1

UNITED STATES ENVIRONMENTAL PROTECTION AGENCY 

Office of Air Quality Planning and Standards 

Research Triangle Park, NC 27711 

April 5, 2011 

MEMORANDUM

SUBJECT: Investigation of 1-hour PM2.5 Mass Concentration Data from EPA-Approved 

Continuous Federal Equivalent Method Analyzers 

FROM: Mark Evangelista, OAR/OAQPS/AQAD /s/ 

TO: PM2.5 NAAQS Review Docket (EPA-HQ-OAR-2007-0492) 

Summary

This memorandum documents systematic analyses of hourly PM2.5 mass concentration data 

obtained from continuous Federal Equivalent Method (FEM) analyzers operated by state and 

local air monitoring agencies and reported to EPA’s Air Quality System (AQS). The quality of 

hourly data from FEM analyzer is of interest in connection with the current review of the 

secondary PM2.5 NAAQS, due to the possible use of such data to calculate PM2.5 light extinction 

on a sub-daily basis (e.g., 1 hour or 4 hours). 

Calendar year 2009 data from 109 FEM instruments were obtained from AQS. As discussed 

later, three sites were eliminated from further consideration based on an obvious data validation 

failure. Of the remaining 106 instruments (105 sites) only one site had collocated instruments of 

the same model, such that instrument readings could be compared to get a sense of data 

reproducibility. At the other 104 single-instrument sites, the analyses of the quality of hourly 

data were based on “common sense” expectations for the range and temporal patterns of actual 

ambient concentrations. 

The following observations were reached: 

 Although FEM instruments are capable of being paired with a digital data logging 

system, not all monitoring programs do so. Some reported concentrations of 

approximately 1000 μg/m3

 were traced to the pairing of one particular instrument model 

with older analog data logging systems. Armed with this awareness, it should be possible 

for monitoring agencies to avoid reporting such erroneous data in the future, even if they 

continue to rely on older analog systems. 

 Data from the one available pair of collocated instruments of the same model show 

periodic inconsistencies in hour-to-hour PM2.5 data. On 26 days in 2009, there was at 

least one hour during which the instruments differed by at least 10 μg/m3

, with one 
2

monitor reading below 10 μg/m3 and the other reading above 20 μg/m3. However, the 

annual 90th and 95th percentile daily maximum 1-hour concentrations for the two 

instruments, respectively, were within 10 percent of each other, indicating that for this 

site and this year the errors did not happen in a pattern that would have greatly affected 

compliance determinations against a standard with this statistical form.1

 Nevertheless, 

this observation from a single year of such data from a single instrument does not provide 

much confidence that measurement errors for hourly concentrations would not affect 

compliance determinations at least some times if NAAQS implementation were based on 

a network of monitoring sites using this instrument model. 

 When data from this same pair of collocated instruments are used to calculate moving 4-

hour average concentrations, the results from the instruments become more similar to 

each other and the occurrence and magnitude of extreme values are reduced, confirming 

the expected tendency of multi-hour averages to mute the effect of hourly measurement 

errors. 

 Among the 106 individual instruments, instances of very high reported 1-hour 

concentrations and/or patterns involving isolated high peaks in concentration were never 

so frequent as to significantly affect the annual 95th percentile or annual 90th percentile 

daily maximum 1-hour concentration, if such 1-hour concentrations were assumed to be 

incorrect readings and were replaced by much lower values. 

It has not been possible to assess the representativeness of the vast majority of the moderately 

high concentrations reported by the 104 single-instrument sites because of the lack of any known 

value for “true” hourly concentrations. Nevertheless, the findings for the one site with a pair of 

collocated instruments, the occurrence of very high and/or highly peaked values at some singleinstrument sites, and the hour-to-hour variability seen in visual time series displays of the 

concentration data suggest that considerable caution is warranted when contemplating the use of 

hourly data from these FEM instruments in any calculations of PM2.5 light extinction. 

