COLORADO STATE 

IMPLEMENTATION PLAN FOR 

REGIONAL HAZE 

TECHNICAL SUPPORT DOCUMENT 

Douglas Pass and Ripple Creek Pass Sites Analyses

Prepared by 

The Colorado Department of Public 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 meteorological parameters in northwest Colorado. The project 

is important to Colorado for defining existing conditions and estimating potential effects 

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

had two parts: 1) Shell Exploration and Production Company consulted and collaborated 

with the State, federal land 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 collaboration with the Colorado Department 

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

awarded a contract through a competitive 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 and 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 measurements. 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 the 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 upwind 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 quality as federally operated 

IMPROVE sites. The same protocols and equipment were utilized. 

Douglas Pass is within 25 miles 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 general 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 states 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 out-of-state haze on Colorado Class I 

areas. 

Correlations. Paired Pearson product-moment correlations 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 operated during this period, missing data, 

operational problems, etc. 

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

for Ripple Creek Pass. A squared Pearson correlation between Ripple Creek Pass and 

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

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

indicates a high correlation of .969 between the two sites. This is an r2

 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 

 ** Correlation 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 from .961 (Ripple Creek) to -.236 (Shamrock Mines). 

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

correlations. Canyonlands NP in Utah was included 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 other sites for each of 

the important visibility impairing species. 
4

Table 2 

Aerosol Extinction Correlations/All Valid Data 2003-2005

Douglas 

Pass 

Ripple 

Creek 

Pass 

Canyonlands 

NP 

Great Sand 

Dunes NP 

Mesa 

Verde NP 

Mt. Zirkel 

WA 

Rocky 

Mountain NP 

Shamrock 

Mines 

Wem

W

Douglas Pass Pearson Correlation 1 .961(**) .382(**) .051 .813(**) .799(**) .211(*) -.236 .8

Sig. (2-tailed) .000 .000 .591 .000 .000 .023 .143 N 220 191 108 114 116 104 116 40 

Ripple Creek Pass Pearson Correlation .961(**) 1 .565(**) .516(**) .751(**) .912(**) .389(**) .409(**) .8

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .003 N 191 286 158 188 164 167 174 51 

Canyonlands NP Pearson Correlation .382(**) .565(**) 1 .408(**) .479(**) .430(**) .348(**) .220 .3

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .114 N 108 158 225 217 209 199 217 53 

Great Sand Dunes NP Pearson Correlation .051 .516(**) .408(**) 1 .623(**) .539(**) .545(**) .659(**) .5

Sig. (2-tailed) .591 .000 .000 .000 .000 .000 .000 N 114 188 217 251 221 221 231 61 

Mesa Verde NP Pearson Correlation .813(**) .751(**) .479(**) .623(**) 1 .714(**) .422(**) .697(**) .7

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 N 116 164 209 221 230 205 223 53 

Mt. Zirkel WA Pearson Correlation .799(**) .912(**) .430(**) .539(**) .714(**) 1 .594(**) .559(**) .7

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 N 104 167 199 221 205 231 215 54 

Rocky Mountain NP Pearson Correlation .211(*) .389(**) .348(**) .545(**) .422(**) .594(**) 1 .440(**) .3

Sig. (2-tailed) .023 .000 .000 .000 .000 .000 .001 N 116 174 217 231 223 215 240 55 

Shamrock Mines Pearson Correlation -.236 .409(**) .220 .659(**) .697(**) .559(**) .440(**) 1 

Sig. (2-tailed) .143 .003 .114 .000 .000 .000 .001 N 40 51 53 61 53 54 55 67 

Weminuche WA Pearson Correlation .859(**) .847(**) .365(**) .547(**) .740(**) .731(**) .356(**) .131 

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 .356 N 112 171 214 226 218 207 228 52 

White River Pearson Correlation .543(**) .765(**) .465(**) .605(**) .640(**) .790(**) .740(**) .530(**) .7

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 .000 N 111 186 220 245 223 224 234 65 

* Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed).
5

Table 3 

Sulfur Correlations/All Valid Data 2003-2005

 *Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed). .

