United States Office of Air Quality EPA-454/R-99-008

Environmental Protection Planning and Standards April 1999

Agency Research Triangle Park, NC 27711

Air

GUIDELINE ON DATA HANDLING

CONVENTIONS FOR THE PM NAAQS
i

EPA-454/R-99-xxx

April 1999 

GUIDELINE ON DATA HANDLING

CONVENTIONS FOR THE PM NAAQS

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Research Triangle Park, North Carolina 27711
ii

Disclaimer

This guidance has been review and approved for publication by the U.S. Environmental

Protection Agency’s Office of Air Quality Planning and Standards. Mention of trade names or

commercial products are not intended to constitute endorsement or recommendation for use.
iii

Contents

What does this guideline cover? ..................................................... 1

How is this guideline presented and organized? ........................................ 1

Chapter 1. Comparing Your Data to the Standards .................................... 3

1. What are the standards for particulate matter? ................................ 3

2. How do I round my numbers? How many decimal places do I keep? ............... 3

3. How do I compute the 3-year average annual mean for PM10? .................... 5

4. How do I compute the 3-year average, spatially averaged, annual mean for PM2.5? ... 6

5. How do I compute the 3-year average 99 percentile for PM ? ................... 8

th

10

6. How do I compute the 3-year average 98 percentile for PM ? .................. 11 th

2.5

7. Is there another way to determine the 98 or 99 percentile? .................... 12 th th

8. How do I make sure my data is complete enough to meet the standards? ........... 15

9. What if I want to show I meet the standards but I don’t have complete data? ....... 15

10. How do I fill in for missing data to show I meet the standards? ................. 16

11. May I use data from a PM Monitor to show that I meet the PM2.5 10

standards? .............................................. 22

12. May I ignore years with high concentrations if they have incomplete data? ........ 22

13. How little data may I use to show I’m NOT meeting the standards? .............. 23

14. May I fill in for missing data to show that I don’t meet the annual standards? ..... 23

15. What if I don’t meet the minimum of 11 samples in a quarter? Can I still show 

I don’t meet the annual standards? .......................... 23

16. May I show that I meet the standards, or that I don’t meet the standards, if I have only

1 or 2 years of data? ...................................... 24

17. What do I do about a monitor that has stopped monitoring? ................... 24

Chapter 2. Sampling Frequency .................................................... 25

18. If I’ve collected more samples than were scheduled, may I use all the data to show I’ve 

met or not met the 24-hour standards? ....................... 25

19. How do I calculate the 98 and 99 percentiles when my sampling frequencies are th th

seasonal? ............................................... 27

20. How do I compute quarterly averages when parts of the quarter are sampled at 

different frequencies? ..................................... 30

21. Under what circumstances may I reduce the required sampling frequency at a site for a

year or season? .......................................... 30

22. May I use a correlated acceptable continuous (CAC) monitor to reduce my sampling 

frequency? .............................................. 31

23. If I’ve missed a scheduled sample, may I make it up? ......................... 32
iv

24. How do I use scheduled samples with make-up and other non-scheduled samples to 

show that I meet or don’t meet the standards? ................. 33

Chapter 3. Monitoring Issues ..................................................... 35

25. Which monitors do I compare to which standards? ........................... 35

26. What are “community monitoring zones” (CMZs) and how do I decide which sites to 

include in a spatial average? ............................... 37

27. If a monitor is reassigned to a different CMZ during the three-year period, which 

CMZ assignment should I use to calculate the spatial average? .... 41

28. How many hourly values make up a valid 24-hour average for a continuous monitor?42

Chapter 4. Miscellaneous Issues ................................................... 43

29. How do I handle data from uncontrollable or natural events? ................... 43

30. How do I determine whether wildland and prescribed fires managed for resource 

benefits significantly contribute to violations of the PM2.5 or PM10

NAAQS? ............................................... 44
1

GUIDELINE ON DATA HANDLING CONVENTIONS FOR THE

PM NAAQS

What does this guideline cover?

This guidance provides information you, the state or local agency responsible for monitoring and

interpreting air quality, need to determine whether you are meeting the standards for particulate

matter in 40 CFR Part 50. It clarifies requirements for data handling and completeness in

Appendix N to 40 CFR Part 50. It tells you how to handle missing data, different sampling

frequencies, and calculating spatial averages for the PM annual standard. 2.5

How is this guideline presented and organized?

This guideline is organized in a question and answer format. Questions are sorted by topic into

chapters:

• Chapter 1 Comparing Your Data to the Standards

• Chapter 2 Sampling Frequency

• Chapter 3 Monitoring Issues

• Chapter 4 Miscellaneous Issues

Chapter 1: Comparing Your Data to the Standards

 1. What are the standards for particulate matter?

 2. How do I round my numbers? How many decimal places do I keep?

 3. How do I compute the 3-year average annual mean for PM10?

 4. How do I compute the 3-year average, spatially averaged, annual mean for PM2.5?

 5. How do I compute the 3-year average 99 percentile for PM ?

th

10

 6. How do I compute the 3-year average 98 percentile for PM ?

th

2.5

 7. Is there another way to determine the 98 or 99 percentile? th th

 8. How do I make sure my data is complete enough to meet the standards?

 9. What if I want to show I meet the standards but I don’t have complete data?

10. How do I fill in for missing data to show I meet the standards?

11. May I use data from a PM10 monitor to show that I meet the PM2.5

standards?

12. May I ignore years with high concentrations if they have incomplete data?

13. How little data may I use to show I’m NOT meeting the standards?

14. May I fill in for missing data to show that I don’t meet the standards?

15. What if I don’t meet the minimum of 11 samples in a quarter? Can I still

show I don’t meet the annual standards?
2

16. May I show that I meet the standards, or that I don’t meet the standards, if

I have only 1 or 2 years of data?

17. What do I do about a monitor that has stopped monitoring?

Chapter 2: Sampling Frequency

18. If I’ve collected more samples than were scheduled, may I use all the data

to show I’ve met or not met the 24-hour standards?

19. How do I calculate the 98 and 99 percentiles when my sampling th th

frequencies are seasonal?

20. How do I compute quarterly averages when parts of the quarter are

sampled at different frequencies?

21. Under what circumstances may I reduce the required sampling frequency at

a site for a year or a season?

22. May I use a correlated acceptable continuous (CAC) monitor to reduce my

sampling frequency?

23. If I’ve missed a scheduled sample, may I make it up?

24. How do I use scheduled samples with make-up and other non-scheduled

samples to show that I meet or don’t meet the standards?

Chapter 3: Monitoring Issues

25. Which monitors do I compare to which standards?

26. What are “community monitoring zones” (CMZs), and how do I decide

which sites to include in a spatial average?

27. If a monitor is reassigned to a different CMZ during the three-year period,

which CMZ assignment should I use to calculate the spatial average?

28. How many hourly values make up a valid 24-hour average for a continuous

monitor?

Chapter 4: Miscellaneous Issues

29. How do I handle data from uncontrollable or natural events?

30. How do I determine whether wildland and prescribed fires managed for

resource benefits significantly contribute to violations of the PM2.5 or PM10

NAAQS?
3

Chapter 1

Comparing Your Data to the Standards

1. What are the standards for particulate matter?

Appendix N of 40 CFR Part 50 says air quality meets

• The annual PM10 standard whenever the 3-year average of the annual mean PM10

concentrations at each monitoring site is less than or equal to 50 μg/m .

3

• The 24-hour PM10 standard whenever the 3-year average of the annual 99 percentile th

values for PM at each monitoring site is less than or equal to 150 μg/m . 10 

3

• The annual PM2.5 standard whenever the 3-year average of the spatially averaged

annual mean PM2.5 concentrations (among designated monitors) is less than or equal to

15.0 μg/m . [Designated monitors are sites designated for spatial averaging in a State 3

PM Monitoring Network Description. This applies only if you opt to do spatial

averaging in your area as discussed in 40 CFR Part 58. When you don’t opt to do

spatial averaging, use the annual average of the single site.]

• The 24-hour PM2.5 standard whenever the 3-year average of the annual 98 percentile th

values for PM at each monitoring site is less than or equal to 65 μg/m . 2.5

3

Each standard is based on three consecutive, complete, calendar years of air quality data.

2. How do I round my numbers? How many decimal places do I keep?

• If you're doing an initial calculation with 24-hour average PM2.5 concentrations, such

as entering 24-hour averages into a computer program, use one decimal place.

Truncate any extra digits.

Example: 10.314 μg/m truncates to 10.3 μg/m 3 3

10.37 μg/m truncates to 10.3 μg/m 3 3
4

• If you're doing an initial calculation with 24-hour average PM10 concentrations, such

as entering 24-hour averages into a computer program, use the integer part. Truncate

any decimal parts.

Example: 45.29 μg/m truncates to 45 μg/m 3 3

45.816 μg/m truncates to 45 μg/m 3 3

• If you’re doing an intermediate calculation, such as a quarterly-average PM10 value

from the 24-hour averages, keep all available digits and decimal places on your

calculator. [Note that for the sake of brevity this Guideline does not include all

available digits in its examples.]

• If you’re comparing a result to a standard, which includes deciding whether to use

incomplete data with high concentrations, round as follows: 

-- Annual PM : Round to the nearest 0.1 μg/m3. Round decimals 0.05 or greater 2.5

up and those less than 0.05 down.

Example: 15.049 rounds to 15.0 μg/m (not above the standard) 3

15.05 rounds to 15.1 μg/m (above the standard) 3

-- 24-Hour PM : Round to the nearest 1 μg/m3. Round decimals 0.5 or greater 2.5

up and those less than 0.5 down.

Example: 65.49 rounds to 65 μg/m (not above the standard) 3 

65.5 rounds to 66 μg/m (above the standard) 3 

-- Annual PM10: Round to the nearest 1 μg/m3. Round decimals 0.5 or greater up

to and those less than 0.5 down.

Example: 50.486 rounds to 50 μg/m (not above the standard) 3 

50.51 rounds to 51 μg/m (above the standard) 3 

-- 24-Hour PM : Round to the nearest 10 μg/m3. Round integers of 5 or greater 10

up and those less than 5 down.

