Co rrelation is significant at the 0.01 level (2-tailed).



# Extinction, All Days. Table 2 presents an entire matrix of aerosol extinction correlations

between all sites that had data during some part of 2003-2005. Of particular interest is

the Douglas Pass column (highlighted in yellow). One can look down it to see

correlations with it ranging fr om .961 (Ripple Creek) to -.236 (Shamrock Mines).

Generally, sites closer to and with a simila r elevation of Douglas Pass have the higher

correlations. Canyonlands NP in Utah was includ ed to examine how related a site that is

further to the west (e.g., upwind) compares to Colorado sites.

Tables 3 thru 8 present correlations between Douglas Pass and the othe r sites for each of

the important visibility impairing species.4Table 2Aerosol Extinction Correlations/All Valid Data 2003-2005DouglasPassRippleCreekPassCanyonlandsNPGreat SandDunes NPMesaVerde NPMt. ZirkelWARockyMountain NPShamrockMinesWeminucheWAWhiteRiverDouglas PassPearson Correlation1.961(**).382(**).051.813(**).799(**).211(*)-.236.859(**).543(**)Sig. (2-tailed).000.000.591.000.000.023.143.000.000N22019110811411610411640112111Ripple Creek PassPearson Correlation.961(**)1.565(**).516(**).751(**).912(**).389(**).409(**).847(**).765(**)Sig. (2-tailed).000.000.000.000.000.000.003.000.000N19128615818816416717451171186Canyonlands NPPearson Correlation.382(**).565(**)1.408(**).479(**).430(**).348(**).220.365(**).465(**)Sig. (2-tailed).000.000.000.000.000.000.114.000.000N10815822521720919921753214220Great Sand Dunes NPPearson Correlation.051.516(**).408(**)1.623(**).539(**).545(**).659(**).547(**).605(**)Sig. (2-tailed).591.000.000.000.000.000.000.000.000N11418821725122122123161226245Mesa Verde NPPearson Correlation.813(**).751(**).479(**).623(**)1.714(**).422(**).697(**).740(**).640(**)Sig. (2-tailed).000.000.000.000.000.000.000.000.000N11616420922123020522353218223Mt. Zirkel WAPearson Correlation.799(**).912(**).430(**).539(**).714(**)1.594(**).559(**).731(**).790(**)Sig. (2-tailed).000.000.000.000.000.000.000.000.000N10416719922120523121554207224Rocky Mountain NPPearson Correlation.211(*).389(**).348(**).545(**).422(**).594(**)1.440(**).356(**).740(**)Sig. (2-tailed).023.000.000.000.000.000.001.000.000N11617421723122321524055228234Shamrock MinesPearson Correlation-.236.409(**).220.659(**).697(**).559(**).440(**)1.131.530(**)Sig. (2-tailed).143.003.114.000.000.000.001.356.000N40515361535455675265Weminuche WAPearson Correlation.859(**).847(**).365(**).547(**).740(**).731(**).356(**).1311.710(**)Sig. (2-tailed).000.000.000.000.000.000.000.356.000N11217121422621820722852235229White RiverPearson Correlation.543(**).765(**).465(**).605(**).640(**).790(**).740(**).530(**).710(**)1Sig. (2-tailed).000.000.000.000.000.000.000.000.000N11118622024522322423465229254* Correlation is significant atthe 0.05 level (2-tailed).** Correlation is significant atthe 0.01 level (2-tailed).5Table 3Sulfur Correlations/All Valid Data 2003-2005*Correlation is significant atthe 0.05 level (2-tailed).** Correlation is significant at the 0.01 level (2-tailed)..DouglasPassRippleCreekPassCanyonlandsNPGreat SandDunes NPMesaVerde NPMt. ZirkelWARockyMountain NPShamrockMinesWeminucheWAWhite RiverDouglas PassPearson Correlation1.804(**).757(**).534(**).580(**).748(**).594(**).596(**).635(**).690(**)Sig. (2-tailed).000.000.000.000.000.000.000.000.000N263237230224223212227143229229Ripple Creek PassPearson Correlation.804(**)1.689(**).618(**).650(**).824(**).695(**).629(**).683(**).802(**)Sig. (2-tailed).000.000.000.000.000.000.000.000.000N237383288288275271288140290293Canyonlands NPPearson Correlation.757(**).689(**)1.630(**).747(**).658(**).541(**).708(**).733(**).713(**)Sig. (2-tailed).000.000.000.000.000.000.000.000.000N230288334323312306322145325329Great Sand Dunes NPPearson Correlation.534(**).618(**).630(**)1.769(**).635(**).624(**).831(**).812(**).744(**)Sig. (2-tailed).000.000.000.000.000.000.000.000.000N224288323333309306321135324328Mesa Verde NPPearson Correlation.580(**).650(**).747(**).769(**)1.602(**).491(**).907(**).884(**).761(**)Sig. (2-tailed).000.000.000.000.000.000.000.000.000N223275312309321297309136313315Mt. Zirkel WAPearson Correlation.748(**).824(**).658(**).635(**).602(**)1.758(**).628(**).667(**).814(**)Sig. (2-tailed).000.000.000.000.000.000.000.000.000N212271306306297317305129310311Rocky Mountain NPPearson Correlation.594(**).695(**).541(**).624(**).491(**).758(**)1.494(**).548(**).711(**)Sig. (2-tailed).000.000.000.000.000.000.000.000.000N227288322321309305333139324327Shamrock MinesPearson Correlation.596(**).629(**).708(**).831(**).907(**).628(**).494(**)1.949(**).791(**)Sig. (2-tailed).000.000.000.000.000.000.000.000.000N143140145135136129139146141144Weminuche WAPearson Correlation.635(**).683(**).733(**).812(**).884(**).667(**).548(**).949(**)1.814(**)Sig. (2-tailed).000.000.000.000.000.000.000.000.000N229290325324313310324141336330White RiverPearson Correlation.690(**).802(**).713(**).744(**).761(**).814(**).711(**).791(**).814(**)1Sig. (2-tailed).000.000.000.000.000.000.000.000.000N229293329328315311327144330339

# 6 

# Sulfur, All Days. Table 3 contains correlations coefficients for sulfur between pairs of sites. With particular

attention to the Douglas Pass column , there are no low or negative corr elations between Douglas Pass and

any other site. Generally 25-50% of sulfur measured at Douglas Pass appears to be associated with sulfur

measured at other sites.

Nitrate, All Days . Table 4 has a strong relati onship between Douglas Pass a nd Ripple Creek Pass nitrate but

very low, no, or negative correlation coefficients with other sites.

Elemental Carbon, All Days . Table 5’s highest correlation is with the White River si te (r=0.78), not with

Ripple Creek (0.58). Other correlat ions range from negative (Shamroc k Mines), to negligible (Rocky

Mountain), to moderate (remaining sites).

Organic Carbon, All Days . Table 6 shows very high correlati ons with Douglas Pass, averaging around

0.90, for all sites except Sham rock Mines (0.20).

