UNITED STATES ENVIRONMENTAL PROTECTION AGENCY 

REGION 8 

1595 Wynkoop Street 

DENVER, CO 80202-1129 

Phone 800-227-8917 

http://www.epa.gov/region08 

April 22, 2015 

Ref: 8P-AR 

MEMORANDUM 

SUBJECT: Utah PM 10 24-hour Design Concentrations 

FROM: Richard M. Payton .uw{~ 

TO: File 

EPA guidance for the calculation of 24-hour PM 10 design concentrations provide four techniques. The 

June 1987 PM10 SIP Development Guideline, EPA-450/2-86-001, describes those methods in Section 

6.3. 

Table look-up 

The first technique discussed is a table look-up method, based on the number of samples being 

considered. The look-up table is reproduced below. 

Table 1 Tabular Estimation of PM10 Desi n Concentration (EPA-450/2-86-001 , . 6-5 

Number of Daily Rank of Upper Bound Rank of Lower Bound Data Point Used for 

Values Desi n Concentration 

< 347 1 Hi est Value 

348-695 1 2 Second Hi est Value 

696-1042 2 3 Third Hi 

1043-1096 3 4 

The first break point, 347 samples, corresponds to a daily monitor with 95% data capture in one year, or 

a 1 in 3 day monitor with 95% data capture of three years. The upper limit of the table, 1096 samples, 

equates to 100% data capture over three years (365+365+366 samples). Of the table, the guideline 

document says 

The look-up procedure is basically a tabular technique for determining what point on the 

empirical frequency distribution corresponds to a frequency of 1/365. By construction, the table 

look-up procedure tends to provide a design concentration slightly lower than would be derived 

using a continuous curve representing a theoretical frequency distribution for PM 10 values. 

The guideline goes on to say that: 

if a more precise design concentration should be desirable, the use of interpolation formulas 

(Section 6.3.2) or more simple graphical procedures (Section 6.3.3) may be necessary. 
The remaining techniques in the guideline are statistical techniques, geared toward estimates of the 

continuous curve representing the theoretical distribution of the data values. 

Fitting on statistical distribution to several years of data 

The first of the statistical methods, in Section 6.3.2 (referred to as interpolation formulas in the quote 

above) is the development of a mathematical representation of the distribution of PM10 data, and then 

using the mathematical representation to determine the concentration occurring at a frequency of l in 

365. The method limits the mathematical curve fit to the high tail of the sampled values, since the design 

concentration and NAAQS attainment are determined simply by the expected number of values greater 

than the NAAQS occurring over a three year period. The guideline recommends fitting the distribution 

of the top 10 or 5% of sampled values. The guideline also refers to the January 1979 Guideline for the 

Interpretation of Ozone Air Quality Standards, EPA-450/4-79-003 for additional information on fitting 

sample distributions. 

Using the empirical frequency distribution of several years of the data (graphical estimation) 

The second statistical technique of the guideline is very similar to the first statistical technique. Where 

the first technique above generates a mathematical representation of the distribution and uses that to 

calculate the concentration occurring at a frequency of I in 365, the graphical technique achieves the 

same result by graphing the data, drawing an estimated best fit line on the graph to represent the upper 

10 or 5% of the data, and using that fit line to graphically determine the concentration corresponding to 

a I in 365 frequency. This technique is also described in more detail in the 1979 ozone ~uideline . 

Conditional probability approach 

The third statistical technique essentially uses one of the two previous statistical techniques, but treats 

the data on an annual rather than multi year basis. The probability of recurrence of the specific 

circumstances of each year are then estimated (say the probability of recurrence of a particular set of 

meteorological conditions for the year, or, appropriate weighting for a single year with incomplete data 

relative to adjacent years with complete data). The conditional probabilities for each year are then used 

to combine the individuaJ distributions and arrive at a generalized I in 365 concentration value. This too 

is described in more detail in the 1979 ozone guideline. 

