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TECHNICAL MEMORANDUM

TO: Tina Ndoh, U.S. EPA/OAQPS/SPPD - Minerals and Manufacturing Group

FROM: Eastern Research Group, Inc.

DATE: May 30, 2014

SUBJECT: Maximum Achievable Control Technology (MACT) Floor Analysis for 

Phosphate Rock Calciners at Phosphoric Acid Manufacturing Plants

1.0 INTRODUCTION

This memorandum presents the methodology and results of the maximum achievable 

control technology (MACT) floor determinations for mercury (Hg) and hydrogen fluoride (HF)

for phosphate rock calciners in the phosphoric acid manufacturing plants (40 CFR part 63, 

subpart AA) source category. The organization of this memo is as follows:

2.0 Summary of Existing Limits

3.0 Background of MACT Floor Methodology

4.0 MACT Floor Determination Methodology

5.0 References

Appendix A: Excel File “MACT_Floor_Calculations_for_Calciners.xlsx.”

2.0 SUMMARY OF EXISTING LIMITS

The existing National Emissions Standard for Hazardous Air Pollutants (NESHAP) for 

phosphate rock calciners in the phosphoric acid manufacturing plants source category (40 CFR 

Part 63, subpart AA) established emission limits for particulate matter (as a surrogate for 

hazardous air pollutant (HAP) metals). Table 2-1 summarizes this emission limit. 

Table 2-1. Existing Emissions Limits for Particulate Matter from Phosphoric Acid 

Manufacturing Plants under 40 CFR Part 63, Subpart AA

Equipment Description Particulate Matter (gr/dscf)

Existing Sources New Sources

Phosphate rock calciner 0.080 0.040

gr/dscf – grains per dry standard cubic foot
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3.0 BACKGROUND OF MACT FLOOR METHODOLOGY

Section 112(d) of the CAA requires EPA to set emissions standards for HAP emitted by 

major stationary sources based on performance of the MACT. The MACT standards for existing 

sources must be at least as stringent as the average emissions limitation achieved by the best 

performing 12 percent of existing sources (for which the Administrator has emissions 

information) or the five best performing sources for source categories or subcategories with less 

than 30 sources (CAA section 112(d)(3)(A) and (B)). This minimum level of stringency is called 

the MACT floor. For new sources, MACT standards must be at least as stringent as the control 

level achieved in practice by the best controlled similar source (CAA section 112(d)(3)). These 

requirements constitute the MACT floor for new and existing sources. EPA may not consider 

costs or other impacts in determining the MACT floors. EPA must consider cost, non-air quality 

health and environmental impacts, and energy requirements in connection with any standards 

that are more stringent than the MACT floor (beyond-the-floor (BTF) controls).

MACT analyses generally involve an assessment of the emissions from the best 

performing units in a source category or subcategory. The assessment can be based on actual 

emissions data, knowledge of the air pollution control in place in combination with actual 

emissions data, or state and Federal regulatory requirements that may enable EPA to estimate the 

actual performance of the regulated units and other relevant emissions information. For each 

source category or subcategory, the assessment involves a review of actual emissions data with 

an appropriate accounting for emissions variability. Other methods of estimating emissions can 

be used provided that the methods can be shown to provide reasonable estimates of the actual 

emissions performance of a source or sources. In March 2007, the District of Columbia Circuit 

Court issued an opinion (Sierra Club v. EPA, 479 F.3d 875 (D.C. Cir. 2007) (Brick MACT)) 

vacating and remanding CAA section 112(d) MACT standards for the Brick and Structural Clay 

Ceramics source categories. Some key holdings in that case were:

 MACT floors for existing sources must reflect the average emission limitation 

achieved by the best performing 12 percent of existing sources, not levels EPA 

considers to be achievable by all sources (479 F.3d at 880–81);

 EPA cannot set floors of “no control.” The Court reiterated its prior holdings, 

including National Lime Association v. EPA, 233 F.3d 625 (D.C. Cir. 2000), 

confirming that EPA must set floor standards for all HAP emitted by the major 

source, including those HAP that are not controlled by at-the-stack control 

devices (479 F.3d at 883); and

EPA cannot ignore non-technology factors that reduce HAP emissions, including when 

determining which sources are best performers for purposes of ascertaining the MACT floor. 

