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. I ntroduction

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 vaca ted 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 (Cl 2 ), mercury (Hg), and non

- Hg HAP metals (or PM surrogate)

.
The objective of this memo randum 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, w e examined the potential for subcategorization based on kiln size, control device type , and fuel

.

II. Kiln Siz e

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 i n 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 ce ilings. 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 t op of the kiln, where they can fire downward to the product. Due to t he difference s in kiln size s and shape s 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 rul e, 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 provid e representative data for the analyses . Because the number of kilns with Cl 2 data (24 kilns)

, Hg data (22 kilns), and non

- Hg HAP metals d ata (22 kilns) were not significant, the emissions for Cl 2 , Hg and non

- Hg HAP metals w ere 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 non

parametric 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 meas ure 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

. I n 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. T he Wilcoxon test results for all three pollutants suggest that the two subcategories seem to define groups of kilns with different median valu es for these relevant measures.Table 1. Median Emission Factor (lb/ton) by Pollutant and Wilcoxon Test Results

Po llutant Design Capacity

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

Median HF lb / ton 0.501 0.019 <0.001

Median HCl lb / ton 0.042 0.023 0.06 0

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 su bcategories ( 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.

Tabl e 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 r esults 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 pollutan ts 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 requ ired 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. Agood 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 stati stics, 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

O bserved 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 dot s 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 clusteringapproach. For the cluster analysis to s upport 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 Smal l 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 thewhite shaded area). The low misclassification rate from the Cluster analysis supports the subcategory de sign capacity cutoff of 10 tph.

Using the results from the Wilcoxon test, the Logistic Model, and the Cluster analysi s, the data support a subcategorization of tunnel kilns at 10 tph design capacity.

I II

. 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 a s 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 tha n 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 c reated 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 compar ed the plots to each other. The box is made up of three points

, Quartile 1 (Q1), Quartil e 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 va lues 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 0.2594Figure 2 . Box and Whisker Plot s

Overall

, the range of data for the kilns with FF

- based APCD is smaller than the range of data for the kilns with o ther APCD and u ncontrolled 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

.

I V

. 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

. S ee Figure s 3 and 4 for these plots . We found that the emissions from tunnel kilns using non

- na tural gas fuel ( i.e. , c oal, 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/ UncontrolledAverage PM for each kiln (lb/ton) 

## Box and Whisker Plot of BSCP Kiln PM Data by APCD Group

Meankiln. 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 subcategorie s 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 1.2PM lb/ton 

## BSCP PM Kiln Data by Fuel Type

Coal Other Natural gas SawdustFigure 4 . BSCP Hg Tunnel Ki ln 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-04Hg lb/ton 

## BSCP Hg Kiln Data by Fuel Type

Coal Natural gas Sawdust