While the land use method is more direct, it can also be labor-intensive to apply. For this discussion, the land use method has been simplified so that it is consistent with EPA guidance (EPA 1986
1; Auer 1978
2), incorporated by reference in s.
NR 660.11, while streamlining the process for the majority of applications so that a clear-cut decision can be made without the need for detailed analysis. Table 6.0-1 summarizes the simplified approach for classifying areas as urban or rural. As shown, the applicant always has the option of applying standard (i.e., more detailed) analyses to more accurately distinguish between urban and rural areas. However, the procedure presented here allows for simplified determinations, where appropriate, to expedite the permitting process.
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1 EPA, Guideline on Air Quality Models (Revised), EPA-450/2-78-027R, Office of Air Quality Planning and Standards, Research Triangle Park, North Carolina, July, 1986, incorporated by reference in s.
NR 660.11.
2 Auer, August H. Jr., ``Correlation of Land Use and Cover with Meteorological Anomalies,'' Journal of Applied Meteorology, pp. 636-643, 1978.
6.2 Simplified Land Use Process
The land use approach considers four primary land use types: industrial (I), commercial (C), residential (R), and agricultural (A). Within these primary classes, subclasses are identified, as shown in table 6.0-1. The goal is to estimate the percentage of the area within a 3-km radius that is urban type and the percentage that is rural type. Industrial and commercial areas are classified as urban; agricultural areas are classified as rural.
The delineation of urban and rural areas, however, can be more difficult for the residential type areas shown in table 6.0-1. The degree of resolution shown in table 6.0-1 for residential areas often cannot be identified without conducting site area inspections and/or referring to zoning maps. This process can require extensive analysis, which, for many applications, can be greatly streamlined without sacrificing confidence in selecting the appropriate urban or rural classification.
The fundamental simplifying assumption is based on the premise that many applications will have clear-cut urban/rural designations, i.e., most will be in rural settings that can be definitively characterized through a brief review of topographical maps. The color coding on USGS topographical maps provides the most effective means of simplifying the typing scheme. The suggested typing designations for the color codes found on topographical maps are as follows:
Green Wooded areas (rural).
White White areas generally will be treated as rural. This code applies to areas that are unwooded and do not have densely packed structures which would require the pink code (house omission tint). Parks, industrial areas, and unforested rural land will appear as white on the topographical maps. Of these categories, only the industrial areas could potentially be classified as urban based on EPA 1986 or Auer 1978 (see footnotes 1 and 2 in Table 6.0-1), incorporated by reference in s.
NR 660.11. Industrial areas can be easily identified in most cases by the characteristics shown in Figure 6.0-1. For this simplified procedure, white areas that have an industrial classification will be treated as urban areas.
Section 7.0—Statistical Methodology for Bevill Residue Determinations
This section describes the statistical comparison of waste-derived residue to normal residue for use in determining eligibility for the Bevill exemption under s.
NR 666.112.
7.1 Comparison of Waste-Derived Residue
to Normal Residue
To be eligible for the Bevill exclusion from the definition of hazardous waste under s.
NR 666.112(2)(a), waste-derived residue may not contain ch.
NR 661 Appendix VIII, constituents that could reasonably be attributable to the hazardous waste (toxic constituents) at concentrations significantly higher than in residue generated without burning or processing hazardous waste (normal residue). Concentrations of toxic constituents in normal residue are determined based on analysis of a minimum of 10 samples representing a minimum of 10 days of operation. The statistically-derived concentrations in normal residue are determined as the upper tolerance limit (95% confidence with a 95% proportion of the sample distribution) of the normal residue concentrations. The upper tolerance limit is to be determined as described in Section 7.2 below. If changes in raw materials or fuels could lower the statistically-derived concentrations of toxic constituents of concern, the statistically-derived baseline shall be re-established for any such mode of operation with the new raw material or fuel.