Specifically, the Court held that “EPA’s decision to base floors exclusively on technology even 

though non-technology factors affect emissions violates the Act.” (479 F.3d at 883).

In addition, the fact that a specific level of performance is not being intentionally 

achieved by the source is not a legal basis for excluding the source’s performance from 

consideration. Sierra Club v. EPA, 479 F.3d at 631-34; National Lime Association (NLA), 233 

F.3d at 640.
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The Brick MACT decision also stated that EPA may account for variability in setting 

floors. However, the Court found that EPA erred in assessing variability, because it relied on 

data from the worst performers to estimate best performers’ variability, and held that “EPA may 

not use emission levels of the worst performers to estimate variability of the best performers 

without a demonstrated relationship between the two” (479 F.3d at 882).

EPA incorporated these decisions into the MACT floor analysis for phosphoric acid 

manufacturing plants.

Incorporating Variability 

In determining the MACT floor for existing sources, EPA may exercise its judgment, 

based on an evaluation of the relevant factors and available data, to determine the level of 

performance that has been achieved by the average of the best-performing five sources under 

variable conditions. The Court has recognized that EPA may consider variability in estimating 

the degree of emissions reduction achieved by best-performing sources and in setting MACT 

floors (see Mossville Envt’l Action Now v. EPA, 370 F.3d 1232, (D.C. Cir. 2004)), holding EPA 

may consider emission variability in estimating performance achieved by best-performing 

sources and may set the floor at a level that best-performing sources can expect to meet “every 

day and under all operating conditions.”

The MACT and other technology-based standards are often derived from short-term 

emissions test data, but such data may not be representative of the range of operating conditions 

that the best performing facilities face on a day-to-day basis. In statistical terms, each test 

produces a limited data sample, not a complete enumeration of the available data for 

performance of the unit over a long period of time.1EPA, therefore, may adjust the short-term 

data to account for these varying conditions. The types of variability that EPA attempts to 

account for include operational distinctions between and within tests at the same unit.

“Between-test variability” can occur even where conditions appear to be the same when 

two or more tests are conducted. Variations in emissions may be caused by differences in 

operating conditions, settings for emissions testing equipment, field teams conducting the testing, 

sample handling or laboratories analyzing the results. Identifying an achieved emission level for 

best performing sources often needs to account for these differences between tests, in order for 

“a uniform standard [to] be capable of being met under most adverse conditions which can 

reasonably be expected to recur[.]” (See NLA I, 627 F.2d at 431, n. 46.) (See also Portland 

Cement Ass’n, 486 F.2d at 396 noting industry point that “a single test offered a weak basis” for 

inferring that plants could meet the standards.)

The same types of differences leading to between-test variability can also cause 

variations in results between various runs comprising a single test, or “within-test variability.” A 

single emissions test at a unit usually includes at least three separate test runs. (See 40 CFR 

63.7(e)(3) for MACT standards under CAA section 112.) Each data point should be viewed as a 

snapshot of actual performance. Along with an understanding of the factors that may affect 

performance, each of these snapshots gives information about the normal, and unavoidable, 

variation in emissions that would be expected to recur over time. 
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Variability can be accounted for using different statistical methods. For example, recent 

rules have used confidence intervals, prediction intervals, and percentiles to determine MACT 

floor emission limits.

A confidence interval represents the interval within which the true population mean (not 

the sample mean) would fall at the specified confidence level. Assume that you calculated a 95 

percent confidence interval for the mean of your sample data. If you were to obtain additional 

samples from the same population, in 95 percent of such cases the calculated interval will 

include the true population mean. A confidence interval is a descriptive interval.

A prediction interval for a single future observation is an interval (with upper and lower 

prediction limits, i.e., UPL and LPL, respectively) that will, with a specified degree of 

confidence, contain the next (or some other pre-specified) randomly selected observation from a 

population. In other words, the prediction interval estimates what future values will be, based on 

present or past background samples taken.

Percentiles are straightforward and are also descriptive. They represent the specified 

“slice” of the sample data. Unlike confidence and prediction intervals, they are distribution-free 

(i.e., to compute a confidence/prediction interval, you need to ascertain that the data follow a 

specific distribution, such as the normal distribution but to compute a percentile you do not need 

to make that kind of a determination).

