# A PPENDICES

Appendix A - List of Supplem ental Docum e nts

........................................... A

- 1
Appendix B - Industrial Categories with Pretreatm e nt Standards

............................. B

- 1

A p p e n d i x C - P o l l u t a n t s R e g u l a t e d b y C a t e g o r i c a l P r e t r e a t m e n t S t a n d a r d s

..................... C

- 1
Appendix D - Clean Water Act Priority Polluta nts and the Federal Water Quality Criteria

......... D

- 1
Appendix E - Federal Se wage Sludge Standards

.......................................... E

- 1
Appendix F - Toxicity Characteristic Leachate Procedure Lim itations

......................... F

- 1

A p p e n d i x G - L i t e r a t u r e I n h i b i t i o n V a l u e s

............................................... G

- 1
Appendix H - Closed-cup Flashpoints for Select Organic Com pounds

......................... H

- 1
Appendix I - Discharge Screening Levels a nd Henry ’ s Law Constants for Organic Com pounds

..... I

- 1
Appendix J - OSHA, ACGIH and NIOSH Exposure Levels

................................. J

- 1
Appendix K - Landfill Leachate Loadings

............................................... K

- 1

A p p e n d i x L - H a u l e d W a s t e L o a d i n g s

.................................................. L

- 1
Appendix M - Hazardous Waste C onstituents - RCRA Appendix VIII

........................ M

- 1

A p p e n d i x N - S t a t i s t i c a l A p p r o a c h t o D e t e r m i n i n g S a m p l i n g F r e q u e n c y

....................... N

- 1

A p p e n d i x O - M i n i m i z i n g C o n t a m i n a t i o n i n S a m p l e s

...................................... O

- 1
Appendix P - Methods for Calculating Rem oval Efficiency

................................. P

- 1
Appendix Q - Methods for Handli ng Data Below Detection Level

............................ Q

- 1

A p p e n d i x R - P r i o r i t y P o l l u t a n t R e m o v a l E f f i c i e n c i e s

...................................... R

- 1
Appendix S - Specific Gravity of Sludge

................................................ S

- 1
Appendix T - Sludge AHL Equations Using Flow (in m e tric units)

........................... T

- 1

A p p e n d i x U - P O T W C o n f i g u r a t i o n s

................................................... U

- 1

A p p e n d i x V - D o m e s t i c P o l l u t a n t L o a d i n g s

.............................................. V

- 1
Appendix W - Best Managem e nt Practices Mini-Case Studies

.............................. W

- 1
Appendix X - Region 1, Reassessm ent of Technically Based Industrial Discharge Lim its Checklist

. . X -1



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## ii-- -- --

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# A PPENDIX 

# A

- L IST OF 

# S UPPLEMENTAL 

# D OCUMENTS

GENERA L GUIDA NCE ON PRETREA T MENT

TITLE DA TE EPA Number NTIS Number ERIC Number CERCLA Site Discharges to POT W s Guidance Manual August 1990 540-G-90-005 PB90-274531 W 150

Control Authority Pr etreatment Audit Checklist and Instructions May 1992

Control of Slug Loadings T o POT W s: Guidance Manual F ebruary 1991 21W -4001

Environmental Regulations and T e chnology : T he National Pretreatment Program July 1986 625-10-86-005 PB90-246521 W 350

Guidance for Conducting a Pretreatment Compliance Inspection September 1991 300-R-92-009 PB94-120631 W 273

Guidance F o r Developing Control Authority Enforcement Response Plans September 1989 PB90- 185083/AS Guidance for Reporting and Evaluating POT W Noncompliance w i th Pretreatment Implementation Requirements September 1987 PB95-157764 W 304

Guidance Manual for POT W Pretreatment Program Development Guidance Manual for POT W s to Calculate the Economic Benefit of Noncompliance Guidance Manual for Preparation and Review of Removal Credit Applications Guidance Manual for Preventing Interference at POT W s Guidance Manual for the Control of W a stes Hauled to Publicly Ow ned T r eatment W o rks Guidance Manual for t he Identification of Hazardous W a stes Delivered to Publicly Ow ned T r eatment W o rks by T r uck, Rail, or Dedicated PipeGuidance Manual for the Use of ProductionBased Pretreatment Standards and the Combined W a stestream F o rmula Guidance Manual on the Development and Implementation of Local Discharge Limitations Under the Pretreatment Program Guidance on Evaluation, Resolution, and Documentation of Analy t ical Problems Associated w i th Compliance Monitoring Guidance on the Privat ization of F ederally F unded W a stew ater T r eatment W o rks Guidance to Protect POT W W o rkers F r om T o xic And Reactive Gases And Vapors October 1983 PB93-186112 W 639

September 1990 833-B-93-007

July 1985 833-B-85-200

September 1987 833-B-87-201 PB92-117969 W 106

September 1999 833-B-98-003

June 1987 PB92-149251 W 202

September 1985 833-B-85-201 PB92-232024 U095

December 1987 833-B-87-202 PB92-129188 W 107

June 1993 821-B-93-001

August 2000 832-B-00-002

June 1992 812-B-92-001 PB92-173236 W 115



### A-1-- --

-- --

-- --

-- --

-- --

-- -- --

-- --

-- -- --

-- -- GENERA L GUIDA NCE ON PRETREA T MENT

TITLE DA TE EPA Number NTIS Number ERIC Number Guides to Pollution Prevention: Municipal Pretreatment Programs Industrial User Inspection and Sampling Manual F o r POT W s Industrial User Permitting Guidance Manual

Metals T r anslator: Guidance for Calculating a T o tal Recoverable Permit Limit from a Dissolved Criterion Model Pretreatment Ordinanc e Multijurisdictional Pr etreatment Programs: Guidance Manual National Pretreatment Program: Report to Congress NPDES Compliance Inspection Manual

Pollution Prevention (P2) Guidance Manual for the Pesticide F o rmulating, Packaging, and Repackaging Industry : Implementing the P2 Alternative POT W Sludge Sampling and Analy s is Guidance Document Prelim User’s Guide, Documentation for the EPA Computer Program/Model for Developing Local Limits for Industrial Pretreatment Programs at Publicly Ow ned T r eatment W o rks, Version 5.0 Pretreatment Compliance Inspection and Audit Manual F o r Approval Authorities Pretreatment Compliance Monitoring and Enforcement Guidance and Softw are (Version 3.0)

Procedures Manual for Review ing a POT W Pretreatment Program Submission Procuring Analy t ical Services: Guidance for Industrial Pretr eatment Programs Region III Guidance for Setting Local Limits for a Pollutant W here the Domestic Loading Exceeds the Maximum Allow able Headw orks Loading Protecting the Nation' s W a ters T h rough Effective NPDES Permits: A Strategic Plan F Y 2001 and Bey ond RCRA Information on Hazardous W a stes for Publicly Ow ned T r eatment W o rks Report to Congress on the Discharge of Hazardous W a stes to Publicly Ow ned T r eatment W o rks October 1993 625-R-93-006

April 1994 831-B-94-001 PB94-170271 W 305

September 1989 833-B-89-001 PB92-123017 W 109

June 1996 823-B-96-007

J une 1992 833-B-92-003 PB93-122414 W 108 June 1994 833-B-94-005 PB94-203544 W 607

July 1991 21-W

- 4004 PB91-228726 W 694

September 1994 300-B-94-014

June 1998 821-B-98-017

August 1989 833-B-89-100

January 1997

July 1986 833-B-86-100 PB90-183625 W 277

(Manual) September 1986 (Softw are) September 1992 (Softw are) 831-F

- 92-001 (Softw are) PB94-118577 (Softw are) W 269

October 1983 833-B-83-200 PB93-209880 W 137

October 1998 833-B-98-004

June 1994

June 2001 833-R-01-001

September 1985 833-B-85-202 PB92-114396 W 351

F ebruary 1986 530-SW -86-004 PB86-184017 & PB95-157228 W 922 & W 692



### A-2--

--

-- --

-- -- GENERA L GUIDA NCE ON PRETREA T MENT

TITLE DA TE EPA Number NTIS Number ERIC Number Supplemental Manual On the Development And Implementation of Local Discharge Limitations Under T he Pretreatment Program May 1991 21W -4002 PB93-209872 W 113



### Source: Updated, originally part of U.S. EPA’s Introduction to the National Pretreatment Program , EPA-833-B-98-002, February 1999, pp. 51-52

GUIDA NCE ON INDUSTRY PRETREA T MENT STA NDA RDS

TITLE DA TE EPA Number NTIS Number ERIC Number Aluminum, Copper, And Nonferrous Metals F o rming And Metal Pow ders Pretreatment Standards: A Guidance Manual December 1989 800-B-89-001 PB91-145441 W 119

Guidance Manual F o r Battery Manufacturing Pretreatment Standards August 1987 440-1-87-014 PB92-117951 W 195

Guidance Manual for Electroplating and Metal F i nishing Pretreatment Standard F ebruary 1984 440-1-84-091-G PB87-192597 W 118

Guidance Manual F o r Implementing T o tal T o xic Organics (T T O ) Pretreatment Standards September 1985 440-1-85-009-T PB93-167005 W 339

Guidance Manual F o r Iron And Steel Manufacturing Pret reatment Standards September 1985 821-B-85-001 PB92-114388 W 103

Guidance Manual for Leather T anning and F i nishing Pretr eatment Standards September 1986 800-R-86-001 PB92-232024 W 117

Guidance Manual for Pulp, Paper, and Paperboard and Builders’ Paper and Board Mills Pretreatment Standards July 1984 PB92-231638 W 196

Guidance Manual for the Use of ProductionBased Pretreatment Standards and the Combined W a stestream F o rmula September 1985 833-B-85-201 PB92-232024 U095

Permit Guidance Document: Pulp, Paper, and Paperboard Manufacturing Point Source Category (40 CF R Section 430) May 2000 821-B-00-003 PB2002- 106590

Permit Guidance Document: T r ansportation Equipment Cleaning Point Source Category (40 CF R 422) March 2001 821-R-01-021

Small Entity Compliance Guide: Centralized W a ste T r eatment Guidelines and Pretreatment St andards (40 CF R 437) June 2001 821-B-01-003



### Source: Updated, originally part of U.S. EPA’s Introduction to the National Pretreatment Program , EPA-833-B-98-002, February 1999, pp. 51-52



### A-3

### This page intentionally left blank.

A-4

# A PPENDIX 

# B

- I NDUSTRIAL 

# C ATEGORIES WITH 

# P RETREATMENT 

# S TANDARDS

Source: U.S. EPA’s Introduction to the National Pretreatment Program , EPA-833-B-98-002, February 1999, Figure 13, p. 14. (Updated) Ca te gory (SIC Codes)* [NA I CS Codes]** 40 CF R Pa rt (Subpa rts ) Ty pe of Standard*** Ov erv i ew o f Pretreatmen t Stan d a rd s

Aluminum F o rming (3353, 3354, 3355, 3357, 3363) [331315, 331316, 331319, 331521] 467(A-F) PSES PSNS Limits are production-based, daily maximums and monthly averages. from certain operations.

Battery Manufacturing (3691, 3692) [335911, 335912] 461(A-G) PSES PSNS Limits are production-based, daily maximums and monthly averages. ed from any process not specifically identified in the regulations. Carbon Black Manufacturing (2895) [325182] 458 (A-D) PSNS L imits are for Oil & Grease only (no limit duration specified).

Centralized W a ste T r eatment (4953) [562211, 562219 ] 437 (A-D) PSES PSNS Limits are concentration-based, daily maximums and monthly averages.
Coil Coating (3411, 3479, 3492) [332431, 332812] 465 (A-D) PSES PSNS Limits are production-based, daily maximums and monthly averages.

Commercial Hazardous W a ste Combustors (4953, 2819, 2869, 3241, 1422, 1429, 1459) [562213, 212312, 325188, 325199, 327310] 444 (A) PSES PSNS Limits are concentration-based daily maximums or maximum monthly averages.

Concentrated Animal F eeding Operations (0211, 0213, 0214, 0241, 0251, 0252, 0253, 0254, 0259, 0272) [112112, 11221, 11241, 11242, 112111, 11212, 11232, 11231, 11233, 11234, 11239, 11292] 412 (B) PSNS Discharge of proce ss w a stew ater is prohibited, except w hen there is an ov erflow resulting from a chronic or catastrophic rainfall event.

Copper F o rming (3351, 3357, 3463) [331421, 331422, 332112] 468 (A) PSES PSNS Limits are production-based, daily maximums and monthly averages.

Electrical and Electronic Components (3671, 3674, 3679) [334411, 334413, 334419] 469 (A-D) PSES PSNS Limits are concentration-based, daily maximums and 30-day averages or monthly averages (varies per subpart and pollutant paramet er). allow ed in lieu of monitoring for certain pollutants
w hen a management plan is approved and implemented. Subpart C prohibits discharges

No discharge is allow

Certification is



### B-1Ca te gory (SIC Codes)* [NA I CS Codes]** 40 CF R Pa rt (Subpa rts ) Ty pe of Standard*** Ov erv i ew o f Pretreatmen t Stan d a rd s

Electroplating (3471, 3672) [332813, 334412] 413 (A-B, D

H) PSES Limits are concentration-based (or alternative massbased equivalents), daily maximums and four consecutive monitoring day s averages. w o sets of limits exist, depending on if facility discharges more or less than 10,000 gallons per day of process
w a stew ater. on is allow ed in lieu of monitoring for certain pollutants w hen a management plan is approved and implemented. Fertilizer Manufacturing (2873, 2874, 2875) [325311, 325312, 325314] 418 (A-G) PSNS L imits may specify zero discharge of w a stew ater pollutants (Subpart A), production-based daily maximums and 30-day averages (Subparts B-E) or concentration-based (Subparts F

- G) w i th no limit duration specified. Glass Manufacturing (3211, 3221, 3296) [327211, 327212, 327993] 426 (H, K

M) PSNS L imits are either conc entration- or production-based, daily maximums and monthly averages.

Grain Mills (2041, 2043, 2044, 2045, 2046, 2047) [311111, 311211, 311212, 311213, 311221, 311230] 406 (A) PSNS Discharge of process w a stew ater is prohibited at a flow rate or mass loading rate w h ich is excessive over any time period during the peak load at a POT W

. Ink Formulating (2893) [325910] 447 (A) PSNS R egulations specif y

no discharge of process
w a stew ater pollutants to the POT W

. Inorganic Chemicals Manufacturing (2812, 2813, 2816, 2819) [325120, 325131, 325181, 325188] 415 (A-BO) PSES PSNS Limits vary for each subpart w i th a majority of the limits concentration-based, daily maximums and 30- day averages, or may specify no discharge of
w a stew ater pollutants. Numerous subparts have no pretreatment standards. Iron and Steel Manufacturing (3312, 3315, 3316, 3317, 3479) [331111, 331210, 331221, 331222, 332812] 420 (A-F , H

J, L, M) PSES PSNS Limits are production-based, daily maximums and 30 day averages, or may specify no discharge of
w a stew ater pollutants.

Leather T anning and F i nishing (3111) [316110] 425 (A-I) PSES PSNS Limits are concentration-based, daily maximums and monthly averages. volume dictates applicabl e pretreatment standards. Metal Finishing (Industry groups: 34, 35, 36, 37, 38) [Industry Subsectors: 332, 333, 334, 336] 433 (A) PSES PSNS Limits are concentration-based, daily maximums and monthly averages. ion is allow ed for certain pollutants w here a m anagement plan is approved and implemented.

Metal Molding and Casting (3321, 3322, 3324, 3325, 3365, 3366, 3369) [331511, 331512, 331513, 331524, 331525, 331528] 464 (A-D) PSES PSNS Limits are primarily production-based, daily maximums and monthly averages. certain processes are pr ohibited (Subparts A-C).

Nonferrous Metals F o rming and Metal Pow ders (3356, 3357, 3363, 3497, 3499) [331491, 331422, 331521, 332117, 332999] 471 (A-J) PSES PSNS Limits are production-based, daily maximums and monthly averages. regulations prohibit the discharge of w a stew ater pollutants. T

Certificati

In certain instances, production

Certificat

Discharges from

In some instances, the



### B-2Ca te gory (SIC Codes)* [NA I CS Codes]** 40 CF R Pa rt (Subpa rts ) Ty pe of Standard*** Ov erv i ew o f Pretreatmen t Stan d a rd s

Nonferrous Metals Manufacturing (2819, 3331, 3334, 3339, 3341) [331311, 331312, 331314, 331411, 331419, 331423, 331492] 421 (B-AE) PSES PSNS Limits are production-based, daily maximums and monthly averages. he majority of the Subparts have both existing and new sour ce limits, w i th others having solely new source requirements. instances, the regulations prohibit the discharge of
w a stew ater pollutants.

Oil and Gas Extraction (1311) [211111] 435 (D) PSES PSNS Regulations specify no discharge of process
w a stew ater (drilling flui eds, deck drainage, etc.) pollutants to the POT W

. Organic Chemicals, Plastics, and Sy nthetic F i bers (2821, 2823, 2824, 2865, 2869) [325211, 325221, 325222, 32511, 325132, 325192, 325188]] 414 (B-H, K) PSES PSNS Limits are mass-based (concentration-based standards multiplied by process flow ), daily maximums and monthly averages. metals and cy anide apply only to metal- or cy anidebearing w a stestreams.

Paint Formulating (2851) [325510] 446 (A) PSNS R egulations specif y

no discharge of process
w a stew ater pollutants to the POT W

. Paving and Roofing Materials (T ars and Asphalt) (2951, 2952, 3996) [324121, 324122, 326192] 443 (A-D) PSNS L imits are for Oil & Grease only (no limit duration specified).

Pesticide Chemicals (2879) [325320] 455 (A, C, E) PSES PSNS Limits are mass-based (concentration-based standards multiplied by process flow ), daily maximums and monthly averages. specifies no discharge of process w a stew ater pollutants but provides for pollution prevention alternatives. process w a stew ater pollutants. Petroleum Refining (2911) [324110] 419 (A-E) PSES PSNS Limits are concentration-based (or mass-based equivalent), daily maximums.

Pharmaceutical Manufacturing (2833, 2834) [325411, 325412] 439 (A-D) PSES PSNS Limits are concentration-based, daily maximums and monthly averages. certify they do not use or generate cy anide in lieu of performing monitoring to demonstrate compliance. Porcelain Enameling (3431, 3469, 3479, 3631, 3632, 3633, 3639) [332116, 332812, 332998, 335221, 335222, 335224, 335228] 466 (A-D) PSES PSNS Limits are concentration-based (or alternative production-based), daily maximums and monthly averages. operations. T

In some

Standards for

Subpart C

Subpart E specifies no discharge of

Subpart A and C facilities may

Subpart B prohibits discharges certain

Pulp, Paper, and Paperboard (2611, 2621, 2631) [322110, 322121, 322122, 322130] 430 (A-G, I

L) PSES PSNS Limits are production-bas ed or concentration-based (or alternative production-based) daily maximums and monthly averages. hes e facilities may certify they do not use certain compounds in lieu of performing monitoring to demonstrate compliance. Facilities subject to Subparts B and E must also implement Best Management Practices as identified. Rubber Manufacturing (2822) [325212] 428 (E-K) PSNS L imits are concentra tion- or production-based, daily maximums and monthly averages. T



### B-3Ca te gory (SIC Codes)* [NA I CS Codes]** 40 CF R Pa rt (Subpa rts ) Ty pe of Standard*** Ov erv i ew o f Pretreatmen t Stan d a rd s

Soap and Detergent Manufacturing (2841) [325611] 417 (O-R) PSNS R egulations spec ify no discharge of process
w a stew ater pollutants to the POT W

.

Steam Electric Pow e r Generating (4911) [221112] 423 PSES PSNS Limits are either concentration-based, daily
max i mums, or “max i mums for any time,” or compliance can be demonstrated through engineering calculations. regulations prohibit the discharge of w a stew ater pollutants. T i mber Products Processing (2421, 2435, 2436, 2491, 2493, 2499) [321114, 321219, 321211, 321212] 429 (F-H) PSES PSNS All PSNS (and PSES for Subpart F) prohibit the discharge of w a stew ater pollutants. Subparts G and H are concentration-based, daily maximums (w ith production-based alternatives).

T r ansportation Equipment Cleaning (4491, 4499, 4741, 7699) [484230, 488320, 488390, 488210] 442 (A-C) PSES PSNS Limits are concentration-based daily maximums. Subpart A and B allow for a pollutant as an alternative to achieving PSES or PSNS. In some instances, the

PSES for



### * SIC = Standard Industrial Classification, 1987 SIC Manual ** NAICS = North Am erican Industry Classification Sy stem , 1997 NAICS Manual. *** PSNS = Pretreatm e nt Standard New Source; PSES = Pretreatm e nt Standard Existing Source

B-4

### C-1 

# A PPENDIX 

# C

- P OLLUTANTS 

# R EGULATED BY 

# C ATEGORICAL 

# P RETREATMENT 

# S TANDARDSAluminum FormingBattery ManufacturingCarbon Black ManufacturingCentralized Waste TreatmentCoil CoatingCopper FormingElectrical and Electronic ComponentsElectroplatingFeedlotsFertilizer ManufacturingGlass ManufacturingGrain MillsInk FormulatingInorganic Chemicals ManufacturingIron and Steel ManufacturingLeather Tanning and FinishingMetal FinishingMetal Molding and CastingNonferrous Metals Form./Metal PowdersNonferrous Metals ManufacturingOil and GasOrganic Chems., Plastics, and Syn.Paint FormulatingPaving and Roofing MaterialsPesticide ChemicalsPetroleum RefiningPharmaceutical ManufacturingPorcelain EnamelingPulp, Paper, and PaperboardRubber ManufacturingSoap and Detergent ManufacturingSteam Electric Power GeneratingTimber Products ProcessingTransportation Equip. CleaningWaste Combustors

Flow Restrictions Onl y X X X X
Ammonia (as N) X X X X X X BO D X COD X X Fluoride X X X X X X Nitrate (as N) X Oil and Grease X X X X X X X X X Oil (mineral) X Organic Nitrogen (as N) X
pH X X X Phenols X X X Phosphorus X X Sulfide X TSS X X 1,1- Dichloroethane X X X X
1,1- Dichloroethy lene X X X X X X
1,1,1- Trichloroethane XXXX X X X X X
1,1,2- Trichloroethane X X X X X
1,1,2,2-Tetrachloroethane X X X
1,2- Dichlorobenzene X X X X X X X
1,2- Dichloroethane X X X X X X X
1,2- Dichloropropane X X X X
1,2-Dipheny lhy drazine X X X X
1,2-transDichloroethy lene X X X XFibers

X

X

X

X

X

X

XAluminum FormingBattery ManufacturingCarbon Black ManufacturingCentralized Waste TreatmentCoil CoatingCopper FormingElectrical and Electronic ComponentsElectroplatingFeedlotsFertilizer ManufacturingGlass ManufacturingGrain MillsInk FormulatingInorganic Chemicals ManufacturingIron and Steel ManufacturingLeather Tanning and FinishingMetal FinishingMetal Molding and CastingNonferrous Metals Form./Metal PowdersNonferrous Metals ManufacturingOil and GasOrganic Chems., Plastics, and Syn.Paint FormulatingPaving and Roofing MaterialsPesticide ChemicalsPetroleum RefiningPharmaceutical ManufacturingPorcelain EnamelingPulp, Paper, and PaperboardRubber ManufacturingSoap and Detergent ManufacturingSteam Electric Power GeneratingTimber Products ProcessingTransportation Equip. CleaningWaste Combustors



### C-21,2,4-Trichlorobenzene X X X X X
1,3-Dichlorobenzene X X X X X
1,3-Dichloropropene X X X X
1,4-Dichlorobenzene X X X X X X
2-Chloroethy l viny l ether (mixed) X X

2-Chloronaphthalene X X
2-Chlorophenol X X X X X 2-Nitrophenol X X X X2,3-Dichloroaniline X
2,3,4,6-Tetrachlorophenol X
2,4- Dichlorophenol X X X X X
2,4-Dimethy l

- phenol X X X
2,4-Dinitrophenol X X2,4-Dinitrotoluene X X X
2,4,5-Trichlorophenol X
2,4,6-Trichlorophenol X X X X X X
2,6-Dinitrotoluene X X X
3,3-Dichlorobenzidine X X
3,4,5-Trichlorocatechol X
3,4,5-Trichloroguaiacol X
3,4,6-Trichlorocatechol X
3,4,6-Trichloroguaiacol X
4-Bromopheny l pheny l ether X XFibers

X

X

X

X X

X

X

X

X

X

X

XAluminum FormingBattery ManufacturingCarbon Black ManufacturingCentralized Waste TreatmentCoil CoatingCopper FormingElectrical and Electronic ComponentsElectroplatingFeedlotsFertilizer ManufacturingGlass ManufacturingGrain MillsInk FormulatingInorganic Chemicals ManufacturingIron and Steel ManufacturingLeather Tanning and FinishingMetal FinishingMetal Molding and CastingNonferrous Metals Form./Metal PowdersNonferrous Metals ManufacturingOil and GasOrganic Chems., Plastics, and Syn.Paint FormulatingPaving and Roofing MaterialsPesticide ChemicalsPetroleum RefiningPharmaceutical ManufacturingPorcelain EnamelingPulp, Paper, and PaperboardRubber ManufacturingSoap and Detergent ManufacturingSteam Electric Power GeneratingTimber Products ProcessingTransportation Equip. CleaningWaste Combustors



### C-3 4-Chloropheny l pheny l ether X X
4-Nitrophenol X X X X X 4,4-DDD X X X 4,4-DDE X X X 4,4-DDT X X4,5,6-Trichloroquaiacol X
4,6-Dinitro-ocresol X X X
Acenaphthene X X X X X Acenaphthy lene X X X X Acetone X Acr o lein X X Acr y lonitr ile X X Aldrin X X Alpha-BHC X XAlphaendosulfan X X

Anthracene X X X X X X X Benzene X X X X X X X Benzidine X X X Benzo (b) fluoranthene X X X X
Benzo (a) anthracene X X X
Benzo (ghi) pery l ene X X X
Benzo (a) py rene X X X X X X Benzo (k) fluoranthene X X X

Beta-BHC X X X Beta-endosulfan X XBis (2-chloroethoxy ) methane X X
Bis (2-chloroisopropy l) ether X X
Bis (2-chloroethy l) ether X X X

