NOTICE

This technical report does not necessarily represent final EPA decisions or 

positions. It is intended to present technical analysis of issues using data 

that are currently available. The purpose in the release of such reports is to 

facilitate the exchange of technical information and to inform the public of 

technical developments.

Assessment and Standards Division

Office of Transportation and Air Quality

U.S. Environmental Protection Agency

National Renewable Energy Laboratory

U.S. Department of Energy

Coordinating Research Council

EPAct/V2/E-89: Assessing the Effect 

of Five Gasoline Properties on Exhaust 

Emissions from Light-Duty Vehicles 

Certified to Tier 2 Standards

Final Report on Program Design and 

Data Collection

Appendix L

LOD/LOQ Method

EPA-420-R-13-004

April 2013
L-2 

LOD/LOQ Method for EPAct/V2/E-89 Program at SwRI® 

This document is a description of the method that will be used for handling chemistry 

data in the EPAct/V2/E-89 Program, with respect to Limit of Detection (LOD) and Limit of 

Quantification (LOQ). In addition to describing the final method for processing the data, the 

methods used to determine and track LOD and LOQ are described, as well as the reasoning 

behind the method and the background and experiments which led to its development. 

Most of this discussion is related specifically to the carbonyl and alcohol data, where 

potential interference from the sampling media is significant issue.

Root Issue

The primary problem for which this method was developed is how to properly address 

media interference in the measurement process when the exhaust samples themselves are at 

levels similar to that interference. This problem arises because we are using methods and media 

which were developed for use at much higher measurement levels (and which are perfectly 

adequate for those higher levels). 

For example, based on our experiments, most of the media interference for carbonyls is 

on the order of 0.5 mg/mile or less, which is similar to the levels were are trying to quantify in 

the exhaust samples. It is important to recognize that the traditional process has been developed 

to determine compliance with formaldehyde standards on the order of 4 to 18 mg/mile, and to 

quantify even higher values on pre-Tier 2 vehicles, where this level of blank interference 

represents at most 10% of the standard. We are trying to use the same process to quantify values 

an order of magnitude lower than that, and this has required some refinement of the process. 

Proposed QA Blank Tracking and Data Analysis Process

It should be noted that all of this tracking is done on a compound-by-compound basis, so 

that (for instance) an issue might be observed only on acrolein while all other compounds could 

still be within limits.

1. The laboratory and field blanks are analyzed in a daily basis to determine a daily 

average blank value ( blanki ).

2. Data from these blanks are tracked over time, and are used to generate a 5-day

moving average ( blank ), and an associated standard deviation (σblank).

3. The daily blanks for a given test day ( blanki ) are first evaluated in comparison to the 

previous 5-day average as follows: 

Is i blank blank blank ≤− 3σ ? 
L-3 

a. If the above answer is Yes, update the 5-day blank and σblank, and proceed with 

data analysis. This indicates that the current blank falls within acceptable 

variation from the running average, and is described in Step 4 below. 

b. If the above answer is No, then the current blank is outside the normal range of 

variation and is flagged for review. This process is described further below in 

Step 5. 

4. Data analysis for “normal” blanks (Case 3a, answer to 3 is Yes). Each analyzed 

sample (which is either a dilution air background or a dilute exhaust sample) is 

compared individually to the 5-day blank and σblank. 

a. Is uncorrected blank sample blank ≤− 3σ ? 

b. If the answer to 4a is No, then calculate the blank corrected sample as follows:

sample sample blank corrected =

uncorrected −

c. If the answer to 4a is Yes, then report = 0 corrected sample for that given 

sample. The means that the difference between the sample and the blank does not 

rise above the level of noise in the blanks (i.e., we cannot tell the difference 

between the sample and a media blank).

d. Analysis then proceeds normally with background correction (using the dilution 

factor) and mass calculations. The final analyzed mass is always reported. 

Negative masses are reported as zero and set to zero on a bag-by-bag basis prior 

to composite calculations.

e. Analysis Complete, Proceed to Reporting. 

5. Review of and processing “outlier” blanks (Case 3b, answer to 3 is No).

a. Manual review of blank data chromatograms to determine if an analytical 

problem is at fault, and correct if possible. 

b. Manual review of blank data and association with sample/background samples to 

determine if the blank itself is an outlier or if there is a shift observed for all 

samples and blanks on that day. 

c. If blank itself is an outlier, discard and process test data (as described in Step 4)

using the previous 5-day average. Do not update blank and σblank. 

d. If the shift is consistent for all samples, backgrounds, and blanks on that day, 

process test data (as described in Step 4) BUT use the daily average blank value 

blanki in place of blank for that day only. Do not update blank and σblank. 
L-4 

6. Media Shift.

a. If outlier behavior is consistent for three days running, blanks may have shifted. 

In other words, if a blank value shifts from low to high, but then stays high for 

three days, this may indicate a shift in the media, rather just an outlier. If this is 

the case, data will be reviewed.

b. If it is determined to be appropriate, blank and σblank will be reset (initially with 

values from the three days of question). Note this will be done on a compoundby-compound basis. 

