ORAL ARGUMENT NOT YET SCHEDULED

UNITED STATES COURT OF APPEALS

FOR THE DISTRICT OF COLUMBIA CIRCUIT

NATIONAL ASSOCIATION OF )

CLEAN WATER AGENCIES, )

)

Petitioner, ) 

)

v. ) No. 11-1131 (consolidated 

) with 11-1167 and 11-1185)

ENVIRONMENTAL PROTECTION )

AGENCY and LISA PEREZ JACKSON, )

ADMINISTRATOR, EPA, )

)

Respondents. )

___________________________________ )

EPA’S OPPOSITION TO EMERGENCY JOINT MOTION

FOR STAY OF THE SEWAGE SLUDGE INCINERATION RULE

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Respondents, the Environmental Protection Agency and Lisa Perez Jackson 

(“EPA” or “Agency”) submit this Opposition to the “Emergency Joint Motion for 

Stay of the SSI Rule” filed by Petitioners, the National Association of Clean Water 

Agencies and Hatfield Township Municipal Authority (collectively “NACWA”). 

See Doc. #1330427 (“NACWA Mot.”). For the reasons stated below, NACWA’s 

Motion must be denied. 

BACKGROUND

Petitioners seek to stay EPA’s final rule entitled “Standards of Performance 

for New Stationary Sources and Emission Guidelines for Existing Sources: 

Sewage Sludge Incineration Units,” 76 Fed. Reg. 15,372 (Mar. 21, 2011) (“SSI 

Rule” or “Rule”). That Rule sets New Source Performance Standards and 

Emission Guidelines under Clean Air Act (“CAA” or “the Act”) Section 129, 42 

U.S.C. § 7429, to reduce emissions of nine air pollutants from new and existing 

sewage sludge incinerators.1

CAA Section 129, entitled “Solid Waste Combustion,” requires EPA to 

establish emissions standards for certain categories of “solid waste incineration 

units.” Id. A “solid waste incineration unit” is “a distinct operating unit of any

 

 

1 The nine pollutants are: cadmium, carbon monoxide, hydrogen chloride, lead, 

mercury, nitrogen oxides, particulate matter, polycholorinated dibenzo-p-dioxins 

and polychlorinated dibenzofurans, and sulfur dioxide. 42 U.S.C. § 7429(a)(4).

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facility which combusts any solid waste material from commercial or industrial 

establishments or the general public (including single and multiple residences, 

hotels, and motels).” Id. § 7429(g)(1). Section 129 specifies categories of solid 

waste incineration units for which EPA must establish standards: those 

combusting municipal solid waste, hospital and medical waste, industrial or 

commercial waste, and “other categories of solid waste incineration units” that 

combust waste not specifically identified. Id. § 7429(a)(1)(A)-(E); see Davis 

County v. Solid Waste Mgmt. v. EPA, 101 F.3d 1395, 1403 (D.C. Cir. 1996); 

NRDC v. EPA, 489 F.3d 1250, 1255 (D.C. Cir. 2007). 

Sewage sludge incinerators are one of the “other categories of solid waste 

incineration units” that EPA is required to regulate under Section 129. A sewage 

sludge incinerator is a device at a wastewater treatment facility that combusts 

domestic sewage sludge to reduce the volume of sludge. 76 Fed. Reg. at 15,374. 

This combustion releases various air pollutants. Id. at 15,375. EPA’s SSI Rule 

regulates two subcategories of new and existing sewage sludge incinerators: 

multiple hearth incinerators and fluidized bed incinerators.2

Section 129 directs EPA to set emissions standards that “reflect the 

 Id. at 15,376. 

 

2 Section 129 gives EPA discretion to “distinguish among classes, types . . . and 

sizes of units within a category” to establish the standards. 42 U.S.C. § 7429(a)(2). 

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maximum degree of reduction in emissions” of air pollutants, “taking into 

consideration the cost of achieving such emission reduction, and any non-air 

quality health and environmental impacts and energy requirements [that EPA] 

determines is achievable for new or existing units in each category.” 42 U.S.C. 

§ 7429(a)(2). This is referred to as the “maximum achievable control technology” 

level of control, or “MACT.” See Ne. Md. Waste Disposal Auth. v. EPA Maryland, 

358 F.3d 936, 940 (D.C. Cir. 2004); NRDC, 489 F.3d at 1254. 

EPA develops MACT standards through a two-step process. First, EPA 

determines the minimum level of stringency that the Act requires – the “MACT 

floor.” For new units, the MACT floor is “the emissions control . . . achieved in 

practice by the best controlled similar unit . . . .” 42 U.S.C. § 7429(a)(2). For 

existing units, the MACT floor is “the average emissions limitation achieved by 

the best performing 12 percent of units in the category.” Id. Second, EPA 

determines whether more stringent beyond-the-floor standards are achievable 

based on costs, non-air quality health and environmental impacts, and energy 

requirements. Id. 

Here, EPA determined that beyond-the-floor MACT standards were not 

warranted and adopted MACT floor standards for each of the nine pollutants 

emitted from the multiple hearth and fluidized bed sewage sludge incinerators. 76 

Fed. Reg. at 15,394. 

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STANDARD OF REVIEW

A stay is a disfavored remedy and “it is the movant’s obligation to justify the 

court’s exercise of such an extraordinary remedy.” Cuomo v. U.S. Nuclear 

Regulatory Comm’n, 772 F.2d 972, 978 (D.C. Cir. 1985). To determine whether a 

stay is warranted, the Court stringently applies four factors: (1) whether the 

movant has demonstrated a substantial likelihood that it will prevail on the merits; 

(2) the prospect of irreparable injury to the movant if relief is withheld; (3) the 

possibility of harm to other parties if relief is granted; and (4) the public interest. 

Nken v. Holder, 129 S. Ct. 1749, 1761 (2009). “A stay is not a matter of right, 

even if irreparable injury might otherwise result.” Id. at 1760 (citation omitted). 

Demonstrating a substantial likelihood of success on the merits requires a 

showing that NACWA is likely to persuade this Court that EPA’s SSI Rule is 

“arbitrary, capricious, an abuse of discretion, or otherwise not in accordance with 

law.” 42 U.S.C. § 7607(d)(9)(A). This narrow, deferential standard presumes the 

validity of agency actions and prohibits the Court from substituting its judgment

for that of the Agency. Motor Vehicle Mfrs. Ass’n v. State Farm Mut. Auto. Ins. 

Co., 463 U.S. 29, 43-44 (1983). Judicial deference also typically extends to any 

agency’s interpretation of a statute it administers. United States v. Mead Corp., 533 

U.S. 218, 227-31 (2001); Chevron, U.S.A., Inc. v. Natural Res. Def. Council, Inc., 

467 U.S.. 837, 842-45 (1984). 

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Irreparable harm requires injury that is “both certain and great, it must be 

actual and not theoretical.” Wis. Gas Co. v. FERC, 758 F.2d 669, 674 (D.C. Cir. 

1985). The injury must be “of such imminence that there is a clear and present 

need for equitable relief to prevent irreparable harm.” Id. (citation omitted); see 

also Nken, 129 S. Ct. at 1761 (more than a “mere possibility” of success on the 

merits is required, and the standard for irreparable harm requires more than 

showing the “possibility” of harm). 

ARGUMENT

I. INDUSTRY PETITIONERS CANNOT DEMONSTRATE A 

SUBSTANTIAL LIKELIHOOD OF SUCCESS ON THE MERITS

A. EPA Reasonably Regulated Sewage Sludge Incinerators Under 

CAA Section 129.

Chevron guides review of NACWA’s claim that the SSI Rule conflicts with 

the Clean Air Act. The Court “must decide (1) whether the statute unambiguously 

forbids the Agency’s interpretation, and if not, (2) whether the interpretation, for 

other reasons exceeds the bounds of the permissible.” Barnhart v. Walton, 535 

U.S. 212, 218 (2002). Here, under Chevron step one, EPA’s regulation of sewage 

sludge incinerators under Section 129 is reasonable. 

First, this Court in Natural Resources Defense Council v. EPA, 489 F.3d 

1250 (D.C. Cir. 2007), specifically determined that Section 129 “plainly and 

broadly” defines “solid waste incineration unit” “to include ‘a distinct operating 

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unit of any facility which combusts any solid waste material from commercial or 

industrial establishments or the general public . . . .’” Id. at 1257 (quoting 42 

U.S.C. § 7429(g)(1)). “The word ‘any’ is usually understood to be all inclusive.” 

Id. (citation and internal quotation marks omitted). Consistent with that 

unambiguously broad language, EPA determined that Section 129 covers 

incineration units that combust sewage sludge – a type of solid waste – and 

established emission standards for such units. NACWA’s attempt to distinguish 

NRDC on the ground that it involved a different type of incinerator is irrelevant. 

NACWA Mot. at 10. The Court’s opinion regarding the definition of “solid waste 

incineration unit” is directly on-point and NACWA should not be allowed to 

constrict “the scope of this plain, broad language.” 489 F.3d at 1258. 

Second, Section 129(a)(1)(E) requires EPA to establish standards for “other 

categories of solid waste incineration units,” in addition to the specific categories 

of solid waste incineration units listed in subsections (a)(1)(B) through (D). 42 

U.S.C. § 7429(a)(1)(B)-(E). As this Court has found, that broad language 

specifically allows EPA the “discretion to identify ‘other categories of solid waste 

incineration units.’” Davis County, 101 F.3d at 1405 (emphasis in original). Here, 

EPA reasonably interpreted that phrase to include sewage sludge incinerators –

which are solid waste incinerators – as one of the “other categories” under Section 

129. 

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NACWA disagrees, arguing that sewage sludge incinerators do not fall 

under “other categories” because sewage sludge comes from within the publicly 

owned treatment works (“POTW”) rather than “’from commercial or industrial 

establishments or the general public’ under § 129(g)(1).” NACWA Mot. at 9. 

This is a distinction without difference. Sewage sludge is “a solid, semi-solid, or 

liquid residue generated during the treatment of domestic sewage in a [POTW] . . . 

.” EPA-HQ-OAR-2009-0559-0028 at 1 (Exh. 1 relevant pages attached hereto). 

“Domestic sewage is generated by residences, businesses, etc. . . .” Id. Sewage 

sludge therefore originates from the general public and residential and commercial 

facilities. The POTW’s treatment of that domestic waste to separate out from the 

waste stream the sewage sludge that will be combusted does not change the 

original source of the sewage sludge. 76 Fed. Reg. at 15,383.3

 

3

 Under Clean Air Act Section 7429(g)(6), the term “solid waste” has the 

definition established by EPA under the Resource Conservation and Recovery Act 

(“RCRA”). NACWA argues that EPA “concluded that these [sewage] sludges are 

not solid waste” in a 1990 rulemaking. NACWA Mot. at 9 (citing 55 Fed. Reg. 

46,354, 46364 (Nov. 2, 1990)). On March 21, 2011, however, EPA finalized a rule 

under RCRA, which included sewage sludge in the definition of non-hazardous 

solid waste. See 76 Fed. Reg. 15,456, 15,513 (Mar. 21, 2011). NACWA filed a 

petition for review of that RCRA solid waste definition in this court. Plus, the 

1990 Federal Register notice to which NACWA refers addresses only hazardous 

waste, for which EPA has established a separate regulatory definition under 

RCRA. See 40 C.F.R. Pt. 261. 

 

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Finally, Section 129 explicitly excludes certain incinerators from the 

definition of solid waste incineration units, 42 U.S.C. § 7429(g)(1); see Davis

County, 101 F.3d at 1398, and sewage sludge incinerators are not among those 

exclusions. “Where Congress explicitly enumerates certain exceptions to a general 

prohibition, additional exceptions are not to be implied, in the absence of a 

contrary legislative intent.” Sierra Club v. EPA, 551 F.3d 1019, 1028 (D.C. Cir. 

2008), cert denied, 130 S. Ct. 1735 (2010) (citation omitted). 

NACWA argues that EPA must regulate sewage sludge incinerators under 

Section 112, which requires EPA to establish emissions standards for sources of 

hazardous air pollutants. NACWA Mot. at 5-8. To support its view, NACWA 

isolates a single provision of Section 112 – subsection (e)(5) – which is contained 

in the section entitled “Schedule for standards and review.” 42 U.S.C. § 7412(e). 

That provision, however, merely provides a deadline for EPA to establish 

emissions standards for hazardous air pollutants from POTWs. 42 U.S.C. 

§ 7412(e)(5); see 76 Fed. Reg. at 15,383. Subsection (e)(5) does not require EPA 

to regulate sewage sludge incinerators under Section 112. 

Reading Section 112 in conjunction with Section 129 dispels NACWA’s 

argument. Read together, it is clear that Congress did not intend for Section 112 to 

trump regulation of solid waste incinerators under Section 129. Section 112 is a 

more general provision that applies to hazardous air pollutants from all listed 

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source categories, whereas Section 129 is more specific and applies to certain 

pollutants (some of which are not hazardous air pollutants) from a particular type 

of source – solid waste incineration units. See NRDC, 489 F.3d at 1256. “The 

principle that a specific statutory provision prevails over a more general provision 

is established beyond question.” F.T.C. v. Manager, Retail Credit Co., Miami 

Beach Branch Office, 515 F.2d 988, 993 (D.C. Cir. 1975) (citations omitted). 

“However inclusive may be the general language of a statute, it will not be held to 

apply to a matter specifically dealt with in another part of the same enactment . . . 

.” Id. at 994, n.10 (citations and internal quotation marks omitted). Here, EPA’s 

interpretation is a reasonable one that gives meaning and effect to both Sections: 

Section 129 requires (and EPA has established) emissions standards for certain 

pollutants from sewage sludge incinerators at POTWs, whereas Section 112 

requires emissions standards for hazardous air pollutants from other parts of the 

POTW that do not combust solid waste and, thus, are not solid waste incineration 

units.

4

NACWA prefers to focus on the interplay between Section 112(e)(5) and the 

Clean Water Act, rather than the structural relationship in the Clean Air Act 

 

 

4 As noted below, EPA set POTW standards years ago under Section 112 and did 

not include sewage sludge incinerators.

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between Sections 112 and 129. NACWA is correct that Section 112(e)(5)

incorporates the Clean Water Act’s definition of POTW or “treatment works.” The 

relationship between Section 112(e)(5) and the Clean Water Act, however, has no 

bearing on which emissions standards – Section 112 or Section 129 – should apply 

to sewage sludge incinerators.5

To buttress its view that EPA must regulate sewage sludge incinerators 

under Section 112, NACWA resorts to prior EPA “regulatory pronouncements.” 

NACWA Mot. at 6, 7. Such statements are irrelevant to whether the plain 

language of Section 129 allows EPA to regulate sewage sludge incinerators. 

Moreover, the regulatory statements on which NACWA relies pre-date this 

Court’s opinion in NRDC.

 The salient point is that Section 112(e)(5) says 

nothing that requires regulation of emissions from solid waste incinerators, 

whereas Section 129 does. 

6

 

5 EPA’s regulation of sewage sludge incinerators under Section 129 does not 

undermine its regulation of the final use and disposal of sewage sludge under the 

Clean Water Act regulations at 40 C.F.R. Pt. 503. See 76 Fed. Reg. at 15,375. 

Importantly, Clean Water Action Section 405 specifically states that nothing is 

intended to waive more stringent requirements of the Clean Water Act or any other 

federal law. 33 U.S.C. § 1345. 

 Even if prior EPA pronouncements were relevant, 

however, NACWA dismisses EPA’s 1999 emissions standards for POTWs under 

6 Additionally, NACWA’s reliance on these notices is misplaced as EPA explained 

in the final rule. 76 Fed. Reg. at 15,383. 

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Section 112(d), which specifically excluded sewage sludge incinerators. See 64 

Fed. Reg. 57,572 (Oct. 26, 1999). In fact, in the proposal for those standards, EPA 

stated that “[s]ewage sludge incineration will be regulated under section 129 of the 

CAA, and will be included in the source category Other Solid Waste 

Incinerators[.]” 63 Fed. Reg. 66,084, 66,087 (Dec. 1, 1998). 

For these reasons, EPA’s interpretation of Section 129 is reasonable. 

B. EPA Reasonably Used A Statistical Methodology To Determine 

The MACT Floor for Existing Units Under Section 129. 

NACWA’s challenge to EPA’s use of a statistical methodology to help 

determine the MACT floors for existing units also is subject to a Chevron

analysis.7

 

7 NACWA does not challenge EPA’s methodology for determining the MACTfloor for new units. 

 Section 129 provides that the MACT floor for existing units “shall not 

be less stringent than the average emissions limitation achieved by the best 

performing 12 percent of units in the category[.]” 42 U.S.C. § 7429(a)(2). There 

are 204 sewage sludge incinerators in operation, and out of those, 144 are multiple 

hearth units and 60 are fluidized bed units. See EPA-HQ-OAR-2009-0559-0157 at 

3 (Exh. 2 attached hereto). Thus, 12 percent of each of those units (rounding up to 

the nearest whole integer) is 18 multiple hearth units and 8 fluidized bed units. Id.

at 6. EPA was able to collect actual data for 20 multiple hearth units and 6 

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fluidized bed units, but not all of these units had data for every pollutant.8

For those pollutants where EPA did not have enough data, EPA used all of 

the quality-assured data it did have to determine the MACT floor, and then verified 

this data using a statistical methodology to determine the minimum number of 

observations (or actual data) needed to reflect the performance of the best 12 

percent of units. Here, the number of observations or data that EPA collected

actually equaled or exceeded the minimum number of observations that were 

necessary according to this statistical methodology. EPA therefore was able to 

determine that the collected data mirrored the data of the best performing 12 

percent of units in the source category. Thus, the MACT floor calculation from the 

collected data is consistent with what it would have been if EPA had collected 

actual data from the best performing 12 percent of units. Exh 2 at 6-10. 

 Id. 

The issue at hand is whether EPA must, in all cases, use only actual data 

from the best performing 12 percent or units, or if EPA can use other approaches 

(such as the statistical methodology used here) in conjunction with actual data to 

determine the MACT floors. In Sierra Club v. EPA, 167 F.3d 658 (D.C. Cir. 

1999), this Court answered that question and determined that the phrase “average 

 

8 EPA collected actual data for mercury and particulate matter for 12 percent of the

multiple hearth units and, thus, was able to base the MACT floor for those units 

and pollutants on that data. 

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emissions limitation achieved by the best performing 12 percent of units” “does 

not by its plain meaning exclude estimation, either by sampling or some other 

reliable means.” Id. at 661-62. Under Chevron, the Court determined that Section 

129 says “nothing about how the performance of the best units is to be calculated.” 