Douglas 

Pass 

Ripple 

Creek 

Pass 

Canyonlands 

NP 

Great Sand 

Dunes NP 

Mesa 

Verde NP 

Mt. Zirkel 

WA 

Rocky 

Mountain NP 

Shamroc

Mines 

Douglas Pass Pearson Correlation 1 .804(**) .757(**) .534(**) .580(**) .748(**) .594(**) .596(

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .0N 263 237 230 224 223 212 227 1

Ripple Creek Pass Pearson Correlation .804(**) 1 .689(**) .618(**) .650(**) .824(**) .695(**) .629(

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .0N 237 383 288 288 275 271 288 1

Canyonlands NP Pearson Correlation .757(**) .689(**) 1 .630(**) .747(**) .658(**) .541(**) .708(

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .0N 230 288 334 323 312 306 322 1

Great Sand Dunes NP Pearson Correlation .534(**) .618(**) .630(**) 1 .769(**) .635(**) .624(**) .831(

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .0N 224 288 323 333 309 306 321 1

Mesa Verde NP Pearson Correlation .580(**) .650(**) .747(**) .769(**) 1 .602(**) .491(**) .907(

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .0N 223 275 312 309 321 297 309 1

Mt. Zirkel WA Pearson Correlation .748(**) .824(**) .658(**) .635(**) .602(**) 1 .758(**) .628(

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .0N 212 271 306 306 297 317 305 1

Rocky Mountain NP Pearson Correlation .594(**) .695(**) .541(**) .624(**) .491(**) .758(**) 1 .494(

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .0N 227 288 322 321 309 305 333 1

Shamrock Mines Pearson Correlation .596(**) .629(**) .708(**) .831(**) .907(**) .628(**) .494(**) 

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 N 143 140 145 135 136 129 139 1

Weminuche WA Pearson Correlation .635(**) .683(**) .733(**) .812(**) .884(**) .667(**) .548(**) .949(

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 .0N 229 290 325 324 313 310 324 1

White River Pearson Correlation .690(**) .802(**) .713(**) .744(**) .761(**) .814(**) .711(**) .791(

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 .0N 229 293 329 328 315 311 327 1
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 correlations 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 relationship between Douglas Pass and 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 site (r=0.78), not with 

Ripple Creek (0.58). Other correlations range from negative (Shamrock Mines), to negligible (Rocky 

Mountain), to moderate (remaining sites). 

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

0.90, for all sites except Shamrock 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. Examining 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 very 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 extinction at Ripple Creek Pass is highly associated (r2

=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 correlated to sites at greater distance. 

• Douglas Pass sulfur is consistently well correlated to all sites with coefficients ranging from 

0.53 to 0.80. This might indicate that sulfur variability in Colorado is quite 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 other sites. Compared to the 

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

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

• Fine mass at Douglas Pass has a strong relationship 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 correlated well with Ripple Creek but shows little to no 

relation to other Colorado sites. 
7

Table 4 

Nitrate Correlations/All Valid Data 2003-2005

Douglas 

Pass 

Ripple 

Creek Pass 

Canyonlands 

NP 

Great 

Sand 

Dunes NP 

Mesa 

Verde NP 

Mt. Zirkel 

WA 

Rocky 

Mountain NP 

Shamrock 

Mines W

Douglas Pass Pearson Correlation 1 .873(**) -.020 -.323(**) .073 .094 .052 -.059 

Sig. (2-tailed) .000 .764 .000 .281 .175 .438 .485 N 239 221 228 228 223 210 225 142 

Ripple Creek Pass 

Pearson Correlation .873(**) 1 .261(**) .597(**) .550(**) .806(**) .184(**) .132 Sig. (2-tailed) .000 .000 .000 .000 .000 .002 .112 N 221 299 275 289 271 262 273 146 

Canyonlands NP 

Pearson Correlation -.020 .261(**) 1 .434(**) .360(**) .198(**) .041 .288(**) Sig. (2-tailed) .764 .000 .000 .000 .001 .462 .000 N 228 275 335 329 319 299 321 149 

Great Sand Dunes NP 

Pearson Correlation -.323(**) .597(**) .434(**) 1 .416(**) .486(**) .202(**) .517(**) Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 N 228 289 329 355 326 314 331 153 