Data Quality Issues 

EPA Region 8 has identified several issues with Utah data quality in AQS. Pertinent to the calculation of 

24-hour PM10 design concentrations, the number of samples Utah reports with 0 μg/m3 has increased 

dramatically in the 2011-20 14 timeframe. Table 2 shows the number of reported 0 concentration PM10 

samples by year in AQS by Utah DAQ. 
Table 2 Reported PM10 Samples ofO μg/m3 by Utah DAQ 

Year(s) Number of Zeros Monitors 

1987-1998 0 - 1999 1 Lindon 

2000-2006 0 - 2007 1 Lindon 

2008 0 - 2009 11 Hawthorne, Lindon, Ogden, 

North Salt Lake, Magna 

2010 IO Cottonwood 

2011 16 Hawthorne, Lindon, Ogden, 

North Salt Lake, Magna, 

Cottonwood, North Provo 

2012 6 Hawthorne, North Salt Lake 

2013 10 Hawthorne, Lindon, Ogden, 

North Salt Lake, Magna, North 

Provo 

2014 4 North Provo 

Zero concentration data is not necessarily suspect data. The pattern above, however, suggests some data 

validation may be in order. Both the table look-up method and the statistical methods for calculating 

design concentrations depend on the number of valid samples collected. Looking at the zero data 

reported in 2011-2014, it is useful to look at any collocated PM2.s data collected at the same monitoring 

site on days where the PM10 monitors reported 0 μg/m3 to judge if the reported value was indicative of a 

very low concentration which would have no direct bearing on the ca1culation of the design value, or if 

it was indication of a missed or invalid sample mistakenly coded as 0 μg/m3 which would affect the total 

number of valid samples relevant to the design value calculation. Table 3 shows those instances where a 

PM1o monitor reported 0 μg/m3 and the associated PM2.s monitor at the same site reported non-zero 

concentration. The table also shows PM10 data recorded on that day by either collocated PM10 monitors 

(where available) or from the nearest PM10 monitor with data for the same day. Days with collocated 

data PM2.s or PM10 greater than 4 μg/m3 or where the nearest PM1omonitor recorded greater than 15 