Fine Mass, All Days . Table 7’s correlation pairs with Douglas Pass are mostly very low (Shamrock Mines,

White River, Rocky Mountain NP, Mt . Zirkel WA, Mesa Verde NP, Great Sand Dunes). Two sites have a

moderate relationship with Douglas Pass fine mass (Weminuche WA, Canyonlands NP) and only one site,

Ripple Creek, has a high correlation. Ex amining the rest of the correlation matrix in Table 7 indicates that

Colorado sites are fairly highly inter-correlated.

Coarse Mass, All Days . Overall, the pairs in Table 8 are not very correlated. The highest is between

Douglas Pass and Ripple Creek (0.88). In contrast with the fine mass correlation matrix, few sites have

much relation with each other suggesting ve ry local source areas for coarse mass.

Summary of Correlations of other IMPROVE monitors with Douglas Pass . These monitoring-based results

are consistent with other evidence. Using Douglas Pass as an indicator/predictor of regional haze

originating out-of-state, shows:

• Worst days aerosol extinc tion at Ripple Creek Pass is highly associated (r 2 

# =94%) with

Douglas Pass. That is, the worst haze at Ripple Creek is “explained” by haze coming from

out-of-state.

• All days aerosol extinction at Douglas Pass, and out-of-state haze, is highly associated with

the sites closest to it and poorly correlat ed to sites at greater distance.

• Douglas Pass sulfur is consiste ntly well correlated to all sites with coeffi cients ranging from

0.53 to 0.80. This might indicate that sulfur variab ility in Colorado is quit e regional in scale.

• In contrast, Douglas Pass nitrate is poorly correlated at all sites except Ripple Creek

indicating the importance of more local source areas.

• Elemental carbon measurements at Douglas Pass have low correlations with all sites except

White River (0.78).

• Organic carbon at Douglas Pass is very high correlated with ot her sites. Compared to the

other visibility impairing species, OC displa ys the highest set of correlations. This

underlines the strong regional and episodic natu re of organic carbon variability in Colorado.

• Fine mass at Douglas Pass has a strong relations hip with Ripple Creek Pass and a moderate

correlation with Weminunche WA but only small or no association with fine mass at other

sites in Colorado. Fine mass at other sites in Colorado does seem to be varying over time in

a fairly correlated manner.

• Coarse Mass at Douglas Pass is again correlate d well with Ripple Creek but shows little to no