Mesa Verde NP Pearson Correlation .073 .550(**) .360(**) .416(**) 1 .340(**) .102 .752(**) 

Sig. (2-tailed) .281 .000 .000 .000 .000 .068 .000 N 223 271 319 326 332 297 319 141 

Mt. Zirkel WA Pearson Correlation .094 .806(**) .198(**) .486(**) .340(**) 1 .220(**) .168(*) 

Sig. (2-tailed) .175 .000 .001 .000 .000 .000 .049 N 210 262 299 314 297 321 302 138 

Rocky Mountain NP 

Pearson Correlation .052 .184(**) .041 .202(**) .102 .220(**) 1 .143 Sig. (2-tailed) .438 .002 .462 .000 .068 .000 .088 N 225 273 321 331 319 302 336 143 

Shamrock Mines 

Pearson Correlation -.059 .132 .288(**) .517(**) .752(**) .168(*) .143 1 Sig. (2-tailed) .485 .112 .000 .000 .000 .049 .088 N 142 146 149 153 141 138 143 160 

Weminuche WA 

Pearson Correlation .133(*) .696(**) .283(**) .452(**) .610(**) .488(**) .100 .258(**) Sig. (2-tailed) .044 .000 .000 .000 .000 .000 .070 .002 N 229 280 329 339 326 307 331 146 

White River Pearson Correlation .109 .831(**) .226(**) .569(**) .511(**) .731(**) .183(**) .309(**) 

Sig. (2-tailed) .101 .000 .000 .000 .000 .000 .001 .000 N 226 288 327 346 324 313 329 156 

** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). 
8

Table 5 

Elemental Carbon Correlations/All Valid Data 2003-2005

Douglas 

Pass 

Ripple 

Creek Pass 

Canyonlands 

NP 

Great Sand 

Dunes NP 

Mesa 

Verde NP 

Mt. Zirkel 

WA 

Rocky 

Mountain NP 

Shamroc

Mines 

Douglas Pass Pearson Correlation 1 .582(**) .408(**) .313(**) .344(**) .399(**) .115 -.09

Sig. (2-tailed) .000 .000 .001 .000 .000 .205 .55N 225 202 122 120 121 111 122 4

Ripple Creek Pass Pearson Correlation .582(**) 1 .662(**) .589(**) .577(**) .782(**) .513(**) .09

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .52N 202 299 181 201 178 182 184 5

Canyonlands NP Pearson Correlation .408(**) .662(**) 1 .514(**) .556(**) .771(**) .260(**) .07

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .57N 122 181 241 238 233 221 236 5

Great Sand Dunes NP Pearson Correlation .313(**) .589(**) .514(**) 1 .495(**) .622(**) .302(**) .11

Sig. (2-tailed) .001 .000 .000 .000 .000 .000 .35N 120 201 238 258 235 238 241 6

Mesa Verde NP Pearson Correlation .344(**) .577(**) .556(**) .495(**) 1 .610(**) .297(**) .05

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .67N 121 178 233 235 238 222 233 5

Mt. Zirkel WA Pearson Correlation .399(**) .782(**) .771(**) .622(**) .610(**) 1 .429(**) .21

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .10N 111 182 221 238 222 241 224 5

Rocky Mountain NP Pearson Correlation .115 .513(**) .260(**) .302(**) .297(**) .429(**) 1 .12

Sig. (2-tailed) .205 .000 .000 .000 .000 .000 .35N 122 184 236 241 233 224 243 5

Shamrock Mines Pearson Correlation -.092 .090 .077 .118 .059 .217 .128 

Sig. (2-tailed) .551 .526 .577 .352 .676 .105 .351 N 44 52 55 64 53 57 55 6

Weminuche WA Pearson Correlation .539(**) .499(**) .502(**) .499(**) .536(**) .481(**) .243(**) -.08

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 .55N 120 185 236 241 233 225 239 5

White River Pearson Correlation .777(**) .661(**) .651(**) .496(**) .538(**) .776(**) .242(**) .11

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 .36N 117 196 235 252 232 235 238 6