Example: 154.893 rounds to 150 μg/m (not above the standard) 3 

155.51 rounds to 160 μg/m (above the standard) 3 
5

3. How do I compute the 3-year average annual mean for PM10?

Follow these steps: 

a. Calculate the four quarterly means for each year. Add all of the 24-hour sample

concentrations within a quarter; then, divide by the number of samples in the quarter. 

Example:

Suppose you took 87 24-hour average PM measurements (in μg/m3) during Quarter 10

1 of Year 1, of which the first eight are 39, 42, 58, 66, 45, 28, 36, and 27. 

In this case, the

quarterly mean = (39 + 42 + 58 + 66 + 45 + 28 + 36 + 27 + . . .) μg/m .

3

 87

b. Calculate the annual mean from the four quarterly means. Add the four quarterly

means; then, divide by 4. 

Example:

Assume the four quarterly means for Year 1 of the three-year period for which you

are to compare readings to the NAAQS are as follows: Quarter 1 - 43.23 μg/m3;

Quarter 2 - 44.72 μg/m3, Quarter 3 - 40.96 μg/m3, and Quarter 4 - 40.77 μg/m3. 

Then, the annual mean = ( 43.23 + 44.72 + 40.96 + 40.77) μg/m3 = 42.42 μg/m3.

 4

 

c. Calculate the 3-year average annual mean. Add the three annual average means; then, 

divide by 3. 

Example:

Assume you’ve also calculated the PM10 annual means for Years 2 and 3 as 57.17 and

53.23 μg/m3, respectively. If so, the 3-year average annual mean =

(42.42 + 57.17 + 53.23) μg/m3 = 50.94 μg/m3.

 3
6

d. Round the 3-year average annual mean. Because you’re trying to calculate the 3-year

average annual mean for PM , round to the nearest integer. Round up decimal parts 10

of 0.5 or greater; round down decimal parts less than 0.5. 

Example:

Round 50.94 μg/m3 up to 51 μg/m3.

e. Compare your result to the standard. 51 μg/m3 is greater than 50 μg/m3, so this

example doesn’t meet the PM annual standard. 10

4. How do I compute the 3-year average, spatially averaged, annual mean for PM2.5?

Spatial averages are computed across all designated sites in a Community Monitoring

Zone (CMZ) (see 40 CFR Part 58). If the CMZ has only one site, you can follow the procedures

described here or the slightly simpler ones in Question 3. If you follow the ones in Question 3, 

round the PM2.5 three-year average annual mean to the nearest 0.1 μg/m before comparing it with 3

the standard. 

Assume you’ve designated three monitors to use for computing the 3-year average,

spatially averaged, annual mean.

a. Calculate the four quarterly means for each site. Add all of the 24-hour sample

concentrations; then, divide by the number of samples.

b. Calculate the annual mean from the four quarterly means. For each site, add the four 

quarterly means; then, divide by 4.

Example: 

 

Assume the four quarterly means for one site for the first year are 11.6, 12.4, 15.1, and

12.1 μg/m3. If so, the annual mean is

(11.6 + 12.4 + 15.1 + 12.1) μg/m3 = 12.8 μg/m3.

 4

Calculate the annual means for the other sites and other years using the same approach

to give the results shown in Table 4-1. Note that site 3 had insufficient data in Year 1

(fewer than 11 samples in one or more quarters, as indicated by “NA”), so the annual

mean is not available.
7

 Table 4-1. Using Annual Means (μg/m ) from Three Sites for Spatial Averaging. 3

Site Number

Year One Two Three

 1 12.8 14.2 NA

 2 13.0 13.5 12.9

 3 15.2 14.8 17.1

c. Calculate the spatially averaged annual mean of the designated monitors in the area.

Add all values for a given year across all sites; then, divide by the number of sites with

data for that year. 

Example:

Year 1. (12.8 + 14.2) μg/m3 = 13.5 μg/m3.

 2

Year 2. (13.0 + 13.5 + 12.9) μg/m3 = 13.13 μg/m3.

 3

Year 3. (15.2 + 14.8 + 17.1) μg/m3 = 15.7 μg/m3.

 3

d. Calculate the 3-year average, spatially averaged, annual mean. Add the three spatially

averaged annual means and divide the total by 3.

Example:

13.50 + 13.13 + 15.70 μg/m3 = 14.11 μg/m3.

 3

e. Round to the nearest 0.1 μg/m . Round decimals 0.05 or greater up and those less 3

than 0.05 down.
8

Example: 

Round 14.11 down to 14.1 μg/m3.

f. Compare your result to the standard. 14.1 is less than 15.0 μg/m , so this example 3

meets the PM annual standard of 15.0 μg/m3. 2.5

5. How do I compute the 3-year average 99 percentile for PM ?

th

10

This computation is explained with an example. If you’ve collected more than the

scheduled number of samples in a year, you will need to modify step c of this calculation as shown

in the example for Question 18.

Examine Table 5-1, which presents some sample data on 24-hour concentrations of PM10

collected once every three days over a three year sampling period. The table shows only the three

highest and three lowest values for each year. In this example, a total of 110 samples were

collected in Year 1; 98 samples in Year 2; and 100 samples in Year 3.

Table 5-1. Sample Values for Computing the 3-year Average 99 Percentile for PM .

th

10

YEAR 1 YEAR 2 YEAR 3

(110 total samples) (98 total samples) (100 total samples)

Value Value Value

(μg/m3) (μg/m3) (μg/m3)

88 150 40

130 93 52

120 90 144

85 97 140

128 148 48

87 143 147

! ! !

a. Sort all data values collected in each year from lowest to highest. The result should

 look like Table 5-2.
9

 Table 5-2. Arranging Data Values in Ascending Order.

YEAR 1 YEAR 2 YEAR 3

Value Value Value

(μg/m3) (μg/m3) (μg/m3)

85 90 40

87 93 48

88 97 52

! ! !

120 143 140

128 148 144

130 150 147

b. Assign a rank to each data value. Assign rank 1 to the lowest 24-hour average in each

 year, rank 2 to the second-lowest average, and so on. The results should look like

 Table 5-3.

Table 5-3. Data Values with Assigned Ranks.

Year 1 Year 2 Year 3

Rank (μg/m3) Rank (μg/m3) Rank (μg/m3)

Value Value Value

1 85 1 90 1 40

2 87 2 93 2 48

3 88 3 97 3 52

! ! ! ! ! !

108 120 96 143 98 140

109 128

110 130 98 150 100 147

97 148 99 144

c. Calculate the rank of the 99 percentile value for each year. 

th

• Multiply the number of samples taken in the year by 0.99. 
10

Example (from Table 5-3):

Year 1: 110 x 0.99 = 108.9

Year 2: 98 x 0.99 = 97.02

Year 3: 100 x 0.99 = 99.0

• Take the integer part of the product and add 1. This step gives you the ranking

that corresponds to the 99 percentile: th

Example:

Year 1. Result is 108.9; integer part is 108. 108 + 1 = 109.

Year 2. Result is 97.02; integer part is 97. 97 + 1 = 98.

Year 3. Result is 99.0; integer part is 99. 99 + 1 = 100.

The 99 percentile ranks are therefore 109, 98, and 100. th

d. Find the value that corresponds to each rank. 

Example:

Referring to Table 5-3, 

Year 1. Rank 109 corresponds to 128 μg/m3.

Year 2. Rank 98 corresponds to 150 μg/m3.

Year 3. Rank 100 corresponds to 147 μg/m3.

e. Calculate the 3-year average of all three values for the 99 percentile. th

Example:

128 + 150 + 147 = 141.67 μg/m3.

 3

f. Round to the nearest 10. In this case, round 141.67 down to 140 μg/m3.

g. Compare your result to the standard. 140 is less than 150 μg/m3. This example meets 

the 24-hour standard of 150 μg/m3 for PM . 10
11

6. How do I compute the 3-year average 98 percentile for PM ?

th

2.5

First, review answer 5, above, which is similar to this problem. Then, work through the

example below. However, if you’ve collected more than the scheduled number of samples in a

year, you will need to modify step c of this calculation as shown in the example for Question 18.

This example assumes you have data on 24-hour concentrations of PM2.5 collected on an

every day sampling schedule over a three year period: 281 samples in Year 1; 304 samples in Year

2; and 296 samples in Year 3.

a. Sort all data values collected in each year from lowest to highest.

b. Assign a rank to each data value. Assign rank 1 to the lowest 24-hour average in each

year, rank 2 to the second lowest, and so on. 

The results for this example site after steps a and b are in Table 6-1, below. 

Table 6-1. Rankings of Sample Values for One Site over Three Years.

Year 1 Year 2 Year 3

Rank (μg/m3) Rank (μg/m3) Rank (μg/m3)

Value Value Value

1 40.7 1 40.6 1 45.1

! ! ! ! ! !

275 58.9 296 54.3 290 66.0

276 59.0 291 68.4

277 62.2 292 69.8

! ! ! ! ! !

281 64.4 304 65.8 296 70.9

297 57.1

298 63.0

c. Calculate the rank of the 98 percentile for each year. th

• Multiply the number of samples taken in the year by 0.98.

Example (from Table 6-1):

Year 1. 281 x 0.98 = 275.38

Year 2. 304 x 0.98 = 297.92 

Year 3. 296 x 0.98 = 290.08 
12

• Take the integer part of the product and add 1. This gives you the ranking that

corresponds to the 98 percentile: th

Example:

Year 1. 275 + 1 = 276 

Year 2. 297 + 1 = 298 

Year 3. 290 + 1 = 291 

The 98 percentile ranks are therefore 276, 298, and 291.

th

d. Find the value that corresponds to each rank. 

Example (from Table 6-1):

Year 1. Rank 276 corresponds to 59.0 μg/m3.

Year 2. Rank 298 corresponds to 63.0 μg/m3.

Year 3. Rank 291 corresponds to 68.4 μg/m3.

e. Calculate the 3-year average of all three values for the 98 percentile. th

Example:

59.0 + 63.0 + 68.4 = 63.47 μg/m3.