relation to other Colorado sites.7Table 4Nitrate Correlations/All Valid Data 2003-2005DouglasPassRippleCreek PassCanyonlandsNPGreatSandDunes NPMesaVerde NPMt. ZirkelWARockyMountain NPShamrockMinesWeminuche WAWhite RiverDouglas PassPearson Correlation1.873(**)-.020-.323(**).073.094.052-.059.133(*).109Sig. (2-tailed).000.764.000.281.175.438.485.044.101N239221228228223210225142229226Ripple CreekPassPearson Correlation.873(**)1.261(**).597(**).550(**).806(**).184(**).132.696(**).831(**)Sig. (2-tailed).000.000.000.000.000.002.112.000.000N221299275289271262273146280288CanyonlandsNPPearson Correlation-.020.261(**)1.434(**).360(**).198(**).041.288(**).283(**).226(**)Sig. (2-tailed).764.000.000.000.001.462.000.000.000N228275335329319299321149329327Great SandDunes NPPearson Correlation-.323(**).597(**).434(**)1.416(**).486(**).202(**).517(**).452(**).569(**)Sig. (2-tailed).000.000.000.000.000.000.000.000.000N228289329355326314331153339346Mesa Verde NPPearson Correlation.073.550(**).360(**).416(**)1.340(**).102.752(**).610(**).511(**)Sig. (2-tailed).281.000.000.000.000.068.000.000.000N223271319326332297319141326324Mt. Zirkel WAPearson Correlation.094.806(**).198(**).486(**).340(**)1.220(**).168(*).488(**).731(**)Sig. (2-tailed).175.000.001.000.000.000.049.000.000N210262299314297321302138307313RockyMountain NPPearson Correlation.052.184(**).041.202(**).102.220(**)1.143.100.183(**)Sig. (2-tailed).438.002.462.000.068.000.088.070.001N225273321331319302336143331329ShamrockMinesPearson Correlation-.059.132.288(**).517(**).752(**).168(*).1431.258(**).309(**)Sig. (2-tailed).485.112.000.000.000.049.088.002.000N142146149153141138143160146156WeminucheWAPearson Correlation.133(*).696(**).283(**).452(**).610(**).488(**).100.258(**)1.760(**)Sig. (2-tailed).044.000.000.000.000.000.070.002.000N229280329339326307331146344337White RiverPearson Correlation.109.831(**).226(**).569(**).511(**).731(**).183(**).309(**).760(**)1Sig. (2-tailed).101.000.000.000.000.000.001.000.000N226288327346324313329156337354** Correlation is significant at the 0.01 level (2-tailed).* Correlation is significant at the 0.05 level (2-tailed).8Table 5Elemental Carbon Correlations/All Valid Data 2003-2005DouglasPassRippleCreek PassCanyonlandsNPGreat SandDunes NPMesaVerde NPMt. ZirkelWARockyMountain NPShamrockMinesWeminucheWAWhite RiverDouglas PassPearson Correlation1.582(**).408(**).313(**).344(**).399(**).115-.092.539(**).777(**)Sig. (2-tailed).000.000.001.000.000.205.551.000.000N22520212212012111112244120117Ripple Creek PassPearson Correlation.582(**)1.662(**).589(**).577(**).782(**).513(**).090.499(**).661(**)Sig. (2-tailed).000.000.000.000.000.000.526.000.000N20229918120117818218452185196Canyonlands NPPearson Correlation.408(**).662(**)1.514(**).556(**).771(**).260(**).077.502(**).651(**)Sig. (2-tailed).000.000.000.000.000.000.577.000.000N12218124123823322123655236235Great Sand Dunes NPPearson Correlation.313(**).589(**).514(**)1.495(**).622(**).302(**).118.499(**).496(**)Sig. (2-tailed).001.000.000.000.000.000.352.000.000N12020123825823523824164241252Mesa Verde NPPearson Correlation.344(**).577(**).556(**).495(**)1.610(**).297(**).059.536(**).538(**)Sig. (2-tailed).000.000.000.000.000.000.676.000.000N12117823323523822223353233232Mt. Zirkel WAPearson Correlation.399(**).782(**).771(**).622(**).610(**)1.429(**).217.481(**).776(**)Sig. (2-tailed).000.000.000.000.000.000.105.000.000N11118222123822224122457225235Rocky Mountain NPPearson Correlation.115.513(**).260(**).302(**).297(**).429(**)1.128.243(**).242(**)Sig. (2-tailed).205.000.000.000.000.000.351.000.000N12218423624123322424355239238Shamrock MinesPearson Correlation-.092.090.077.118.059.217.1281-.085.115Sig. (2-tailed).551.526.577.352.676.105.351.550.360N44525564535755675265Weminuche WAPearson Correlation.539(**).499(**).502(**).499(**).536(**).481(**).243(**)-.0851.600(**)Sig. (2-tailed).000.000.000.000.000.000.000.550.000N12018523624123322523952243238White RiverPearson Correlation.777(**).661(**).651(**).496(**).538(**).776(**).242(**).115.600(**)1Sig. (2-tailed).000.000.000.000.000.000.000.360.000N11719623525223223523865238255** Correlation is significant at the 0.01 level (2-tailed).9Table 6Organic Carbon Correlations/All Valid Data 2003-2005DouglasPassRipple CreekPassCanyonlandsNPGreat SandDunes NPMesaVerde NPMt. ZirkelWARockyMountain NPShamrockMinesWeminucheWAWhiteRiverDouglas PassPearson Correlation1.924(**).929(**).894(**).867(**).928(**).683(**).198.913(**).964(**)Sig. (2-tailed).000.000.000.000.000.000.197.000.000N22520212212012111112244120117Ripple Creek PassPearson Correlation.924(**)1.888(**).836(**).643(**).961(**).762(**).593(**).850(**).903(**)Sig. (2-tailed).000.000.000.000.000.000.000.000.000N20229918120117818218452185196Canyonlands NPPearson Correlation.929(**).888(**)1.794(**).712(**).874(**).639(**).441(**).847(**).895(**)Sig. (2-tailed).000.000.000.000.000.000.001.000.000N12218124123823322123655236235Great Sand DunesNPPearson Correlation.894(**).836(**).794(**)1.666(**).826(**).581(**).740(**).894(**).850(**)Sig. (2-tailed).000.000.000.000.000.000.000.000.000N12020123825823523824164241252Mesa Verde NPPearson Correlation.867(**).643(**).712(**).666(**)1.652(**).553(**).672(**).757(**).690(**)Sig. (2-tailed).000.000.000.000.000.000.000.000.000N12117823323523822223353233232Mt. Zirkel WAPearson Correlation.928(**).961(**).874(**).826(**).652(**)1.788(**).689(**).834(**).900(**)Sig. (2-tailed).000.000.000.000.000.000.000.000.000N11118222123822224122457225235Rocky Mountain NPPearson Correlation.683(**).762(**).639(**).581(**).553(**).788(**)1.694(**).581(**).625(**)Sig. (2-tailed).000.000.000.000.000.000.000.000.000N12218423624123322424355239238Shamrock MinesPearson Correlation.198.593(**).441(**).740(**).672(**).689(**).694(**)1.362(**).706(**)Sig. (2-tailed).197.000.001.000.000.000.000.008.000N44525564535755675265Weminuche WAPearson Correlation.913(**).850(**).847(**).894(**).757(**).834(**).581(**).362(**)1.900(**)Sig. (2-tailed).000.000.000.000.000.000.000.008.000N12018523624123322523952243238White RiverPearson Correlation.964(**).903(**).895(**).850(**).690(**).900(**).625(**).706(**).900(**)1Sig. (2-tailed).000.000.000.000.000.000.000.000.000N11719623525223223523865238255** Correlation is significant at the 0.01 level (2-tailed).10Table 7Fine Mass Correlations/All Valid Data 2003-2005DouglasPassRippleCreek PassCanyonlandsNPGreatSandDunes NPMesaVerde NPMt. ZirkelWARockyMountain NPShamrockMinesWeminucheWAWhiteRiverDouglas PassPearson Correlation1.918(**).691(**).107.296(**).291(**).142(*).073.683(**).373(**)Sig. (2-tailed).000.000.115.000.000.035.394.000.000N234208224216217209220140223220Ripple Creek PassPearson Correlation.918(**)1.711(**).651(**).704(**).880(**).610(**).789(**).579(**).811(**)Sig. (2-tailed).000.000.000.000.000.000.000.000.000N208300272286260269272143275288Canyonlands NPPearson Correlation.691(**).711(**)1.461(**).537(**).590(**).527(**).381(**).471(**).626(**)Sig. (2-tailed).000.000.000.000.000.000.000.000.000N224272339325317310326149329331Great Sand DunesNPPearson Correlation.107.651(**).461(**)1.672(**).634(**).508(**).835(**).305(**).648(**)Sig. (2-tailed).115.000.000.000.000.000.000.000.000N216286325348311320323148326340Mesa Verde NPPearson Correlation.296(**).704(**).537(**).672(**)1.698(**).543(**).857(**).512(**).708(**)Sig. (2-tailed).000.000.000.000.000.000.000.000.000N217260317311326301313140317317Mt. Zirkel WAPearson Correlation.291(**).880(**).590(**).634(**).698(**)1.699(**).866(**).517(**).822(**)Sig. (2-tailed).000.000.000.000.000.000.000.000.000N209269310320301334309144314325Rocky MountainNPPearson Correlation.142(*).610(**).527(**).508(**).543(**).699(**)1.627(**).341(**).826(**)Sig. (2-tailed).035.000.000.000.000.000.000.000.000N220272326323313309337143328329Shamrock MinesPearson Correlation.073.789(**).381(**).835(**).857(**).866(**).627(**)1.266(**).849(**)Sig. (2-tailed).394.000.000.000.000.000.000.001.000N140143149148140144143162145158Weminuche WAPearson Correlation.683(**).579(**).471(**).305(**).512(**).517(**).341(**).266(**)1.843(**)Sig. (2-tailed).000.000.000.000.000.000.000.001.000N223275329326317314328145340332White RiverPearson Correlation.373(**).811(**).626(**).648(**).708(**).822(**).826(**).849(**).843(**)1Sig. (2-tailed).000.000.000.000.000.000.000.000.000N220288331340317325329158332354** Correlation is significant at the 0.01 level (2-tailed).* Correlation is significant at the 0.05 level (2-tailed).11Table 8Coarse Mass Correlations/All Valid Data 2003-2005**** Correlation is significant at the 0.01 level (2-tailed).DouglasPassRippleCreek PassCanyonlandsNPGreat SandDunes NPMesaVerde NPMt. ZirkelWARockyMountain NPShamrockMinesWeminucheWAWhiteRiverDouglas PassPearson Correlation1.881(**).358(**).234(**).053.030-.007.335(**).049.461(**)Sig. (2-tailed).000.000.000.416.642.912.000.453.000N241210240239239239239159239238Ripple Creek PassPearson Correlation.881(**)1.371(**).263(**).037-.054-.015.449(**).042.556(**)Sig. (2-tailed).000.000.000.523.351.794.000.474.000N210306293306293304293161293305Canyonlands NPPearson Correlation.358(**).371(**)1.094.016-.011.007.038.095.213(**)Sig. (2-tailed).000.000.073.754.832.893.617.069.000N240293367366366366366174366365Great Sand Dunes NPPearson Correlation.234(**).263(**).0941-.082.003-.003-.063.004.159(**)Sig. (2-tailed).000.000.073.117.959.951.392.933.002N239306366380366378366187366378Mesa Verde NPPearson Correlation.053.037.016-.0821-.155(**)-.004.121-.096.012Sig. (2-tailed).416.523.754.117.003.933.111.066.822N239293366366366366366174366365Mt. Zirkel WAPearson Correlation.030-.054-.011.003-.155(**)1-.001-.081-.001.019Sig. (2-tailed).642.351.832.959.003.980.269.979.712N239304366378366378366186366377Rocky Mountain NPPearson Correlation-.007-.015.007-.003-.004-.0011.582(**).007-.005Sig. (2-tailed).912.794.893.951.933.980.000.894.922N239293366366366366366174366365Shamrock MinesPearson Correlation.335(**).449(**).038-.063.121-.081.582(**)1.071.051Sig. (2-tailed).000.000.617.392.111.269.000.355.485N159161174187174186174187174186Weminuche WAPearson Correlation.049.042.095.004-.096-.001.007.0711.011Sig. (2-tailed).453.474.069.933.066.979.894.355.838N239293366366366366366174366365White RiverPearson Correlation.461(**).556(**).213(**).159(**).012.019-.005.051.0111Sig. (2-tailed).000.000.000.002.822.712.922.485.838N238305365378365377365186365378