** Correlation is significant at the 0.01 level (2-tailed). 
9

Table 6 

Organic Carbon Correlations/All Valid Data 2003-2005

Douglas 

Pass 

Ripple Creek 

Pass 

Canyonlands 

NP 

Great Sand 

Dunes NP 

Mesa 

Verde NP 

Mt. Zirkel 

WA 

Rocky 

Mountain NP 

Sham

Mi

Douglas Pass Pearson Correlation 1 .924(**) .929(**) .894(**) .867(**) .928(**) .683(**) 

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 N 225 202 122 120 121 111 122 

Ripple Creek Pass Pearson Correlation .924(**) 1 .888(**) .836(**) .643(**) .961(**) .762(**) .59

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 N 202 299 181 201 178 182 184 

Canyonlands NP Pearson Correlation .929(**) .888(**) 1 .794(**) .712(**) .874(**) .639(**) .44

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 N 122 181 241 238 233 221 236 

Great Sand Dunes NP 

Pearson Correlation .894(**) .836(**) .794(**) 1 .666(**) .826(**) .581(**) .74Sig. (2-tailed) .000 .000 .000 .000 .000 .000 N 120 201 238 258 235 238 241 

Mesa Verde NP Pearson Correlation .867(**) .643(**) .712(**) .666(**) 1 .652(**) .553(**) .67

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 N 121 178 233 235 238 222 233 

Mt. Zirkel WA Pearson Correlation .928(**) .961(**) .874(**) .826(**) .652(**) 1 .788(**) .68

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 N 111 182 221 238 222 241 224 

Rocky Mountain NP Pearson Correlation .683(**) .762(**) .639(**) .581(**) .553(**) .788(**) 1 .69

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 N 122 184 236 241 233 224 243 

Shamrock Mines Pearson Correlation .198 .593(**) .441(**) .740(**) .672(**) .689(**) .694(**) 

Sig. (2-tailed) .197 .000 .001 .000 .000 .000 .000 N 44 52 55 64 53 57 55 

Weminuche WA Pearson Correlation .913(**) .850(**) .847(**) .894(**) .757(**) .834(**) .581(**) .36

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 N 120 185 236 241 233 225 239 

White River Pearson Correlation .964(**) .903(**) .895(**) .850(**) .690(**) .900(**) .625(**) .70

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 N 117 196 235 252 232 235 238 

** Correlation is significant at the 0.01 level (2-tailed). 
10

Table 7 

Fine Mass Correlations/All Valid Data 2003-2005

Douglas 

Pass 

Ripple 

Creek Pass 

Canyonlands 

NP 

Great 

Sand 

Dunes NP 

Mesa 

Verde NP 

Mt. Zirkel 

WA 

Rocky 

Mountain NP 

Shamrock 

Mines 

Wemin

W

Douglas Pass Pearson Correlation 1 .918(**) .691(**) .107 .296(**) .291(**) .142(*) .073 .68

Sig. (2-tailed) .000 .000 .115 .000 .000 .035 .394 N 234 208 224 216 217 209 220 140 

Ripple Creek Pass Pearson Correlation .918(**) 1 .711(**) .651(**) .704(**) .880(**) .610(**) .789(**) .57

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 N 208 300 272 286 260 269 272 143 

Canyonlands NP Pearson Correlation .691(**) .711(**) 1 .461(**) .537(**) .590(**) .527(**) .381(**) .47

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 N 224 272 339 325 317 310 326 149 

Great Sand Dunes NP 

Pearson Correlation .107 .651(**) .461(**) 1 .672(**) .634(**) .508(**) .835(**) .30Sig. (2-tailed) .115 .000 .000 .000 .000 .000 .000 N 216 286 325 348 311 320 323 148 

Mesa Verde NP Pearson Correlation .296(**) .704(**) .537(**) .672(**) 1 .698(**) .543(**) .857(**) .51

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 N 217 260 317 311 326 301 313 140 

Mt. Zirkel WA Pearson Correlation .291(**) .880(**) .590(**) .634(**) .698(**) 1 .699(**) .866(**) .51

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 N 209 269 310 320 301 334 309 144 