 3

f. Round to the nearest integer. Round decimals 0.5 or greater up and those less than 0.5

down. 

Example:

Round 63.47 down to 63 μg/m3. 

g. Compare your result to the standard. 63 is less than 65 μg/m3, so this example meets

the 24-hour standard of 65 μg/m3 for PM . 2.5

7. Is there another way to determine the 98 or 99 percentile? th th

Yes, you may also use Table 7-1, below, to calculate the 98 and 99 percentiles. Note th th

that for this method PM concentrations are ranked from highest to lowest (i.e., 1 = highest

concentration). If you’ve collected more than the scheduled number of samples in a year, see

Question 18 for an example of how to modify the calculations.
13

Table 7-1. 98 and 99 Percentiles Defined by Appendix N. th th

Samples n Max. for 98 n Max. for 99 1 th th

Percentile Percentile 2

Value of n Value of n

th th

3

1-50 1 1

4

51-100 2 1

5,6

101-150 3 2

7

151-200 4 2

201-250 5 3

251-300 6 3

301-350 7 4

351-366 8 4

Samples are all the monitored daily PM values in a year unless you’ve collected more than the scheduled number of samples. In that 1

case, see Question 18.

n Max for the 98 percentile is the n highest value in a year that represents the 98 percentile. 2 th th th th

 (1 is the highest value measured in a year, 2 is the second highest value, etc.)

n Max for the 99 percentile is the n highest value in a year that represents the 99 percentile. 3 th th th th

1 in 6 day sampling achieving 75 to 85% data completeness would be in this category. 4

1 in 6 day sampling achieving 85 to 100% data completeness would be in this category. 5

1 in 3 day sampling achieving 75 to 82% data completeness would be in this category. 6

1 in 3 day sampling achieving 83 to 100% data completeness would be in this category. 7

Work through the example below, which uses the same PM2.5 data as the example for answer 6. In

years 1, 2, and 3, your data sets from the site contained 281, 304, and 296 samples, respectively.

a. Sort all of the data values collected in any given year from highest to lowest. The

results should look like Table 7-2, which is a rearrangement of Table 6-1 (the values

go from the highest to the lowest). This table has the n highest value listed for each n th

so it is easy to use it with Table 7-1.

b. Find n from Table 7-1 and find the n highest value from Table 7-2. For PM ,

th

2.5

calculate the 98 percentile. For PM , calculate the 99 percentile. th th

10
14

Table 7-2. Maxima of Sample PM Values from One Site for a Three-Year Period. 2.5

 Year 1 Year 2 Year 3

n (μg/m ) (μg/m ) (μg/m )

n highest value n highest value n highest value th

3

th

3

th

3

1 64.4 65.8 70.9

2 63.7 65.2 70.8

3 63.1 64.9 70.3

4 62.8 64.5 70.0

5 62.2 64.1 69.8

6 59.0 63.8 68.4

7 57.9 63.0 66.0

8 57.3 57.1 65.6

9 54.7 54.3 65.6

! ! ! !

Example:

In Year 1 there were 281 PM samples. For PM , you calculate the 98 percentile. 2.5 2.5

th

From Table 7-1, n equals 6. From Table 7-2, the n highest value equals 59.0 μg/m .

th 3

Similarly, for Years 2 and 3, n equals 7 and 6, and the n highest values equal 63.0 and th

68.4 μg/m , respectively. 3

c. Calculate the 3-year average of all three values for the 98 or 99 percentile. th th

Example:

59.0 + 63.0 + 68.4 = 63.47 μg/m3.

 3

d. Round to the nearest integer for PM and to the nearest multiple of 10 for PM For 2.5 10.

PM2.5, round decimals 0.5 or greater up and those less than 0.5 down. For PM10, round

integers 5 or greater up and those less than 5 down. 
15

Example:

Round 63.47 down to 63 μg/m3. 

e. Compare your result to the standard. 63 is less than 65 μg/m3 so this example meets

the 24-hour standard of 65 μg/m3 for PM . 2.5

8. How do I make sure my data is complete enough to meet the standards?

Table 8-1 summarizes how complete data must be to show you meet the standards. 

Table 8-1. How Complete Data Must Be to Show that an Area Meets the NAAQS for PM.

Standard Data Completeness to Show You Meet the Standards

Daily PM Single site: at least 75% of the scheduled sampling days per quarter 2.5

Daily PM Single site: at least 75% of the scheduled sampling days per quarter 10

Annual Single site: if each quarter has at least 75 % of the scheduled sampling days, the

PM annual mean for that year and site is valid. 2.5

Community monitoring zone: In each of the three years, at least one site must

have a valid annual mean. The valid sites may be the same every year, or may

vary from year to year.

Annual Single site: at least 75% of the scheduled sampling days per quarter

PM10

9. What if I want to show I meet the standards but I don’t have complete data?

Appendix N says you may have compelling reasons to use less complete data, but the 

Regional Administrator must approve it. The Regional Administrator may want to consider filling

in for missing scheduled sampling days using the procedures in Question 10 if you

• Have at least 50% of the scheduled number of samples for each quarter for all three

years.
16

• Show that the emissions and meteorology for the substitute quarters compare to the

emissions and meteorology for the quarters in question. 

• Meet the standards based on the incomplete data.

10. How do I fill in for missing data to show I meet the standards?

First, you should meet the criteria from Question 9. Then, you may use either of two

approaches to fill in for missing scheduled sampling days:

(1) Replace missing data with collocated data for the same year and quarter.

(2) Replace missing data with the maximum data value across all three years for the same

quarter. 

Approach No. 1: How you may use collocated data to substitute for missing data

You may use collocated data in either of two ways:

(1) If you have collocated PM10 or TSP data from a monitor for the same year and

quarter, you may use it to replace missing PM10 or PM2.5 values on scheduled

sampling days. 

(2) If you have collocated PM2.5 data from a monitor for the same year and

quarter, you may use it to replace missing PM2.5 values on scheduled sampling

days.

The following notes apply in either case:

• You must substitute for all missing scheduled sampling days where collocated data

is available, not just for selected days in that quarter. 

• If you didn’t collect the collocated data on the same day as the scheduled sampling

day, you can use collocated data from the nearest day (within two days before or

after) to replace missing PM concentrations. The emissions and meteorology for

the substitute day must compare to the emissions and meteorology for the missing

day.

• The collocated PM or PM monitor must use a Federal Reference Method or 10 2.5

Equivalent Method, or must be a collocated QA monitor.
17

Example: How to Substitute Collocated PM10 Data for Missing Data and Use It to

Calculate the Annual PM2.5 Standard

For this example, assume

• Your PM monitor is on a once every three days schedule, and it has a collocated 2.5

PM monitor that has collected three years of data and measured it using a Federal 10

Reference Method.

• The site is the only site in a community monitoring zone.

• In Years 1 and 3 of sampling, the annual average concentrations of PM2.5 are 12.84

and 12.43 μg/m3, respectively. 

• In Quarter 3 of Year 2 (see Table 10-1), you got only 23 samples from the 31

scheduled sampling days. 

• The total PM concentration of the 23 samples is 292.1 μg/m3. 2.5

• You have collocated PM data for only three of the eight scheduled sampling days 10

that are missing PM data. The three PM values are 30, 21, and 54 μg/m3. 2.5 10

• You meet the criteria from Question 9. [To show that you meet the annual PM2.5

standard based on the incomplete data, repeat steps b to f below without using the

substituted data. In step b, the average for Quarter 3 of Year 2 is 12.70 μg/m3

without using the substituted data. Then, in step e, the resulting rounded 3-year

average annual mean is 12.8 μg/m3, which meets the annual PM2.5 standard of 15.0

μg/m3.]

For Quarter 3 of Year 2, you had 31 scheduled sampling days but only 23 samples. 75%

of 31 equals 24 (rounding up to the next integer). So you don’t meet the data completeness

requirement of 24 samples. You need at least one more sample.
x =

+ ×

+

=

+

=

2921 401 8

23 8

2921 3208

31

. ( . )

. .

19.77 g / m3 m

y2

1189 14 69 19 77 1321

4

=

+ + +

=

. . . .

14.89 g / m

3 m

y =

+ +

=

12 84 13 77 12 43

3

. . .

13.01 g / m

3 m

(x )

q

18

 Table 10-1. PM Quarterly Statistics for Year 2 (in μg/m ). 2.5

3

Quarter Number of Total* Quarterly Average*

Samples

1 28 332.9 11.89

2 29 426.0 14.69

3 23 292.1 12.70

4 28 369.9 13.21

*Calculated from available PM concentrations. 2.5

a. Combine the three PM samples with the 23 PM samples collected in Quarter 3 of 10 2.5

Year 2. This total of 26 samples constitutes a valid quarter because 26 is at least 75%

of 31. Thus, you can use the combined data from this quarter to show you’ve met the

standard.

b. Calculate the average for Quarter 3 of Year 2.

c. Calculate the average for Year 2.

d. Calculate the 3-year average.
19

e. Apply the rounding convention. Round the 3-year average annual mean for PM2.5 to

the nearest 0.1, so 13.01 μg/m3 rounds down to 13.0 μg/m3.

f. Compare your result to the standard. Because 13.0 μg/m3 is below the annual standard

for PM of 15.0 μg/m3, you’ve met the standard. 2.5

Example: How to Substitute Collocated PM10 Data for Missing Data and Use It to

Calculate the 24-Hour PM2.5 Standard

Consider the same example (see Table 10-1) and also assume that

• In Years 1 and 3 of sampling the annual 98 percentile values are 58.7 and 54.3 μg/m3, th

respectively.

• In Year 2, the highest three of the 108 measured PM concentrations are 75.7, 70.4, 2.5

and 50.9 μg/m3.