μg/m3 are highlighted as suspect. 
Table 3 

Date 

4/29/2011 

5/31/2011 

6/8/2011 

6/1612011 

6/20/2011 

6/27/2011 

8/4/2011 

8/7/2011 

8/13/2011 

10/3/2011 

10/6/2011 

10/6/201 l 

10/6/201 1 

111/2012 

6/2412012 

7/1/2012 

8/13/2012 

11/8/2012 

11/17/2012 

1/28/2013 

2/4/2013 

2/9/2013 

2/21/2013 

3/11/2013 

3/25/2013 

4/29/2013 

7/20/2013 

7/29/2013 

1116/2013 

12/13/2014 

12/22/2014 

12/25/2014 

12/28/2014 

Utah PM10 Nonattainment Area PM10 and PM2.s Data on Days PM10 was Reported as 

Zero; Data Indicating Possible Errors Recommended for Validation in Bold 

Site PM10 PM2.s Nearest PM10 Site PM10 at 

Reported Reported Nearest Site 

Ogden 0 2.4 Hawthorne 2.2 

Hawthorne 0 4.4 North Salt Lake 15 

North Provo, POC 3 0 4.4 North Provo POC 2 21 

Lindon 0 6.5 Hawthorne 12 

Lindon 0 4.0 North Provo IO 

Lindon 0 5.5 Hawthorne 15 

North Salt Lake 0 NA Hawthorne, Magna 12 

Ma2na 0 5.1 Hawthorne 11 

Mama 0 6.6 Hawthorne 24 

02den 0 s.o North Salt Lake 33 

Magna 0 No Data Hawthorne 1 

North Provo POC 2 0 No Data Lindon 2 

North Provo POC 3 0 No Data Lindon 2 

Hawthorne 0 17.1 North Salt Lake 36 

North Salt Lake 0 NA Hawthorne 24 

North Salt Lake 0 NA Hawthorne 29 

North Salt Lake 0 NA North Salt Lake POC 30 

POC3 2 

North Salt Lake 0 NA Hawthorne 49 

North Salt Lake 0 NA North Salt Lake POC 13 

POC2 3 

North Salt Lake 0 NA North Salt Lake POC 17 

POC3 2 

North Salt Lake 0 NA Hawthorne 61 

Hawthorne 0 6.5 North Salt Lake 13 

Magna 0 3.8 Hawthorne 8 

Ogden 0 3.6 Hawthorne 17 

North Salt Lake 0 NA Hawthorne 10 

North Salt Lake 0 NA Hawthorne 21 

Lindon 0 8.6 Hawthorne 19 

Lindon 0 2 Hawthorne 9 

Lindon 0 4.4 North Provo 13 

North Provo POC 3 0 1.9 North Provo POC 2 4 

North Provo 0 1.5 Lindon 3 

North Provo POC 2 0 No Data North Provo POC 3 3 

North Provo 0 No Data Lindon 2 

Another issue with the North Provo data is data completeness. In 40 CFR Part 50, Appendix K 

(Interpretation of the National Ambient Air Quality Standards for Particulate Matter) data completeness 

requirements are provided for data sets that will be compared to the PM10 NAAQS in order to determine 

if the NAAQS is attained. This attainment comparison is based on the expected number of exceedances 

over a three year period. Appendix K, Section 2.3(a) (Data Requirements) states that "For the purposes 

of making comparisons with the particulate matter standards, aJI data produced by State and Local Air 
Monitoring Stations (SLAMS) and other sites submitted to EPA in accordance with the part 58 

requirements must be used, and a minimum of 75 percent of the scheduled PM1o samples per quarter are 

required." For the North Provo PM10 monitor, the second quarter of 2013 has only 59% of scheduled 

samples in AQS, and the fourth quarter of 2014 has only 74% of scheduled samples. Consequently, 

under nominal conditions the data from the North Provo monitor cannot be used in a comparison of the 

calculated expected exceedances to the NAAQS for either 2011-2013 or 2012-2014. The site cannot be 

used to show that the Provo PM10 nonattainment area has attained the PM1o standard in these years, even 

though no exceedances were recorded at the monitor in the 2011-2014 period. 1 Fortunately, the Lindon 

monitor is also in the Provo PM10 nonattainment area. The Lindon monitor has a higher sampling 

frequency (daily compared to 1 day in 3 at North Provo), has historically recorded higher concentration 

data than has North Provo (as is shown below) and has complete data{> 75% of scheduled days) for all 

quarters in 2011 through 2014. The Lindon monitor therefore shows attainment for the nonattainment 

area, with no exceedances recorded 201 1-2014. 

Data requirements for design concentration calculation purposes, in contrasts to requirements for 

comparison to the NAAQS to determine attainment, are provided in the 1987 PM10 SIP Development 

guideline. The guideline states: "Ideally, ... 3 years of representative air quality measurements should 

be considered in determining 24-hour design concentrations." With respect to data completeness, the 

guideline says, "All portions of the year should be adequately reflected in the measurements." 

Figure 1 shows the time series of PM10 measurements from North Provo PM10 monitor for 2011 through 

2014; quarters 2 of2013 and 4 of2014 with less than 75% data capture are indicated. Any of the design 

concentration methods in the guideline are ultimately most dependent on the highest concentrations 

within the data set. Thus if data is incomplete during seasons where the highest concentrations in the 

distribution rarely occur, the missing data will not degrade design concentrations predicted from the 

high concentrations in the distribution. Looking at Figure 1, the highest concentrations generalJy 

occurred in quarter 1, with second highest generally in the fourth quarter. All first quarters have greater 

than 75% data in 2011-2014. For fourth quarter, that of 2014 is short 1 sampler from being 75% 

complete. The upper part of the distribution is dominated by data from the first quarter of 2013, a severe 

winter pollution year. Thus the missing data from second quarter 2013 and fourth quarter 2014 will 

likely have little or no impact on the design concentration derived from the upper 10% of data points 

collected in either 2011-2013 or 2012-2014 (both of which contain 2013 data), because those quartersw 

were unlikely to contain high values. The less than complete data appears to be suitable for design 

concentration calculation (but not for NAAQS comparison).