Rocky Mountain NP 

Pearson Correlation .142(*) .610(**) .527(**) .508(**) .543(**) .699(**) 1 .627(**) .34Sig. (2-tailed) .035 .000 .000 .000 .000 .000 .000 N 220 272 326 323 313 309 337 143 

Shamrock Mines Pearson Correlation .073 .789(**) .381(**) .835(**) .857(**) .866(**) .627(**) 1 .26

Sig. (2-tailed) .394 .000 .000 .000 .000 .000 .000 N 140 143 149 148 140 144 143 162 

Weminuche WA Pearson Correlation .683(**) .579(**) .471(**) .305(**) .512(**) .517(**) .341(**) .266(**) 

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 .001 N 223 275 329 326 317 314 328 145 

White River Pearson Correlation .373(**) .811(**) .626(**) .648(**) .708(**) .822(**) .826(**) .849(**) .84

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 .000 N 220 288 331 340 317 325 329 158 

** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). 
11

Table 8 

Coarse Mass Correlations/All Valid Data 2003-2005 

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

Douglas 

Pass 

Ripple 

Creek Pass 

Canyonlands 

NP 

Great Sand 

Dunes NP 

Mesa 

Verde NP 

Mt. Zirkel 

WA 

Rocky 

Mountain NP 

Shamrock 

Mines 

Douglas Pass Pearson Correlation 1 .881(**) .358(**) .234(**) .053 .030 -.007 .335(**) 

Sig. (2-tailed) .000 .000 .000 .416 .642 .912 .000 N 241 210 240 239 239 239 239 159 

Ripple Creek Pass Pearson Correlation .881(**) 1 .371(**) .263(**) .037 -.054 -.015 .449(**) 

Sig. (2-tailed) .000 .000 .000 .523 .351 .794 .000 N 210 306 293 306 293 304 293 161 

Canyonlands NP Pearson Correlation .358(**) .371(**) 1 .094 .016 -.011 .007 .038 

Sig. (2-tailed) .000 .000 .073 .754 .832 .893 .617 N 240 293 367 366 366 366 366 174 

Great Sand Dunes NP Pearson Correlation .234(**) .263(**) .094 1 -.082 .003 -.003 -.063 

Sig. (2-tailed) .000 .000 .073 .117 .959 .951 .392 N 239 306 366 380 366 378 366 187 

Mesa Verde NP Pearson Correlation .053 .037 .016 -.082 1 -.155(**) -.004 .121 

Sig. (2-tailed) .416 .523 .754 .117 .003 .933 .111 N 239 293 366 366 366 366 366 174 

Mt. Zirkel WA Pearson Correlation .030 -.054 -.011 .003 -.155(**) 1 -.001 -.081 

Sig. (2-tailed) .642 .351 .832 .959 .003 .980 .269 N 239 304 366 378 366 378 366 186 

Rocky Mountain NP Pearson Correlation -.007 -.015 .007 -.003 -.004 -.001 1 .582(**) 

Sig. (2-tailed) .912 .794 .893 .951 .933 .980 .000 N 239 293 366 366 366 366 366 174 

Shamrock Mines Pearson Correlation .335(**) .449(**) .038 -.063 .121 -.081 .582(**) 1 

Sig. (2-tailed) .000 .000 .617 .392 .111 .269 .000 N 159 161 174 187 174 186 174 187 

Weminuche WA Pearson Correlation .049 .042 .095 .004 -.096 -.001 .007 .071 

Sig. (2-tailed) .453 .474 .069 .933 .066 .979 .894 .355 N 239 293 366 366 366 366 366 174 

White River Pearson Correlation .461(**) .556(**) .213(**) .159(**) .012 .019 -.005 .051 

Sig. (2-tailed) .000 .000 .000 .002 .822 .712 .922 .485 N 238 305 365 378 365 377 365 186 
12

Back Trajectories. The National Oceanic and Atmospheric Administration (NOAA) Air 

Resources Laboratory (ARL) HYbrid Single-Particle Lagrangian Integrated Trajectory 

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

days in 2005 at the Douglas Pass and Ripple Creek 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 trajectory points for every two-hour period during a sample 

day. This spread of points provides a comprehensive picture 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 Creek are shown in Figure 1. The distribution 

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

northwestern Colorado. Utah, Wyoming, northwestern 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 with calculated extinctions within the worst 

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

through the counties in the vicinity of the site, the highest density of points is near Ripple 

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

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

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

trajectory points using a moving 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 the 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 lead to trajectory location 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 and 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 along 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 degree of correspondence between local terrain 

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

For this analysis, the extinction or concentration 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 degrees 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 method. Limiting the averaging to nodes with a 

high number of points reduces the chances that a few points associated with a high or low 

sample concentration or extinction bias the estimates of source contributions from that 

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

covered by the analysis. 

