Date: November 6, 2014

To: Jeff Telander, EPA/OAQPS/SPPD

From: Gabrielle Raymond, Breda Munoz, and Kristin Sroka, RTI International

Subject: Analysis of Potential Subcategories for BSCP Tunnel Kilns

I. Introduction

In accordance with section 112 of the Clean Air Act (CAA), the U.S. Environmental 

Protection Agency (EPA) established national emission standards for hazardous air pollutants 

(NESHAP) on May 16, 2003 for the brick and structural clay products (BSCP) manufacturing 

industry. The NESHAP regulated emissions of hydrogen fluoride (HF), hydrogen chloride (HCl), 

and particulate matter (PM)—a surrogate for hazardous air pollutant (HAP) metals—from tunnel 

kilns located at BSCP facilities. On March 13, 2007, the D.C. Circuit Court vacated the emission 

standards for BSCP manufacturing due to issues associated with the methodology used to 

determine the minimum regulatory “floors” for new and existing units.1 Therefore, the EPA is 

proposing new standards for BSCP manufacturing, including standards for HF, HCl, chlorine 

(Cl2), mercury (Hg), and non-Hg HAP metals (or PM surrogate).

The objective of this memorandum is to describe the determination of subcategories for

BSCP tunnel kilns. Under CAA section 112(d)(1), the EPA has the discretion to “...distinguish 

among classes, types, and sizes of sources within a category or subcategory in establishing...” 

standards. For BSCP tunnel kilns, we examined the potential for subcategorization based on kiln 

size, control device type, and fuel.

II. Kiln Size

We first considered subcategorization of tunnel kilns based on kiln size. There are several 

differences between the design, operation, and efficiency of larger kilns and smaller kilns. In 

particular, many small kilns are the older, less efficient kilns in the industry, and newer kilns can 

be constructed to be larger and more efficient due to advances in design. Smaller, older kilns 

were constructed with large amounts of heavy refractory brick and are typically narrow and tall 

in shape, with high arched ceilings. Larger, newer kilns can be constructed with more efficient 

refractories and can include features such as fiber linings and insulating brick, resulting in a 

wider kiln with lower ceilings. In addition, the burners in a small kiln are generally less efficient 

 1 Sierra Club v. EPA, 479 F.3d 875 (D.C. Cir. 2007).
and are located near the bottom of the kiln, where some of the heat is absorbed by the cars that 

move the bricks through the kiln rather than by the bricks themselves. In a large kiln, the burners 

are more efficient and are often located at the top of the kiln, where they can fire downward to 

the product. Due to the differences in kiln sizes and shapes and in burner efficiency and location

between large and small kilns, a large kiln provides a more even temperature distribution 

throughout the kiln and product than a small kiln.

To assess whether these design and operation differences have an effect on emissions 

and, if so, to provide support for defining size subcategories in the proposed rule, we conducted a 

set of statistical analyses on the emissions data set. In the vacated rule, “small kilns” were 

defined as kilns with a design capacity less than 10 tons per hour (tph), and “large kilns” were 

defined as kilns with a design capacity of 10 tph or greater. The main goal of the statistical 

analyses was to determine if these definitions are supported by our current data set.

The number of kilns with HF data (108 kilns), HCl data (84 kilns), and PM data (92 

kilns) was sufficient enough to provide representative data for the analyses. Because the number 

of kilns with Cl2 data (24 kilns), Hg data (22 kilns), and non-Hg HAP metals data (22 kilns) were 

not significant, the emissions for Cl2, Hg and non-Hg HAP metals were not used in this analysis.

We used three statistical tests to evaluate the 10 tph cutoff: Wilcoxon statistical test, Logistic

Model, and a Cluster Analysis.

A. Wilcoxon Test

Table 1 displays the median emission factor (lb/ton) for each design capacity subcategory 

by pollutant and the corresponding p-value for the Wilcoxon Test. The Wilcoxon test is a nonparametric statistics test used to evaluate the hypothesis that the median emission factors from 

the two subcategories (Small and Large) are the same. A small p-value (usually p<0.05) suggests 

that there is enough evidence in the data to reject the null hypothesis of equality of the medians 

(Hollander and Wolfe, 1973). For a skewed or long-tail distribution, such as that of each of the 

three pollutants, the median is preferred as a measure of centrality over the average. A long-tail 

distribution is likely to violate the assumption of normality; for this reason, a non-parametric test 

is preferred over the standard t-statistic tests for comparing the averages. Table 1 suggests that 

the HF lb/ton medians for Small and Large and PM lb/ton medians for Small and Large 

subcategories are statistically different from each other. In other words, we can say with 