• You meet the criteria from Question 9. [To show that you meet the 24-hour PM2.5

standard based on the incomplete data, repeat steps b to f immediately below without

using the substituted data. In step c, the number of samples equals 108, n equals 3

from Table 7-1, and the 3 highest value equals 50.9 μg/m3 without using the rd

substituted data. Then, in step e, the resulting rounded 3-year average 98 percentile is th

55 μg/m3, which meets the 24-hour PM standard of 65 μg/m3.] 2.5

Follow these steps.

a. Combine the three PM samples with the 23 PM samples collected in Quarter 3 of 10 2.5

Year 2. This total of 26 samples constitutes a valid quarter because 26 is at least 75%

of 31. Thus, you can use the combined data from this quarter to show you’ve met the

standard. In Year 2 there are 108 measured PM2.5 concentrations. The combined data

has a total of 111 samples in Year 2. 

b. Sort the combined data from highest to lowest. For Year 2, the original three highest

values are 75.7, 70.4, and 50.9 μg/m3. The three substituted PM samples are 30, 21, 10

and 54 μg/m3. Then, the highest four of the 111 samples in the combined data are

75.7, 70.4, 54, and 50.9 μg/m3.

c. Find n from Table 7-1 and find the n highest value. There are 111 samples. From th

Table 7-1, n equals 3. The 3 highest value equals 54 μg/m3.

rd
20

d. Calculate the 3-year average of all three values for the 98 percentile. th

58.7 + 54 + 54.3 = 55.67 μg/m3.

 3

e. Round to the nearest integer for PM2.5. For PM2.5, round decimals 0.5 or greater up

and those less than 0.5 down.

Round 55.67 up to 56 μg/m3. 

f. Compare your result to the standard. 56 is less than 65 μg/m3 so this example meets

the 24-hour standard of 65 μg/m3 for PM . 2.5

Approach No. 2: How you may use maximum observed values to substitute for missing

data

Replace each missing scheduled sampling day in an incomplete quarter with the maximum

observed value from the same site and the same quarter (from any of the three years). 

Example: How to Substitute Maximum Observed Values for Missing Data and Use

Them to Calculate the Annual PM2.5 Standard

Consider the same set of PM observations used in the example above (see Table 10-1). 2.5

Assume 

• You have no collocated data available, so you must substitute for each of the eight

scheduled PM sampling days that are missing in Quarter 3 of Year 2. 2.5

• Emissions and meteorology of the quarter in question are typical for Quarter 3

throughout the three years. 

With these assumptions, you can substitute data from Quarter 3 of all three years to show

you meet the annual PM standard. 2.5

a. Combine eight substituted maximum values with the 23 PM2.5 samples collected in

Quarter 3 of Year 2. Substitute the maximum PM concentration observed in Quarter 2.5

3 in any of the most recent three years for all eight missing samples. The maximum

may occur on one of the 23 non-missing days in Quarter 3 of Year 2, or in Quarter 3

of Year 1 or Year 3. 
x =

+ ×

+

=

+

=

2921 401 8

23 8

2921 3208

31

. ( . )

. .

19.77 g / m

3 m

y2

1189 14 69 19 77 1321

4

=

+ + +

=

. . . .

14.89 g / m

3 m

y =

+ +

=

12 84 14 89 12 43

3

. . .

13.39 g / m3 m

y1 = y3 =

21

You review Quarter 3 data for all three years, and find the maximum value is 40.1

μg/m3. It occurred in Quarter 3 of Year 2. (You would get the same results if the

maximum for that quarter occurred in Year 1 or Year 3). Substitute 40.1 μg/m3 for

each of the eight missing sampling days scheduled in Year 2, Quarter 3. 

b. Calculate the average for Quarter 3 of Year 2.

c. Calculate the average for Year 2.

d. Calculate the 3-year average. Because the averages for Year 1 and Year 3 (from 

the previous example) are 

12.84 μg/m3 and 12.43 μg/m3, 

the three-year average is

e. Apply the rounding convention and compare your result to the standard. This threeyear average of 13.39 μg/m3 rounds to 13.4 μg/m3, which is below the PM annual 2.5

standard. Thus, this example meets the PM annual standard. 2.5

Example: How to Substitute Maximum Observed Values for Missing Data and Use

Them to Calculate the 24-Hour PM2.5 Standard

Continue the same example.
22

a. Combine the eight substituted maximum values with the 23 PM2.5 samples collected in

Quarter 3 of Year 2. In Year 2 there are 108 measured PM2.5 concentrations. The

combined data has a total of 116 samples in Year 2. 

b. Sort the combined data from highest to lowest. For Year 2, the original three highest

values are 75.7, 70.4, and 50.9 μg/m3. The eight substituted maximum values are all

equal to 40.1 μg/m3. Then, the highest three of the 116 samples in the combined data

are also 75.7, 70.4, and 50.9 μg/m3.

c. Find n from Table 7-1 and find the n highest value. There are 116 samples. From th

Table 7-1, n equals 3. The 3 highest value equals 50.9 μg/m3.

rd

d. Calculate the 3-year average of all three values for the 98 percentile. th

58.7 + 50.9 + 54.3 = 54.63 μg/m3.

 3

e. Round to the nearest integer for PM2.5. For PM2.5, round decimals 0.5 or greater up

and those less than 0.5 down.

Round 54.63 up to 55 μg/m3. 

f. Compare your result to the standard. 55 is less than 65 μg/m3 so this example meets

the 24-hour standard of 65 μg/m3 for PM . 2.5

11. May I use data from a PM10 Monitor to show that I meet the PM2.5 standards?

Yes, if the monitor meets PM2.5 siting requirements and the acceptable PM2.5 sampling

schedule, you may treat the PM data as if it were PM data and compare it to the standards as 10 2.5

in Questions 4 and 6. However, you may not use PM10 data to show that you do not meet the

PM standards. 2.5

12. May I ignore years with high concentrations if they have incomplete data?

No. For the 24-hour standards, Appendix N says you must include data from years with

quarters which have less than 75% completeness if the resulting annual 98 or 99 percentile th th

(rounded under the conventions in Question 2) exceeds the standard. For the annual standards,

Appendix N says you must include data from years with less than 75% completeness but at least

11 samples per quarter, if the resulting annual mean (rounded under the conventions in Question

2) exceeds the standard. 
23

13. How little data may I use to show I’m NOT meeting the standards?

Generally, you must use data from years meeting the rules for completeness in Table 8-1

above. Plus, Appendix N says you must keep data from years with high values that don’t meet

those rules. The minimum data you need to show you’re not meeting the standards follows: 

• For the 24-hour standards: You must keep any year where the annual percentile

exceeds the standard, even if there was only one measurement in that year and that

single measurement exceeded the standard.

• For the annual PM standard: You must keep any year where 10

-- the annual mean exceeds the standard, and 

-- there were at least 11 samples in each quarter.

• For the annual PM standard: For one monitor, the rule is the same as for the annual 2.5

PM10 standard. When there are multiple monitors designated for spatial averaging in a

CMZ, you must keep any year where

-- the spatially averaged annual mean exceeds the standard, and 

-- at least one of the monitors has in each quarter at least 11 samples. 

Of course, in all of these cases, data from three years is averaged.

14. May I fill in for missing data to show that I don’t meet the annual standards?

No, except in some of the situations described in the answer to Question 15, where the

annual mean exceeds the standard but you don’t meet the minimum of 11 samples in a quarter.

15. What if I don’t meet the minimum of 11 samples in a quarter? Can I still show I

don’t meet the annual standards?

Appendix N says that situations may arise in which there are compelling reasons to retain

years containing quarters which do not meet the minimum number of 11 samples, and the use of

less than complete data is subject to the approval of the Regional Administrator.

If, for example, a site is missing part or all of a quarter, the Regional Administrator may

want to consider examining data from the same quarters in the other two years and/or data from

nearby monitors during the same quarter for all 3 years. Consistent meteorology and emissions

data between the quarter with missing data and the one from which data are substituted would

also be a factor to examine.
24

If, as another example, a site is missing part or all of a quarter, the Regional Administrator

may want to consider substituting 0 (zero) for the missing data to demonstrate that the site has an

annual average that does not meet the standard. The Regional Administrator could also consider

substituting the historically lowest 24-hr concentration observed at the site. Since this is a

compelling argument, the Regional Administrator might consider using this approach for several

quarters with no data.

In any situation, the Regional Administrator could combine these approaches to strengthen

the argument for a determination that the site does not meet the standards.

16. May I show that I meet the standards, or that I don’t meet the standards, if I have

only 1 or 2 years of data?

• For the 24-hour PM standards and the annual PM10 standard, you must wait for the

third year of data if your site is still operating.

• For the annual PM standard, a site may contribute only 1 or 2 years to the spatial 2.5

average across a CMZ, as long as in each of the 3 years, at least one site contributes.

For more on this method, see Question 4 of this guidance or Example 1 in Appendix

N.

17. What do I do about a monitor that has stopped monitoring?

The appropriate Regional Office must be notified prior to shutting down a NAMS or

SLAMS site that has exceeded the level of the particulate matter NAAQS. The air quality status

at a NAMS or SLAMS site that has stopped monitoring after two years must be handled on a

case-by-case basis. Factors to consider are the reasons that the site stopped monitoring, the

magnitude of the particulate matter concentrations measured, and the likelihood that additional

monitoring data may be available in future years. 
25

Chapter 2

Sampling Frequency

18. If I’ve collected more samples than were scheduled, may I use all the data to show

I’ve met or not met the 24-hour standards?

Yes, but the 98 and 99 percentiles are based on the applicable number of samples, th th

rather than the actual number of samples.

For the 24-hour standards, you won’t receive credit for more samples than the maximum

number of scheduled sampling days in a quarter. For each quarter, the applicable number of

samples is the lower of the actual number of samples and the scheduled number of samples. The

percentile will be calculated as in Question 7 using the applicable number of samples for the year

in Table 7-1. This policy ensures the annual 98 or 99 percentiles aren’t biased low by extensive th th

sampling over a short period when values are low. If “extra” samples are collected when values

are high, those samples could contribute to a violation. If this occurs for an uncontrollable or

natural event, see Question 29 for additional data handling considerations.

If you prefer to use the calculations from Questions 5 and 6, then you must modify step c.