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

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

this causes the irregular distribution 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 Corners region and an area that may include 

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

be significant source areas for higher extinction. 

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

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

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

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

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

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

extinction days. 

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

of points shows that the air arriving at Douglas Pass typically passes through the western 

one-fifth of Colorado. Utah, Wyoming, northwestern 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 shown in Figure 6. Since all paths to Douglas 

Pass must travel through the counties in the vicinity of the site, the highest density of 

points is near Douglas Pass. The trajectory points 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 average source-region analysis for calculated 

extinction (Bext) at Douglas Pass are presented in Figure 7. Contours were not generated 

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

distribution of the contoured areas. This map suggests that the regions that contributed to 

the highest average extinction values included the Four Corners region, southwestern 

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

analysis of positive matrix factorization results 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 applied to the trajectory points for days that 

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

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

that the highest source regions are in southwestern Colorado, 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 suggest that these source 

regions were significant for high extinction days. 

Figure 1. NOAA ARL HYSPLIT back trajectory 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 period during a sample day. 
15

Figure 2. NOAA ARL HYSPLIT back trajectory points for Ripple Creek 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. 
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 spatial moving average analysis of extinction 

values associated with HYSPLIT back trajectory 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 extinction 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 extinction values associated with HYSPLIT back trajectory 

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 extinction during 

the worst 20 percentile events is above 40 inverse megameters.
18

Figure 5. NOAA ARL HYSPLIT back trajectory 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 period 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 extinction at Douglas Pass in 2005 when the air 

mass is from a specific region (based on a spatial moving average analysis of extinction 

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

eastern Utah, southwestern Colorado, 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 extinction 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 extinction values 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 these regions, for example, the 

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

inverse megameters. 

Positive Matrix Factorization. Positive Matrix Factorization (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 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 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 diagnostics attached to the end of 
22

this TSD. 

Four source categories were identified for Douglas Pass. These categories 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 used. Although most of the diagnostics were 

good, the regression of total PMF concentrations 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 modeled contributions would often provide a poor representation 

of reality. 

Figure 10 presents the mean 2004-2005 concentrations (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 represents power generation sources and probably also 

includes mobile sources and oil and gas 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 highest in the spring, and 

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

is lower in the winter and higher 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 there are a few major episodes in late 2005. 
23

Sum of PMF Concentrations vs Gravimetric Mass 

Concentrations y = 0.9702x + 0.0178

R2

 = 0.9227

-5

0

5

10

15

20

25

30

-5 0 5 10 15 20 25

IMPROVE gravimetric mass concentrations ug/m3

Sum of PMF concentrations ug/m3

 

Figure 9. The sum of PMF concentrations versus gravimetric mass concentrations for 

Douglas Pass (2004-2005). The linear regression 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 concentrations (source profiles) for each 

category in ug/m3.
24

0

3

5

8

10

20042005

Year

Concentration in ug/m3

Variables

sulfate

geologic

woodburning

nitrate

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

each sample day for 2004. 
25

0

5

10

15

20

20052006

Year

Concentration in ug/m3

Variables

sulfate

geologic

woodburning

nitrate

Figure 12. Time series of PMF 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 contributions from each of the four PMF source 

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

sulfate group source corridor from northwest New Mexico northward through 

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

geologic source factor are presented in Figure 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 exceedance events for fine particulates at 

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

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

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

similar to the patterns for both the sulfate and nitrate source groups. As discussed in the 

Methods and Model Diagnostics section 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 show 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 Utah, southwestern Colorado, and over Mesa 

County. 
26

Figure 13. A map of the mean PMF sulfate group 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 concentrations at Douglas 

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

average analysis of geologic factor concentrations 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 average geologic source category concentration 

at Douglas Pass is above 1 ug/m3. 
28

Figure 15. A map of the mean PMF wood combustion 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 highest source regions are in northwestern New 

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

the average wood combustion category concentration 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. When an air mass is from these areas, the 

average wood combustion category concentration 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 are roughly proportional to the sources’ annual 

emissions. 

