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November 29, 2010

United States Environmental Protection Agency

Mailcode 6102T 1200 Pennsylvania Ave., NW. Washington, DC 20460, Attn: Docket ID No. EPA–HQ–OAR–2009–0559

Re: Standards of Performance for New Stationary Sources and Emission Guidelines for Existing Sources: Sewage Sludge Incineration Un its; Proposed Rule (75 Fed. Reg. 63,260 (October 14, 2010))

The Energy Recovery Council (“ERC”) appreciates the opportunity to submit to the United States Environmental Protection Agency (“EPA ”) the following comments on Standards of Performance for New Stationary Sources and Emission Guidelines for Existing Sources: Sewage Sludge Incineration Units; Proposed Rule.

ERC is the national trade association representing companies and communities engaged in the waste-to-energy sector. Waste-to-energy fac ilities produce clean, renewable energy through the combustion of municipal solid wa ste in specially designed power plants equipped with the most modern pollution cont rol equipment to clean emissions. ERC members include: Covanta Energy Corporation, Wh eelabrator Technologies Inc., and Babcock & Wilcox, several dozen business organi zations in the municipal waste management and energy fields, and 28 municipalities that are served by waste-to-energy plants. ERC members own or operate 67 of the 86 wa ste-to-energy facilities in the

United States. Members of ERC have a vital stake i n the outcome of this rulemaking as owners and operators of facilities regulated under Section 129 of the Clean Air Act and which may combust sewage sludge in conjunction with municipal solid waste.

Comment 1. EPA should exempt all solid waste incine rator units which are subject

to other Section 129 standards.

The proposed rules define a Sewage Sludge Incinerat ion (SSI) unit as “an incineration unit combusting sewage sludge for the purpose of reducing the volume of the sewage sludge by removing combustible matter.” (75 FR 63313; 75 FR 63338.) The definition explicitly includes fluidized bed and mu ltiple hearth SSI unit designs, but does not limit the definition to only these designs and does not require that a minimum amount of sewage sludge be combusted to be considered a SS I unit. As such, other solid waste incineration unit designs could be considered SSI u nits and subject to the rules if they combust any amount of sewage sludge. This would include Munici pal Waste Combustors (MWCs) currently subject to other Clean Air Act (CA A) Section 111/129 New Source Performance Standards (NSPS) and Emission Guideline s (EG).Docket ID No. EPA–HQ–OAR–2009–0559 Comments of Energy Recovery Council

November 29, 2010 Page 2 of 14

EPA proposes to exclude from SSI regulation those c ommercial and industrial incinerators subject to the Commercial and Industri al Solid Waste Incinerator (CISWI)

standards 1 , and seeks comment on whether other combustion uni ts combust sludge and such units should be subject to SSI standards or to other Section 129 standards. (75 FR 63268.) ERC believes that MWCs have the potential t o fire sewage sludge and that the exemption afforded to CISWI-regulated units should be extended to both Large and Small MWCs subject to new or existing Section 129 M WC standards.

It is appropriate to consider MWCs as potential com bustors of sewage sludge. MWC units are or may in the future be permitted to accept it as a supplemental waste. There is no intrinsic technical limitation to accep ting sewage sludge at MWCs. Material handling, combustion and emission control systems a t many MWCs are appropriately configured or could be modified to successfully bur n sewage sludge, and sludge content does not present unique challenges meeting MWC stan dards when fired with municipal solid waste. Co-fired amounts would be operationall y limited by the relatively high water content of the sewage sludge, even if dewatered. Fr om a policy perspective MWCs offer an environmentally sound alternative management opt ion for municipal officials seeking to acceptably dispose POTW sewage sludge and recove r its renewable energy potential. MWCs are typically located in urban areas proximate to sewage sludge generators and thereby represent a convenient, low transportation option.

EPA should extend the Section 60.4780 and Section 6 0.5065 exclusions to all solid waste incineration units that meet the applic ability requirements of other Section 129 NSPS and EG rules, including MWCs regulated und er Subparts Ea, Eb, Cb, AAAA, and BBBB. This exemption is consistent with EPA’s p roposed CISWI NSPS and EG rules which explicitly exempt MWC units subject to these other Section 129 rules. (75 FR 31974, Section 60.2020(c); 75 FR 31990, Section 60.2555(c).)

Comment 2. EPA should exempt co-fired combustors wh ich fire less than 30% sewage sludge by weight.