Percentiles are based on the distribution of the available emission observations (e.g., test 

runs), and do not embody a predictive aspect that a prediction interval does. The UPL represents 

the value the mean of future test run observations can be expected to fall below, based on the 

results of the independent sample of size (n) from the same population. Therefore, should a 

future test condition be selected randomly from any of these sources (e.g., average of three runs), 

one can be confident (99 percent confident in the case of a 99 percent UPL) that the reported 

level will fall below a MACT floor emission limit calculated using a UPL. 

The UPL is the appropriate statistical tool to use to account for variability in the 

concentration data from phosphoric acid manufacturing plants. For these emission points, where 

there is a limited sampling of the source category and EPA does not have test data from all of the 

emission points for a pollutant in the in the subcategory, the predictive aspect of the UPL 

calculation is especially important. 

4.0 MACT FLOOR DETERMINATION METHODOLOGY FOR PHOSPHATE 

ROCK CALCINERS

The following sections describe the methodology used to calculate MACT floors for HF 

and total Hg for phosphate rock calciners in phosphoric acid manufacturing plants.

Only one facility currently operates phosphate rock calciners; PCS Aurora. In response to 

an April 2010 CAA section 114 request, PCS Aurora provided Hg (by US EPA Method 30B) 

emissions testing results for one of the six calciners to the EPA. The facility provided HF 

emissions testing results (by US EPA Method 320) for the same calciner in response to a January 

2014 CAA section 114 request. In addition, the facility provided Hg emissions testing results (by 

US EPA Method 30B) for another, previously untested calciner also in response to the January 
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2014 CAA section 114. For additional information, refer to the separate memorandum entitled 

“Information Collection and Additional Data Received for the Phosphoric Acid and Phosphate 

Fertilizer Production Source Categories2,” available in the docket for this action.

As previously stated, the EPA received HF emissions testing results by US EPA Method 

320 for one phosphate rock calciner in response to the January 2014 CAA section 114 request. 

Of the six test runs reported to EPA, four were reported as below the method detection limit. The 

detected concentrations were, on average, only 20 percent above the method detection limit. The 

expected measurement imprecision for an emissions value occurring at or near the method 

detection limit is about 40 to 50 percent. Because the HF emission levels are near or below the 

method detection limit, the measured concentration values are questionable for HF. As a result, 

the true levels of HF emitted from phosphate rock calciners are uncertain.

CAA section 112(h)(1) states that the Administrator may prescribe a work practice 

standard or other requirements, consistent with the provisions of CAA sections 112(d) or (f), in 

those cases where, in the judgment of the Administrator, it is not feasible to enforce an emission 

standard. CAA section 112(h)(2)(B) further defines the term “not feasible” in this context to 

apply when “the application of measurement technology to a particular class of sources is not 

practicable due to technological and economic limitations.”

Because approximately 67 percent of the HF data collected using US EPA Method 320 

were below the method detection limit, and the fact that the detected concentrations were, on 

average, only 20 percent above the method detection limit, it was concluded that HF emissions 

from phosphate rock calciners cannot practicably be measured. As a result, work practice 

standards were identified in place of numeric emission limits for HF from phosphate rock 

calciners.

According to information provided by PCS Aurora3, phosphate rock calciners are 

operated to remove organic content from the phosphate rock in efforts to produce products with 

low organic content. Based on review of available literature, removal of fluorine takes place at 

temperatures between approximately 2,500 and 2,750 degrees Fahrenheit (in addition to adding 

defluorinating agents), whereas removal of organic matter and dissociation of carbonates is 

typically carried out between 1,200 and 1,830 degrees Fahrenheit.4Process flow diagrams 

submitted by PCS Aurora in response to the April 2010 and January 2014 CAA section 114 

requests indicate that the phosphate rock calciners currently in operation maintain a calcination 

temperature of less than 1,600 degrees Fahrenheit. Based on this information, maintaining the 

temperature of the phosphate rock calciner fluidized bed at less than 1,600 degrees Fahrenheit 

will minimize emissions of HF.