Bi s (2-ethy l

hexy l ) phthalate X X X X X X X X

Bromoform X X X XFibers

X

X

X

X

X X

X X X X

X

X X

X

X

X

X

X

X

X

X

XAluminum FormingBattery ManufacturingCarbon Black ManufacturingCentralized Waste TreatmentCoil CoatingCopper FormingElectrical and Electronic ComponentsElectroplatingFeedlotsFertilizer ManufacturingGlass ManufacturingGrain MillsInk FormulatingInorganic Chemicals ManufacturingIron and Steel ManufacturingLeather Tanning and FinishingMetal FinishingMetal Molding and CastingNonferrous Metals Form./Metal PowdersNonferrous Metals ManufacturingOil and GasOrganic Chems., Plastics, and Syn.Paint FormulatingPaving and Roofing MaterialsPesticide ChemicalsPetroleum RefiningPharmaceutical ManufacturingPorcelain EnamelingPulp, Paper, and PaperboardRubber ManufacturingSoap and Detergent ManufacturingSteam Electric Power GeneratingTimber Products ProcessingTransportation Equip. CleaningWaste Combustors



### C-4 Buty l benzy l phthalate X X X X X

Carbon tetrachloride X X X X X X

Carbazole X Chlordane (tech. mix

. & metabolites) X X

Chlorobenzene X X X X X X XChlorodibromomethane X X X

Chloroethane X X X C h l o r o f o r m XXXX X X X X X X X Chry sene X X X X Cresol X Delta-BHC X X X Di -n-buty l phthalate X X X X X X X

Di -n-oc t y l phthalate X X
Dibenzo (a,h) anthracene X X X

Dichlorobromomethane X X X X X

Dieldrin X X Diethy l phthalate X X X X X X Diethy lamine X Dimethy l phthalate X X X X

Endosulfan sulfate X X X

Endrin X X X Endrin aldehy de X X X X Ethy l acetate X Ethy lbenzene X X X X X X X Fluoranthene X X X X X X X Fluorene X X X X X Gamma-BHC X X X Heptachlor epoxide X X
Heptachlor X XFibers

X

X

X

X

X

X

X

X

X

X

X

X X X

X

XAluminum FormingBattery ManufacturingCarbon Black ManufacturingCentralized Waste TreatmentCoil CoatingCopper FormingElectrical and Electronic ComponentsElectroplatingFeedlotsFertilizer ManufacturingGlass ManufacturingGrain MillsInk FormulatingInorganic Chemicals ManufacturingIron and Steel ManufacturingLeather Tanning and FinishingMetal FinishingMetal Molding and CastingNonferrous Metals Form./Metal PowdersNonferrous Metals ManufacturingOil and GasOrganic Chems., Plastics, and Syn.Paint FormulatingPaving and Roofing MaterialsPesticide ChemicalsPetroleum RefiningPharmaceutical ManufacturingPorcelain EnamelingPulp, Paper, and PaperboardRubber ManufacturingSoap and Detergent ManufacturingSteam Electric Power GeneratingTimber Products ProcessingTransportation Equip. CleaningWaste Combustors



### C-5Hexachlorobenzene X X X X

Hexachlorobutadiene X X

Hex a chlor o cy clo pentadiene X X X

Hexachloroethane X X X
Indeno (1,2,3- cd)py r ene X X X
Isobuty l aldehy de X Isophorone X X X X Isopropy l acetate X Isopropy l ether X Methy l formate X Methy l bromide X X X X Methy l cellosolve X Methy l Isobuty l Ketone X
Methy l chloride X X X X X Methy l ene chlor i de XXXX X X X X X X
n-Amy l acetate X n-Buty l acetate X n-Decane X n-Heptane X n-Hexane XN-nitrosodi-npropy lamine X X X X

N-nitrosodimethy l amine X X X

N-nitrosodipheny lamine X X X X X

n-Octadecane X Naphthalene X X X X X X X X X Nitrobenzene X X X Non-polar materi al (SGTHEM) X

Parac h l o rometacresol X X X X

PCB–1016 X X X XFibers

X

X

X

X

X

X

X

X

X

X X

XAluminum FormingBattery ManufacturingCarbon Black ManufacturingCentralized Waste TreatmentCoil CoatingCopper FormingElectrical and Electronic ComponentsElectroplatingFeedlotsFertilizer ManufacturingGlass ManufacturingGrain MillsInk FormulatingInorganic Chemicals ManufacturingIron and Steel ManufacturingLeather Tanning and FinishingMetal FinishingMetal Molding and CastingNonferrous Metals Form./Metal PowdersNonferrous Metals ManufacturingOil and GasOrganic Chems., Plastics, and Syn.Paint FormulatingPaving and Roofing MaterialsPesticide ChemicalsPetroleum RefiningPharmaceutical ManufacturingPorcelain EnamelingPulp, Paper, and PaperboardRubber ManufacturingSoap and Detergent ManufacturingSteam Electric Power GeneratingTimber Products ProcessingTransportation Equip. CleaningWaste Combustors



### C-6 PCB–1221 X X X PCB–1232 X X X PCB–1242 X X X X PCB–1248 X X X X PCB–1254 X X X PCB–1260 X X XPentachlorophenol X X X X X

Phenanthrene X X X X X X X X Phenol X X X X X Py rene X X X X X TCDF XTetrachlorocatechol X

Tetrachloroethy lene X X X X X X X X X

Tetrachloroguaiacol X
Tetrahy drofuran X Toluene XXXX X X X X X X Toxaphene X XTrichloroethy lene X XXX X X X X
Trichlorosy r ingol X T r iethy l amine X Viny l chlor i de X X X X Xy lenes X 2,3,7,8-tetrachlorodibenzo-p-dioxin X X X

Organic Pesticide Active Ingredients X

Antimony X X X X X X Arsenic X X X X X X Asbestos X Barium X Ber y llium X Cadmium X X X X X X X X X X C h r o m i u m , T o t a l X X XXXXX XXXX X X X X X X X X XFibers

X X

X X

X

X X X

X

X X

X

X

X XAluminum FormingBattery ManufacturingCarbon Black ManufacturingCentralized Waste TreatmentCoil CoatingCopper FormingElectrical and Electronic ComponentsElectroplatingFeedlotsFertilizer ManufacturingGlass ManufacturingGrain MillsInk FormulatingInorganic Chemicals ManufacturingIron and Steel ManufacturingLeather Tanning and FinishingMetal FinishingMetal Molding and CastingNonferrous Metals Form./Metal PowdersNonferrous Metals ManufacturingOil and GasOrganic Chems., Plastics, and Syn.Paint FormulatingPaving and Roofing MaterialsPesticide ChemicalsPetroleum RefiningPharmaceutical ManufacturingPorcelain EnamelingPulp, Paper, and PaperboardRubber ManufacturingSoap and Detergent ManufacturingSteam Electric Power GeneratingTimber Products ProcessingTransportation Equip. CleaningWaste Combustors



### C-7 Chromium, Hexavalent X X

Cobalt X X X X Copper X X X X X X X X X X X X X Cy anide, Total X X X X X X X X X X X X X X Cy anide, Amenable X

Gol d X Indium X Iron X X Lead X X X X X X X X X X X X X X X X X X Manganese X X Mercury X X X X X X X Moly bdenum X X X N i c k e l X X X X X X X Palladium X Platinum X Selenium X X X S i l v e r X X X X X X X Tantalum X T hallium X Ti n X X Titanium X X X Tungsten X Vanadium X
Z i n c XX XXXXX X X XXX X X X X X X X X



### Source: Updated from the 1991 National Pretreatment Program Report to Congress , pp. 5-6.Fibers

X

X

X X X X X

X X X

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C-8

# A PPENDIX 

# D

- C LEAN 

# W ATER 

# A CT 

# P RIORITY 

# P OLLUTANTS AND THE 

# F EDERAL 

# W ATER 

# Q UALITY 

# C RITERIA

The appendix below lists, in three tables, the National Recom m e nded Water Quality Criteria for:

• S pecific chem ical com pounds that are identifie d by unique Chem ical Abstract Service (CAS) registry num bers;

• P riority pollutants in the form of the Criteria Maxim u m Concentration (CMC) and Criterion Continuous Concentration (CCC);

• N on-priority pollutants in the form of the Cr iteria Maxim u m Concentration (CMC) and Criterion Continuous Concentration (CCC) for non-priority pollutants;

• O rganoleptic effects in the form of Organoleptic Effect Criteria.
Please see page D-16 for further discussion and definitions of these criteria.

D-1

### D-2NATIONAL RECOMMENDED WATER QUALITY CRITERIA FOR PRIORITY POLLUTANTSPriorityPollutantCASNumberFreshwaterSaltwaterHuman Health ForConsumption of:FRCite/SourceCMC(:g/L)CCC(:g/L)CMC(:g/L)CCC(:g/L)Water +Organism(:g/L)OrganismOnly(:g/L)1Antimony744036014 B,Z4300 B57FR608482Arsenic7440382340 A,D,K150 A,D,K69 A,D,bb36 A,D,bb0.018 C,M,S0.14 C,M,S62FR4216057FR608483Beryllium7440417J,ZJ62FR421604Cadmium74404394.3 D,E,K2.2 D,E,K42 D,bb9.3 D,bbJ,ZJ62FR421605aChromium III16065831570 D,E,K74 D,E,KJ,Z TotalJEPA820/B-96-00162FR421605bChromium VI1854029916 D,K11 D,K1,100 D,bb50 D,bbJ,Z TotalJ62FR421606Copper744050813 D,E,K,cc9.0 D,E,K,cc4.8 D,cc,ff3.1 D,cc,ff1,300 U62FR421607Lead743992165 D,E,bb,gg2.5 D,E,bb,gg210 D,bb8.1 D,bbJJ62FR421608Mercury74399761.4 D,K,hh0.77 D,K,hh1.8D,ee,hh0.94 D,ee,hh0.050 B0.051 B62FR421609Nickel7440020470 D,E,K52 D,E,K74D,bb8.2 D,bb610 B4,600 B62FR4216010Selenium7782492L,R,T5.0 T290 D,bb,dd71 D,bb,dd170Z11,00062FR42160IRIS 09/01/9111Silver74402243.4 D,E,G1.9 D,G62FR4216012Thallium74402801.7 B6.3 B57FR6084813Zinc7440666120 D,E,K120 D,E,K90 D,bb81 D,bb9,100 U69,000 U62FR42160IRIS 10/01/92

### D-3NATIONAL RECOMMENDED WATER QUALITY CRITERIA FOR PRIORITY POLLUTANTSPriority PollutantCASNumberFreshwaterSaltwaterHuman Health ForConsumption of:FR Cite/SourceCMC(:g/L)CCC(:g/L)CMC(:g/L)CCC(:g/L)Water +Organism(:g/L)OrganismOnly(:g/L)14Cyanide5712522 K,Q5.2 K,Q1 Q,bb1 Q,bb700 B,Z220,000 B,HEPA820/B-96-00157FR6084815Asbestos13322147 millionfibers/L57FR60848162,3,7,8-TCDD (Dioxin)17460161.3E-8 C1.4E-8 C62FR4216017Acrolein10702832078057FR6084818Acrylonitrile1071310.059 B,C0.66 B,C57FR6084819Benzene714321.2 B,C71 B,C62FR4216020Bromoform752524.3 B,C360 B,C62FR4216021Carbon Tetrachloride562350.25 B,C4.4 B,C57FR6084822Chlorobenzene108907680 B,Z21,000 B,H57FR6084823Chlorodibromomethane1244810.41 B,C34 B,C62FR4216024Chloroethane75003252-Chloroethylvinyl Ether11075826Chloroform676635.7 B,C470 B,C62FR4216027Dichlorobromomethane752740.56 B,C46 B,C62FR42160281,1-Dichloroethane75343291,2-Dichloroethane1070620.38 B,C99 B,C57FR60848301,1-Dichloroethylene753540.057 B,C3.2 B,C57FR60848I

### D-4NATIONAL RECOMMENDED WATER QUALITY CRITERIA FOR PRIORITY POLLUTANTSPriority PollutantCASNumberFreshwaterSaltwaterHuman Health ForConsumption of:FRCite/SourceCMC(:g/L)CCC(:g/L)CMC(:g/L)CCC(:g/L)Water +Organism(:g/L)OrganismOnly(:g/L)311,2-Dichloropropane788750.52 B,C39 B,C62FR42160321,3-Dichloropropene54275610 B1,700 B57FR6084833Ethylbenzene1004143,100 B,Z29,000 B62FR4216034Methyl Bromide7483948 B4000 B62FR4216035Methyl Chloride74873JJ62FR4216036Methylene Chloride750924.7 B,C1600 B,C62FR42160371,1,2,2-Tetrachloroethane793450.17 B,C11 B,C57FR6084838Tetrachloroethylene1271840.8 C8.85 C57FR6084839Toluene1088836,800 B,Z200,000 B62FR42160401,2-Trans-Dichloroethylene156605700 B,Z140,000 B62FR42160411,1,1-Trichloroethane71556J,ZJ62FR42160421,1,2-Trichloroethane790050.60 B,C42 B,C57FR6084843Trichloroethylene790162.7 C81 C57FR6084844Vinyl Chloride750142.0 C525 C57FR60848452-Chlorophenol95578120 B,U400 B,U62FR42160462,4-Dichlorophenol12083293 B,U790 B,U57FR60848472,4-Dimethylphenol105679540 B,U2,300 B,U62FR42160482-Methyl-4,6-Dinitrophenol53452113.476557FR60848492,4-Dinitrophenol5128570 B14,000 B57FR60848502-Nitrophenol88755514-Nitrophenol100027523-Methyl-4-Chlorophenol59507UU

### D-5NATIONAL RECOMMENDED WATER QUALITY CRITERIA FOR PRIORITY POLLUTANTSPriority PollutantCASNumberFreshwaterSaltwaterHuman Health ForConsumption of:FRCite/SourceCMC(:g/L)CCC(:g/L)CMC(:g/L)CCC(:g/L)Water +Organism(:g/L)OrganismOnly(:g/L)53Pentachlorophenol8786519F,K15F,K13bb7.9bb0.28 B,C8.2 B,C,H62FR4216054Phenol10895221,000 B,U4,600,000B,H,U62FR4216057FR60848552,4,6-Trichlorophenol880622.1 B,C,U6.5 B,C62FR4216056Acenaphthene833291,200 B,U2,700 B,U62FR4216057Acenaphthylene20896858Anthracene1201279,600 B110,000 B62FR4216059Benzidine928750.00012 B,C0.00054 B,C57FR6084860Benzo (a) Anthracene565530.0044 B,C0.049 B,C62FR4216061Benzo (a) Pyrene503280.0044 B,C0.049 B,C62FR4216062Benzo (b) Fluoranthene2059920.0044 B,C0.049 B,C62FR4216063Benzo (ghi) Perylene19124264Benzo (k) Fluoranthene2070890.0044 B,C0.049 B,C62FR4216065Bis 2-Chloroethoxy Methane11191166Bis 2-Chloroethyl Ether1114440.031 B,C1.4 B,C57FR6084867Bis 2-Chloroisopropyl Ether396383291,400 B170,000 B62FR4216057FR6084868Bis 2-Ethylhexyl PhthalateX1178171.8 B,C5.9 B,C57FR60848694-Bromophenyl Phenyl Ether10155370Butylbenzyl PhthalateW856873,000 B5,200 B62FR42160712-Chloronaphthalene915871,700 B4,300 B62FR42160

### D-6NATIONAL RECOMMENDED WATER QUALITY CRITERIA FOR PRIORITY POLLUTANTSPriority PollutantCASNumberFreshwaterSaltwaterHuman Health ForConsumption of:FRCite/SourceCMC(:g/L)CCC(:g/L)CMC(:g/L)CCC(:g/L)Water +Organism(:g/L)OrganismOnly(:g/L)724-Chlorophenyl Phenyl Ether700572373Chrysene2180190.0044 B,C0.049 B,C62FR4216074Dibenzo (a,h) Anthracene537030.0044 B,C0.049 B,C62FR42160751,2-Dichlorobenzene955012,700 B,Z17,000 B62FR42160761,3-Dichlorobenzene5417314002,60062FR42160771,4-Dichlorobenzene106467400 Z260062FR42160783,3'-Dichlorobenzidine919410.04 B,C0.077 B,C57FR6084879Diethyl PhthalateW8466223,000 B120,000 B57FR6084880Dimethyl PhthalateW131113313,0002,900,00057FR6084881Di-n-Butyl PhthalateW847422,700 B12,000 B57FR60848822,4-Dinitrotoluene1211420.11 C9.1 C57FR60848832,6-Dinitrotoluene60620284Di-n-Octyl Phthalate117840851,2-Diphenylhydrazine1226670.040 B,C0.54 B,C57FR6084886Fluoranthene206440300 B370 B62FR4216087Fluorene867371,300 B14,000 B62FR4216088Hexachlorobenzene1187410.00075 B,C0.00077 B,C62FR4216089Hexachlorobutadiene876830.44 B,C50 B,C57FR6084890Hexachlorocyclopentadiene77474240 B,U,Z17,000 B,H,U57FR6084891Hexachloroethane677211.9 B,C8.9 B,C57FR60848

### D-7NATIONAL RECOMMENDED WATER QUALITY CRITERIA FOR PRIORITY POLLUTANTSPriority PollutantCASNumberFreshwaterSaltwaterHuman Health ForConsumption of:FRCite/SourceCMC(:g/L)CCC(:g/L)CMC(:g/L)CCC(:g/L)Water +Organism(:g/L)OrganismOnly(:g/L)92Indeno (1,2,3-cd) Pyrene1933950.0044 B,C0.049 B,C62FR4216093Isophorone7859136 B,C2,600 B,CIRIS11/01/9794Naphthalene9120395Nitrobenzene9895317 B1,900 B,H,U57FR6084896N-Nitrosodimethylamine627590.00069 B,C8.1 B,C57FR6084897N-Nitrosodi-n-Propylamine6216470.005 B,C1.4 B,C62FR4216098N-Nitrosodiphenylamine863065.0 B,C16 B,C57FR6084899Phenanthrene85018100Pyrene129000960 B11,000 B62FR421601011,2,4-Trichlorobenzene120821260 Z940IRIS 11/01/96102Aldrin3090023.0 G1.3 G0.00013 B,C0.00014 B,C62FR42160103alpha-BHC3198460.0039 B,C0.013 B,C62FR42160104beta-BHC3198570.014 B,C0.046 B,C62FR42160105gamma-BHC (Lindane)588990.95K0.16 G0.019 C0.063 C62FR42160106delta-BHC319868107Chlordane577492.4G0.0043G,aa0.09G0.004G,aa0.0021 B,C0.0022 B,C62FR42160IRIS 02/07/981084,4-DDT502931.1G0.001G,aa0.13G0.001G,aa0.00059 B,C0.00059 B,C62FR421601094,4-DDE725590.00059 B,C0.00059 B,C62FR421601104,4-DDD725480.00083 B,C0.00084 B,C62FR42160

### D-8NATIONAL RECOMMENDED WATER QUALITY CRITERIA FOR PRIORITY POLLUTANTSPriority PollutantCASNumberFreshwaterSaltwaterHuman Health ForConsumption of:FRCite/SourceCMC(:g/L)CCC(:g/L)CMC(:g/L)CCC(:g/L)Water +Organism(:g/L)OrganismOnly(:g/L)111Dieldrin605710.24K0.056K,O0.71G0.0019G,aa0.00014 B,C0.00014 B,C62FR42160112alpha-Endosulfan9599880.22G,Y0.056G,Y0.034G,Y0.0087G,Y110 B240 B62FR42160113beta-Endosulfan332136590.22G,Y0.056G,Y0.034G,Y0.0087G,Y110 B240 B62FR42160114Endosulfan Sulfate1031078110 B240 B62FR42160115Endrin722080.086K0.036K,O0.037G0.0023G,aa0.76 B0.81 B,H62FR42160116Endrin Aldehyde74219340.76 B0.81 B,H62FR42160117Heptachlor764480.52G0.0038G,aa0.053G0.0036G,aa0.00021 B,C0.00021 B,C62FR42160118Heptachlor Epoxide10245730.52G,V0.0038G,V,aa0.053G,V0.0036G,V,aa0.00010 B,C0.00011 B,C62FR42160119Polychlorinated BiphenylsPCBs:0.014 N,aa0.03 N,aa0.00017 B,C,P0.00017 B,C,P62FR4216063FR16182120Toxaphene80013520.730.0002aa0.210.0002aa0.00073B,C0.00075B,C62FR42160Footnotes:AThis recommended water quality criterion was derived from data for arsenic (III), but is applied here to total arsenic, whichmight imply that arsenic (III)and arsenic (V) are equally toxic to aquatic life and that their toxicities are additive.A 440/5-84-033, January 1985),Species Mean Acute Values are given for both arsenic (III) and arsenic (V) for five species and the ratios of the SMAVs for each speciesrange from 0.6 to1.7.hronic values are available for both arsenic (III) and arsenic (V) for one species; for the fathead minnow, the chronicvalue for arsenic (V) is 0.29times the chronic value for arsenic (III).be available concerning whether the toxicities of the forms ofarsenic to aquatic organismsare additive.BThiscriterion hasbeen revised to reflect The Environmental Protection Agency’sq1* or RfD, ascontained in the Integrated Risk Information System(IRIS) as of April 8, 1998.factor (BCF) from the 1980 Ambient Water Quality Criteria document was retained in eachcase.CThiscriterion isbased on carcinogenicity of 10-6risk.klevelsmay be obtained by moving the decimal point (e.g., for a risklevel of 10-5,move the decimal point in the recommended criterion one place to the right).DFreshwater and saltwater criteria for metals are expressed in terms of the dissolved metal inthe water column.ed water quality criteriavalue wascalculated by using the previous304(a) aquatic life criteria expressed in termsof total recoverable metal, and multiplying it by a conversionfactor (CF).onversion Factor" (CF) representsthe recommended conversion factor for converting a metal criterion expressed as the totalrecoverable fraction in the water column to a criterion expressed asthe dissolved fraction in the water column.onversion Factors for saltwater CCCsare not currently available.ater CMCs have been used for both saltwater CMCs and CCCs).See "Office of WaterPolicyIn the arsenic criteria document (EPCNo data are known toThe fish tissue bioconcentrationAlternate risThe recommendThe term "C(CConversion factors derived for saltwhardness of 100 mg/L.Criteria values for other hardness may becalculated from the following: CMC (dissolved) = exp{mA [ln( hardness)]+ bA} (CF),or CCC (dissolved) = exp{mC[ln (hardness)]+ bC} (CF) and the parameters specified in AppendixB to the Preamble- Parameters for CalculatingFreshwater Dissolved Metals Criteria That Are Hardness-Dependent.This recommended water quality criterion is expressed in terms oftotal recoverable metal in the water column.It is scientifically acceptable to use theThis value was announced (61FR58444-58449, November 14, 1996) asa proposed GLI 303(c) aquatic life criterion.EPA is currently working on this440/5-80-019), Chlordane (EPA 440/5-80-027), DDT (EPA 440/5-80-038), Endosulfan (EPA 440/5-80-046), Endrin (EPA 440/5-80-047),Heptachlor(440/5-80-052), Hexachlorocyclohexane (EPA 440/5-80-054), Silver (EPA 440/5-80-071).The Minimum Data Requirements and derivationprocedures were different in the 1980 Guidelines than in the1985 Guidelines.For example, a “CMC” derived using the 1980 Guidelines was derivedto be used as an instantaneous maximum.If assessment is to be done using an averaging period, the values given should be divided by 2 to obtain avalue that is more comparable to a CMC derived using the 1985 Guidelines.No criterion for protection of human health from consumptionof aquatic organisms excluding water was presented in the 1980 criteria document or inPCBs are a class of chemicals which include aroclors, 1242, 1254, 1221, 1232, 1248, 1260,and 1016, CAS numbers 53469219, 11097691, 11104282,This recommended criterion is based on a 304(a) aquatic life criterion that was issued in the1995 Updates: Water Quality Criteria Documents for theThe CMC = 1/[(f1/CMC1) + (f2/CMC2)] where f1 and f2 are the fractions of total selenium that are treated as selenite and selenate, respectively, andProtection of Aquatic Life in Ambient Water,(EPA-820-B-96-001, September 1996).This value was derived using the GLI Guidelines(60FR15393-15399, March 23, 1995; 40CFR132 Appendix A); the differencebetween the 1985 Guidelines and the GLI Guidelines are explained onpage iv of the 1995 Updates.None of the decisions concerning the derivation of this criterion were affected by any considerations that are specific tothe Great Lakes.Freshwater aquatic life values for pentachlorophenol are expressed as a function of pH, and are calculated as follows: CMC = exp(1.005(pH)-4.869);The derivation of the CCC for this pollutant did not considerexposure through the diet, which is probably important for aquatic life occupying upperNevertheless, sufficient information was presented in the 1980 document to allow the calculation of a criterion,and Technical Guidance on Interpretation and Implementation of Aquatic Life Metals Criteria,” October 1, 1993, by Martha G. Prothro, ActingAssistant Administrator for Water, available from the Water Resource Center, USEPA, 401 M St., SW, mail code RC4100, Washington, DC 20460;and 40CFR§131.36(b)(1).Conversion Factors applied in the tablecan be found in Appendix A to the Preamble- Conversion Factorsfor DissolvedMetals.EPA has not calculated human health criterion for this contaminant.However, permit authorities should address this contaminant in NPDES permitEPA is currently reassessing the criteria for arsenic.Uponcompletion of the reassessment the Agency will publish revised criteria as appropriate.The freshwater criterion for this metal is expressed as a function of hardness (mg/L) in the water column.The value given here corresponds to aThis Criterion is based on 304(a) aquatic life criterion issued in1980, and was issued in one of the following documents: Aldrin/Dieldrin (EPAThis criterion for asbestos is the Maximum Contaminant Level (MCL) developed under the Safe Drinking Water Act (SDWA).conversion factor of 0.922 that was used in the GLI to convert this to a value that is expressed in terms of dissolved metal.11141165, 12672296, 11096825 and 12674112 respectively.The aquatic life criteria apply to this set of PCBs.This criterion applies to total pcbs, i.e., thesum of all congener or all isomer analyses.This recommended water quality criterion is expressed as:g free cyanide (as CN)/L.CCC = exp(1.005(pH)-5.134).Values displayed in table correspond to a pH of 7.8.even though the results of such a calculation were not shown in the document.This recommended water quality criterion refers to the inorganic form only.criterion and so this value might change substantially in the near future.CMC1 and CMC2 are 185.9:g/L and 12.83:g/L, respectively.actions using the State's existing narrative criteria for toxics.the1986 Quality Criteria for Water.trophic levels.KLMNOPQRSTFGHEIJ



