OAR Box 1115 

Prepped by Ryan Dugan 

Document Number: 

93) IV-B-1 

Docket Number: 

A-84-22 

Printed 4/18/2008 10:52:09 AM Sheet 93 of 94 

p.1 
P.2 

RADIAN 

CORPORATIO N 

MEMORANDU M 

A-84-22 

IV-B-1 

TO: 

FROM: 

DATE: 

Opacity Method Project File 

Kim S. Kepford, Radian Corporation 

February 6, 1991 

jjUaJLUj/ 

i H ! 

1 " - j L 

EPA AIR DOCKET 

SUBJECT: Considerations of Opacity Error in Visible Emissions Testing 

1.0 INTRODUCTION 

This memorandum discusses the concepts of error and bias. It includes 

sections on how these concepts are characterized in each of the three proposed 

methods for measuring opacity, and how they influence compliance 

determination. 

The three methods referred to in this memorandum are as follows: 

(1) Method 203A -- Visual Determination of Opacity Emissions From 

Stationary Sources for Time-Averaged Regulations; 

(2) Method 203B -- Visual Determination of Opacity Emissions From 

Stationary Sources for Time-Exception Regulations; and 

(3) Method 203C -- Visual Determination of Opacity Emissions From 

Stationary Sources for Instantaneous-Limitation Regulations. 

Sections 2.0, 2.1, and 2.2 provide general discussions of decision and 

method errors. Section 2.0 is an introduction to the types of error discussed 

in the memorandum. Section 2.1 presents the descriptions and theoretical 

considerations of decision errors. Section 2.2 provides a description of 

possible method errors and the relative importance of each. Section 3.0 

discusses bias, along with the concepts of precision and accuracy. Also 

included is an*explanation of how bias and error are related. Section 4.0 

presents a brief description of the proposed opacity measurement methods. 

Sections 4.1, 4.2, and 4.3 discuss the error and bias associated with each of 

the three methods. Information provided in these sections includes the 

positive and negative error estimates for each of the methods. Section 5.0 

provides a discussion on how the error and bias associated with each method 

should be considered when choosing a standard for the regulation of opacity. 

Particular attention is paid to how these values affect compliance 

determination. 

Progress Center/3200 E. Chapel Hill Rd./Nelson Hwy./P.O. Box 13000/Research Triangle Park. N.C 27709/(919)541 9100 
P.3 

2.0 INTRODUCTION TO ERROR 

Error can be thought of as uncertainty or variability in a given method 

or in the use of that method. There can be error in the procedures within a 

test method or in the application of method results, and these sources of 

error will be considered separately. Section 2.1 discusses errors in the 

decision-making process. Errors in the test method result in either an overor underestimation of the actual opacity, and will be discussed in 

Section 2.2. 

2.1 Decision Error 

In the decision-making process, two types of error are possible. The 

first type of error ("Type I") would be to falsely cite a source as a 

violation when in fact it is not out of compliance. The second type of error 

("Type II") would be to fail to cite a source that actually is out of 

compliance. The two types of error are inversely related, and a State must 

decide which type of error it wishes to reduce the most. 

The amount of negative or positive error within a test method affects the 

error made in the decision-making process. Thus, when considering the 

relative amount of each type of decision-making error to accept, it is 

important to have information on both the negative and positive error 

resulting from the test method. Section 5.0 discusses in general terms how 

the error associated with each of the methods affects compliance 

determination. 

2.2 Method Error 

There are several types of method error which contribute to the total 

possible error for each of the opacity test methods. This memorandum 

discusses error resulting from the sampling, measurement, and observation 

processes, along with gross errors (mistakes) in any aspect of these 

procedures. 

Sampling error is that error due to the sample selection process. It 

results from the natural variation in opacity and the fact that measurements 

are not taken continuously. As in Method 9, each of the observations is 

intended to represent the "average" opacity over the 15 second interval.-

Since opacity is not constantly monitored, the values reported are actually 

only a subset of the true opacity, which is continuous. Constant monitoring 

is not economically feasible, and the natural variation in opacity cannot be 

dkd.127 2 
P.4 

eliminated. Effort was made to reduce this type of error during the 

development of the three proposed test methods. Thus, the small amount of 

sampling error will not be discussed further. 