“Co-fired combustor” is defined in Sections 60.4930 and 60.5250 as follows:

“ Co-fired combustor means a unit combusting sewage sludge or dewatered sludge pellets with other fuels or wastes ( e.g. , coal, clean biomass, municipal solid waste, commercial or institutional waste hospital infectio us medical waste, unused pharmaceuticals, other solid waste) and subject to an enforceable requirement limiting the unit to combusting a fuel stream, 10 p ercent or less of the weight of

1 In proposed Section 60.5065 EPA excludes existing CISWI units from Subpart CCCC, but this exclusion
should be from Subpart DDDD which applies to existi ng CISWI units.Docket ID No. EPA–HQ–OAR–2009–0559 Comments of Energy Recovery Council

November 29, 2010 Page 3 of 14

which is comprised, in aggregate, of sewage sludge. ”(75 FR 63312; 75 FR 63337.)

The inclusion of this definition in the proposal su ggests EPA’s intent to limit applicability of the SSI rule to combustors firing more than 10% sewage sludge by weight. However, applicability criteria in proposed Subpart LLLL Section 60.4770 and Subpart MMMM Section 60.5005 do not explicitly excl ude co-fired combustors. Indeed,

once defined the term “co-fired combustor” is never used again anywhere in the proposed Subpart LLLL or Subpart MMMM regulations. We wonder why EPA defined the term if

it did not intend to use it to exclude co-fired com bustors from the SSI rules.

EPA should exempt all co-fired combustors from Subp art LLLL and Subpart MMMM regulations. The co-fired combustor definition should place an upper limit on the amount of sewage sludge that could be co-fired and still meet the exemption. However, EPA should apply the exemption to units fi ring 30% or less by weight instead of 10% or less by weight. Because sewage sludge has high moisture content its heating value (kilojoules per kilogram as fired) is low, an d a 10% limit by weight would effectively limit firing to a much lower fraction o f total heat input, providing little incentive for operators to make any physical and pe rmitting modifications necessary to accept sewage sludge. The 30% limit is also consist ent with the co-fired combustor definition contained in MWC Section 129 rules, e.g. Subpart Eb rules at 40 CFR 60.51b.

Comment 3. EPA should modify the definition of “co- fired combustor” to include all biomass, not just clean biomass.

EPA’s co-fired combustor definition applies to fuel s and wastes and includes “clean biomass” and “other solid wastes” (75 FR 75 FR 63312; 75 FR 63337.) In the final rule EPA should assure consistency with the I dentification of Non-Hazardous Secondary Materials That Are Solid Waste Rule (NHSM Rule) which was proposed June 4, 2010 and is expected to be promulgated by Decemb er 16, 2010. Depending on the outcome of the NHSM Rule it is possible that some b iomass materials (say, for example, urban demolition wood) would be neither “clean” nor classified as “solid waste”, thereby excluding these materials from the co-fired combust or definition. EPA can solve this potential problem by striking the adjective “clean” from biomass in the definition. Since EPA’s intent appears to be to include all biomass, either as clean material or solid waste, deleting “clean” from the definition would assure i ts intent is fulfilled regardless of the

outcome of the NHSM Rule.

Comment 4. EPA should change its MACT floor-setting methodology to conform to the law’s “average emissions limitation achieved” r equirement.

EPA’s proposed methodology sets a Maximum Achievabl e Control Technology (MACT) floor for each pollutant from an average of best performing 12% of units and then employs a statistical methodology which accoun ts for emissions variability to setDocket ID No. EPA–HQ–OAR–2009–0559 Comments of Energy Recovery Council

November 29, 2010 Page 4 of 14

MACT floor emissions limits. As explained in an Apr il 2010 memorandum from Roy Oommen, ERG, to Amy Hambrick, U.S. EPA, entitled “M ACT Floor Analysis for the Sewage Sludge Incinerator Source Category”, (hereaf ter called the “ERG Floor Memorandum”), EPA distinguishes between a “MACT flo or” (an average of emissions from the best performing units), and a “MACT floor emission limit” (the value determined from its variability analysis). Accordin g to the ERG Floor Memorandum, for existing units “The MACT floor was calculated as the average of the test runs fro m the best performing (i.e. lowest emitting) 12% percent of sources” and for new units “The

MACT floor

was calculated as the average of the test runs fro m the best performing single unit (i.e. lowest emitting).” (ERG Floor Mem orandum, page 10, emphasis added). EPA then conducted a variability analysis using the UPL statistic to determine the

“MACT floor emission limit ”. (ERG Memorandum, page 12, emphasis added).