### D-90.025:g/L given on page 23 of the criteria document is based on the Final Residue Value procedure in the 1985 Guidelines.Since thepublication of theGreatThis recommended water quality criterion was derived on page 43 of the mercury criteria document (EPA 440/5-84-026, January 1985).The saltwater CCC ofWater Quality Criteria for the Protection of Aquatic Organisms and Their Uses, PB85-227049, January 1985) and was issued in one of the following criteriadocuments: Arsenic (EPA 440/5-84-033), Cadmium (EPA 440/5-84-032), Chromium (EPA 440/5-84-029), Copper (EPA 440/5-84-031), Cyanide (EPA 440/5-84-028), Lead (EPA 440/5-84-027), Nickel (EPA 440/5-86-004), Pentachlorophenol (EPA 440/5-86-009), Toxaphene, (EPA 440/5-86-006), Zinc (EPA440/5-87- 003).1995 (60FR15393-15399, March 23, 1995), the Agency no longer uses theFinal Residue Value procedure for deriving CCCs for new or revised 304(a) aquaticlife criteria.criteria.It is anticipated that industry intends to publish in the peer reviewed literature draft aquatic life criteria generated in accordance with EPA Guidelines.EPA will review such criteria forpossible issuance as national WQC.Although EPA has not published a final criteria document for thiscompound it is EPA’s understanding that sufficient data exist to allow calculation of aquaticThis recommended water quality criterion wasderived from data for inorganic mercury (II),but is applied here to total mercury.If a substantial portion of theThis water quality criterion is based on a 304(a) aquaticlife criterion that was derived using the 1985 Guidelines (Guidelines for Deriving Numerical NationalThis CCC is based on the Final Residue Value procedure in the 1985 Guidelines.Since the publication of theGreat LakesAquatic Life Criteria Guidelinesinmercury in the water column is methylmercury, this criterion will probably be under protective.In addition, even though inorganic mercury is converted tomethylmercury and methylmercury bioaccumulates to a great extent, this criterion does not account for uptake via the food chainbecausesufficient data werenot available when the criterion was derived.The selenium criteria document (EPA 440/5-87-006, September 1987) provides that if selenium is as toxic to saltwater fishes in the field as it is to freshwaterfishes in the field, the status of thefish community should be monitored wheneverthe concentration of selenium exceeds 5.0:g/L in salt water becausetheAmore stringent MCL has been issued by EPA.Refer to drinking water regulations (40 CFR 141) or Safe Drinking Water Hotline(1-800-426-4791) forLakes Aquatic Life Criteria Guidelinesin 1995 (60FR15393-15399, March 23, 1995), the Agency no longer uses the Final Residue Value procedure forderiving CCCs for new or revised 304(a) aquatic life criteria.This value was derived from data for heptachlor and the criteria document provides insufficient data to estimate the relativetoxicities of heptachlor andThis recommended water quality criterion was derived inAmbient Water Quality Criteria Saltwater Copper Addendum(Draft, April 14, 1995) and wasWhen the concentration of dissolved organic carbon is elevated, copper is substantially less toxic and use of Water-Effect Ratios might be appropriate.This value was derived from data for endosulfan and is most appropriately appliedto the sum of alpha-endosulfan and beta-endosulfan.EPA is actively working on this criterionand so this recommended water quality criterion may change substantially in the near future.There is a full set of aquatic life toxicitydata that show that DEHP is not toxicto aquatic organisms at or below its solubility limit.promulgated in the Interim final National Toxics Rule (60FR22228-222237, May 4, 1995).saltwater CCC does not take into account uptake via the food chain.heptachlor epoxide.values.VWXYZaabbccddeeffgghh



### D-10

### D-11NATIONAL RECOMMENDED WATER QUALITYCRITERIAFOR NON-PRIORITY POLLUTANTSPriority PollutantCAS NumberFreshwaterSaltwaterHuman Health ForConsumption of:FR Cite/SourceCMC(:g/L)CCC(:g/L)CMC(:g/L)CCC(:g/L)Water +Organism(:g/L)OrganismOnly(:g/L)1Alkalinity--*20000 F*GoldBook2Aluminum pH 6.5 - 9.07429905750 G,I87 G,I,L*53FR331783Ammonia7664417FRESHWATER CRITERIA ARE pHDEPENDENT -- SEE DOCUMENTSALTWATER CRITERIA ARE pHAND TEMPERATURE DEPENDENTEPA822-R-98-008EPA440/5-88-0044Aesthetic Qualities--NARRATIVE STATEMENT -- SEE DOCUMENTGold Book5Bacteria--FOR PRIMARY RECREATION AND SHELLFISH USES -- SEE DOCUMENTGold Book6Barium74403931,000 AGold Book7Boron--NARRATIVE STATEMENT -- SEE DOCUMENTGold Book8Chloride16887006860000 G230000 G53FR190289Chlorine77825051911137.5CGold Book10Chlorophenoxy Herbicide2,4,5,-TP9372110 AGold Book11Chlorophenoxy Herbicide2,4-D94757100 A,CGold Book12Chlorpyrifos29218820.083 G0.041 G0.011 G0.0056 GGold Book13Color--NARRATIVE STATEMENT -- SEE DOCUMENTGold Book14Demeton80654830.1 F0.1 FGold Book15Ether, Bis Chloromethyl5428810.00013 E0.00078 EIRIS 01/01/9116Gases, Total Dissolved--NARRATIVE STATEMENT -- SEE DOCUMENTGold Book17Guthion865000.01 F0.01 FGold Book18Hardness--NARRATIVE STATEMENT -- SEE DOCUMENTGold Book******DFF

### D-12NATIONAL RECOMMENDED WATER QUALITYCRITERIAFOR NON-PRIORITY POLLUTANTSPriority PollutantCAS NumberFreshwaterSaltwaterHuman Health ForConsumption of:FR Cite/SourceCMC(:g/L)CCC(:g/L)CMC(:g/L)CCC(:g/L)Water +Organism(:g/L)OrganismOnly(:g/L)19Hexachlorocyclo-hexaneTechnical3198680.01230.0414Gold Book20Iron74398961000 F300 AGold Book21Malathion1217550.1 F0.1 FGold Book22Manganese743996550 A100 AGold Book23Methoxychlor724350.03 F0.03 F100 A,CGold Book24Mirex23858550.001 F0.001 FGold Book25Nitrates1479755810,000 AGold Book26Nitrosamines--0.00081.2427Dinitrophenols255505877014,000Gold Book28Nitrosodibutylamine,N9241630.0064 A0.587 AGold Book29Nitrosodiethylamine,N551850.0008 A1.24 AGold Book30Nitrosopyrrolidine,N9305520.01691.9Gold Book31Oil and Grease--NARRATIVE STATEMENT -- SEE DOCUMENTGold Book32Oxygen, Dissolved7782447WARMWATER AND COLDWATERMATRIX -- SEE DOCUMENTGold Book33Parathion563820.065 J0.013 JGold Book34Pentachlorobenzene6089353.5 E4.1 EIRIS 03/01/8835pH--6.5 - 9 F6.5 - 8.5 F,K5- 9Gold Book36Phosphorus Elemental77231400.1 F,KGold Book37Phosphate Phosphorus--NARRATIVE STATEMENT -- SEE DOCUMENTGold BookFO

### D-13NATIONAL RECOMMENDED WATER QUALITYCRITERIAFOR NON-PRIORITY POLLUTANTSPriority PollutantCAS NumberFreshwaterSaltwaterHuman Health ForConsumption of:FR Cite/SourceCMC(:g/L)CCC(:g/L)CMC(:g/L)CCC(:g/L)Water +Organism(:g/L)OrganismOnly(:g/L)38Solids Dissolved andSalinity--250,000 AGold Book39Solids Suspended andTurbidity--NARRATIVE STATEMENT -- SEE DOCUMENTGold Book40Sulfide-Hydrogen Sulfide77830642.0 F2.0 FGold Book41Tainting Substances--NARRATIVE STATEMENT -- SEE DOCUMENTGold Book42Temperature--SPECIES DEPENDENT CRITERIA -- SEE DOCUMENTGold Book43Tetrachlorobenzene,1,2,4,5-959432.3 E2.9 EIRIS03/01/9144Tributyltin TBT--0.46 N0.063 N0.37 N0.010 N62FR4255445Trichlorophenol,2,4,5-959542,600 B,E9800 B,EIRIS 03/01/88Footnotes:AThis human health criterion is the same as originally published in the Red Book which predates the 1980 methodology and did not utilize the fish ingestionBCF approach.lue is now published in the Gold Book.BThe organoleptic effect criterion is more stringent thanthe value presented in the non priority pollutants table.CAmore stringent Maximum Contaminant Level (MCL) has been issued by EPA under the SafeDrinking Water Act.water regulations40CFR141 or Safe Drinking Water Hotline (1-800-426-4791) for values.DAccording to the procedures described in theGuidelines for Deriving Numerical National Water Quality Criteria for the Protection of Aquatic Organisms andTheirUses, except possibly where a very sensitive species is important at a site, freshwater aquatic life should be protected if both conditions specified inAppendix C to the Preamble--Calculation ofFreshwater Ammonia Criterion are satisfied.EThis criterion has been revised to reflect The Environmental Protection Agency’s q1* or RfD, as contained in the Integrated Risk Information System (IRIS) asof April 8, 1998.used to derive the original criterion was retained in each case.FThe derivation of this value is presented in the Red Book (EPA 440/9-76-023, July, 1976).GThis value is based on a 304(a) aquatic life criterion that was derived using the 1985 Guidelines (Guidelines for Deriving Numerical National Water QualityCriteria for the Protection ofAquatic Organisms and Their Uses, PB85-227049, January 1985) and was issued inone of the following criteria documents:Aluminum (EPA 440/5-86-008); Chloride (EPA440/5-88-001); Chlorpyrifos (EPA 440/5-86-005).IThis value is expressed in terms of total recoverable metal in the water column.JThis value is based on a 304(a) aquatic life criterion that was issued in the1995 Updates: Water Quality Criteria Documents for the Protection of Aquatic Lifein Ambient Water(EPA-820-B-96-001).Guidelines (60FR15393-15399, March 23, 1995; 40CFR132 Appendix A); thedifferences between the 1985 Guidelines and the GLI Guidelines are explained on page ivof the 1995 Updates.rning this criterion wasaffected by any considerationsthat are specific to the Great Lakes.FMThis same criterion vaRefer to drinkingThe fish tissue bioconcentration factor (BCF)This value was derived using the GLINo decision conceFor open ocean waters where the depth is substantially greater than the euphotic zone,the pH should not be changed more than 0.2 units from the naturallyoccurring variation or any case outside the range of 6.5 to 8.5.For shallow, highly productive coastal and estuarine areas where naturally occurring pHvariations approach the lethal limits of some species, changes in pH should be avoided but in any case should not exceed the limits established for freshwater, i.e., 6.5-9.0.This value was announced (62FR42554, August 7, 1997) as a proposed 304(a) aquatic life criterion.Although EPA has not responded to public comment, EPAbass in water with pH= 6.5-6.6 and hardness<10 mg/L.Data in “Aluminum Water-EffectRatio for the 3M Plant Effluent Discharge, Middleway, WestVirginia” (May 1994) indicate that aluminumis substantially less toxic at higher pH and hardness, but the effects of pH and hardness are not well quantified atthis time.(2) In tests with the brook trout at low pH and hardness, effects increased with increasing concentrations of totalaluminum even though theconcentration of dissolved aluminum was constant, indicating that total recoverable isa more appropriate measurement than dissolved, at least when particulatealuminum is primarily aluminum hydroxide particles.In surface waters, however, the total recoverable procedure might measurealuminum associated withclay particles, which might be less toxic than aluminum associated with aluminum hydroxide.(3)EPA is aware of field data indicating that many high qualitywaters in the U.S. contain more than 87:g aluminum/L, when either total recoverable or dissolved is measured.There are three major reasonswhy the use of Water-Effect Ratios might be appropriate.(1) The value of 87:g/L is based on a toxicity test with the stripedU.S. EPA. 1973.Water Quality Criteria 1972.EPA-R3-73-033. National Technical Information Service, Springfield, VA.; U.S. EPA. 1977.TemperatureU.S. EPA. 1986.Ambient Water Quality Criteria for Dissolved Oxygen. EPA 440/5-86-003. National Technical Information Service, Springfield, VA.is publishing this as a 304(a) criterion in today’s notice as guidance for Statesand Tribes to consider when adopting water quality criteria.Criteria for Freshwater Fish: Protocol and Procedures. EPA-600/3-77-061. National Technical Information Service, Springfield, VA.According to page 181 of the Red Book:KLMNO



### D-14

### D-15NATIONAL RECOMMENDED WATER QUALITY CRITERIA FOR ORGANOLEPTIC EFFECTSPollutantCAS NumberOrganoleptic Effect Criteria(:g/L)FR Cite/Source1Acenaphthene8332920Gold Book2Monochlorobenzene10890720Gold Book33-Chlorophenol--0.1Gold Book44-Chlorophenol1064890.1Gold Book52,3-Dichlorophenol--0.04Gold Book62,5-Dichlorophenol--0.5Gold Book72,6-Dichlorophenol--0.2Gold Book83,4-Dichlorophenol--0.3Gold Book92,4,5-Trichlorophenol959541Gold Book102,4,6-Trichloropehnol880622Gold Book112,3,4,6-Tetrachlorophenol--1Gold Book122-Methyl-4-Chlorophenol--1800Gold Book133-Methyl-4-Chlorophenol595073000Gold Book143-Methyl-6-Chlorophenol--20Gold Book152-Chlorophenol955780.1Gold Book16Copper74405081000Gold Book172,4-Dichlorophenol1208320.3Gold Book182,4-Dimethylphenol105679400Gold Book19Hexachlorocyclopentadiene774741Gold Book20Nitrobenzene9895330Gold Bookbe exposed briefly without resulting in an unacceptable effect.The Criterion Continuous Concentration (CCC) is an estimate ofthe highest concentration of aThe CMC and CCC are just two ofmaterial in surface water to which an aquatic community can beexposed indefinitely without resulting in an unacceptable effect.the six parts of a aquatic life criterion; the other four partsare the acute averaging period, chronic averaging period, acutefrequency of allowed exceedence, andchronic frequency of allowed exceedence.Because 304(a) aquaticlife criteria are national guidance, they are intended to be protective of the vast majority of theaquatic communities in the United States.This compilation lists all priority toxic pollutants and some non priority toxic pollutants, and both human health effect and organoleptic effect criteriaissued pursuant to CWA §304(a).Blank spaces indicate that EPA has no CWA §304(a) criteria recommendations.For a number of non-priority toxic pollutants notlisted, CWA §304(a) “water + organism” human health criteria are not available, but, EPA has published MCLs under the SDWA thatmay be used in establishingwater quality standards to protect water supply designated uses.Because of variationsin chemical nomenclature systems, thislisting of toxic pollutants does notduplicate the listing in Appendix A of 40 CFR Part 423.Also listed are the Chemical Abstracts Service CAS registry numbers, which provide a uniqueidentification for each chemical.The Criteria Maximum Concentration (CMC) isan estimate of the highest concentration ofa material in surface water to which anaquatic community canThe human health criteria for the priority and non priority pollutants are based on carcinogenicity of 10 -6 risk.Alternate risk levels may be obtained bymoving the decimal point (e.g., for a risklevel of 10 -5 , move the decimal point in the recommended criterion one place to the right).Becauseof variationsin chemical nomenclature systems,this listing of pollutants doesnot duplicate the listing in Appendix A of 40 CFR Part 423.Alsolisted are the Chemical Abstracts Service (CAS) registry numbers, which provide aunique identification for each chemical.NATIONAL RECOMMENDED WATER QUALITY CRITERIAFR Cite/SourceNATIONAL RECOMMENDED WATER QUALITY CRITERIA FOR ORGANOLEPTIC EFFECTS45FR79341Gold BookGold BookOrganoleptic Effect CriteriaCAS NumberCriteria Recommendations for Priority Pollutants,NonPriority PollutantsandOrganoleptic Effects(:g/L)500030300Criteria Maximum Concentration andCriterion Continuous ConcentrationThese criteria are based on organoleptic (taste and odor) effects.744066687865108952Human Health RiskPollutantPentachlorophenolAdditional Notes:General Notes:PhenolZinc2122231.1.2.3.



### D-16The compilation contains 304(a) criteria for pollutants with toxicity-based criteria as well as non-toxicity based criteria.The basis for the non-toxicity basedcriteria are organoleptic effects (e.g., taste and odor) which would make water and edible aquatic life unpalatable but not toxic to humans.The table includes criteriafor organoleptic effects for 23 pollutants.Pollutants with organoleptic effect criteria more stringent than the criteria based on toxicity (e.g., included in both thepriority and non-priority pollutanttables) are footnoted as such.The Chemical Abstract Services number (CAS) for Bis(2-Chloroisopropyl) Ether, has been corrected in the table.The correct CASnumber for this chemical is39638-32-9.Previous publications listed 108-60-1 as the CAS number for this chemical.In the 1980 Selenium document, a criterion for the protection ofhuman health from consumption ofwater and organisms was calculated based on a BCF of 6.0L/kg and a maximum water-related contribution of 35 mg Se/day.Subsequently, the EPA Office of Health and Environmental Assessment issued an errata notice(February 23, 1982), revising the BCF for selenium to 4.8 L/kg.In 1988, EPA issued an addendum (ECAO-CIN-668) revising the human health criteria forselenium.Later in the final National Toxic Rule (NTR, 57 FR60848), EPA withdrew previously published selenium human health criteria, pending Agency reviewof new epidemiological data.The 304(a) criteria for metals, shown as dissolved metals, are calculated in one of two ways.For freshwater metals criteria that are hardness-dependent, thedissolved metal criteria were calculated using a hardness of 100mg/L as CaCO3 for illustrative purposes only.Saltwater and freshwater metals’ criteria that are nothardness-dependent are calculated by multiplying the total recoverable criteria before rounding by the appropriate conversion factors.The final dissolved metals’criteria in the table are rounded to two significant figures.Information regarding the calculation of hardness dependent conversion factorsare included in thefootnotes.The compilation includes footnotes for pollutants with Maximum Contaminant Levels (MCLs) more stringent than the recommended water quality criteria inthe compilation.MCLs for these pollutants are not included in the compilation, but can be found in the appropriate drinking water regulations (40 CFR 141.11-16Many of the values in the compilation were published in the proposed California Toxics Rule(CTR, 62FR42160).Although such values were publishedpursuant to Section 303(c) of the CWA, theyrepresent the Agency’s most recent calculation of water quality criteria and thus are published today as the Agency’s304(a) criteria.Water quality criteria published in the proposed CTR may be revised when EPA takes final action on the CTR.In the 1980 criteria documents, certain recommended water quality criteria were published for categories of pollutants rather than for individual pollutantswithin that category.Subsequently, in a series of separate actions, the Agency derived criteria for specific pollutantswithin a category.Therefore, in thiscompilation EPA is replacing criteria representing categories with individual pollutantcriteria (e.g., 1,3-dichlorobenzene, 1,4-dichlorobenzene and1,2-dichlorobenzene).and 141.60-63), or can be accessed through the Safe Drinking Water Hotline (800-426-4791) or the Internet (http://www.epa.gov/ost/tools/dwstds-s.html).Water Quality Criteria published pursuant to Section 304(a) or Section 303(c) of the CWACorrectionof Chemical Abstract ServicesNumberCalculation of Dissolved Metals CriteriaMaximum Contaminant LevelsSpecific Chemical CalculationsHuman HealthOrganoleptic EffectsCategory CriteriaSelenium(1)A.9.10.4.5.6.7.8.



### D-17This compilation includes human health criteria for selenium, calculated using a BCF of 4.8 L/kgalong with the current IRIS RfD of 0.005 mg/kg/day.EPAincluded these recommended water quality criteria in the compilation because the data necessary for calculating a criteria in accordance with EPA’s 1980 humanhealth methodology are available.Aquatic Life(2)This compilation contains aquatic life criteria for selenium thatare the same as those publishedin the proposed CTR.In theCTR, EPA proposed an acutecriterion for selenium based on the criterionproposed for selenium in the Water QualityGuidance for the Great Lakes System (61 FR 58444).The GLI and CTRproposals take into account data showing thatselenium’s two most prevalent oxidationstates, selenite and selenate, present differing potentials for aquatic toxicity,as well as new data indicating that variousforms of selenium are additive.The new approach produces a different selenium acute criterion concentration, or CMC,depending upon the relative proportions of selenite, selenate, and other forms of selenium that are present.EPA notes it is currently undertaking a reassessment of selenium, and expects the 304(a) criteria for selenium will be revisedbased on the final reassessment(63FR26186).However, until such time as revised water qualitycriteria for selenium are published by the Agency, the recommended water quality criteria in thiscompilation are EPA’s current 304(a) criteria.1,2,4-Trichlorobenzene and ZincB.Human health criteria for 1,2,4-trichlorobenzene and zinc have notbeen previously published.Sufficient information is now available for calculating waterquality criteria for the protection of human health from the consumption of aquatic organisms andthe consumption of aquatic organisms and water for both thesecompounds.Therefore, EPA is publishing criteria for these pollutants in this compilation.Chromium (III)C.The recommended aquatic life water quality criteria for chromium(III) included in the compilation are based on the values presented in the document titled:1995 Updates: Water Quality Criteria Documents for the Protection of Aquatic Life in Ambient Water, however, this document contains criteria based on the totalrecoverable fraction.The chromium (III) criteria in this compilation were calculated by applying the conversion factors usedin the Final Water Quality Guidancefor the Great Lakes System (60 FR 15366) to the 1995 Update document values.Ether, Bis (Chloromethyl), Pentachlorobenzene, Tetrachlorobenzene 1,2,4,5-, TrichlorphenolD.Human health criteria for these pollutants were last published inEPA’s Quality Criteria for Water 1986 or “Gold Book”.Some of these criteria were calculatedusing Acceptable Daily Intake (ADIs) ratherthan RfDs.Updated q1*s and RfDs are nowavailable in IRIS for ether, bis (chloromethyl), pentachlorobenzene,tetrachlorobenzene 1,2,4,5-, and trichlorophenol, and were usedto revise the water quality criteria for these compounds.Therecommended water quality criteria forether, bis (chloromethyl) wererevised using an updated q1*, while criteria forpentachlorobenzene, and tetrachlorobenzene 1,2,4,5-, and trichlorophenol werederived using an updated RfD value.PCBsE.These criteria replace theIn this compilation EPA is publishing aquaticlife and human health criteria based on total PCBs rather than individual arochlors.previous criteria for the seven individual arochlors.Thus, there are criteria for a total of 102 of the 126 priority pollutants.



### D-18------------ConversionFactorsfor DissolvedMetalsMetalConversion Factorfreshwater CMCConversion Factorfreshwater CCCConversion Factorsaltwater CMCConversion Factorsaltwater CCC1Arsenic1.0001.0001.0001.000Cadmium1.136672-[(lnhardness)(0.041838)]1.101672-[(lnhardness)(0.041838)]0.9940.994Chromium III0.3160.860Chromium VI0.9820.9620.9930.993Copper0.9600.9600.830.83Lead1.46203-[(lnhardness)(0.145712)]1.46203-[(lnhardness)(0.145712)]0.9510.951Mercury0.850.850.850.85Nickel0.9980.9970.9900.990Selenium0.9980.998Silver0.850.85Zinc0.9780.9860.9460.946



### D-19------Parameters* for Calculating Freshwater Dissolved Metals Criteria That Are Hardness-DependentChemicalmAbAmCbCFreshwater Conversion Factors (CF)AcuteChronicCadmium1.128-3.68670.7852-2.7151.136672-[ln(hardness)(0.041838)]1.101672-[ln(hardness)(0.041838)]Chromium III0.81903.72560.81900.68480.3160.860Copper0.9422-1.7000.8545-1.7020.9600.960Lead1.273-1.4601.273-4.7051.46203-[ln(hardness)(0.145712)]1.46203-[ln(hardness)(0.145712)]Nickel0.84602.2550.84600.05840.9980.997Silver1.72-6.520.85Zinc0.84730.8840.84730.8840.9780.986*WheremAandbAare conversionfactorsto calculate CMC and mCand bCareconversion factors necessary to calculate CCC



### D-20

### Appendix C - Calculation of Freshwater Ammonia Criterion

1. The one-hour average concentration of total am m oni a nitrogen (in m g N/L) does not exceed, m o re than once every three years on the average, the CM C calculated using the following equation:


# 0.275 39.0 CM C = 1 1 0 7

. 204 − pH 

# + 1 1 0 p H − 7.2 0 4 

# + +

In situations where salm onids do not occur, the CMC m a y be calculated using the following equation:



# 0. 411 58.4 CMC = 1 1 0 7.20 4 − p H 

# + 1 1 0 p H − 7 .20 4 

# + +

2. The thirty-day average concentration of total am m onia nitrogen (in m g N/L) does not exceed, m o re than once every three years on the average, the CCC calculated using the following equation:


# 0.0858 3.70 CCC = 1 1 0 7.6 8 8 − p H 

# + 1 1 0 p H − 7 .688 

# + +

and the highest four-day average within the 30-day period does not exceed twice the CCC.



### Source: U.S. EPA’s National Recommended Water Quality Criteria-Correction , EPA-822-Z-99-001, April 1999, pp. 7-25; http://www.epa.gov/OST/standards/wqcriteria.htm l

D-21

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D-22

# A PPENDIX 

# E

- F EDERAL 

# S EWAGE 

# S LUDGE 

# S TANDARDSPollutantCeiling Concentration*Monthly Average PollutantConcentration*Cumulative PollutantLoading Rates*Annual Pollutant Loading Rate*(Table 1, 40 CFR 503.13)(Table 3, 40 CFR 503.13)(Table 2, 40 CFR 503.13)(Table 4, 40 CFR 503.13)mg/kglbs/1000 lbsmg/kglbs/1000 lbskg/hectarelbs/acre**kg/hectare/365-day periodlbs/acre/365-day period**Arsenic75754141413721.8Cadmium8585393939351.91.7Copper4,3004,3001,5001,5001,5001,3387567Lead8408403003003002681513Mercury5757171717150.850.76Molybdenum7575------Nickel4204204204204203752119Selenium1001001001001008954.5Zinc7,5007,5002,8002,8002,8002,498140125BiosolidsLand Application Limitations***Calculated using metric standards specified in40 CFR 503.13 multiplied by the conversion factor of 0.8922.Dry weight.Source:40 CFR §503.13, Tables 1-4, October 25, 1995



### E-1Su rface Disp o sal

Dis t a n c e from the Bounda ry of A c tiv e Bio s o lid s Un it to Su rface Disp o sal Site Prope rty Line (me t e r s ) Polluta nt Conc e n tra t ion*
Arsenic (mg/kg) Chromium (mg/kg) Nickel (mg/kg)

0 to less than 25 30 200 210

25 to less than 50 34 220 240

50 to less than 75 39 260 270

75 to less than 100 46 300 320

100 to less than 125 53 360 390

125 to less than 150 62 450 420 Equal to or greater than 150 73 600 420



### * D ry -weight.

Source: 40 CFR Part 503.23 Table 1 and 2.