Id. at 661. Also, “EPA typically has wide latitude in determining the extent of 

data-gathering necessary to solve a problem” and the Court “generally defer[s] to 

an agency’s decision to proceed on the basis of imperfect scientific information, 

rather than to invest the resources to conduct the perfect study.” Id. at 662 (citation 

and internal quotation marks omitted). As long as EPA’s approach for making the 

estimation bears a “rational relationship to the reality it purports to represent,” 

EPA’s methodology will be upheld. Id. 

Here, EPA’s statistical methodology is permissible because it allowed a 

“reasonable inference” based on actual data “as to the performance of the top 12 

percent of units.” Sierra Club, 167 F.3d at 663. EPA also explained “why its 

methodology yields the required estimate.” Cement Kiln Recycling Ass’n v. EPA, 

255 F.3d 855, 862 (D.C. Cir. 2001); see Exh. 2 at 6-10. As EPA explained, this 

statistical methodology assures that the data EPA collected adequately represent 

data from the best performing 12 percent of units in each subcategory. Exh. 2 at 9. 

The methodology also accounts for various factors to ensure that the minimum 

number of observations necessary to make that determination is not an arbitrary 

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number. The fact that EPA’s collected data equaled or exceeded the minimum 

number of observations determined under the methodology assures that the MACT 

floor calculations are what they would have been if EPA had been able to collect a 

complete dataset from the best performing 12 percent of units.

9

C. EPA Reasonably Accounted For Variability In Sewage Sludge 

Emissions. 

 

This Court has recognized EPA’s authority to account for variability in 

setting MACT floors, Cement Kiln, 255 F.3d at 863 (quoting Sierra Club, 167 F.3d 

at 665), which reflects the fact that even the performance of the best units will not 

always be the same. Here, EPA collected emissions data from nine different 

facilities in nine different states across the country. 76 Fed. Reg. at 15,391. The 

facilities represent residential and commercial populations as well as varying 

winter and early spring climates – the seasons during which the majority of the air 

 

9 Section 129’s MACT floor provision for existing sources stands in contrast to the 

corresponding MACT floor provision in Section 112. Section 112 requires the 

MACT floor to reflect the average emissions limitation achieved by the best 

performing 12 percent of sources “for which the Administrator has emissions 

information.” 42 U.S.C § 7412(d)(3)(A). Section 129 does not contain this 

qualifier. Because Congress envisioned that Section 129 standards would be based 

on the average of all sources in the top 12 percent of the source category, the 

ability to reasonably estimate the sources’ emissions, rather than rely only on 

actual emissions testing data for each source, is appropriate to ensure that MACT 

floor standards are established in a timely manner, and do in fact reflect the 

emissions performance of the top 12 percent of sources.

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emissions testing were conducted. Id. Taken together, these data – from a variety 

of facilities – adequately incorporate variability in air emissions from wastewater 

treatment systems across the country. Id. 

NACWA wants EPA to account for variability in a different way – by 

looking at the variability of the sludge feed in addition to looking at the variability 

of the air emissions. NACWA Mot. at 12-14. EPA, however, already 

appropriately accounted for geographic variability by gathering emissions data 

from facilities that are spread across the country in different climates. 76 Fed. Reg. 

at 15,391. Additionally, EPA applied a statistical methodology (the Upper 

Predictive Limit) that analyzed variability of air emissions tests within a unit over 

time based on various factors to account for future variability. Exh. 2 at 10-14, 11-

12. Thus, the MACT-floors reflect the potential variations in the air emissions 

that may result from variations in the sludge content.10

II. INDUSTRY PETITIONERS CANNOT DEMONSTRATE AN 

IMMINENT THREAT OF IRREPARABLE HARM

 

None of NACWA’s alleged economic injuries is sufficient to warrant a stay. 

 

10 NACWA does not dispute the reasonableness of EPA’s use of the UPL to 

account for variability and, instead, argues that EPA should have used inadequate 

sampling data under the Clean Water Act and incomplete stack test data to account 

for variability. NACWA Mot. at 13-14. It is not for this Court, however, to 

substitute its judgment for that of the Agency concerning how to best account for 

variability. Motor Vehicle Mfrs. Ass’n, 463 U.S. at 43-44. 

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First, NACWA relies on a request by New York State asking POTWs either to 

certify, by June 30, 2011, that they will comply with the SSI Rule, or to shut down 

their sewage sludge incinerator by March 21, 2012. See NACWA Mot. at 15. 

NACWA also argues that the Commonwealth of Virginia is “planning to ‘move 

quickly’ to implement the SSI Rule.” NACWA Mot. at 15, Ex. 3, ¶ 8. Both of 

these State actions or representations – which neither EPA nor the SSI Rule 

requires – come at least two and a half years before the Rule’s compliance date for 

existing units.11

Second, NACWA claims its members will suffer unrecoverable economic 

losses because the installation of new pollution control devices is time-consuming 

and costly. NACWA Mot. at 17-18. Allegations of mere economic injuries, 

 Moreover, NACWA’s purported harm from independent actions 

by States – not EPA – to require early compliance commitments or shutdowns, or 

to fast-track implementation of the SSI Rule, does not “directly result from the 

action which the movant seeks to enjoin,” i.e., the SSI Rule. See Wis. Gas, 758 

F.2d at 674 (emphasis added). 

 

11 Under the SSI Rule, the compliance date for new sources is the earlier of 60 days 

after the unit reaches the feed rate at which it will operate or within 180 days after 

initial startup. 76 Fed. Reg. at 15,373. Existing sources must come into 

compliance either three years after EPA approves a state plan implementing the 

emissions guidelines, or five years after promulgation of the Rule, whichever is 

earlier. 

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“however substantial, in terms of money, time, and energy” simply are not enough 

to support granting a stay. Wis. Gas, 758 F.2d at 674. NACWA’s expenditures for 

pollution control equipment represent the costs of compliance with government 

regulation, which “ordinarily is not irreparable harm.” Am. Hosp. Ass’n v. Harris, 

625 F.2d 1328, 1331 (7th Cir. 1980). As the Third Circuit has explained, “[a]ny 

time a corporation complies with a government regulation that requires corporation 

action, it spends money and loses profits; yet it could hardly be contended that 

proof of such an injury, alone, would satisfy the requisite for a preliminary 

injunction,” i.e., an irreparable injury. A.O. Smith Corp. v. FTC, 530 F.2d 515, 527 

(3rd Cir. 1976). 

Compliance costs would be sufficient only if the “very existence” of the 

POTW itself is threatened, see Wis. Gas, 758 F.2d at 674, which none of 

NACWA’s declarations alleges.

12

 

12 None of NACWA’s declarants state that their POTWs actually will decide to 

shut down their sewage sludge incinerators or that doing so would threaten the 

existence of the entire facility as opposed to just the use of the sewage sludge 

incinerator within the facility. 

 Moreover, out of the 204 operating sewage 

sludge incinerators, EPA estimates that 155 of them currently are meeting the 

emissions limitations in the SSI Rule. 76 Fed. Reg. at 15,386. The fact that 

approximately 75% of sewage sludge incinerators already meet the SSI Rule 

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standards belies NACWA’s notion that compliance costs are so harmful as to 

warrant a stay, or that installation of pollution control measures is impracticable. 

Third, NACWA claims that the SSI Rule will cause environmental harm 

because some facilities will be “forced” to switch to landfilling, and some may 

stop conducting environmentally beneficial projects such as incineration. See

NACWA Mot. at 15-16.13 As EPA explained, the method chosen to treat sewage 

sludge is a local choice – not a choice “forced” upon these facilities by EPA or the 

SSI Rule – which depends on “environmental protection concerns, community 

needs, geographic constraints, and economic conditions.” 76 Fed. Reg. at 

15,395.14

Finally, NACWA argues that if POTWs install new control equipment to 

comply with the SSI Rule and the Court invalidates the SSI Rule, EPA will be able 

 Thus, a municipality’s choice to pursue a potentially more costly 

landfilling option rather than the less costly incineration option is not a harm that 

directly results from the SSI Rule – it results from the municipality’s choice. 

 

13 NACWA also argues that there will be “environmental impacts” if 

municipalities are “forced” to switch to landfilling, such as increased methane 

emissions from the landfill and increased emissions from trucking the sewage 

sludge to the landfills. NACWA Mot. at 27. Although EPA disagrees with 

NACWA’s increased greenhouse gas argument, such an impact, even if true, is not 

an irreparable harm to NACWA. 

14 Some municipalities may find landfilling to be a more cost-effective and 

environmentally-friendly alternative. 76 Fed. Reg. at 15,395. 

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to use the lower emissions from the new control equipment to set future MACT 

floors. NACWA Mot. at 17. This alleged harm, however, is too speculative, i.e., if 

a unit installs pollution control equipment, if the Rule is vacated, and if EPA 

subsequently uses those new controls to set MACT floors, NACWA may be 

harmed. Such conjectural harm is insufficient. See Nken, 129 S. Ct. at 1761. 

III. A STAY WOULD HARM THE ENVIRONMENT AND THIRD 

PARTIES AND BE CONTRARY TO THE PUBLIC INTEREST

We address the last two stay factors together because the harm to each is 

similar. As to the harm to third parties, the Court must consider whether “despite 

showings of probable success and irreparable injury on the part of petitioner, the 

issuance of a stay would have a serious adverse effect on other interested persons.” 

Va. Petroleum Jobbers Ass’n v. Fed. Power Comm’n, 259 F.2d 921, 925 (D.C. Cir. 

1958). As for where the public interest lies, “[i]n litigation involving the 

administration of regulatory statutes designed to promote the public interest, this 

factor necessarily becomes crucial.” Id. Here, the interests of NACWA “must 

give way to the realization of public purposes.” Id. 

The Clean Air Act “protect[s] and enhance[s] the quality of the Nation’s air 

resources so as to promote the public health and welfare and the productive 

capacity of its population.” 42 U.S.C. § 7401(b)(1). To that end, the SSI Rule 

reduces the emission of nine air pollutants that are known or suspected of causing 

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20

cancer and other serious health effects. Specifically, EPA estimates that the Rule 

will reduce nationwide emissions from sewage sludge incinerators by: 

• 19-30 tons per year of hydrogen chloride

• 430-700 tons per year of acid gases

• 58-70 tons per year of particulate matter, and 

• 1.7 tons per year of lead

76 Fed. Reg. at 15,398; see id. 15,400 (charts summarizing monetized benefits); 

EPA-HQ-OAR-2009-0559-0159 (Exh. 3 attached hereto) (summarizing predicted 

health incidences for particulate matter); EPA-HQ-OAR-2009-0559-0042 at

Section 5 (Exh. 4 relevant pages attached hereto). Also, EPA expects that the SSI 

Rule will result in annual health benefits that will decrease premature death, certain 

illnesses, incidences of emergency room visits, days when people miss work, and 

days when people must restrict their activities. Exh. 4 at Section 5. Finally, as 

compared to the compliance costs about which NACWA complains, EPA 

estimates the net benefits from the SSI Rule to be $26 million to $220 million, 

depending on the type of discount rate applied. Id. p. 1-2 and Section 5. A stay of 

this Rule will only further delay these important environmental and health benefits. 

CONCLUSION

For the foregoing reasons, EPA respectfully requests that the Court deny the 

Emergency Joint Motion for Stay of the SSI Rule. 

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21

Respectfully Submitted,

IGNACIA S. MORENO

Assistant Attorney General

/s/ Michele L. Walter

Michele Walter, DC Bar # 487329

United States Department of Justice

Environmental & Natural Resources 

 Division

P.O. Box 23986

Washington, D.C. 20026-3986

Michele.Walter@usdoj.gov

Tel: 202-514-2795

Attorney for Respondents 

Dated: October 11, 2011

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22

CERTIFICATE OF SERVICE

The undersigned hereby certifies that, on October 11, 2011, a true and 

correct copy of the foregoing EPA’S OPPOSITION TO EMERGENCY JOINT 

MOTION FOR STAY OF THE SEWAGE SLUDGE INCINERATION RULE, 

was served electronically through the ECF system on all registered counsel. 

_/s/ Michele L. Walter __________

MICHELE L. WALTER

 

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Exhibit 1

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Differences between Sewage Sludge, Medical Wastes,

Hazardous Wastes and Municipal Solid Wastes and

the Incinerators Used to Combust these Materials

X X X X X X X Y X X Y X ?C X X X X X X X Y X X Y X X X X X X X X X X X X X X X X X X X X X X X Y X X X Y X X X ~C X X X Y X

A. Sewage Sludge

Sewage sludge (biosolids) is defined as a solid, semi-solid, or liquid residue generated

during the treatment of domestic sewage in a publicly owned treatment works (POTV~,

and consists of solids removed in primary, secondary, or advanced wastewater treatment

processes and any material derived from sewage sludge (e.g., blended sewage

sludge/fertilizer product).

Domestic sewage is generated by residences, businesses, etc., located in the POTWs

services area, along with Stormwater runoff, and is transported to the POTWs via sewers.

All of the sewage is treated at POTWs which are owned by municipalities or other

governmental agencies, as a service to its rate payers.

The land application, surface disposal and incineration of sewage sludge is regulated

under 40 CFR Part 503 (Use or Disposal of Sewage Sludge).

Sewage sludge incinerators are regulated under Subpart E of the Part 503 Regulation,

which established pollutant limits, operational standards and recording, monitoring &

reporting requirement that ensure protection of human health and the environment. This

includes, but is not limited to:

- Site specific emission limits for arsenic, cadmium, chromium, lead and nickel

- Compliance with the NESHAP limits for beryllium and mercury

- Compliance with an operational standard for total hydrocarbon or an alternative

standard for carbon monoxide

Sewage sludge incinerators are also regulated under 40 CFR Part 60, Subpart O (New

Source Performance Standards for Sewage Sludge Treatment Plants) and Section 112 of

the Clean Air Act (Hazardous Air Pollutants).

There are two types of incinerators that are used to combust sewage sludge (i.e., multiple

hearths and fluidized beds).

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Exhibit 2

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vyu~rw.~r~.~t~fn

MEMORANDUM

To: Amy Hambrick, U.S. EPA, Sector Policies and Programs Division/Natural

Resources and Commerce Group

From: Eastern Research Group, Inc.

Date: January 2011

Subject: Revised MACT Floor Analysis for the Sewage Sludge Incinerator Source

Category

1.0 INTRODUCTION AND SUMMARY OF RESULTS

The U.S. Environmental Protection Agency (EPA), under section 129 of the Clean Air

Act (CAA), is required to regulate emissions of nine pollutants from sewage sludge incineration

(SSI) units. The nine pollutants are: hydrogen chloride (HCI), carbon monoxide (CO), lead (Pb),

cadmium (Cd), mercury (Hg), particulate matter (PM), total mass basis dioxins/furans (TMB

PCDD/PCDF) and toxic equivalency basis dioxin/furans (TEQ PCDD/PCDF), nitrogen oxides

(NOX), and sulfur dioxide (SOZ). The purpose of this memorandum is to present the methodology

used to determine the ma~mum achievable control technology (MACT) floor limits for SSI units.

The MALT floor analysis was conducted using emissions data obtained from responses to an

Information Collection Request (ICR). Emissions test information for each of the Section 129

pollutants was analyzed, MACT floor averages were calculated, and then a data variability

analysis was conducted. The MACT floor emissions limits were determined based on the results

of these analyses.

Table 1-1 summarizes the MACT floor limits for existing sources regulated by the SSI

emission guidelines (EG) and the MALT floor limits for new sources regulated by the SSI new

source performance standards (NSPS). The table presents limits for two subcategories under the

EG and the NSPS: multiple hearth incineration units (MH) and fluidized bed incineration units

(FB). For both the MH and FB new source subcategories, the best performing FB unit was used

as the best performing similar source. The discussion of the SSI subcategories is presented in

section 3.0.

Table 1-1. Summary of MALT Floor Limits for Sewage Sludge Incinerators

Pollutant Units

EG NSPS

FB MH FB MH

Cd m /dscm 0.0016 0.095 0.0011 0.0024

Pb m /dscm 0.0074 0.30 0.00062 0.0035

H m /dscm 0.037 0.28 0.0010 0.15

PM m /dscm 18 80 9.6 60

HCl mvd 0.51 1.2 0.24 1.2

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Table 1-1. Summary of MACT Floor Limits for Sewage Sludge Incinerators

(Continued)

Pollutant Units

EG NSPS

FB MH FB MH

SOZ mvd 15 26 5.3 26

NOX mvd 150 220 30 210

CO mvd 64 3,800 27 52

PCDD/PCDF

TMB

ng/dscm 1.2 5.0 0.013 0.045

PCDD/PCDF

TEQ

ng/dscm 0.10 0.32 0.0044 0.0022

The memorandum is organized as follows:

2.0 Background of MALT Floor Methodology

3.0 Development of Subcategories for SSI Source Category

4.0 MALT Floor Determination Methodology

5.0 Summary of Results

6.0 References

2.0 BACKGROUND OF MACT FLOOR METHODOLOGY

All standards established pursuant to CAA Section 129(a)(2) must reflect MALT, the

maximum degree of reduction in emissions of certain listed air pollutants that the Administrator,

taking into consideration the cost of achieving such emission reduction, and any non-air quality

health and environmental impacts and energy requirements, determines is achievable for each

category. This level of control is referred to as a MALT standard.

The minimum level of stringency is called the MALT "floor," and CAA Section 129(a)(2)

sets forth differing levels of minimum stringency that EPA's standards must achieve, depending

on whether they regulate new or existing sources. For new units, the MACT floor cannot be less

stringent than the emission control that is achieved in practice by the best controlled similar unit.

Emission standards for existing units may be less stringent than standards for new units, but

cannot be less stringent than the average emission limitation achieved by the best performing 12

percent of units in the category. These requirements constitute the MALT floor for new and

existing sources; however, EPA may not consider costs or other impacts in determining the

MALT 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 controls).

MALT analyses involve an assessment of the emissions from the best performing units in a

source category. The assessment can be based on actual emissions data, on knowledge of the air

pollution control in place in combination with actual emissions data, or on state regulatory

2

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requirements that may enable EPA to estimate the actual performance of the regulated units and

other relevant emissions information. For each source category, 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 a March 2007 court decision (Sierra Club v. EPA, 479 F. 3d 875 [DC Cir. 2007])

(Brick MAC7~ EPA was directed to set MALT floors with the following guidance:

• Floors for e~sting 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, 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).

• EPA cannot ignore non-technology factors that reduce HAP emissions. Specifically, the

court held that "EPA's decision to base floors exclusively on technology even though nontechnology factors affect emissions violates the Act" (479 F. 3d at 883).

• 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).

These decisions were incorporated into the MALT floor analysis for SSI units.

3.0 DEVELOPMENT OF SUBCATEGORIES FOR SSI SOURCE CATEGORY

The Clean Air Act allows EPA to subcategorize a source category based on differences in

class, type, or size. There are two types of incinerators currently used to combust sewage sludge

(or biosolids): MH units and FB units. Of the 204 sewage sludge incineration units in operation,

60 use the FB design, while 144 use the MH design. The two types are significantly different

combustor designs.