CAA Section 129(a)(2) states “Emission standards fo r existing units in a category...shall not be less stringent than the avera ge emissions limitation achieved by the best performing 12 percent of units in the cate gory.” A plain reading of this requirement with the key phrase “average emissions limitation achieved” directs that the limitation achieved be determined first, and then a n average taken to determine the applicable least stringent standard. EPA’s method i s inconsistent with this Section 129 language. In EPA’s method there is no averaging of the achieved emissions limitations

, an average is only used to select the best performi ng units and set a “MACT floor” value before considering variability.

EPA should employ an alternate methodology which de termines the emissions limitation achieved for each best performing unit f irst , and then averages these limitations to determine the least stringent standard, or MACT floor. This method conforms to the plain meaning of the phrase “average emissions limi tation achieved”. Table 1 shows the

difference between the EPA and alternate methods:

Table 1. MACT Floor-Setting Methodology Comparison Step EPA Method Alternate Method
Select best performing

12% units based on... average emissions. average emissions.

Set emissions

limitation achieved

based on... the upper limit of all data from selected units, i.e., pool

all the emission data from

selected units and calculate the upper limit value. the average of upper limit values for the selected units, i.e., determine the upper limit

value for each of the selected

units and then calculate the average of those values.

The alternate method also conforms with EPA’s own e xplanation of the Mossville case findings: “The Court has recognized that EPA m ay consider emission variability in estimating the degree of emission reduction achieve d by the best-performing sources and in setting MACT floors that the best-performing sources can expect to mee t ‘every dayDocket ID No. EPA–HQ–OAR–2009–0559 Comments of Energy Recovery Council

November 29, 2010 Page 5 of 14

and under all operating conditions’.” (75 FR 63269, emphasis added.) In “setting the

MACT floors” the floor value should include the eff ect of variability. In EPA’s method the “floor” is the average of the best performing u nits before variability is considered. EPA then adjusts for the variability effects. In th e alternate method the floor value already considers variability, consistent with the Mossville findings.

From a statistics perspective, the alternate method is also superior. EPA’s method pools data from all best performing units for the s tatistical calculations which leads to biased results from 1) over-weighting the effect of units that have more data (see Comment 5), and 2) running statistical calculations on a pool of data from units representing distinct populations due to difference s in design, feed, operating characteristics, and emission controls (see Comment 6). The alternate method avoids both these problems; data from each unit is given equal weight (one emissions limitation achieved calculated per unit) and statistical calcu lations are run using data from only one unit (distinct population) at a time.

Comment 5. EPA’s floor-setting methodology biases r esults in favor of units that

have more data. EPA should use the alternate method ology presented in Comment 4 which avoids this problem.

In setting MACT standards for existing units, EPA p ools and utilizes data from all available test runs for the best performing units w ithout regard to the number of data points available for each unit. For all pollutants the number of test runs varies from unit to unit. Using data this way biases the statistical results and ultimately the standards by over-weighting the performance of the units that ha ve more data. As an example, in the ERG Floor Memorandum, Attachment A-2, page 2, “Vari ability Analysis for CO from Existing FB Units” the five best performing units include a total of 112 data points, distributed as shown in Table 2. EPA’s analysis bia ses the MACT floor results toward Units 3, 4, and 5 which constitute 92% of the data points even though they make up only 60% of the units, and under-weights Units 1 and 2 w hich are 40% of the units but only

contribute 1/12 th of the data. This example clearly shows EPA’s floo r-setting methodology is scientifically and statistically fla wed.

Table 2. EPA's CO Variability Analysis for Existing FB SSIs

Unit Number of

Data Points % of units % of Data

1 6 20% 5%

2 3 20% 3%

3 35 20% 31%

4 30 20% 27%

5 38 20% 34%

All 112 100% 100%Docket ID No. EPA–HQ–OAR–2009–0559 Comments of Energy Recovery Council

November 29, 2010 Page 6 of 14

EPA should avoid this serious flaw simply by using the alternate methodology presented in Comment 4. In this alternate method ea ch best performing unit’s emissions

limitation achieved is determined first and then th at single value is averaged along with the single values from the other best performing un its to determine the floor. All best performing units are thereby weighted equally.