Conversion Factors
pounds per acre (lbs/ac) x 1.121 = kilogram s per hectare (kg/ha)
kilogram s per hectare (kg/ha) x 0.8922 = pounds per acre (lbs/ac)
pound (lb) = 0.4536 kilogram (kg)
kilogram (kg) = 2.205 pounds (lbs)
English ton = 0.9072 m e tric tonne

m e tric tonne = 1.102 English ton

E-2

# A PPENDIX 

# F

- T OXICITY 

# C HARACTERISTIC 

# L EACHATE 

# P ROCEDURE 

# L IMITATIONS

EPA Ha za rdous W aste No

. Co n t amin an t C A S No

. 1 Re gula t ory Le v e l (mg/L)

D004 Arsenic 7440–38–2 5.0 D005 Barium 7440–39–3 100.0 D018 Benzene 71–43–2 0.5 D006 Cadmium 7440–43–9 1.0 D019 Carbon tetrachloride 56–23–5 0.5 D020 Chlordane 57–74–9 0.03 D021 Chlorobenzene 108–90–7 100.0 D022 Chloroform 67–66–3 6.0 D007 Chromium 7440–47–3 5.0 D024 o-Cresol 95-48-7 200.0 2
D024 m-Cresol 108–39–4 200.0 2
D025 p-Cresol 106–44–5 200.0 2
D026 Cresols 200.0 2
D016 2,4-D 94–75–7 10.0 D027 1,4-Dichlorobenzene 106–46–7 7.5 D028 1,2-Dichloroethane 107–06–2 0.5 D029 1,1-Dichloroethy lene 75–35–4 0.7 D030 2,4-Dinitrotoluene 121–14–2 0.13 3
D012 Endrin 72–20–8 0.02 D031 Heptachlor (and its epoxide) 76–44–8 0.008

D032 Hexachlorobenzene 118–74–1 0.13 3
D033 Hexachlorobutadiene 87–68–3 0.5 D034 Hexachloroethane 67–72–1 3.0 D008 Lead 7439–92–1 5.0 D013 Lindane 58–89–9 0.4 D009 Mercury 7439–97–6 0.2 D014 Methoxy c hlor 72–43–5 10.0 D035 Methy l ethy l ketone 78–93–3 200.0
D036 Nitrobenzene 98–95–3 2.0 D037 Pentachlorophenol 87–86–5 100.0 D038 ridine 110–86–1 5.0 3
D010 Selenium 7782–49–2 1.0 D011 Silver 7440–22–4 5.0 D039 T e trachloroethy lene 127–18–4 0.7 D015 T o xaphene 8001–35–2 0.5 D040 T r ichloroethy lene 79–01–6 0.5 Py



### F-1EPA Ha za rdous W aste No

. Co n t amin an t C A S No

. 1 Re gula t ory Le v e l (mg/L)

D041 2,4,5-T r ichlorophenol 95–95–4 400.0 D042 2,4,6-T r ichlorophenol 88–06–2 2.0 D017 2,4,5-T P (Silvex) 93–72–1 D043 Viny l chloride 75–01–4 1.0 0.2



### 1 C hem i cal Abstracts Service num ber.

2 If o-, m

- , and p-Cresol concentrations cannot be differentiated, the total cresol (D026) concentration is used. The regulatory level of total cresol is 200 m g /L.

3 Q uantitation lim it is greater than the calcula ted regulatory level. The quantitation lim it therefore becom es the regulatory level.

Source: 40 CFR 261.24

F-2

# A PPENDIX 

# G

- L ITERATURE 

# I NHIBITION 

# V ALUES

Pollutant Re porte d Ra nge of A c tiv a te d Sludge Inhibition Thre s hold Lev e ls, mg/L Referen ces*

MET A LS/NONMET A L INORGANICS Ammonia 480 (4) Arsenic 0 .1 (1), (2), (3) Cadmium 1 - 10 (2), (3) Chromium (VI) 1 ( 2), (3) Chromium (III) 10 - 50 (2), (3) Chromium (T otal) 1 - 100 (1) Copper 1 ( 2), (1), (3) Cy anide 0.1 - 5 5 (1), (2), (3) (1) Iodine 10 (4) Lead 1.0 - 5.0 10 - 100 (3) (1) Mercury 0 .1 - 1 2.5 as (2), (3) (1) Nickel 1 .0 - 2.5 5 (2), (3) (1) Sulfide 25 -30 ( 4) Z i nc 0.3 - 5 5 - 10 (3) (1) Hg (II)

ORGANICS Anthracene 500 (1) Benzene 100 - 500 125 - 500 (3) (1) 2-Chlorophenol 5 20 - 200 (2) (3) 1,2 Dichlorobenzene 5 ( 2) 1,3 Dichlorobenzene 5 ( 2) 1,4 Dichlorobenzene 5 ( 2) 2,4-Dichlorophenol 64 (3) 2,4 Dimethy lphenol 40 - (3) 2,4 Dinitrotoluene 5 ( 2) 1,2-Dipheny lhy d razine 5 ( 2) Ethy lbenzene 200 (3) Hexachlorobenzene 5 ( 2) Naphthalene 500 500 500 (1) (2) (3) Nitrobenzene 30 - 500 500 500 (3) (1) (2) 200



### G-1Re porte d Ra nge of A c tiv a te d Sludge Pollutant Inhibition Thre s hold Referen ces* Lev e ls, mg/L Pentachlorophenol 0.95 (2) 50 (3) 75 - 150 (1) 500 (1) 500 (2) 50 - 200 (3) 200 (2) 200 (1) 200 (3) 50 - 100 (1) Surfactants 100 - 500 (4) Phenanthrene

Phenol

T o luene 2,4,6 T r ichlorophenol

Pollutant Re porte d Ra nge of Tric k ling Filte r Referen ces* Inhibition Thre s hold Le v e ls , mg/L Chromium (III) 3.5 - 67.6 ( 1) Cy anide 30 (1)

Pollutant Re porte d Ra nge of Nitrific a t ion Inhibition Thre s hold Le v e ls , mg/L Referen ces*

MET A LS/NONMET A L INORGANICS Arsenic 1 .5 (2) Cadmium 5 .2 (1), (2) Chloride 180 (4) Chromium (VI) 1 - 10 [as (CrO 4 ) 2- ] ( 1 ) Chromium (T ) 0 .25 - 1.9 1 - 100 (trickling filter) (1), (2), (3) (1)

Copper 0.05 - 0.48 (2), (3) Cy anide 0.34 - 0.5 ( 2), (3) Lead 0.5 ( 2), (3) Nickel 0 .25 - 0.5 5 (2), (3) (1) Z i nc 0.08 - 0.5 ( 2), (3) ORGANICS Chloroform 10 (2) 2,4-Dichlorophenol 64 (3) 2,4-Dinitrophenol 150 (2) Phenol 4 4 - 10 (2) (3)



### G-2Pollutant Re porte d Ra nge of A n a e r obic Dig estio n Inhibition Thre s hold Lev e ls, mg/L Referen ces*

MET A LS/NONMET A L INORGANICS Ammonia 1500 - 8000 (4) Arsenic 1 .6 (1) Cadmium 2 0 ( 3) Chromium (III) 130 (3) Chromium (VI) 110 (3) Copper 40 (3) Cy anide 4 - 100 1 - 4 (1) (2), (3) Lead 340 (3) Nickel 1 0 136 (2), (3) (1) Silver 13 - 65** ( 3) Sulfate 500 - 1000 (4) Sulfide 50 - 100 (4) Z i nc 400 (3) ORGANICS Acry lonitrile 5 5 (3) (2) Carbon T e trachloride 2 .9 - 159.4 10 - 20 2.0 (1) (3) (2) Chlorobenzene 0.96 - 3 0.96 (1) (2) Chloroform 1 5 - 16 10 - 16 (2) (1) (3) 1,2-Dichlorobenzene 0.23 - 3.8 0.23 (1) (2) 1,4-Dichlorobenzene 1.4 - 5.3 1.4 (1) (2) Methy l chloride 3.3 - 536.4 100 (1) (2) Pentachlorophenol 0.2 0.2 - (2) (1) T e trachloroethy lene 20 (2) T r ichloroethy lene 1 - 20 20 20 (1) (2) (3) T r ichlorofluoromethane

- ( 2) 1.8



### * T otal pollutant inhibition levels, unless otherwise indicated. ** Dissolved m e tal inhibition levels.
(1) Jenkins, D.I., and Associates. 1984. Impact of Toxics on Treatment Literature Review

.

G-3

### (2) R ussell, L. L., C. B. Cain, and D.I. Jenkins. 1984. Impacts of Priority Pollutants on Publicly Owned Treated Works Processes: A Literature Review

. 1984 Purdue Industrial Waste Conference.
(3) A nthony , R. M., and L. H. Briem burst. 1981. Determining Maximum Influent Concentrations of Priority Pollutants for Treatment Plants

. Journal Water Pollution Control Federation 53(10):1457-1468.
(4) U .S. EPA. 1986, Working Document; Interferences at Publicly Owned Treatment Works

. Septem ber 1986.

Source: EPA’s Guidance Manual on the Development and Implementation of Local Discharge Limitations Under the Pretreatment Program , Decem ber 1987, pp. 3-44 to 3-49.

G-4

# A PPENDIX 

# H

- C LOSED



# - CUP 

# F LASHPOINTS FOR 

# S ELECT 

# O RGANIC 

# C OMPOUNDS

Pollutant Closed Cup Fla s hpoint ( / F)

Acrolein -15 Acry lonitrile 30 Benzene 12 Chlorobenzene 82 Chloroethane (Ethy l chloride) -58 1,1-Dichloroethane 2 1,2-Dichloroethane (Ethy l ene dichloride) 56 1,1-Dichloroethy lene (Viny lidene chloride) -2 T r ans-1,2-Dichloroethy lene, (1,2-Dichloroethy lene) 36-39 1,2-Dichloropropane (Propy lene dichloride) 60 Ethy lbenzene 55 T o luene 40



### Source: Online NIOSH Pocket Guide to Chemical Hazards at http://www.cdc.gov/niosh/npg/npg.htm l

.

H-1

### This page intentionally left blank.

H-2

# A PPENDIX 

# I

- D ISCHARGE 

# S CREENING 

# L EVELS AND 

# H ENRY 

# ’ S 

# L AW 

# C ONSTANTS FOR 

# O RGANIC 

# C OMPOUNDS

Disch arg e Screen in g L e v e ls Based o n Exp l o s iv ity

Pollutant LELs (1 ) % v o lume /

vo l u m e LELs (mol/m 3 )

Acrolein 2.8 1 .15 Acry lonitrile Benzene Chlorobenzene 1.3 Chloroethane 3.8 1,1-Dichloroethane 5.4 1,2-Dichloroethane 6.2 1,1-Dichloroethy lene 6.5 T r ans-1,2-Dichloroethy lene 5.6 1,2-Dichloropropane 3.4 Ethy l benzene 0.8 Hy drogen Cy anide 5.6 Hy drogen Sulfide 4 .0 Methy l bromide 10.0 Methy l chloride 8.1 Methy l ene Chloride 13.0 T o luene 1.1 1,1,2-T r ichloroethane 6. 0 1,1,1-T r ichloroethane 7. 5 3.0 1.2

T r ichloroethy lene 8.0 (F ) Viny l Chloride 3.6 1 .23 0 .49 0 .53 1 .55 2 .21 2 .54 2 .66 2 .29 1 .39 0 .33 2 .30 1.64 4 .09 3 .31 5 .32 0 .45 2 .45 3 .07 3 .20 1 .47 He nry ' s La w Cons ta nt (mol/m 3 )/(mg/L)

8 .7E-05 8 .4E-05 2 .9E-03 1 .3E-03 7 .0E-03 2 .4E-03 4 .9E-04 1 .2E-02 4 .0E-03 1 .0E-03 3 .1E-03 1 .7E-4 1.7E-2 2 .7E-03 7 .4E-03 1 .2E-03 3 .0E-03 2 .6E-04 5 .2E-03 3 .1E-03 1 .7E-02 MW (g/mol) Disch arg e Screen in g Le v e l (mg/L)

56.1 13163 53.1 14586 78.1 169 112.6 395

65.5 222

99 909 99 5221 97 215 97 571 113 1326 106.2 106 2 7 13529 34 96 95 1521 50.5 450 84.9 4307 92.1 152 133.4 9611 133.4 591 131.4 1029

62.5 8 8


### Lower Explosive Lim its (LELs) assum e d for 25°C unless noted otherwise. MW = m o lecular weight

Source: Updated in 2002 via the online NIOSH Pocket Guide to Chemical Hazards at http://www.cdc.gov/niosh/npg/npg.htm l

I-1Discharge Screening Lev e ls Based upon Fume Toxicity

Pollutant Ex pos ure Limit* (mg/m 3 ) Henry ' s La w Cons ta nt (mg/m 3 /

mg/L) Disch arg e Screen in g Le v e l (mg/L)

Acrolein 0.23 4.9 0 .047 Acry lonitrile 21.70 4.5 4 .822 Benzene 228.0 0 .014 Bromoform 5.17 22.8 0 .227 Carbon T e trachloride 12.58 1185.0 0 .011 Chlorobenzene 345.75 151.0 2 .290 Chloroethane 2,640.00 449.0 5 .880 Chloroform 9.76 163.5 0 .060 Dichloroethane, 1,1- 405. 00 240.4 1 .685 Dichloroethane,1,2

- 8.10 48.1 0 .168 Dichloroethy lene, 1,1- 19.80 1202.1 0 .016 T r ans-Dichloroethy lene,1, 2

- 794.00 389.3 2 .040 Dichloropropane,1,2 20 118.5 4 .289 Ethy l benzene 542.50 327.0 1 .659 Hy drogen Cy anide 5.17 4.5 1

. 149 Hy drogen Sulfide 14.00 414.4 0 .034 Methy l bromide 77.80 255.5 0 .305 Methy l chloride 207.00 371.6 0 .557 Methy l ene Chloride 433.75 104.8 4 .139 T e trachlorethane, 1,1,2, 2- 34.35 18.6 1 .847 T e trachloroethy lene 678. 00 717.1 0 .945 T o luene 565.50 272.5 2 .075 T r ichloroethane, 1,1,2- 54.60 34.1 1 .601 T r ichloroethane, 1,1,1 1, 911.00 692.7 2 .759 T r ichloroethy lene 10.74 408.7 0 .026 Viny l Chloride 12.80 1048.0 0 .012 3.19

508. Sourc e

T L V-ST EL PEL-Ceiling, REL- Ceiling REL-ST EL PEL-T W A , T L V-T W A, REL-T W A REL-ST EL PEL-T W A PEL-T W A REL-ST EL PEL-T W A , T L V-T W A, REL-T W A REL-ST EL T L V-T W A PEL-T W A , T L V-T W A, REL-T W A T L V-ST EL T L V-ST EL, REL-ST EL T L V-Ceiling, REL-ST EL REL-Ceiling PEL-Ceiling T L V-ST EL PEL-ST E L PEL-T W A T L V-ST EL REL-ST EL PEL-T W A , T L V-T W A, REL-T W A REL-Ceiling REL-Ceiling PEL Ceiling


### *Exposure lim its are lowest of acute toxicity data (NIOSH REL-STEL, ACGIH TLV-STEL, OSHA PELSTEL, NIOSH REL-Ceiling, ACGIH TLV-Ceiling, OSHA PEL-Ceiling) converted from ppm to m g /m 3 

### through conversion factor. If acute toxicity data we re not available, the highest chronic exposure lim it (NIOSH REL-TWA, ACGIH TLV-TWA, OSHA PEL-TWA) was used. See Appendix J of this m a nual for full list of acute and chronic exposure data.
Discharge Screening Level = Exposure Lim it / Henry ’ s Law Constant.

I-2Hen r y ' s L a w Co n s tan t s Exp r essed in A l tern ate Un its

Pollutant He nry ' s La w Constant(2) M/atm @ 298 K (25°C) He nry ' s La w Cons ta nt (atm m 3 / mol) He nry ' s La w Cons ta nt (mol/m 3 / mg/L) He nry ' s La w Cons ta nt (mg/m 3 / mg/L)

Acrolein 8.2 0 .00012 0.000087 4.9 Acry lonitrile 0.00011 0.000084 4.5 Benzene 0.0056 0.0029 228 Bromoform 0 .00056 0.000091 23 Carbon T e trachlori de 0.029 0.0077 1185 Chlorobenzene 0.27 0.0037 0.0013 151 Chloroethane 0.089 0.011 0.007 449 Chloroform 0.25 0.004 0.00137 164 1,1-Dichloroethane 0.17 0.0059 0.0024 240 1,2-Dichloroethane 0.85 0.0012 0.00049 48 1,1-Dichloroethy l ene 0.034 0.029 0.012 1202 T r ans-1,2-Dichloroethy lene 0.105 0.0095 0.004 389 1,2-Dichloropropane 0.345 0.0029 0.001 119 Ethy l benzene 0.125 0.008 0.0031 327 Hy drogen Cy anide 9.3 0 .00011 0.00017 4.5 Hy drogen Sulfide 0 .1 0.01 0.017 414.4 Methy l bromide 0.16 0.0063 0.0027 256 Methy l chloride 0.11 0.0091 0.0074 372 Methy l ene Chloride 0.39 0.0026 0.0012 105 1,1,2,2,-T etrachlore thane 2.2 0 .00045 0.00011 19 T e trachloroethy l ene 0.057 0.018 0.00432 717 T o luene 0.15 0.0067 0.003 273 1,1,2-T r ichloroet hane 1.2 0 .00083 0.00026 34 1,1,1-T r ichloroet hane 0.059 0.017 0.0052 693 T r ichloroethy lene 0.1 0 .01 0 .0031 409 Viny l Chloride 0.039 0.026 0.017 1048 9.15 0.18

1.8 0.034



### Source: Compilation of Henry’s Law Constants for Inorganic and Organic Species of Potential Importance in Environmental Chemistry , R. Sanders 1999 (version 3). H (atm m 3 

### /m ol) = [ 986.9 * H (M/atm )] -1 H (m g/m 3 

### / m g /L) = 40,893 * H(atm m 3 

### / m o l) H (m ol/m 3 

### m g /L) = H (m g/m 3 

### / m g /L) / (1000 * MW) MW = m o lecular weight in gram s per m o le

I-3

### This page intentionally left blank.

I-4

# A PPENDIX 

# J

- OSHA, ACGIH AND 

# NIOSH E XPOSURE 

# L EVELS

EXPOSURE LIMITS FROM VA RIOUS A G E NCIES FOR VOLA TILE ORGA NIC PRIORITY POLLUTA NTS
OSHA Permissible Ex pos ure Limits A C GIH Thre s hold Limit Va lue s NIOSH Reco mmen d e d Ex pos ure Limits Volatile Organic Compounds mg/m 3 pe r ppm TWA ppm STEL ppm TVA ppm STEL ppm TWA ppm STEL ppm Acrolein 2.29 0.1 0.1 0.1 0 .3 Acry lonitrile 2.17 2 C 10 2 1 C 10 Benzene 3.19 1 5 .5 2.5 0 .1 1 Bromoform 10.34 0.5 0.5 Carbon T e trachloride 6.29 10 C 25 5 10 2 Chlorobenzene 4.61 75 Chloroethane (Ethy l chloride) 2.64 1000
Chloroform 4.88 C 50 10 2 Dichloroethane, 1,1- 4.05 100 100 Dichloroethane,1,2

- (Ethy l ene dichloride) 4.05 50 C 100 10 1 2 Dichloroethy lene, 1,1

- (Viny lidene chloride) 3.96 5
transDichloroethy lene,1,2

- (1,2-Dichloroethy lene) 3.97 200 200 200
Dichloropropane,1,2

- (Propy lene dichloride) 4.62 75 75 110 Ethy l benzene 4.34 100 100 100 125 Hy drogen Cy anide 1.10 10 C 4.7 4 .7 Hy drogen Sulfide 1.40 C 20 10 15 C 10 Methy l bromide 3.89 C 20 1 Methy l chloride 2.07 100 C 200 50 100 Methy l ene Chloride (Dichloromethane) 3.47 25 125 50
T e trachlorethane, 1,1,2,2- 6.87 5
T e trachloroethy lene (Perchloroethy lene) 6.78 100 C 200 25 100
T o luene 3.77 200 C 300 50 100 150 T r ichloroethane, 1,1,2- 5.46 10 10 T r ichloroethane, 1,1,1 (Methy l Chloroform) 5.46 350 350 450 350
T r ichloroethy lene 5.37 100 C 200 50 100 25 C 2 Viny l Chloride 2.56 1 5 C

0.5

10
100

100

125

1

10
C

C 5



### J-1

### Occupational Safety and Health Administration P ermissible Exposure Limits (PELs) (29 CFR 1910.1000) PEL tim e-weighted average (TWA) concentrations m u st not be exceeded during any 8-hour workshift of a 40-hour workweek. PEL short-term exposure lim it (STEL) m u st not be exceeded over a 15-m i nute period unless noted otherwise. PEL ceiling concentrati ons (designated by “C” preceding the value in the STEL colum n ) m u st not be exceeded during any part of the workday ; if instantaneous m onitoring is not feasible, the ceiling m u st be assessed as a 15-m i nute TWA exposure. OSHA values were updated in 2002 via the online NIOSH Pocket Guide to Chemical Hazards

. http://www.cdc.gov/niosh/npg/npg.htm l

. American Conference of Governmental Industrial Hy gienists (ACGIH) Threshold Limit Values (TLVs) TLV Tim e -weighted average (TVA) concentrations are for a conventional 8-hour workday and a 40-hour workweek for which it is believed that nearly all workers m a y be repeatedly exposed, day after day , without adverse effect. TLV short-term exposur e lim it (STEL) concentrations are the 15-m i nute TWA exposure which should not be exceeded at any tim e dur ing a workday even if the 8-hour TWA is within the TLV-TWA. TLV ceiling concentrations (designated by a “C” preceding the value in the STEL colum n ) should not be exceeded during any part of the working exposure. ACGIH values found in the ACGIH 2002 TLVs and BEIs.
National Institute for Occupational Safety and Hea lth (NIOSH) Recommended Exposure Limits (RELs) REL tim e-weighted average (TWA) concentrations m u st not be exceeded over a 10-hour workday during a 40-hour workweek. REL short-term exposure lim its (STELs) are a 15-m i nute TWA exposure that should not be exceeded at any tim e during a workday

. A ceiling REL, designated by “C” preceding the value in the STEL colum n , should not be exceeded at any tim e. NIIOSH values updated in 2003 via the online NIOSH Pocket Guide to Chemical Hazards at http://www.cdc.gov/niosh/npg/npg.htm l

.

J-2

# A PPENDIX 

# K

- L ANDFILL 

# L EACHATE 

# L OADINGS

Landfill Leachate Monitoring Data*

Maximu m Pollutant Min i mu m Co n cen tratio n (mg/L)

Antimony Arsenic Barium Cadmium Chromium (T ) Copper Cy anide Iron Lead Manganese Mercury Nickel Selenium Silver Z i nc

Acetone Benzene Benzoic Acid Chlorobenzene Chloroethane p-chloro-m-Cresol 1,4-Dichlorobenzene 1,1-Dichloroethane 1,2-Dichloroethane Ethy lbenzene Methy l Buty l Ketone Methy l Ethy l Ketone 4-Methy l phenol Naphthalene N-Nitrosodipheny lamine Pentachlorophenol Phenol Conc e n tra t ion (mg/L)
INORGANICS 0.008 0 .002

< 0 .1

< 0.001 0 .007 0.007 0.04 1.5 0.005 0 .63

< 0002 0 .003

< 002

< 0.01

< 01 0.3 0.13 0.55 1.25 12.1 10.87 0.05 4500

9.8 73.2 0.002 12.09 0.02 0.05 58 ORGANICS 2.8

< 0.002

0.02 0.011

< 0.001 0 .018

< 0.005

< 0.001

< 0.005 0.017 0.028

5.3 0.065

< 0.01 0.011 0.016 0.008 2 .8 0.031

< 0.4 0.011

< 0.1 0.018

< 0.4 0.052

6.8 0.54 0.16 2 9 0.065

< 0 .4 0.011 0.016

2.9 A v erag e Co n cen tratio n (mg/L)

0 .142 0.042 0.201 0.03 0 .633 0.395 0.029 33.8 0 .156 13.224 0.002 0.55 0.01 0.019 12.006

2.8 0.025 0 .19 0.011 0 .021 0.018 0 .101 0.002 1 .136 0.171 0.094 13.633 0.065 0.113 0.011 0.016 1 .06



### K-1Pollutant Min i mu m Co n cen tratio n (mg/L)
T o luene T r ichloroethene 1,1,1-T r ichloroet hane 2,4-Dimethy l Phenol Diethy l Phthalate Dimethy l Phthalat e Di-N-Buty l Phthal ate Viny l Acetate Viny l Chloride 0.0082

< 0.001 0.011 0.005 0 .11 0 .0049 0 .0044

0 .25

< 0.002 Maximu m Conc e n tra t ion (mg/L)
1.6

< 0.1 0.022

< 0.4 0 .11 0.0049 0.0044

0 .25 0.21 A v erag e Co n cen tratio n (mg/L)
0 .735 0 .025 0.019 0 .107 0 .11 0.0049 0.0044

0 .25 0.067

* N umber of detections/number of observations could not be determined from data provided.



### Source: U.S. EPA’s Supplemental Manual on the Development and Implementation of Local Discharge Limitations Under the Pretreatment Programs , May 1991, pp. 1-30 and 1-31. “Pollutant levels reported below specified detection lim it were consid ered in the data analy s is and, for the purpose of statistical analy s is, were considered to be equal to the detection lim it.”