# 12 

# Back Trajectories. The National Oceanic and Atmos pheric Administra tion (NOAA) Air

Resources Laboratory (ARL) HYbrid Single-Part icle Lagrangian Integrated Trajectory

model or HYSPLIT has been used to calcula te back trajectories for IMROVE sample

days in 2005 at the Douglas Pass and Ripple Cr eek monitoring sites. A total of 576 back

trajectory points have been calculated for each sample day at each site. These points

represent 48 hours of back tr ajectory points for every two- hour period during a sample

day. This spread of points provides a comprehensive pict ure of the possible air mass

source areas for the sample during the previous two days. With about 120 sample days in

2005, this exercise yields about 70,000 back trajectory points for each monitoring site.

The 2005 back trajectory points for Ripple Cree k are shown in Figure 1. The distribution

of points shows that the air arriving at Ripple Creek t ypically passes through western and

northwestern Colorado. Utah, Wyoming, north western New Mexico and Arizona are key

source regions for this site. There was very little transport from eastern Colorado during

this period.

The back trajectory points for those samples w ith calculated extinctions within the worst

20 percentile for 2005 are shown in Figure 2. Si nce all paths to Ripple Creek must travel

through the counties in the vicin ity of the site, the highest de nsity of points is near Ripple

Creek. The trajectory points also suggest that the Salt Lake City a nd Four Corners areas

may be source regions for the samples with the worst calculated extinction levels.

It is possible to estimate the average air qua lity contributions of any area covered by back

trajectory points using a movi ng spatial average method. This approach eliminates the

central tendency bias near the monitor caused by the convergence of all trajectories at this

point. The use of the HYSPLIT model and th e EDAS40 input meteorological data set are

key steps in the process.

The meteorological data that drives the HYSPLIT model has a grid resolution of 40

kilometers. While this grid resolution can l ead to trajectory locati on errors, particularly

in areas of complex terrain, the input weather data and HYSPLIT have incorporated

adjustments that improve the mean accuracy of calculated back trajectories. The input

meteorological data is a hybrid of modeled and measured data, and it has been heavily

weighted by actual observations from a very large number of surface stations, aircraft

sensors, and other sources of real time weather information. HYSPLIT also employs

routines for interpolating the data to a resolution much finer than 40 kilometers. While

individual HYSPLIT back trajectories can suffer from errors associated with poor

matches between the HYSPLIT wind fields an d actual wind fields, it seems that on

average the back trajectories provide a good representation of air mass source regions.

When applied to ozone source regions al ong Colorado’s Front Range, HYSPLIT back

trajectories and the moving spatial average accurately located source regions within

urban area valleys known to have persistent cold pooling and emissions trapping (see

reference 1). There appeared to be a high de gree of correspondence between local terrain

features that affect emissions and the analyzed contributions of these areas.

For this analysis, the extinction or concentra tion values associated with a given sample

were assigned to each of the calculated back trajectory points for that sample. A moving

spatial average was applied to all of the point concentrations or extinction values for the

# 13 

# entire year. A computer routine placed an oval window with a height of 0.4 degrees

latitude and width of 0.4 degr ees longitude over each grid node (the grid node spacing

was 0.2 degrees latitude in the Y direction and 0.2 degrees longitude in the X direction).

If the number of points in the oval was 130 or higher, the routine calculated the mean

extinction or concentration for that node. The distribution of mean values on the map

was then contoured using the Kriging met hod. Limiting the averaging to nodes with a

high number of points reduces th e chances that a few points a ssociated with a high or low

sample concentration or extinction bias the es timates of source contributions from that

locale. The resulting map provides an estim ate of the mean contribution for any area

covered by the analysis.

The results for Ripple Creek calculated extin ction (Bext) are presented in Figure 3.

Contours were not generated for nodes or averaging windows with too few points, and

this causes the irregular di stribution of contoured areas. This map suggests that the

regions that contributed to the highest average extinction values were generally some

distance from the site. Both the Four Co rners region and an area that may include

sources in southern Idaho, s outhwestern Wyoming, and the Salt Lake City area seem to

be significant source area s for higher extinction.

The moving spatial average method was also app lied to the trajectory points for days that

account for the worst 20-percentile calculated extinction. The sp atial threshold for

averaging was lowered to 30 points in this ca se. The results shown in Figure 4 highlight

a corridor from northwest Colorado to southw estern Wyoming, the Salt Lake City area,

and southern Idaho. While the results in Figur es 3 and 4 could benefit from the inclusion

of data for other years, they suggest that distant source regi ons were significant for high

extinction days.

The 2005 back trajectory points for Douglas Pa ss are shown in Figure 5. The distribution

of points shows that the air arriving at Douglas Pass t ypically passes th rough the western

one-fifth of Colorado. Utah, Wyoming, north western New Mexico and Arizona are key

source regions for this site. There was very little transport from eastern Colorado during

this period.

The back trajectory points for those Douglas Pass samples with calculated extinctions

within the worst 20 percentile for 2005 are s hown in Figure 6. Since all paths to Douglas

Pass must travel through the counties in the vi cinity of the site, th e highest density of

points is near Douglas Pass. The trajectory po ints also suggest that central and eastern

Utah, Arizona, and the Four Corners areas may be source regions for the samples with

the worst calculated extinction levels.

The results for the spatial moving averag e source-region analysis for calculated

extinction (Bext) at Douglas Pass are presente d in Figure 7. Contour s were not generated

for nodes or averaging windows with too few points, and this expl ains the irregular

distribution of the contoured ar eas. This map suggests that th e regions that contributed to

the highest average extincti on values included the Four Corners region, southwestern

Colorado in general, eastern Utah, and Me sa County in Colorado. A similar spatial

analysis of positive matrix factorization result s described later in this section sheds some

light on emissions sources that may be responsible for these “hotspots”.

# 14 

# The moving spatial average method was also app lied to the trajectory points for days that

account for the worst 20-percentile calculated extinction. The sp atial threshold for

averaging was lowered to 30 points in this ca se. The results shown in Figure 8 suggest

that the highest source regions are in southwestern Colo rado, northwestern New Mexico,

and eastern Utah. While the results in Figures 3 and 4 are subject to further review and

could benefit from the inclusion of data for other years, they sugge st that these source

regions were significant for high extinction days.