It should be noted that there are multiple review steps throughout this process in the event 

of outliers. It is hoped that over the course of these review steps, any process issues which may 

have contributed to either an increase in the frequency of outliers or a shift in blank values can be 

identified and corrected.

Background on Process Development

To establish an appropriate method for determination of the LOQ for a given 

measurement, it is necessary to understand the key factors driving measurement variability. 

These factors will vary depending in the measurement in question. 

In the case of gaseous HC speciation measurements, the primary driver is analytical 

variability. Experiments have indicated that the bag media do not contribute in a significant 

manner to the measurement, which is not unexpected given the requirements on the media with 

respect to HC off-gassing, as well as the multiple purge-evacuation processes designed to 

eliminate carryover. Therefore, the repeatability of the GC instrument was quantified and an 

LOQ was established in terms of raw area counts at 200 counts. This analytical LOQ was 

determined by examining repeat measurements of low-level standards. LOD and LOQ are then 

determined by examining the ratio between the standard peak height and the noise response. The 

LOD is the lowest concentration where the standard-to-noise ratio is 3 to 1, while the LOQ is 

defined as the lowest concentration where the standard-to-noise ratio is 10 to 1. These ratios 

follow standard good laboratory practice for GC analysis. Any analyzer response below this 

LOQ in terms of area counts is reported as a zero, because we cannot reliably quantify a number 

below this threshold. Note that this process is done on every individual measurement, including 

samples and backgrounds, before the numbers are fed into calculations to determine mass. 

For the carbonyls, and to a lesser extent alcohols, the sample media have a much higher 

potential for interference. Table 1 shows a comparison of analytical instrument LOD versus the 

average blank levels, with the data given as raw area counts. The values in the table compare 3σ 

for the instrument (as quantified by multiple analyses of a standard of 0.0003 ug/ml) against 3σ 

for the blanks (as quantified by examining multiple blanks). This comparison demonstrates that 

the variation in blank area counts is at a much higher level than the instrument LOD, which 

indicates that blank variation is the dominant source of variability in the measurement. 
L-5 

TABLE 1. COMPARISON OF INSTRUMENT AND BLANK VARIABILITY FOR 

CARBONYLS (AREA COUNTS)

To determine an appropriate method to deal with this variation, it was also necessary to 

determine if this variation is present on a day-to-day basis, a batch-to-batch basis (i.e., different 

batches or lots of cartridges), or on the basis of individual blanks. Two data sets were used for 

this analysis. One data set is similar to the set shown above which includes area count values for 

blanks determined over a period of three months’ time, including about 60 days of data and 

covering more than one batch. The second data set was generated but taking 10 blanks from a 

single batch and analyzing all of them in a single analytical run on the same day. The results are 

summarized below in Table 2, with the data given as raw area counts. 

TABLE 2. COMPARISON OF LONG-TERM AND SHORT-TERM CARBONYL 

COMPOUND BLANK VARIABILITY

Compound

Instrument 

LOD

Blank 

Average

FORMALDEHYDE 984 2823

ACETALDEHYDE 1285 9217

ACROLEIN 757 1115

ACETONE 661 39183

PROPIONALDEHYDE 850 1070

CROTONALDEHYDE 392 1605

N-BUTYRALDEHYDE + MEK 785 6002

BENZALDEHYDE 437 1707

HEXANALDEHYDE 326 1154

ISOVALERALDEHYDE 371 4155

VALERALDEHYDE 716 976

O-TOLUALDEHYDE 389 1659

M/P-TOLUALDEHYDE 383 1762

DIMETHYLBENZALDEHYDE 496 1291

90-day Single batch 90-day Single batch

Formaldehyde 3460 3647 941 1070

Acetaldehyde 7858 6791 3072 2677

Acrolein 253 354 372 389

Acetone 21482 20767 13061 5020

Propionaldehyde 256 389 357 409

Crotonaldehyde 432 430 535 465

N-butyraldehyde & MEK 2172 1722 2001 2303

Benzaldehyde 783 593 569 316

Isovaleraldehyde 262 381 385 486

Valeraldehyde 1787 1733 1385 1283

o-Tolualdehyde 128 43 325 108

m/p-Tolualdehyde 437 255 553 149

Hexanaldehyde 1512 1898 587 746

Dimethylbenzaldehyde 267 8 430 25

Average Standard Deviation

Compound
L-6 

In most cases the average and standard deviations for both data sets were very similar. 

This indicates that the variation exists on a cartridge-to-cartridge basis, even within a single 

batch, and that this short-term media variation cannot be distinguished from any longer term 

factors. There are a few compounds that do not follow this trend such as acetone, wherein the 

variations are likely also driven by daily variations in the laboratory environment. 

As a result of this determination, the practice of using a daily blank for correction of 

samples for that given day is problematic, because the cartridge-to-cartridge variability means 

that a daily blank set may not necessarily represent the sample cartridges. Therefore, it is 

necessary to average a number of blanks over time to represent the media correctly. However, 

any method must also track sudden shifts in media which do occur from time to time, as well as 

dealing with individual outliers.