Background

Consideration of potential revised secondary standards for PM2.5 to address PM-related welfare 

effects involves addressing visibility impairment, which is caused by light scattering and 

absorption by suspended particles and gases. Initial drafts of the Policy Assessment for the 

current review of the secondary PM2.5 NAAQS contemplated the use of calculated PM2.5 light 

extinction, estimated based on 24-hour PM2.5 speciation data and hourly PM2.5 mass data, as an 

indicator for a revised secondary standard. The contemplated method includes simple algorithms 

to estimate light extinction attributed to each of the major PM2.5 components. By applying the 

speciation profile associated with a Chemical Speciation Network monitor to the hourly PM2.5 

concentration values reported by a collocated continuous mass monitor implementing a Federal 

 

1

 A revised PM2.5 NAAQS could be based on a three-year 90th or 95th percentile form. To calculate these percentiles, 

I determined a percentile value depending upon the number of days in the data set, rounded up to the nearest 10 (for 

the 90th percentile case) or 20 (for the 95th percentile case) days. For example, for a full year of data (365 days) the 

95th percentile daily maximum value is the 19th highest value. This method is based on the method used to identify 

98th percentile concentrations for the 2006 24-hour PM2.5 NAAQS, as described in 40 CFR 50 Appendix N.
3

Equivalent Method (FEM), hourly values of the major PM2.5 components could be estimated 

and input into a version of the IMPROVE algorithm for calculating light extinction. 

The analyses in this memo were designed to highlight outlier concentration values that were so 

high that they seemed highly unlikely to represent actual ambient conditions, especially if they 

were frequent and had no obvious explanation based on the monitoring site. However, some of 

the same (largely unknown) causes of such very high outlier concentrations may, to a lesser 

degree, also affect reported concentration values in or near the range 20-50 μg/m3. This range of 

PM2.5 concentration is of interest because, depending on species mix and relative humidity, 

PM2.5 concentrations in this range can translate into PM2.5 light extinction levels in the range of 

25-30 deciviews, which is the range of most interest in the Policy Assessment. 

Federal Equivalent Method Monitors and the Data Set Used in this Investigation

Monitor models, manufacturers, and monitoring methods used to obtain the data set for this 

investigation are listed in Table 1. 

Table 1. Information about monitoring instrumentation. The legend for the table is found in the 

first row. 

Number 

of 

Monitors 

in 2009 

Data Set 

Monitor Model Name 

Website of the monitor model manufacturer

Federal Equivalent Method [and reference location] 

91* 

Met One BAM-1020 Mass Monitor w/VSCC 

www.metone.com/particulate.php 

Method EQPM-0308-170 [Federal Register: April 25, 2008 (Vol. 73, Num 81) Pg. 22362-22363] 

1 

Thermo Scientific 1405-DF Dichotomous FDMS 

www.globalspec.com/FeaturedProducts/Detail/ThermoFisherScientificAirQualityInstruments

Method EQPM-0609-182 [Federal Register: June 17, 2009 (Vol. 74, Num 115) Pg. 28696-28698] 

15 

Thermo Scientific 8500C FDMS w/VSCC 

www.thermoscientific.com/wps/portal/ts/products/detail 

Method EQPM-0609-181 [Federal Register: June 17, 2009 (Vol. 74, Num 115) Pg. 28696-28698] 

2 

Thermo Scientific Model 5030 SHARP w/VSCC 

www.globalspec.com/FeaturedProducts/Detail/Model_5030_RealTime_Particulate_Monitor/17839/0

Method EQPM-0609-184 [Federal Register: June 17, 2009 (Vol. 74, Num 115) Pg. 28696-28698] 

* Data from three of the sites with this instrument model were set aside early in the conduct of these analyses, as 

described in the body of this memo. 

I examined hourly PM2.5 concentration values reported by FEM monitors for 2009. The 

investigation began in early 2010, and 2009 provided the latest seasonally-balanced data set for 

an entire year at that time. 
4

For this investigation, I obtained data from all 109 FEM monitors that had some data for 2009. 