To calculate the rank of the 99 percentile for each year: Multiply the applicable number of th

samples by 0.99. Take the integer part of the product, add 1, and then add the number of extra

samples beyond the number of scheduled samples. To calculate the rank of the 98 percentile for th

each year: Multiply the applicable number of samples by 0.98. Take the integer part of the

product, add 1, and then add the number of extra samples beyond the number of scheduled

samples. 

The answers to this question and to Question 20 show that the calculations for the 24-

hour and annual standards use all the samples that were collected. Therefore, all measured sample

concentrations must be reported to AIRS, including any extra samples beyond the number of

scheduled samples.
26

Example:

Assume that in Year 1 the scheduled sampling frequency for PM2.5 was once every three

days. Assume that the numbers of samples collected and scheduled were as shown in

Table 18-1. Assume that the four highest values in the year (among all 228 samples) were

66.7, 62.2, 62.1, and 62.1 μg/m3.

 Table 18-1. Actual and Applicable Number of Samples for Year 1.

Quarter Actual Number of Scheduled Number Applicable Number

Samples of Samples of Samples =

Minimum (Actual,

Scheduled)

1 90 30 30

2 89 30 30

3 25 30 25

4 24 31 24

Total 228 121 109

Using Table 7-1

a. Calculate the applicable number of samples. For each quarter, the applicable number of

samples is the lower of the scheduled number of samples and the actual number of

samples. For each year, the applicable number of samples is the total of the applicable

number of samples in each quarter. From Table 18-1, in quarters 1 and 2 the scheduled

number of samples was 30, but you collected 90 samples in quarter 1 and 89 samples

in quarter 2. For each of those quarters you will only be credited with 30 samples. For

quarters 3 and 4 you collected less than the scheduled number of samples, so the

applicable number of samples is the actual number of samples. The total number of

applicable samples for Year 1 is 109.

b. Find n from Table 7-1 and the n highest value from the sorted values. For PM you th

10

calculate the 99 percentile. For PM you calculate the 98 percentile. The total th th

2.5

number of applicable samples is 109. The value of n from Table 7-1 is 3. The 98th

percentile for Year 1 is the 3 highest value from all 228 samples, i.e. 62.1 μg/m3.

rd
( ) ( ) F ( x),

d d

d

F x

d d

d

W x Low

High Low

Low

High

High Low

High

+

+

+

=

F x

number of samplesin season a that are x

number of samplesin season a

a

( ) = ,

≤

27

Using the Calculated Rank

a. Calculate the applicable number of samples. As before, from Table 18-1 the total

number of applicable samples for Year 1 is 109.

b. Multiply the number of applicable samples by 0.98 or 0.99. For PM10 you multiply by

0.99. For PM you multiply by 0.98. 2.5

109 x 0.98 = 106.82.

c. Take the integer part of the product, add 1, and add the number of extra samples

beyond the number of scheduled samples. From Table 18-1, the number of extra

samples was 228 actual samples minus 109 applicable samples, i.e., 119. (You get the

same number of extra samples if you add the 60 extra samples in quarter 1 to the 59

extra samples in Quarter 2.)

106 + 1 + 119 = 226.

The value with rank 226 is the 226 lowest value, which equals 62.1 μg/m3.

th

19. How do I calculate the 98 and 99 percentiles when my sampling frequencies are th th

seasonal?

You calculate the percentiles by finding the smallest measured concentration, x, that

makes W(x) > .98 (or .99) where:

d = number of calendar days in the “High” season, High

d = number of calendar days in the “Low” season, (d + d = days in a year), Low High Low

 such that a can be either High or Low,

x is the measured concentration, and

dHigh/(dHigh + dLow) and dLow /(dHigh + dLow) are constant and are called seasonal “weights.”
105

105

70

70

104

105

69

70

28

The following example shows how you find the percentile using this formula. Suppose you

expect high PM2.5 values during January, February, November, and December, but you expect low

PM concentrations the rest of the year. You now know that dHigh = 120 and dLow = 245, so you

can calculate the seasonal weights as 0.329 and 0.671.

You schedule samples to be taken every day during the High months and once every 3

days the rest of the year. In the year in question, suppose you actually got 105 samples out of a

possible 120 in the High season and you got 70 samples out of a possible 82 for the Low season.

Rank or sort the samples from each season. The highest concentrations have the highest

ranks. Suppose the results are as shown in Table 19-1. 

 Table 19-1. Ranking of High Values in High and Low Seasons. 

Concentrations in μg/m3.

High Season Low Season

Rank of x Rank of x

Samples Concentration Samples Concentration

105 74.3 70 60.9

104 71.2 69 58.7

103 70.0 68 43.6

102 67.5 67 35.7

101 64.8 66 28.5

100 59.2 65 25.0

99 55.1 64 20.3

98 50.0 63 20.1

Ranking will allow you to calculate FHigh(x) and FLow(x). Looking at the first line in the table, you

calculate FHigh(74.3) as = 1, because 105 of 105 samples are # 74.3. Similarly, calculate

F (60.9) as = 1. Likewise, calculate F (71.2) as = 0.990 and F (58.7) as Low High Low

= 0.986. Your results are in Table 19-2, below.
29

 Table 19-2. Distribution Functions of High Values in High and Low Seasons.

Concentrations in μg/m3.

High Season Low Season

Rank of x Rank of x

Samples Concentration F (x) Samples Concentration F (x) High Low

105 74.3 1.000 70 60.9 1.000

104 71.2 0.990 69 58.7 0.986

103 70.0 0.981 68 43.6 0.971

102 67.5 0.971 67 35.7 0.957

101 64.8 0.962 66 28.5 0.943

100 59.2 0.952 65 25.0 0.929

99 55.1 0.943 64 20.3 0.914

98 50.0 0.933 63 20.1 0.900

Next, you rank or sort all the values in a year (both seasons) to help you calculate W(x). If

a concentration value wasn’t measured in the Low (or High) season, you find the value of FLow

(or FHigh ) from the next lowest measured concentration. For example, 74.3 wasn’t measured in

the Low season; the next lowest measured concentration in that season was 60.9. FLow (74.3) =

FLow (60.9) = 1.000. As another example, FHigh (58.7) = FHigh (55.1) = 0.943. Table 19-3 shows the

results with the calculated values of W(x).

 Table 19-3. Calculating Weighted Percentiles for Two Seasons.

Concentrations in μg/m3.

Concentration Weight F (x) Weight F (x) W(x)

Seasonal Seasonal

High Low

74.3 0.329 1.000 0.671 1.000 1.000

71.2 0.329 0.990 0.671 1.000 0.997

70.0 0.329 0.981 0.671 1.000 0.994

67.5 0.329 0.971 0.671 1.000 0.990

64.8 0.329 0.962 0.671 1.000 0.988

60.9 0.329 0.952 0.671 1.000 0.984

59.2 0.329 0.952 0.671 0.986 0.975

58.7 0.329 0.943 0.671 0.986 0.972

55.1 0.329 0.943 0.671 0.971 0.962

50.0 0.329 0.933 0.671 0.971 0.959
30

Using the formula for W(x), calculate W(74.3) as 0.329×1.000 + 0.671×1.000 = 1.000. 

You calculate W(71.2), W(70.0), and so on, in the same way. Now you can find the smallest x for

which W(x) is > 0.98 by moving up the right hand column until you find the first number greater

than but not equal to 0.98 (0.984). This value corresponds to the concentration of 60.9 μg/m3,

which is the 98 percentile for this year. th

This method has two advantages. First, the seasons don’t have to be contiguous months

and can even be planned episodic monitoring with concurrence from the appropriate regional

administrator. Second, if you set one of the seasons to have 0 days, the method will calculate a

percentile identical to one calculated using the procedures in Appendix N.

20. How do I compute quarterly averages when parts of the quarter are sampled at

different frequencies?

You should use the same calculation as in Question 3 (for PM ) or Question 4 (for 10

PM ). The quarterly average is the arithmetic mean of the measured concentrations in that 2.5

quarter, so you add all the sampled values and divide by the number of samples. [Note that

“extra” samples beyond the scheduled sampling days are included when you calculate the annual

mean.] The arithmetic mean should represent the true average concentrations during that quarter

reasonably well, even if the scheduled sampling frequencies change.

21. Under what circumstances may I reduce the required sampling frequency at a site

for a year or season?

Review our Interim Guidelines for Granting Schedule Exemptions for PM Monitoring, 10

(attachment to Waivers for PM10 Sampling Frequency memorandum, from William F. Hunt, Jr. to

EPA Regional Office Air Program Directors, December 2, 1997) which describes methods and

decision rules for shifting certain sites from every-day and one-in-three-day sampling for PM10

schedules to one-in-six-day sampling. You must show there is little or no chance of exceeding the

daily standard. A brief summary follows. [Note that EPA is developing similar guidance for PM2.5

sampling frequencies. For initial guidance on PM sampling frequencies see Guidelines for 2.5

Granting Exceptions for Daily PM2.5 Monitoring memorandum, from William F. Hunt, Jr. to EPA

Regional Office Air Program Directors, April 9, 1998. For information on PM2.5 sampling

frequencies for calendar year 1999 only, see PM2.5 Site Types and Sampling Frequency During

CY-99 memorandum, from William F. Hunt, Jr. to EPA Regional Office Air Program Directors,

May 18, 1998.]
31

Requesting a Year-Round Exemption for PM10

The Regional Administrator may grant a year-round exemption that allows you to sample

only once in six days. To request this year-round exemption, you must, at least, show one of the

following:

• The mean 99 percentile for the most recent three years is statistically significantly th

lower than the 24-hour standard. Apply a statistical t-test at the 10 percent significance

level. 

• The annual standard is the controlling standard. Show that the three-year average

annual mean divided by the annual standard level is greater than the three-year average

annual 99 percentile divided by the 24-hour standard level. th

• The 99 percentile estimated using an exponential distribution is lower than the 24- th

hour standard. Fit an exponential distribution to the top 25 percent of the

concentrations measured over the most recent three years. Show that the fitted 99th

percentile is below the daily standard level. 