2 

1 40 CFR Part 50, Appendix K Section 2.3(b) states that "In all cases, 3 years of representative monitoring data 

that meet the 75 percent criterion of the previous paragraph should be utilized, if available, and would suffice. 

More than 3 years may be considered, if all additional representative years of data meeting the 75 percent 

criterion are utilized. Data not meeting these criteria may also suffice to show attainment; however, such 

exceptions will have to be approved by the appropriate Regional Administrator in accordance with EPA 

guidance." Because complete higher frequency PM10 data showing attainment is available from the Lindon 

monitor, it is not necessary to seek approval from the Regional Administrator to use the less than complete North 

Provo data to also show attainment. 

2 EPA guidance (Memorandum, William G. Laxton to Regional Air Directors, Use of Collocated Data, Ju ly 24, 

1989) allows data collected by a collocated quality assurance monitor to be substituted for missing data from a 

primary monitor when the primary monitor fails to collect a valid sample on a scheduled sampling day. Two such 

samples were obtained in second quarter 2013, but none were collected in fourth quarter 2014. Substitution of the 

2 samples in second quarter 2013 only raises the data completeness from 59% to 65%. Because the two available 

substitution concentrations were relatively low (13 and 23 μg/m3

) and the design concentration is dependent only 

on the high concentration data, this substitution was not utilized. 
160 

140 

120 

100 

80 

60 

• 

40 . • 

20 

0 

Figure I 

North Provo PMlO Data, 2011-2014 

• 

• 

• 

• 

• 

• 

... • • 

• • 

• • ... • 'I• • • • • 

--~·-~-""~· .. -·ill 

. . 

. 

... 

• . .... ~. . I 

• 

• • .: ,, . 

• • • 

.... - ':!'.~· 

• 

North Provo PM10 Time Series and Quarters with Incomplete Data; All 1st and 4th Quarter 

Data Outlined 

Design Concentration for Utah Nonattainment Areas 

Utah has 3 PM10 nonattainment areas (Ogden City, Salt Lake County, and Utah County). The areas have 

included seven PM10 monitors operating for some or all of the 2011-2014 time period. For most 

historical applications, use of the table look-up method has proven adequate in providing a design 

concentration for PM10 attainment demonstrations. For the contemplated PM10 maintenance plan and 

redesignation request, unique circumstances at the North Provo PM10 monitor in the Utah County PM10 

nonattainment area make the table look-up result for that monitor problematic. 

January 2013 was an exceptionally severe season for secondary aerosol formation in Utah County. The 

highest PM2.s concentrations ever recorded (1998-2015) were recorded in Utah County that month, as 

well as the highest winter PM 10 concentration since 1996. This means that three year periods including 

2013 could have distributions with more high concentrations than a typical historical distribution. 

Table 4 shows the number of samples collected by the seven monitors in the 2011-2013 and 2012-2014 

timeframe, and the table look-up based design concentration resulting. For this table, missing sample 

values from the primary sampler at each site have not been replaced by concentrations collected by any 

collocated samplers which might be resident at the site. 
Table 4 Utah PM10 Nonattainment Area PM1 0 Design Concentrations, 2011-2014, Table Look-Up 

Monitor Nonattainrnent 2011-2013 2011-2013 2012-2014 2012-2014 

Area Sample Count Design Sample Count Design 

Concentration Concentration 

Ogden Ogden City 1060 90 ul!/m3 1036 95 1 .. udm3 

Cottonwood* Salt Lake 171 79 μg/m3 0 NA 

(No data after County 

2011) 