A MH unit consists of a vertical cylinder containing from 6 to 12 horizontal hearths and a

rotating center shaft with rabble arms. Biosolids enter the top hearth and flows downward while

combustion air flows from the bottom to the top. The MH is divided into three zones. The upper

hearths comprise the drying zone in which biosolids, water, and some organic compounds are

evaporated. The middle hearths comprise the combustion zone. The exposure to the combustion

gas and biosolids to high temperature is only in this section and residence time of the gas is short.

The lower hearths form the cooling zone, where ash is cooled as its heat is transferred to the

incoming combustion air. Multiple hearth units are sensitive to any change in the feed, such as

feed moisture and feed rate. Since the emissions of CO and organic compounds are dependent on

the temperature of the top hearth, any changes occurring in the biosolids input can cause

operational upset with momentary drop in top hearth temperature and increase in emissions.'

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In an FB unit, the reactor is a vertical steel shell comprised of four sections. The lower

section is called the windbox and acts as a plenum in which combustion air is received. Above the

windbox is a refractory arch. The section above the refractory arch is filled with sand and is

called the bed area or combustion zone. Hot air is distributed homogeneously throughout the

fluidized bed. The intensive mixing of the solid and gas in the fluidized state results in a high heat

transfer resulting in rapid combustion of the biosolids. The section above the bed is the freeboard

or disengagement zone. The freeboard provides six to seven seconds of gas residence time which

complete the combustion of any volatile hydrocarbons escaping from the bed. ~

The differences between the two combustor designs result in significant differences in

emissions, size of the flue gas stream, ability to handle variability in the feeds, control of

temperature and other process variables, auxiliary fuel use, and other characteristics. Generally,

FB units have lower emissions of organic compounds, PCDD/PCDF, and CO than MH units due

to the combustion temperature, mv~ing, and residence time differences.''2 This comparison is

demonstrated in EPA's Compilation of Emission Factors (also referred to as AP-42)2, and in the

emissions information gathered to support developing these proposed standards.3 Intermittent

operations, involving frequent shutdown and startup, are generally easier and more rapid for FB

units than MH units. Additionally, FB units have better capability of handling feeds with varying

moisture and volatile contents. Lower excess air and auxiliary fuel is required to operate FB units

resulting in smaller flue gas flow rates and consequently smaller sized downstream control

devices.

To reflect the differences in their combustion mechanisms, two subcategories, FB and

MH, were developed for existing and new SSI sources.

4.0 MACT FLOOR DETERMINATION METHODOLOGY

Section 129(a)(2) of the CAA requires that EPA determine the emission control that is

achieved in practice by the best controlled similar unit when establishing the MALT floor for new

units, and the average emission limitation achieved by the best performing 12 percent of units

when establishing the MALT floor for existing units. EPA must exercise its judgment, based on

an evaluation of the relevant factors and available data, to determine the level of emission control

that has been achieved by the best performing SSI units 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 MALT floors. (See Mossville Envt'1 Action

Now v. EPA, (370 F.3d 1232, 1241-42 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").

Maximum Achievable Control Technology and other technology-based standards are

necessarily derived from short-term emissions test data, but such data are not 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.4 EPA, therefore, often

needs to adjust the short-term data to account for these varying conditions. The types of

4

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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 different settings for

emissions testing equipment, different field teams conducting the testing, differences in sample

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

best performing sources 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 Assn, 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 also cause variations in

results between various runs comprising a single test, or "within-test variability." A single test at

a unit usually includes at least three separate test runs. (See 40 CFR 63.7(e)(3) for MALT

standards under CAA Section 112 and 40 CFR 60.80 for NSPS under CAA Section 111.) 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.

Variability can be accounted for using different statistical methods. For example, recent

rules have used the upper limit (UL) or the Upper Prediction Limit (UPL) to determine the

MALT floor emission limits. A UL is based on the distribution of the available emission

observations (e.g., test runs), and does not embody a predictive aspect that a UPL does. A

prediction interval (e.g., a UPL) for a future observation is an interval 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. Given this defuution, the UPL

represents the value the mean of three future test run observations (three-run average) 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 (i.e., average of three runs), we can be 99 percent confident that the reported level will

fall below a MACT floor emission limit calculated using a UPL. Since a source must demonstrate

compliance with the MALT floor using the average of a three-run test, the appropriate test

condition to use to assess variability is three. If a source had to demonstrate compliance by

showing that each individual test run was below the MALT floor emission limit, it would be

appropriate to use a future test condition of one.

The UPL is an appropriate statistical tool to use in determinuig variability in the SSI data.

For this source category, where there is a limited sampling of the source category and we do not

have test data from all of the SSI units in the best performing 12% for each subcategory, the

predictive aspect of the UPL calculation is especially important. (See sections 4.1 through 4.3 for

further discussion of the dataset and the statistical approach.)

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4.1 MACT Floor Analvses Dataset

As stated earlier, the CAA requires that MALT for new sources be no less stringent than

the emission control achieved in practice by the best controlled similar unit. For existing sources,

the CAA requires that MALT be no less stringent than the average emission limitation achieved

by the best performing 12 percent of units in a source category. Because the number of units in

different subcategories maybe different, the number of units that represent the best performing 12

percent of sources in different subcategories may be different. Also, mathematically, the number

of units that represent the best performing 12 percent of the units in a subcategory will not always

be an integer. To ensure that each MALT standard is based on at least 12 percent of the units in

a subcategory, EPA has determined that it is appropriate to always round up to the nearest integer

when 12 percent of a given subcategory is not an integer. For example, if 12 percent of a

subcategory is 4.1, the standards will be based on the best performing five units even though

rounding conventions would normally lead to rounding down to four units. As discussed earlier,

there are 204 SSI units, composed of 144 multiple hearth units and 60 fluidized bed units. This

procedure results in a top 12 percent comprising 18 MH units and eight FB units.

Information collection request surveys were sent to nine municipalities operating SSI units

to collect emissions information. To select the surveyed owners, EPA reviewed the inventory of

SSI units for the control devices being operated, and identified a subset of units expected to have

the lowest emissions based on the type of unit and the installed air pollution controls. EPA

believes these controls achieve the most reductions possible for the CAA Section 129 pollutants,

and thereby allow EPA to identify for each pollutant the units with the lowest emissions. For

example, units were selected that operated more than one of the following technologies: activated

carbon injection to reduce Hg and PCCD/PCDF; regenerative thermal oxidizers or afterburners to

reduce CO and organics; wet electrostatic precipitators to reduce fine particulate; high efficiency

scrubbers such as packed bed scrubbers and impingement tray scrubbers to reduce PM, Cd, Pb,

particulate Hg, and acid gases such as HCl and SO2i and units with multiple control devices that

could reduce PM, Cd, Pb, particulate Hg, such as venturi scrubber in combination with

impingement scrubbers and wet ESPs or with another particulate control device.

EPA considered whether non-technology factors, such as the characteristics of the waste

combusted, would influence which sources are the best performing. In contrast to regulated

MWC units or CISWI units, SSI units receive a more homogenous type of waste to burn. There

are variations in the amount of each of the CAA Section 129 pollutants present depending on the

regions, climates, and sources of wastewater (commercial or residential). The variations are not

as significant as variations that would occur if different types of materials were combusted, e.g.,

sewage sludge, coal, gas, oil, wood, municipal waste, etc. All SSI units are required to meet the

EPA's Office of Water SSI discharge and emission requirements (40 CFR part 503). Part 503

establishes daily average concentration limits for Pb, Cd, and other metals in sewage sludge that is

disposed of by incineration. Part 503 also requires that 5SI units meet the National Emission

Standards for Beryllium and Hg in subparts C and E, respectively, of 40 CFR part 61. In order to

meet the 40 CFR part 503 standards, facilities are already incorporating management practices

and measures to reduce waste and limit the concentration of pollutants in the sludge sent to SSI

units, such as segregating contaminated and uncontaminated wastes and establishing discharge

limits or pre-treatment standards for non-domestic users discharging wastewater to publicly

owned treatment works (POTWs). Thus, non-technology measures to reduce emission are

6

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already being taken. As a result, the data used to develop the MALT emission limits reflects the

control technologies used at each facility, and the other HAP emission reduction approaches, such

as management practices each facility is following to comply with the part 503 standards.

The nine companies selected to receive the ICR operate 20 SSI units with the most

efficient pollution controls that would reduce emissions of CAA section 129 pollutants of interest.

Therefore, the sources identified for testing and the resulting emission information received from

the surveyed SSI units represent the best performing SSI units. Six of the nine operate MH units,

resulting in 13 MH units surveyed. Three of the nine operate FB units, resulting in seven FB units

surveyed. Some owners of multiple units at a facility provided information for less than the total

number they operated, e.g. 1 unit instead of 2, because not all units were in operation during the

test period. Of those 20 units from the nine surveyed municipalities, EPA collected data from 17

units that were in operation (11 MH units and six FB units). The surveyed information was

supplemented with test information for nine MH SSI units collected from state environmental

agencies public databases. In total, emissions information were collected from FB units and

20 MH units from facilities responding to the ICR and additional test reports provided by state

environmental agencies. Attachment H identifies the control devices used at FB and MH units

with advanced controls and identifies the nine companies that were sent ICR surveys.

Although emissions information was collected from six FB units and 20 MH units, not

every test report contained information on all pollutants. Except for PCDD/PCDF and SO2, test

information for most of the nine CAA 129 pollutants was available from FB units.

PCDD/PCDF data was provided for five FB units, and SOZ data were only available for four units.

One facility (operating two FB units) provided "zero" values for SOZ emissions, without

providing detection limits. Depending on the pollutant, the number of MH units with emissions

information ranges from five to 19. Table 4-1 presents the distribution of emissions information

for the CAA section 129 pollutants. The floor analysis was then conducted using all the quality

assured emissions information for each pollutant in each subcategory (e.g, all FB units for Cd,

and all 14 MH units for Cd), as this information includes emissions data from the population of

best performing units. For Hg, data from the top 18 MH units (or 12% of the 144 MH units in

the total inventory) were used.

Table 4-1. Summary of the Number of Units with

Test Information nn the Section 129 Pollutants

Subcategory Pb Cd Hg PM HCl SOS NOS CO PCDD/PCDF

FB 6 6 6 6 6 4 6 6 5

MH 14 14 19 18 7 11 8 9 5

Test results from each of these units are based on the results of at least three individual

runs per test, meaning that one would expect MACT floor calculations based on a population of

21 FB runs (7 FB units * 3 runs per FB unit) and on a population of 60 MH runs (20 MH unit * 3

runs per MH unit).

While EPA does not have actual emissions test data for the population of units that

represent the best performing 12 percent, the statistical technique described below is the approach

7

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used to establish the existing source MALT floor. The MACT floor calculations are based on all

of the actual quality assured data received (for example, a population of 15 MH runs from five

MH incinerators for PCDD/PCDF~. Because the emission data are normally distributed, or can be

transformed to be normally distributed (using the log-normal transformation of the data as

described in Section 4.3), a statistical technique can be employed to determine the minimum

number of observations needed to accurately characterize the distribution of the best performing

12 percent of units in each subcategory. EPA used this statistical technique because of the lack of

data from the full set of the best performing 12 percent of sources. While Congress adopted

identical language describing the MALT floor calculation in section 129(a)(2) as it did in section

112(d)(3), the latter section includes a provision stating that the floor for existing sources cannot

be less stringent than "the average emission limitation achieved by the best performing 12 percent

of the existing sources (for which the Administrator has emissions information)[.]" Section 129,

however, simply states that the existing source floor cannot be less stringent than the average

emission limitation achieved by the best performing 12 percent of the e~sting sources in the

category. Therefore, while EPA believes Congress intended for the MACT floor calculation

under each section of the Act to be the same, this difference in the text of the two provisions

requires us to establish the MACT floor for section 129 source categories based on the best

performing 12 percent of sources in the category. Because EPA does not have that data at this

time, the statistical technique described below is the only manner in which we can establish the

existing source floor on that basis. This technique (shown in Equation 1) is necessary to provide

the assurance that the characteristics of the sampled data set mirror those of the best performing

12 percent of units in the source category. Equation 1 shows that the number of observations is a

function of the total population size (in each subcategory), the level of confidence, the level of

precision, and degree of the variability (expressed as probability of occurrence).5

Equation 1:

where:

n= N*Z*Z*p~q /[(E *E*(N- 1))+(Z*Z'~p *q)]

n = minimum number of observations or sample size,

N = population sizes,

Z =the Z-score associated with a specific confidence level,

E =level of precision or error tolerance,

p =degree of variability of the observations, and

q =1 —p.

The number of runs test associated with the total population is N, since EPA set the

confidence level at 90°Io for this source category, the Z-score is 1.645); as EPA's level of

precision for this case is 20%, E = 0.2; and the degree of variability of the observations is 0.5

a For this calculation, the population size is the number of test runs that correspond to 12% of units in each subcategory

(the MACT floor population). Performance tests consist of 3 individual test runs. For the FB subcategory, 12% of 60

units is 8 units, so the population size is 24 observations (8 FB * 3 runs per FB). For the MH subcategory, the

population size is 54 observations (18 MH * 3 runs per MH).

8

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(which means p and q are both 0.5). Note that a variability of 0.5 maximizes the number of

observations required for collection. Therefore, using a variability of 0.5 is a conservative

approach in that its use overestimates the minimum number of observations which may actually

overestimate the minunum sample size.

In order to assess whether or not the number of observations collected adequately

characterizes the population, Equation 1 was applied to each subcategory for each pollutant. If

the number of observations collected equaled or exceeded the minimum number of observations

calculated using Equation 1, then the MALT floor calculations of the sampled data set are

consistent with what the MACT floor calculations would have been had they been performed on

the complete data set from the best performing 12% of the population. Table 4-2 demonstrates

(for the pollutants with the lowest number of observations) the data utilized by EPA meets or

exceeds the number of observations necessary to provide an accurate representation of that data

distributed from the best performing 12% of the source population as determined using

Equation 1.

Table 4-2. Minimum Size Sample Calculation

Number of observations

Minimum number of collected and used (for

observations (calculated pollutants with lowest

Subcategory Population size, N using Equation 1) number of test data)

FB 24 11 12

MH 58 14 15

For units whose emissions testing results were below detection levels and who provided

numerical detection levels, EPA used the detection levels in the MALT floor analysis. If a source

reported a "zero" or a negative value, that run was not used to calculate the average of the test.

Each data set used to calculate the MALT floor limits were reviewed for outliers. If emission

levels appeared to be three standard deviations from the mean, the test reports containing the

emissions information were reviewed to determine if there were any irregularities during the

operation of the SSI unit, or during the testing or analysis. Information from three sources was

removed from the MALT floor calculations during the review. The HCl emissions for test run 1

conducted by Columbia Metro were two orders of magnitude greater than the other two test runs.

Calls to the facility indicated that a data entry error was made for test run 1. The SOZ emissions

from Ypsilanti were two orders of magnitude greater than all the other SOZ test information. A

review of the source test report indicated a different test method was used than used at all the

other facilities providing SOz data. Additionally, the reported emissions in the report could not be

replicated using the raw data. The CO emissions reported from one run from tests conducted at

one of The Metropolitan Council's St. Paul units was 3 standard deviations greater than the other

two test runs in the test. The high value was also 3 standard deviations greater than other tests

conducted at the unit. A review of the source test report did not indicate any irregularities during

the test run. However, because the value was not consistent with the other tests conducted at the

facility and also greater than 3 standard deviations from the other tests, this data point was

removed from the MACT floor analysis.

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The MALT floor analysis for PM was conducted on emissions measured using only EPA

Test Method 5, or equivalent State test methods. Although PM emissions were reported to be

collected using other test methods, such as OTM 27, the results from the different test methods

were not combined because they do not measure equivalent PM fractions or species.

4.2 MALT Floor Calculation

For each unit providing emissions information for a pollutant, the average (mean) of the

test runs for all the tests conducted on the unit was calculated. If one test were conducted

consisting of three test runs, the emissions from the unit would be the average of the three test

runs. If two tests were conducted each consisting of three test runs, the average of all 6 test. runs

would be calculated.

To conduct the existing source MALT floor analysis for each pollutant, individual SSI

units in each subcategory for which EPA had emissions test data were ranked based on their

average emissions levels of the pollutant from lowest to highest. The MALT floor was calculated

as the average of the test runs from the best performing (i.e., lowest emitting) 12 percent of

sources. For the SSI source category, all the quality assured emissions information from the ICR

responses and additional test reports collected were used in the MALT floor calculation. That is,

for each pollutant the MACT floor emissions level was calculated as the average emission limit for

all the test runs from the quality assured emissions data collected.

As discussed in section 3.0, two subcategories were developed for new sources. For the

FB new source subcategory, the best performing FB unit was used as the best performing similar

source. For the MH new source subcategory, the best performing MH unit was used as the best

performing similar source.

To conduct the new source MACT floor analysis for each pollutant, the SSI unit in each

subcategory with the lowest emission test average (mean) from 3 test runs for the pollutant was

identified. The MALT floor was calculated as the average emission level of the test runs from the

best performing single unit (i.e., lowest emitting). If the lowest emitting source provided

emissions information from less than 3 test runs, then the MALT floor was calculated from the

next lowest emitting source with at least 3 test runs. This procedure was followed because

compliance is based on the average of 3 test runs.

4.3 Incorporating Data Variability

The first step in the statistical analysis includes a determination of whether the data used

for each MALT floor calculation were normally or log-normally distributed. If the data were

normally distributed (e.g., similar to a typical bell curve), then furthervariability analyses could be

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), then the type of distribution (e.g., log-normal) was

determined and a data transformation was performed (e.g., taking the natural log of the data) 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. A skewness statistic that is

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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 SES = SQRT(6/N) where N is the sample size. According to the small sample

skewness hypothesis test, if the skewness statistic (S) is greater than two times 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 kurtosis statistic that is greater (less) than zero indicates a relatively peaked (flat)

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

SQRT(24/N) where N is the sample size. According to the small sample kurtosis hypothesis test,

if K is greater than two times the SEK, the data distribution is typically considered to be nonnormal.

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

and the lognormal values of the reported test values. If the S and K statistics of the reported

dataset were both less than twice the SES and SEK, respectively, the dataset was classified as

normally distributed. An EPA guidance document shows that environmental data are more likely

to be log-normally distributed than normally distributed.6 If neither of the S and K statistics or

only one of these statistics was less than twice the SES or SEK, respectively, then the skewness

and kurtosis hypothesis tests were conducted for the natural log-transformed data. If both the

reported values and the natural-log transformed reported values had S and K statistics that were

greater than twice the SES or SEK, respectively, the log-normally distributed dataset was selected

as the basis of the floor. If the results of the skewness and kurtosis hypothesis tests were mixed

for the reported values and the natural log-transformed reported values, the log-normal

distribution was chosen.