Most Common Landfill Leachates*

Pollutant Conc e n tra t ion Ra nge (parts per million)

INORGANICS** Arsenic 0 .0002 - 0.982 Barium 0.11 - 5 Cadmium 0 .007 - 0.15 Chloride 31 - 5,475 Chromium (T otal) 0 .0005 - 1.9 Copper 0.03 - 2.8 Iron 0.22 - 2,280 Lead 0.005 - 1.6 Manganese 0 .03 - 79 Nickel 0 .02 - 2.2 Nitrate 0.01 - 51 Sodium 12 - 2,574 Sulfate 8 - 1,400 ORGANICS*** 1,1-Dichloroethane 0.004 - 44 T r ans-1,2-Dichloroethy lene 0.002 - 4.8 Ethy lbenzene 0.006 - 4.9 Methy l ene Chloride 0.002 - 220 Phenol 0.007 - 28.8 T o luene 0.006 - 18



### K-2

### * L eachate data is com p iled from a database of 70 MSWLFs [ U .S. EPA 1988. Summary of Data on Municipal Solid Waste Landfill Leachate Characteristics-Criteria for Municipal Solid Waste Landfills (40 CFR Part 258) - Subtitle D of Resource Conservation and Recovery Act (Background Docum e nt)] . Washington, DC: Office of Solid Waste.

** Leachate data from 62 landfills.

*** Leachate data from 53 landfills.

Source: U.S. EPA’s National Pretreatment Program Report to Congress , July 1991, p. 3-81.

Co n t amin an t Co n cen tratio n Ran g es in Mu n i cip a l L each ate

Pollutant Parameter Ge orge (1972) Chain /DeWalle

(1977)

BOD

pH T SS 9 - 54,610

3.7 - 8.5 6 - 2,685

Alkalinity 0 - 20,850 Bicarbonate Chlorides COD

F l uorides Hardness 0 - 22,800 NH 3 -Nitrogen 0 - 1,106 NO -Nitrogen 34 - 2,800 0 - 89,520

Organic NitrogenOrthoPhosphorus Sulfates Sulfide T O C T D S

To t a l

- K

Nitrogen To t a l Phosphorus T o tal Solids 0 - 1,300

1 - 1,826

0 - 42,276

0 - 1,416

1 - 154

Aluminum Arsenic Barium Bery llium 81 - 33,360 2,200 - 720,000 3.7 - 8.5 3 .7 - 8. 5 10 - 700 13 - 26,500 Metry / Cro ss (1977) CONVENT IONAL Cameron (1978) Wis c ons in Re port (20 Sites) Sobotk a Re port (44 Sites)

9 - 55,000 ND - 195,000 7 - 21,600

3.7 - 8.5 5 - 8.9 5.4 - 8.0 2 - 140,900 28 - 2,835 NON-CONVENT IONAL 0 - 20,850 310 - 9,500 0 - 20,900 ND - 15,050 0 - 7,375 3,260 - 5,730 4.7 - 2,467 47 - 2,350 34 - 2,800 2 - 11,375 120 - 5,475 40 - 89,520 800 - 750,000 0 - 9,000 6.6 - 97,900 440 - 50,450

0 - 2.13 0 - 0.74 0.12 - 0.790 0 - 22,800 35 - 8, 700 0 - 22,800 52 - 225,000 0.8 - 9,380

0 - 1,106 0. 2 - 845 0 - 1,106 11.3 - 1,200 0.2 - 1,0.29 4.5 - 18 0 - 5,0.95

2.4 - 550 4.5 - 78.2

6.5 - 85 0.3 - 136 0 - 154

1 - 1,558 20 - 1,370 0 - 1,826 ND - 1,850 8 - 500 0 - 0.13 256 - 28,000 ND - 30,500 5 - 6,884 584 - 44,900 100 - 51, 000 0 - 42,300 584 - 50,430 1,400 - 16,120 2 - 3,320 47.3 - 938

0 - 130 ND - 234

0 - 59,200 1,900 - 25,873 MET A LS 0 - 122 ND - 85 0.010 - 5.07 0 - 11.6 ND - 70.2 0 - 0.08

0 - 5.4 ND - 12.5 0.01 - 10 0 - 0.3 ND - 0.36 0.001 - 0.01



### K-3Pollutant Parameter Ge orge (1972) Chain /DeWalle

(1977) Metry / Cro ss (1977) Cameron (1978) Wis c ons in Re port (20 Sites) Sobotk a Re port (44 Sites)
Boron 0.3 - 73 0.867 - 13 Cadmium 0.03 - 17 0 - 0.19 ND - 0.04 0 - 0.1 Calcium 5 - 4,080 60 - 7,200 240 - 2, 570 5 - 4,000 200 - 2,500 95.5 - 2,100 To t a l Chromium 0 - 33.4 ND - 5.6 0.001 - 1.0
Copper 0 - 9.9 0 - 9.9 0 - 10 ND - 4.06 0.003 - 0.32 Cy anide 0 - 0.11 ND - 6 0 - 4.0 Iron 0.2 - 5,500 0 - 2,820 0.12 - 1, 700 0.2 - 5,500 ND - 1,500 0.22 - 1,400 Lead 0 - 0.5 < 0 .10 - 2.0 0 - 5.0 0 - 14.2 0.001 - 1.11 Magnesium 16.5 - 15,600 17

- 15,600 64 - 547 16.5 - 15,600 ND - 780 76 - 927 Manganese 0.06 - 1,400 0.09 - 125 13 0.06 - 1,400 ND - 31.1 0.03 - 43 Mercury 0 - 0.064 ND - 0.01 0 - 0.02 Moly bdenum 0 - 0.52 0.01 - 1.43 Nickel 0.01 - 0.8 ND - 7.5 0.01 - 1.25 Potassium 2.8 - 3,770 28 - 3,770 28 - 3,800 2.8 - 3,770 ND - 2,800 30 - 1,375 Sodium 0 - 7,700 0 - 7,700 85 - 3,800 0 - 7,700 12 - 6,010 T i tanium 0 - 5.0 < 0 .01 Vanadium 0 - 1.4 0.01 Z i nc 0 - 1,000 0 - 370 0.03 - 135 0 - 1,000 ND - 731 0.01 - 67



### All concentrations in m g /L, except pH (standard units). ND = Non-detect

Source: U .S. EPA’s Technical Development Document for Proposed Effluent Limitations Guidelines and Standards for the Landfills Point Source Category , EPA 821-R-97-022, January 1998, Table 6-3.

* Literature sources were:
George, J. A., Sanitary Landfill-Gas and Leachate Control, the National Perspective , Office of Solid Waste Managem e nt Program s, U.S. EPA, 1972.
Chian, E. S., and F. B. DeWalle, Evaluation of Leachate Treatment, Volume I, Characterization of Leachate , EPA-600/2-77-186a.
Metry , A. A., and F. L. Cross, Leachate Control and Treatment , Volum e 7, Environm ental Monograph Series, Technom ic Publishing Co., Westport, CT, 1977.

Cam e ron, R. D., The Effect of Solid Waste Landfill Leachates on Receiving Waters , Journal AWWA, March 1978.
McGinley , Paul M., and Peter Met. Formation, Characteristics, Trea tment and Disposal of Leachate from Municipal Solid Waste Landfills , Wisconsin Departm e nt of Natura l Resources Special Report, August 1, 1984.
Sobotka & Co., Inc., Case History Data Com p iled a nd Reported to the U.S. Environm ental Protection Agency Econom ic Analy s is Branch, Office of Solid Waste, 1986.

K-4

# A PPENDIX 

# L

- H AULED 

# W ASTE 

# L OADINGS

Se pta g e Ha ule r Monitoring Da ta

Pollutant Number of Detectio n s Number of Samples Minimum Co n cen tratio n (mg/L) Maximu m Co n cen tratio n (mg/L) A v erag e Co n cen tratio n (mg/L)
INORGANICS 144 145 0 3 .5 0.141 128 128 0.002 202 5.758 825 1097 0.005 8.1 0 .097 931 1019 0.01 34 0.49

1 6 3 2 < 0.003 3.45 0.406 963 971 0.01 260.9 4 .835 575 577 0.001 1.53 0.469 464 464 0.2 2740 39.287 962 1067 < 0.025 118 1.21

5 5 0 .55 17.05 6.088 582 703 0.0001 0.742 0.005 813 1030 0.01 37 0.526 237 272 < 0.003 5 0 .099

1 1 2 5 < 015 1 0 .076 959 967 < 0.001 444 9.971 Arsenic Barium Cadmium Chromium (T ) Cobalt Copper Cy anide Iron Lead Manganese Mercury Nickel Silver T i n Z i nc

COD

Acetone Benzene Ethy lbenzene Isopropy l Alcohol Methy l Alcohol Methy l Ethy l Ketone Methy l ene Chloride T o luene Xy lene NONCONVENT IONALS

ORGANICS

183 510

118 112 115 117 117 115 115 113

87 118 0 112 0.005 115 0.005 117 1 117 1 115 1 115 0.005 113 0.005

87 0.005 183 117500 21247.951

210 3.1 1.7 391 396 240 2.2 1.95 0.72 10.588 0 .062 0 .067 14.055

15.84

3.65 0 .101

0.17 0.051



### Source: U.S. EPA’s Supplemental Manual on the Development and Implementation of Local Discharge Limitations Under the Pretreatment Programs , 21W-4002, May 1991, pp. 1-27 and 1-28. “Pollutant levels reported below specified detec tion lim it were considered in the data analy s is and, for the purpose of statistical analy s is, we re considered equal to the detection lim it.”

L-1

### This page intentionally left blank.

L-2

# A PPENDIX 

# M

- H AZARDOUS 

# W ASTE 

# C ONSTITUENTS



# - RCRA A PPENDIX 

# VIII

Constituent CA S No. Ha za rdous W aste No

. A2213 30558-43-1 U 394 Acetonitrile 75-05-8 U 003 Acetophenone 98-86-2 U 004 2-Acety l aminefluarone 53-96-3 U 005 Acety l chloride 75-36-5 U 006 1-Acety l -2-thiourea 591-08-2 P 002 Acrolein 107-02-8 P 003 Acry lamide 79-06-1 U 007 Acry lonitrile 107-13-1 U 009 Aflatoxins 1402-68-2

- Aldicarb 116-06-3 P 070 Aldicarb sulfone 1646-88-4 P 203 Aldrin 309-00-2 P 004 Ally l alcohol 107-18-6 P 005 Ally l chloride 107-18-6

- Aluminum phosphide 20859-73-8 P 006 4-Aminobipheny l 92-67-1

- 5-(Aminomethy l)-3-isoxazolol 2763-96-4 P 007 4-Aminopy ridine 504-24-5 P 008 Amitrole 61-82-5 U 011 Ammonium vanadate 7803-55-6 P 119 Aniline 62-53-3 U 012 Antimony 7440-36-0

- Antimony compounds, N.O.S.

- - Aramite 140-57-8

- Arsenic 7440-38-2

- Arsenic compounds, N.O.S.

- - Arsenic acid 7778-39-4 P 010 Arsenic pentoxide 1303-28-2 P 011 Arsenic trioxide 1327-53-3 P 012 Auramine 492-80-8 U 014 Azaserine 115-02-6 U 015 Barban 101-27-9 U 280 Barium 7440-39-3

- Barium compounds, N.O.S.

- - Barium cy anide 542-62-1 P 013 Bendiocarb 22781-23-3 U 278 Bendiocarb phenol 22961-82-6 U 364 Benomy l 17804-35-2 U 271 Benz[c]acridine 225-51-4 U 016 Benz[a]anthracene 56-55-3 U 018 Benzal chloride 98-87-3 U 017 Benzene 71-43-2 U 019 Benzenearsonic acid 98-05-5

- Benzidine 92-87-5 U 021 Benzo[b]fluoranthene 205-99-2

- Benzo[j]fluoranthene 205-82-3

- Benzo(k)fluoranthene 207-08-9

- Benzo[a]py rene 50-32-8 U 022 p-Benzoquinone 106-51-4 U 197 Benzotrichloride 98-07-7 U 023 Benzy l chloride 100-44-7 P 028 Bery llium pow der 7440-41-7 P 015 Bery llium compounds, not otherw i se specified (NOS)

- -



### M-1Constituent CA S No. Ha za rdous W aste No

. Bis(pentamethy lene)-thiur am tetrasulfide 120-54-7

- Bromoacetone 598-31-2 P 017 Bromoform 75-25-2 U 225 4-Bromopheny l pheny l ether 101-55-3 U 030 Brucine 357-57-3 P 018 Buty l benzy l phthalate Buty late Cacody lic acid Cadmium 85-68-7

- 2008-41-5

- 75-60-5 U 136 7440-43-9

- Cadmium compounds, NOS Calcium chromate - 13765-19-0 - U 032

Carbofuran Carbofuran phenol Carbon disulfide Carbon oxy f luoride Carbon tetrachloride Carbosulfan Chloral Calcium cy anide Carbary l Carbendazim 592-01-8 P 021 63-25-2 U 279 10605-21-7 U 372

1563-66-2 P 127 1563-38-8 U 367

75-15-0 P 022 353-50-4 U 033

56-23-5 U 211 55285-14-8 P 189 75-87-6 U 034 305-03-3 U 035

57-74-9 U 036 Chlorambucil Chlordane Chlordane (alpha and gamma isomers) Chlorinated benzenes, NOS - U 036

- - Chlorinated ethane, NOS Chlorinated fluorocarbons, NOS -

- -

-

Chlorobenzene Chlorobenzilate p-Chloro-m-cresol 2-Chloroethy l viny l ether Chloroform Chloromethy l methy l ether beta-Chloronaphthalene o-Chlorophenol 1-(o-Chloropheny l)thiourea Chloroprene 3-Chloropropionitrile Chromium Chromium compounds, NOS Chry sene Citrus red No. 2 Coal tar creosote Copper cy anide Copper dimethy l dithiocarbamate Creosote Cresol (Cresy lic acid) Crotonaldehy de m-Cumeny l methy l carbamate Cy anides (soluble salts and complexes), NOS

- P030 Cy anogen 460-19-5 P 031 Cy anogen bromide 506-68-3 U 246 Cy anogen chloride 506-77-4 P 033 Chlorinated naphthalene, NOS Chlorinated phenol, NOS Chlornaphazin Chloroacetaldehy de Chloroalky l ethers, NOS p-Chloroaniline - -

- - 494-03-1 U 026 107-20-0 P 023

- - 106-47-8 P 024 108-90-7 U 037 510-15-6 U 038

59-50-7 U 039 110-75-8 U 042

67-66-3 U 044 107-30-2 U 046

91-58-7 U 047 95-57-8 U 048 5344-82-1 P 026

126-99-8

- 542-76-7 P 027 7440-47-3

-

- - 218-01-9 U 050 6358-53-8

- 8007-45-2

-

544-92-3 P 029 137-29-1

-

- U051 1319-77-3 U 052 4170-30-3 U 053 64-00-6 P 202



### M-2Constituent CA S No. Ha za rdous W aste No

. Cy casin 14901-08-7

- 1134-23-2

-

131-89-5 P 034

50-18-0 U 058 94-75-7 U 240 Cy cloate 2-Cy clohexy l -4,6-dinitrophenol Cy clophosphamide 2,4-D 2,4-D, salts, esters Daunomy c in Dazomet - U 240 20830-81-3 U 059 533-74-4

-

72-54-8 U 060 72-55-9

- 50-29-3 U 061 2303-16-4 U 062

226-36-8

- DDD DDE DDT Diallate Dibenz[a,h]acridine Dibenz[a,j]acridine Dibenz[a,h]anthracene 7H-Dibenzo[c,g]ca rbazole Dibenzo[a,e]py rene Dibenzo[a,h]py rene Dibenzo[a,i]py rene 1,2-Dibromo-3-chloropropane Dibuty l phthalate 224-42-0

- 53-70-3 U 063 194-59-2

- 192-65-4

- 189-64-0

- 189-55-9 U 064

96-12-8 U 066 84-74-2 U 069 95-50-1 U 070 541-73-1 U 071 o-Dichlorobenzene m-Dichlorobenzene p-Dichlorobenzene Dichlorobenzene, NOS 106-46-7 U 072 25321-22-6

- 3,3’-Dichlorobenzidine 1,4-Dichloro-2- butene 91-94-1 U 073 764-41-0 U 074 Dichlorodifluoromethane Dichloroethy lene, NOS 1,1-Dichloroethy lene 1,2-Dichloroethy lene Dichloroethy l ether Dichloroisopropy l ether Dichloromethoxy ethane Dichloromethy l ether 2,4-Dichlorophenol 2,6-Dichlorophenol Dichloropheny larsine Dichloropropane, NOS Dichloropropanol, NOS Dichloropropene, NOS 1,3-Dichloropropene Dieldrin 75-71-8 U 075 25323-30-2

- 75-35-4 U 078 156-60-5 U 079 111-44-4 U 025 108-60-1 U 027 111-91-1 U 024 542-88-1 P 016 120-83-2 U 081

87-65-0 U 082 696-28-6 P 036 26638-19-7

- 26545-73-3

- 26952-23-8

- 542-75-6 U 084

60-57-1 P 037 1,2:3,4-Diepoxy butane Diethy l arsine Diethy l ene gly c ol, dicarbamate 1,4-Diethy l eneoxide Diethy l hexy l phthalate N,N' -Diethy l hy drazine O,O-Diethy l S-methy l dithiophosphate Diethy l -p-nitropheny l phosphate Diethy l phthalate O,O-Diethy l O-py raziny l phosphoro- thioate Diethy l stilbesterol Dihy d rosafrole Diisopropy lfluorophosphat e (DFP) Dimethoate 1464-53-5 U 085 692-42-2 P 038 5952-26-1 U 395

123-91-1 U 108 117-81-7 U 028 1615-80-1 U 086 3288-58-2 U 087

311-45-5 P 041

84-66-2 U 088 297-97-2 P 040

56-53-1 U 089 94-58-6 U 090 55-91-4 P 043 60-51-5 P 044 3,3' -Dimethoxy benzidine 119-90-4 U 091 p-Dimethy laminoazobenzene 60-11-7 U 093



### M-3Constituent CA S No. Ha za rdous W aste No

. 7,12-Dimethy lbenz[a] anthracene 57-97-6 U 094 119-93-7 U 095

79-44-7 U 097 57-14-7 U 098 540-73-8 U 099 122-09-8 P 046 105-67-9 U 101 3,3' -Dimethy lbenzidine Dimethy lcarbamoy l chloride 1,1-Dimethy lhy d razine 1,2-Dimethy lhy d razine alpha,alpha-Dimethy lphenet hy lamine 2,4-Dimethy lphenol Dimethy l phthalate Dimethy l sulfate 131-11-3 U 102 77-78-1 U 103 644-64-4 P 191 25154-54-5

- Dimetilan Dinitrobenzene, NOS 4,6-Dinitro-o-cresol 4,6-Dinitro-o-cresol salts 534-52-1 P 047

- P047 2,4-Dinitrophenol 2,4-Dinitrotoluene 51-28-5 P 048 121-14-2 U 105 2,6-Dinitrotoluene Dinoseb 606-20-2 88-85-7 U 106 P 020 Di-n-octy l phthalate Dipheny lamine 1,2-Dipheny lhy d razine Di-n-propy lnitrosamine Disulfiram 117-84-0 U 017 122-39-4

- 122-66-7 U 109 621-64-7 U 111

97-77-8

- 298-04-4 P 039 541-53-7 P 049 115-29-7 P 050 145-73-3 P 088

72-20-8 P 051

- P 051 106-89-8 U 041 Disulfoton Dithiobiuret Endosulfan Endothall Endrin Endrin metabolites Epichlorohy drin Epinephrine EPT C 51-43-4 P 042 759-94-4

-

F l uoroacetic acid, sodium salt F o rmaldehy de F o rmetanate hy drochloride F o rmic acid F o rmparanate Gly c idy l aldehy de U 238 P 101

- U 114 U 114 U 067 U 077 U 359 P 054 U 115 U 116 U 076 U 118 U 119 P 097

- 206-44-0 U 120 7782-41-4 P 056

640-19-7 P 057

62-74-8 P 058 50-00-0 U 122 23422-53-9 P 198 64-18-6 U 123 17702-57-7 P 197

765-34-4 U 126 Ethy l carbamate (urethane) Ethy l cy anide Ethy l Z i ram Ethy lenebisdithiocar bamic acid Ethy lenebisdithiocarbamic acid, salts and esters Ethy lene dibromide Ethy lene dichloride Ethy lene gly c ol monoethy l ether Ethy leneimine Ethy lene oxide Ethy lenethiourea Ethy lidene dichloride Ethy l methacry late Ethy l methanesul fonate F a mphur F e rbam 51-79-6 107-12-0 14324-55-1

111-54-6

- 106-93-4 107-06-2 110-80-5 151-56-4

75-21-8 96-45-7 75-34-3 97-63-2 62-50-0 52-85-7 14484-64-1 F l uoranthene F l uorine F l uoroacetamide



### M-4Constituent CA S No. Ha za rdous W aste No

. Halomethanes, NOS

- - Heptachlor Heptachlor epoxide Heptachlor epoxide (alpha, beta, and gamma isomers) Heptachlorodibenzofurans Heptachlorodibenzo-p-dioxins Hexachlorobenzene 76-44-8 P 059 1024-57-3

-

- -

- -

- - 118-74-1 U 127 Hexachlorobutadiene Hexachlorocy clopentadiene Hexachlorodibenzo-p-dioxins Hexachlorodibenzofurans 87-68-3 U 128 77-47-4 U 130

- -

- - Hexachloroethane Hexachlorophene Hexachloropropene Hexaethy l tetraphosphate Hy drazine Hy drogen cy anide Hy drogen fluoride Hy drogen sulfide Indeno[1,2,3-cd]p y r ene 3-Iodo-2-propy ny l n-but y l carbamate Isobuty l alcohol Isodrin 67-72-1 U 131 70-30-4 U 132 1888-71-7 U 243

757-58-4 P 062 302-01-2 U 133

74-90-8 P 063 7664-39-3 U 134 7783-06-4 U 135

193-39-5 U 137 55406-53-6

- 78-83-1 U 140 465-73-6 P 060 119-38-0 P 192 120-58-1 U 141 Isolan Isosafrole Kepone Lasiocarpine Lead 143-50-0 U 142 303-34-1 U 143 7439-92-1

-

- - 301-04-2 U 144 7446-27-7 U 145 1335-32-6 U 146

58-89-9 U 129 Lead compounds, NOS Lead acetate Lead phosphate Lead subacetate Lindane Maleic anhy dride Maleic hy drazide Malononitrile 108-31-6 U 147 123-33-1 U 148 109-77-3 U 149 15339-36-3 P 196 Manganese dimethy l dithiocarbamate Melphalan Mercury Mercury compounds, NOS Mercury fulminate Metam Sodium 148-82-3 U 150 7439-97-6 U 151

- - 628-86-4 P 065 137-42-8

- 126-98-7 U 152 Methacry lonitrile Methapy rilene Methiocarb 91-80-5 U 155 2032-65-7 P 199 Methomy l Methoxy c hlor Methy l bromide 74-83-9 U 029 Methy l chloride 74-87-3 U 045 Methy l chlorocarbonate 79-22-1 U 156 Methy l chloroform 71-55-6 U 226 3-Methy l cholanthrene 56-49-5 U 157 4,4’-Methy l enebis(2-chloroaniline) 101-14-4 U 158 Methy l ene bromide 74-95-3 U 068 Methy l ene chloride 75-09-2 U 080 Methy l ethy l ketone (MEK) 78-93-3 U 159 Methy l ethy l ketone peroxide 1338-23-4 U 160 Methy l hy drazine 60-34-4 P 068 Methy l iodide 74-88-4 U 138 16752-77-5 P 066 72-43-5 U 247



### M-5Constituent CA S No. Ha za rdous W aste No

. Methy l isocy anate 624-83-9 P 064 75-86-5 P 069 80-62-6 U 162 66-27-3

- 298-00-0 P 071

56-04-2 U 164 1129-41-5 P 190 2-Methy llactonitrile Methy l methacry late Methy l methanesulfonate Methy l parathion Methy l thiouracil Metolcarb
Mitomy cin C MNNG 315-18-4 P 128 50-07-7 U 010 70-25-7 U 163 2212-67-1

- Mexacarbate

Molinate Mustard gas Naphthalene 1,4-Naphthoquinone alpha-Naphthy lamine beta-Naphthy lamine alpha-Naphthy lthiourea Nickel 505-60-2

- 91-20-3 U 165 130-15-4 U 166 134-32-7 U 167

91-59-8 U 168 86-88-4 P 072 7440-02-0

-

- - Nickel compounds, NOS Nickel carbony l Nickel cy anide Nicotine 13463-39-3 P 073 557-19-7 P 074

54-11-5 P 075

- P075 10102-43-9 P 076

100-01-6 P 077

98-95-3 U 169 10102-44-0 P 078 Nicotine salts Nitric oxide p-Nitroaniline Nitrobenzene Nitrogen dioxide Nitrogen mustard Nitrogen mustard, hy drochloride salt Nitrogen mustard N-oxide Nitrogen mustard, N-oxide, hy dro- chloride salt Nitrogly c erin p-Nitrophenol 2-Nitropropane Nitrosamines, NOS 51-75-2

-

- - 126-85-2

-

- - 55-63-0 P 081 100-02-7 U 170

79-46-9 U 171 35576-91-1D

- N-Nitrosodi-n-buty l amine N-Nitrosodiethanolamine 924-16-3 1116-54-7 U 172 U 173 N-Nitrosodiethy lamine N-Nitrosodimethy l amine N-Nitroso-N-ethy l urea N-Nitrosomethy l ethy lamine N-Nitroso-N-methy l urea N-Nitroso-N-methy l urethane N-Nitrosomethy l viny lamine N-Nitrosomorpholine N-Nitrosonornicotine 55-18-5 U 174 62-75-9 P 082 759-73-9 U 176 10595-95-6

-

684-93-5 U 177 615-53-2 U 178 4549-40-0 P 084 59-89-2

- 16543-55-8

-

100-75-4 U 179 N-Nitrosopiperidine N-Nitrosopy rrolidine N-Nitrososarcosine 930-55-2 U 180 13256-22-9

- Octamethy l py rophosphoramide Osmium tetroxide 99-55-8 U 181 152-16-9 P 085 20816-12-0 P 087 23135-22-0 P 194 5-Nitro-o-toluidine

Oxamy l Paraldehy de Parathion 123-63-7 U 182 56-38-2 P 089 Pebulate Pentachlorobenzene 1114-71-2

- 608-93-5 U 183

- -

- - Pentachlorodibenzo-p-dioxins Pentachlorodibenzofurans



### M-6Constituent CA S No. Ha za rdous W aste No

. Pentachloroethane 76-01-7 U 184 Pentachloronitrobenzene (PCNB) Pentachlorophenol Phenacetin 82-68-8 U 185 87-86-5 F 027 62-44-2 U 187 108-95-2 U 188 Phenol Pheny lenediamine Pheny lmercury acetate Pheny lthiourea Phosgene Phosphine Phorate 25265-76-3