Requesting a Seasonal Exemption for PM10

The Regional Administrator may grant a seasonal exemption to allow your site to sample

once in three days or once in six days instead of every day, but you must show

• The site has met Appendix N’s minimum requirements for data completeness.

• Every value during that season for the past two years was below the level of the 24-

hour standard.

22. May I use a correlated acceptable continuous (CAC) monitor to reduce my sampling

frequency?

You may use the CAC monitor to reduce the required PM2.5 sampling frequency from

every day to once in three days at a core monitor under the following conditions, as given in 40

CFR 58.13(f).

(1) You must show the CAC monitoring data is correlated with PM2.5 data from a

collocated monitor using a Federal Reference Method or Equivalent Method.

(2) In a Priority 1 PM monitoring area, you must collect at least two complete years of

PM2.5 monitoring data using a Federal Reference Method or Equivalent Method before
32

being considered for permission to reduce the sampling frequency. A Priority 1 PM

monitoring area, as defined in 40 CFR Part 58(f)(2):

• Is a Metropolitan Statistical Area with a population of 1 million or more

• Has an average annual 98 percentile PM greater than or equal to 80% of th

2.5

the 24-hour standard level for PM . 2.5

23. If I’ve missed a scheduled sample, may I make it up?

EPA publishes schedules for each calendar year that give the dates on which you should

collect particulate matter samples at State and Local Air Monitoring Stations (SLAMS) or other

monitoring sites. EPA publishes these schedules for one-in-three-day sampling and for one-in-sixday sampling. Your scheduled sample might be missed or invalidated for various reasons,

including sampler malfunctions and power outages. EPA encourages but does not require you to

make up missing scheduled samples by collecting samples on other days, referred to as

replacement sampling days. You may want to use these make-up samples to help meet the

requirements for data completeness. 

The EPA Regional Office is responsible for ensuring that uses of make-up samples avoid

bias by being consistent with this and other EPA guidance and with the data validation procedures

in the State’s Quality Assurance Project Plan (QAPP). If you decide to use make-up samples,

You must

• Make up the sample no later than one week after the scheduled sampling day 

You should

• Discuss the use of make-up sampling in the QAPP

• Report the reason why the scheduled sample was missing or invalidated to the

Regional Office

• Include the reason why the scheduled sample was missing or invalidated in the State’s

internal database

You must not

• Intentionally invalidate or fail to collect scheduled samples
33

• Select whether a missing scheduled sample will be made up based on the level of the

expected concentrations

• Select replacement sampling days based on the level of the expected concentrations 

You should not 

• Make-up 6 or more scheduled samples in any calendar quarter

• Consistently have to use make-up samples to meet requirements for data completeness

The preferred approach is to make up all missed or invalid scheduled samples whenever

practicable within the one week time limit. If the QAPP does not include such a plan, the EPA

Regional Office may not want to approve make-up samples on days that are not representative of

the corresponding scheduled sampling day. For example, this might include make-ups after a

severe stagnation or after a brief period of extremely high emissions.

The replacement sampling day should be chosen as follows:

Preferred Approach: Sample before the next scheduled sampling day

• If your monitor samples once every six days, and if other sites in the same network

sample once every three days, choose the next scheduled one-in-three-day sampling

day. This gives additional spatial resolution and is likely to be most convenient.

• Otherwise, choose the earliest possible day before the next scheduled sampling day.

This increases the probability that the replacement day has similar meteorological

conditions.

Alternative Approach: Sample exactly one week later

• This approach reduces potential biases due to the variation of emissions patterns with

the day of the week.

24. How do I use scheduled samples with make-up and other non-scheduled samples to

show that I meet or don’t meet the standards?

Treat make-up samples and other non-scheduled samples exactly the same as the

scheduled samples for the calculations showing that you meet or don’t meet the particulate matter

standards:
34

• The comparisons with the standards use all valid 24-hour samples except for data

affected by natural or uncontrollable events and excluded under Appendix N

provisions (see Question 29). The actual number of samples (in Question 18) is the

total number of

-- samples on scheduled sampling days 

-- make-up samples on non-scheduled sampling days, as described in the

answer to Question 23

-- samples substituted for missing data as described in the answers to

Questions 10 and 15

-- any other non-scheduled samples 

• For quarters with one-in-three-day or one-in-six-day sampling, the scheduled number

of samples is the number of sampling days in the published schedule for the sampling

frequency

• For quarters with every day sampling, the scheduled number of samples is the number

of days in the calendar quarter

• Data completeness percentages are given by the ratios of the actual number of samples

to the scheduled number of samples
35

Chapter 3

Monitoring Issues

25. Which monitors do I compare to which standards?

• Is the PM monitoring data collected using a Federal Reference Method or Federal

Equivalent Method?

• Does the monitored data meet the Part 58 requirements for data quality assurance,

sampling frequency, monitor siting, and, if necessary, spatial averaging?

If so, you may compare that data with the applicable PM or PM standards, as 10 2.5

described below. If not, you generally may not compare that data to the PM standards in order to

decide whether or not you’ve met the standards. Part 58 and its appendices discuss some

exceptions. 

Handling PM Data: 10

Compare your PM10 data with the annual and 24-hour standards for PM10 if you collect PM10 data

• At State and Local Air Monitoring Stations (SLAMS) or at National Air Monitoring

Stations (NAMS)—a subset of the SLAMS network. 

• At a Special Purpose Monitor (SPM) that the State intends to use to show that the

PM10 standards are met (i.e., they are included in the PM Monitoring Network

Description). 
Special purpose monitors that meet Part 58 requirements will be exempt from NAAQS *

comparisons with the PM2.5 NAAQS for the first two calendar years of their operation to

encourage PM monitoring initially (see 40 CFR 58.14(b)). 2.5

36

Handling PM data: 2.5

Follow Table 25-1 below to determine how to compare your PM data to the standards. 2.5

Table 25-1. How to compare your PM data to the standards. 2.5

If your monitor And if the (see Part 58, location these PM standards

is a . . . location is . . . App D, 2.8) is represents

And if the

spatial scale And if the Then compare with

. . .

2.5 

SLAMS or population- neighborhood, annual and 24-hour

NAMS or SPM oriented urban, or *

regional

SLAMS or population- micro or many annual and 24-hour

NAMS or SPM oriented middle scale locations in *

the area

SLAMS or population- micro or a unique 24-hour only

NAMS or SPM oriented and a middle scale area *

local hot spot

SLAMS or a background neighborhood, annual and 24-hour, but

NAMS or SPM site urban, or address failures to meet *

regional the NAAQS at these

sites as part of the PM

implementation

program
Table 25-1. How to compare your PM data to the standards. 2.5

If your monitor And if the (see Part 58, location these PM standards

is a . . . location is . . . App D, 2.8) is represents

And if the

spatial scale And if the Then compare with

. . .

2.5 

37

SLAMS or a transport site urban or annual and 24-hour, but

NAMS or SPM regional address failures to meet *

the NAAQS at these

sites as part of the PM

implementation

program

SLAMS or not population neither standard

NAMS or SPM oriented, and *

not a

background or

transport site

SPM with at population- neither standard, but if

most two years oriented your data violates the

of data NAAQS, report the

results in the State’s

annual monitoring

report and consider

whether these sites

should become SLAMS 

Special purpose monitors that meet Part 58 requirements will be exempt from NAAQS *

comparisons with the PM2.5 NAAQS for the first two calendar years of their operation to

encourage PM monitoring initially (see 40 CFR 58.14(b)). 2.5

26. What are “community monitoring zones” (CMZs) and how do I decide which sites

to include in a spatial average?

A community monitoring zone (CMZ) is an area that contains one or more sites with

relatively similar concentrations of PM that are impacted by similar emission sources. The states 2.5

define CMZs in their PM Monitoring Network Description, based on 40 CFR Part 58. For the

PM2.5 annual standard, the states may spatially average the annual means over the sites in the
38

CMZ (each site-year with data is given equal weight in the computation of the annual spatial

average). They compare the average of the three annual spatial averages to the level of the

standard. CMZs are defined to meet the following three conditions, only the first of which is

required (see Part 58, Appendix D, §2.8.1.6.1):

• The annual average concentrations at every site in the CMZ must be no less than 80%

or more than 120% of the annual spatial average [required]

• The 24-hour average concentrations should have strong correlations (a correlation

coefficient of 0.6 or greater) [recommended]

• Emissions from the same source or types of sources of PM2.5 affect the entire CMZ

[recommended]

State and local agencies that regulate air quality must develop descriptions for the PM2.5

network. In those descriptions, they must group sites into CMZs if they choose to do spatial

averaging. If the CMZ’s data does not meet the three conditions above, then states may need to

redefine the CMZs before comparing the data to the NAAQS. To decide which sites to include in

the spatial average, follow these steps: 

a. Calculate the annual mean for each year and site. Average the four quarterly means.

Don’t use data for a year and site that does not meet the criteria for data completeness

of the annual mean.

b. Calculate the spatially averaged annual mean for each year. Average the annual means

across the designated sites in the CMZ.

c. Check each year to see whether all sites’ annual means are greater than or equal to 80

percent and are less than or equal to 120 percent of the spatially averaged annual mean

for that year.

• YES. The annual averages meet the first condition for a CMZ. Check the other

two conditions.

• NO. The annual averages do not meet the first condition for a CMZ. Redefine the

CMZ.

d. Calculate the spatial correlations between the 24-hour average concentrations.

Compute the Pearson correlation coefficients between the 24-hour average

concentrations at each pair of sites for each year. Suppose the n pairs of

concentrations at sites X and Y measured on the same day in a year are (X , Y ),(X , 1 1 2
r

X Y

X Y

n

X

X

n

Y

Y

n

xy

i i

i

n i

i

n

i

i

n

i

i

n i

i

n

i

i

n i

i

n

=

−

−









































−









































=

= =

=

=

=

=

∑

∑ ∑

∑

∑

∑

∑

1

1 1

2

1

1

2

2

1

1

2

39

Y2), (X3, Y3), . . . (Xn, Yn). Then the Pearson correlation coefficient, rxy, for sites X

and Y in a year is given by the formula:

e. Check to see whether all the spatial correlations between the 24-hour average

concentrations are at least 0.6.