Concentrations of toxic constituents in waste-derived residue are determined based on the analysis of one or more samples collected over a compositing period of not more than 24 hours. Multiple samples of the waste-derived residue may be analyzed or subsamples may be composited for analysis, if the sampling period does not exceed 24 hours. If more than one sample is analyzed to characterize the waste-derived residue generated over a 24-hour period, the arithmetic mean of the concentrations shall be used as the waste-derived concentration for each constituent.
The concentration of a toxic constituent in the waste-derived residue is not considered to be significantly higher than in the normal residue (i.e., the residue passes the Bevill test for that constituent) if the concentration in the waste-derived residue does not exceed the statistically-derived concentration.
7.2 Calculation of the Upper Tolerance Limit
The 95% confidence with 95% proportion of the sample distribution (upper tolerance limit) is calculated for a set of values assuming that the values are normally distributed. The upper tolerance limit is a one-sided calculation and is an appropriate statistical test for cases in which a single value (the waste-derived residue concentration) is compared to the distribution of a range of values (the minimum of 10 measurements of normal residue concentrations). The upper tolerance limit value is determined as follows:
UTL = X + (K)(S)
where X = mean of the normal residue concentrations, X = Xi /n,
K = coefficient for sample size n, 95% confidence and 95% proportion,
S = standard deviation of the normal residue concentrations,
S = (Ó(Xi – X) 2/(n – 1))0. 5, and
n = sample size.
The values of K at the 95% confidence and 95% proportion, and sample size n are given in Table 7.0-1.
For example, a normal residue test results in 10 samples with the following analytical results for toxic constituent A:
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The mean and the standard deviation of these measurements, calculated using the above equations, are 11.5 and 2.9, respectively. Assuming that the values are normally distributed, the upper tolerance limit (UTL) is given by:
UTL = 11.5+(2.911)(2.9) = 19.9 ppm
Thus, if the concentration of constituent A in the waste-derived residue is below 19.9 ppm, then the waste-derived residue is eligible for the Bevill exclusion for constituent A.
7.3 Normal Distribution Assumption
As noted in Section 7.2 above, this statistical approach (use of the upper tolerance limit) for calculation of the concentration in normal residue is based on the assumption that the concentration data are distributed normally. The department is aware that concentration data of this type may not always be distributed normally, particularly when concentrations are near the detection limits. There are a number of procedures that can be used to test the distribution of a data set. For example, the Shapiro-Wilk test, examination of a histogram or plot of the data on normal probability paper, and examination of the coefficient of skewness are methods that may be applicable, depending on the nature of the data (References 1 and 2).
If the concentration data are not adequately represented by a normal distribution, the data may be transformed to attain a near normal distribution. The department has found that concentration data, especially when near detection levels, often exhibit a lognormal distribution. The assumption of a lognormal distribution has been used in various programs at EPA, such as in the Office of Solid Waste Land Disposal Restrictions program for determination of BDAT treatment standards. The transformed data may be tested for normality using the procedures identified above. If the transformed data are better represented by a normal distribution than the untransformed data, the transformed data should be used in determining the upper tolerance limit using the procedures in Section 7.2 above.
In all cases where the owner or operator wishes to use other than an assumption of normally distributed data or believes that use of an alternate statistical approach is appropriate to the specific data set, the owner or operator shall provide supporting rationale in the operating record that demonstrates that the data treatment is based upon sound statistical practice.
7.4 Nondetect Values
The department is developing guidance regarding the treatment of nondetect values (data where the concentration of the constituent being measured is below the lowest concentration for which the analytical method is valid) in carrying out the statistical determination described above. Until the guidance information is available, facilities may present their own approach to the handling of nondetect data points, but shall provide supporting rationale in the operating record for consideration by the department.
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7.5 References
1. Shapiro, S.S. and Wilk, M.B. (1965), “An Analysis of Variance Test for Normality (complete samples)," Biometrika, 52,591-611.