Measurement errors result from the collection process, and are due to 

such factors as environmental conditions, equipment characteristics, or the 

location of the opacity observer. Measurement errors can be either random or 

systematic. If a certain piece of equipment is calibrated incorrectly, the 

errors may be systematic. If the equipment is malfunctioning, the resulting 

errors could be random. Measurement errors can be reduced by the 

standardization of equipment and monitoring procedures. Every effort was made 

to reduce these errors during the development of the proposed test methods. 

This type of error will not affect a State's decision on which method to 

implement, and will not be discussed further. 

Gross errors are mistakes in any of the steps in data collection or 

analysis. They range from incorrectly placed decimal points to the application 

of invalid statistical procedures. Gross errors can be reduced by carefully 

checking all data recording and transferring steps, along with the 

appropriateness of each statistical procedure. The reduction of these errors 

has also been dealt with during the development of the test methods, and can 

be achieved by standard quality assurance methods. 

The major source of error in the opacity test methods is observational. 

Observation error is the discrepancy between observed opacity and actual 

opacity (as measured by a transmissometer). It is due to the use of observers 

who estimate the opacity of a plume based on their own judgement. Efforts to 

reduce observational error include the certification of all observers, and the 

use of averaging techniques on repeated observations. Certification ensures 

that observers have been exposed to proper observational techniques and that 

they have met required proficiency levels for estimating opacity. Taking 

multiple observations increases the size of the sample population, which 

reduces the difference between observed and actual opacity levels. However, 

due to the human judgement involved, observation error remains the largest 

contributor to the total error in all three test procedures. 

died. 127 
P.5 

3.0 BIAS, PRECISION, AND ACCURACY 

Bias can be defined as a consistent under- or overestimation of a value. 

For opacity, positive bias is a result of estimations that are greater than 

the actual opacity and negative bias is a result of observations that are 

consistently less than actual opacity. This memorandum considers two types of 

bias, (1) statistical bias, and (2) measurement bias. 

Statistical bias is the difference between the expected value of the 

estimator (observation value) and the population parameter being estimated 

(actual value). Often, it is the result of the incorrect use of a statistical 

procedure such as averaging. In most cases, statistical bias can be reduced 

or eliminated by increasing the number of observations. All of the tests 

performed on the three opacity measurement methods contain a sufficient number 

of observations to sufficiently reduce statistical bias; as do the.methods 

themselves. 

Measurement bias is another type of consistent under- or overestimation 

of the population parameter. It can result from any of the errors discussed 

in Section 2.2. The largest potential source of bias in the three test 

methods is observer error. All of the opacity test methods showed a negative 

bias. Bias is an important consideration when determining compliance, and 

Section 5.0 discusses it for each of the methods. 

Precision is a measure of the closeness of agreement between two 

individual observations. Precision can be calculated for the observations 

made by one individual, or between two different observers measuring the same 

plume. 

Bias and precision combine to give accuracy. The classic example 

showing the relationship between bias, precision, and accuracy is to describe 

shots at a target, as seen in Figure 3-1. Figures 3-l(a) and 3-l(c) show the 

effects of bias on the estimation. Although Figure 3-l(c) is precise, in that 

each of the shots is close to the others, it is not accurate due to the 

presence of bias. Figure 3-1(b) is unbiased, but 1s not accurate due to the 

lack of precision. Figure 3-1(d) is. both precise, and unbiased, and is 

therefore accurate. 

The precision of each observer is measured as part of the State 

certification process. Observers must be recertified periodically to ensure 

their precision is kept within the standard set by that State. Ensuring the 

died. 127 
P.6 '• -J-T- n-^ja-ja^fr,-

(a) (b) 

(c) (d) 

Figure 3-1. Bias and Precision. 

Patterns of shots at a target (after Jessen, 1978, Fig. 1)2. (a): high bias 

+ low precision = low accuracy; (b): low bias + low precision =• low accuracy; 

(c): high bias + high precision - low accuracy; (d): low bias + high precision 

= high accuracy. 

dkd.127 
P.7 

precision and ability of certified, observers is the responsibility of each 

state, and 1s one of the primary ways to reduce error 1n estimates. 

4.0 METHOD DESCRIPTION 

This section briefly describes opacity measurement Methods 203A, 203B 

and 203C. For a more detailed description of these methods, the reader is 

referred to the preamble to the proposed Methods. The statistics on error and 

bias discussed in this memorandum are derived from field studies performed by 

EPA for use with Method 9.3 

Method 203A is used with time-averaged regulations. Field studies on 

this method involve calculating the average opacity value for observations 

taken in 15-second intervals for time periods ranging from 15 seconds to 

8 minutes. For time-averaged regulations, compliance would be met by not 

exceeding a specified average opacity level. 