• YES. The 24-hour average concentrations meet the second condition for a CMZ. 

Check the third condition.

• NO. The 24-hour average concentrations do not meet the second condition for a

CMZ. Consider redefining the CMZ.

f. Check to see if emissions from the same source or source-types affect the CMZ. For

help with this, see the Guidance for Network Design and Optimum Site Exposure for

PM and PM , December 15, 1997, Section 5.5. 2.5 10

• YES. The emissions meet the third condition for a CMZ. 

• NO. The emissions do not meet the third condition for a CMZ. Consider

redefining the CMZ.

Example: How to check site annual averages across a CMZ for consistency

For this example, examine Table 26-1 below. Assume the CMZ contains 5 sites, numbered

1 to 5. Assume each site and quarter is at least 75% complete.

a. Calculate the annual means. The annual means are as shown in Table 26-1.
40

b. Calculate the spatially averaged annual mean. The spatially averaged annual means

(10.80, 14.08, and 13.99 μg/m3) for each year are shown in the row “Spatial mean." 

For example, in Year 1, the spatially averaged annual mean is

10.38 + 13.71 + 10.62 + 10.50 + 8.79 = 10.80 μg/m3.

5

c. Check whether each site’s annual means are between 80 and 120 percent of the

spatially averaged annual mean. The spatially averaged annual means multiplied by 0.8

and by 1.2 are shown in the rows “spatial mean × 80%" and “spatial mean × 120%."

For example, in Year 1, 80% of the spatially averaged annual mean is 10.80 × 0.8 =

8.64 μg/m3. In year 2, all five sites have annual means between 80 and 120 percent of

the spatially averaged annual mean (i.e., between 11.26 and 16.90). However, in Year

1, site 2 is more than 120% of the spatially averaged annual mean (13.71 > 12.96). In

Year 3, site 1 is less than 80% of the spatially averaged annual mean (9.71 < 11.19). In

Year 3, site 2 is more than 120% of the spatially averaged annual mean (20.36 >

16.79). Thus, the first condition for a CMZ is not met for this example. Redefine the

CMZ before evaluating whether or not the NAAQS is met.

Table 26-1. Example of Consistency Checks for the CMZ’s Annual Average. (PM2.5

concentrations in μg/m3).

Site Year 1 Year 2 Year 3

1 10.38 13.21 9.71

2 13.71 16.32 20.36

3 10.62 12.50 16.11

4 10.50 12.51 11.66

5 8.79 15.84 12.10

Spatial mean 10.80 14.08 13.99

Spatial mean × 80% 8.64 11.26 11.19

Spatial mean × 120% 12.96 16.90 16.79
41

Example: How to check spatial correlations between 24-hour average concentrations

across a CMZ for consistency

d. Calculate the spatial correlations between the 24-hour average concentrations.

Suppose that a CMZ has 3 sites, numbered 1, 2, and 3. Table 26-2 shows correlation

coefficients for one year calculated by the equation given in step 4 above: 

Table 26-2. Example of Consistency Checks for the CMZ’s Spatial Correlations.

Correlations Site 1 Site 2 Site 3

Site 1 X 0.68 0.71

Site 2 X 0.63

Site 3 X

e. Check to see whether all the spatial correlations between the 24-hour average

concentrations are at least 0.6. Since the correlations are all greater than 0.6, sites 1, 2,

and 3 in this year meet the second condition for a CMZ. To complete the test of the

condition for strong spatial correlations, check whether all other correlations between

pairs of sites in the rest of the years are all greater than or equal to 0.6.

f. Check to see if emissions from the same source or source-types affect the CMZ.

Assume that using the guidance provided in the document sited in step 6 above shows

that PM2.5 emissions are from the same source type throughout the CMZ. The third

condition for a CMZ is met. 

27. If a monitor is reassigned to a different CMZ during the three-year period, which

CMZ assignment should I use to calculate the spatial average?

Apply the most recent definition of the CMZ to all three years.

Example:

Suppose that in Year 1, sites 1, 2, and 3 formed one CMZ, and site 4 formed another. In

Year 3, the state or local agency reassigns site 2 to the second CMZ. Calculate whether you meet

the standard based on having sites 1 and 3 in one CMZ and sites 2 and 4 in the other CMZ. Use

all valid annual means for the three-year period. Apply the three conditions for a CMZ described

in the answer to Question 26 to each of these CMZs, so you can decide whether you need to

subdivide one or both CMZs.
Note: See 40 CFR Part 50 Appendix J (for PM ) and Appendix L (for PM ) for the **

10 2.5

rules on determining how many hours make a valid 24-hour average for an integrated 24-hour

measurement.

42

28. How many hourly values make up a valid 24-hour average for a continuous 

monitor?**

• If measurements are available for at least 75 percent (18 or more) of the hours during

the 24-hour period, the 24-hour average is valid. You’ll compute it by summing the

hourly concentrations and dividing by the number of hourly measurements.

• If measurements are available for less than 75 percent (17 or less) of the hours during

the 24-hour period, you must treat the 24-hour average as invalid, unless the

concentrations are too high to be ignored, as determined by the following calculation: 

a. Compute the lower bound. Substitute zero for each missing hour and

compute the average over all 24 hours.

b. Round the lower bound. Round the lower bound to the nearest 1 μg/m3 for

PM concentrations (round up decimals 0.5 or greater). Round the lower 2.5

bound to the nearest 10 μg/m3 for PM concentrations (round up integers 10

of 5 or greater). 

c. Compare the rounded lower bound to the standard. If the rounded lower

bound exceeds the level of the 24-hour standard, use the unrounded lower

bound as the valid 24-hour average. Otherwise, treat the 24-hour average

as invalid.

Example: How to calculate 24-hour averages from hourly concentrations

Suppose the hourly PM10 measurements are as follows, where “M” denotes a missing

value: M, M, M, M, M, M, 112, M, 109, 95, 110, 113, 140, 150, 160, 160, 160, 165, 160, 170,

140, 130, 135, 140 μg/m3. You have 17 hourly concentrations and 7 missing values. Because

there are fewer than 18 valid hours, you must calculate the lower bound. The lower bound equals

(0 + 0 + 0 + 0 + 0 + 0 + 112 + 0 + 109 + . . . + 135 + 140) /24 = 97.9 μg/m3. Rounding to the

nearest 10 gives a rounded value of 100 μg/m3. This value is lower than the level of the 24-hour

standard, 150 μg/m3, so the 24-hour average for this day is invalid.

.
43

Chapter 4

Miscellaneous Issues

29. How do I handle data from uncontrollable or natural events?

First, you should review the following three documents for guidance on how to address

PM data that are affected by uncontrollable or natural events.

• Guideline on the Identification and Use of Air Quality Data Affected by Exceptional

Events, EPA 450/4-86-007, July, 1986.

• Areas Affected by PM-10 Natural Events, Memorandum from Mary D. Nichols,

Assistant Administrator for Air and Radiation, to EPA Regional Office Air Program

Directors, May 30, 1996.

• Interim Air Quality Policy on Wildland and Prescribed Fires, Memorandum from

Richard D. Wilson, Acting Assistant Administrator for Air and Radiation, to EPA

Regional Administrators, May 15, 1998.

The Guideline on the Identification and Use of Air Quality Data Affected by Exceptional

Events addresses uncontrollable events such as structural fires, high pollen count, chemical spills

and industrial accidents, and activities that temporarily affect a nearby monitor. The Natural

Events Policy for the Particulate Matter National Ambient Air Quality Standards addresses the

treatment of data that are affected by volcanic and seismic activities, unwanted wildland fires

(wildfires), and high wind events. The Interim Air Quality Policy on Wildland and Prescribed

Fires addresses the treatment of air quality data that are affected by wildland and prescribed fires

that are managed to achieve resource benefits. Actions to be taken depend upon whether the

wildland and prescribed fires managed for resource benefits significantly contribute to violations

of the PM NAAQS; the answer to Question 30 shows you how to decide this. 

 

Appendix N generally sets forth what is needed to determine whether the PM standards

are met, based on three consecutive, complete years of air quality data. Section 1.0(b) of

Appendix N allows EPA to give special consideration to data affected by uncontrollable or natural

events. While all valid ambient air quality data should be submitted to the EPA Aerometric

Information Retrieval System (AIRS), appendix N provides that in some cases it may be

appropriate for you to exclude such data from the calculations because they could result in

inappropriate values to compare with the levels of the PM standards. In other cases, you should

use all the data to calculate the comparison with the standards and you should then follow the

regulatory response determined to be appropriate by EPA, which could include approaches
44

outlined in the Natural Events and Interim Air Quality policies. The appropriate Regional

Administrator is responsible for approving the exclusion, adjustment, or retention of data affected

by uncontrollable or natural events.

30. How do I determine whether wildland and prescribed fires managed for resource

benefits significantly contribute to violations of the PM2.5 or PM10 NAAQS?

The Interim Air Quality Policy on Wildland and Prescribed Fires addresses the flagging

(in section VII.B) and treatment of air quality data that are affected by wildland and prescribed

fires that are managed to achieve resource benefits. Actions to be taken depend upon whether the

wildland and prescribed fires managed for resource benefits significantly contribute to violations

of the PM NAAQS. You may consider fires managed for resource benefits to have significantly

contributed to a violation of the PM or PM NAAQS if the corresponding standard was not 2.5 10

met and if 

C For the 24-hour standards: 25 percent or more of all the PM concentrations that are

above the level of the standards have been flagged as being due to fire impacts.

C For the annual standards: the sum of all measured PM concentrations flagged as being

due to fire impacts, divided by the total number of sample days (fire days plus non-fire

days) is greater than or equal to 25 percent of the annual standard.