After determining the distribution of each dataset, a student's t-test statistic was used.

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 is calculated using the following Excel equation:

Equation 2:

Where:

t-statistic = TINV(2*(1-0.99),n-1)

n =the number of test runs from units in the top 12 percent

Next, the average (or sample mean) and sample .standard deviation of the test runs were

calculated.

For the existing source variability analysis, all the quality assured emission test runs

reported for the best performing 12 percent of units in each subcategory were identified. By

including multiple emission tests from units with a test average in the top 12 percent, EPA can

evaluate intra-unit variability of emission tests over time, considering variability in control device

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performance, unit operations, and fuels fired during the test. As discussed previously, the UPL

was used for the SSI MALT floor variability analysis.

For the e~cisting source analysis, the 99% UPL values was calculated for each pollutant

and for each subcategory using the test run data for those units in the best-performing 12 percent.

Since the compliance with the MALT floor emission limit is based on the average of a three-run

test, the UPL is calculated by:

Equation 3:

Where:

~~L= x +t~0.9~,n -1~x sax~1+ 1~

n era

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

m =the number of test runs in the compliance average

s =standard deviation of the emission test data

x =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.

This calculation was performed using the following two Excel functions:

Normal distribution: 99°~o UPL =AVERAGE (Test Runs in Top 12°Io) + [STDEV(Test Runs in

Top 12%) x TINV(2 * 0.99, n-1 degrees of freedom)*SQRT((1/n)+(1/3))], for aone-tailed tvalue (with 2 x probability), probability of 0.01, and sample size of n

Lognormal distribution: 99% UPL = EXP{AVERAGE(Natural Log Values of Test Runs in Top

12%) + [STDEV(Natural Log Values of Test Runs in Top 12%) x TINV(2 * 0.99, n-1 degrees of

freedom) * SQRT((1/n)+(1/3))] }, for aone-tailed t-value (with 2 x probability), probability of

0.01, and sample size of n

The 99 percent UPL represents the value which one can expect the mean of future 3-run

performance tests from the best-performing 12 percent of sources to fall below with 99 percent

confidence, based upon the results of the independent sample of observations from the same best

performing sources. In establishing the limits, the UPL values were rounded up to two significant

figures. For example, a value of 1.42 would be rounded to 1.5 (as has been done for other CAA

section 129 rules) because a limit of 1.4 would be lower than the calculated MALT floor value.

The SSI emissions database for FB units contains data from six units at four facilities. The

entities surveyed were requested to provide recent (within the previous five years) emissions test

reports. Most survey recipients provided only the most recent report. One facility, with three

units, provided results of emissions test conducted for compliance reports spanning a 10 year

period. This facility also uses the most advanced pollution controls on their FB units in the

subcategory. The one facility comprises 70 percent of the Cd and Pb data, 90 percent of the CO

and Hg data, and 75 percent of the HCl data and PM data. As a result, the existing source

MALT floors calculated using the UPL methodology, and all the test run data from the one

facility, effectively results in calculating more stringent limits more akin to a new source MALT

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floor than an e~sting source MACT floor. In order to adequately incorporate the emissions from

all the SSI units in the FB subcategory, a weighted UPL was used for calculating the existing

source MALT floors for the final rule. The weighted UPL is calculated from a weighted mean

and weighted variance as described below.

There are many different types of weighting procedures. The most straightforward

methodology is to base it on the number of data points (i.e., test runs) from each SSI unit. This

weighting scheme ensures that no facility in the MACT best performers pool is over-represented

in the computation of the MACT floor. The first step in weighting procedure is to assign a

weighting factor to each test run by multiplying each observation for source i and run j with a

weight term, w,;, as shown in equation 4:

Equation 4:

Where:

w~~ = x —

1 1~

M; N

M~ =the number of observations (i.e., runs) for source i and

N =the number of best performing sources in the MACT pool.

The second step is to calculate the mean and total variance for the weighted data from the

weight terms using equations 5 and 6:

Equation 5:

N M;

~~W~ixii

Weighted Mean: xweighted _ ~=1 j=1

N Mr

w!%

i=1 j=1

Equation 6:

Where:

N M;

r , weighted 12

w~' ~xi' — XU 1

Weighted Variance: v'Ne`g'"ed = `_' ;=1

N M.

~K–l~~~w~~

t=1 )=1

13

K

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N =the total number of observations in the MACT best performers pool.

K=~M~

Whei~lthe weights are equal to one, the above equations reduce to those for un-weighted

data, as expected. As shown in equation 7, the weighted mean and weighted variance are then

used in the UPL calculation instead of the simple (i.e., un-weighted) mean and variance.

Equation 7:

UPL = x 

weighted 

+ t(~.99~ n~ — 1) X ~ 

weighted lx 1 + 1

1

i2~ YYI~

For MH units, there are more emissions data from a larger number of facilities/units. For

example, we have data on Cd and Pb from 11 facilities with 14 units, Hg from 11 facilities with 18

units. The MACT floor calculations are not skewed by 1 or 2 units or facilities. Consequently,

the MACT floor for e~sting MH units does not need to be calculated using a weighted UPL.

To determine the MACT floor for new sources, a UPL calculation similar to that for

existing sources was conducted, except the best performing unit's data within a subcategory was

used to calculate the MALT floor emission limit for each pollutant instead of the average of the

best performing 12 percent of units. The best performing unit was identified as the lowest

emitting source with at least 3 test runs. In summary, the approach ranks individual SSI units

based on actual performance and establishes MACT floors based on the best performing source

for each pollutant and subcategory, with an appropriate accounting of emissions variability. In

other words, the UPL was determined for the data set of individual test runs for the single best

performing source for each regulated pollutant from each subcategory.

4.4 Use of Non-Detect Values

Non-detect values comprise more than 50 percent of the emission data for HCl from FB

units and PCDD/PCDF from both MH and FB units and may influence the MACT floor analysis

for other pollutants. EPA developed a methodology to account for the imprecision introduced by

incorporating non-detect data into the MACT floor calculation.

At very low emission levels where emission tests result in non-detect 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 ar near the method detection level is

about 40 to 50 percent. Pollutant measurement imprecision decreases to a consistent level of 10

to 15 percent for values measured at a level about three times the method detection level.

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One approach that can be applied to account for measurement variability in this situation

starts with defining a method detection level that is representative of the data used in the data

pool. The first step in this approach would be identify the highest test-specific method detection

level reported in a data set that is also equal to or less than the average emission calculated for the

data set. This approach has the advantage of relying on the data collected to develop the floor

emission limit, while to some degree, minimizing the effect of a tests) 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 deternuning the detection level was other than that

specified).

The second step is to determine the value equal to three times the representative method

detection level and compare it to the calculated floor emission limit. If three times the

representative method detection level were less than the calculated floor emission limit, we would

conclude that measurement variability is adequately addressed and we would not adjust the

calculated floor emission limit. If, on the other hand, the value equal to three times the

representative method detection level were greater than the calculated floor emission limit, we

would conclude that the calculated floor emission limit does not account entirely for measurement

variability. Therefore, the use the value equal to three times the method detection level in place of

the calculated floor emission limit ensures that the floor emission limit accounts for measurement

variability and imprecision.

The approach discussed above was used to calculate the proposed MALT floor limit for

HCl from FB units, and Hg from MH units. The following additional procedures were followed

for PCDD/PCDF total mass basis (TMB) and to~c equivalency (TEQ) basis limits. To calculate

a TMB limit, all the 17 congeners of interest were identified and non-detect values that are

associated with each were indicated. The mean of the non-detect values was calculated and

multiplied by 17 (for the total number of congeners of interest). The mean value was then used as

the detection limit of the run. Then, each data set was reviewed to identify the highest testspecific method detection level reported that was also equal to or less than the average emission

level (i.e., unadjusted for probability confidence level) calculated for the data set. The second

step discussed above and also used for HCl was used to set the limit.

To calculate a limit on a TEQ basis, first, the mean of the non-detect values was

calculated. Then the tonic equivalency factor (TES for each congener was multiplied by the

mean to determine the TEQ for each congener. TEQs for each congener were summed to

calculate a TEQ sum value. The TEQ sum was then used as the detection limit for the test run.

The second step discussed above and also used for HCl was used to set the limit.

4.4 CO Span Adjustments

Carbon monoxide values obtained from emission test reports were reviewed to determine

whether the span of the test used was capable of accurately reading the reported value. If the span

was inconsistent with the reported value, the CO levels were adjusted to provide a value that was

more consistent with the span.

EPA Method 10 is structured such that measurement data quality relative to the

calibration span of the instrument can be assessed. For example, the allowable calibration error,

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system bias, and drift requirements are directly proportional to the site-specific instrument

calibration span (i.e., ± 2.0 percent of the calibration span value). For instrument calibration span

values of 25 ppmv and less, the allowable calibration error, bias, or drift values are each ± 0.5

ppmv.

For a measurement made using an instrumental test method, the equivalent of the method

detection level can be assessed using: a square root formula, the reported calibration span value,

and the allowable data quality criteria (i.e. the allowable calibration error, bias, and drift values).

The estimated CO measurement error resulting from the square root formula was adjusted by a

factor of three to be consistent with the methodology EPA applied for non-detect data (where

limits no less than three times the method detection level were established).

The calculations used to estimate the measurement error are shown in Equations 8 and 9:

Equation 8:

Where:

For tests where calibration span is greater than 25 ppm:

0.02*calibration span =Method 10 allowable calibration error, system bias, and drift

requirement for calibration spans greater than 25 ppm

Equation 9:

Where:

For tests where calibration span is 25 ppm or less:

0.5 =Method 10 allowable calibration error, system bias, and drift requirement for

calibration spans less than or equal to 25 ppm

In order to develop a basis for measurement error, instrument calibration spans in available

test reports were reviewed. In most cases, a calibration span was specified in the report. If no

span value was reported, the maximum concentration from a protocol gas certificate was used in

lieu of the span. In several cases a test report with the exact test date in question was not

available to the Agency to extract the calibration span or protocol gas certificates. In order to fill

these data gaps, test reports from the same units on alternate dates or test reports from similar

units at the same facility were reviewed to identify a likely calibration span for the units in

question. If alternate reports were available, the report with the date closest to the test date

associated with the CO runs in question was used. Where no span values could be found, it was

assumed that if the test was conducted on or before May, 2008, the associated CO span would be

1000 ppm, and tests conducted after May 2008 would have a CO span of 100 ppm. This

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assumption was made because, before revisions were made to Method 10 in May of 2008, it was

common that units were using the prescriptive span guidance that was listed in the old method.'

The current version of EPA Method 10 does not include these span requirements but instead

requires the tester to choose calibration ranges that reflect the range of expected emission

concentrations at the unit.$

Span values were input into Equations 8 and 9 in order to estimate the measurement error

for each run. In cases where the reported emission concentrations were lower than their

corresponding measurement errors, the default measurement errors were used in lieu of the

reported concentration.

Attachment H shows a detailed list of each of the test reports reviewed. The attachment

indicates whether a substitution was made to fill data gaps for CO. Additional background on CO

measurement imprecision and the basis for this CO span adjustment methodology can be found in

separate memoranda.9,'o

5.0 RESULTS

Table 5-1 summarizes the MACT floor variability analysis results for existing units. Table

5-2 summarizes the MACT floor variability analysis results for new units.

Attachments A-1 through A-8 contain the data and detailed calculation of the UPL for Cd,

Pb, Hg, filterable PM, HCI, 502, NOX, and CO at existing FB units.

Attachments B-1 through B-8 contain the data and detailed calculation of the UPL for Cd,

Pb, Hg, filterable PM, HCI, SO2, NOX, and CO at existing MH units.

Attachments C-1 through C-8 contain the data and detailed calculation of the UPL for Cd,

Pb, Hg, filterable PM, HCI, SO2, NOX, and CO at all new FB units.

Attachments D-1 through D-8 contain the data and detailed calculation of the UPL for Cd,

Pb, Hg, filterable PM, HCI, SOZ, NOX, and CO at all new MH units.

Attachments E-1 through E-5 summarize the MACT floor variability analysis for

PCDD/PCDF at existing and new FB units.

Attachments F-1 through F-5 summarize the MACT floor variability analysis for

PCDD/PCDF at existing and new MH units.

Attachment G identifies the control devices used at FB and MH units with advanced

controls and identifies the nine companies that were sent ICR surveys.

Attachment H lists CO data extracted from test reports and shows the calculations made

to determine the appropriate span adjusted values to use in determining the MACT floors for CO.

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6.0 REFERENCES

A Comparison of Fluid Bed and Multiple Hearth Biosolids Incineration. Ky Dangtran,

John Mullen, and Dale Mayrose. Paper presented at the 14`h Annual Residuals and

Sludge Management Conference. February 27-March 1, 2000, Boston MA.

2. Compilation of Air Pollutant Emission Factors, Volume I: Stationary Point and Area

Sources. Fifth Edition. U.S. Environmental Protection Agency. January 1995.

3. Facility, Unit, and Emissions Test Database for the Sewage Sludge Incineration

Source Category. Memorandum from ERG to Amy Hambrick, U.S. EPA. June 2010.

4. Natrella, Experimental Statistics, National Bureau of Standards Handbook 91, chapter

1 revised ed., 1966.

5. Sample Size Requirements for Studying Small Populations in Gerontology Research;

Robert B. Noble, A. John Bailer, Suzanne R. Kunkel &Jane K.

Straker; Health Services Outcomes Research Methodologies (2006) 6:59-67

6. Guidance for Data Quality Assessment: Practical Methods for Data Analysis"

EPA/600/R-96/084, July 2000.

7. USEPA, Emissions Measurement Center. Old Version of EPA Method 10. [Online]

http://www.epa.gov/ttn/emc/promgate/m- l0.pdf.

8. USEPA, Emissions Measurement Center. US EPA Method 10 (Revised). [Online]

2008 May. http://www.epa.gov/ttn/emc/promgate/method10r06.pdf.

9. Westlin, Peter. Measurement Imprecision for Carbon Monoxide. s.l.: EPA Emissions

Measurement Center, January 2011.

10. Incorporating Measurement Error in Reported Carbon Monoxide (CO) and Total

Hydrocarbon (THC) Data. Memorandum from ERG to Brian Shrager, U.S. EPA.

January 2011.

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Table 5-1. Summary of MACT Floor Results for Existing Units

Pollutant Units

FB Units MH Units

Avg of top

12%

(wei hted)

99% of

UPL

(wei hted) Limits

Avg of top

12%

99% of

UPL Limits

Cd m /dscm 0.00049 0.00151 0.0016 0.030 0.0947 0.095

Pb m /dscm 0.0026 0.00736 0.0074 0.082 0.295 0.30

H m /dscm 0.010 0.0367 0.037 0.095 0.2722 0.28

PM m dscm 43 17.4 18 41.3 79.1 80

HCl mvd 0.19 0.5072 0.51 0.53 1.14 1.2

SOz mvd 2.8 14.04 15 9.4 25.7 26

NOX mvd 56 143 150 130 217 220

CO mvd 27 63.2 64 1,015 3,775 3,800

PCDD/PCDF n /dscm 0.199 1.15 1.2 0.69 4.95 5.0

PCDD/PCDF

TEQ

ng/dscm 0.016 0.0998 0.10 0.047 0.314 0.32

~ Proposed limits were rounded up to 2 significant figures.

2 The value shown is the result of the non-detect analysis, which results in using the limit that is based on 3

times the highest detection limit that is less than the average.

Table 5-2. Summary of MALT Floor Results for New Units

Pollutant Units

FB Units MH Units

Avg of top

unit

99% of

UPL Limits

Avg of top

unit

99 °l0 of

UPL Limit'

Cd m /dscm 0.00017 0.001098 0.0011 0.00033 0.00231 0.0024

Pb m /dscm 0.00031 0.000618 0.00062 0.00084 0.00343 0.0035

H m /dscm 0.00036 0.000992 0.0010 0.051 0.1502 0.15

PM m /dscm 1.41 9.53 9.6 1.2 59.9 60

HCl mvd 0.044 0.2301 0.24 0.201 0.512 1.23

SO2 mvd 0.62 5.26 5.3 1.2 265 263

NOX mvd 14.9 29.6 30 68.5 206 210

CO mvd 8.21 26.93 27 27.6 51.4 52

PCDD/PCDF n /dscm 0.0109 0.0128 0.013 0.0095 0.0447 0.045

PCDD/PCDF

TEQ

ng/dscm 0.0025 0.00431 0.0044 0.00095 0.00212 0.0022

' Proposed limits were rounded up to 2 significant figures.

z The value shown is the result of the non-detect analysis, which results in using the limit that is based on 3

times the highest detection limit that is less than the average.

3 Limit calculated for NSPS was higher than for EG. NSPS limits cannot be less stringent than EG limits.

Therefore, the EG lunits were used.

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Attachment A

Detailed MACT Floor Analysis for Existing FB Sources

Cd, Pb, Hg, PM, HCI, SO2, NOX, and CO

See MACTFIoor_AttachA_F'BVariability.xlsx

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Attachment B

Detailed MALT Floor Analysis for Existing MH Units

Cd, Pb, Hg, PM, HCI, S02, NOX, and CO

See MACTFIoor_AttachB_MHVariability.~sx

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Attachment C

Detailed MACT Floor Analysis for New FB Units

Cd, Pb, Hg, PM, HCI, SO2, NOX, and CO

See MACTFIoor_AttachC_FBVariability_NSPS.~sx

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Attachment D

Detailed MACT Floor Analysis for New MH Units

Cd, Pb, Hg, PM, HCI, S02, NOX, and CO

See MACTFIoor_AttachD_MHVariability_NSPS.~sx

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Attachment E

Detailed MACT Floor Analysis for New and

Existing FB and MH Sources

PCDD/PCDF

See MACTFIoor_AttachE_FBVariability_DF.~sx

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Attachment F

Detailed MACT Floor Analysis for New and

Existing MH Sources

PCDD/PCDF

See MACTFIoor_AttachF_MHVariability_DF.~sx

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Attachment G

List of SSI Units with Advanced Control Devices and Companies that Received

the ICR Survey

See MACTFIoor Attache Units w advanced controls.xls

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Attachment H

Attachment H. CO Spans and Estimated Measurement Error

See MACTFIoor_AttachH_CO_SpanAdjustments.xlsx

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Exhibit 3

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MEMORANDUM

TO: SSI NSPSAND EG DOCKET

FROM: AMY LAMSON, RISK AND BENEFITS GROUP

SUBJECT: HEALTH INCIDENCES FOR SSI FINAL RULE

DATE: 2/18/2011

Due to time limitations under the court-ordered schedule, we were unable to fully update

the RIAs for the SSI NSPS and EG for the final rule. We provide this memo to the docket as

documentation of the estimated health incidences to support the information in the

communication materials. These health incidences were calculated using the methodology

described in the proposal RIA using the emission reductions estimated for the final rule, which is

available at http://www.epa.~ov/ttn/ecas/ria.html .