Comment 6. The UPL approach does not work because b est performing MACT units represent distinct populations due to variabi lity in design, sludge characteristics, operating characteristics, operati ng/maintenance procedures and

emission controls.

Best performing MACT units represent separate and d istinct populations due to variability in design, sludge characteristics, oper ating characteristics, operating/maintenance procedures and emission contr ols

. All these variables are independent from one best performing source to the next. Therefore the only way a UPL based approach could statistically work is if varia bility from individual best performing sources are treated independently and data from eac h source is given equal weight. EPA has used the square root of sample variance instead of the square root of total variance

. By substituting sample variance for total variance EPA has not properly accounted for total variability. Given these two assumptions then between source variance and within source variance must be additive not pooled and tot al variance between the best performing sources would be used in UPL approach as was proposed in the Hazardous Waste Combustor (HWC) MACT Rule. 2

The equation for the UPL that addresses total varia tion from the HWC MACT

Rule is:

UPL = Mean + t √ V T

Where, Mean is the average of the best performing M ACT unit averages and V T is the

total variance determined as the sum of the within (intra) source variance and the between (inter) source variance.

Total Variance of V T = V B + V W ,

Where, V B is the variance of the average of the best perform ing unit averages. As

described above V B is additive because the variance of each MACT source unit is independent of one another and dependent on individ ual units sludge characteristics, sludge combustor type, air pollution control type a nd sample matrix affects. V W is the

within or intra source variance and is calculated a s the sum of the variances of individual

2 Technical Support Document for HWC MACT Standards, Volume III: Selection of MACT Standards. U.S. Environmental Protection Agency, Office of Sol id Waste and Emergency Response. September 2005, Pages 7-5 to 7-7. EPA-HQ-OAR-2004-0022-0453.pdfDocket ID No. EPA–HQ–OAR–2009–0559 Comments of Energy Recovery Council

November 29, 2010 Page 7 of 14

runs within each of the best performing sources sin ce individual unit test results will vary with sludge characteristics, and process and air po llution control operation conditions during each test run and test event.

Using the UPL outlined in the HWC MACT Rule that ac counts for total variance between best performing source provides another alt ernative to calculating MACT limits

that is in line with the 99% UL calculated from the 3 run averages. 99% UPL MACT limit for CO and Cd calculated using total variance approach is 73.3 ppm and 0.0020 mg/dscm respectively. Similar results would be expe cted for the other limits as well.

Comment 7. In its variability analysis EPA should n ot use the upper prediction limit (UPL) to set MACT floors because the random data di stribution premise of the UPL

calculation is incorrect. EPA should instead use ac tual 3-run averages and calculate the floor using the upper limit (UL) statistic. If EPA cannot use 3-run averages due to a paucity of data then EPA should use individual test runs and the UL.

EPA determines the MACT floor limit for each pollut ant using individual test run values and an upper predictive limit (UPL) calculat ion. (75 FR 63269.) The UPL is a statistical tool that is misapplied in this case an d should not be used. The UPL recognizes that the variability of 3-run averages is different from that of individual test runs, i.e. they have different frequency distributions. Mathematica lly the UPL accounts for this difference by adjusting the upper limit determined from individual runs to that using multiple runs using the factor SQRT ((1/n) + (1/m)) , where m=3, the number of runs per complete performance test. However, this adjustment presumes that each test run result within a given 3-run test occurs randomly over the entire population distribution. EPA provides no evidence supporting this presumption. I n fact, as will be seen in the examples below, run-to-run results within performance tests are not distributed randomly across the entire distribution. Rather, each of the three runs reflects conditions affecting a unit’s emissions at the time of the test. Many of these co nditions (e.g., sludge characteristics, condition of combustor and air pollution control eq uipment, season of the year) are likely

to remain the same or at least similar over the sho rt duration (up to a few days) of a performance testing program. Individual run results in a short term performance test will therefore not vary randomly across the entire distr ibution of emissions that would be observed over longer (monthly or annual) time perio ds. For example, suppose testing is performed with sludge that has seasonally higher-th an-average sulfur content leading to

elevated SO 2 emissions. EPA’s UPL calculation presumes that SO 2 emissions during any test run is just as likely to be below the average SO 2 emissions as above when in fact all three runs are likely to be above the average becau se the sulfur content is higher than normal over the entire course of the short term per formance test. In short, the frequency distribution of 3-run averages will exhibit a great er standard deviation and hence greater variability than that predicted by EPA’s UPL. EPA’s UPL approach under-estimates 3- run average performance test variability thereby se tting MACT floor limits which have not been achieved in practice and failing to comply with CAA Section 129.Docket ID No. EPA–HQ–OAR–2009–0559 Comments of Energy Recovery Council