North Salt* Salt Lake 892 86 μg/m3 560 (no 2014 82 μg/m3 

Lake (shut County data) 

down in 2013) 

Magna Salt Lake 349 54 μg/m3 355 62 μg/m3 

County 

Hawthorne Salt Lake 1047 86 μg/m3 1050 87 μg/rn3 

County 

North Provo* Utah County 316 149 Wllm3 315 149 u.e/m3 

Lindon Utah County 1014 124 ull/m3 1031 124 ull/m3 

*Monitors with incomplete data for one or more years, 2011-2014 

The 149 μg/m3 for North Provo is problematic. The North Provo and Lindon monitors are located some 

6.6 miles apart. The Lindon monitoring site is 2.4 miles northeast from the former site of the Geneva 

Steel smelter. North Provo is 6 miles southeast of the smelter's former location. The smelter 

discontinued operations in November of 2001 and has since been demolished. 

The Lindon monitoring station has reported PMw data since April of 1985. The North Provo site has 

reported PM1o since January of 1986. Prior to November 2001 , with the nearby steel smelter in 

operation, Lindon typically reported higher PM10 than did North Provo. As shown in Figure 2, the slope 

of a least squares regression line for the data collected from 1987 through November of2001 is 1.217, 

with a y-intercept very close to zero. This indicates that on average, data from the Lindon monitor was 

about 22% higher than the corresponding data from the North Provo monitor. The r2 coefficient of 

determination for the regression is 0.86. 
M 

E 

~ ;) 

0 

300 

250 

200 

..... 150 

100 

50 

0 

Figure 2 

0 

Lindon vs. N. Provo, 1987-November 2001 

• 

• 

• 

•• 

• 

50 

• 

• 

y = 1.217x - 0.0243 .·· 

.... ······· ~ 

. . .. ······ . 

. ,. .. ·; 

. ..·· 

. . ...... ....... ...... . ........ 

• 

. 

' .... ····· _. ...• 

·.·.·-;. .. , '-~- • • 

• 

• 

• 

• 

100 150 200 

North Provo PMlO, ug/m3 

Linear Regression of Lindon PM10 data vs. North Provo PM10 Data for the Period in 

which Geneva Steel was in Operation. 

250 

Since Geneva Steel ceased operations in November 2001, PM10 data from the two sites is much more 

comparable, as shown in Figure 3. The least squares regression line has a y-intercept of 1.77 μg/m3

, but 

the slope of the regression line is 1.017, indicating that on average Lindon data is 1. 77 μg/m3 + 1. 7% 

higher than the North Provo data. Here, the r2 coefficient of determination has increased somewhat, to 

0.88. Given this linear relationship, one would expect data from the two monitors to yield similar design 

concentrations for data sets of similar size covering the same time period at the two sites. Since the 

table look-up design concentration for the large data set at Lindon was 124 μg/m3 for both 2011-2013 

and 2012-2014, while the North Provo design concentration for both those periods with a much smaller 

sample size was 149 μg/m3, this substantiates that an alternative design value calculation technique 

might produce more reliable results for North Provo. 
4SO 

400 

350 

M 300 

E 

....... 

co 

::: 2SO 0 

2 

~ 200 0 

-0 

c:: 

·.:::; 1SO 

100 

so 

0 

0 

Figure 3 

Lindon vs. N. Provo, after Geneva Steel Shutdown, November 2001-

2014 

•... ·· ... ··"~ ... . ~=r··· · 

so 100 1SO 

.•' .. ··· 

.... ······ 

.·· 

200 

... ······ •' 

2SO 

North Provo PMlO, ug/m3 

300 

• 

y = 1.0174x + 1.770~ .·· 

3SO 400 450 

Linear Regression of Lindon PM10 data vs. North Provo PM10 Data for the Period After 

Geneva Steel Ceased Operations. 