Sulfur dioxide sources and source area contours for the Douglas Pass sulfate factor are 

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

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

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

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

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

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

to the Huntington power generation facility in east-central 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 sulfate is a secondary aerosol, and it often forms in areas 

downwind of sources. Source areas may sometimes 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 Utah. Nitrate is also a secondary aerosol, and it often forms 

in areas downwind of sources. Source areas may 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 accelerated nitrate formation as can emission of NOx into low 

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

Yampa Valley sources do not seem to play a major 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 eastward transport of secondaries towards the 

Mt. Zirkel Wilderness Area. The major canyons 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 trajectories and their implications for the 

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

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

Services Program, Air Pollution Control Division, 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 sources’ 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 areas 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 areas 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 areas 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 areas 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 analytical procedure that can be used to 

identify air pollution source categories and to estimate the contributions 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 category 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 values and zero-value data were replaced with 

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

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

also yield better results), the reported IMPROVE 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 diagnostics for Ripple Creek 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 Creek 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, total gravimetric fine mass, magnesium, 

nickel, nitrate, nonsoil potassium, organic carbon, lead, phosphorous, rubidium, 

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

carbon concentrations were calculated using the component carbon measurements and the 

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

http://vista.cira.colostate.edu/improve/Publications/OtherDocs/IMPROVEDataGuide/IMPROVEDataGuide

.htm

Nonsoil potassium was also calculated using the formula presented in the IMPROVE 

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

were derived by taking the square root of the sum of the squares 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 other 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-squared 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 with than less than ideal diagnostics can be 

included, but this may degrade the final resolution 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 identified for Douglas Pass (2004 and 2005 data). 

The four source categories are a sulfate 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 series 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 high in selenium, phosphorous, lead, and elemental 

carbon. The geologic source factor is very high 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, which 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 represents power generation sources and probably also 
41

includes mobile sources and oil and gas 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 highest in the spring, and 

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

is lower in the winter and higher 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 there 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 variability in the percentage 

of species apportioned to a source factor and 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 and are close to the box, then there is good 

reproducibility. The black star that is overlaid is the value from 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”. Through these iterations the model attempts to 

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

many times before the user is likely to find the most appropriate set of species and 

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

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

reproducibility increases the likelihood of a good fit with reality. Figures 27 through 30 

generally show good reproducibility 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 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 used. Although most of the diagnostics were 

good, the regression of total PMF concentrations 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 modeled 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 Laboratory, Research Triangle Park, NC 

27711, 32 pp. 

Kim, E., Hopke, P.K., Edgerton, E.S., 2004. Improving 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. Source 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. Aerosol Science and Technology 38, 724-738. 

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

factors into multilinear receptor model studies of Atlanta aerosol. Atmospheric 

Environment 37, 5009-5021. 

Lee, Eddie; Chan, Chak K.; Paatero, Pentti, 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 formulation 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 species 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 species for Factor 2 (Geologic 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 per species for Factor 3 (Wood Combustion 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). 
46

Figure 26. Mass of species and percent of mass per species for Factor 4 (Nitrate 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 apportioned for Factor 1, Sulfate group, (bottom). 
48

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

and variability in mass of species apportioned for Factor 2, Geologic group, (bottom). 
49

Figure 29. Box plots of variability in percentage of species apportioned 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 apportioned for Factor 4, Nitrate group, (bottom). 
51

Sum of PMF Concentrations vs Gravimetric Mass 

Concentrations y = 0.9702x + 0.0178

R2

 = 0.9227

-5

0

5

10

15

20

25

30

-5 0 5 10 15 20 25

IMPROVE gravimetric mass concentrations ug/m3

Sum of PMF concentrations 

ug/m3

Figure 31. The sum of PMF concentrations versus gravimetric mass concentrations for Douglas Pass (2004-

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