Figure 1. NOAA ARL HYSPLIT back trajecto ry points for Ripple Creek for roughly

120 IMPROVE sample days in 2005. A total of 576 back trajectory points have been

calculated for each sample day. These 576 points represent 48 hours of back trajectory

points for every two-hour pe riod during a sample day.

# 15 

# Figure 2. NOAA ARL HYSPLIT back trajec tory points for Ripple Creek for the

IMPROVE sample days with the worst 20 pe rcentile calculated extinction in 2005. A

total of 576 back trajectory points have been calculated for each worst-case day. These

points represent 48 hours of back trajector y points for every two-hour period during a

sample day.

# 16 

# Figure 3. A map of the mean calculated extinction at Ripple Creek in 2005 when the air

mass is from a specific region (based on a sp atial moving average an alysis of extinction

values associated with HYSPLIT back trajector y points). The highest source region is in

northern Utah and southern Idaho. When an air mass is from this region, for example,

the mean IMPROVE calculated extincti on is above 20 inverse megameters.

# 17 

# Figure 4. A map of the mean calculated extinction at Ripple Creek in 2005 for the worst

20 percentile sample days when the air mass is from a specific region (based on a spatial

moving average analysis of ex tinction values associated w ith HYSPLIT back trajectory

points). The highest source regi on is in northern Utah and sout hern Idaho. When an air

mass is from this region, for example, the mean IMPROVE calculated extinction during

the worst 20 percentile events is above 40 inverse megameters.

# 18 

# Figure 5. NOAA ARL HYSPLIT back trajecto ry points for Douglas Pass for roughly

120 IMPROVE sample days in 2005. A total of 576 back trajectory points have been

calculated for each sample day. These 576 points represent 48 hours of back trajectory

points for every two-hour pe riod during a sample day.

# 19 

# Figure 6. NOAA ARL HYSPLIT back trajectory points for Douglas Pass for the

IMPROVE sample days with the worst 20 percentile calculated extinction in 2005. A

total of 576 back trajectory points have been calculated for each worst-case day. These

points represent 48 hours of back trajectory points for every two-hour period during a

sample day.

# 20 

# Figure 7. A map of the mean calculated extin ction at Douglas Pass in 2005 when the air

mass is from a specific region (based on a sp atial moving average an alysis of extinction

values associated with HYSPLIT back traject ory points). The highest source areas are in

eastern Utah, southwestern Co lorado, and northwestern New Mexico. When an air mass

is from these regions, for example, the mean IMPROVE calculated extinction is above 22

inverse megameters.

# 21 

# Figure 8. A map of the mean calculated extin ction at Douglas Pass in 2005 for the worst

20 percentile sample days when the air mass is from a specific region (based on a spatial

moving average analysis of ex tinction values associated w ith HYSPLIT back trajectory

points). The highest source region is in southwestern Colorado, northwestern New

Mexico, and eastern Utah. When an air ma ss is from these regions, for example, the

mean IMPROVE calculated extinction during th e worst 20 percentile events is above 60

inverse megameters.

Positive Matrix Factorization. Positive Matrix Factorizatio n (PMF) has been applied to

IMPROVE sample data for Douglas Pass and Ripple Creek for 2004 and 2005. PMF is

an analytical procedure that can be used to identify air pollution source categories and to

estimate the contributions of these source cate gories for individual air quality samples in

a time series. In this sense it is similar to Chemical Mass Balance analysis or CMB, but

emissions or source profiles are not needed in PMF. PMF, which is similar to factor

analysis, identifies source prof iles through statistical analyses. Unlike factor analysis or

principal component analysis, PMF yields non-n egative estimates of the contributions of

each source category to the total measurement for each sample. In the analysis reported

here, PMF has been applied to IMPROVE aerosol fine mass concentrations. A

description of the method and discussion of the output diagno stics attached to the end of

# 22 

# this TSD.

Four source categories were identified for D ouglas Pass. These cat egories are a sulfate

group or source factor, a geologic material source factor, a wood combustion source

factor, and a nitrate group or source factor. The final diagnostic for the analysis is shown

in Figure 9. This is a linear regression of the sum of the concentrations from all four

source categories against the measured or gravimetric fine-mass concentrations from the

IMPROVE sample. The slope is near one at 0.97. This shows that on average the PMF

results reproduce the total measured concentrations from the samples. The intercept is

near zero, and the R-squared is high at 0.92.

The PMF results for Ripple Creek were not us ed. Although most of the diagnostics were

good, the regression of total PMF concentratio ns against measured concentrations was

weak. The slope was nearly one at 0.996, but the intercept was relatively high at 1.2

ug/m3, and the R-squared value was quite low at 0.34. There was too much scatter in the

plot, suggesting that the mode led contributions would often provide a poor representation

of reality.

Figure 10 presents the mean 2004-2005 concentr ations (source profiles) for each category

in ug/m3. Time series of source factor contributions for each sample day are presented in

Figures 11 and 12 for 2004 and 2005, respectively.

The sulfate group probably repr esents power generation sources and probably also

includes mobile sources and oil and ga s combustion. The nitrate group probably

represents power generation sources, mobile sources, and fuel combustion in general.

There does not seem to be enough of a signal in the Douglas Pass data to separate these

two groups into smaller categories. Model performance declined significantly when

more than four categories were included. Both groups represent secondary aerosols.

The sulfate group shows weak seasonality with lower values in the winter and a few

major episodes. The geologic group is lowest in the winter and highe st in the spring, and

this is consistent with the annual patterns for blowing dust. The wood combustion group

is lower in the winter and highe r in summer and late fall. This is consistent with the

seasonal patterns for wildfire and controlled burning smoke. The nitrate group is highest

in the late winter and spring, and ther e are a few major episodes in late 2005.

# 23 

# Sum of PMF Concentrations vs Gravimetric Mass

Concentrations y = 0.9702x + 0.0178

R 2 

# = 0.9227

-5 0 5 10 15 20 25 30

-5 0 5 10 15 20 25



# IMPROVE gravimetric mass concentrations ug/m3Sum of PMF concentrations ug/m3



# Figure 9. The sum of PMF concentrations ve rsus gravimetric mass concentrations for

Douglas Pass (2004-2005). The linear re gression shows a nearly one-to-one

correspondence and an R-squared of 0.92.

0.631

0.568 1.105 0.084 

# sulfate

geologic

woodburning

nitrate

Figure 10. Mean 2004-2005 Douglas Pass conc entrations (source profiles) for each

category in ug/m3.

# 24 0 3 5 8 1020042005



# YearConcentration in ug/m3 

# Variables

sulfate

geologic

woodburning

nitrate



# Figure 11. Time series of PM F source factor contributions at Douglas Pass in ug/m3 for

each sample day for 2004.

# 25 0 5 10 15 2020052006



# YearConcentration in ug/m3 

# Variables

sulfate

geologic

woodburning

nitrate



# Figure 12. Time series of PM F source factor contributions at Douglas Pass in ug/m3 for

each sample day for 2005.