When the data from Lindon and North Provo are plotted in a log-linear fashion, with concentration 

increasing along the x-axis and the fraction of samples less than or equal to the concentration increasing 

along the y-axis, the concentration distributions for the higher concentrations are approximately linear, 

indicating an exponential distribution for that portion of the data. The distributions for Lindon and 

North Provo for 2011-2013 are shown in Figures 4 and 5. Both distributions when graphed in a loglinear fashion show a non-linear segment for concentrations below 20 μg/m3

, a linear segment between 

about 20 μglm3 and about 70 μg/m3

, and a second linear segment with a different slope above about 40 

μg/m3 . 
Lindon 2011-2013 

c: 

.Q 

~ 

c 

OJ 

.... 

0 

v 0.1 

ro 

.8 

:J 

er 

u.I 

c 

.s 0.01 

~ .,, 

<I.I • 7 

.,., • (lJ • a. 

c; 

0.001 '1) 

V\ • '-0 

c: 

.Q 

'-' 

~ u.. 

0.0001 

0 20 40 60 80 100 120 140 160 

PMlO, ug/m3 

Figure 4 Lindon PM10 Sample Distribution, 2011-2013. 
N Provo, 2011-2013 

c 

.2 

~ 

... 

c 

<ll 

u 

c 

0 

u 

ro 

£ 0.1 ~ 

& 

UJ 

0 '-

c ,, <O 

.s::. 

..... 

v \ ~ <lJ • • 19 

.,, 0.01 • 

• Cl.I 

a. 

E 

.,; • 0 

c • 0 

·;::; 

u 

~ u. 

0.001 

0 20 40 60 80 100 120 140 160 

PMlO, ug/m3 

Figure 5 North Provo PM10 Sample Distribution, 2011-2013. 

Modem spreadsheets provide curve fitting tools that were not available at the time of the 1979 ozone 

guideline and the 1987 PM10 SIP Development guideline. If the upper 10 or 5% of data are separated 

out, modem spreadsheets can generate a least squares exponential curve fit equation and line, allowing 

simultaneous design value calculation using both the "Fitting on statistical distribution to several years 

of data" method and the "Using the empirical frequency distribution of several years of the data 

(graphical estimation)" method. 

Figures 6 and 7 show the Lindon and North Provo 2011-2013 data, with the upper 10 percent of data 

points highlighted, and the best fit equation and line for the upper 10 percent of data. 
0.1 

0.01 

0.001 

0.0001 

0 

Figure 6 

1/365 

20 40 

Lindon 2011-2013 

y = 0.5074e-0.o44x 

• 

60 80 100 

• • • • 

120 

Lindon PM10 Distribution Best Exponential Fit, Top 10%, 2011-2013. 

• 

• 

140 160 
0.01 

0.001 

0 

Figure 7 

• Lower 90% of data 

20 40 

N Provo, 2011-2013 

Top 10% of data - 1/365 Probability ....... Expon. (Top 10% of data) 

60 

······ ... 

"···-·. 

• 

·· .. 

80 

·· ... y = 0.292Se·0.035x 

·· ... .. .. ·· .. 

100 

·•·· .. ················· ... 

120 

.. • 

········· ... 

140 

North Provo PM10 Distribution Best Exponential Fit, Top 10%, 2011-2013. 

The equation for the North Provo fit line is l-F(PM10) = 0.2925e--0.o35CPMio). Solving this equation for 

PM10 concentration at a probability of 1/365 gives: 

l-F(PM10) = 11365 = 0.2925e-<>.OWfPMiol 

Similarly for Lindon, 

Ln (1/365) = Ln (0.2925)-{0.035*[PM10]) 

0.035*[PM10] = Ln(0.2025 - Ln(l/365) 

[PM10] = 1/0.035 * {Ln(0.2925)-Ln(l/365) = 133.5 μg/m3 

[PM1o]ov = 1/0.044 * (Ln(0.5074)- Ln(l/365)) = 118.7 μg/m3 

160 

The 1979 ozone guideline recommends that fits be evaluated for appropriateness. As an example, the 

guideline says 

.. if the fitted distribution underpredicts all of the last eight data points by more than 5%, then it 
must be established that the fitted distribution is reasonable. Such an argument might involve 

showing that the majority of these data points all occurred in the same period and that the 

meteorology for these particular days was extremely unusual." 