2. Bhattacharyya, G.K. and R.A. Johnson (1977), Statistical Concepts and Methods, John Wiley and Sons, New York.
Section 8.0—
Procedures for Determining Default Values for Air Pollution Control System Removal Efficiencies
During interim license, owners or operators of boilers and industrial furnaces burning hazardous waste shall submit documentation to the department that certifies that emissions of HCl, Cl
2, metals, and particulate matter (PM) are not likely to exceed allowable emission rates. See certification of precompliance under s.
NR 666.103(2). This documentation also establishes interim license feed rate and operating limits for the facility. For the initial certification, estimates of emissions and system removal efficiencies (SREs) can be made to establish the operating limits. Subsequently, owners or operators shall use emissions testing to demonstrate that emissions do not exceed allowable levels, and to establish operating limits (see s.
NR 666.103(3)). However, initial estimates of emissions for certification of precompliance can be based on estimated or established SREs.
The SRE combines the effect of partitioning of the chorine, metals, or PM and the air pollution control system removal efficiency (APCS RE) for these pollutants. The SRE is defined as:
SRE = (species input – species emitted) / species input
The SRE can be calculated from the partitioning factor (PF) and APCS RE by the following formula:
SRE=1 – [(PF/l00) X (1 – APCS RE/100)]
where:
PF = percentage of the pollutant partitioned to the combustion gas
Estimates of the PF and/or the APCS RE can be based on either EPA's default values or engineering judgement. EPA's `default values for the APCS RE for metals, HCl, Cl2, and PM are described in this section. EPA's default values for partitioning of these pollutants are described in section 9.0.
Guidelines for the use of engineering judgement to estimate APCS REs or PFs are described in section 9.4.
8.1 APCS RE Default Values for Metals
EPA's default assumptions for APCS RE for metals are shown in Table 8.1-1. The default values in the table are conservative estimates of the removal efficiencies for metals in BIFs, depending on the volatility of the metal and the type of APCS.
The volatility of a metal depends on the temperature, the thermal input, the chlorine content of the waste, and the identity and concentration of the metal. Metals that do not vaporize at combustion zone temperatures are classified as “nonvolatile". Such metals typically enter the APCS in the form of large particles that are removed relatively easily. Metals that vaporize in the combustion zone and condense before entering the APCS are classified as “volatile". Such metals typically enter the APCS in the form of very fine, submicron particles that are rather inefficiently removed in many APCSs. Metals that vaporize in the combustion zone and do not condense before entering the APCS are classified as “very volatile". Such metals enter the APCS in the form of a vapor that is very inefficiently removed in many APCSs.
Typically, BIFs have combustion zone temperatures high enough to vaporize any hazardous metal at concentrations sufficient to exceed risk-based emission limits. For this reason, the default assumption is that there are no nonvolatile metals. Tables 8.1-2 and 8.1-3 are used to determine whether metals are classified as “volatile" or “very volatile" depending on the temperature entering the APCS, the thermal input, and whether the waste is chlorinated or nonchlorinated.
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WS = Wet Scrubber including: Sieve Tray Tower, Packed Tower, Bubble Cap Tower
VS-20 = Venturi Scrubber, ca. 20-30 in W.G. Ä p
VS-60 = Venturi Scrubber, ca. >60 in W.G. Ä p
ESP-l = Electrostatic Precipitator; 1 stage
ESP-2 = Electrostatic Precipitator; 2 stage
ESP-4 = Electrostatic Precipitator; 4 stage
IWS = Ionizing Wet Scrubber
DS = Dry Scrubber
FF = Fabric Filter (Baghouse)
SD = Spray Dryer (Wet/Dry Scrubber)
1 Interpolation of thermal input is not allowed. If a BIF fires between 2 ranges, the APCS temperature under the higher thermal input shall be used. Example: For a BIF firing 10-100 MMBtu/hr, Mercury is considered very volatile at APCS temperatures above 260 F and volatile at APCS temperatures of 260 F and below.