In addition, based on process flow diagrams submitted by PCS in response to the April 

2010 and January 2014 section 114 requests, particulate emissions from the calciners currently in 

operation are controlled using a combination of an absorber (i.e., a venturi type wet scrubbing 

system) and an electrostatic precipitator. A PCS facility representative confirmed that the venturi

scrubbers operate whenever the calciners are in operation.5 As discussed in a separate 

memorandum,6other facilities in the phosphoric acid source category use venturi scrubbers to 

control HF emissions from various processes located at the source category. Because hydrogen 

fluoride is highly soluble in water, if hydrogen fluoride is present in the calcination exhaust 
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stream in any amount, it is expected that the absorbers currently in operation are achieving some 

level of emission reduction. Based on this information, routing emissions from phosphate rock 

calciners to an absorber was identified as a work practice standard for HF from phosphate rock 

calciners.

For Hg, we calculated MACT floors for phosphate rock calciners after evaluating the 

variability in the concentration data reported by PCS Aurora. Hg emissions data were available 

for two phosphate rock calciners, see Table 4-1 for the average Hg concentration for each. 

Calciner Hg emissions are the result of Hg contained in the fuel and raw materials. Because the 

six phosphate rock calciners are designed to be identical and use the same raw materials and 

fuels, Hg emissions from the six calciners are expected to be identical. Therefore, all Hg data 

from the calciners were combined into one dataset to determine both new and existing MACT 

floors. 

Table 4-1. Average Concentration of Mercury from Phosphate Rock Calciners (as 

Measured by US EPA Method 30B)

Rank Facility and Process 

Line

Average Hg Concentration 

(mg/dscm @ 3% O2)

1 PCS Aurora (202) 9.87E-02

2 PCS Aurora (203) 9.92E-02

mg/dscm @ 3% O2 – milligrams per dry standard cubic meter 

corrected to 3 percent oxygen

Hg - mercury

Incorporating Data Variability

As discussed in Section 2.0, the UPL was used for the MACT floor variability analysis.

The first step included a determination of whether the data from the sources were normally or 

log-normally distributed. If the data were normally distributed (e.g., similar to a typical bell 

curve), further variability analyses were conducted on the data set. If the data were not normally 

distributed (for example, if the data were asymmetric or skewed to the right or left), the type of 

distribution (e.g., log-normal) was determined and a data transformation (e.g., taking the natural 

log of the data) was performed to normalize the data prior to conducting the variability analysis. 

Two statistical measures, skewness and kurtosis, were examined to determine if the data were 

normally or log-normally distributed. 

The skewness statistic (S) characterizes the degree of asymmetry of a given data 

distribution. Normally distributed data have a skewness of zero. An S value that is greater (less) 

than zero indicates that the data are asymmetrically distributed with a right (left) tail extending 

toward positive (negative) values. Further, the standard error of the skewness statistic (SES) is 

given by Equation 1:

 

 

 

(Eq. 1)

Where:

N = the sample size
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According to the small sample skewness hypothesis test, if S is greater than twice the 

SES, the data distribution can be considered non-normal. 

The kurtosis statistic (K) characterizes the degree of peakedness or flatness of a given 

data distribution in comparison to a normal distribution. Normally distributed data have a 

kurtosis of zero. A K value that is greater (less) than zero indicates a relatively peaked (flat) 

distribution. Further, the standard error of the kurtosis statistic (SEK) is given by Equation 2:

 

 

 

(Eq. 2)

Where:

N = the sample size

According to the small sample kurtosis hypothesis test, if K is greater than twice the 

SEK, the data distribution is typically considered to be non-normal.

Skewness and kurtosis hypothesis tests were applied to both the reported test values and 

the lognormal values of the reported test values for Hg. The S and K statistics of the reported 

data set were both less than twice the SES and SEK, respectively, and as a result, the data set 

were classified as normally distributed. 

After determining the distribution of each data set, a student’s t­test statistic was 

determined to calculate the UPL value. The t-test was used in the promulgated hospital and 

medical waste incineration (HMWI) rulemaking and proposed Portland Cement rulemaking, and 

it is more appropriate for smaller sample sizes. The t-statistic can be found in standard statistical 

reference tables or by using the Excel function “TINV,” each requiring the degrees of freedom 

and the desired confidence interval. The degrees of freedom was calculated as one less than the 

number of test runs and the confidence interval used was 99 percent.