Summary of Reductions in Health Incidences from PM2,5 for the Final SSI NSPS and EG in 2015*

Final MALT Floor

Avoided Premature Mortality

Pope et al. 2

Laden et al. 6

Avoided Morbidity

Chronic Bronchitis 2

Acute Myocardial Infarction 4

Hospital Admissions, Respiratory 1

Hospital Admissions, Cardiovascular 1

Emergency Room Visits, Respiratory 2

Acute Bronchitis 4

Work Loss Days 320

Asthma Exacerbation 42

M RAD 1,900

Lower Respiratory Symptoms 46

Upper Respiratory Symptoms 35

*These health incidences are 81% lower than those shown in the proposal RIA for this rule.

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EX~llblt 4

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September 2010

Regulatory Impact Analysis:

Standards of Performance for New

Stationary Sources and Emission

Guidelines for Existing Sources:

Sewage Sludge Incineration Units

Prepared for

Tom Walton

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards (OAQPS)

Air Benefit and Cost Group

(MD-C439-02)

Research Triangle Park, NC 27711

Prepared by

RTI International

3040 Cornwallis Road

Research Triangle Park, NC 27709

RTI Project Number 0209897.004.074

USCA Case #11-1131 Document #1334650 Filed: 10/11/2011 Page 2 of 40

(Page 57 of Total)
RTI Project Number

0209897.004.074

Regulatory Impact Analysis:

Standards of Performance for New

Stationary Sources and Emission

Guidelines for Existing Sources:

Sewage Sludge Incineration Units

September 2010

Prepared for

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards (OAQPS)

Air Benefit and Cost Group

(MD-C439-02)

Research Triangle Park, NC 27711

Prepared by

RTI International

3040 Cornwallis Road

Research Triangle Park, NC 27709

RTI International is a trade name of Research Triangle Institute.

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SECTION 1

INTRODUCTION

Section 129 ofthe Clean Air Act (CAA) requires that performance standards for new

units and emission guidelines (EG) for existing units be established for each category of solid

waste incineration units. In previous actions, the U.S. Environmental Protection Agency (EPA)

has promulgated rules and EG for hospital medical and infectious waste incinerators (HMIWI),

commercial and industrial solid waste incinerators (CISWI), and other solid waste incineration

(OSWI) units. These actions did not apply to sewage sludge incinerators (SSI). EPA is proposing

new source performance standards (NSPS) and EG for SSI units. As part of the regulatory

process, EPA is required to develop a regulatory impact analysis (RIA). The RIA includes an

economic impact analysis (EIA) and a small entity impacts analysis and documents the RIA

methods and results.

1.1 Executive Summary

The key results of the RIA are as follows:

• Options Analyzed: EPA analyzed the following options and selected Option 2:

— Option 1 is the MACT floor level of control for the two subcategories developed

for existing sewage sludge incineration (SSI) units, multiple hearth (MH) units,

and fluidized bed (FB) units.

— Option 2 is the same as Option 1, with the addition of activated carbon injection

for additional mercury (Hg) emissions reduction from MH units.

— Option 3 is the same as Option 2, with the addition of an afterburner on all MH

■ 

units for additional carbon monoxide (CO) emissions reduction.

Engineering Cost Analysis: EPA estimates the proposed rule's total annualized costs

■ 

will be $92 million (2008$).

Social Cost Analysis: Because the proposed regulatory option affects governmental

entities (96 of the 97 owners are governmental entities) providing services not

provided in a market, the Office of Air Quality Planning and Standards (OAQPS) has

used the direct compliance cost method as a measure of social costs. The social cost is

■ 

approximately $92 million (2008$).

Small Entity Analyses: EPA performed a screening analysis for impacts for 18 small

government entities by comparing compliance costs to revenues (e.g., revenue tests).

■ 

EPA's analysis found the tests were below 1%for small entities.

Benefits Analysis: In the year of full implementation (2015), EPA estimates the

monetized PM2.5 benefits of the proposed NSPS and EG are $130 million to $320

million and $120 million to $290 million, at 3%and 7% discount rates, respectively.

1-1

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All estimates are in 2008$ for the year 2015. Using alternate relationships between

PM2.5 and premature mortality supplied by experts, higher and lower benefits

estimates are plausible, but most of the expert-based estimates fall between these

estimates. The benefits from reducing other air pollutants have not been monetized in

this analysis, including reducing 2,900 tons of CO, 96 tons of HC1, 3.0 tons of Pb, 1.6

tons of Cd, 5,500 pounds of mercury (Hg), and 90 grams of total dioxins/furans

(CDD/CDF) each year. In addition, ecosystem benefits and visibility benefits have

not been monetized in this analysis.

• Net Benefits: The net benefits for the NSPS and EG are $37 million to $220 million

and $26 million to $190 million, at 3%and 7% discount rates, respectively (Table 1-

1). All estimates are in 2008$ for the year 2015.

1.2 Organization of this Report

EIA:

The remainder of this report supports and details the methodology and the results of the

• Section 2 describes the SSI process, alternative disposal methods, and affected

entities.

• 

■ 

Section 3 describes the engineering cost analysis.

Section 4 describes the economic impact and small entity analyses.

• Section 5 presents the benefits estimates.

1-2

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Table 1-1. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the SSI

NSPS and EG in 2015 (millions of 2008$)a

3% Discount Rate 7% Discount Rate

Proposed: Option 2

Total Monetized Benefits" $130 to $320 $120 to $290

Total Social Costs` $92 $92

Net Benefits $37 to $220 $26 to $190

26,000 tons of carbon monoxide

96 tons of HCl

5,500 pounds of mercury

1.6 tons of cadmium

Non-monetized Benefits 3.0 tons of lead

90 grams of dioxins/furans

Health effects from NOZ and SOZ exposure

Ecosystem effects

Visibility impairment

Option 1

Total Monetized Benefitsb $130 to $320 $120 to $290

Total Social Costs` $63 $63

Net Benefits $66 to $250 $55 to $220

2,900 tons of carbon monoxide

96tons of HCl

820 pounds of mercury

1.6 tons of cadmium

Non-monetized Benefits 3.0 tons of lead

74 grams of dioxins/furans

Health effects from NOZ and SOZ exposure

Ecosystem effects

Visibility impairment

Option 3

Total Monetized Benefits° $130 to $310 $120 to $290

Total Social Costs` $132 $132

Net Benefits -$5.4 to $180 -$14 to $150

26,000 tons of carbon monoxide

96 tons of HCl

5,500 pounds of mercury

1.6 tons of cadmium

Non-monetized Benefits 3.0 tons of lead

90 grams of dioxins/furans

Health effects from NOZ and SOZ exposure

Ecosystem effects

Visibility impairment

All estimates aze for the implementation year (2015), and are rounded to two significant figures. These results include 2 new FB

incinerators anticipated to come online by 2015 and the large entities comply and small entities landfill assumption.

b The total monetized benefits reflect the human health benefits associated with reducing exposure to PM2.5 through reductions of

directly emitted PM2.5 and PMZ,S precursors such as NOX and SOZ. It is important to note that the monetized benefits include

many but not all health effects associated with PMZ,S exposure. Benefits aze shown as a range from Pope et al. (2002) to Laden

et al. (200. These models assume that all fine particles, regardless of their chemical composition, aze equally potent in causing

premature mortality because there is no clear scientific evidence that would support the development of differential effects

estimates by particle type.l'hese estimates include energy disbenefits valued at $0.5 million at a 3%discount rate from COZ

emissions.

`The annual compliances costs serve as a proxy for the annual social costs of this rule given the lack of difference between the two.

1-3

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SECTION 5

HUMAN HEALTH BENEFITS OF EMISSIONS REDUCTIONS

5.1 Synopsis

In this section, we provide an estimate of the monetized benefits associated with reducing

particulate matter (P1V~ for the proposed Sewage Sludge Incinerator (SSI) New Source

Performance Standard (NSPS) and Emissions Guidelines (EG). For this rule, the PM reductions

are the result of emission limits on PM, emission limits on PM2.5 precursors such as NOX and

SO2, as well as emission limits on other pollutants. The total PM2_5 reductions are the

consequence ofthe technologies installed or waste diversion to meet these multiple limits. These

estimates reflect the monetized human health benefits of reducing cases of morbidity and

premature mortality among populations exposed to the PM2.5 precursors reduced by this

rulemaking. Using a 3%discount rate, we estimate the total monetized benefits including energy

disbenefits ofthe proposed SSI NSPS and EG to be $130 million to $320 million in the

implementation year (2015). Using a 7% discount rate, we estimate the total monetized benefits

including energy disbenefits ofthe proposed SSI NSPS and EG to be $120 million to $290

million in the implementation year. All estimates are in 2008$.

These estimates reflect EPA's most current interpretation of the scientific literature.

Higher or lower estimates of benefits are possible using other assumptions; examples of this are

provided in Figure 5-2. Data, resource, and methodological limitations prevented EPA from

monetizing the benefits from several important benefit categories, including benefits from

reducing hazardous air pollutants, ecosystem effects, and visibility impairment. The benefits

from reducing other air pollutants have not been monetized in this analysis, including reducing

2,900 tons of CO, 96 tons of HCI, 3.0 tons of Pb, 1.6 tons of Cd, 5,500 pounds of mercury (Hg),

and 90 grams of total dioxins/furans (CDD/CDF) each year.

5.2 Calculation of PM2,5 Human Health Benefits

This rulemaking would reduce emissions of PM2.5, 502, and NO2. Because SOX and NOZ

are also precursors to PM2_5, reducing these emissions would also reduce PM2.5 formation, human

exposure, and the incidence of PM2.5-related health effects. For this rule, the PM reductions are

the result of emission limits on PM, emission limits on PM2.5 precursors such as NOX and SO2, as

well as emission limits on other pollutants. The total PM2.5 reductions are the consequence of the

technologies installed or waste diversion to meet these multiple limits. Due to analytical

limitations, it was not possible to provide a comprehensive estimate of PM2.5-related benefits.

Instead, we used the "benefit-per-ton" approach to estimate these benefits based. on the

5-1

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methodology described in Fann, Fulcher, and Hubbell (2009). The key assumptions are

described in detail below. These PM2.5benefit-per-ton estimates provide the total monetized

human health benefits (the sum of premature mortality and premature morbidity) of reducing one

ton of PMz.s from a specified source. EPA has used the benefit per-ton technique in several

previous RIAs, including the recent NOZ NAAQS RIA (U.S. EPA, 2010b). Table 5-1 shows the

quantified and unquantified benefits captured in those benefit-per-ton estimates.

Table 5-1. Human Health and Welfare Effects of PM2.5

Pollutant Quantified and Monetized Unquantified Effects

Effect in Primary Estimates Changes in:

PM2.5 Adult premature mortality Subchronic bronchitis cases

Bronchitis: chronic and acute Low birth weight

Hospital admissions: respiratory and Pulmonary function

cardiovascular Chronic respiratory diseases other than chronic

Emergency room visits for asthma bronchitis

Nonfatal heart attacks (myocardial infarction) Non-asthma respiratory emergency room visits

Lower and upper respiratory illness Visibility

Minor restricted-activity days Household soiling

Work loss days

Asthma exacerbations (asthmatic population)

Infant mortality

Consistent with the Portland Cement NESHAP (U.S. EPA, 2009a), the PM2.5 benefits

estimates utilize the concentration-response functions as reported in the epidemiology literature,

as well as the 12 functions obtained in EPA's expert elicitation study as a sensitivity analysis.

One estimate is based on the concentration-response (C-R) function developed from

the extended analysis ofAmerican Cancer Society (ACS) cohort, as reported in Pope

et al. (2002), a study that EPA has previously used to generate its primary benefits

estimate. When calculating the estimate, EPA applied the effect coefficient as

reported in the study without an adjustment for assumed concentration threshold of 10

μg/m3 as was done in recent (2006-2009) Office of Air and Radiation RIAs.

One estimate is based on the C-R function developed from the extended analysis of

the Harvard Six Cities cohort, as reported by Laden et al. (2006). This study,

published after the completion of the Staff Paper for the 2006 PM2.5 NAAQS, has

been used as an alternative estimate in the PMz.S NAAQS RIA and PM2.5 benefits

estimates in RIAs completed since the PM2.5 NAAQS. When calculating the estimate,

EPA applied the effect coefficient as reported in the study without an adjustment for

assumed concentration threshold of 10 μg/m3 as was done in recent (2006-2009)

RIAs.

5-2

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Twelve estimates are based on the C-R functions from EPA's expert elicitation study

(IEc, 2006; Roman et al., 2008) on the PM2.5 -mortality relationship and interpreted

for benefits analysis in EPA's final RIA for the PM2.5 NAAQS. For that study, twelve

experts (labeled A through L) provided independent estimates of the PM2.5 -mortality

concentration-response function. EPA practice has been to develop independent

estimates of PM2.5 -mortality estimates corresponding to the concentration-response

function provided by each of the twelve experts, to better characterize the degree of

variability in the expert responses.

The effect coefficients are drawn from epidemiology studies examining two large

population cohorts: the American Cancer Society cohort (Pope et al., 2002) and the Harvard Six

Cities cohort (Laden et al., 2006).' These are logical choices for anchor points in our presentation

because, while both studies are well designed and peer reviewed, there are strengths and

weaknesses inherent in each, which we believe argues for using both studies to generate benefits

estimates. Previously, EPA had calculated benefits based on these two empirical studies, but

derived the range of benefits, including the minimum and maximum results, from an expert

elicitation of the relationship between exposure to PM2.5 and premature mortality (Roman et al.,

2008).2 Within this assessment, we include the benefits estimates derived from the concentrationresponse function provided by each of the twelve experts to better characterize the uncertainty in

the concentration-response function for mortality and the degree of variability in the expert

responses. Because the experts used these cohort studies to inform their concentration-response

functions, benefits estimates using these functions generally fall between results using these

epidemiology studies (see Figure 5-2). In general, the expert elicitation results support the

conclusion that the benefits of PM2,5 control are very likely to be substantial.

Readers interested in reviewing the general methodology for creating the benefit-per-ton

estimates used in this analysis should consult Fann, Fulcher, and Hubbell (2009). As described in

Fann, Fulcher, and Hubbell (2009), benefit-per-ton estimates are developed for selected

pollutant/source category combinations. The per-ton values calculated therefore apply only to

tons reduced from those specific pollutant/source combinations (e.g., S02 emitted from electric

generating units; NO2 emitted from mobile sources). In this analysis, we apply the national

average benefit-per-ton estimate fora 2015 analysis year and multiply it by the corresponding

emission reductions of directly emitted PM2,5, SO2, and NOx to quantify the benefits of this rule.

The benefit-per-ton estimates found in Fann, Fulcher, and Hubbell (2009) reflect a specifc set of

key assumptions and input data. As we update these underlying assumptions to reflect the

'These two studies specify multi-pollutant models that control for SOZ, among other pollutants.

ZPlease see the Section 5.2 of the Portland Cement proposal RIA in Appendix SA for more information regarding the

change in the presentation of benefits estimates.

5-3

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scientific literature, we re-estimate the benefit-per-ton estimates and post the updated estimates

at http://www.epa.gov/air/benmap/bpt.html. In addition, we adjust these estimates to match the

currency year for the costs in this analysis.

These models assume that all fine particles, regardless of their chemical composition, are

equally potent in causing premature mortality because there is no clear scientific evidence that

would support the development of differential effects estimates by particle type. Directly emitted

PM, SO2, and NOx are the primary PM2.5 precursors affected by this rule. Even though we

assume that all fine particles have equivalent health effects, the benefit-per-ton estimates vary

between precursors because each ton of precursor reduced has a different propensity to form

PM2.5. For example, NOX has a lower benefit-per-ton estimate than direct PM2.5 because it does

not form as much PM2.5, thus the exposure would be lower, and the monetized health co-benefits

would be lower.

The benefit-per-ton coefficients in this analysis were derived using modified versions of

the health impact functions used in the PM NAAQS Regulatory Impact Analysis. Specifically,

this analysis uses the benefit-per-ton method first applied in the Portland Cement NESHAP RIA

(U.S. EPA, 2009a), which incorporated three updates: a new population dataset, an expanded

geographic scope of the benefit-per-ton calculation, and the functions directly from the

epidemiology studies without an adjustment for an assumed threshold.3 Removing the threshold

assumption is a key difference between the method used in this analysis of PM benefits and the

methods used in RIAs prior to Portland Cement, and we now calculate incremental benefits

down to the lowest modeled PM2.5 air quality levels.

EPA strives to use the best available science to support our benefits analyses, and we

recognize that interpretation of the science regarding air pollution and health is dynamic and

evolving. Based on our review of the current body of scientific literature, EPA now estimates

PM-related mortality without applying an assumed concentration threshold. EPA's Integrated

Science Assessment for Particulate Matter (U.S. EPA, 2009b), which was recently reviewed by

EPA's Clean Air Scientific Advisory Committee (U.S. EPA-SAB, 2009a; U.S. EPA-SAB,

2009b), concluded that the scientific literature consistently finds that a no-threshold log-linear

model most adequately portrays the PM-mortality concentration-response relationship while

recognizing potential uncertainty about the exact shape of the concentration-response function.

Since then, the Health Effects Subcommittee (U.S. EPA-SAB, 2010) of EPA's Council

3The benefit-per-ton estimates have also been updated since the Cement RIA to incorporate a revised VSL, as

discussed on the next page.

5-4

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concluded, "The HES fully supports EPA's decision to use a no-threshold model to estimate

mortality reductions. This decision is supported by the data, which are quite consistent in

showing effects down to the lowest measured levels. Analyses of cohorts using data from more

recent years, during which time PM concentrations have fallen, continue to report strong

associations with mortality. Therefore, there is no evidence to support a truncation of the CRF."

In conjunction with the underlying scientific literature, this document provided a basis for

reconsidering the application of thresholds in PM2.5 concentration-response functions used in

EPA's RIAs. For a summary of these scientific review statements and the panel members

commenting on thresholds since 2002, please consult the Technical Support Document (TSD)

Summary of Expert Opinions on the Existence of a Threshold (U.S. EPA, 2010c), which is

provided as an appendix to this RIA.