The North Provo fit looks reasonable, but for the Lindon fit, the highest seven days all lie above the fit 

line. Figure 8 shows that all these days occurred in January 2013 (purple points), which Utah has shown 

had the most severe meteorology in Utah County for PM2.s since 2000. 

0.001 

0.0001 

0 

Figure 8 

1/365 

20 

Lindon 2011-2013, January 2013 in Purple 

40 60 80 100 

•• • • 

120 

Lindon Top 10% Days Recorded in January 2013 (in Purple). 

• 

• 

140 160 

The upper seven points are tested against the fit line in Table 5. Two of the top seven values are 

underpredicted by less than 5%, two are underpredicted by just over 5%, and 3 are underpredicted by 7 

to 8%. All the top seven values occurred between January 7 and January 23, 2013, when Utah County 

was experiencing prolonged and unusually severe temperature inversions. Therefore, as suggested in the 

1979 ozone guideline, the fit is acceptable even with systematic underprediction of the top values, since 

most three year periods would be expected to experience values this high at less frequency than do 

periods containing 2013 data. Figure 9 shows the Lindon fit with the +/- 5% range of concentrations 

shown by orange lines. 
Table 5 Comparison of Top 7 Concentrations with Fit Line Concentration Predictions 

Date PM10 Fit PM10 at same % Difference 

Frequency 

1/19/2013 106 97.6 7.8% 

117/2013 109 101.2 7.2% 

1120/13 110 105.3 4.3% 

1/8/2013 I I 7 110.4 5.6% 

1/21/2013 124 116.9 5.7% 

1/22/2013 136 126. I 7.3% 

1/23/2013 148 141.9 3.5% 

Lindon 2011-2013, January 2013 in Purple 

1 

0.1 

y = 0.5074e-0.044x 

0.01 

• 

1/365 

0.001 

0.0001 

0 20 40 60 80 100 120 140 160 180 

Figure 9 Lindon Distribution Fit, Monitored Data, and +/- 5% Ranges Indicated in Orange. 

Using the table look-up method, Lindon had a design concentration 20 I 1-2013 of 124 μg/m3

, and North 

Provo had a design concentration of 149 μg/m3. The North Provo table look-up design concentration is 

120% of the Lindon design concentration. Overall, the Lindon and North Provo monitors had very well 

correlated PM10 data from 2001 to 2014, so given similar sample sizes over the same time period, better 

agreement in design concentration would be expected than was shown by the table look-up method. The 

distribution fit to the upper 10% method (and the graphical method) result in design concentrations of 

118.7 at Lindon, and 133.5 at North Provo. In this case, the North Provo design concentration is 112% 
of the Lindon concentration, so the two design concentrations for the monitors are in better agreement. 

For consistency, the table look-up and top 10% distribution fit design concentrations are shown in Table 

6 for all the monitors in the Ogden, Salt Lake County and Utah County non-attainment areas with 

monitoring data in all quarters of 2011-2013. Both design concentrations can be considered valid, but 

the top 10% curve fit design concentration could be considered more representative of long term data 

(less susceptible to interannual weather variation). 

Table 6 

Site 

Ogden 

Hawthorne 

Magna 

Lindon 

North Provo 

Comparison of Table Look-Up and Top 10% Distribution Fit Design Concentrations for 

2011-2013. 