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1 Interpolation of thermal input is not allowed. If a BIF fires between two ranges, the APCS temperature under the higher thermal input shall be used. Example: For a BIF firing 10-100 MMBtu/hr, Mercury is considered very volatile at APCS temperatures above 260 F and volatile at APCS temperatures of 260 F and below.
A waste is considered chlorinated if chlorine is present in concentrations greater than 0.1% by weight. In the EPA guidance document “Guidance for Metals and Hydrogen Chloride Controls for Hazardous Waste Incinerators, Volume IV of the Hazardous Waste Incineration Guidance Series,"(1) one percent is used for the chlorinated/nonchlorinated cutoff. However, best engineering judgement, based on examination of pilot-scale data reported by Carroll et al. (2) on the effects of waste chlorine content on metals emissions, suggests that the one percent cutoff may not be sufficiently conservative.
Tables 8.1-2 and 8.1-3 were compiled based on equilibrium calculations. Metals are classified as very volatile at all temperatures above the temperature at which the vapor pressure of the metal is greater than 10% of the vapor pressure that results in emissions exceeding the most conservative risk-based emissions limits.
8.2 APCS RE Default Values for HCl and Cl2
Default assumptions for APCS RE for HCl in BIFs are shown in Table 8.2-1. This table is identical to the column for other BIFs except that cement kilns have a minimum HCl removal efficiency of 83%. Because of the alkaline nature of the raw materials in cement kilns, most of the chlorine is converted to chloride salts. Thus, the minimum APCS RE for HCl for cement kilns is independent of the APCS train.
Removal efficiency of Cl2 for most types of APCS is generally minimal. Therefore, the default assumption for APCS RE for Cl2 for all APCSs is 0%. This is applicable to all BIFs, including cement kilns.
8.3 APCS RE Default Values for Ash
Default assumptions for APCS RE for PM are also shown in Table 8.1-4. These figures are conservative estimates of PM removal efficiencies for different types of APCSs. They are identical to the figures in the Nonvolatile APCS RE column for hazardous metals presented in Table 8.1-1 because the same collection mechanisms and collection efficiencies that apply to nonvolatile metals also apply to PM.
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WS = Wet Scrubber including: Sieve Tray Tower, Packed Tower, Bubble Cap Tower
PS = Proprietary Wet Scrubber Design (A number of proprietary wet scrubbers have come on the market in recent years that are highly efficient on both particulates and corrosive gases. Two such units are offered by Calvert Environmental Equipment Co. and by Hydro-Sonic Systems, Inc.).
VS-20 = Venturi Scrubber, ca. 20-30 in W.G. Ä p
VS-60 = Venturi Scrubber, ca. >60 in W.G. Ä p
ESP-l = Electrostatic Precipitator; 1 stage
ESP-2 = Electrostatic Precipitator; 2 stage
ESP-4 = Electrostatic Precipitator; 4 stage
IWS = Ionizing Wet Scrubber
DS = Dry Scrubber
FF = Fabric Filter (Baghouse)
SD = Spray Dryer (Wet/Dry Scrubber)
8.4 References
1. U.S. Environmental Protection Agency. “Guidance on Metals and Hydrogen Chloride Controls for Hazardous Waste Incinerators," Office of Solid Waste, Washington, DC, August 1989.
2. Carroll, G.J., R.C. Thurnau, R.E. Maurnighan, L.R. Waterland, J.W. Lee, and D.J. Fournier. The Partitioning of Metals in Rotary Kiln Incineration. Proceedings of the Third International Conference on New Frontiers for Hazardous Waste Management. NTIS Document No. EPA/600/9-89/072, p. 555 (1989).
Section 9.0—Procedures for Determining
Default Values
for Partitioning of
Metals, Ash,
and Total Chloride/Chlorine
Pollutant partitioning factor estimates can come from 2 sources: default assumptions or engineering judgement. The department's default assumptions are discussed below for metals, HCl2, Cl, and PM. The default assumptions are used to conservatively predict the partitioning factor for several types of BIFs. Engineering judgement-based partitioning factor estimates are discussed in section 9.4.