Of the 109 monitors, 53 reported data for the entire year 2009, 21 monitors reported about 9 

months of data, 15 monitors reported about six months of data, and 20 monitors reported data for 

3 months or less. I included data from all monitors in the analyses without regard for data 

completeness. Of course, determination of design values for regulatory purposes would depend 

upon data completeness. 

Instructions for access to the data used in this investigation are given in Appendix A. 

First Indication of a Data Quality Issue

One of the first observations in examining the data set was that three sites had reported PM2.5

concentration values of 985 μg/m3. These values were considered to represent self-evident 

monitoring system errors, and their cause was investigated by EPA staff in the Ambient Air 

Monitoring Group. It was determined that the Met One BAM-1020 Mass Monitor used at these 

three sites, when paired with an analog data logging system, is typically run with a voltage range 

from 0 to 1 volt corresponding to a concentration range from -0.015 mg/m3 to 0.985 mg/m3 (i.e., 

-15 μg/m3to 985 μg/m3). This offset allows for slight negative values, which may be within the 

noise of the instrument output when the atmosphere approaches an ambient concentration value 

near zero, to be included in the reported hourly data. As a means of sending an error code when 

a malfunction or out-of-limits condition is detected, the monitor is programmed to send its 

analog output to the full-scale voltage of 1 volt, rather than report a questionable concentration 

value.2 If the data logger (and/or downstream data processing steps) is designed to convert every 

recorded voltage into a concentration using a simple linear algorithm, the hour for which the 

voltage output was 1 volt will be treated as having a concentration value of 985 μg/m3. 

I eliminated these three sites from the investigation data set, leaving data from 106 instruments at 

105 monitoring sites. Additionally, OAQPS and Regional Office staff are discussing this issue 

with Met One and with State monitoring operators to encourage corrective action, and to ensure 

monitoring agencies remove such clearly incorrect data before submission.

Analytical Approaches

Several analytical approaches were followed. 

1. A qualitative, exploratory data inspection in which time series graphics were generated 

for every FEM monitor with at least one “daylight” 1-hour concentration in excess of 150 

μg/m3

. 

2. Graphical analysis of the time-matched data from the one site with a pair of collocated 

instruments. 

3. Analysis of the effect of 4-hour averaging on the annual daily maximum concentration 

values. 

4. Application of two different approaches for qualitatively defining (“flagging”) what data 

could potentially be erroneous. Data identified by these approaches were graphically 

 

2

 When the instrument is paired with a digital data logger, a separate data channel is used to report such conditions 

explicitly.
5

inspected in temporal and spatial context to determine whether it might be plausible that 

the data represent legitimate measurements. 

5. An analysis in which reported 1-hour concentrations that arguably could be potentially 

erroneous, as identified by Approach 4, were replaced with plausible values. The point 

of the substitution process was to produce an alternate data set that might better represent 

true ambient concentrations, to create a baseline for quantifying the effect of possible 

measurement errors in the actual reported data. I examined how the substitutions affected 

the annual maximum, the annual 95th percentile, and the annual 90th percentile values of 

the daily maximum 1-hour PM2.5 concentration values. The effect of errors/substitutions 

on the annual 95th percentile and annual 90th percentile values of the daily maximum 1-

hour PM2.5 concentration values was of interest because a revised PM2.5 NAAQS could 

be based on a three-year 90th or 95th percentile form. 

1. Qualitative, Exploratory Data Inspections

I immediately noticed that some FEM monitors occasionally reported high hourly PM2.5

concentrations. These concentration spikes sometimes occur for only one hour, with the hours 

immediately before and after showing a significantly lower concentration. Other high hourly 

PM2.5 concentration values appear to take several hours to build and subside. Also, there were 

cases in which a very high reading was unique during the year and other cases where high 

readings occur more frequently. For example, a time series of hourly data reported by a monitor 

in Zion National Park, UT (AQS ID 49-053-0130) for 2009 (Figure 1) shows one hourly 

concentration of nearly 250 μg/m3, but no other hourly concentration values greater than 80 

μg/m3 were reported in all of 2009. In contrast, Figure 2 shows the hourly time series for 2009 

concentrations at a monitor in Allentown, PA (AQS ID 42-095-0025), where high concentrations 

occurred several times during the year. In both cases, these high concentration values may 

represent the actual pattern of ambient particulate matter. Or, they may be erroneous values that 

are due to instrument issues such as siting, maintenance, calibration, instrument design, or oneoff instrument defects. Errors in data handling and transfer are also a possibility. 