November 29, 2010 Page 8 of 14

Examples Demonstrate the Validity of Our Comment

EPA’s inappropriate use of the UPL is demonstrated in examples for three pollutants

taken from EPA’s MACT Floor calculations for existi ng FB units (ERG Floor Memorandum, “Attachment A., Detailed MACT Floor Ana lysis for Existing FB Sources Cd, Pb, Hg, HCl, SO 2 , NOx, and CO”, Docket # EPA-HQ-OAR-2009-0059-0006. 1).

Attachment 1 to these comments is an Excel file whi ch repeats EPA’s floor calculations

and also contains seven additional columns of calcu lations we have added for CO, Cd, and Hg, highlighted in yellow. First, let’s assess the accuracy of EPA’s 99% UPL calculation for CO, which is shown in pink-highligh ted cell G132 as 51.1 ppm. Given the relatively large CO database (112 test runs) we can test the CO UPL accuracy with confidence by calculating 3-run performance test av erages 3 and the resulting 99% UL, and then comparing this 3-run average 99% UL with E PA’s 99% UPL. Column H shows this calculation which yields a 99% UL of 74.1 ppm (green-shaded cell H130), 34% higher than EPA’s value. Again, this 74.1 ppm value represents the true 99% upper limit of variability because it uses the actual 3-run ave rage performance test data. So what

accounts for this 34% difference? It’s the fact tha t EPA’s UPL statistic presumes run-torun variability is random across the entire distrib ution of data, whereas the actual data show the variability is not random. We can duplicat e EPA’s UPL value if we randomly sort the raw data using the Excel RANDBETWEEN funct ion in Column I and recalculate the 99% UL. The resulting 55.0 ppm 99% UL value as (orange-shaded cell K130) closely matches EPA’s value, verifying that the only way to support EPA’s UPL approach is to incorrectly presume that run-to-run data occur rand omly across the entire data distribution. (The calculations in Column K will no t match EPA’s UPL exactly because the limited data introduce some error, but the calc ulated values will always be similar.)

Similar conclusions result from an assessment of Cd and Hg data and also Lntransformed data for CO, Cd, and Hg shown in Column s L, M & O. The results, summarized in Table 3, demonstrate that EPA’s UPL u nderstates the variability of 3-run performance test averages and should not be used.

3 For two of the tests only 2 valid runs were availab le so in those instances we used 2-run averages.Docket ID No. EPA–HQ–OAR–2009–0559 Comments of Energy Recovery Council

November 29, 2010 Page 9 of 14

Table 3. Summary of UPL and UL calculations
CO (ppm) Cd (mg/dscm) Hg (mg/dscm) MACT Floor Using... N LN N LN N LN 99% UPL (EPA’s approach) 55.1 105.4 0.0014 0.0019 0.0109 0.0032

99% UL with Actual 3-run Test Averages (Statistically valid approach) 76.1 435.1 0.0020 0.0092 0.0177 0.0153 99% UL with Randomly

Distributed Data and 3-run Test Averages (Random data approach) 55.0 110.2 0.0013 0.0017 0.0109 0.0036 N = Normal statistics, LN = Log-normal statistics

EPA should avoid the entire issue of adjustments fo r the number of runs per performance test by using 3-run test averages in it s database and calculating the 3-run average upper limits (UL) for each pollutant. The c alculated UL values will appropriately reflect the true 3-run performance test variability . If EPA cannot use 3-run averages due

to a lack of data then it should use individual tes t run results and the UL value that is unadjusted for the number of runs per performance t est, i.e. m=1. EPA should not penalize the regulated community with more restrict ive emission standards because it has not obtained enough test data to provide for valid statistical analysis.

Comment 8. For the purposes of characterizing emiss ion data distributions EPA

should default to non-normal unless sufficient data are available to conduct robust analyses which unambiguously shows the distribution can only be described by

normal statistics.