Method 203B 1s used for determining compliance for time-exception 

regulations. It consists of aggregating the amount of time corresponding to 

the number of opacity readings (taken at 15 second intervals over a specified 

time period) which are over a specified level. Time-exception regulations 

limit the total time opacity can be above the specified level, and compliance 

would be met by keeping the time above the specified opacity at or below this 

1imit. 

Method 203C is used for instantaneous-limitation regulations. 

Observations are taken at 5-second intervals averaged over a one minute time 

period. Instantaneous-limitation regulations prohibit opacity values above 

the given limit at any time. 

4.1 Error and Bias 1n Method 203A 

As mentioned in Section 4.0, the field studies used to evaluate the 

opacity error for Method 203A were originally performed to test Method 9.* 

For studies on time-averaged methods, the raw data contained individual 15-

second observations, with corresponding transmissometer readings. The 

observations were recorded sequentially and rolling averages were calculated 

over time periods ranging from 15 seconds to 8 minutes. The percent error was 

then determined for each of the averages. The average error (1n terms of 

percent opacity) was then grouped Into eleven classes to determine frequency 

distributions. 

dkd.127 
P.8 

Table 4-1 summarizes the percentage distribution of opacity errors by 

averaging time. The table shows there is both positive and negative error at 

all of the averaging times. It also reveals that as averaging time increases, 

the percentage of positive and negative error decreases. This reflects the 

concept that as sample size increases, the expected value of the sample 

approaches the true population value (as measured by transmissometer). 

Table 4-2 shows the percentage of data with negative opacity error. The 

numbers indicate a slight negative bias for all of the averaging times, with 

at least 55 percent of all data having negative error. Figure 4-1 shows a 

sample frequency distribution for percent error for a six-minute averaging 

time. The errors appear to follow a normal distribution, and 52 percent of 

all data are within +5 percent and -5 percent opacity error. Approximately 

95 percent of all data show less than +15 percent opacity error. Jhis means 

that if a given observation (averaged over six minutes) reveals an opacity of 

50 percent, then 95 times out of 100, the true opacity is within the range of 

35 to 65 percent. However, it also means that if the true opacity is 20 

percent, then the confidence interval is from 5 to 35 percent opacity. This 

indicates that the influence of error is greater for low opacity readings. 

The effect this has on compliance determination is discussed in Section 5.0. 

4.2 Error and Bias in Method 203B 

Field studies done on the error in time-aggregated methods summed total 

time that opacity exceeded a standard during a given averaging period.5 

Observations were taken 1n 15-second intervals. The standard used was 

20 percent opacity, and the averaging time was 6 minutes. The aggregated time 

the observer indicated opacity exceeded 20 percent was compared to the actual 

time opacity exceeded this level as measured by a transmissometer. 

The error was reported 1n seconds over or under the actual number 

measured by the transmissometer. Error was grouped in 15-second increments, 

and ranged from +360 seconds to -360 seconds. An error of -360 seconds meant 

that the observer did not record any observations as over 20 percent opacity 

when the transmissometer measured the entire 6 minutes as exceeding 

20 percent. Table 4-3 shows the cummulative distribution of opacity errors. 

Figure 4-2 shows the distribution of the number of data sets by error 

(in seconds). All field studies have both negative and positive error, and 

67 percent of all data sets have an error of between -15 and +15 seconds. 

dkd.127 
P.9 

TABLE 4-1. PERCENTAGE DISTRIBUTION OF OPACITY ERRORS BY AVERAGING TIME*** 

Error Direction 

and Range 

Positive error: 

More than +15 % 

+ 15 % or less 

+ 10 % or less 

+ 7.5 % or less 

+ 5 % or less 

+ 1 % or less 

Negative error: 

More than 0 % 

-1 % or more 

-5 % or more 

-10 % or more 

-15 % or more 

15s 

4 

96 

91 

86 

80 

65 

55 

55 

34 

19 

8 

30s 

4 

96 

91 

87 

80 

64 

56 

54 

33 

17 

7 

lm 

3 

97 

91 

87 

81 

63 

57 

53 

32 

16 

6 

Averaging Time 

2m 

2 

98 

93 

87 

81 

63 

57 

52 

32 

15 

5 

3m . 

