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 Richard M
. Payton

## .uw{~ FROM: TO: File EPA guidance for the calculation of24-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 ofDaily 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 ofthree years. The upper limit of the table, 1096 samples , equates to 100% data capture over three years (365 +365 +366 samples)
. Ofthe 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 : ifa 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 ofthe continuous curve representing the theoretical distribution ofthe data values
. 

### Fitting on statistical distribution to several years of data The first ofthe statistical methods , in Section 6.3.2 (referred to as interpolation formulas in the quote above) is the development ofa mathematical representation of the distribution of

### PM1 0data, 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 ofthe sampled values , since the design concentration and NAAQS attainment are determined simply by the expected number ofvalues 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 ofOzone 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% ofthe 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 multiyear basis
. The probability ofrecurrence 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/m 3 has increased dramaticall y in the 2011-20 14 timeframe. Table 2 shows the number of reported 0 concentration 

### PM 1 0 samples by year in AQS by Utah DAQ.Table 

### 2 Reported PM10 Samples ofO μg/m 3 by Utah DAQ Year(s) Number ofZeros 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 ofvalid 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/m 3 tojudge ifthe reported value was indicative ofa 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/m 3 which would affect the total number of valid samples relevant to the design value calculation. Table 3 shows those instances where a PM 1 o monitor reported 

### 0 μg/m 3 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 PM 1 0 greater than 4 μg/m 3 or where the nearest PM1 omoni tor recorded greater than 

### 15 μg/m 3 are highlighted as suspect.Table 3
Date 4/29/2011 

### 5/31/2011 6/8/2011 6/1612011 6/20/20 1 1 

### 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 2/2 1/ 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 PM1 0 Nonattainment Area PM1 0 and PM2
.s Data on Days PM 1 0 was Reported as Zero ; Data Indicating Possible Errors Recommended for Validation in Bold

Site PM1 0 PM2
.s Nearest PM 10 Site PM1 0 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 (Interpreta tion ofthe National Ambient Air Quality Standards for Particulate Matter) data completeness requirements are provided for data sets that will be compared to the PM1 0NAAQS 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 AirMonitoring Stations (SLAMS) and other sites submitted to EPA in accordance wi th the part 58 requirements must be used, and a minimum of 75 percent ofthe scheduled PM 1 o samples per quarter are required. " For the North Provo PM 1 0 monitor, the second quarter of2013 has only 59% of scheduled samples in AQS , and the fourth quarter of2014 has only 74% of scheduled samples. Consequently, under nominal conditions the data from the North Provo monitor cannot 

### be used in a comparison ofthe calculated expected exceedances to the NAAQS for either 2011-2013 or 2012-2014
. The site cannot be used to show that the Provo PM 1 0 nonattainment area has attained the PM 1o standard in these years , even though no exceedances were recorded at the monitor in the 2011-2014 period. 1Fortunately, 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 PM 1 0 SIP Development guideline. The guideline states: "Ideally, ... 3 years ofrepresentative 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 ofthe design concentration methods in the guideline are ultimately most dependent on the highest concentrations within the data set. Thus 

### ifdata 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 ofthe distribution is dominated by data from the first quarter of2013 , 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 ofwhich 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 C FR 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 ofdata 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 gu idance ." Because complete higher frequency PM1 0 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 ofCollocated Data, Ju l y 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 ofthe 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/m 3) and the design concentration is dependent only on the high concentration data, this substitution was not utilized
.160
140
120
100
80
60 

# • 40
. 

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Figure I 

### North Provo PMlO Data, 2011-2014



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North Provo 

### PM 10 Time Series and Quarters with Incomplete Data; 

### All 1s t 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 

### PM 10 monitors operating for some or all of the 2011-2014 time period
. For most historical applications , use ofthe table look-up method has proven adequate in providing a design concentration for 

### PM1 0 attainment demonstrations. For the contemplated 

### PM10 maintenance plan and redesignation request, unique circumstances at the North Provo 

### PM10 monitor in the Utah County 

### PM 1 0 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 1 0 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 2012-20 14 Area Sample Count Design Sample Count Design Concentration Concentration Ogden Ogden City 1060 90 ul!/m 3 1036 95 1
.. udm3 Cottonwood* Salt Lake 171 79 μg/m 3 0 