I next conducted a visual inspection of the hourly data from each monitor that included at least 

one “daylight” concentration value in excess of 150 μg/m3

. This was the lowest round value that 

would cause both sites mentioned in the previous paragraph to be included. I used a smaller 

dataset of only “daylight” hours for expediency.3

 In the 2009 data, I identified 12 monitors that 

had at least one “daylight” concentration value greater than 150 μg/m3

. Figure 3 shows the 

highest daily maximum concentration value for each of these monitors, compared to the second 

highest daily maximum concentration value. Figure 4 shows the annual time series for the site at 

Turlock, CA (AQS ID 06-099-0006, denoted as monitor 3 in Figure 3). This time series shows 

 

3

 The methods contemplated in the Policy Assessment to estimate visibility from, among other information, PM2.5

values reported by FEM monitors, involve several steps and inputs. Two such steps are screening the data to include 

only daylight hours, and screening the data to include only those hours when relative humidity is reported below a 

certain value. To allow full use of values from sites with only a few months of reported data, and because there was 

no a priori reason to suspect that the time of day or high humidity play a role in the generation of questionable 

concentration values, I tested most flagging algorithms on the full data set without filtering for relative humidity or 

daylight hours. However, computational limitations sometimes required a smaller data set, and in those cases the test 

was performed on data for only a subset of hours. Those tests performed on this subset—referred to as “daylight” 

hours, defined as the hours 5:00 AM to 7:00 PM local time (no Daylight Savings correction)—are clearly identified.
6

one value above 150 μg/m3, but several other high values as well. In this context, perhaps the 

high value is valid as there are other days with relatively high concentrations in the same period. 

Of the 12 monitors, only one had a second highest daily maximum also greater than 150 μg/m3. 

In fact, four monitors had a second highest daily maximum below 75 μg/m3

, less than one-half 

the highest value. These four monitors each appeared to have one high concentration value 

unique to the rest of the year’s data. Figures 5-7 each depict a time series of the 2009 hourly 

concentration values for one of these monitors. In Figure 5, the high value at the Honolulu site 

may have resulted from an upstream volcanic eruption. Figure 6 shows another high value that, 

while it is much lower than the highest value, may lend plausibility to that highest value. 

Additionally, the high value occurs within 90 days of the start of monitoring operation, perhaps 

suggesting an instrument issue. This possibility is more clearly illustrated in the time series for 

another monitor in Figure 7. 

2. Examination of Data from One Site with a Pair of Collocated Instruments

In 2009, data was reported from only one site with collocated FEM monitors—Missoula, 

Montana (AQS ID 30-063-0024, POC3 and POC4)—of the same model and method (Met One 

BAM-1020 Mass Monitors w/VSCC, Method ID EQPM-0308-170). Hourly PM2.5 mass 

measurements were reported from both Missoula monitors for the full year 2009. Figure 8 

shows a scatter plot of the paired hourly PM2.5 measurements reported by both monitors. I 

identified 26 days on which there was an hour when the concentration measured by one monitor 

was reported to be greater than 20 μg/m3

, while the measurement by the collocated monitor for 

the same hour was reported to be less than 10 μg/m3

. I examined these individual days in greater 

detail. Time series for some of these days are found in Figures 9-11. In the figures, differences 

up to 10 μg/m3 for the same hour can sometimes be seen between POCs 3 and 4, and these 

differences generally lasted 4 or more hours when they did occur. 