2 

98 

93 

88 

81 

63 

57 

52 

31 

14 

5 

4m 

CM 

98 

93 

88 

81 

63 

57 

52 

31 

14 

5 

5m 

2 

98 

93 

88 

81 

63 

57 

52 

31 

13 

4 

6m 

2 

99 

94 

89 

82 

63 

57 

51 

31 

13 

4 

7m 

2 

98 

94 

89 

82 

63 

- 57 

51 

31 

13 

4 

8m 

1 

99 

94 

89 

82 

63 

56 

51 

31 

13 

4 

TABLE 4-2. PERCENT OF DATA WITH NEGATIVE ERROR? 

Averaging Time 

15s 

30s 

lm 

2m 

3m 

4m 

5m 

6m 

7m 

j . 8m 

Percent of Data 

with Negative Error 

55 

56 

57 

57 

57 

57 

57 

58 

57 

56 | 

8 
25 

20 

27 

25 

15 

18 

SO 

c 

3 

cr 

tu 

s10 11 

-15 -10 -5 0 5 

Error (% opacity) 

10 15 

Figure 4-1. Freguency distribution for percent error for time-averaged 

methods. 
P.11 

TABLE 4-3. 

Error 

Seconds 

-360 

-345 

-330 

-315 

-300 

-285 

-270 

-255 

-240 

-225 

-210 

-195 

-180 

-165 

-150 

-135 

-120 

-105 

-90 

-75 

-60 

-45 

-30 

-15 

0 

15 

30 

45 

60 

75 

90 

105 

120 

135 

150 

165 

180 

195 

210 

225 

240 

255 

270 

285 

300 

315 

330 

345 

360 

CUMULATIVE PERCENTAGE DISTRIBUTION OF TIME AGGREGATION ERRORS FOR 

ESTIMATING PLUME OPACITY DURATION OVER A S 

No. of Data Sets by Fi 

RTPB 

7 

23 

142 

46 

9 

6 

2 

TPW 

1 

16 

197 

59 

33 

16 

6 

5 

1 

1 

FL1B 

12 

2 

2 

2 

5 

3 

14 

5 

1 

2 

15 

5 

5 

3 

2 

2 

2 

8 

2 

2 

2 

6 

70 

1 

2 

1 

2 

1 

1 

eld Evaluation- Study 

^

Liw 

3 

1 

1 

3 

2 

1 

2 

3 

3 

1 

1 

3 

2 

2 

2 

1 

5 

91 

3 

5 

1 

2 

2 

2 

1 

5 

2 

6 

4 

2 

3 

2 

7 

1 

1 

1 

3 

FL2B 

1 

1 

1 

1 

1 

2 

1 

2 

1 

1 

4 

2 

1 

2 

2 

4 

2 

26 

11 

2 

3 

6 

4 

2 

1 

6 

FL2W 

42 

1 

1 

11 

3 

2 

3 

19 

1 

1 

2 

4 

TOTAL 

X MINUT E PERIOD8 

Sure! %J. Cum % 

15 

3 

3 

2 

5 

2 

4 

15 

8 

3 

3 

18 

10 

6 

8 

4 

4 

9 

4 

11 

6 

4 

15 

52 

568 

121 

50 

37 

21 

14 

8 

23 

6 

4 

12 

1 

2 

4 

4 

2 

4 

2 

7 

1 

0 

0 

1 

1 

3 

1 1110 

1 

0 

0 

0 

0 

0 

0 

1 

1 

0 

0 

2 

1-

1 

1 

0 

0 

1 

0 

1 

1 

0 

1 

5 

51 

11 

5 

3 

2 

1 

1 

2 

1 

0 

1 

0 

0 

0 

0 

0 

0 

0 

1 

0 

0 

0 

0 

0 

0 

1 

2 

2 

2 

3 

3 

3 

4 

5 

5 

6 

7 

8 

9 

9 

10 

10 

11 

11 

12 

13 

13 

25 

29 

70 

81 

86 

89 

91 

92 

93 

95 

96 

96 

97 

97 

97 

98 

98 

98 

99 

99 

99 

100 

100 

100 

100 

100 

100 

1 
a * 

>» 

50 

40 

30 

20 

10 

1 1 1 

2 2 

1 1 1 1 

51% 

6 

have zero error 

16 

5 

2 

'3 

1 1 

-360 -330 -300 -270 -240 -210 -180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180 210 240 270 300 360 

error in seconds 

Fgure 4-2. Frequency distribution for percent error for time-aggregated methods. 
P.13 

is a percent frequency distribution for error. It shows the precision of the 

method, with 51 percent of the data sets having no error. Although the data 

sets show a strong central tendency, there is a large spread of extreme 

values. The range for error to include 95 percent of the data 1s between -240 

seconds and +120 seconds (-4 minutes to +2 minutes). This large spread 1n 

error is important when setting compliance levels, and will be discussed 

further in Section 5.0. 