- 62-38-4 P 092 103-85-5 P 093

75-44-5 P 095 7803-51-2 P 096

298-02-2 P 094 Phthalic acid esters, NOS Phthalic anhy dride Phy s ostigmine Phy s ostigmine salicy l ate 2-Picoline - - 85-44-9 U 190 57-47-6 P 204 57-64-7 P 188 109-06-8 U 191

- Poly chlorinated bipheny ls, NOS Potassium cy anide Potassium dimethy l dithiocarbamate Potassium n-hy droxy m ethy l-n -methy l -dithiocarbamate Potassium n-methy l dithiocarbamate 151-50-8 P 098 128-03-0

- 51026-28-9

- 137-41-7

- 7778736

- 506-61-6 P 099 2631-37-0 P 201 23950-58-5 U 192 Potassium pentachlorophenate Potassium silver cy anide Promecarb Pronamide 1,3-Propane sultone n-Propy lamine Propargy l alcohol Propham Propoxur Propy lene dichloride 1,2-Propy lenimine Propy lthiouracil Prosulfocarb 1120-71-4 U 193 107-10-8 U 194 107-19-7 P 102 122-42-9 U 373 114-26-1 U 411

78-87-5 U 083 75-55-8 P 067 51-52-5

- 52888-80-9 U 387 110-86-1 U 196 Py ridine Reserpine Resorcinol 50-55-5 U 200 108-46-3 U 201

Selenium compounds, NOS Selenium dioxide 81-07-2 U 202

- U202 94-59-7 U 203 7782-49-2

-

- - 7783-00-8 U 204 7488-56-4 U 205

144-34-3

- 630-10-4 P 103 7440-22-4

-

- - 506-64-9 P 104 Saccharin Saccharin salts Safrole Selenium

Selenium sulfide Selenium, tetrakis(dimethy l-dithiocarbamate) Selenourea Silver Silver compounds, NOS Silver cy anide Silvex (2,4,5-T P) Sodium cy anide Sodium dibuty l dithiocarbamate Sodium diethy ldit hiocarbamate Sodium dimethy l dithiocarbamate Sodium pentachlorophenate Streptozotocin Stry chnine 93-72-1 F 027 143-33-9 P 106 136-30-1

- 148-18-5

- 128-04-1

-

131522 None 18883-66-4 U 206 57-24-9 P 108

- P108 95-06-7

- Stry chnine salts Sulfallate



### M-7Constituent CA S No. Ha za rdous W aste No

. T CDD 1746-01-6

- T e trabuty l thiuram disulfide 1,2,4,5-T e trachl orobenzene 1634-02-2

- 95-94-3 U 207 T e trachlorodibenzo-p-dioxins T e trachlorodibenzofurans -

- -

- T e trachloroethane, NOS 1,1,1,2-T e trachl oroethane 25322-20-7

- 630-20-6 U 208

2,3,4,6-T e trac hlorophenol 2,3,4,6-tetrachlorophenol , potassium salt 2,3,4,6-tetrachlorophenol , sodium salt T e traethy ldithiopy rophosphate T e traethy l lead T e traethy l py rophosphate T e tramethy lthiuram monosulfide T e tranitromethane 79-34-5 U 209 127-18-4 U 210

58-90-2 F 027 53535276

- 25567559

- 3689-24-5 P 109

78-00-2 P 110 107-49-3 P 111

97-74-5

- 509-14-8 P 112 7440-28-0

-

- - 1314-32-5 P 113

563-68-8 U 214 1,1,2,2-T e trachl oroethane T e trachloroethy lene

T hallium T hallium compounds, NOS T hallic oxide T hallium(I) acetate T hallium(I) carbonate T hallium(I) chloride T hallium(I) nitrate T hallium selenite 6533-73-9 U 215 7791-12-0 U 216 10102-45-1 U 217 12039-52-0 P 114 T hallium(I) sulfate T h ioacetamide 7446-18-6 62-55-5 P 115 U 218 59669-26-0 U 410 39196-18-4 P 045 74-93-1 U 153 23564-05-8 U 409 T h iodicarb T h iofanox T h iomethanol T h iophanate-methy l T h iophenol T h iosemicarbazide 108-98-5 P 014 79-19-6 P 116 T h iourea 62-56-6 U 219 T h iram 137-26-8 U 244 T i rpate 26419-73-8 P 185 T o luene 108-88-3 U 220 T o luenediamine 25376-45-8 U 221 T o luene-2,4-diamine 95-80-7

- T o luene-2,6-diamine 823-40-5

- T o luene-3,4-diamine 496-72-0

- T o luene diisocy anate 26471-62-5 U 223 o-T o luidine 95-53-4 U 328 o-T o luidine hy drochloride 636-21-5 U 222 p-T o luidine 106-49-0 U 353 T o xaphene 8001-35-2 P 123 T r iallate 2303-17-5 U 389 2,4,6-T r ibromophenol 118-79-6 U 408 1,2,4-T r ichlorobenzene 120-82-1

- 1,1,2-T r ichlor oethane 79-00-5 U 227 T r ichloroethy lene 79-01-6 U 228 T r ichloromethanethiol 75-70-7 P 118 T r ichloromonofluor omethane 75-69-4 U 121 2,4,5-T r ichlorophenol 95-95-4 F 027 2,4,6-T r ichlorophenol 88-06-2 F 027 2,4,5-T 93-76-5 F 027



### M-8Constituent CA S No. Ha za rdous W aste No

. T r ichloropropane, NOS 25735-29-9

- 1,2,3-T r ichloropropane 96-18-4

- T r iethy l amine 121-44-8 U 404 O,O,O-T r iethy l phosphorothioate 126-68-1

- 1,3,5-T r initr obenzene 99-35-4 U 234 T r is(1-aziridiny l)phosphine sulfide 52-24-4

- T r is(2,3-dibromopropy l) phosphate 126-72-7 U 235 T r y pan blue 72-57-1 U 236 Uracil mustard 66-75-1 U 237 Vanadium pentoxide 1314-62-1 P 120 Vernolate 1929-77-7

- Viny l chloride 75-01-4 U 043 W a rfarin, concentrations less than 0.3% 81-81-2 U 248 W a rfarin, concentrations greater than 0.3% 81-81-2 P 001 W a rfarin salts, w hen pr esent at concentrations less than 0.3%. - U 248
W a rfarin salts, w hen present at concentrations greater than 0.3%. - P 001
Z i nc cy anide 557-21-1 P 121 Z i nc phosphide, w hen present at concentrations greater than 10%. 1314-84-7 P 122
Z i nc phosphide, w hen present at concentrations of 10% or less. 1314-84-7 U 249
Z i ram 137-30-4 P 205



### Source: 40 CFR Part 261, Subpart D and Appendix VIII - Hazardous Constituents.

M-9

### This page intentionally left blank.

M-10

# A PPENDIX 

# N

- S TATISTICAL 

# A PPROACH TO 

# D ETERMINING 

# S AMPLING 

# F REQUENCY

The use of statistical analy ses can help establis h an acceptable m i nim u m num ber of sam ples needed to adequately represent a population of pollutants influe nt and effluent at an acceptable confidence level.
The procedure for establishing an acceptable m i nim u m num ber of sam ples is calculated using the technique described in: Statistical M e thods for Environmental Pollution M onitoring (Gilbert, 1987). This text is frequently cited in envi ronm entally related statistical work. The m e thod utilizes Equation 1 to calculate the sam ple size required to estim ate the true m ean of a population, based on the coefficient of variation, a confidence level, and a relative error. The m e thod assum es a norm a l distribution of sam ples.

n = (Z 1- "

/2



### 0



### /d

r 

### ) 2



### Where:

n = S am ple size required for estim ating the true m ean, : 

### Z

1- "

/2 

### = N orm a l deviate of desired confidence level 0 

### = C oefficient of variation d

r 

### = Relative error

The coefficient of variation is determ ined by Equation 2.

0 

### = s / 

# 0

Where:

s = Standard deviation 

# 0 = M ean

The sam ple standard deviation is determ ined by Equation 3. Eq. 1

Eq. 2

Eq. 3

The m ean and standard deviation u sed above should be taken from an acceptable past available sam ple. Both an acceptable confidence level and an acceptable relativ e error m u st be selected , each of which will vary d e p e n d i n g o n t h e ty pe of pollutant being m easured. Se lection of both levels shoul d be determ ined by t h e POTW based on the situation. The confidence level e xpresses the certainty of the estim ated m ean while the relative error indicates the accuracy of the es tim ated m ean com p ared to the true m ean.

Table 1-1 is an exam ple m a trix which applies Equation 1 to calculate sam ple size.

N-1Ta ble 1

- 1

. Confidence Level

(1- 

### " )

0.80 (Z 0.90 = 1.28) Relative Error Sa mple Size s Re quire d for Es tima ting the True Me a n Coefficient of Variation ( 

### 0 )

0 .10 0 .30 0 .50 1 .00 1 .50 2 .00

2 1 5 4 1 164 369 656

0.90 (Z 0.975 = 1.645) 1083 7

1 1 ( d r )

0.10

0.25

- 3 7 2 7 5 9 105

0.50

- 1 2 7 15 27

1

- - - 2 4

0.10 3 2 5 6 8 271 609

0.25

- 4 11 44 98 174

0.50

- 1 3 1 1 2 5 4 4

1

- - 1 3 7



### As shown in Table 1-1, estab lishing the num ber of sam ples needed to estim ate the true m ean is critically dependent on a data set’s coe fficient of variation (CV).

For exam ple, a past, reliable sam ple produced a data set with standard deviation of 2 m g /L and a m ean of 2

m g /L, resulting in CV equal to one. If a confidence level of 0.80 (with a corresponding Z 1- "

/2 

### = 1.28) and a relative error of 0.25 are determ ined to be adequate, then Equation 1 is used as follows:

n = (1.28 * 1 / .25) 2 

### = 26.21

The sam ple size m u st then be rounded to th e next whole num ber, in this case, 27. The 27 sam ples m a y be taken throughout the y ear if d e si r e d , o r as determ ined by the POTW. In the case of taking the sam ples throughout the y ear, the POTW m i ght take two sam ples per m onth and an additional three sam ples at random tim es during the y ear. One sam ple may be evaluate d for m u ltiple contam inants; however, each location would need to be sam pled independently

. Under these conditions, there would be 80% confid ence that t h e estim ated m ean from 27 sam ples (as illustrated in Table 1-1) would be within + 25% of the true m ean. Therefore, if the estim ated m ean is 4 m g /L, there would be 80% confidence that the t r ue m ean is within the interval of 3 to 5 ( i.e. , 4 + 1). If a confidence level of 0

. 9 0 and relative error of 0.10 were desire d, the num ber of sam ples would increase substantially

. Under these conditions, there would be 90% confid ence that the estim ated m ean from 271 sam ples (as illustrated in Table 1-1) would be within + 10% of the true m ean. Therefore, if the estim ated m ean was 2

m g /L, there would be 90% confidence that the true m ean was within the interval of 1.8 to 2.2 ( i.e. , 2 + 0.2).

Source: SAIC. 1998. POTW Metals Analy s is Project, Task 3 Deliverable to U.S. EPA Region VIII, EPA, Contract No. 68-C4-0068; Work Assignm ent Num b er PS-3-1, SAIC Project Num b er 01-0833-08- 2696-800, August 25, 1998.

N-2

# A PPENDIX 

# O

- M INIMIZING 

# C ONTAMINATION IN 

# S AMPLES

Som e of the data reported as below the detection le vel (BDL) m a y be the result of t h e POTW sam pling techniques and chosen analy tical m e thods. With the need to accurately detect trace levels of pollutan t s, POTWs should thoroughly exam ine potential sources of gross and trace contam ination and select analy tical

m e thods that can detect very low levels of po llutants. EPA has established new perform ance based 1 

### sam pling and analy s is m e thods (1600 series) for m easuring 13 toxi c m e tals in the low ppt to ppb range. While these

m e thods were developed for am bient water quality m on itoring, POTWs m a y apply som e of the concepts in Method 1669, Sampling Ambient Water for Determination of Metals at EPA Water Quality Criteria Levels , to im prove the reliability of data collected, potentia lly even utilizing analy tical m e thods 1631, 1632, 1636-40.
Excerpts from Section 4.2.2 of Method 1669 are provided below.

M

i nimizing Contamination: Sampling Location, Sampling Equipment and M a terials, and Chemicals:

C 

### Where possible, limit exposure of the sample and equi pment in areas of higher contamination, e.g., downwind from the sludge beds.

C 

### M i nimize contact with airborne dust, dirt, p a r ticulate matter, or vapors from automobile exhaust; cigarette smoke; nearby corroded or rusted bri dges, pipes, poles, or wires; nearby roads; and even human breath. Areas where nearby soil is bare and subject to wind erosion should be avoided.

C 

### Clean the sampling equipment and minimize the time between cleaning of equipment and use.

C 

### Use metal-free equipment, i.e., equipment shoul d be nonmetallic and free of material that ma y contain metals of interest. When it is not possible to obtain equipment that is completely free of the metal(s) of interest, the sample should not come into direct contact with the equipment.

C 

### Do not use sampling equipment where there are indica tions that it may not be clean, e.g., sampler tubing or collection bottle is stained, has not been changed out in some time, was used to collected a sample of a slug load that hit the W W TP, etc.

C 

### Avoid contamination b y c a r r y o ver. Contamination may occur when a sample containing low concentrations of metals is processed immedi ately after a sample containing relatively high concentrations of these metals.

C 

### Where possible, do not collect, process, or ship sa mple s c o n t a i n i ng high concentrations of metals (e.g., untreated effluents, in-process waters, landfill le achates) at the same time as samples being collected for trace metals determinations.

C 

### Wear clean, non-talc dusted gloves during all opera tions involving handling of equipment, samples, and blanks. Change gloves once they have become contaminated.

1 An alternate procedure or technique may be used so l ong as neither samples nor blanks are contaminated when following alternate procedures.



### O-1

### C 

### Fluoropolymer (FEP, PTFE),

co n ven tional or linear polyethylene, po l y c a r b o nate, polysulfone, polypropylene, or

u ltra pure quartz are th e preferred materials for coming in contact wi th samples. Fluoropolymer o r g l a s s
co ntainers are preferred for samp les that will be analyzed for mercur y because mercury

va

p o rs can diffuse in or out of other materials

, resulting either in contamination or low-biased results. L o t A n aly ses o f Metals in Differen t Grad es o f Nitric A c id (SOURCE-FISHER-INTERNET) Highest Grade Higher Grade High Grade Antimony < 0 .01 ppb < 0 .1 ppb Arsenic < 0.1 ppb < 0 .3 ppb # 4 ppb Cadmium < 0.005 ppb < 0 .1 ppb Chromium < 0 .03 ppb # 9 ppb # 100 ppb Copper # 0.05 ppb < 1 ppb # 50 ppb Lead # 0.01 ppb < 0 .3 ppb # 100 ppb Mercury < 0.1 ppb < 0 .5 ppb Nickel # 0.1 ppb < 1 ppb # 50 ppb Selenium < 0 .5 ppb Silver < 0 .005 ppb < 0 .1 ppb



### C 

### The following materials have been found to cont ain trace met a l s : Pyrex, Kimax, methacrylate, polyvinyl chloride, nylon, Vycor, highly colored plastics, paper cap liners, pigm e n t s u s e d t o mark increments on plastics, and rubber. It is recommended that these materials not be used to hold liquids that come in contact with the sample or must not contact the sample.

C 

### Use an appropriate grade of chemicals when prep p i n g equipment/materials and chemically preserving samples.
Quality Control:

C 

### Serial numbers should be indelibly marked or etched on each piece of Apparatus so that contamination can be traced, and logbooks sh ould be maintained to track the sample from the container through the sampling process to shipment to the laboratory. Chain-of-custody procedures should be used so that contamination can be traced to particular handling procedures or lab personnel.

C 

### Equipment blanks should be periodically generate d and analyzed to identify contamination that may result from improper preparation or handling of sampling equipment and bottles in the laboratory. Equipment blanks include processing reagent water (i.e., water known not to contain pollutants at detectable levels) through sampling equipment and sa mple bottle(s) prior to taking t h e equipment or bottle(s) to the field.

C 

### A trip blank should be periodically generated and analyzed to identify incidental contamination that may occur to sampling equipment/bottles while in transit to and from the sampling lo ca tio n. Essential, reagent water is place in a sa mple bottle prior to going to the field.

C 

### Field blanks should be periodically generated and anal yzed to identify contamination that may occur to sampling equipment/bottles while in the field. Like equipment blanks, it involves process reagent water through the sampling equipment/bottle

.

O-2

# A PPENDIX 

# P

- M ETHODS FOR 

# C ALCULATING 

# R EMOVAL 

# E FFICIENCY

There are three m e thods of calculating rem oval effi ciencies: av erag e daily rem oval efficiency (ADRE)

m e thod, m ean rem oval efficiency (MRE) m e thod, and the decile approach. ADRE across a plant is defined as:

Where: R

potw 

### = P lant rem oval efficiency from headwo rks to plant effluent (as decim a l) I n 

### = POTW g /L E

potw, n 

### = POTW

n = Paired observations, num bered 1 to N
As defined in Equation 5.2, the MRE across a plant is defined as:

Where: R

potw 

### = P lant rem oval efficiency from head works to plant effluent (as decim a l) I

r 

### = P OTW g /L E

potw, t 

### = P OTW g /L t = P lant effluent sam ples, num bered 1 to T

r = P lant influent sam ples, num bered 1 to R

It is im portant to realize that the portion of the po llutant rem oved through a treatm e nt process is transferred t o a n o t h e r w a s t e s t r e a m , t y p i c a l l y t h e s l udge. For c o n s e r v a t i v e p o l l u t a n t s , s u c h a s m e t a l s , a l l t h e p o l l u t a n t fro m th e influent ends up in either the effluent or the sludge. ple, a 93% overall plan t remo v a l

m eans that 93% of the cadm i um in the influent is tran sferred to the sludge, while 7% rem a ins in the effluent wastewater.

1. R EVIEW O F TH E 

### D ATA 

### S ET AND 

### E X C L U SION OF 

### C ERTAIN 

### D ATA



### A good first step in determ ining rem oval efficiencies is to review the data set. identify any data values that are extrem ely high or low. e isolated extrem e values, there are form al statistical procedures that can be applied to evaluate whether a va lue can be classified as an “ outlier” relative to the rest of the data set. e thods m o st widely used to m a ke this determ ination are described in the following two paragraphs.

If the data is kn o w n t o cl o s ely follow a norm a l distribu tion, then any data point that lies m o re than two standard deviations from the m ean is considered an outlier. Co n s i d er, for exam ple, the DRE data values As defined in Equation 5.1, the

influent pollutant concentration at headworks , m effluent pollutant concentration

influent pollutant concentration at headworks, m effluent pollutant concentration, m

For exam

This review can If there ar

Two m

P-1

### located i n T a b l e 1 of this appendix, and assum e that this data is from a norm a l distribution. The 15 observations have a m ean of 52.69 and a standard devia tion of 34.65. Using this m e thod, any data point that lies outside of the range -16.61 to 121.99, or 52.69 ± 2*34. 65, can be considered an outlier. In this case, one value, -20.25, falls outside of the range a nd can be determ ined to be an outlier.
However, the DRE data values do not approxim a te a “be ll-shaped” norm a l distribution. In this case, outliers can be determ ined based on the interquartile range (IQR) of the data set. First, order the data from sm allest to largest and locate the data points that fall at the 25 th 

### percentile (also referred to as the first quartile or Q1), and the 75 th 

### percentile (also referred to as the third quartile or Q3). The IQR is equal to the value o f t h e observation at Q3 m i nus the value of the observation at Q1. Any data point that lies m o re than 1.5 tim es this IQR below Q1, or above Q3, is considered an outlier. Again, consider the data in Table 1, but now m a ke no

assum p tions about the distribution of the population from which the sam ple was taken. The Q1 and Q3 of this data set are located at 38.04 and 78.5 respectively

. Based on these values, the IQR is equal to 40.46 (78.5 - 38.04). Any value that falls below -22.65 (38. 04 - 1.5*40.46), or above 139.19 (78.5 + 1.5*40.46), can be considered an outlier. In t h i s case, there are no valu es that fall outside of the range and, consequently , no values should be determ ined to be outliers.

Both of these m e thods are m eant to determ ine any valu es that m a y be candidates for exclusion from the data set. Data exclusion should be perform ed only if techni cal justification exists to support such action (e.g., poor

rem ovals due to tem porary main ten a nce or opera tional problem s or known sam pling problem s). For

exam ple, if an exam ination of the data set shows that an unusually high influent valu e is from the sam e
sam pling day / event as an unusually high effluent valu e, this occurrence of corresponding ex trem e values should be investigated to determ ine if the data valu es can be explained by technical or operational problem s not related to treatm e nt sy stem perform ance (e.g., m a inte nance, repair, or sam pling problem s). If this is the case, dropping the data pair from the data set m a y be appropriate.

Review of the data m a y also show patterns such as increasing effluent values over tim e. If a sim ilar pattern is not observed for the influent values, this will gene rate a pattern of decreasing DREs over tim e. A graph or plot of DRE against sam pling day / event (in order fro m first to m o st recent sam ple) can help identify such trends. This may alert th e POTW to operational problem s that should be investigated. A plot can also highlight unus ua lly low DREs that call for further re view, such as checking laboratory quality control sam ples to determ ine if blank or duplicate sam ples indicate any t hing out of the ordinary

. If abnorm a lities are found in la bora t ory QA/QC (quality assurance/quality control) data, the POTW m a y consider excluding the affected values from the data set.

Table 1 contains an exam ple data set of 15 influent and effluent sam ple pairs for zinc. The in flu e n t an d effluent concentrations have been converted to load ings using the POTW flo w s fo r the sam ple day s

. The influent and effluent concentrations m a y be used in stead of converting to loadings. Whether loadings or concentrations are used will likely have little im pact on the results of the ADRE and decile approaches.
Influent and effluent flows are probably sim ilar (if not the sam e) for a data pair and therefore will have little effect on the relative size of the influent and effluent values, so DREs will change little. However, converting to loadings m a y have a noticeable im pact on the MRE m e thod if a POTW has high variability in its flows. Because influent and effluent loadings for high flow da y s will increase m o re relative to influent and effluent l o ad i n g s for low flow day s , the net effect is to give greater weight to the rem oval rates on those day s with high flows. If the POTW has high variability in its fl ows, it should evaluate whether its rem oval rates tend to go up and down in relation to flow. If so, the POTW should co n s ider calculating an MRE using both concentrations and loadings and ev aluating which is m o re appropriate.

P-21 2 3 4 5 6 7 8 9 10 11 12 13 14 15 T a b l e 1. Remo v a l Efficien cy Examp l e
Sample

Day D ate Influe nt Loa d (lbs/day ) Efflue nt Loa d (lbs/day ) DRE (% ) 3 /4/99 518.22 111.41 78.50 3 /5/99 163.98 173.99 -6.10 3 /6/99 110.15 97.64 11.36 3 /7/99 1739.93 474.41 72.73 3 /8/99 266.48 320.45 -20.25 4 /15/99 170.48 105.15 38.32 5 /11/99 473.16 132.67 71.96 5 /12/99 314.19 148.96 52.59 5 /13/99 306.68 132.69 56.73 5/14/99 232.57 92.63 60.17 5/15/99 226.52 72.60 67.95 6/15/99 533.25 98.87 81.46 7/1/99 141.43 87.63 38.04 7/15/99 1166. 77 103.90 91.10 8/1/99 2301.00 97.88 95.75 Average 577.65 150.06 52.69



### Review of the data shows that:

C 

### The data set does not require rem oval of outliers.

C 

### The three particularly high influent valu es (samp l e d a y s 4, 14, and 15) all have DREs of m o re than 70% , so the high influent values do not appear to m a ke the data candidates for elim ination.

C 

### There are two effluent values (sam ple day s 4 and 5) that are significantly higher than the others. For one, the corresponding influent v a lue is also high and the DRE is 73%. For the other day , the DRE is negative (-20%) because the influent value is relatively l o w

. These results are from sam ples taken on two consecutive day s (March 7 and March 8), which m a y indicate that the POTW treatm e nt sy stem was experiencing som e operational difficulties or interference at the tim e. The POTW should investigate the m a tter to determ in e i f t h er e are valid reasons for dropping these data from the rem oval calculations data set.

C 

### There are two negative DREs (one for March 8) calculated from the influent and effluent data pairs. They occurred three day s apart and m a y indicate tem porary operational problem s, so the POTW should investigate the m a tter (as noted above).
A plot of the data m a y help the POTW identify any data concerns that should be investigated. Based on the review of data for this exam ple, it was determ ined th at no justification exists for excl u d i n g any of the data from the data set.

2. C ALCULATIO N O F 

### R EMO VAL 

### E FFICIENCIES



### Once the data set has been review ed, the POTW can proceed to c a l c u l a ting rem oval efficiencies. The following sections describe each of the m e thods for cal c ulating rem oval efficiencies and perform the calculations using the exam ple data set in Table 1.

P-3

### 2.1 A verage Daily Removal Efficiency (ADRE)
The ADRE is calculated by f i r s t c a l c u l a ting a DRE for each pair of influent and effluent values (i.e., an influent value and an efflue n t v a l u e f r om the sam e sam pling day / event are used to calculate a DRE). This set of DREs is then averaged to determ ine the ADR E for a pollutant. Use of the ADRE m e thod requires that a POTW only use data for the sam pling day s /events for which it has both an influent and an effluent value, and the influent value is greater than zero.
Example

For the exam ple data set in Table 1, the ADRE is calculated as:
ADRE = [ 78.5+(-6.1)+11.36+72.73+(-20.25)+ 38.32+71.96+52.59+56.73+ 60.17+67.95+81.46+38.04 +91.10+95.75)] / 15 = 52.69%

2.2 M ean Removal Efficiency (MRE)
The MRE is calculated by using the sam e form ula as for the DRE (shown at the beginning of the Appendix), but instead of using individual infl uent and effluent values from sam pling day s /events, the set of influent values is first averaged to determ in e the average influent value and the sam e is done for the set of effluent values (either concentrations or loadings). These aver age values are then used in the DRE equation to result in the MRE for a pollutant. Unlike the ADRE m e thod, the MRE m e thod does not require paired influent and effluent values from the sam e sam pling day s /events. Th e MRE can be based on influent and effluent sam ple values that a r e n o t alway s paired (e.g., one effluent sam ple is lost or destroy e d, so the influent average is based on one m o re value than the effluent average). However, the POTW should use caution in building the data sets for calculating influent and effluent aver ages because if too many unpa ired values are used the

rem oval efficiencies m a y be m eaningless because the infl uent data and effluent data m a y represent different tim e periods, and treatm e nt plant conditions do vary over tim e. Example

For the exam ple data set in Table 1, the MRE is calculated as:
Average of the influent values = 577.65 lbs/day Average of the effluent values = 150.06 lbs/day MRE = 100*(577.65-150.06)/577.65 = 74.02%

2.3 C omparison of Results from ADRE and MRE Methods

No te that the MRE (74.02%) is higher than the ADRE ( 52.69% ). The three day s with the highest in flu e n t loadings have relatively high DREs and the two negative DREs (Day 2 a nd Day 5) occur on day s with values that are not significantly greater than the other day s

. In the ADRE calculation, each day / DRE is given the

sam e weig h t as th e others, while the MRE m e thod gives greater weight to the day s with greater loadings. This m eans that the high rem ovals on the day s with high influent loadings affect the MRE m o re than the other day s do, leading to a higher MRE, while the negativ e values do not have as great an im pact because they occur on day s with less elevated influ e n t and efflue nt values If each DRE were to be weighted by its proportion of the total loading, the result would be the sam e as with the MRE m e thod.