High PM2.5 concentration measurements are not specific to one single monitor at this site. Both 

monitors did report high PM2.5 concentration values, but never on the same day. The monitor 

identified as POC3 reported more high values than POC4. Figure 9 illustrates all days on which 

POC3 reported a high PM2.5 concentration while POC4 did not. Figure 10 illustrates all days on 

which POC4 reported a high concentration while POC3 did not. Additionally POC4 reported 

measurements in a more erratic pattern than POC3 on several days, as illustrated by Figure 11, 

while POC3 exhibited no such erratic pattern. 

 

3. Analysis of 4-Hour Averages

 

Four-hour averages were determined from the hourly data. If one hourly measurement was 

missing, an average was determined of the remaining three hourly measurements. If there were 

two or more missing hourly measurements, the 4-hour average was not computed. 

The 4-hour averaging was applied to the data from the one site with a pair of collocated 

instruments. Scatter plots of the 4-hour averages are shown in Figure 12. Not surprisingly, 

correlation of the 4-hour averages (0.910) was greater than correlation of the hourly data shown 

in Figure 8 (0.825). Figure 12 does not have any 4-hour values above about 100 μg/m3

, while 

the 1-hour data set depicted in Figure 8 contained three such values. Also, there are fewer cases 
7

in Figure 12 of data pairs in which the two POCs reported much different 4-hour values than 

there are of cases in Figure 8 in which they reported much different 1-hour values. 

Figure 13 shows a scatter plot of the annual maximum daily maximum PM2.5 concentrations for 

1-hour vs. 4-hour values. Figures 14 and 15 show the same comparisons for the 95th and 90th

percentiles. 

Averaging would obviously reduce the impact of an outlier value, and it is reasonable to find 

several points in Figure 13 that represent 1-hour values greater than 50 μg/m3

, and therefore 

highly likely to correspond to a light extinction value that exceeds 30 deciviews (dv), with a 

corresponding 4-hour average value within the 20-50 μg/m3

 range, where light extinction values 

greater than 30 dv may be less likely given other atmospheric circumstances. Figure 14 shows 

the same comparison for the 95th percentile and has a similar number of points of the same type 

as Figure 13. Figure 15, representing the comparison for the 90th percentile has fewer points of 

this type, as one would expect from the lower percentile. Additionally, some 95th and 90th

percentile 4-hour average values are less than 20 μg/m3

with a corresponding 1-hour value well 

within the 20-50 μg/m3 range. 

4. Application of Two Data Flagging Algorithms

Algorithms using two different methods were developed for this portion of the investigation: 

flagging data greater than a certain threshold value (“‘high value”), and flagging data exceeding 

the average of adjacent values by a threshold (“spikes”). These algorithms were applied to the 

full 2009 dataset, and the flagged data were examined for plausibility. Similar results were 

obtained from applying the two algorithms. For illustration, I will describe only the algorithm 

and results for flagging data spikes exceeding the average of adjacent values by a threshold. 

This algorithm flagged any value more than 75 μg/m3greater than the average of concentration 

values immediately before and after the examined value. In other words, the concentration value 

was compared to the average of the concentration values for the previous hour and the following 

hour. This average is the linearly interpolated value between those adjacent hours, which would 

be a reasonable initial estimate of a missing value between these adjacent values. If the 

measured concentration value exceeded the interpolated value by more than 75 μg/m3

, the 

concentration value was flagged. This threshold value was chosen because one hourly value 75 

μg/m3

 higher than the adjacent average would be sufficient to create 4-hour averages above 20 

μg/m3 even if the other hourly values in the average were very low. 

Of the 106 instruments in the 2009 data, this algorithm flagged data for 31 instruments. Ten 

monitors had 2-4 flagged concentration values, and 20 monitors had only one hour flagged. The 

only monitor with more than 4 flagged concentration values was located in the West 

Yellowstone City Center, Montana (AQS ID 30-031-0016), which had 9 such hourly values. 

Most of the flagged hours were in the evening or night and all were in the cool season, indicating 

possible wood burning under night time inversions as the cause of legitimate measurements 

rather than instrument errors. The 2009 time series for this monitor is shown in Figure 16. 
8

5. Analysis of Data Substitution Algorithms

Hourly concentration values that had been flagged by two algorithms in Approach 4—high-value 

and spikes—were eliminated from the data and replaced by predetermined concentration values. 