Consistent with this recent scientific advice, we are replacing the previous threshold

sensitivity analysis with a new "Lowest Measured Level" (LML) assessment. This information

allows readers to determine the portion of population exposed to annual mean PM2.5 levels at or

above the LML of each study; in general, our confidence in the estimated PM mortality

decreases as we consider air quality levels further below the LML in major cohort studies that

estimate PM-related mortality. While an LML assessment provides some insight into the level of

uncertainty in the estimated PM mortality benefits, EPA does not view the LML as a threshold

and continues to quantify PM-related mortality impacts using a full range of modeled air quality

concentrations. It is important to emphasize that we have high confidence in PM2,5-related effects

down to the lowest LML of the major cohort studies. Just because we have greater confidence in

the benefits above the LML, this does not mean that we have no confidence that benefits occur

below the LML.

For this analysis, policy-specific air quality data is not available due to time or resource

limitations. For these rules, we are unable to estimate the percentage of premature mortality

associated with this specific rule's emission reductions at each PMz.51eve1. However, we believe

that it is still important to characterize the distribution of exposure to baseline air quality levels.

As a surrogate measure of mortality impacts, we provide the percentage of the population

exposed at each PM2.5 level using the most recent modeling available from the recently proposed

Transport Rule (U.S. EPA, 2010e). It is important to note that baseline exposure is only one

parameter in the health impact function, along with baseline incidence rates population, and

change in air quality. In other words, the percentage of the population exposed to air pollution

below the LML is not the same as the percentage of the population experiencing health impacts

as a result of a specific emission reduction policy. The most important aspect, which we are

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unable to quantify for rules without air quality modeling, is the shift in exposure associated with

this specific rule. Therefore, caution is warranted when interpreting the LML assessment. For

more information on the data and conclusions in the LMI, assessment for rules without policyspecific air quality modeling, please consult the LML TSD (U.S. EPA, 2010d), which is

provided as an appendix to this RIA. The results of this analysis are provided in Section 5.4.

As is the nature of Regulatory Impact Analyses (RIAs), the assumptions and methods

used to estimate air quality benefits evolve over time to reflect the Agency's most current

interpretation of the scientific and economic literature. For a period of time (2004-2008), the

Office of Air and Radiation (OAR) valued mortality risk reductions using a value of statistical

life (VSL) estimate derived from a limited analysis of some of the available studies. OAR arrived

at a VSL using a range of $1 million to $10 million (2000$) consistent with two meta-analyses of

the wage-risk literature. The $1 million value represented the lower end ofthe interquartile range

from the Mrozek and Taylor (2002) meta-analysis of 33 studies. The $10 million value

represented the upper end ofthe interquartile range from the Viscusi and Aldy (2003) metaanalysis of 43 studies. The mean estimate of $5.5 million (2000$)4 was also consistent with the

mean VSL of $5.4 million estimated in the Kochi et al. (2006) meta-analysis. However, the

Agency neither changed its official guidance on the use of VSL in rule-makings nor subjected

the interim estimate to a scientific peer-review process through the Science Advisory Board

(SAB) or other peer-review group.

During this time, the Agency continued work to update its guidance on valuing mortality

risk reductions, including commissioning a report from mete-analytic experts to evaluate

methodological questions raised by EPA and the SAB on combining estimates from the various

data sources. In addition, the Agency consulted several times with the Science Advisory Board

Environmental Economics Advisory Committee (SAB-EEAC) on the issue. With input from the

meta-analytic experts, the SAB-EEAC advised the Agency to update its guidance using specific,

appropriate meta-analytic techniques to combine estimates from unique data sources and

different studies, including those using different methodologies (i.e., wage-risk and stated

preference) (U.S. EPA-SAB, 2007).

Until updated guidance is available, the Agency determined that a single, peer-reviewed

estimate applied consistently best reflects the SAB-EEAC advice it has received. Therefore, the

Agency has decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines

4 After adjusting the VSL to account for a different currency year (2008$) and to account for income growth to

2015, the $5.5 million VSL is $7.9 million.

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for Preparing Economic Analyses (U.S. EPA, 2000)5 while the Agency continues its efforts to

update its guidance on this issue. This approach calculates a mean value across VSL estimates

derived from 261abor market and contingent valuation studies published between 1974 and

1991. The mean VSL across these studies is $6.3 million (2000$).6 The Agency is committed to

using scientifically sound, appropriately reviewed evidence in valuing mortality risk reductions

and has made significant progress in responding to the SAB-EEAC's specific recommendations.

Figure 5-1 illustrates the relative breakdown of the monetized PM2,5 health benefits.

Resp

0.01%

'/o

v NNci ncaN.~yiiiN v.v~~

Lower Resp Symp 0.00'

ERVisits, Resp0.00%

Figure 5-1. Breakdown of Monetized PM2,5 Health Benefits using Mortality Function from

Pope et al. (2002)a

a This pie chart breakdown is illustrative, using the results based on Pope et al. (2002) as an example. Using the

Laden et al. (2006) fiznction for premature mortality, the percentage of total monerized benefits due to adult

mortality would be 97%. This chart shows the breakdown using a 3%discount rate, and the results would be

similar if a 7% discount rate was used.

Tables 5-2 and 5-3 provide a general summary of the all units comply assumption and

large entities comply and small entities landfill assumption results by pollutant, including the

SIn the (draft) update of the Economic Guidelines (U.S. EPA, 2008), EPA retained the VSL endorsed by the SAB

with the understanding that further updates to the mortality risk valuation guidance would be forthcoming in the

near future. Therefore, this report does not represent final agency policy.

6In this analysis, we adjust the VSL to account for a different currency year (2008$) and to account for income

growth to 2015. After applying these adjustments to the $63 million value, the VSL is $9.1 million.

5-7

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emission reductions and monetized benefits-per-ton at discount rates of 3%and 7%.' Table 5-4

provides a summary of the reductions in health incidences as a result of the pollution reductions

for the large entities comply and small entities landfill results. In Table 5-5, we provide the

benefits using our anchor points of Pope et al. and Laden et al. as well as the results from the

expert

Table 5-2. Summary of Monetized Benefits Estimates for Proposed SSI NSPS and EG in

2015 (2008$) (large entities comply and small entities landfill)a

Emissions Benefit per Benefit per

Benefit Benefit per

Total

Monetized

Total

Monetized

Pollutant Reductions ton (Pope, ton (Laden, Per ton

Pope,

ton

(Laden, Benefits Benefits

(tons) 3%) 3%) 7%) 7%) millions (millions

2008$ at 3%)2008$ at 7%)

Direct PMZ.S 254 $230,000 $560,000 $210,000 $500,000 $58 to $140 $52 to $130

.. PMZ.S Precursors

0

SOZ 2,298 $29,000 $72,000 $27,000 $65,000 $68 to $170 $61 to $150

a

~ NOZ 824 $4,900 $12,000 $4,400 $11,000 $4.0 to $9.8 $3.6 to $8.8

Total $130 to $320 $120 to $290

~'' DirectPM,; 254 $230,000 $560,000 $210,000 $500,000 $58 to $140 $52 to $130 c

0

PM, 5 Precursors

O

.~ SOZ 2,298 $29,000 $72,000 $27,000 $65,000 $68 to $170 $61 to $150

NOz 824 $4,900 $12,000 $4,400 $11,000 $4.0 to $9.8 $3.6 to $8.8

0

a Total $130 to $320 $120 to $290

Direct PM2.5 254 $230,000 $560,000 $210,000 $500,000 $58 to $140 $52 to $130

M PM2.5 Precursors

e

•,°; SOz 2,298 $29,000 $72,000 $27,000 $65,000 $68 to $170 $61 to $150

cL

~ NOZ 824 $4,900 $12,000 $4,400 $11,000 $4.0 to $9.8 $3.6 to $8.8

Total $130 to $320 $120 to $290

'To comply with Circular A-4, EPA provides monetized benefits using discount rates of 3%and 7% (OMB, 2003).

These benefits are estimated for a specific analysis year (i.e., 2015), and most of the PM benefits occur within that

year with two exceptions: acute myocardial infarctions (AMIs) and premature mortality. For AMIs, we assume 5

years of follow-up medical costs and lost wages. For premature mortality, we assume that there is a "cessation"

lag between PM exposures and the total realization of changes in health effects. Although the structure of the lag

is uncertain, EPA follows the advice of the SAB-HES to assume a segmented lag structure chazacterized by 30%

of mortality reductions in the first year, 50%over years 2 to 5, and 20%over the years 6 to 20 after the reduction

in PM2.5 (U.S. EPA-SAB, 2004). Changes in the lag assumptions do not change the total number of estimated

deaths but rather the timing of those deaths. Therefore, discounting only affects the AMI costs after the analysis

year and the valuation of premature mortalities that occur after the analysis year. As such, the monetized benefits

using a 7%discount rate are only approximately 10%less than the monetized benefits using a 3%discount rate.

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All estimates are for the implementation year (2015), and are rounded to two significant figures so numbers may

not sum across columns. All fine particles are assumed to have equivalent health effects, but the benefit per ton

estimates vary because each ton of precursor reduced has a different propensity to form PM2,5. The monetized

benefits incorporate the conversion from precursor emissions to ambient fine particles. These results include 2

new FB incinerators anticipated to come online by 2015. These estimates do not include energy disbenefits valued

at $0.5 million at a 3%discount rate for COZ emissions.

elicitation on PM mortality. Figures 5-2 through 5-4 provide a visual representation of the range

of benefits estimates and the pollutant breakdown of the monetized benefits.

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Table 5-3. Summary of Monetized Benefits Estimates for Proposed SSI NSPS and EG in

2015 (2008$) (all units comply)e

Total Total

Benefit Benefit per Benefit Benefit Monetized Monetized

Emissions per ton ton per ton per ton Benefits Benefits

Reductions (Pope, (Laden, (Pope, (Laden, (millions 2008$ (millions 2008$

Pollutant (tons) 3%) 3%) 7%) 7%) at 3%) at 7%)

Direct PMz.s

.. PM2,5 Precursors

a

SOZ

a

~ NOZ

~ Direct PM2.5

0

a, PM2,5 Precursors

O

,fl SOz

0

a

NOZ

0

~,

a

209 $230,000 $560,000 $210,000 $500,000 $48.0 to $120.0 $43.0 to $110.0

2,193 $29,000 $72,000 $27,000 $65,000 $65 to $160 $59 to $140

5 $4,900 $12,000 $4,400 $11,000 $.02 to $.06 $.02 to $.OS

Total $110 to $270 $100 to $250

209 $230,000 $560,000 $210,000 $500,000 $48.0 to $120.0 $43.0 to $110.0

2,193 $29,000 $72,000 $27,000 $65,000 $65 to $160 $59 to $140

5 $4,900 $12,000 $4,400 $11,000 $.02 to $.06 $.02 to $.OS

Total $110 to $270 $100 to $250

Direct PMZ.S 209 $230,000 $560,000 $210,000 $500,000 $48 to $120 $43 to $110

c•, PM2,5 Precursors

C

SOZ 2,193 $29,000 $72,000 $27,000 $65,000 $65 to $160 $59 to $140

a

~ NOZ 5 $4,900 $12,000 $4,400 $11,000 $.02 to $.06 $.02 to $.OS

Total $110 to $270 $100 to $250

a All estimates are for the implementation year (2015), and are rounded to two significant figures so numbers may

not sum across columns. All fine particles are assumed to have equivalent health effects, but the benefit per ton

estimates vary because each ton of precursor reduced has a different propensity to form PM2.5. The monetized

benefits incorporate the conversion from precursor emissions to ambient fine particles. These results include 2

new FB incinerators anticipated to come online by 2015. These estimates do not include energy disbenefits valued

at $0.5 million at a 3%discount rate for COZ emissions.

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Table 5-4. Summary of Reductions in Health Incidences from PM2,; Benefits for the

Proposed SSI NSPS and EG in 2015a

Option 1 Proposed: Option 2 Option 3

Avoided Premature Mortality

Pope et al. 14 14 14

Laden et al. 36 36 36

Avoided Morbidity

Chronic Bronchitis 10 10 10

Acute Myocardial Infarction 23 23 23

Hospital Admissions, Respiratory 3 3 3

Hospital Admissions, Cardiovascular 7 7 7

Emergency Room Visits, Respiratory 14 l4 14

Acute Bronchitis 23 23 23

Work Loss Days 1,900 1,900 1,900

Asthma Exacerbation 250 250 250

Minor Respiratory Symptoms 11,000 11,000 11,000

Lower Respiratory Symptoms 270 270 270

Upper Respiratory Symptoms 210 210 210

a All estimates are for the analysis year (2015) and are rounded to whole numbers with two significant figures. All

fine particles are assumed to have equivalent health effects, but each PMZ.S precursor pollutant has a different

propensity to form PM2,5. Confidence intervals are unavailable for this analysis because of the benefit-per-ton

methodology. These results include 2 new FB incinerators anticipated to come online by 2015 and the large

" entities comply and small entities landfill assumption.

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Table 5-5. All PM2,5 Benefits Estimates for the Proposed SSI NSPS and EG at Discount

Rates of 3% and 7% in 2015 (in millions of 2008$)a

Proposed:

Option 1 Option 3

Option 2

3% 7% 3% 7% 3% 7%

Benefit-per-ton Coefficients derived from Epidemiology Literature

Pope et al. $130 $120 $130 $120 $130 $120

Laden et al. $320 $290 $320 $290 $320 $290

Benefit-per-ton Coefficients Derived from Expert Elicitation

ExpertA $340 $300 $340 $300 $340 $300

ExpertB $260 $230 $260 $230 $260 $230

ExpertC $260 $230 $260 $230 $260 $230

ExpertD $180 $160 $180 $160 $180 $160

Expert E $420 $380 $420 $380 $420 $380

ExpertF $230 $210 $230 $210 $230 $210

ExpertG $150 $140 $150 $140 $153 $139

ExpertH $190 $170 $190 $170 $190 $170

ExpertI $250 $230 $250 $230 $250 $230

Expert) $210 $190 $210 $190 $210 $190

ExpertK $51 $46 $51 $46 $51 $46

ExpertL $190 $170 $190 $170 $190 $170

All estimates are rounded to two significant figures. Estimates do not include confidence intervals because they

were derived through the benefit-per-ton technique described above. The benefits estimates from the Expert

Elicitation are provided as a reasonable characterization of the uncertainty in the mortality estimates associated

with the concentration-response function. These results include 2 new FB incinerators anticipated to come online

by 2015 and the large entities comply and small entities landfill assumption. These estimates do not include

energy disbenefits valued at $0.5 million at a 3%discount rate for COZ emissions.

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S400

$350

$300

$250

0

0

`. $200

c

0

~ $150

$100

$50

$0

Benefits estimates derived from 2 epidemiology functions and 12 expert functions

Figure 5-2. Total Monetized PMZ,S Benefits for the Proposed SSI NSPS and EG in 2015

a This graph shows the estimated benefits at discount rates of 3% and 7% using effect coefficients derived from the

Pope et al. study and the Laden et al study, as well as 12 effect coefficients derived from EPA's expert elicitation

on PM mortality. The results shown are not the direct results from the studies or expert elicitation; rather, the

estimates are based in part on the concentration-response function provided in those studies. These results include

2 new FB incinerators anticipated to come online by 2015 and the large entities comply and small entities landfill

assumption. These estimates do not include energy disbenefits valued at $0.5 million at a 3% discount rate for

COz emissions.

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Figure 5-3. Breakdown of Monetized Benefits for the Proposed SSI NSPS and EG by

PMZ,S Precursor Pollutant and Source

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iearth

Fluidized Bed

10%

Figure 5-4. Breakdown of Monetized Benefits for the Proposed SSI NSPS and EG by

Subcategory

5.3 Energy Disbenefits

Electricity usage associated with the operation of control devices is anticipated to

increase emissions of pollutants from utility boilers that supply electricity to the sewage sludge

incinerators. For example, increased scrubber pump horsepower and sorbent injection controls

may cause slight increases in electricity consumption. We estimate that the increased electricity

consumption associated with the proposed option would be 12 million kWh if all entities

comply, and 12 million kWh if the small entities landfill. Using national emission factors from

eGRID for electrical generating units (EGUs), we estimate the increased emissions to be 19,000

tpy of CO2 for the proposed option assuming that small entities landfill.8 Since NOx and SOZ are

covered by capped emissions trading programs, we are only estimating the COZ emission

increases from the increased electricity demand. The methodology used to calculate these

g Option 3 has additional energy disbenefits associated with the supplemental fuel required to run the afterburners,

which results in additional emissions of CO2i CO, and NOx. The COZ energy disbenefits for Option 3 are

shown in Tables 5-7 and 5-8. The additional NOx disbenefits (as a precursor to PMz.s using the methodolology

described in Section 5.2) for Option 3 are $0.4 million to $0.9 million, which do not affect the rounded benefits

results.

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emission increases is described "Secondary Impacts of Control Options for the Sewage Sludge

Incineration Source Category", which is available in the docket.

5.3.I Social Cost of Carbon and Greenhouse Gas Disbenefits

EPA has assigned a dollar value to reductions in carbon dioxide (CO2) emissions using

recent estimates of the "social cost of carbon" (SCC). The SCC is an estimate of the monetized

damages associated with an incremental increase in carbon emissions in a given year. It is

intended to include (but is not limited to) changes in net agricultural productivity, human health,

property damages from increased flood risk, and the value of ecosystem services due to climate

change. The SCC estimates used in this analysis were developed through an interagency process

that included EPA and other executive branch entities, and concluded in February 2010. EPA

first used these SCC estimates in the benefits analysis for the final joint EPA/DOT Rulemaking

to establish Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average

Fuel Economy Standards; see the rule's preamble for discussion about application of SCC (75

FR 25324; 5/7/10). The SCC Technical Support Document (SCC TSD) provides a complete

discussion of the methods used to develop these SCC estimates.9

The interagency group selected four SCC values for use in regulatory analyses, which we

have applied in this analysis: $5, $21, $35, and $65 per metric ton of CO2 emissions10 in 2010, in

2007 dollars. The first three values are based on the average SCC from three integrated

assessment models, at discount rates of 2.5, 3, and 5 percent, respectively. SCCs at several

discount rates are included because the literature shows that the SCC is quite sensitive to

assumptions about the discount rate, and because no consensus exists on the appropriate rate to

use in an intergenerational context. The fourth value is the 95th percentile of the SCC from all

three models at a 3 percent discount rate. It is included to represent higher-than-expected impacts

from temperature change further out in the tails of the SCC distribution. Low probability, high

impact events are incorporated into all ofthe SCC values through explicit consideration of their

effects in two of the three models as well as the use of a probability density function for

9 Docket ID EPA-HQ-OAR-2009-0472-114577, Technical Support Document: Social Cost of Carbon for

Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of

Carbon, with participation by Council of Economic Advisers, Council on Environmental Quality, Department of

Agriculture, Department of Commerce, Department of Energy, Deparhnent of Transportation, Environmental

Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of Management

and Budget, Office of Science and Technology Policy, and Department of Treasury (February 2010). Also

available at http://www.ena.eov/otaq/climate/reeulations.htm

10 The interagency group decided that these estimates apply only to COZ emissions. Given that warming profiles and

impacts other than temperature change (e.g. ocean acidification) vary across GHGs, the group concluded

"transforming gases into COZ-equivalents using GWP, and then multiplying the carbon-equivalents by the SCC,

would not result in accurate estimates of the social costs of non-COZ gases" (SCC TSD, pg 13).