The moving spatial average method was also applied to the Douglas Pass trajectory

points associated with the sample-day contri butions from each of the four PMF source

groups for Douglas Pass for the year 2005. Th e results shown in Figure 13 highlight a

sulfate group source corridor from no rthwest New Mexico northward through

southwestern Colorado and another source area in eastern Utah. Contours for the

geologic source factor are presented in Figur e 14. These show significant source areas in

the deserts of southern Utah and northern Arizona. These are known source areas for

blowing dust NAAQS particulate matter exceed ance events for fine particulates at

Western Colorado monitoring sites. On averag e, transport from these areas results in a

source group concentration of 1.2 ug/m3 or hi gher at Douglas Pass. Results for the wood

combustion source factor are shown in Figur e 15. The pattern for wood combustion is

similar to the patterns for bot h the sulfate and nitr ate source groups. As discussed in the

Methods and Model Diagnostics sect ion at the end of this TSD, this may in part be due to

inadequate separation between the sulfate and wood combustion categories in the PMF

analysis. The wood combustion category does sh ow a small hotspot in Mesa County (the

large corbel-shaped county south of Douglas Pass), which would be consistent with

residential wood combustion and agricultural burning. Wildfires and controlled burns in

eastern Utah and western Colorado may have influenced the overall pattern. An

investigation of specific wildfire events and their possible effects on these results has not

been completed. Finally, the results for the nitrate group are shown in Figure 16.

Hotspot source areas are found in eastern Ut ah, southwestern Colorado, and over Mesa

County.

# 26 

# Figure 13. A map of the mean PMF sulfate gr oup concentrations at Douglas Pass in 2005

when the air mass is from a specific region (based on a spatial moving average analysis

of sulfate factor concentrations associated with HYSPLIT back trajectory points). The

highest source region is in southwestern Colorado, northwestern New Mexico, and

eastern Utah. When an air mass is from northwestern New Mexico or southwestern

Colorado, the average sulfate group concentration at Douglas Pass is above 1 ug/m3.

# 27 

# Figure 14. A map of the mean PMF geologic source category concentr ations at Douglas

Pass in 2005 when the air mass is from a specific region (based on a spatial moving

average analysis of geologic factor con centrations associated with HYSPLIT back

trajectory points). The highest source region is in southern Utah and northern Arizona.

When an air mass is from these areas, the av erage geologic source ca tegory concentration

at Douglas Pass is above 1 ug/m3.

# 28 

# Figure 15. A map of the mean PMF wood co mbustion category concentrations at

Douglas Pass in 2005 when the air mass is from a specific region (based on a spatial

moving average analysis of wood combustion factor concentrations associated with

HYSPLIT back trajectory points). The highe st source regions are in northwestern New

Mexico, southwestern Colorado, and eastern Uta h. When an air mass is from these areas,

the average wood combustion category con centration at Douglas Pass is above 2.0

ug/m3.

# 29 

# Figure 16. A map of the mean PMF nitrate group category concentrations at Douglas

Pass in 2005 when the air mass is from a specific region (based on a spatial moving

average analysis of nitrate factor concentrations associated with HYSPLIT back

trajectory points). The highest source regions are in Mesa County (Colorado),

southwestern Colorado, and eastern Utah. Wh en an air mass is from these areas, the

average wood combustion category concentr ation at Douglas Pass is above 0.2 ug/m3.

Point sources for SO2 and NOx are mapped in Figures 17 and 18, respectively. The

diameters of the source locator symbols ar e roughly proportional to the sources’ annual

emissions.

Sulfur dioxide sources and source area contou rs for the Douglas Pa ss sulfate factor are

shown in Figures 19 and 20. These clearly s how that there is a correspondence between

large SO2 sources and the analyzed source regi ons for the sulfate factor (based on back

trajectories and monitoring at Douglas Pa ss). The source area for the highest

concentrations is anchored to the Four Corn ers and San Juan power generation facilities

in northwest New Mexico. It also lines up with oil and gas so urces in the area and

numerous smaller sources in southwestern Co lorado. A source area in Utah is anchored

to the Huntington power gene ration facility in east-centr al Utah. These three power

# 30 

# generation facilities appear to have had a major impact on worst-case concentrations in

2005. It is worth noting that su lfate is a secondary aerosol, and it often forms in areas

downwind of sources. Source areas may some times be offset from point sources for

SO2.

Nitrogen dioxide and nitrogen oxide sources and source area contours for the Douglas

Pass nitrate factor are shown in Figures 21 and 22. One of the source areas for the

highest concentrations is anchored to the Four Corners and San Juan power generation

facilities in northwest New Mexico. There are also key source areas in Mesa County

(Colorado) and east-central Uta h. Nitrate is also a seconda ry aerosol, and it often forms

in areas downwind of sources. Source areas ma y sometimes be offset from point sources

for NOx and located in areas favorable for nitrate formation. Pooling of cold humid air

in valleys can lead to accel erated nitrate formation as can emission of NOx into low

clouds. There is not yet a clear explanati on for the source areas showing up in eastCentral Utah.

Yampa Valley sources do not seem to play a ma jor role at either Ripple Creek or Douglas

Pass. In some ways this is not surprising. Previous studies have demonstrated the role of

Yampa Valley cold pooling and subsequent east ward transport of secondaries towards the

Mt. Zirkel Wilderness Area. The major ca nyons and valleys in western Colorado can

have a profound effect on local transport and can channel emissions away from some

locations and towards others.

Reference:

1) Reddy, P. J., D. Wells, K. Briggs , C. Machovec, 2006. “Meteorological &

climatological analysis of ozone back traj ectories and their implications for the

role of oil and gas field VOC emissi ons in Colorado and Front Range ozone

formation”, Modeling, Meteorology, and Emissions Inventory Unit, Technical

Services Program, Air Pollution Control Di vision, Colorado Department of Public

Health and Environment.

# 31 

# Figure 17. Sulfur dioxide point sources. The diameters of the source locator symbols are

roughly proportional to the s ources’ annual emissions.

# 32 

# Figure 18. NOx point sources. The diameters of the source locator symbols are roughly

proportional to the sources’ annual emissions.

# 33 

# Figure 19. SO2 point sources, scaled by the magnitude of annual emissions, and contours

of the Douglas Pass sulfate factor source ar eas from Figure 13 (based on a spatial moving

average analysis of sulfate factor concentrations associated with HYSPLIT back

trajectory points).

# 34 

# Figure 20. SO2 point sources, scaled by the magnitude of annual emissions, and contours

of the Douglas Pass sulfate factor source ar eas from Figure 13 (based on a spatial moving

average analysis of sulfate factor concentrations associated with HYSPLIT back

trajectory points).

# 35 

# Figure 21. NOx point sources, scaled by the magnitude of annual emissions, and

contours of the Douglas Pass nitrate factor source area s from Figure 13 (based on a

spatial moving average analysis of nitrate factor concentrations associated with

HYSPLIT back trajectory points).

# 36 

# Figure 22. NOx point sources, scaled by the magnitude of annual emissions, and

contours of the Douglas Pass nitrate factor source area s from Figure 13 (based on a

spatial moving average analysis of nitrate factor concentrations associated with

HYSPLIT back trajectory points).