P-4

### Usually , the MRE and ADRE are slightly different from each other, a nd can be quite different (as in the

exam ple presented here). The POTW can calcu l a t e b o t h and decide if one of the estim ates is the m o st appropriate for use in AHL calculations. The POTW can also use the d e c i l e a p p r o a c h to determ ine representative rem oval efficiencies.

2.4 D ecile Approach
The decile approach, unlike the above m e thods, consid ers how often the actual DRE will be above or below

a specified rem oval rate, thereby taking into account the variability of POTW rem oval efficiencies over tim e. The decile approach involves putting the set of DREs (calculated using the form ula presented at the beginning of this appendix) in order from least to greatest and then determ ining nine decile values. Each decile is the

v a l u e b e l o w which a certain percentage of the DREs fall. For exam ple, the first decile is the value below which 10% of the DREs fall. Sim ilarly , the second d ecile is the value below which 20% of the DREs fall, on up to the ninth decile, which is the value below which 90% of the DREs fall. The fifth decile is the m e dian and half of the DREs fall below this num ber. To a pply the decile approach, a m i nim u m of nine DREs are required. If exactly nine DREs are available, the nine estim ated deciles are sim p ly the nine DREs. If m o re then nine DREs are used, the POTW need s to calculate the nine decile estim ates.
Tables 2 and 3 below illustrate use of the decile appr oach for the exam ple zinc data set. The steps are:

C 

### Step 1: Take the set of DREs and put the values in order from sm allest to largest (see Table 2).

C 

### Step 2: The entries for Colum n 1 are obtained by perform ing the two calculations. First, define the location for the first decile and then calculate the next eight m u ltiples of that locati o n v a lue to determ ine the location for the second through ninth dec iles. The first location is determ ined by the equation: (N+1)/10, where N = the num ber of data pairs/DREs used. For the exam ple data set, N=15, so the location for the first decile is (15+1)/10 = 1.6. The location for the second decile is 2 x 1.6 = 3.2, the location for the third decile is 3 x 1.6 = 4. 8, and so on up to the ninth decile of 9 x 1.6 = 14.4. (Colum n 1 in Table 3)

C 

### Step 3: For each decile, take the whole num ber part of the value in Colum n 1 and place it in Colum n 2 (e.g., the first decile is 1.6 , so the whole num ber pa rt is 1; the fourth decile is 6.4, so the whole

num ber part is 6).

C 

### Step 4: The entries in Colum n 3 of Table 3 are taken from the ordered list of DREs in Table 2. The whole num ber values in Colum n 2 correspond to the entry in the ordered list in Table 2 [ e .g., the whole num ber part for the first decile is 1, so en try 1 (-20.25% ) from Table 2 is the correct value and is placed in C o l u mn 3 of Table 3; sim ilarly , the fourth decile whole num ber part is 6, so value 6 (52.59%) is placed in Colum n 3 of Table 3 for the fourth decile]

.

C 

### Step 5: Following a sim ilar procedure as in Step 4, values for Colum n 4 are taken from Table 2 and place in Table 3, except that this tim e the va l u es taken from Table 2 are the ones that im m e diately follow the Colum n 3 entries [e.g., fo r the first decile, the value placed in Colum n 4 is -6.10, which is value 2 (the value im m e diately after value 1) from Table 2; for the fourth decile, the value placed in Colum n 4 is 56.73, which is value 7 from Table 2]

.

C 

### Step 6: Fill in Colum n 5 by subtracting Colum n 3 from Colum n 4 and entering the result.

C 

### Step 7: Sim ilar to the process for filling Colum n 2 (explai ned in Step 3) of Table 3, place the decim a l part of the Colum n 1 entries in Colum n 6 of Table 3 (e.g., for the first decile, use 0.6; for the fourth decile, use 0.4).

P-5

### C 

### Step 8: Fill in Colum n 7 by m u ltiply ing the values in Colum n 5 b y t h e v a l u es in Colum n 6 and entering the result.

C 

### Step 9: Add Colum n 3 and Colum n 7 and enter the resu lt in Colum n 8 of Table 3. These values are the estim ated deciles.

Ta ble 2

. Se t of DREs Sorte d in A s c e nding Orde r
1 3 1 0 1 1 1 2 1 3 1 4 1 5

-20.25 -6.1 11.36 38.04 38.32 52.59 56.73 60.17 67.95 71.96 72.73 78.50 81.46 91.10 95.75 2 9 8 7 6 5 4

Ta ble 3

. De c ile A pproa c h for Zinc Ex a m ple De c ile s C olumn 1 C olumn 2 C olumn 3 C olum n 4 C olumn 5 C olumn 6 C olumn 7 C olumn 8 1st 1 .6 1

- 20.25 -6.10 14.15 0.6 8 .490 -11.76 2nd 3.2 3 11.36 38.04 26. 68 0.2 5 .336 16.70 3rd 4 .8 4 38.04 38.32 0.28 0.8 0 .224 38.26 4th 6 .4 6 52.59 56.73 4.14 0.4 1 .656 54.25 5th 8 .0 8 60.17 67.95 7.78 0 0 .000 60.17 6th 9 .6 9 67.95 71.96 4.01 0.6 2 .406 70.36 7th 11.2 1 1 72.73 78.50 5.77 0.2 1 .154 73.88 8th 12.8 1 2 78.50 81.46 2.96 0.8 2 .368 80.87 9th 14.4 1 4 91.10 95.75 4.65 0.4 1 .860 92.96



### T h e m a in value of the decile approach is that it pr ovides an estim ate of how often a POTW is ex p ected to exceed certain rem oval values, such as the ADRE and MRE. For the exam ple, the ADRE is 53% and the M R E i s calculated as 74%. If the POTW uses either one of these values, what am ount of the tim e will i t s
rem oval efficiency exceed those values? This can be estim ated usi n g t h e decile approach. The ADRE of 53% falls between the third and fourth deciles (38. 26% and 54.25%, respectively ) , m eaning that the actual

rem oval ef f i c i ency is estim ated to exceed the ADRE 60% to 70% of the tim e [(e.g., the third decile m eans that 30% of the tim e values will fall below that value (38.26% in this case)]

. The MRE of 74% lies between the se v e n t h a nd eight deciles (73.88% and 80.87%, resp ectively ) , so the POTW is estim ated to exceed the MRE 20% to 30% of the tim e. In developing local lim its, appropriate rem oval effici encies m u st be selected for calculation of AHLs for each pollutant. POTWs have often selected a pollutant’s ADRE for local lim its calculations. EPA recom m e nds t h at POTWs consider using the decile approach or the MRE m e thod because they better account f o r variabilities in rem oval efficiencies over tim e. For ex am ple, because a higher rem oval efficiency m eans m o re pollutant is rem oved to the sludge, if the POTW u sed the ADRE fro m th e above exam ple (which is likely exceeded 60% to 70% of the tim e) to calculate an AHL to protect sludge quality , the resulting AHL m a y not be adequately protective. More pollutant will lik ely be rem oved to the sludge 60% to 70% of the tim e, so loadings in the sludge will higher than was estim ated in the AHL calculations and m a y lead to exceedances of sludge disposal standards.
A different approach that m a y address this concern is to use one decile for AHL calculations to protect sludge quality (for sludge disposal an d fo r sludge digester i nhibition for conservative pollutants) and a different decile for AHL calculations for protection against P ass Through concerns (e.g., NPDES perm it lim its). For

exam ple, a POTW can base its sludge quality -based AHL s on the seventh decile rem oval which m eans that greater rem ovals to sludge and hence greater sludge lo adings would be estim ated to occur 30% of the tim e.

P-6

### Sim ilarly , the POTW can use the third decile fo r calculating its water q u a lity -based AHLs because lower

rem ovals (and hence higher effluent loadings) would be estim ated to occur about 30% of the tim e. use of these deciles estimat es t h at AHLs would be exceed ed 30% of the tim e, in reality this is not highly likely

. If the entire AHL is allocated to IUs, all IUs would have to discharge at their m a xim u m allowed level to reach the AHL. the rem oval achieved is gr eater than the seventh decile, m o re loading would go to th e sludge than is provided for with the AHL. m e IUs discharge at below their allocated loadings, which is very likely at any given tim e, the likelihood of exceeding the allowed loading to the sludge is m u ch lower.

3. N ON 

### -C O N SERVATIVE 

### P O LLUTANTS



### The above discussion of rem oval efficiency calculations applies to co n s erv a tive pollutants (e.g., m e tals). However rem oval efficiencies for non-conservative pollu tants can be used to calculate AHLs based on Pass Through criteria (e.g., biological process inhibition da ta, NPDES perm it lim its) and the guidance above can be used for non-conservative pollutants only in these cases. Conservative pollutant rem oval efficiencies are determ ined by pollutant concentrations in the POTW in fluent and effluent stream s. p tion applied to conservative pollutants (that rem oved pollutants are exc l u s i v e l y t r a n sferred to the POTW’s sludge stream s) cannot be extended to non-conservative pollutants because losses through degradation and volatilization do not contribute to pollutant loadings in sludge. efo r e, non-conservative pollutant rem oval efficiencies cannot be used in deriving AHLs from criteria/standard s applicable to the POTW’s sludge stream s (e.g., digester inhibition, sludge disposal).
Equation 5.13,

- c o n s ervativ e pollutants, based on criteria for sludge digester inhibition, is:

Where: AHL dgstr 

### = AHL based on sludge digestion inhibition, lb/day L infl 

### = P OTW influent loading, lb/day C

dgstinhib 

### = S ludge digester inhibition criterion, m g /L C

dgstr 

### = E xisting pollutant level in sludge, m g /L
The equation can be rewritten as:

Where the factor C dgstr 

### /L infl 

### is a partitioning factor that relates the pollutant level in the POTW sludge, C dgstr 

### , to the headworks loading of the pollutant, L infl 

### . a t i on of an allowable loading based upon sludge digestion inhibition, AHL dgstr 

### , from a sludge digester inhibition criteria, C dgstinhib 

### , for a non-conservative pollutant. ine the par titioning factor for a particular pollutant, the POTW’s influent and sludge m u st be routinely sam pled for that pollutant. Although

Then if If so

The presum

Ther

for calculating AHLs for non

The partitioning factor enables calcul

To determ

P-7

### The factor C dgstr 

### /L infl 

### expresses non-conservative pollutant rem ovals to sludge. Non-c onservative pollutant

rem ovals to sludge are highly variable , and are dependent on such factors as wastewater tem p erature, am bient air tem p erature, biodegradation rates (which are tem p erature dependent), aeration rates, and POTW influent flow. Because non-conservative pollutant rem ovals to sludge are highly variable, the variability in nonconservative pollutant sludge partitioning factors should be addressed in the l o c a l l i m i t s de ve l opm e n t pr oc e s s. The procedures and recom m e ndations presented in this m a nual for addressing rem oval efficiency variability for conservative pollutants (e.g., the calculation of m ean rem ovals and the decile approach) can be extended to addressing variability in non-conservative pollutant sludge partitioning factors. I n cal cu lating sludge AHLs, the sludge partitioning factor should be used in place of the rem oval efficiency for non-conservative pollutants.

P-8

# A PPENDIX 

# Q

- M ETHODS FOR 

# H ANDLING 

# D ATA 

# B ELOW 

# D ETECTION 

# L EVEL

The occurren ce o f values below the detection lim it (DL) in environm ental data sets is a m a jor statistical

com p lication. Uncertainty about the actual wastewater treatm e nt plant influent and effluent values below the DL can bias subsequent statistical analy ses to determ ine the rem oval efficiencies.
The various approaches to handling belo w detection level (BDL) data can be broken into three m a in categories:

C 

### Regression order statistic (ROS) and probability plotting (MR) m e thods

C 

### Maxim u m likelihood estim ation (MLE) m e thods

C 

### Sim p le replacem ent of a single value (e.g., de tection lim it or one half detection lim it).
Although this discussion focuses on handling data below the detection limit, the same techniques can be applied to those data below the minimum level of quan titation (ML) as well. These methods can be applied by those without a background in statistics. How ever, EPA strongly recommends a statistician perform these data manipulations.

R EGRESSION 

### O RDER 

### S TATISTIC 

### (ROS) AND 

### P RO BABILITY 

### P LO TTING 

### (MR) M E T HOD S 

### Both the original ROS and the MR m e thods are b ased on ordered statistics of observed data and the

assum p tion that data com e from a norm a l or log-norm a l distribution. If Y is from a norm a l distribution with

m ean 

### : and standard deviation 

### F ( Y ~ N ( 

### : , 

### F )) and Z is from a norm a l distribution with m ean 0 and standard deviation 1 ( Z ~ N (0, 1)), statistical theories show that Y = : 

### + 

### F Z when Y and Z are at the sam e percentiles in their respective distributions. For a given observation (sam pling result) Y that is above the detection lim it, we can calculate the “ord er st at i s t i c”, i.e., the proportion of observations that are less than Y

. This order statistic of Y is an estim ate of the percentile. The corresponding Z value is available by either using existing

com puter program or checking the norm a l distribution tabl e. In other words, we have a list of observations that are above the detectio n limit ( Y 1 

### , Y 2 

### , . .., Y m 

### ) and a list of Z values ( Z 1 

### , Z 2 

### , ..., Z m 

### ) that are of the sam e per centiles as the respective Y values. By perform ing a regression analy s is of Y against Z , the resulting intercept and slope are estim ates of the m ean a nd standard deviation of the distribution of Y

.
When the data are from a log-norm a l 2 

### distribution, a log transform a tion is needed before the regression. The estim ated m ean and standard deviation is for the log- transform e d variable. To convert the estim ates to the original m e tric, the standard log-norm a l distribution results should be used. For exam ple, if Y is froma lognorm a l distribution, and estim ated m ean and variance for log( Y ) are : 

### and 

### F , the m ean of Y is and the

+  

### variance of Y is e 2 μ σ 2    e σ 2

− 1  



### .  


### Alternatively , one m a y use the regression eq u a t i o n t o “f ill in” the m i ssing (BDL) values. This is possible because we can calculate the order statistics for all BD L values. For exam ple, suppose we have 20 out of 100 observations are BDL. The order statistics for the 20 BDL values are 0.01, 0.02, ..., 0.20. Using these order statistics, we can get the corresponding Z values Z 1 

### , Z 2 

### , ..., Z 20 

### . Substitute these Z values into the regression

m odel, we have the 20 fill-in Y values.

2 Log-norm a l distributions are probability distributions whic h are closely related to norm a l distributions: if X is a normally distributed random variable, then e xp(X) has a log-normal distribution. In other words: the natural logarithm of a log-normally distributed variable is normally distributed.



## Q-1

### To recap, we first define the variables used in this m e thod:

n = Total num ber of observations k = Num b er of BDL observations Y

i 

### = Value of the i th 

### ranked observation

To utilize the ROS m e thod, data are first ranked from sm allest to largest so that Y n 

### is the largest data value and Y 1 

### through Y k 

### are the unknown BDL values. If an approxim a tely norm a l distribution is expected, each Y

i 

### is plotted on the y

- axis against the expected norm a l order statistic Z i 

### for each rank i

. The following linear regression is used to obtain : 

### and 

### F , using only the points above the DL (i.e., i = k +1,..., n ).

Y

i 

### = : 

### + 

### F Z i


### One m a y use the estim ated intercept and slope as the m ean and standard deviation. Alternatively , one m a y
use the above equation to obtain appropriate “fill-in” values for each of the k BDLs using the Z -statistic. The

m ean and standard deviation are then calculated usi ng traditional form ulas applied to both the observed and filled-in data. Thus, the estim ated data are based on the assum p tion of norm a lity , while the o b s er v e d data

are used directly with no assum p tion about their distribu tion. This m e thod is relatively robust to departures from norm a lity or lognorm a lity (Gilliom and Helsel 1986).
If a distribution is expected to be skewed, then log( Y i 

### ) is plotted against Z i 

### and the fitted data and the observed data are transform e d back to original units from wh ic h the m ean and s t a nda rd deviation are calculated (Gilliom and Helsel 1986). Transform a tion of the data, ra ther than the sum m a ry statistics, avoids inherent transform a tion bias (Helsel 1990).

MR M E T HOD



### The MR m e thod, an extension of the ROS m e thod, account s for m u ltiple detection lim its. When there is only one detection lim it, the k -BDL values are assigned order statistics of 1 through k

. When there are m u ltiple detection lim its, it is not obvious how to assign the order statistic for som e of the data, both below or above

som e detection lim its. For exam ple, suppose we have the following five observations: <100, 110, <200, 250, and 300. It is obvious that the two largest observati ons, 250 and 300 should receive order statistics of 4 and 5. But the rest is not clear, b ecause the value labeled as <200 can be 199 or 9. Helsel and Cohn (1988) developed a plotting position m e thod for assigning order st atistics when there are m u ltiple detection lim its. The idea is that although we don’t know exactly wh ere th e value, say <200, should fall, we can lay out all possible positions for this particular value and take th e average rank of all possible ranks. For exam ple, the value labeled as <200 can be the sm allest (rank 1), th e second sm allest (rank 2), or the third sm allest (rank 3), the average rank i s ( 1 +2 +3)/3 = 2. The value 110 can be the second sm allest or the third sm allest, therefore a rank of (2+3)/2 = 2.5. Finally , the observation <100 receives a rank of (1+2)/2=1.5. Once the order statistics are assigned, one may u s e t h e sam e regression analy s is m e thod in the ROS m e thod. When there is only one detection lim it, the MR m e thod is the sam e as the ROS m e thod.

Helsel and Cohn (1988) found that if a single estim ating m e thod for several descriptive statistics is desired and the sam pling distribution of a data set is unknown, the MR m e thod should be utilized. The actual plotting procedure for the MR m e thod is detailed in Appendix B of Estimation of Descriptive Statistics for Multiple Censored Water Quality Data (Helsel and Cohn, 1988).



## Q-2

### M AX IMUM 

### L IKE L I HOOD 

### E STIMATIO N 

### (MLE) M E T HOD



### The MLE m e thod is based on a specific probabilistic assum p tion about the observations. For exam ple, suppose the data we observed ( Y 1 

### , Y 2 

### , ..., Y n 

### ) are from a norm a l distribution with unknown m ean and standard deviation. The likelihood of observing a specific value, say Y i 

### , is calculated by the norm a l distribution density function: ( Y i − μ 

### ) 2 

# ( ) = 2 πσ 1 e − 2 σ 2 

## L Y i



### The likelihood for a BDL value is:

  

### 2 



### e − 1 2 2 2

πσ σ  

### X

− μ   

### DL 

# ( )
= ∫ dX L Y k −∞

The likelihood of observing all the data ( Y 1 

### , Y 2 

### , ..., Y n 

### ), both below and above the detection lim it is the product of all individual likelihoods. The likelihood of obser ving all data is a very com p licated function of : 

### and 

### F

. A different set of : 

### and 

### F values will lead to a different likelihood value. The m a xim u m likelihood estim ator is the pair of : 

### and 

### F values that m a xim i ze the likelihood function. Because the likelihood function is often very com p licated, com putation of the MLE m e thod is difficult.

Gilliom and Helsel (1986) found that the ROS and the MR m e thods appear to be m o re robust to departures from distributional assum p tions.

MLE m e thods have been shown to have the sm allest m ean-squared error (i.e., higher accuracy ) of available techniques when the data distribution is exactly norm a l or lognorm a l (Harter and Moore 1966). However, sim u lation results indicate that ROS and MR m e thods are superior when d i stribution shape population is unknown (Gilliom and Helsel 1986).

In a sim u lation study by Newm an et al. (1989) com p aring m ean and standard deviation estimates between MLE and ROS, the results were sim ilar. However, the MLE m e thod provided slightly m o re accurate results when BDL values com p rised less than 30 percent of th e data set, while ROS m e thods provided slightly m o re accurate results when BDL values represented 30 percent or m o re.

S IMPLE 

### S UBSTITUTIO N 

### M E T HOD S



### Sim p le substitution m e thods sim p ly replace the below det ection value with another value, such as zero, the detection lim it, or one-half the detection lim it. Bo th ROS an d MLE m e thods o ffer substantial advantages

over m o st simp le rep l acem ent m e thods (Gilbert 1987, Gleit 1985, Helsel and Gilliom 1986, Newm an et al.1989).
In general, replacem ent m e thods result in a greater bi as when cal cu lating the m ean or standard deviation. Additionally , their relative perform ance worsens as the proportion of BDLs i n cr eases (Gilliom and Helsel 1986). Helsel (1989) reasons that because large differe nces m a y occur in the resulting estim ates for any given population, and because the choice of the replacem ent value is essentially arbitrary without som e knowledge of instrum e nt readings below the reporting lim it, estim ates resulting from sim p le substitution are not defensible.



## Q-3

### C O NCLUSIO N



### The MR m e thod is m o st applicable for use in local lim its developm ent because of the data set’s mu ltip le detection lim its and unknown parent distribution. A dditionally , the MR m e thod is recom m e nded when the data set contains a relatively hi gh percentage of BDL values.

Further inform ation on statistical m e thods can be found in the literature listed below.

L ITERATURE 

### R EVIEW 

### L IST 

### /R EFERENCES



### Gilliom and Helsel 1986. Estimation of distribution parameters for censored trace level water quality data: 1 Estimation techniques

. Water Resources Research 22:135-146.

Gleit 1985. Estimation for small normal data sets with det ection limits. Environmental Science Technology 19:1201-1206.

Harter and Moore 1966. Local-Maximum-Likelihood estimation of the parameters of t h ree- p a rameter lognormal populations from co mplete and cen so red sa mples. Journal of Am erican Statistical Association 61:842-851.
Helsel, D. R. 1990. Less Than Obvious: Statistical treatment of data below the detection limit. Environm ental Science and Technology 24:1766-1774.

Helsel and Cohn 1988. Estimation of Descriptive Statistics fo r Mu ltiple Censored Wa ter Quality Data. Water Resources Research 24:1997-2004.

Newm an, Dixon, Looney , and Pinder 1989. Estimating the mean and variance for environmental samples with below detection limit observations. Water Resources Bulletin 25:905-916.
Porter, Ward, and Bell 1988. The Detection Limit: W a ter quality m onitoring data are plagued with levels of chemicals that are too low to be measured precisely

. Environm ental Science Technology Vol. 22, No. 8.

Travis and Land. 1990. Estimating the Mea n of Data Sets with Nondetectable Values

. Environm ental Science Technology Vol. 24, No. 7.



## Q-4

### A TTACH MENT


### - D ESCRIPTIO N O F TH E 

### MR M E T HOD



### Method : (1) If an analy tical result is reported as ND (to be referred to as a nondetect), set th e resu lt c i 

### = 1. Annotate the result with a “<” and consider th is observation to be “< a detection lim it.”
(2) D ivide the observations into two groups: Nondetect s, those observations annotated with a “<” sign, and detects.
(3) L et m = num ber of distinct detection lim its.
(4) L et A j 

### = num ber of detected observations at or above the jth detection lim it (j = 1,...,m ) and below the next highest detection lim it.
(5) L et B j 

### = num ber of detected and nondetected observati ons below the jth detection lim it (j = 1,...,m ).
(6) L et p e,j 

### = p e,j+1 

### + (A j 

### /[ A j 

### + B j 

### ] )(1 - p e,j+1 

### ), and solve iteratively for j = m , m
- 1,...,2,1. By convention, p e,m + 1 

### = 0.
(7) D eterm i ne plotting positions, p(i), for detected observations as:
p(i) = (1 - p e,j 

### ) + (p e,j 

### - p e,j+1 

### )
@ 

### r/(A j 

### + 1), wh ere r is the rank of the ith observation above the jth detection lim it. If detected observations are “tied, ” arbitrarily order the “tied” observations before assigning ranks. Whether the “tied” observations are arbitrarily ordered or assigned the sam e m i drank (average of the corresponding ranks) is expected to be of negligible im portance. If detected observations are present below the lowest detection lim it, assum e the “0th detection lim it” is 0, and consequently p e,0 

### = 1.
(8) A ssign plotting positions, pc(i), for nondetected observations as:
pc(i) = (1- p e,j 

### )
@ 

### r/(C j 

### + 1), r = 1,...,C j 

### . C j 

### is the num ber of nondetected values known only to be less than the jth detection lim it (j = 1,...m ). The form ula for C j 

### is: C j 

### = B j 

### - (A j-1 

### + B j-1 

### ), where A 0 

### = B 0 

### = 0. Plotting positions are therefore assigned separately within the j groups of nondetects (j=1,...,m ).
(9) P erform a sim p le linear regression using only the detected observations. The natura l l o g a r i t h m of the detected observations (z i 

### = ln(y i 

### )) is the dependent variable, and the norm a l quantile associated with the corresponding plotting position ( M -1 

### (p(i))) is the independent variable, where M -1 

### (
@ 

### ) is the

norm a l quantile.

^ ^ ^ 

### (10) Use the estim ated regression line (z i 

### = b 0 

### + b 1 

### @ M -1 

### (pc(i))) to “fill in” (using the term inology of Helsel and Cohn) est i m at ed natural logarithm values for nondetected observations, based on the norm a l quantile associated with the calculated plotting position (pc(i)).
(11) Calculate a n a t u r a l l o g me an ( : 

### ^) and log standard deviation ( F 

### ^ ) of the detected and “filled in” observations using the form ulas below. Assum e z i 

### = ln(y i 

### ), where z i 

### represents the natural logarithm of detected observations where available, and "f illed in" estim ated natural l o g a ri t h m values where nondetects were observed.