4.3 Error and Bias In Method 203C 

Reference Method 9 specifies that opacity observations be taken at 15-

second intervals; 24 consecutive observations are then averaged to yield a 6-

minute average for compliance determination.3 Many state implementation plans 

(SIPs) include opacity standards where this 6-minute average is not 

appropriate. One example of where this type of opacity measurement method 

does not apply is for an instantaneous-limitation regulation. 

Method 203C is the proposed method for determining compliance with 

instantaneous-limitation regulations. As mentioned in Section 4.0, it 

involves observations taken at 5-second intervals averaged over a 1-minute 

time period. Taking observations at shorter intervals and averaging over a 

shorter time period is intended to more accurately reflect the concept of 

"instantaneous" opacity. 

A field study was conducted to determine the ability of observers to 

accurately measure opacity at 5-second intervals. The study consisted of 

eight observers participating in ten runs. Each observer recorded thirteen 5-

second observations over a 1-minute averaging period. Observer opacity 

estimates were compared with actual values as measured by a transmissometer to 

determine error. Data sets were grouped according to their error, and 

compared to the error results from previous methods (Method 9, and proposed 

Method F-1). Table 4.4 shows the distribution of opacity errors. 

The observations showed a slight negative bias, with 53 percent of the 

averages having negative error. Figure 4-3 shows the frequency distribution 

for error. The negative bias can be seen in the skewed distribution of the 

frequency bars toward the negative side of the graph. The data showed a 

strong central tendency, with 56 percent of the errors between +5 and -5 

percent opacity. For this study, 95 percent of the error was between +15 and 

-15 percent opacity. 

dkd.127 12 
TABLE 4-4. DISTRIBUTION OF OPACITY ERROR** 

to 

Opacity Error 

Percent Level 

Number of Sets 

Cumulative 

Number of Sets 

Cumulative 

Percent of Sets 

>15 

3 

3 

3 

>10 

2 

5 

6.3 

>7.5 

1 

6 

7.5 

>5 

7 

13 

16.3 

>1 

18 

31 

38.8 

>0 

5 

36 

45.0 

>-l 

8 

44 

55.0 

>-5 

14 

58 

72.5 

>-10 

10 

58 

85.0 

>-15 

11 

79 

98.8 

<-15 

1 

80 1 

100.0 1 
26 

24 24 

>» 

u 

c 

a> 

3 

O* 

0) 

22 

20 

18 

21 

-10 -5 0 5 10 

Error (% opacity) 

Figure 4-3. Frequency Distribution for percent error for time-exception methods. 

15 

Ul 
P.16 

These results are quite similar to those found in the previously 

mentioned field studies, using the 15-second observation interval methods. 

There does not appear to be any significant increase in error when using 

5-second observation intervals. 

5.0 REGULATION AND COMPLIANCE DETERMINATION 

It is Important to have an understanding of the sign (+ or -) and 

relative magnitude of the error involved in each of the proposed opacity 

measurement methods. The amount of error or the presence of bias in a 

measurement method affects compliance determination. Thus, States should 

consider the sign and magnitude of any error or bias when choosing the method 

of regulation and when determining compliance. 

An understanding of the spread (and distribution) of error is. important 

when making compliance determinations. If the error shows a strong central 

tendency, meaning it is concentrated around zero, then the amount of 

uncertainty involved in an observation is less. In this case, a State may 

decide to seek enforcement against all sources slightly over the standard. 

Whereas if the spread of error is large, the State may seek enforcement 

against only those sources which are over the standard by an obviously larger 

amount. The distribution of error allows a State to determine the range of 

opacity observations it would consider to be in clear violation of the 

standard. 