Daily maximum PM2.5 concentrations for the data set including the substituted values were 

compared to daily maximum PM2.5 concentrations for the original data without any flagging or 

substitution. Comparisons involving the two flagging/substitution algorithms yielded similar 

results. This analysis involving the spike algorithm is explained in detail. 

I assumed all the flagged concentrations values were invalid, and I substituted the linearly 

interpolated values for the flagged values at these 31 monitors. The annual maximum daily 

maximum concentration values for the 2009 data with replacement of flagged values was 

compared to that for the 2009 data without any replacement in Figure 17. Replacement of data 

flagged using this algorithm appeared to affect the annual 95th percentile daily maximum 

concentration values at about 10% of the monitors (Figure 18). The annual 90th percentile daily 

maximum concentration values at only three monitors appeared to be affected by the flagging 

and substitution algorithm (Figure 19). 

The flagging and substitution approaches were also applied to the data from both POCs at the 

collocated site, and the results compared. Table 2 shows the annual maximum daily maximum, 

the annual 95th percentile daily maximum, and the annual 90th percentile daily maximum for 

POC3 and POC4, determined from the original data, as well as from the results of substitution by 

two different algorithms. 

The high-value algorithm did not change the annual 95th percentile daily maximum or the 90th

percentile daily maximum at POC3, presumably because the day with the flagged concentration 

value did not fall below the percentile day when re-ranked after substitution or, if the day fell 

below the 95th or 90th percentile day, the next day was also 37.1 μg/m3. However, the spike 

algorithm and substitution did change the annual 95th and annual 90th percentile daily maximum 

values, presumably because the 3 flagged values did not all occur on the dame day, and the 

substituted days changed the ranking of the percentile days. These changes were less than 10 

percent. 

At POC4, neither substitution algorithm altered the annual 95th or 90th percentile daily maximum 

concentration values, presumably because the day with the flagged concentration value did not 

fall below the percentile day when re-ranked after substitution or, if the flagged day fell below 

the 95th or 90th percentile day, the next day was of the same value as the original 95th or 90th

percentile day. 

The flagging and techniques do not offer much if any insight into the unexplained serious lack of 

reproducibility in the bulk of the collocated data. 

 
9

Table 2. Comparison of Annual Daily Maximum values for the collocated monitors at the 

Missoula MT site (AQS ID 30-063-0024). Values are substituted by the algorithm to flag 

values above a threshold (Hi-Val) and by the algorithm to flag values considered sudden 

spikes (Spike). Column designated Original is data without any flagging or substitution. 

Annual Maximum 

Daily Maximum 

(μg/m3) 

Annual 95th Percentile 

Daily Maximum 

(μg/m3) 

Annual 90th Percentile 

Daily Maximum 

(μg/m3) 

Hourly Values 

Flagged 

Algorithm Original Hi-Val Spike Original Hi-Val Spike Original Hi-Val Spike Hi-Val Spike 

POC 3 187.8 128.6 105.7 37.1 37.1 34.8 30.9 30.9 30.7 1 3 

POC 4 201.2 99.0 99.0 40.1 40.1 40.1 32.5 32.5 32.5 1 1 

 
10
11
12
13

 
14
15
16
17
18
19
20
21

 
22
23
24
25

 
26

 
27
28

 
29

Appendix A 

Instructions for Access to Data Used in This Investigation 

2009 Hourly PM2.5 concentration values from EPA-Approved Continuous Federal Equivalent 

Method Monitors.xlsx. This file can be downloaded at: http://www.epa.gov/ttn/analysis/pm.htm. 

Monitoring site information for PM2.5 monitors, including those used in this investigation, can be 

downloaded at http://www.epa.gov/airexplorer/monitor_kml.htm. Select the file PM2.5 CSV 

(11.4MB) under the heading “Criteria Pollutants.”