# 37 

# Positive Matrix Factorization for Douglas

Pass and Ripple Creek IMPROVE Data

for

2004-2005

Methods and Model Diagnostics

# 38 

# Positive Matrix Factorization (PMF) is an an alytical procedure that can be used to

identify air pollution source ca tegories and to estimate the c ontributions of these source

categories for individual air quality samples in a time series. In this sense it is similar to

Chemical Mass Balance analysis or CMB, but emissions or source profiles are not needed

in PMF. PMF, which is similar to factor analysis, identifies source profiles through

statistical analyses. Unlike factor analysis or principal component analysis, PMF yields

non-negative estimates of the contributions of each source ca tegory to the total

measurement for each sample. In the analysis reported here, PMF has been applied to

IMPROVE aerosol fine mass concentrations.

The Environmental Protection Agency’s (EPA ’s) PMF 1.1 software was used to estimate

the contributions of four categories of sources for 2004 and 2005 for each of 220 sampledays at the Douglas Pass and Ripple Creek IMPROVE monitors. The EPA PMF 1.1

software requires non-missing, non-zero, and non-negative data for each sample and

every aerosol species used in the analysis. EPA PMF 1.1 also requires uncertainty values

for each species. Where possible, missing valu es and zero-value data were replaced with

a number equivalent to half of the minimu m detection level (MDL). Although there are

other methods for estimating sample uncertainties for these data replacements (that may

also yield better results), the reported IMPR OVE sample uncertainties were used for all

replaced data.

Hundreds of PMF 1.1 runs were completed for each of these two sites. This iterative

process makes it possible to select a nearly optimal subset of analysis species and an

optimal number of source categories for each site/data set. While this process yielded

good results for the Douglas Pass data, the dia gnostics for Ripple Cr eek showed that the

results for that site were not useable. A brief summary of the results for Douglas Pass

follows. A much briefer synopsis for Ripple Cree k is included at the end of this section.

Twenty-two species were used in the final run for Douglas Pass. These species and the

EPA PMF 1.1 signal to noise values for each are presented in Table 9. In order of

appearance in Table 9, these fine mass species are aluminum, bromine, calcium,

chromium, copper, elemental carbon, iron, to tal gravimetric fine mass, magnesium,

nickel, nitrate, nonsoil potassium, orga nic carbon, lead, phosphorous, rubidium,

selenium, silicon, sulfate, strontium, titanium , and zinc. Elemental carbon and organic

carbon concentrations were calculated usi ng the component carbon measurements and the

IMPROVE formulas for these categories . See the IMPROVE Data Guide at:

http://vista.cira.colostate.edu/improve/Publicati ons/OtherDocs/IMPROVEDataGuide/IMPROVEDataGuide

.htm



# Nonsoil potassium was also calculated us ing the formula presen ted in the IMPROVE

Data Guide. Uncertainties for elementa l carbon, organic carbon, and non-soil potassium

were derived by taking the square root of the sum of the squa res of the uncertainties for

each component species used in the formulas for the concentrations.

# 39 

# Table 9. Species selected and PMF signal to noise values for the final PMF run for

Douglas Pass.

Species Signal To Noise

ALf 4.19

BRf 6.23

CAf 4.36

CRf 0.92

CUf 1.06

EC 1.1

FEf 8.69

MF 4.94

MGf 1.87

NIf 0.45

NO3f 7.76

NonsoilK 2.15

OC 1.64

PBf 1.18

Pf 0.53

RBf 0.63

SEf 0.74

SIf 4.33

SO4f 3.14

SRf 1.77

TIf 5.13

ZNf 3.19



# EPA PMF 1.1 generates tables and plots for evaluating each run. Key diagnostics for the

success or goodness of fit of the analysis are shown in Table 10. The values of Q

(Robust and True) should be close to each ot her and not radically different from the

product of the number of species times the number of samples. In this case, this product

is 4840 (22 species times 220 sample days). The Q Robust and Q True numbers are close

enough to 4840 to suggest a successful run. Q Robust and Q true are also very close to

each other. The model run must also show convergence and Table 10 shows that it does.

Table 10 Key diagnostics for selected PMF run for Douglas Pass.

RANDOM

RUN Q (ROBUST) Q (TRUE) CONVERGED (Y/N) STEPS

1 3501.2 3603.21 Yes 709



# Additional diagnostics for the selected PMF run for Douglas Pass are presented in Table

11. Species with consistently low R-square d values in initial runs were assigned a

“weak” species status in EPA PMF 1.1. EPA PMF 1.1 automatically triples the

uncertainties for these species. Ideally, intercepts should be close to zero, slopes should

be near 1, root mean square errors should be small compared to the species mean, and Rsquared values should be near 1. Species wi th than less than ideal diagnostics can be

included, but this may degrade the final reso lution of the results. An additional data

# 40 

# uncertainty factor of 5% was selected for each use of PMF 1.1.

Table 11. Additional diagnostics for selected PMF run for Douglas Pass.

DIAGNOSTICS FOR POSITIVE MATRIX FACTORIZATION

FOR DOUGLAS PASS IMPROVE DATA 2004-2005 (BEST RUN)

SPECIES 

# INTERCEPT SLOPE ROOT MEAN SQUARE

ERROR R-SQUARED

Strong Species Used in Analysis:



# NO3f 0.01 0.94 0.02 1.00

FEf 0 1.00 0 0.99

SIf -0.01 1.08 0.02 0.98

SO4f 0.05 0.91 0.06 0.98

CAf 0 0.9 0.01 0.96

TIf 0 0.98 0 0.96

BRf 0 0.85 0 0.95

MF 0.02 0.97 0.62 0.92

OC 0.04 0.83 0.21 0.88

ALf 0 0.85 0.02 0.85

MGf 0 0.71 0 0.83

EC 0.02 0.69 0.06 0.70

Pf 0 0.67 0 0.69

SRf 0 0.54 0 0.67

PBf 0 0.49 0 0.62

Weak Species Used in Analysis:



# CUf 0 0.38 0 0.55

RBf 0 0.45 0 0.52

NIf 0 0.52 0 0.46

Nonsoil Potassium 0 0.22 0.01 0.35

ZNf 0 0.32 0 0.27

CRf 0 0.16 0 0.24

SEf 0 0.42 0 0.13



# Four source factors or categories were iden tified for Douglas Pass (2004 and 2005 data).

The four source categories are a sulfat e group, geologic group, wood combustion group,

and nitrate group. Figures 23 through 26 show the mass of the species and percent of

mass per species for each factor and the time se ries of contribution scalars. The sample

scalar times the factor’s data set mean is the sample concentration. The sulfate source

factor is high in sulfate and relatively hi gh in selenium, phosphorous, lead, and elemental

carbon. The geologic source factor is very hi gh in soil and geologic material metals and

cations including aluminum, calcium, iron, magnesium, lead, silicon, strontium, and

titanium. The wood combustion source factor is high in bromine, organic carbon,

elemental carbon, and nonsoil potassium, wh ich is an indicator for wood combustion.