95 percent confidence that the two medians are different. HCl has borderline significance (i.e., 

p-value close to 0.05), in the sense that a larger sample set might result in either significant 

difference or confirm that there is no difference between the medians. The Wilcoxon test results 

for all three pollutants suggest that the two subcategories seem to define groups of kilns with 

different median values for these relevant measures.
Table 1. Median Emission Factor (lb/ton) by Pollutant and Wilcoxon Test Results

Pollutant 

Design Capacity

Small (<10 tph) Large (≥10 tph) p-value Wilcoxon Test

Median HF lb/ton 0.501 0.019 <0.001

Median HCl lb/ton 0.042 0.023 0.060

Median PM lb/ton 0.243 0.078 0.003

B. Logistic Model

Next we explored the association between emissions of each of the pollutants and the 

design capacity classification (i.e., the correlation between or dependence of the pollutant 

emissions on the design capacity). A logistic model was used to evaluate the association between 

the design capacity subcategories (less than 10, or “<10,” and greater than or equal to 10, or 

“≥10”) and each of the three pollutants. A small p-value indicates that changes in the pollutant 

are associated with the classification of “<10” and “≥10”. Pollutants associated with the design 

capacity classification are considered good predictors to be used in the clustering exercise, which 

will determine a possible clustering structure based only on the pollutants. Table 2 displays the 

p-values from each of the logistic models.

Table 2. Summary of p-values from Logistic Model

Pollutant p-value Logistic Model

HF lb/ton <0.001

HCl lb/ton 0.014

PM lb/ton 0.006

Small p-values (less than 0.05) indicate good predictors of the classification; in other 

words, they are highly associated with the classification. The Logistic Model results suggested 

that HF, HCl, and PM emission factors are all good predictors of the classification status in each 

of the two subcategories.

C. Cluster Analysis

These three pollutants were then considered in a cluster analysis to investigate whether 

the combined effect of these pollutants suggest a different partition of the data. For this type of 

analysis, complete data (i.e., values for all three pollutants HF, HCl, and PM) is required for all 

kilns. Only 72 kilns out of 226 (32 percent) have values for these three pollutants (HF, HCl, and 

PM). Figure 1 shows the output of a Support Vector Machine (SVM) approach used to cluster 

the 72 kilns with complete data. Support Vector Machine comprises a series of learning and 

modeling algorithms used for classification purposes (Karatzoglou et al, 2006). The prediction 

capability of the cluster using HF, HCl, and PM was higher than that of the cluster approach 

using only HF and HCl, favoring the selection of the clustering using HF, HCl, and PM. 

Sensitivity, also known as true positive rate, refers to the proportion of Small correctly identified; 

specificity, also known as true negative, refers to the proportion of Large correctly identified. A 
good clustering exercise will have high scores (close to 1) for both sensitivity and specificity. 

Omission is the proportion of misclassified and proportion of correct is a combination of both 

specificity and sensitivity (i.e., the proportion of correct classified). The Kappa statistic measures 

the degree of agreement between the clustering and the observed classification. Complete 

agreement corresponds to a value of Kappa equal to 1 and lack of agreement to a value of Kappa 

equal to 0. Based on the statistics, the clustering exercise including all three pollutants matches 

the best the current classification. Table 3 and Table 4 present the prediction statistics for the two 

clusterings.

Table 3. Prediction Matrix for Clustering Including HF, HCl, and PM

Observed

Large Small Total

From clustering Large 60 2 62

Small 0 10 10

Total 60 12 72

Kappa Statistic: 0.8929

Omission: 0.028

Specificity: 1

Sensitivity: 0.8333

Proportion of correct: 0.9722

Table 4. Prediction Matrix for Clustering Including HF and HCl

Observed

Large Small Total

From clustering Large 59 3 62

Small 1 9 10

Total 60 12 72

Kappa Statistic: 0.7857

Omission: 0.056

Specificity: 0.9833

Sensitivity: 0.75

Proportion of correct: 0.9444

Figure 1 shows the classification structure predicted by the clustering approach using HF, 

HCl, and PM. (Note that only HF and PM are shown on the axes because the results can only be 

shown on a two-dimensional plot, but data for all three pollutants are included in the results.) 

The green and white shades denote the partition suggested by clustering analysis. The green 

shaded area in the plot covers the kilns classified as Large by the cluster analysis, and the white 

shaded area covers the kilns classified as Small by the cluster analysis. Black dots represent large 

kilns (i.e., those with design capacity ≥10 tph), and red dots represent small kilns (i.e., those with 

design capacity <10 tph). The pollutants PM and HF were used as the axes to plot the clustering 
approach. For the cluster analysis to support the subcategory design capacity cutoff of 10 tph, a 

perfect alignment would be to see all the black dots in the green shaded area and all the red dots 

in the white shaded area.