An area where additional study is needed is to determine the 

relationship between opacity error and actual opacity level. This would give 

States an understanding of how opacity error changes with the level of opacity 

being estimated (or if it does not change). This becomes Increasingly 

important as the standard for opacity decreases. It would be most useful for 

States to have an estimate of the percentage error as a function of opacity 

level. As mentioned in Section 4.1, a +15 or -15 percent opacity error can be 

significant 1f the standard 1s set at 20 percent opacity. 

Section 2.1 discussed the concept of decision error. It presented the 

two types of error a State must accept when setting a standard. It is up to 

the State to determine which type of error It finds most acceptable or 

defensible (1n legal terms). Previous error analyses have dealt only 1n terms 

of the positive error from measurement methods. This Informs-the State of the 

probability of making a Type I error (falsely citing a source that 1s not 

truly out of compliance). 

dkd.127 15 
P.17 

Defining a method in terms of its positive error allows a State to set 

standards for each type of regulation at levels which minimize Type I error. 

For example, if a State knows that the 95 percent confidence Interval for 

average error for a time-average regulation is 10 percent, (using an observer 

who 1s State- certified to read opacity within 7.5 percent) they may decide to 

define compliance with a 20 percent opacity standard as any average which 1s 

under 23 percent opacity. 

However, it should be noted that the probability of a Type II error 

increases as the probability for Type I decreases. States will eventually be 

called upon by public interest or environmental groups to defend Type II 

errors (failing to cite a source which is out of compliance). Thus, it 1s 

important for States to understand the amount of negative error, or 

underestimation of opacity contained in each of the methods. 

At present, observer error cannot be eliminated from opacity test 

methods. Strict adherence to prescribed methodologies, and rigorous State 

certification programs are recommended to reduce observer error. States 

should set and enforce standards within the range of positive and negative 

error for the chosen measurement methodology. It 1s recommended that States 

specify the opacity level at which a citation will be Issued (see Appendix A: 

Sections of Tennessee regulations). If the average or aggregate opacity of a 

source exceeds this level, then the source would be considered out of 

compliance, unless it can be shown that the individual observer shows an error 

outside the expected range. 

dkd.127 16 
P.18 

! « • a 

REFERENCES 

1. 40 CFR 60. Appendix A, Method 9. 1987, as amended. Visual 

Determination of the Opacity of Emissions from Stationary Sources. 

2. Gilbert, Richard 0. Statistical Methods for Environmental Pollution 

Monitoring. Van Noshand Reinhold Company, Inc. New York. 1987. 

pg. 11. 

3. U.S. Environmental Protection Agency, Office of Air Quality Planning and 

Standards. Opacity Errors for averaging and Nonaveraging Data Reduction 

and Reporting Techniques. Final Report SR-1-6-85. Research Triangle 

Park, North Carolina. June 1985. 

/ 

4'. See Reference 3. 

5. See Reference 3. 

6. See Reference 3. pg. 12. 

7. See Reference 3. pg. 13. 

8. See Reference 3. pg. 17. 

9. See Reference 1. 

10. Rose, Thomas H., Eastern Technical Associates, Raleigh, North Carolina 

Collaborative Study of Opacity Observations at Five-second Intervals by 

Certified Observers. Prepared for U.S. Environmental Protection Agency, 

Stationary Source Compliance Division, Washington, D.C. September 1990. 

11. See Reference 10. 

12. Tennessee Visible Emission Evaluation Method 1. 1984, as amended. 

13. Tennessee Visible Emission Evaluation Method 2. 1984. 

dkd.127 17 
P.19 

a » a » 

APPENDIX A 
P.20 

t (f r » 

APPENDIX Al2,13 

TVEE Method 1 

Section No. 4 

Revision No. 3 

Date May 29, 1984 

Page 1 of 1 Pages 

Opacity of Emissions From Nontraditional sources 

(Roads and Parking Areas) 

4.0 Observation Error 

4.1 A positive observational error of 8.8 percent opacity shall be taken 

into account prior to issuing a Notice of Violation on nontraditional 

sources (roads and parking areas). 

TVEE Method 2 

Section No. 3 

Revision No. 0 

Date May 18, 1984 

Page 1 of 1 Pages 

Visual Determination of Opacity 

For Aggregate Count Visible Emission Standards 

Observation Error 

In order for a Notice of Violation to be issued excess visible emissions 

must be recorded that are equal to or greater than 15% opacity above the 

allowable opacity standard. 

ddk.127 . i