The first step in statistical analysis on a dataset is to determine the type of distribution the data follow, typically characterized as normal or n on-normal (log-normal). In its proposed SSI rule methodology EPA employs simplisti c criteria using two Excel functions (skewness and kurtosis) to decide whetheremission data are normally or nonnormally distributed.

Using its criteria EPA finds that the data used for SSI floors follow normal distributions for 45% of the pollutants for EG and 90% for NSPS. (ERG Floor Memorandum, Attachments A-E.) This finding is incon sistent with both conventional wisdom and EPA’s own Guidance for Data Quality Asse ssment Manual, 4 (hereafter referred to as “EPA Guidance for Data Assessment”) which hold that it is more likely that environmental data are distributed log-normall y. EPA’s Guidance for Data Assessment provides that the log-normal distributio n is “a commonly met distribution in

4 U.S. Environmental Protection Agency, Office of Env ironmental Information, “Guidance for Data Quality
Assessment: Practical Methods for Data Analysis”, E PA/600/R-96/084, July 2000Docket ID No. EPA–HQ–OAR–2009–0559 Comments of Energy Recovery Council

November 29, 2010 Page 10 of 14

environmental work” (page 2-40), also stating “Envi ronmental data commonly exhibit frequency distributions that are non-negative and s kewed with heavy or long right tails” (page 4-7), and “The lognormal distribution is a co mmonly used distribution for modeling environmental contaminant data” (page 4-7) . It is reasonable to believe that

environmental emissions distributions are non-norma l, since frequency plots typically show many readings approaching zero and fewer large readings forming an elongated tail to the right. Normal distributions may exist for ce rtain pollutants where the entire dataset is many standard deviations away from zero, and val ues are controlled by an air pollution control process with set points, and feedback and c ontrol loops.

EPA’s characterization approach using Excel functio ns is not robust. The

Guidance for Data Assessment states that skewness a nd kurtosis tests are “rarely used as they are less powerful than many alternatives.” It is therefore reasonable to conclude that EPA’s data distribution designations are flawed and that they bias the resulting MACT

floor determinations.

Distribution determination is not an exact science, even with datasets that are representative of the entire data population. Graph ical methods and mathematical methods such as Shapiro Wilk are frequently employe d but can still lead to ambiguous results. Add the additional confounding factor of E PA’s small database for many of the units and the determination becomes highly uncertai n. For new units where normal distributions were assigned to 90% of the pollutant s, EPA is applying its Excel criteria to as few as three data points! Any conclusion reached from such a cursory analysis is extremely uncertain and speculative. In addition da tasets do not lend themselves to simple black and white normal/non-normal designatio ns, there are shades of grey. EPA’s forced, binary analysis belies this fact.

The normal/non-normal designation is too important to be left to chance. In the SSI proposal 99% UPL values differ significantly be tween normal and non-normal; for

new units the normal distribution 99% UPLs are on a verage only 60% of the non-normal distribution 99% UPLs. EPA’s criteria for assigning data distributions risks setting MACT floors which have not been achieved in practic e.

EPA must, by default, assume that emissions data it analyzes are non-normally distributed unless rigorous statistical analysis co nducted in accordance with EPA guidance unambiguously show otherwise. This approac h is reasonable and consistent with the need to err on the side of achievability i n order to assure compliance with the Section 129 requirement that emissions limitations be achieved in practice. In setting MACT floors the stakes are too high for EPA to rely on cursory Excel tests and criteria applied to small and likely non-representative data sets to determine distributions.Docket ID No. EPA–HQ–OAR–2009–0559 Comments of Energy Recovery Council

November 29, 2010 Page 11 of 14

Comment 9. In its statistical calculations EPA shou ld select the 99.9% upper limit (and not the 99% upper limit) as representative of emissions limitation achieved. EPA should reject its proposed 95% UPL alternative.