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equilibrium climate sensitivity. Treating climate sensitivity probabilistically results in more high

temperature outcomes, which in turn lead to higher projections of damages.

The SCC increases over time because future emissions are expected to produce larger

incremental damages as physical and economic systems become more stressed in response to

greater climatic change. Note that the interagency group estimated the growth rate of the SCC

directly using the three integrated assessment models rather than assuming a constant annual

growth rate. This helps to ensure that the estimates are internally consistent with other modeling

assumptions. The SCC estimates for the analysis years of 2015, in 2008$ are provided in Table

5-6.

When attempting to assess the incremental economic impacts of carbon dioxide

emissions, the analyst faces a number of serious challenges. A recent report from the National

Academies of Science (NRC, 2008) points out that any assessment will suffer from uncertainty,

speculation, and lack of information about (1) future emissions of greenhouse gases, (2) the

effects of past and future emissions on the climate system, (3) the impact of changes in climate

on the physical and biological environment, and (4) the translation ofthese environmental

impacts into economic damages. As a result, any effort to quantify and monetize the harms

associated with climate change will raise serious questions of science, economics, and ethics and

should be viewed as provisional.

The interagency group noted a number of limitations to the SCC analysis, including the

incomplete way in which the integrated assessment models capture catastrophic and noncatastrophic impacts, their incomplete treatment of adaptation and technological change,

uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk

aversion. The limited amount of research linking climate impacts to economic damages makes

the interagency modeling exercise even more difficult. The interagency group hopes that over

time researchers and modelers will work to fill these gaps and that the SCC estimates used for

regulatory analysis by the Federal government will continue to evolve with improvements in

modeling. Additional details on these limitations are discussed in the SCC TSD.

In light of these limitations, the interagency group has committed to updating the current

estimates as the science and economic understanding of climate change and its impacts on

society improves over time. Specifically, the interagency group has set a preliminary goal of

revisiting the SCC values within two years or at such time as substantially updated models

become available, and to continue to support research in this area.

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Applying the global SCC estimates to the estimated increases in COz emissions for the

range ofpolicy scenarios, we estimate the dollar value of the climate-related disbenefits captured

by the models for each analysis year. For internal consistency, the annual disbenefits are

discounted back to NPV terms using the same discount rate as each SCC estimate (i.e. 5%, 3%,

and 2.5%) rather than 3%and 7%.I ~ These estimates are provided in Tables 5-7 and 5-8.

Table 5-6. Social Cost of Carbon (SCC) Estimates (per tonne of CO2) for 2015 a

Discount Rate and Statistic SCC estimate (2008$)

5% Average $5.9

3% Average $24•~

2.5%Average $39.9

3% 95%ile $75.6

a The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of

climate impacts.

Table 5-7. Monetized SCC-derived Disbenefits of COz Emission Increases in 2015

(all units comply, millions of 2008$)a

Proposed:

Discount Rate and Statistic Option 1 Option 2 Option 3

24,900 tpy COZ 24,900 tpy COZ 126,000 tpy COz

5% Average $0.1 $0.1 $0.7

3% Average $0.6 $0.6 $3.1

2.5%Average $1.0 $1.0 $5.0

3% 95°/vile $1.9 $1.9 $9.5

a The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of

climate impacts. These results include 2 new FB incinerators anticipated to come online by 2015.

Table 5-8. Monetized SCC-derived Disbenefits of COZ Emission Increases in 2015

(large entities comply and small entities landfill, millions of 2008$)a

Proposed:

Discount Rate and Statistic Option 1 Option 2 Option 3

21,782 tpy COZ 21,782 tpy COZ 114,784tpy COZ

5% Average $0.1 $0.1 $0.7

3% Average $0.5 $0.5 $2.8

2.5%Average $0.9 $0.9 $4.6

" It is possible that other benefits or costs of proposed regulations unrelated to COZ emissions will be discounted at

rates that differ from those used to develop the SCC estimates.

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3% 95%ile $1.6 $1.6 $8.7

The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of

climate impacts. These results include 2 new FB incinerators anticipated to come online by 2015.

5.4 Unquantified Benefits

The monetized benefits estimated in this RIA only reflect the portion of benefits

attributable to the health effect reductions associated with ambient fine particles. Data, resource,

and methodological limitations prevented EPA from quantifying or monetizing the benefits from

several important benefit categories, including benefits from reducing toxic emissions,

ecosystem effects, and visibility impairment. The health benefits from reducing hazardous air

pollutants (HAPs) and carbon monoxide have not been monetized in this analysis. In addition to

being a PM2.5 precursor, SOZ emissions also contribute to adverse effects from acidic deposition

in aquatic and terrestrial ecosystems, increased mercury methylation, as well as visibility

impairment. The benefits from reducing other air pollutants that have not been monetized in this

analysis including 2,900 tons of carbon monoxide, 96 tons of HCI, 3.0 tons of lead, 1.6 tons of

cadmium, 5,500 pounds of mercury, and 90 grams of total dioxins/furans each year.

5.4.1 Carbon Monoxide Benefits

Carbon monoxide (CO) exposure is associated with a variety of health effects. Without

knowing the location of the emission reductions and the resulting ambient concentrations using

fine-scale air quality modeling, we were unable to estimate the exposure to CO for nearby

populations. Due to data, resource, and methodological limitations, we were unable to estimate

the benefits associated with the reductions of CO emissions that would occur as a result of this

rule.

Carbon monoxide in ambient air is formed primarily by the incomplete combustion of

carbon-containing fuels and photochemical reactions in the atmosphere. The amount of CO

emitted from these reactions, relative to carbon dioxide (COz), is sensitive to conditions in the

combustion zone, such as fuel oxygen content, burn temperature, or mixing time. Upon

inhalation, CO diffuses through the respiratory system to the blood, which can cause hypoxia

(reduced oxygen availability). Carbon monoxide can elicit a broad range of effects in multiple

tissues and organ systems that are dependent upon concentration and duration of exposure.

The Integrated Science Assessment for Carbon Monoxide (U.S. EPA, 2010a) concluded

that short-term exposure to CO is "likely to have a causal relationship" with cardiovascular

morbidity, particularly in individuals with coronary heart disease. Epidemiologic studies

associate short-term CO exposure with increased risk of emergency department visits and

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hospital admissions. Coronary heart disease includes those who have angina pectoris (cardiac

chest pain), as well as those who have experienced a heart attack. Other subpopulations

potentially at risk include individuals with diseases such as chronic obstructive pulmonary

disease (COPD), anemia, or diabetes, and individuals in very early or late life stages, such as

older adults or the developing young. The evidence is suggestive of a causal relationship

between short-term exposure to CO and respiratory morbidity and mortality. The evidence is also

suggestive of a causal relationship for birth outcomes and developmental effects following longterm exposure to CO, and for central nervous system effects linked to short- and long-term

exposure to CO.

5.4.2 Other S02 Benefits

In addition to being a precursor to PM2.5, S02 emissions are also associated with a variety

of respiratory health effects. Unfortunately, we were unable to estimate the health benefits

associated with reduced SOz exposure in this analysis because we do not have air quality

modeling data available. Without knowing the location of the emission reductions and the

resulting ambient concentrations, we were unable to estimate the exposure to SOZ for nearby

populations. Therefore, this analysis only quantifies and monetizes the PM2.5 benefits associated

with the reductions in SOZ emissions.

Following an extensive evaluation of health evidence from epidemiologic and laboratory

studies, the U.S. EPA has concluded that there is a causal relationship between respiratory health

effects and short-term exposure to SOz (U.S. EPA, 2008). According to summary of the ISA in

EPA's risk and exposure assessment (REA) for the SOZ NAAQS, "the immediate effect of SOz

on the respiratory system in humans is bronchoconstriction" (U.S. EPA, 2009c). In addition, the

REA summarized from the ISA that "asthmatics are more sensitive to the effects of SOZ likely

resulting from preexisting inflammation associated with this disease." A clear concentrationresponse relationship has been demonstrated in laboratory studies following exposures to S02 at

concentrations between 20 and 100 ppb, both in terms of increasing severity of effect and

percentage of asthmatics adversely affected (U.S. EPA, 2009c). Based on our review of this

information, we identified four short-term morbidity endpoints that the SOZ ISA identified as a

"causal relationship": asthma exacerbation, respiratory-related emergency department visits, and

respiratory-related hospitalizations. The differing evidence and associated strength ofthe

evidence for these different effects is described in detail in the S02 ISA. The SOZ ISA also

concluded that the relationship between short-term SOZ exposure and premature mortality was

"suggestive of a causal relationship" because it is difficult to attribute the mortality risk effects to

S02 alone. Although the SOz ISA stated that studies are generally consistent in reporting a

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relationship between SOZ exposure and mortality, there was a lack of robustness of the observed

associations to adjustment for pollutants.

S02 emissions also contribute to adverse welfare effects from acidic deposition, visibility

impairment, and enhanced mercury methylation. Deposition of sulfur causes acidification, which

can cause a loss of biodiversity of fishes, zooplankton, and macro invertebrates in aquatic

ecosystems, as well as a decline in sensitive tree species, such as red spruce (Picea rubens) and

sugar maple (Ater saccharum) in terrestrial ecosystems. In the northeastern United States, the

surface waters affected by acidification are a source of food for some recreational and

subsistence fishermen and for other consumers and support several cultural services, including

aesthetic and educational services and recreational fishing. Biological effects of acidification in

terrestrial ecosystems are generally linked to aluminum toxicity, which can cause reduced root

growth, which restricts the ability of the plant to take up water and nutrients. These direct effects

can, in turn, increase the sensitivity of these plants to stresses, such as droughts, cold

temperatures, insect pests, and disease leading to increased mortality of canopy trees. Terrestrial

acidification affects several important ecological services, including declines in habitat for

threatened and endangered species (cultural), declines in forest aesthetics (cultural), declines in

forest productivity (provisioning), and increases in forest soil erosion and reductions in water

retention (cultural and regulating) (U.S. EPA, 2008d).

Reducing SOz emissions and the secondary formation of PM2.5 would improve the level

of visibility throughout the United States. Fine particles with significant light-extinction

efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soil (Sider, 1996).

These suspended particles and gases degrade visibility by scattering and absorbing light. Higher

visibility impairment levels in the East are due to generally higher concentrations of fine

particles, particularly sulfates, and higher average relative humidity levels. In fact, particulate

sulfate is the largest contributor to regional haze in the eastern U.S. (i.e., 40% or more annually

and 75%during summer). In the western U.S., particulate sulfate contributes to 20-50% of

regional haze (U.S. EPA, 2009c). Visibility has direct significance to people's enjoyment of

daily activities and their overall sense of wellbeing. Good visibility increases the quality of life

where individuals live and work, and where they engage in recreational activities.

5.4.3 HAP Benefits

Due to data, resource, and methodology limitations, we were unable to estimate the

benefits associated with the hazardous air pollutants that would be reduced as a result of this

rule. Available emissions data show that several different HAPs are emitted from SSI. This rule

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is anticipated to reduce 96 tons of HCI, 3.0 tons of lead, 1.6 tons of cadmium, 5,500 pounds of

mercury, and 90 grams of total dioxins/furans each year. In the absence of air quality modeling

and/or concentration-response functions, we are unable to quantify the magnitude of the

reduction in human exposure to these pollutants associated with the emission reductions from

this rule.

5.4.3.1 Mercury

Mercury is a highly neurotoxic contaminant that enters the food web as a methylated

compound, methylmercury (U.S. EPA, 2008d). The contaminant is concentrated in higher

trophic levels, including fish eaten by humans. Experimental evidence has established that only

inconsequential amounts of methylmercury can be produced in the absence of sulfate (U.S. EPA,

2008d). Current evidence indicates that in watersheds where mercury is present, increased sulfate

deposition very likely results in methylmercury accumulation in fish (Drevnick et al., 2007;

Munthe et al, 2007). The SOZ ISA concluded that evidence is sufficient to infer a casual

relationship between sulfur deposition and increased mercury methylation in wetlands and

aquatic environments (U.S. EPA, 2008d).

In addition to the role of sulfate deposition on methylation, this proposed rule would also

reduce mercury emissions. Mercury is emitted to the air from various man-made and natural

sources. These emissions transport through the atmosphere and eventually deposit to land or

water bodies. This deposition can occur locally, regionally, or globally, depending on the form of

mercury emitted and other factors such as the weather. The form of mercury emitted varies

depending on the source type and other factors. Available data indicate that the mercury

emissions from these sources are a mixture of gaseous elemental mercury, inorganic ionic

mercury, and particulate bound mercury. Gaseous elemental mercury can be transported very

long distances, even globally, to regions far from the emissions source (becoming part of the

global "pool") before deposition occurs. Inorganic ionic and particulate bound mercury have a

shorter atmospheric lifetime and can deposit to land or water bodies closer to the emissions

source. Furthermore, elemental mercury in the atmosphere can undergo transformation into ionic

mercury, providing a significant pathway for deposition of emitted elemental mercury.

This source category emitted about 3.1 tons of mercury in the air in 2008 in the U.S.

Based on the EPA's National Emission Inventory, about 103 tons of mercury were emitted from

all anthropogenic sources in the U.S. in 2005. Moreover, the United Nations has estimated that

about 2,100 tons of mercury were emitted worldwide by anthropogenic sources in 2005. We

believe that total mercury emissions in the U.S. and globally in 2008 were about the same

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magnitude in 2005. Therefore, we estimate that in 2008, these sources emitted about 3% of the

total anthropogenic mercury emissions in the U.S. and about 0.15% of the global emissions.

Overall, this rule would reduce mercury emissions by about 5,500 pounds per year from current

levels, and therefore, contribute to reductions in mercury exposures and health effects. Due to

time and resource limitations, we were unable to model mercury dispersion, deposition,

methylation, bioaccumulation in fish tissue, and human consumption ofinercury-contaminated

fish that would be needed in order to estimate the human health benefits from reducing mercury

emissions.

Potential exposure routes to mercury emissions include both direct inhalation and

consumption offish containing methylmercury. In the U.S., the primary route of human

exposure to mercury emissions from industrial sources is generally indirectly through the

consumption offish containing methylmercury. As described above, mercury that has been

emitted to the air eventually settles into water bodies or onto land where it can either move

directly or be leached into waterbodies. Once deposited, certain microorganisms can change it

into methylmercury, a highly toxic form that builds up in fish, shellfish and animals that eat fish.

Consumption offish and shellfish are the main sources of methylmercury exposure to humans.

Methylmercury builds up more in some types offish and shellfish than in others. The levels of

methylmercury in fish and shellfish vary widely depending on what they eat, how long they live,

and how high they are in the food chain. Most fish, including ocean species and local freshwater

fish, contain some methylmercury. For example, in recent studies by EPA and the U.S.

Geological Survey (USGS) offish tissues, every fish sampled from 291 streams across the

country contained some methylmercury (Scudder, 2009).

The majority of fish consumed in the U.S. are ocean species. The methylmercury

concentrations in ocean fish species are primarily influenced by the global mercury pool.

However, the methylmercury found in local fish can be due, at least partly, to mercury emissions

from local sources. Research shows that most people's fish consumption does not cause a

mercury-related health concern. However, certain people may be at higher risk because of their

routinely high consumption of fish (e.g., tribal and other subsistence fishers and their families

who rely heavily on fish for a substantial part oftheir diet). It has been demonstrated that high

levels of methylmercury in the bloodstream of unborn babies and young children may harm the

developing nervous system, making the child less able to think and learn. Moreover, mercury

exposure at high levels can harm the brain, heart, kidneys, lungs, and immune system of people

of all ages.

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Several studies suggest that the methylmercury content offish may reduce these cardioprotective effects offish consumption. Some of these studies also suggest that methylmercury

may cause adverse effects to the cardiovascular system. For example, the NRC (2000) review of

the literature concerning methylmercury health effects took note of two epidemiological studies

that found an association between dietary exposure to methylmercury and adverse cardiovascular

effects.'Z Moreover, in a study of 1,833 males in Finland aged 42 to 60 years, Solonen et al.

(1995) observed a relationship between methylmercury exposure via fish consumption and acute

myocardial infarction (AMI or heart attacks), coronary heart disease, cardiovascular disease, and

all-cause mortality.13 The NRC also noted a study of 917 seven year old children in the Faroe

Islands, whose initial exposure to methylmercury was in utero although post natal exposures may

have occurred as well. At seven years of age, these children exhibited an increase in blood

pressure and a decrease in heart rate variability.14 Based on these and other studies, NRC

concluded in 2000 that, while "the data base is not as extensive for cardiovascular effects as it is

for other end points (i.e. neurologic effects) the cardiovascular system appears to be a target for

methylmercury toxicity."15

Since publication ofthe NRC report there have been some 30 published papers

presenting the findings of studies that have examined the possible cardiovascular effects of

methylmercury exposure. These studies include epidemiological, toxicological, and toxicokinetic

investigations. Over a dozen review papers have also been published. If there is a causal

relationship between methylmercury exposure and adverse cardiovascular effects, then reducing

exposure to methylmercury would result in public health benefits from reduced cardiovascular

effects.

In early 2010, EPA sponsored a workshop in which a group of experts were.asked to

assess the plausibility of a causal relationship between methylmercury exposure and

cardiovascular health effects and to advise EPA on methodologies for estimating population

'Z National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Committee on the

Toxicological Effects of Methylmercury, Board on Environmental Studies and Toxicology. National Academies

Press. Washington, DC. pp. 168-173.

13Salonen, J.T., Seppanen, K. Nyyssonen et al. 1995. "Intake of mercury from fish lipid peroxidarion, and the risk of

myocardial infarction and coronary, cardiovascular and any death in Eastern Finnish men." Circulation, 91

(3):645-655.

14Sorensen, N, K. Murata, E. Budtz-Jorgensen, P. Weihe, and Grandjean, P., 1999. "Prenatal Methylmercury

Exposure As A Cardiovascular Risk Factor At Seven Years of Age", Epidemiology, pp370-375.

'sNational Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Committee on the

Toxicological Effects of Methylmercury, Board on Environmental Studies and Toxicology. National Academies

Press. Washington, DC. p. 229.

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level cardiovascular health impacts of reduced methylmercury exposure. The report from that

workshop is in preparation.