Figure 1. SVM Classification Plot of HF and PM Data for Large and Small Kilns

Two kilns with design capacities <10 tph were misclassified as Large by the cluster 

analysis (i.e., two red dots appear in green shaded area), and 1 kiln with design capacity ≥10 tph 

is borderline misclassified as Small by the clustering analysis (i.e., one black dot appears in the 
white shaded area). The low misclassification rate from the Cluster analysis supports the 

subcategory design capacity cutoff of 10 tph.

Using the results from the Wilcoxon test, the Logistic Model, and the Cluster analysis, 

the data support a subcategorization of tunnel kilns at 10 tph design capacity.

III. Kiln Control Device Group Type

Additionally, we examined the possibility of subcategorizing by the air pollution control 

device (APCD) group type. In other source categories, EPA has found that as a group, fabric 

filter-based control devices, including dry injection/fabric filters (DIFF), dry lime scrubbers 

(DLS) with fabric filters (FF), and stand-alone FF are better performers in terms of controlling 

PM emissions than non-fabric filter-based control devices such as dry limestone adsorbers 

(DLA), wet scrubbers (WS), and spray dryer/electrostatic precipitators (SD/ESP), as well as

uncontrolled kilns.

To evaluate whether this observation applies to BSCP kilns as well, we used a box and 

whisker plot, which is a statistical test for plotting data. We created one box and whisker plot for 

all the PM data from fabric filter-based control devices and another for all the other control 

devices and uncontrolled kilns and compared the plots to each other. The box is made up of three 

points, Quartile 1 (Q1), Quartile 2 (Q2), and Quartile 3 (Q3). Twenty five percent of the data lie 

between the minimum data point and Q1. Fifty percent of the data lie between Q1 and Q3, which 

is referred to as the Interquartile range (IQR). Twenty five percent of the data lie between Q3 and 

the maximum data point. The Minimum Whisker is equal to the minimum data point. The 

Maximum Whisker is equal to the maximum data point. Table 5 displays the values graphed in 

Figure 2 box and whisker plots for the two sets of data by APCD group.

Table 5. Parameters of Box and Whisker Plots

Parameter Kilns with FF-based APCD Kilns with Other APCD or Uncontrolled

Minimum Whisker 0.0060 0.0116

Q1 0.0182 0.0784

Median 0.0390 0.1779

Q3 0.0508 0.3757

Maximum Whisker 0.2397 1.1877

IQR 0.0398 0.2973

Mean 0.0539 0.2594
Figure 2. Box and Whisker Plots

Overall, the range of data for the kilns with FF-based APCD is smaller than the range of 

data for the kilns with other APCD and uncontrolled kilns. The graphs also show that the best 

performing data are from kilns with FF-based APCD. As such, we decided to use the data from 

the FF-based kilns for setting the MACT floor for PM because these kilns represent the best 

performers.

IV. Kiln Fuel

We also explored the possibility of subcategorizing by fuel type to see if the fuel type had 

any significant effect on the emissions. BSCP tunnel kilns are designed to operate with a primary

fuel, such as natural gas, coal, sawdust, or other. We first graphed the tunnel kilns for which we 

had PM and Hg data by their emissions and primary fuel type. See Figures 3 and 4 for these 

plots. We found that the emissions from tunnel kilns using non-natural gas fuel (i.e., coal, 

sawdust, other) were spread out among the emissions from tunnel kilns using natural gas as the 

primary fuel. There were no distinct groupings of the emissions by the fuel type of the tunnel 

0.0

0.2

0.4

0.6

0.8

1.0

1.2

FF-based Other APCD/ Uncontrolled

Average PM for each kiln (lb/ton)

Box and Whisker Plot of BSCP Kiln PM Data by 

APCD Group

Mean
kiln. As such, based on the available emissions test data, we could not discern differences in 

emissions based on fuel type, and for that reason, we have not created subcategories by fuel type

in the proposed rule.

Figure 3. BSCP PM Tunnel Kiln Data by Fuel Type

0

0.2

0.4

0.6

0.8

1

1.2

PM lb/ton

BSCP PM Kiln Data by Fuel Type

Coal Other Natural gas Sawdust
Figure 4. BSCP Hg Tunnel Kiln Data by Fuel Type

V. References

Hollander, M and D.A. Wolfe. 1973. Nonparametric Statistical Methods. New York: John Wiley 

& Sons, 1973. Pages 27–33 (one-sample), 68–75.

Karatzoglou A, D. Meyer, and K. Hornik. 2006. “Support Vector Machines in R.” Journal of 

Statistical Software. 15(9), April 2006.

0.00E+00

2.00E-05

4.00E-05

6.00E-05

8.00E-05

1.00E-04

1.20E-04

1.40E-04

1.60E-04

1.80E-04

2.00E-04

Hg lb/ton

BSCP Hg Kiln Data by Fuel Type

Coal Natural gas Sawdust