In its proposal EPA determined MACT floor emission limits for existing and new

units based on a 99 th percentile UPL. In Comments 6 and 7 we explained o ur concerns about EPA’s use of the UPL versus the UL. In this c omment we address the appropriateness of the selected numerical percentil e value and will refer to it simply as the “% limit”. From the statistical point of view a 99% limit is the emission value that the unit will meet with 99% probability. While this wou ld seem to suggest a high likelihood of compliance, probability must be viewed from a vi olation perspective, i.e. that there is a 1% likelihood that a given pollutant test on a give n unit will exceed the selected limitation. In this regard the 99% limit is inadequ ate. EPA’s rules propose annual performance tests to demonstrate compliance with ea ch of 11 emission limits. For each unit the probability of passing all 11 pollutant em ission tests in an annual performance testing program is (0.99) 11 = 90%, i.e. the probability of at least one emissi on violation is 10%, excluding the additional compliance risk assoc iated with continuous emission monitoring (CEM). These are unacceptable risks to a ny responsible operator, and should likewise be unacceptable to EPA.

The consequences of emission limit exceedances are severe. Operators face stiff fines, retesting costs, additional regulatory scrut iny, bad press, citizen suits, and negative public opinion. All diligent operators try to keep emissions well below limits in order to account for emissions variability and thereby avoid violations and their consequences.

If EPA employs the statistical limit to set MACT fl oor emission limits it should use the 99.9% limit. This represents a 0.1% probabi lity of a failure for individual tests, or a 1% per unit non-compliance probability per annual performance test program. This value better encompasses unit emissions variability and represents a manageable risk to the responsible facility operator. 5

EPA originally proposed using the 99.9% UL in its H ospital/Medical/Infectious Waste Incinerator (HMIWI) MACT rule (73 FR 72962), stating “The 99.9 percent UCL 6
is appropriate for use in this analysis because sou rces must meet the standards at all times, and as mentioned above, the limited amount o f test data introduces a degree of uncertainty.” (73 FR 72977). EPA changed its protoc ol to 99% in the final HMIWI rule

5 We are concerned that EPA’s emissions database doe s not account for emissions variability occurring
during startup, shutdown, and malfunction periods. This comment does not address that concern and the
resulting additional compliance risk.

6 The HMIWI rule used the term upper confidence limi t (UCL); however, EPA acknowledged in its
proposed CISWI rule that the term as used in the HM IWI rule is the same as the upper limit (UL). See 7 5

FR 31943, footnote 3.Docket ID No. EPA–HQ–OAR–2009–0559 Comments of Energy Recovery Council

November 29, 2010 Page 12 of 14

in response to public comments, “The 99 percent UCL values are more in line with the highest test runs for the MACT floor units than the other percentiles, indicating that the 99 percent UCL provides a more reasonable compensat ion for variability.” (74 FR 51387). EPA’s conclusion is invalid because, like t he SSI unit database, the HMIWI database consisted of scant emission test results, few enough to require EPA to use

individual test runs in order to have more data to use in its statistical analyses. With so few data points it is highly unlikely that the high est emissions and full variability would have been observed. Indeed, the inability to captur e and describe the full range of emissions and emission distribution is the very rea son for relying on statistics to predict upper limits! EPA’s conclusion to use 99% in the HM IWI rule therefore has no valid statistical basis and only serves to increase the p robability of emissions violations. In the SSI MACT rule EPA should use 99.9% limit as origina lly proposed in the HMIWI rule.

EPA requests comment on an alternative to use 95% l imits (95% UPL) to calculate MACT floors. (75 FR 63274.) EPA observed that certain calculated floors were higher than SSI test data so it is considering the use a 95% upper limit to get lower floors and tighter limits. In short, it appears EPA didn’t like the results so it’s thinking about changing the % limit to get results that are more s tringent. This is purely arbitrary and capricious.

In addition, EPA’s lack-of-data rationale to reduce the UPL % is inconsistent with its conclusion that it has enough data (sample size ) to characterize emissions. (75 FR 63270-63271). If EPA argues that there are enough d ata to determine the floors EPA cannot also argue that insufficient data is causing the need to make a calculation change.

Rather than making arbitrary changes to fit a desir ed outcome, EPA should instead be comparing the data scatter in the SSI da tabase with that in other MACT rules (which also suffer from limited data) to determine whether scatter (standard deviation) is indeed higher and if so examine its methodology to explain it. What EPA might find is that the combination of the way units were selected (using data from all units thought to be best performers based on air pollution control d evices rather than selecting best performing units from the universe of SSIs) and EPA ’s pooled data approach (running statistics on pooled data from units representing d istinct data populations due to differences in design, feed, operating characterist ics, and emission controls) is what results in higher standard deviations.