5.4.3.2 Cadmium

Breathing air with very high levels of cadmium can severely damage the lungs and may

cause death. In the United States, where proper industrial hygiene is generally practiced, inhaling

very high levels of cadmium at work is expected to be rare and accidental. Breathing air with

lower levels of cadmium over long periods of time (for years) results in a build-up of cadmium

in the kidney, and if sufficiently high, may result in kidney disease. Lung cancer has been found

in some studies of workers exposed to cadmium in the air and studies of rats that breathed in

cadmium. The U.S. Department of Health and Human Services (DHHS) has determined that

cadmium and cadmium compounds are known human carcinogens. The International Agency for

Research on Cancer (IARC) has determined that cadmium is carcinogenic to humans. The EPA

has determined that cadmium is a probable human carcinogen.16

5.4.3.3 Lead

The main target for lead toxicity is the nervous system, both in adults and children. Longterm exposure of adults to lead at work has resulted in decreased performance in some tests that

measure functions of the nervous system. Lead exposure may also cause weakness in fingers,

wrists, or ankles. Lead exposure also causes small increases in blood pressure, particularly in

middle-aged and older people. Lead exposure may also cause anemia. At high levels of

exposure, lead can severely damage the brain and kidneys in adults or children and ultimately

cause death. In pregnant women, high levels of exposure to lead may cause miscarriage. Highlevel exposure in men can damage the organs responsible for sperm production.

We have no conclusive proof that lead causes cancer (is carcinogenic) in humans. Kidney

tumors have developed in rats and mice that had been given large doses of some kind of lead

compounds. The Department of Health and Human Services (DHHS) has determined that lead

and lead compounds are reasonably anticipated to be human carcinogens based on limited

evidence from studies in humans and sufficient evidence from animal studies, and the EPA has

determined that lead is a probable human carcinogen. The International Agency for Research on

Cancer (IARC) has determined that inorganic lead is probably carcinogenic to humans. IARC

determined that organic lead compounds are not classifiable as to their carcinogenicity in

humans based on inadequate evidence from studies in humans and in animals.

16 Agency for Toxic Substances and Disease Registry (ATSDR). 2008. Public Health Statement for Cadmium.

CAS# 1306-19-0. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service.

Available on the Internet at <httn://www.atsdr.cdc.gov/PHS/PHS.asp?id=46&tid=15>.

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Children are more sensitive to the health effects of lead than adults. No safe blood lead

level in children has been determined. Lead affects children in different ways depending on how

much lead a child swallows. A child who swallows large amounts of lead may develop anemia,

kidney damage, colic (severe "stomach ache"), muscle weakness, and brain damage, which

ultimately can kill the child. In some cases, the amount of lead in the child's body can be

lowered by giving the child certain drugs that help eliminate lead from the body. If a child

swallows smaller amounts of lead, such as dust containing lead from paint, much less severe but

still important effects on blood, development, and behavior may occur. In this case, recovery is

likely once the child is removed from the source of lead exposure, but there is no guarantee that

the child will completely avoid all long-term consequences of lead exposure. At still lower levels

of exposure, lead can affect a child's mental and physical growth. Fetuses exposed to lead in the

womb, because their mothers had a lot of lead in their bodies, may be born prematurely and have

lower weights at birth. Exposure in the womb, in infancy, or in early childhood also may slow

mental development and cause lower intelligence later in childhood. There is evidence that these

effects may persist beyond childhood."

5.4.3.4 Hydrogen Chloride (HCl)

Hydrogen chloride gas is intensely irritating to the mucous membranes ofthe nose,

throat, and respiratory tract. Brief exposure to 35 ppm causes throat irritation, and levels of 50 to

100 ppm are barely tolerable for 1 hour. The greatest impact is on the upper respiratory tract;

exposure to high concentrations can rapidly lead to swelling and spasm of the throat and

suffocation. Most seriously exposed persons have immediate onset of rapid breathing, blue

coloring of the skin, and narrowing of the bronchioles. Patients who have massive exposures

may develop an accumulation of fluid in the lungs. Exposure to hydrogen chloride can lead to

Reactive Airway Dysfunction Syndrome (BADS), a chemically- or irritant-induced type of

asthma. Children may be more vulnerable to corrosive agents than adults because of the

relatively smaller diameter of their airways. Children may also be more vulnerable to gas

exposure because of increased minute ventilation per kg and failure to evacuate an area promptly

when exposed. Hydrogen chloride has not been classified for carcinogenic effects.18

"Agency for Toxic Substances and Disease Registry (ATSDR). 2007. Public Health Statement for Lead. CAS#:

7439-92-1. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. Available on

the Internet at < http://www.atsdr.cdc.gov/ToxProfiles/phsl3.htm1>.

'$ Agency for Toxic Substances and Disease Registry (ATSDR). Medical Management Guidelines for Hydrogen

Chloride (HCl). CAS#: 7647-01-0. Atlanta, GA: U.S. Department of Health and Human Services, Public Health

Service. Available on the Internet at <htt~//www.atsdr.cdc.gov/Mhmi/mmg173.html>.

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5.4.3.5 Dioxins (Chlorinated dibenzodioxins (CDDs)

A number of effects have been observed in people exposed to 2,3,7,8-TCDD levels that

are at least 10 times higher than background levels. The most obvious health effect in people

exposure to relatively large amounts of 2,3,7,8-TODD is chloracne. Chloracne is a severe skin

disease with acne-like lesions that occur mainly on the face and upper body. Other skin effects

noted in people exposed to high doses of 2,3,7,8-TCDD include skin rashes, discoloration, and

excessive body hair. Changes in blood and urine that may indicate liver damage also are seen in

people. Alterations in the ability ofthe liver to metabolize (or breakdown) hemoglobin, lipids,

sugar, and protein have been reported in people exposed to relatively high concentrations of

2,3,7,8-TCDD. Most ofthe effects are considered mild and were reversible. However, in some

people these effects may last for many years. Slight increases in the risk of diabetes and

abnormal glucose tolerance have been observed in some studies of people exposed to 2,3,7,8-

TCDD. We do not have enough information to know if exposure to 2,3,7,8-TCDD would result

in reproductive or developmental effects in people, but animal studies suggest that this is a

potential health concern.

In certain animal species, 2,3,7,8-TCDD is especially harmful and can cause death after a

single exposure. Exposure to lower levels can cause a variety of effects in animals, such as

weight loss, liver damage, and disruption of the endocrine system. In many species of animals,

2,3,7,8-TCDD weakens the immune system and causes a decrease in the system's ability to fight

bacteria and viruses at relatively low levels (approximately 10 times higher than human

background body burdens). In other animal studies, exposure to 2,3,7,8-TCDD has caused

reproductive damage and birth defects. Some animal species exposed to CDDs during pregnancy

had miscarriages and the offspring of animals exposed to 2,3,7,8-TCDD during pregnancy often

had severe birth defects including skeletal deformities, kidney defects, and weakened immune

responses. In some studies, effects were observed at body burdens 10 times higher than human

background levels.19

5.4.3.6 Furans (Chlorinated dibenzofurans (CDFs))

Most of the information on the adverse health effects comes from studies in people who

were accidentally exposed to food contaminated with CDFs. The amounts that these people were

exposed to were much higher than are likely from environmental exposures or from a normal

diet. Skin and eye irritations, especially severe acne, darkened skin color, and swollen eyelids

19 Agency for Toxic Substances and Disease Registry (ATSDR). 1999. ToxFAQs for Chlorinated Dibenzo-p-dioxins

(CDDs) (CAS#: 2,3,7,8-TCDD 1746-01-6). Atlanta, GA: U.S. Department of Health and Human Services, Public

Health Service. Available on the Internet at http://www.atsdr.cdc.~ov/tfacts104.html.

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with discharge, were the most obvious health effects of the CDF poisoning. CDF poisoning also

caused vomiting and diarrhea, anemia, more frequent lung infections, numbness, effects on the

nervous system, and mild changes in the liver. Children born to exposed mothers had skin

irritation and more difficulty learning, but it is unknown if this effect was permanent or caused

by CDFs alone or CDFs and polychlorinated biphenyls in combination.

Many of the same effects that occurred in people accidentally exposed also occurred in

laboratory animals that ate CDFs. Animals also had severe weight loss, and their stomachs,

livers, kidneys, and immune systems were seriously injured. Some animals had birth defects and

testicular damage, and in severe cases, some animals died. These effects in animals were seen

when they were fed large amounts of CDFs over a short time, or small amounts over several

weeks or months. Nothing is known about the possible health effects in animals from eating

CDFs over a lifetime.20

5.5 Characterization of Uncertainty in the Monetized PM2.5 Benefits

In any complex analysis, there are likely to be many sources of uncertainty. Many inputs

are used to derive the final estimate of economic benefits, including emission inventories, air

quality models (with their associated parameters and inputs), epidemiological estimates of

concentration-response (C-R) functions, estimates of values, population estimates, income

estimates, and estimates of the future state of the world (i.e., regulations, technology, and human

behavior). For some parameters or inputs it may be possible to provide a statistical representation

of the underlying uncertainty distribution. For other parameters or inputs, the necessary

information is not available.

The annual benefit estimates presented in this analysis are also inherently variable due to

the processes that govern pollutant emissions and ambient air quality in a given year. Factors

such as hours of equipment use and weather are constantly variable, regardless of our ability to

measure them accurately. As discussed in the PM2,5 NAAQS RIA (Table 5-5) (U.S. EPA, 2006),

there are a variety of uncertainties associated with these PM benefits. Therefore, the estimates of

annual benefits should be viewed as representative of the magnitude of benefits expected, rather

than the actual benefits that would occur every year.

It is important to note that the monetized benefit-per-ton estimates used here reflect

specific geographic patterns of emissions reductions and specific air quality and benefits

20 Agency for Toxic Substances and Disease Regishy (ATSDR). 1995. ToxFAQsiM for Chlorodibenzofurans

(CDFs). Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. Available on the

Internet at <httn://www.atsdr.cdc.EOV/tfacts32.htm1>.

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modeling assumptions. For example, these estimates do not reflect local variability in population

density, meteorology, exposure, baseline health incidence rates, or other local factors. Use of

these $/ton values to estimate benefits associated with different emission control programs (e.g.,

for reducing emissions from large stationary sources like EGUs) may lead to higher or lower

benefit estimates than if benefits were calculated based on direct air quality modeling. Great care

should be taken in applying these estimates to emission reductions occurring in any specific

location, as these are all based on national or broad regional emission reduction programs and

therefore represent average benefits-per-ton over the entire United States. The benefits-per-ton

for emission reductions in specific locations may be very different than the estimates presented

here.

PM2.5 mortality benefits are the largest benefit category that we monetized in this

analysis. To better characterize the uncertainty associated with mortality impacts that are

estimated to occur in areas with low baseline levels of PM2.5, we included the LML assessment.

Without policy-specific air quality modeling, we are unable to quantify the shift in exposure

associated with this specific rule. For this rule, as a surrogate measure of mortality impacts, we

provide the percentage of the population exposed at each PMZ.S level using the most recent

modeling available from the recently proposed Transport Rule (U.S. EPA, 2010e). A very large

proportion of the population is exposed at or above the lowest LML of the cohort studies

(Figures 5-5 and 5-6), increasing our confidence in the PM mortality analysis. Figure 5-5 shows

a bar chart of the percentage of the population exposed to various air quality levels in the preand post-policy policy. Figure 5-6 shows a cumulative distribution function of the same data.

Both figures identify the LML for each of the major cohort studies. As the policy shifts the

distribution of air quality levels, fewer people are exposed to PMZ.S levels at or above the LML.

Using the Pope et al. (2002) study, the 85% of the population is exposed to annual mean PM2.5

levels at or above the LML of 7.5 μg/m3. Using the Laden et al. (2006) study, 40% of the

population is exposed above the LML of 10 μg/m3. As we model mortality impacts among

populations exposed to levels of PM2.5 that are successively lower than the LML of the lowest

cohort study, our confidence in the results diminishes. However, the analysis above confirms that

the great majority of the impacts occur at or above the lowest cohort study's LMI.,. It is important

to emphasize that we have high confidence in PM2.5-related effects down to the lowest LML of

the major cohort studies. Just because we have greater confidence in the benefits above the LML,

this does not mean that we have no confidence that benefits occur below the LML.

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zsr

Pope ex al. 2002 Laden et al. 2006

20%

15/

10%

5%

0%

The control svategy lowers PMZ.S

levels substantially, parcicularly

among highly exposed

populations. In the baseline, 85%

of the population lived in areas

where PM2,5 levels above the

lowest measured levels of the

Pope study, increasing our

confidence in the estimated

mortality reductions for this rule.

1 2 3 4 5 5.8 6 7 7.5 

■ 

8 9 10 11 12 13 14 15 16 17 18 19 20

Post-control ■Baseline

Figure 5-5. Percentage of Adult Population by Annual Mean PM2,5 Exposure

(pre- and post-policy policy)

iouss -

Pope etal, 2002 Laden et al, 2006

y0°'o

SO°o

7096

~~~;

50%

4o°n

30°0

24'n

10?6

0°'

The control strategy lowers PM25 levels

substantially, particularly am o ng h igh ly

exposed populations, In the baseline, 89% of

the population lived in areas where PM25

levels above the lowest measured levels of

the Pope study, increasing our confidence in

the estimated mortality reductions forthis

rule.

------ --

1 2 3 4 5 5.~ 6 7 7.5 ~ 9 1Q 11 12 13 14 15 1r, 1' 1S 15 20

—Port-control —Baselin?

Figure 5-6. Cumulative Distribution of Adult Population at Annual Mean PMZ,S levels

(pre- and post-policy policy)

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Above we present the estimates of the total monetized benefits, based on our

interpretation of the best available scientific literature and methods and supported by the SABHES and the NAS (NRC, 2002). The benefits estimates are subject to a number of assumptions

and uncertainties. For example, for key assumptions underlying the estimates for premature

mortality, which typically account for at least 90% of the total monetized benefits, we were able

to quantify include the following:

1. PM2.5 benefits were derived through benefit per-ton estimates, which do not reflect

local variability in population density, meteorology, exposure, baseline health

incidence rates, or other local factors that might lead to an over-estimate or underestimate of the actual benefits of controlling directly emitted fine particulates.

2. We assume that all fine particles, regardless of their chemical composition, are

equally potent in causing premature mortality. This is an important assumption,

because PM2.5 produced via transported precursors emitted from EGUs may differ

significantly from direct PM2,5 released from diesel engines and other industrial

sources, but no clear scientific grounds exist for supporting differential effects

estimates by particle type.

3. We assume that the health impact function for fine particles is linear down to the

lowest air quality levels modeled in this analysis. Thus, the estimates include health

benefits from reducing fine particles in areas with varied concentrations of PM2.5,

including both regions that are in attainment with fine particle standard and those that

do not meet the standard down to the lowest modeled concentrations.

4. To characterize the uncertainty in the relationship between PM2.5 and premature

mortality (which typically accounts for 85% to 95% of total monetized benefits), we

include a set of twelve estimates based on results of the expert elicitation study in

addition to our core estimates. Even these multiple characterizations omit the

uncertainty in air quality estimates, baseline incidence rates, populations exposed and

transferability ofthe effect estimate to diverse locations. As a result, the reported

confidence intervals and range of estimates give an incomplete picture about the

overall uncertainty in the PM2.5 estimates. This information should be interpreted

within the context of the larger uncertainty surrounding the entire analysis. For more

information on the uncertainties associated with PM2.5 benefits, please consult the

PM2.5 NAAQS RIA (Table 5-5).

This RIA does not include the type of detailed uncertainty assessment found in the PM

NAAQS RIA because we lack the necessary air quality input and monitoring data to run the

benefits model. In addition, we have not conducted any air quality modeling for this rule.

Moreover, it was not possible to develop benefit-per-ton metrics and associated estimates of

uncertainty using the benefits estimates from the PM RIA because of the significant differences

between the sources affected in that rule and those regulated here. However, the results of the

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Monte Carlo analyses of the health and welfare benefits presented in Chapter 5 of the PM RIA

can provide some evidence ofthe uncertainty surrounding the benefits results presented in this

analysis.

5.6 Comparison of Benefits and Costs

Using a 3%discount rate, we estimate the total monetized benefits ofthe proposed SSI

NSPS and EG including energy disbenefits to be $130 million to $320 million in the

implementation year (2015). Using a 7% discount rate, we estimate the total monetized benefits

ofthe SSI NSPS and EG including energy disbenefits to be $120 million to $290 million. The

annualized costs are $92 million at a 7% interest rate.21 Thus, net benefits are $37 million to $220

million at a 3%discount rate for the benefits and $26 million to $190 million at a 7% discount

rate. All estimates are in 2008$.

Table 5-9 shows a summary of the monetized co-benefits, social costs, and net benefits

for the SSI NSPS and EG, respectively. Figures 5-7 and 5-8 show the full range of net benefits

estimates (i.e., annual co-benefits minus annualized costs) utilizing the 14 different PM2,5

mortality functions at discount rates of 3%and 7%. In addition, the benefits from reducing 2,900

tons of carbon monoxide, 96 tons of HCI, 3.0 tons of lead, 1.6 tons of cadmium, 5,500 pounds of

mercury, and 90 grams oftotal dioxins/furans each year have not been included in these

estimates.

21 For more information on the annualized costs, please refer to Section 4 of this RIA. There are no estimates of

costs available at a 3%discount rate.

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S3so

S3oo

Szso

~.

Szoo

00

Slso

0

~ $100

$50

$0

-$50

Cost estimate combined with total monetized benefits estimates derived from 2

epidemiology functions and 12 expert functions

Figure 5-7. Net Benefits for the Proposed SSI NSPS and EG at 3% Discount Rate a

a Net Benefits are quantified in terms of PM2.5 benefits for implementation year (2015). This graph shows 14 benefits

estimates combined with the cost estimate. All combinations are treated as independent and equally probable. All

fine particles are assumed to have equivalent health effects, but the benefit per ton estimates vary because each ton

of precursor reduced has a different propensity to become PM2 5. The monetized benefits incorporate the

conversion from precursor emissions to ambient fine particles. These estimates include energy disbenefits valued at

$0.5 million at a 3% discount rate for COz emissions.

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$300

$250

$200

o $150

0

N

C

O ~1~~

Sso P

So

_...

-Sso --

Cost estimate combined with total monetized benefits estimates derived from 2

epidemiology functions and 12 expert functions

Figure 5-8. Net Benefits for the Proposed SSI NSPS and EG at 7% Discount Rate a

aNet Benefits are quantified in terms of PM2.5 benefits for implementation year (2015). This gaph shows 14 benefits

estimates combined with the cost estimate. All combinations are treated as independent and equally probable. All

fine particles are assumed to have equivalent health effects, but the benefit per ton estimates vary because each ton

of precursor reduced has a different propensity to become PM2 5. The monetized benefits incorporate the

conversion from precursor emissions to ambient fine particles. These estimates include energy disbenefits valued at

$0.5 million at a 3°/o discount rate for COZ emissions.

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