### NA (No data after County 2011) North Salt* Salt Lake 892 86 μg/m 3 560 (no 2014 82 μg/m 3 Lake (shut County data) down 

### in 2013) Magna Salt Lake 349 54 μg/m 3 355 62 μg/m 3 County Hawthorne Salt Lake 1047 86 μg/m 3 1050 87 μg/rn 3 County North Provo* Utah County 316 149 Wllm 3 315 149 

### u.e/m3 Lindon Utah County 1014 124 ull/m 3 1031 124 ull/m 3 *Monitors with incomplete data for one or more years , 2011-2014 The 149 μg/m 3 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 ofthe Geneva Steel smelter
. North Provo is 6 miles southeast of the smelter' s former location
. The smelter discontinued operations in November of2001 and has since been demolished. The Lindon monitoring station has reported PMw data since April of 1985
. The North Provo site has reported PM 1 

### o 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



# • •

• 

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.217x

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• 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/m 3, but the slope of the regression line is 1
. 017, indicating that on average Lindon data is 1. 77 μg/m 3 

### + 1. 7% higher than the North Provo data
. Here, the 

### r2 coefficient ofdetermination 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/m 3 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/m 3 , 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



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.··

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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/m 3 , a linear segment between about 

### 20 μglm 3 and about 

### 70 μg/m 3 , and a second linear segment with a different slope above about 

### 40 μg/m3
.

### Lindon 2011-2013

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### .... 0 0
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.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

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.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 ofthe 1979 ozone guideline and the 1987 PM10 SIP Development guideline. 

### Ifthe upper 10 or 5% ofdata 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 ofthe data (graphical estimation)" method. Figures 6 and 7 show the Lindon and North Provo 

### 2011-2013 data, with the upper 

### 10 percent ofdata 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
.o 44x 

# •

60 80 100 

# •

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120 Lindon 

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

# • •

140 1600.01

0.001 0 Figure 7 • Lower 90% of data

20 40 

### N Provo, 2011-2013 Top 10% of data
- 1/365 Probab ili ty
....... Expon
. (Top 10% of data)

60

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### = 0.292Se · 0
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120 

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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
. o 35CPMio). 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/m 3
[PM1o]ov = 1/0.044 * (Ln(0.5074)- Ln(l/365)) = 118.7 μg/m 3 160

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

..ifthe fitted distribution underpredicts all of the last eight data points by more than 5%, then itmust be established that the fitted distribution is reasonable. Such an argument might involve showing that the majority ofthese 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
.00 1

0.000 1 0 Figure 8 1/365

20 

### Lindon 2011-2013, January 2013 in Purple

40 60 80 1 00 

# •• 

# • •

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 ofthe 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 ofconcentrations shown by orange lines
.Table 5 Comparison ofTop 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 I7 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/m 3
, and North Provo had a design concentration of 149 μg/m 3

. 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%ofthe 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 ofTable 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/rn 3 Salt lake County 58 uwm3 69
. 8 ue/rn3 Salt lake County 124 ue/m 3 118.7 uwm3 Salt Jake County 149 μg/m 3 133.5 ue/m3 The 1987 PM 1 0 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 ofthe 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 ofthe 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% ofvalues. Using 5% rather than 10% at Hawthorne reduces the curve fit design concentration from 102.0 to 95.6 μg/m 3 , while using the top 5% at Magna has virtually no impact on the curve fit design concentration (69.8 μg/m 3 is reduced to 69.3 μg/m 3

. 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 ofthe distribution curve fit design values should yield a more consistent design concentration Jess reflective ofthe individual vagaries ofa given data set than does the table look up method
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### Hawthorne PM 10 

### Data Distribution, 2011 - 2013 

### e PMlO 

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20 40 Top 10% of Data

- 1/365 F requency Expon
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. ( T

op 5% )



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80 100

Hawthorne 

### PM10 Distribution, with Upper l 0% and Upper 5% Curve F its 120

### Magna PM 10 

### Data, 2011-2013 

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### 7 .3802e·O.l1 4x 0 c .2 0
.001 

### u 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