Following the determination of the student’s t-test statistic, the 99 percent UPL values for 

Hg were calculated using the test run data. Equation 3 was used to calculate the UPL for a 

MACT floor emission limit for which compliance is based on the average of a three-run test:

(Eq. 3)

Where:

n = the number of test runs (i.e., sample size)

m = the number of test runs in the compliance average (m-value), 3. 

s = standard deviation of the emission test data

 = mean, i.e., average of the emission test data

t(0.99, n – 1) = t-statistic for 99% significance and a sample size of n
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The 99 percent UPL represents the value which one can expect the mean of future threerun performance tests to fall below with 99 percent confidence, based upon the results of the 

independent sample of observations from the same sources. There were no non-detect values for 

the Hg test data from the phosphate rock calciners. However, imprecision due to potential nondetect values was investigated after determining the 99 percent UPL values. 

Use of Non-Detect Values

At very low emission levels where emission tests resulted in ND values, the inherent 

imprecision in the pollutant measurement method has a large influence on the reliability of the 

data underlying the regulatory floor emission limit. Because of sample and emission matrix 

effects, laboratory techniques, sample size and other factors, method detection levels normally 

vary from test to test for any specific test method and pollutant measurement. The confidence 

level that a value measured at the detection level is greater than zero is about 99 percent. The 

expected measurement imprecision for an emission value occurring at or near the method 

detection level is about 40 to 50 percent. Pollutant measurement imprecision decreases to a 

consistent level of 10 to 20 percent for values measured at a level about three times the method 

detection level. 

The procedure for determining a representative detection limit (RDL) starts with 

identifying all of the available reported pollutant specific method detection levels for the bestperforming units regardless of any subcategory (e.g., existing or new, fuel type, etc.). From that 

combined pool of data, the arithmetic mean value is calculated. By limiting the dataset to those 

tests used to establish the floor or emissions limit (i.e., best performers), the result is 

representative of the best-performing testing companies and laboratories using the most sensitive 

analytical procedures. The outcome should minimize the effect of a test(s) with an inordinately 

high method detection level (e.g., the sample volume was too small, the laboratory technique was 

insufficiently sensitive, or the procedure for determining the minimum value for reporting was 

other than the detection level). The resulting mean of the method detection levels is designated as 

the RDL as characteristic of accepted source emissions measurement performance. 

The second step in the process is to calculate three times the RDL to compare with the 

calculated floor or emissions limit. The multiplication factor of three is used to reduce the 

imprecision of the analytical method until the imprecision in the field sampling reflects the 

relative method precision as estimated by the Reference Method Accuracy and Precision 

(ReMAP) study. 7That study indicates that such relative imprecision remains a constant 10 to 20 

percent, over the range of the method. For assessing the calculated floor results relative to 

measurement method capabilities, if three times the RDL were less than the calculated floor or 

emissions limit (e.g., calculated from the UPL), it would be determined that measurement 

variability was adequately addressed. The calculated floor or emissions limit would need no 

adjustment. If, on the other hand, the value equal to three times the RDL were greater than the 

UPL, it would be concluded that the calculated floor or emissions limit does not account entirely 

for measurement variability. Where such was the case, the value equal to three times the RDL

would be substituted for the calculated floor or emissions limit, which results in a concentration 

where the method would produce measurement accuracy on the order of 10 to 20 percent, which 

is similar to other EPA test methods and the results found in the ASME ReMAP study. 
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We used a procedure similar to the one discussed above to calculate the three times RDL 

value for Hg measured by US EPA Method 30B. Facilities submitted US EPA Method 30B 

concentration data (including non-detect values) in response to the April 13, 2010 section 114 

request and PCS Aurora submitted additional US EPA Method 30B concentration data in 

response to the January 24, 2014 section 114 request. The Hg concentration data included testing 

from phosphate rock calciners and dryers, WPPA, SPA, and phosphate fertilizers process lines.

First, we identified the method detection levels reporting by the best performing units. We then 

treated the average of the method detection levels as the RDL value for Hg. We calculated three 

times the RDL value and corrected the resulting concentration to a three percent oxygen basis for 

use in the phosphate rock calciner MACT floor variability calculation.