EPA should not attempt to solve one problem with it s methodology (the data scatter largely caused by its use of pooled data fr om different units) by creating another problem (promoting acceptability of a 5% failure ra te per pollutant per test). In fact,

based on the above discussion even a 1% failure rat e is unacceptable. EPA should instead revise its overall methodology in accordance with C omment 4 and calculate the 99.9% UL as the MACT floors. EPA should not arbitrarily c hange the % limit. Where there is uncertainty, EPA must err on the side of achievabil ity; otherwise it risks setting theDocket ID No. EPA–HQ–OAR–2009–0559 Comments of Energy Recovery Council

November 29, 2010 Page 13 of 14

technology-based MACT standards at levels which tec hnology cannot achieve, in

contradiction to the CAA.

EPA can use its authority under the law to go beyon d-the-floor in instances where derived floors do not reflect “maximum degree in re duction of emissions”. In fact, with

its limited emissions database and statistical tool s EPA should anticipate that some calculated floor values may be higher than is ultim ately achievable. This is result of these limitations. In such cases EPA should rely on its b eyond-the-floor evaluation to set the standards. EPA can also set more stringent standar ds in the residual risk and five-year review processes wherein a more robust data populat ion would be available.

Comment 10. EPA correctly omitted a PM 2.5 standard.

EPA requests comment on whether it should set PM 2.5 standards for SSIs and whether OTM 27 and 28 are appropriate measurement t echniques. (75 FR 63275.) ERC believes that EPA acted properly by excluding a PM 2.5 emission standard from its proposal and that it should not recognize the OTM 2 7/28 methods for PM performance testing.

Section 129 standards are based on emissions reduct ions achieved using MACT. MACT air pollution control (APC) equipment for part iculate matter (PM) emissions is limited by its ability to capture particulate that exists at the APC device operating temperature. This temperature is much higher than t he ambient temperatures at which much of the condensable particulate forms. The MACT standard and associated performance test method should reflect the PM contr ol device’s capability at operating flue gas temperatures. The OTM 27/28 methods measur e not only PM actually present in the flue gas but also any particulate matter formed from the interaction and condensation of gaseous organic/inorganic flue gas constituents in the sampling train, and thereby measure and report PM which is beyond the capabilit y of the PM control device selected as MACT. In addition, to our knowledge OTM 27/28 m ethods have not been validated on SSIs as mandated by Section 129. As such the SSI MACT standards should regulate PM as measured by EPA Reference Method 5, and not O TM 27/28.

PM 2.5 emission standards are appropriate in the context of Ambient Air Quality Standards or other ambient health-based standards, and are set as part of source PSD/Title V permitting processes which evaluate both solid an d condensable PM emissions and their impacts. Regulation under MACT is inappropria te and unnecessary.

Comment 11. EPA’s two dioxin/furan emission standar ds are duplicative and unnecessary. EPA should set a single standard for d ioxin/furan total mass basis.

EPA’s proposal contains two emission standards for dioxin/furan, one as total mass basis (TMB) and the other as toxic equivalents (TEQ). These standards are redundant, only a TMB standard is appropriate. The same test, EPA Reference MethodDocket ID No. EPA–HQ–OAR–2009–0559 Comments of Energy Recovery Council

November 29, 2010 Page 14 of 14

23, is used to measure both TMB and TEQ, the only d ifference being a mathematical calculation using TEQ factors to convert the measur ed TMB values to TEQ values. TEQ factors have changed over the years from the origin al 1989 I-TEQ to the presently used 2005 World Health Organization Toxic Equivalence Fa ctors, so setting a TEQ standard represents a potentially moving target. Two standar ds are not required by Section 129 which lists “dioxins and dibenzofurans” only once a nd without distinction between TMB and TEQ. (Section 129(a)(4).) Finally, MACT standar ds are based on emissions reductions at best performing units, i.e. are techn ology based as opposed to health based. Considering these facts, EPA should promulgate a si ngle dioxin/furan standard, as TMB, or if compelled to regulate both TMB and TEQ should base compliance on achieving

either one of the two limits.

Conclusion

The Energy Recovery Council shares EPA’s commitment to improving emissions. As an industry, our members have demonstrated this commitment with a constant drive to enhance technology and reduce emissions. ERC appre ciates the opportunity to submit these detailed comments. Of course, we would be pl eased to answer any questions you may have.

Sincerely,

Ted Michaels

President

# Attachment 1

(This was filed as an Excel spreadsheet in conjunction with these comments)