Follow the calculations in these examples, which also show how you should apply the

rounding conventions: 

 Example: How to Decide if Fires Managed for Resource Benefits Have Significantly

Contributed to a Violation of the 24-hour PM NAAQS. 10

Steps a to e of this example follow the steps given in Question 7 to decide if the site meets

or does not meet the 24-hour standard for PM10. If the site does not meet the standard, follow

steps f and g to decide whether fires managed for resource benefits have significantly contributed

to a violation of the PM NAAQS. 10

a. Sort all of the data values collected in any given year from highest to lowest. Consider

the set of PM10 observations given in Table 30-1. The values have been sorted from

highest to lowest. Assume 111 values were measured in Year 1, 99 values in Year 2,

and 101 values in Year 3. Assume one of the values (in Year 2) has been flagged as

being due to fire impacts, as shown by the superscript f.
45

Table 30-1. Maxima of Sample PM Values from One Site for a Three-Year Period. 10

 Year 1 Year 2 Year 3

n n highest value n highest value n highest value

111 values 99 values 101 values

th

(μg/m ) (μg/m ) (μg/m )

3

th

3

th

3

1 170 203 175 f

2 130 155 147

3 128 152 144

4 120 143 140

5 95 133 133

6 90 120 120

7 90 100 100

8 88 97 99

9 86 90 99

! ! ! !

Concentration flagged as being due to fire impacts. f 

b. Find n from Table 7-1 and find the n highest value from Table 30-1. The 99 th th

percentile value for Year 1 is the 2 highest value (130 μg/m ), for Year 2 is the nd 3

highest value (203 μg/m ), and for Year 3 is the 2 highest value (147 μg/m ). 3 nd 3

c. Calculate the 3-year average of all three values for the 99 percentile. th

130 + 203 + 147 = 160.0 μg/m3.

 3

d. Round to the nearest multiple of 10 Round integers of 5 or greater up and those less .

than 5 down. Round 160.0 to 160 μg/m3. 

e. Compare your result to the standard. 160 is greater than 150 μg/m3 so this example

does not meet the 24-hour standard of 150 μg/m3 for PM . 10

f. Count the number of rounded concentrations that exceed the standard (exceedances),

For the 24-hour standard for PM10, concentrations are rounded to the nearest 10

before being compared to the standard of 150 μg/m3. Therefore the exceedances are 

any concentrations that are greater than or equal to 155 μg/m3. Table 30-1 shows 4

exceedances (in bold).
46

g. Calculate the percentage of flagged concentrations among the exceedances. 1 out of 4

exceedances are flagged as being due to fire impacts, which is 25 percent. Since 25

percent or more of the exceedances standard have been flagged as due to fire impacts, 

fires have caused or significantly contributed to the 24-hour PM10 standard being

violated in this area.

Example: How to Decide if Fires Managed for Resource Benefits Have Significantly

Contributed to a Violation of the 24-hour PM NAAQS. 2.5

Steps a to e of this example follow the steps given in Question 7 to decide if the site meets

or does not meet the 24-hour standard for PM2.5. If the site does not meet the standard, follow

steps f and g to decide whether fires managed for resource benefits have significantly contributed

to a violation of the PM NAAQS. 2.5

a. Sort all of the data values collected in any given year from highest to lowest. Consider

the set of PM2.5 observations given in Table 30-2. The values have been sorted from

highest to lowest. Assume 281 values were measured in Year 1, 304 values in Year 2,

and 296 values in Year 3. Assume 13 values have been flagged as being due to fire

impacts, as shown by the superscripts f.

Table 30-2. Maxima of Sample PM Values from One Site for a Three-Year Period. 2.5

 Year 1 Year 2 Year 3

n n highest value n highest value n highest value

281 values 304 values 296 values

th

(μg/m ) (μg/m ) (μg/m )

3

th

3

th

3

1 64.9 f

 72.0 73.0 f f

2 64.6 f

 70.0 71.0 f f

3 64.5 f

 68.4 70.5 f f

4 64.4 67.8 70.0 f f

5 64.2 67.2 69.8 f f

6 64.0 66.5 68.4

7 57.9 65.5 68.4

8 54.0 65.4 66.0

9 54.0 54.3 62.7

! ! ! !

Concentration flagged as being due to fire impacts. f 
47

b. Find n from Table 7-1 and find the n highest value from Table 30-2. The 98 th th

percentile value for Year 1 is the 6 highest value (64.0 μg/m ), for Year 2 is the 7 th 3 th

highest value (65.5 μg/m ), and for Year 3 is the 6 highest value (68.4 μg/m ). 3 th 3

c. Calculate the 3-year average of all three values for the 98 percentile. th

64.0 + 65.5 + 68.4 = 65.97 μg/m3.

 3

d. Round to the nearest integer Round decimals of 0.5 or greater up and those less than 

.

0.5 down. Round 65.97 to 66 μg/m3. 

e. Compare your result to the standard. 66 is greater than 65 μg/m3 so this example does

not meet the 24-hour standard of 65 μg/m3 for PM . 2.5

f. Count the number of rounded concentrations that exceed the standard (exceedances).

For the 24-hour standard for PM , concentrations are rounded to the nearest integer 2.5

before being compared to the standard of 65 μg/m3. Therefore the exceedances are 

any concentrations that are greater than or equal to 65.5 μg/m3. Table 30-2 shows 15

exceedances (in bold).

g. Calculate the percentage of flagged concentrations among the exceedances. 10 out of

15 exceedances are flagged as being due to fire impacts, which is 66.7 percent. Since

25 percent or more of the exceedances standard have been flagged as due to fire

impacts, fires have caused or significantly contributed to the 24-hour PM2.5 standard

being violated in this area.

Example: How to Decide if Fires Managed for Resource Benefits Have Significantly

Contributed to a Violation of the Annual PM NAAQS. 10

This example uses the same data as the example given in Table 30-1. 

a. Use the calculations given in Chapter 1 to decide if the site meets or does not meet the

annual standard for PM10. Assume for this example that the site does not meet the

annual standard for PM . 10

b. Calculate the sum of all measured PM10 concentrations flagged as being due to fire

impacts. From Table 30-1, only one concentration is flagged, and this sum equals 203

μg/m3.
203

111+ 99+101

= 0.65 g/ m

3 m

893 7

281 304 296

.

+ +

= 1.01 g/ m

3 m

48

c. Divide by the number of sample values. Include fire days and non-fire days. For this

example, the ratio equals

d. Compare the result to 12. 25 percent of the annual standard for PM equals 12 μg/m3 10

(after rounding to the nearest integer). Since 0.65 is less than (and not equal to) 12

μg/m3, fires have not caused or significantly contributed to the annual PM10 standard

being violated in this area.

Example: How to Decide if Fires Managed for Resource Benefits Have Significantly

Contributed to a Violation of the Annual PM NAAQS. 2.5

This example uses the same data as the example given in Table 30-2. Assume that the area

has not been designated for spatial averaging and has a single site.

a. Use the calculations given in Chapter 1 to decide if the area meets or does not meet

the annual standard for PM2.5. Assume for this example that the area does not meet the

annual standard for PM . 2.5

b. Calculate the sum of all measured PM2.5 concentrations flagged as being due to fire

impacts. If the area has been designated for spatial averaging, sum across all flagged

days and across all sites in the CMZ. From Table 30-2, 13 concentrations were

flagged, and their sum equals 

64.5 + 64.6 + 64.9 + 67.2 + 67.8 + 68.4 + 70.0 + 72.0 + 69.8 + 70.0 + 70.5 + 71.0 +

73.0 = 893.7 μg/m3.

c. Divide by the number of sample values. Include fire days and non-fire days. If the area

has been designated for spatial averaging, sum across all sites in the CMZ. For this

example, the ratio equals

d. Compare the result to 4. 25 percent of the annual standard for PM equals 4 μg/m3 2.5

(after rounding to the nearest integer). Since 1.01 is less than (and not equal to) 4
49

μg/m3, fires have not caused or significantly contributed to the annual PM2.5 standard

being violated in this area.
TECHNICAL REPORT DATA

(Please read Instructions on reverse before completing)

1. REPORT NO. 2. 3. RECIPIENT'S ACCESSION NO.

 EPA-454/R-99-009

4. TITLE AND SUBTITLE 5. REPORT DATE

Guideline on Data handling Conventions for the PM NAAQS April 1999

 6. PERFORMING ORGANIZATION CODE

7. AUTHOR(S) 8. PERFORMING ORGANIZATION REPORT NO.

 Jonathan Cohen (ICF Kaiser), Terence Fitz-Simons, Miki Wayland

 

9. PERFORMING ORGANIZATION NAME AND ADDRESS 10. PROGRAM ELEMENT NO.

 Air Quality Trends Analysis Group (MD-14)

 U.S. Environmental Protection Agency

 Office of Air Quality Planning and Standards

 Research Triangle Park, NC 27711

11. CONTRACT/GRANT NO.

 

12. SPONSORING AGENCY NAME AND ADDRESS 13. TYPE OF REPORT AND PERIOD COVERED

 Director

 Office of Air Quality Planning and Standards

 Office of Air and Radiation

 U.S. Environmental Protection Agency

 Research Triangle Park, NC 27711

14. SPONSORING AGENCY CODE

EPA/200/04

15. SUPPLEMENTARY NOTES

Document is available electronically at “http://www.epa.gov/ttn/oarpg/”

16. ABSTRACT

This report provides technical guidance on the data handling conventions for the Particulate Matter National

Ambient Air Quality Standards (NAAQS). The report is written in the “plain English” question and answer

(Q&A) style. The following data handling issues are addressed:

1) Comparing your data to the standards

2) Sampling frequency

3) Monitoring issues

4) Uncontrollable or Natural events

17. KEY WORDS AND DOCUMENT ANALYSIS

a. DESCRIPTORS b. IDENTIFIERS/OPEN ENDED TERMS c. COSATI Field/Group

 Particulate Matter, PM, PM PM Air Pollution Control 10, 2.5

 Compliance

 PM Standard 

 Ambient Air Quality Standards (NAAQS) 

18. DISTRIBUTION STATEMENT 21. NO. OF PAGES

 Release Unlimited

19. SECURITY CLASS (Report)

 Unclassified 54

20. SECURITY CLASS (Page)

 Unclassified

22. PRICE

EPA Form 2220-1 (Rev. 4-77) PREVIOUS EDITION IS OBSOLETE