Blank data for both formaldehyde and acetaldehyde are given in Figure 1 and Figure 2, 

respectively, showing different kinds of behavior that need to be addressed. In the case of 

formaldehyde, the blanks are generally well behaved, with some slow movement over time 

indicated. However, there are also individual outliers that need to be dealt with. On the other 

hand, the acetaldehyde blanks show periodic and significant shifts, such as a shift which occurs 

at about Blank 59 from a low level to a higher level. On review this shift is also present in actual 

test samples and backgrounds from that day, indicating a real shift in the media or process. 

Similar data were examined for the heavier carbonyls, and a similar analysis was also 

performed for the alcohols (although in the case of alcohols only the methanol data showed 

evidence of variability). Based on all of these data, it was determined that a 5-day moving 

window for would be appropriate to properly characterize media blank variation, while also 

being short enough to detect movement of the blanks. In addition, the inclusion of a 3-sigma 

outlier test to trigger manual data review accounts for detection of outlier days, which might 

result from either an individual blank issue or a shift which affects an entire test day. Finally, a 

provision is included to reset the daily average, in the event that a real long-term shift is observed 

over several days, such as observed from time to time in the acetaldehyde data.

Figure 3 shows acetone blanks over the same time window. In this case there are several 

outlier days, which upon analysis were reflected in the data for that day, but then the blank levels 

returned to normal. Thus far, we believe that acetone is uniquely affected by laboratory 

environment given the presence of acetone in many places. In these cases, the blanks for that 

day should be used to process that data for that day, but the running average should not be 

disturbed for an outlier day. 
L-7 

FIGURE 1. FORMALDEHYDE BLANK TRACKING

FIGURE 2. ACETALDEHYDE BLANK TRACKING

Formaldehyde Blank Tracking (March 2009 - June 2009)

0

1000

2000

3000

4000

5000

6000

7000

0 20 40 60 80 100 120 140

Blank Number (2 per day)

Area Counts

Formaldehyde Blank Area Counts 5-Day Moving Average

Acetaldehyde Blank Tracking (March 2009 - June 2009)

0

2000

4000

6000

8000

10000

12000

14000

16000

0 20 40 60 80 100 120 140

Blank Number (2 per day)

Area Counts

Acetaldehyde Blank Area Counts 5-Day Moving Average
L-8 

FIGURE 3. ACETONE BLANK TRACKING

Figure 4 illustrates a running example of the final blank process that was developed using 

formaldehyde as an example. The figure shows the five-day running average blank value (note 

that there are two blanks analyzed per day, as well as the five-day running 3σ upper and lower 

control limits. As noted there are occasional single blanks which rise above those limits, which 

would trigger review. In these cases, the review indicates that these were outliers that were not 

represented in the data, and therefore, these measurements were not used to modify the running 

average or control limits. In one case, near Run 45 the control limits are very tight due to a 

period of time when the variation was small. In this case, although the blanks shown are slightly 

above the 3σ limit, the movement is small and still well within the historical norm, and in that 

case the judgment is made to utilize the data. This example also underscores the fact that a set of 

process rules are not a complete replacement for good engineering judgment.

Acetone Blank Tracking (March 2009 - June 2009)

0

10000

20000

30000

40000

50000

60000

70000

80000

0 20 40 60 80 100 120 140

Blank Number (2 per day)

Area Counts

Acetone Blank Area Counts Series2 5-Day Moving Average

Outlier days - reflected in data for 

that day but not stable for more 

than 2-days - blanks used to 

process that day but NOT in 

running average
L-9 

FIGURE 4. FINAL BLANK PROCESS EXAMPLE WITH FORMALDEHYDE

Figure 5 illustrates a similar example for methanol tracking. Methanol appears to be 

affected by periodic issues which generate a low level noise in both blanks and samples, which 

could result in erroneous reporting of methanol and therefore affect NMOG calculation. This is 

confirmed by the fact that when the blanks are clean there is also no methanol detected in 

exhaust samples. The developed QA process is able to detect this noise and account for its effect 

on the data, so that only a significant quantity of real methanol emission would be reported, and 

the data are not disturbed by this process noise. In areas of media/process variability, the 3-

sigma screening value (yellow triangles) rises, as shown in Figure 5. Therefore, the LOQ 

screening process outlined earlier of checking the different between sample and blanks against 

the five-day 3-sigma would be an effective screening for this issue.

Formaldehyde Blank Tracking (March 2009 - June 2009)

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

0 10 20 30 40 50 60 70

Day Number

Area Counts

5-DayAverage 5-Day 3Sigma + 5-Day 3Sigma - Individual Blanks

Outlier blanks not used in 5-day 

average

Small shift outside 

current 3sigma but 

still acceptable
L-10 

FIGURE 5. FINAL BLANK PROCESS EXAMPLE WITH METHANOL

Methanol Blank Tracking (March 2009 - June 2009)

10

210

410

610

810

1010

1210

0 10 20 30 40 50 60 70 80

Test Day

Area Counts

Individual Blanks 5-Day Average 5-Day 3Sigma +

Outlier day, 

significant effect 

on samples Region of blank noise, similar 

behavior of samples, 3sigma 

data screening cleans up this 

process issue in data

Clean blanks, 

no methanol in 

samples either