The wood combustion source factor is also somewhat high in selenium, and this suggests

that there may be less than ideal separation between the PMF source categories for sulfate

and wood combustion. The nitrate source factor is very high in nitrate and relatively high

in elemental carbon, lead, phosphorous and zinc.

The sulfate group probably repr esents power generation sources and probably also

# 41 

# includes mobile sources and oil and ga s combustion. The nitrate group probably

represents power generation sources, mobile sources, and fuel combustion in general.

There does not seem to be enough of a signal in the Douglas Pass data to separate these

two groups into smaller categories. Model performance declined significantly when

more than four categories were included. Both groups represent secondary aerosols.

The sulfate group shows weak seasonality with lower values in the winter and a few

major episodes. The geologic group is lowest in the winter and highe st in the spring, and

this is consistent with the annual patterns for blowing dust. The wood combustion group

is lower in the winter and highe r in summer and late fall. This is consistent with the

seasonal patterns for wildfire and controlled burning smoke. The nitrate group is highest

in the late winter and spring, and ther e are a few major episodes in late 2005.

Figure 27 through 30 present another set of diagnostic plots for the EPA PMF 1.1

analysis. Each figure contains box and whisker plots of the va riability in the percentage

of species apportioned to a source factor a nd the variability in the mass of species

apportioned for that factor. According to the EPA PMF 1.1 Users Guide (see references

below), when “plus signs are clustered a nd are close to the box, then there is good

reproducibility. The black star th at is overlaid is the value fr om the base case run. Ideally,

the black star should lie within the box. If this is not the case, then the base case likely

has some observations that are atypical and these observations are influencing the

factorization.”

The PMF process is iterative in two ways. Any given model run involves many, internal

iterations, starting from a random “seed”. Thro ugh these iterations th e model attempts to

converge on a reproducible solu tion. In addition the model itself must be implemented

many times before the user is likely to fi nd the most appropriat e set of species and

factors. Good reproducibility refers to the fact that the internal it erations will probably

yield the same result given the same inputs with each re-use of the model. This

reproducibility increases the li kelihood of a good fit with re ality. Figures 27 through 30

generally show good reproducib ility and minimal influences from extreme events,

especially for the key species for each source group. The geologic source group,

however, seems to show the effects of atypical events. This makes sense since blowing

dust events, which may occur a few times a year, probably have a strong impact on the

results for this category.

The final diagnostic for the analysis is shown in Figure 31. This is a linear regression of

the sum of the concentrations from all four source categories against the measured or

gravimetric fine-mass concentrations from th e IMPROVE sample. The slope is near one

at 0.97. This shows that on average the PMF results reproduce the total measured

concentrations from the samples. The intercep t is near zero, and the R-squared is high at

0.92.

The PMF results for Ripple Creek were not us ed. Although most of the diagnostics were

good, the regression of total PMF concentratio ns against measured concentrations was

weak. The slope was nearly one at 0.996, but the intercept was relatively high at 1.2

ug/m3, and the R-squared value was quite low at 0.34. There was too much scatter in the

plot, suggesting that the mode led contributions would often provide a poor representation

of reality.

# 42 

# Select References on Positive Matrix factorization (PMF):

Eberly, Shelly, 2005. “EPA PMF 1.1 User’s Guide,” U.S. Environmental Protection

Agency, National Exposure Research La boratory, Research Triangle Park, NC

27711, 32 pp.

Kim, E., Hopke, P.K., Edgerton, E.S., 2004. Im proving source identification of Atlanta

aerosol using temperature resolved carbon fractions in positive matrix

factorization. Atmospheric Environment 38, 3349-3362.

Kim, E., Hopke, P.K., Edgerton, E.S., 2003. S ource Identification of Atlanta Aerosol by

Positive Matrix Factorization. Journal of Air & Waste Management Association

53, 731-739.

Kim, E., Hopke P.K., 2004. Source Apportionment of Fine Particles in Washington, DC,

Utilizing Temperature-Resolved Carbon Fractions. Journal of Air & Waste

Management Association 54, 773-785.

Kim, E., Hopke, P.K., Larson, T.V., Maykut, N ., Lewtas, J., 2004. Factor Analysis of

Seattle Fine Particles. Aeroso l Science and Technology 38, 724-738.

Kim, E., Hopke, P.K., Paatero, P., Edgert on, E.S., 2003. Incorporation of parametric

factors into multilinear r eceptor model studies of Atlanta aerosol. Atmospheric

Environment 37, 5009-5021.

Lee, Eddie; Chan, Chak K.; Paatero, Pe ntti, 1999. Application of positive matrix

factorization in source apportionment of particulate pollutants in Hong Kong

, Atmospheric Environment. Vol. 33, no. 19, pp. 3201-3212. 1999.

Paatero P., 1997, Least squares formulati on of robust non-negative factor analysis,

Chemometrics and Intelligent Laboratory Systems, Volume 37, Number 1, May

1997, pp. 23-35(13).

# 43 

# Figure 23. Mass of species and percent of mass per sp ecies for Factor 1 (Sulfate group) (top graph) and

time series of contribution scalars (the sample scalar times the factor’s data set mean is the sample

concentration) (bottom graph).

# 44 

# Figure 24. Mass of species and percent of mass per sp ecies for Factor 2 (Geol ogic group) (top graph) and

time series of contribution scalars (the sample scalar times the factor’s data set mean is the sample

concentration) (bottom graph).

# 45 

# Figure 25. Mass of species and percent of mass pe r species for Factor 3 (W ood Combustion group) (top

graph) and time series of contributio n scalars (the sample scalar times the factor’s data set mean is the

sample concentration) (bottom graph).

# 46 

# Figure 26. Mass of species and percent of mass per sp ecies for Factor 4 (Nitrat e group) (top graph) and

time series of contribution scalars (the sample scalar times the factor’s data set mean is the sample

concentration) (bottom graph).

# 47 

# Figure 27. Box plots of variability in percentage of species apportioned to Factor 1, Sulfate group, (top) and

variability in mass of species apportione d for Factor 1, Sulfate group, (bottom).

# 48 

# Figure 28. Box plots of variability in percentage of species apportione d to Factor 2, Geologic group, (top)

and variability in mass of species apportio ned for Factor 2, Geologic group, (bottom).

# 49 

# Figure 29. Box plots of variability in percentage of species appor tioned to Factor 3, Wood Combustion

group, (top) and variability in mass of species apportioned for Factor 3, Wood combustion group, (bottom).

# 50 

# Figure 30. Box plots of variability in percentage of species apportioned to Factor 4, Nitrate group, (top) and

variability in mass of species apportione d for Factor 4, Nitrate group, (bottom).

# 51 

# Sum of PMF Concentrations vs Gravimetric Mass

Concentrations y = 0.9702x + 0.0178

R 2 

# = 0.9227

-5 0 5 10 15 20 25 30

-5 0 5 10 15 20 25

IMPROVE gravimetric mass concentrations ug/m3Sum of PMF concentrationsug/m3



# Figure 31. The sum of PMF concentr ations versus gravimetric mass c oncentrations for Douglas Pass (2004-

2005). The linear regression shows a nearly one- to-one correspondence and an R-squared of 0.92.