Results of the variability analyses of Hg emissions data from calciners are presented in 

Table 4-2. 

As is discussed in a separate BTF Analysis memorandum,8 more stringent BTF limits are 

being proposed for Hg at phosphate rock calciners. Therefore, the proposed limits for Hg from 

phosphate rock calciners are not equal to the calculated MACT limits but rather the BTF limits. 

The BTF limits were determined by estimating the emissions which would be achievable using a 

Hg control device (e.g., fixed-bed carbon adsorption or activated carbon injection) at phosphate 

rock calciners. Based on the available data, it was determined that 90 percent Hg reduction is 

achievable. Therefore, the proposed BTF limits for Hg, as seen in Table 4-2, are 10 percent of 

the calculated MACT limits. 

Table 4-2. MACT Floor, Variability Analysis Results, and 

BTF Limits for Mercury (by US EPA Method 30B) – Phosphate Rock Calciner

Parameter Value

Sample Size 12

Non-Detects 0

Average (mg/dscm @ 3% O2) 9.88E-02

Median (mg/dscm @ 3% O2) 1.020E-01

Standard Deviation (mg/dscm @ 3% O2) 2.33E-02

Minimum (mg/dscm @ 3% O2) 5.83E-02

Maximum (mg/dscm @ 3% O2) 1.42E-01

Skewness -1.01E-02

SE Skewness 7.07E-01

Skewness Test Normal

Kurtosis 1.26E-01

SE Kurtosis 1.41

Kurtosis Test Normal

99.0% t-statistic for UPL 2.72

99.0% UPL (mg/dscm @ 3% O2) 1.40E-01

Detection Limit Analysis

Average (mg/dscm @ 3% O2) 9.88E-02

RDL (mg/dscm @ 3% O2) 1.90E-03

3x RDL (mg/dscm @ 3% O2) 5.71E-03

Calculated MACT Floor Emission Limit for Hg[1]

(mg/dscm @ 3% O2) 1.40E-01

Proposed Beyond-the-Floor Limit for Hg (mg/dscm @ 3% O2) 1.40E-02
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[1] - MACT floor limits were rounded up to two significant figures (e.g., 100.1 would be rounded to 110).

SE – standard error (applied to skewness or kurtosis)

mg/dscm @ 3% O2 – milligrams per dry standard cubic meter corrected to 3 percent oxygen

UPL - upper prediction limit

RDL – representative detection limit

5.0 REFERENCES

1. Natrella, Experimental Statistics, National Bureau of Standards Handbook 91, Chapter 1 

revised ed., 1966.

2. Eastern Research Group, Inc. “Information Collection and Additional Data Received for 

the Phosphoric Acid and Phosphate Fertilizer Production Source Categories.” Docket ID: 

EPA-HQ-OAR-2012-0522. May 2014. 

3. Eastern Research Group, Inc. “Summary of August 14, 2012 US EPA Meeting with PCS 

Phosphate.” Docket ID: EPA-HQ-OAR-2012-0522. August 14, 2012.

4. Abouzeid, A. M. “Physical and thermal treatment of phosphate ores – An overview,” 

International Journal of Mineral Processing, 85 (2008) 59-84.

5. Eastern Research Group, Inc. “Summary of May 19, 2014 Telephone Communication 

between PCS Phosphate and ERG.” Docket ID: EPA-HQ-OAR-2012-0522. May 19, 

2014

6. Eastern Research Group, Inc. “Beyond-the-Floor Analysis for Phosphate Rock Calciners 

at Phosphoric Acid Manufacturing Plants.” Docket ID: EPA-HQ-OAR-2012-0522. May 

2014.

7. “Reference Method Accuracy and Precision (ReMAP): Phase 1, Precision of Manual 

Stack Emission Measurements” American Society of Mechanical Engineers, Research 

Committee on Industrial and Municipal Waste. February 2001. 

8. Eastern Research Group, Inc. “Beyond-the-Floor Analysis for Phosphate Rock Calciners 

at Phosphoric Acid Manufacturing Plants.” Docket ID: EPA-HQ-OAR-2012-0522. May 

2014.