Nonattainment Area Table Look-Up Design Distribution Fit Design 

Concentration Concentration 

Ogden City 91 ue/m3 85.7 uwm3 

Salt lake County 86 ue/m3 102.0 ue/rn3 

Salt lake County 58 uwm3 69.8 ue/rn3 

Salt lake County 124 ue/m3 118.7 uwm3 

Salt Jake County 149 μg/m3 133.5 ue/m3 

The 1987 PM10 SIP Development guideline indicates that in general, the table look-up method should 

yield a design value somewhat lower than that given by an appropriate distribution fit method. From 

Table 6, this is true for Hawthorne and Magna, but not for Ogden, Lindon and North Provo. 

Examination of the curve fits for Hawthorne and Magna shows that the fitted line for the top 10% at 

those sites lies above the measured data for a given frequency at the upper end of the distribution, as 

shown in Figures 10 and 11. Unlike Lindon and North Provo, where the January 2013 severe winter 

period dominates the top concentrations, for both Hawthorne and Magna January 2013 only represents 

four of the top twenty concentrations in the 2011-2013 period. Fewer of the top concentrations could be 

used to attempt to better fit the top values. Figures 10 and 11 show curve fits for Hawthorne and Magna 

using the top 10 and 5% of values. Using 5% rather than 10% at Hawthorne reduces the curve fit design 

concentration from 102.0 to 95.6 μg/m3, while using the top 5% at Magna has virtually no impact on the 

curve fit design concentration (69.8 μg/m3 is reduced to 69.3 μg/m3

. 

For Ogden, North Provo and Lindon, the indexed value corresponding to the table look-up value for the 

appropriate number of samples in each case lies significantly above the expected frequency given by the 

curve fit (see Figure 7, for North Provo, where the l 51 maximum indexed value lies well above the curve 

fit. 

In conclusion, the differences shown between the table look-up design value and the top 10% 

distribution fit design value are consistent with the circumstances unique to the actual measurements for 

the top few concentrations at each monitor. Use of the distribution curve fit design values should yield a 

more consistent design concentration Jess reflective of the individual vagaries of a given data set than 

does the table look up method. 
c: 

.Q 

~ .... 

... 

c: 

~ c: 

0 

u 

c: 

~ 

G 

ti) 

0 

... 

~ 

:::> 

0.1 

g 0.01 

0 

c: 

ta 

.I:. 

1-

... 

QJ 

.. 

~ 0.001 

l5 

VI 

Q) 

Q. 

E 

ta 

V'I 

.... 

~ 0.0001 

.g 0 u 

~ u. 

Figure 10 

Hawthorne PM10 Data Distribution, 2011-2013 

e PMlO 

e Top5% 

20 40 

Top 10% of Data - 1/365 Frequency 

Expon. (Top 10% of Data) ······· Expon. (Top 5%) 

., 

, ... 

• ··· ... y = l.2518e·

0

·

06

-... x 

60 

PM10, ug/m3 

···· ... • 

y = 4.3106e 077~ .. ··· ...• 

80 100 

Hawthorne PM10 Distribution, with Upper l 0% and Upper 5% Curve Fits 

120 
Magna PM10 Data, 2011-2013 

• PMlO • Top 10% - 1/365 • Top5% · · Expon. (Top 10%) ••••• •• Expon. (Top 5%) 

1 c: 

0 

~ 

=-c: 

<lJ 

u 

c 

0 

v 

c: 

<lJ 

> 

0 0.1 C1: 

.8 

-;;; 

::i 

er 

.... ..... 

,_ 

0 

t1; 

c: ··. -. ., ·1 .i::. 

I- .. .. ··-. <I> 0.01 ... ~ Q) . ·· .. 

"v 'J;,.s.ss1se-0·

1ix l'.3 

V) • Q) "· a. 

E 

"' .. VJ

0 y = 7 .3802e·O.l14x c 

.2 

u 0.001 ti) 

... 0 10 20 30 40 50 60 70 80 u. 

PMlO, ug/m3 

Figure 11 Magna PM10 Distribution, with Upper 10% and Upper 5% Curve Fits