## Q-5

### (1)

(2)

(12) Use the values of : ^ 

### and F ^ 

### to estim ate a 90th percentile using a lognorm a l distribution: 90 

### = exp ( : ^ 

### + 1.282 @ F ^ 

### ).

An exam ple of the MR m e thod is given below.

C o mme n t s : Although the algorithm for determ ining plotting positi ons when m u ltiple detection lim its are present appears rather cum b ersom e , as descr ibed in the 12-step process above, the process of fitting a regression line to order statistics is well-established as a m e thod for de term ining param e ters of a distribution.

m e thod utilizes plotting pos itions to “ s pre a d ” nondetected observations along a continuum , rather than sim p ly substituting an arbitrary value for each nondetected m easu rem e nt. values to be “spread out” rather than all fixed at a single point, as would be the case with sim p le substitution

m e thods.

The MR m e thod described above di r ect l y mi m i cs the m e thods of Helsel and Cohn. article by Helsel and Cohn contains an inaccurate form ula for C j 

### , which has been revised above. the article did not address ties in detected observations and detected observations below the lowest detection lim it.
At least two detected observations are necessary to e s t i m a t e a regression line. , this procedure is not useful when 0 or only 1 detected observation is present.

Software which utilizes the MR m e thod to com pute sum m a ry statistics is available. The feasibility of utilizing the software available at this site fo r im plem entation am ong num erous POTWs m u st be explored further

. For exam ple, the software is restrictive in som e way s , such as the form at of data which can be processed.

Reference:
Helsel, D.R., and T.A. Cohn. Estimation of Descriptive Statistics for Multiple Censored Water Quality Data. Water Resources Research 24:1997-2004. P

The ROS
In practice, one would expect nondetected

However, the

In addition,
These questions have been addressed in Steps 7 and 9 above.
Consequently

1988.



## Q-6

### EXAMPLE OF THE MR METHOD

Suppose we have a set of data from m u ltiple sources with vary ing detection lim its. When com b ined, the data set is ordered as follow:

Da ta Summa ry

<50 < 200 <400 100 300 500 <50 < 200 <400 100 300 500 <50 < 200 <400 700 1000 1200
In order to provide estim ates of the m ean and standard deviation, it is necessary to fill-in the non-detected values. Once the non-detected values are filled-in, sam pl e m ean and standard deviation can be estim ated. The following are the MR steps for filling in the nondetected values.

1. Sum m a ry statistics:

n = 18

m

= 3 (1st detection lim it = 50, 2nd detection lim it = 200, 3rd detection lim it = 400)

A 1 

### = 2 (2 detects $ 

### 50 but <200) A 2 

### = 2 (2 detects $ 

### 200 but <400) A 3 

### = 5 (5 detects $ 

### 400)

B 1 

### = 3 (3 nondetects <50) B 2 

### = 8 (3 nondetects <50, 3 nondetects <200, and 2 detects <200) B 3 

### = 13 (3 nondetects <50, 3 nondetects <200, 3 nondet ects <400, 2 detects <200, and 2 detects <400)

C 1 

### = 3 (3 nondetects <50) C 2 

### = 3 (3 nondetects <200) C 3 

### = 3 (3 nondetects <400)

p e,3 

### = p e,4 

### + (A 3 

### /[ A 3 

### + B 3 

### ] )(1 - p e,4 

### ) = 0 + (5/[ 5+13] )
@ 

### 1 = 0.278 p e,2 

### = p e,3 

### + (A 2 

### /[ A 2 

### + B 2 

### ] )(1 - p e,3 

### ) = 0.278 + (2/[ 2+8] )
@ 

### (1-0.278) = 0.422 p e,1 

### = p e,2 

### + (A 1 

### /[ A 1 

### + B 1 

### ] )(1 - p e,2 

### ) = 0.422 + (2/[ 2+3] )
@ 

### (1 - 0.422) = 0.653



## Q-7

### -----------------------------------------------------------------------------------------------------------------

------------------------------------------------------------------------------------------------------------------

----------------------------------------------------------------------------------- 2. Determ ination of plotting positions:

Nondetected observations: Plotting Position

x i 

### j r p e,j 

### C j 

### pc(i) = (1- p e,j 

### )
@ 

### r/(C j 

### + 1)

<50 1 1 0 .653 3 0 .087 <50 1 2 0 .653 3 0 .173 <50 1 3 0 .653 3 0 .260

<200 2 1 0 .422 3 0 .144 <200 2 2 0 .422 3 0 .289 <200 2 3 0 .422 3 0 .433

<400 3 1 0 .278 3 0 .181 <400 3 2 0 .278 3 0 .361 <400 3 3 0 .278 3 0 .542

Detected observations: Plotting Position

x i 

### j r p e,j 

### p e,j+1 

### A j 

### p(i) = (1 - p e,j 

### ) + (p e,j 

### - p e,j+1 

### )
@ 

### r/(A j 

### + 1)

100 1 1 0 .653 0.422 2 0 .424 100 1 2 0 .653 0.422 2 0 .500

300 2 1 0 .422 0.278 2 0 .626 300 2 2 0 .422 0.278 2 0 .674 500 3 1 0 .278 0 5 0.769 500 3 2 0 .278 0 5 0.815 700 3 3 0 .278 0 5 0.861 1000 3 4 0 .278 0 5 0.907 1200 3 5 0 .278 0 5 0.954

3. Linear regression

A s i m p le linear regression is then perform ed usi ng the following detected observations and t h e i r associated plotting points. The regression is based on z i 

### as the dependent variable and p(i) as the independent variable.

x i 

### z i 

### = ln(x i 

### ) p (i) M -1 

### (p(i))

100 4.605 100 4.605 300 5.704 300 5.704 500 6.215 500 6.215 700 6.551 1000 6.908 1200 7.090 0.424 -0.192 0.500 0.000 0.626 0.321 0.674 0.451 0.769 0.736 0.815 0.896 0.861 1.085 0.907 1.323 0.954 1.685



## Q-8

### ------------------------------------------------------------------------ The regression equation based on these nine detected observations is:

^ 

### z i 

### = 4.9614 + 1.4186 @ M -1 

### (p(i))

4. Fill-in
This equation is used to “fill in” estim ated nondetect va lues for the nine nondetects above. The results of the calculation are shown below:

^ 

### pc(i) M -1 

### (pc(i)) z i


### <50 0 .087 <50 0 .173 <50 0 .260 <200 0.144 <200 0.289 <200 0.433 <400 0.181 <400 0.361 <400 0.542 -1.360 3.032 -0.942 3.625 -0.643 4.049 -1.063 3.453 -0.556 4.173 -0.169 4.722 -0.912 3.668 -0.356 4.456 0.106 5.112

^ 

### The z
^ 2 

### are shown below:

4.605 5.704 6.551 3.032 3.453 3.668 4.605 6.215 6.908 3.625 4.173 4.456 5.704 6.215 7.090 4.049 4.722 5.112 i 

### and the z i 

### from the two tables above are then com b ined to estim ate a natural log mean an d a log

^ 

### standard deviation. The data and calculated values for : 

### and F

^ 

### : 

### = 4.9937

^ 

### F ^ 2 

### = 1.5632 ( F 

### = 1.2503)

^ ^ 

### The calculated values for : 

### and F 

### can then be used for estim ating the arithm e tic m ean of the sam ple: m = exp(

^ 

### + 0.5 F : ^ 2 

### ) = 322.241 and sam ple standard deviation 

## s = m e $ σ 2 

## 1 − = 626.168. In som e instances, one m a y

^ 

### be interested in the 90 th 

### percentile of the data, which can be estim ated as P 90 

### = exp ( : 

### + 1.282 @ F ^ 

### ) = 732.585. It is worthwhile to note that these calculations are based on the assum p tion that the data follow a log-norm a l distribution. For m o st water quality related variables, such as BOD concentration, t h e l og-norm a l distribution is ap p r o p r iate. However, when percent rem oval is the variable of concern, log-norm a l is no longer an appropriate probability distribution. Instead, one m a y apply the MR m e thod to the concentration variables first and calculate the percent rem oval after the non- detected concentration values have been filled-in.



## Q-9 x i

### This page intentionally left blank.



## Q-10

# A PPENDIX 

# R

- P RIORITY 

# P OLLUTANT 

# R EMOVAL 

# E FFICIENCIES

Priority Polluta nt Re mov a l Effic i e n c i e s (% ) Through Prima r y Tre a t me nt*

Priority Pollutant Median Numbe r of POTWs w i th Remov a l Data**
MET A L/NONMET AL INORGANICS Cadmium 1 5 6 of 40 Chromium 27 12 of 40 Copper 22 12 of 40 Cy anide 27 12 of 40 Lead 57 1 of 40 Mercury 1 0 8 of 40 Nickel 1 4 9 of 40 Silver 20 4 of 40 Z i nc 27 12 of 40 ORGANICS Benzene 25 8 of 40 Chloroform 14 11 of 40 1,2-trans-Dichloroet hy lene 36 9 of 40 Ethy lbenzene 13 12 of 40 Naphthalene 44 4 of 40 Phenol 8 11 of 40 Buty l benzy l phthalate 6 2 4 of 40 Di-n-buty l phthalate 3 6 3 of 40 Diethy l phthalate 5 6 1 of 40 T e trachloroethy lene 4 12 of 40 1,1,1-T r ichlor oethane 40 10 of 40 T r ichloroethy lene 20 12 of 40



### * P ollutant rem ovals between POTW in fluent and prim ary effluent. From Fate of Priority Pollutants in Publicly Owned Treatment Works, Volume I (EPA 440/1-82/303), U.S. Environm ental Protection Agency , Washington, D.C., Septem ber 1982, p. 61.

** Median rem oval efficiencies from a data base of rem oval efficiencies for 40 POTWs. Only POTWs with ave r a g e i n fluent concentrations exceedi ng three tim es each pollutant’ s detection lim it were considered.

Source: U.S. EPA’s Guidance Manual on the Development and Implementation of Local Discharger Limitations Under the Pretreatment Program , Decem ber 1987, p. 3-55.

R-1Priority Polluta nt Pe rc e n t Re mov a l Effic i e n c i e s (% ) Through A c tiv a te d Sludge Tre a t me nt*

Priority Pollutant Range

MET A LS/NONMET A L INORGANICS** Arsenic 11-78 Cadmium 25-99 Chromium 25-97 Copper 2-99 Cy anide 3-99 Lead 1-92 Mercury 1 -95 Nickel 2 -99 Selenium 25-89 Silver 17-95 Z i nc 23-99 31 33 68 67 41 39 5 0 2 5 33 50 64 ORGANICS** 44 50 50 50 67 36 40 37 75 47 50 39 39 76 50 80 75 75 67 91 80 96 67 83 67 91 86 97 62 77 78 90 68 86 90 98 72 87 67 92 64 87 62 90 86 95 80 93 93 98 85 94 89 98 Anthracene 29-99 Benzene 25-99 Chloroform 17-99 1,2-trans-Dichloroethy l ene 17-99 Ethy lbenzene 25-99 Methy l ene chloride 2-99 Naphthalene 25-98 Phenanthrene 29-99 Phenol 3-99 Bis (2-ethy l hexy l ) phthalate 17-99 Buty l benzy l phthalate 25-99 Di-n-buty l phthalate 11-97 Diethy l phthalate 17-98 Py rene 73-95 T e trachloroethy lene 15-99 T o luene 25-99 1,1,1-T r ichloroethane 18-99 T r ichloroethy lene 20-99 Se c ond Decile Median Eight Decile Numbe r of POTWs
w i th Remov a l Data

45 53 5 of 26 67 91 19 of 26 82 91 25 of 26 86 95 26 of 26 69 84 25 of 26 61 76 23 of 26 6 0 7 9 20 of 26 4 2 6 2 23 of 26 50 67 4 of 26 75 88 24 of 26 79 88 26 of 26

5 of 26 18 of 26 24 of 26 17 of 26 25 of 26 26 of 26 16 of 26

6 of 26 19 of 26 25 of 26 16 of 26 19 of 26 15 of 26

2 of 26 26 of 26 26 of 26 23 of 26 25 of 26



### * P ollutant rem ovals betw een PO TW influent and sec ondary effluent (including secon d a ry clarification). Based on a com puter analy s is of PO TW rem oval effici ency data (derived from actual POTW influent and effluent sam p ling data) provided in U

. S. EPA ’ s Fate of Priority Pollutants in Publicly Owned Treatment Works, Volume II (EPA 440/1-82/303), Septem ber 1982.

** For the purpose of deriving rem oval efficiencies, effluent levels reported as below detection w e re set equal to the reported detection lim its. A ll secondary activated sludge treatm e nt plants sam p led as part o f the study w e re considered.

So u r ce: U.S. EPA’s Guidance Manual on the Development and Implem entation of Local Discharger Limitations U

nder the Pretreatm e nt Program , Decem ber 1987, p. 3-56.



### R-2Priority Polluta nt Re mov a l Effic i e n c i e s (% ) Through Tric k ling Filte r Tre a t me nt 

### *

Priority Pollutant

Cadmium Chromium Copper Cy anide Lead Mercury N i c k e l Silver Z i nc

Benzene Chloroform 1,2-trans-Dichloroethy l ene Ethy lbenzene Methy l ene chloride Naphthalene Phenol Bis (2-ethy l hexy l ) phthalate Buty l benzy l phthalate Di-n-buty l phthalate Diethy l phthalate T e trachloroethy lene T o luene 1,1,1-T r ichloroethane T r ichloroethy lene Range Se c ond Decile Median Eighth Decile Numbe r of POTWs
w i th Remov a l Data
MET A LS/NONMET A L INORGANICS** 93 6 of 11 71 9 of 11 89 9 of 11 79 8 of 11 70 6 of 11 62 9 of 11

7 9 o f 1 1 86 8 of 11 81 9 of 11 5 33-96

5-92 12-97

7-88 4-84 14-80

7

- 7 2 11-93 14-90 33 68 34 55 32 61 33 59 25 55 33 50

1 9 38 66 34 67 1 2

ORGANICS** 5-98 21-94 14-99 45-97

5-98 33-93 50-99

4 -98 25-90 29-97 17-75 26-99 17-99 23-99 50-99 50 75 50 73 50 50 50 80 28 70 40 71 75 84 2 1 5 8 37 60 41 60 40 57 53 80 80 93 75 89 67 94 93 7 of 11 84 9 of 11 96 7 of 11 91 10 of 11 85 10 of 11 87 6 of 11 96 8 of 11 8 1 10 of 11 77 9 of 11 82 10 of 11 67 8 of 11 93 10 of 11 97 10 of 11 97 10 of 11 98 10 of 11



### * P ollutant rem ovals between POTW influent and sec ondary effluent (including secondary clarification). Based on a com puter analy s is of POTW rem oval effici ency data (derived from actual POTW influent and effluent sam pling data) provided in U.S EPA’s Fate of Priority Pollutants in Publicly Owned Treatment Works, Volume II , (EPA 440/182/303), Septem ber 1982.

** For the purpose of deriving rem oval efficiencies, e ffluent levels reported as below detection were set equal to the reported detection lim its

. All secondary trickling filter plants sam pled as part of the study were considered.

Source: U.S. EPA’s Guidance Manual on t h e Development and Implementation of Local Discharger Limitations Under the Pretreatment Program , Decem ber 1987, p. 3-57.

R-3Priority Polluta nt Re mov a l Effic i e n c i e s (% ) Through Te rtia ry Tre a t me nt*

Priority Pollutant Range

MET A LS/NONMET A L INORGANICS** Cadmium 33-81 Chromium 22-93 Copper 8-99 Cy anide 20-93 Lead 4-86 Mercury 33-79 N i c k e l 4

- 7 8 Silver 27-87 Z i nc 1-90 Se c ond Decile Median Eighth Decile

50 50 62 72 58 85 32 66

9 5 2 43 67

7 7 55 62 50 78 1 1

ORGANICS** 40 50 32 53 50 83 80 89 31 57 33 73 80 88 59 76 50 63 27 50 29 38 80 91 83 94 79 94 62 93 54 2 of 4 64 3 of 4 93 2 of 4 94 3 of 4 78 4 of 4 86 3 of 4 96 4 of 4 94 4 of 4 85 4 of 4 70 4 of 4 50 3 of 4 97 4 of 4 97 4 of 4 97 4 of 4 98 4 of 4 Benzene 5-67 Chloroform 16-75 1,2-trans-Dichloroethy l ene 50-96 Ethy lbenzene 65-95 Methy l ene Chloride 11-96 Naphthalene 25-94 Phenol 33-98 Bis (2-ethy l hexy l ) phthalate 45-98 Buty l benzy l phthalate 25-94 Di-n-buty l phthalate 14-84 Diethy l phthalate 20-57 T e trachloroethy lene 67-98 T o luene 50-99 1,1,1-T r ichloroethane 50-98 T r ichloroethy lene 50-99 Numbe r of POTWs
w i th Remov a l Data

73 3 of 4 89 4 of 4 98 4 of 4 83 4 of 4 7 7 3 of 4 75 4 of 4

7 3 o f 4 82 3 of 4 88 4 of 4 5



### * P ollutant rem ovals between POTW in fluent and tertiary effluent (i n c l u d i n g final clarification). Based

on a com puter analy s is of PO T W rem oval efficien cy data (derived from actual POTW influent and effluent sam pling data) provided in U.S. EPA’s Fate of Priority Pollutants in Publicly Owned Treatment Works, V o l u m e I I (EPA 440/1-82/303), Septem ber 1982. Tertiary treatm e nt was taken to include P O T W s with effluent m i croscreening, m i xed m e dia filtration, post aeration, and/or nitrification/denitrification.

** For the purp o s e o f deriving rem oval efficiencies, e ffluent levels reported as below detection were set equal to the reported detection lim its. All tertiary treatm e nt plants sam pled as part of the study w e r e considered.

Source: U.S. EPA’s Gu i dance Manual on the Development and Implementation of Local Discharger Limitations Under the Pretreatment Program , Decem ber 1987, p. 3-58.

R-4

# A PPENDIX 

# S

- S PECIFIC 

# G RAVITY OF 

# S LUDGE

Equation to determ ine specific gravity of wet sludge The allowable headworks loading (AHL) equations presen ted in C h a p t e r 5 f o r sewage sludge disposal contain a factor for the specific gravity of sludge (sludge dens ity ). This factor accounts for differences in the density of sludge based on the percent solids of sludge to disposal. e unit conversion factor (8.34) in the sam e equations converts the overall units in to pounds per day (lbs/day ), using a specific gravity or density of sludge equal to 1 kg/L, which assum es that sludge has the sam e density as water. sludge density is different from the density of water, the unit conversi on factor is not fully accurate. of a sludge increases, the density of the sludge increases and therefore the error introduced by the inaccurate unit conversion factor increases. in accuracy , the num erator of the AHL equation should be

m

u ltiplied by the specific gravity of the dewatered sludge (as noted in Ch ap ter 6). dewatered before disposal, the inaccuracy produced by using the unit conver s i o n f actor (8.34) without a specific gravity factor would probably not be significant.

The POTW can determ ine the specific gravity (den sity ) of its sludge prior to disposal through a sim p le laboratory m easu r emen t

. T h e POTW should take this m easurem e nt as part of its local lim its m onitoring program and average the resulting data set ( e

. g

. , 7

- 1 0 data points) to determ ine a representative sludge specific gravity (density ) factor for use in local lim its calculations. ate the specific gravity of its sludge using the equations below and inform ation on the percent solids.
For a ty pical wet sludge at 10% solids, the approxim a te density is 1.03 kg/L. For a ty pical dewatered sludge at 30% solids, the approxim a t e d e n s ity is 1.11 kg/L. a y reach a density of 1.2 to 1.3 kg/L, which would result in a 20% to 30% conserva tive error in the calculation of an AHL if a specific g r av i t y f act or is not used. ount of volatile solids in the sludge i n
com p arison with the am ount of fixed m i neral solids, wh ich vary with percent solids, and the densities of each of these ty pes of solids.

Where: M WS 

### = M ass of wet sludge (kg) S WS 

### = S pecific gravity of wet sludge (kg/L) M S 

### = M ass of dry sludge solids (kg) S S 

### = S pecific gravity of sludge solids (kg/L) M W 

### = M ass of water (kg) S W 

### = S pecific gravity of water (kg/L) Th

If the dewatered As the percent solids

To correct this If a sludge is not

The POTW can also estim

A sludge at 50% solids m
All of these values depend on the am



## Equation to determ ine specific gravity of dry sludge solids

S-1

### Where: M F 

### = M ass of fixed solids (kg) S F 

### = S pecific gravity of fixed solids (kg/L) M V 

### = M ass of volatile solids (kg) S V 

### = S pecific gravity of volatile solids (kg/L)

The result from the second equation is used in the first equation.
Example
Sludge is 10% solids:

Assum e solids consist of 33% fixed m i neral solids with a specific gravity of 2.5 kg/L and 67% volatile solids with a specific gravity of 1.2 kg/L.

To determ ine the specific gravity of the dry sludge solids, use the second equation:

which results in S s 

### = 1.45 kg/L.

yi e l d s S WS 

### = 1.03 kg/L. Using this value in the first equation:

S-2

# A PPENDIX 

# T

- S LUDGE 

# AHL E QUATIONS 

# U SING 

# F LOW 

# ( IN METRIC UNITS 

# )

Som e POTWs m a y have sludge flow data available in dr y m e tric tons per day , rather than MGD. The AHL equations for sludge disposal in Chapter 6 can be convert ed to use sludge flow data in these units. e of the equations i n C h a p t e r 6 are presented below using fl ows in dry m e tric tons per day

. Use of these “dry flows” elim inates the need for the speci fic gravity factor in the equations.

G ENERAL 

### S LUDG E 

### E Q UATIO N FO R 

### C O N SERVATIVE 

### P O LLUTANTS



### Where: L IN FL 

### = A llowable influent loading, lbs/day C CRI T 

### = S ludge criteria, m g /kg dry sludge Q SLDG 

### = T otal sludge flow to disposal, dry m e tric tons per day R POTW 

### = R em oval efficiency across POTW (as decim a l) 0.0022 = U nit conversion factor

L AND 

### A PPLICATIO N



### A s e xpla i ne d in C h a p te r 6, de te r m ining the la nd a pplic ation sludge crite r i a f o r

us e in the ge ne r a l s l udge equation requires that the POTW first convert 40 CF R §503 Table 2 and Table 4 sludge criteria into values in m g /kg of dry sludge units. Table 4 criteria are in m e tric units (kg/ha), they m u st be converted into English units (lbs/acre) so that they can be used with the equations in Chap ter 6 which use other English units (e.g., flow in MGD, area in acres)

. p r o v i ded in both

m e tric and English units in Appendix E.
Another option is for POTWs to use the land applica tion criteria equations in m e tric units (e.g., area in hectares, flow in dry m e tric tons per day ) , thus elim inating the need to convert Table 2 and Table 4 values to English units. These equations are provided below. These equations avoid the need for a specific gravity factor because they use also use a “dry flow” for sludge.

Where: C CRI T 

### = S ludge criteria, m g /kg dry sludge C CUM 

### = F ederal (Table 2 of 40 CFR 503.13) or State land application cum u lative pollutant loading rate, kg/ha SA = S ite area, hectares SL = S ite life, y ears Q LA 

### = S ludge flow to bulk land application at an agri cultural, forest, public contact, or reclam ation site, dry m e tric tons per day 0.365 = U nit conversion factor Som

Because Table 2 and

Table 2 and Table 4 criteria are

T-1

### Arsenic, Cadm ium , Chrom i um , Nickel

Lead

Bery llium , Mercury , pollutants with State lim its Where: C CRI T 

### = S ludge criteria, m g /kg dry sludge C ANN 

### = F ederal (Table 4 of 40 CFR 503.13) or State land application annual po llutant loading rate, kg/ha AWSAR = Annual whole sludge application rate, m e tric tons per hectare per y ear dry weight basis 0.001 = U nit conversion factor

I NCINERATIO N 

### Sludge standards for m a xim u m pollutant concentrations in sludge feed to the incinerator need to be in m g /kg dry sludge to be used in the equations at the be ginning of Section 6.2.3 to calculate AHLs. disposing of sludge through incineration m a y already have sludge standard s in mg/kg dry sludge, such as through a waste disposal agreem ent with the operator of a sludge incincerator. in Chapter 6, if no sludge standards have been calculated for the sludge f eed to the incinerator, POTWs should use the Part 503 equations (provided below) to determ ine the m a x i m u m pollutant concentrations for the incinerator feed. These m a xim u m concentrations are then used in the equations at the beginning of Section 6.2.3 to calculate AHLs.

Where: C CRI T 

### = S ludge criteria, m g /kg dry sludge NESHAP = N ational em ission standard for bery llium or m e rcury from 40 CFR Part 61, g/day NAAQS = N ational Am bient Air Quality Standard for lead, ug/m 3 

### RSC = F ederal risk specific concentration lim it for arsenic, cadm i um , chrom i um , or nickel from 40 CFR 503.43, ug/m 3 

### CE = C ontrol efficiency (rem oval efficiency ) for sewage sludge incinerator for the given pollutant (as a decim a l) Q IN C 

### = S ludge flow to incinerator (i.e., sewage sl udge feed rate), dry m e tric tons per day DF = D ispersion factor, ug/m 3 

### /g/sec 0.1 and 86,400 = U nit conversion factors
For pollutants with State incinerator em issions standard s, lim its should be entered in g/day in place of the NESHAPs lim its in the first equation above. A POTW

As noted

T-2

# A PPENDIX 

# U

- POTW C ONFIGURATIONS

The diagram s and discu s sions below dem onstrate sam pling locations to develop allowable headworks loadings (AHLs). For illustrative purposes, in this appendix all three plants m u st determ ine the AHLs based upon effluent lim itation s , secondary treatm e nt inhibiti on, sludge digester inhibition, and sludge land appli c a tion. Three different plants, with very diffe rent secondary treatm e nt trains, are diagram e d below along with sam pling points for the AHL calculations.

Diagram A Raw W astewater

Bar Screen and Grit Chamber Effluent

Land Application Primary Clarifier Secondary Clarifier

High Rate

Sludge Digester Gravity Thickener Belt Filter Press Secondary

Clarifier T rickling

Filter

Sludge Degritter Aeration Basin Chlorine Contact Chambers

X X

X X 

### A

B

C D E

X



### AHL FOR 

### S ECO NDARY 

### T REATMENT 

### I NHIBITION 

### At this POTW a trickling filter and an activated sludge sy stem (aeration basin) operated in parallel provide secondary treatm e nt of the raw wastewater. The concentration of a pollutant that could cause inhibition at the trickling filter m a y be different than the pollu tant concentration that causes inhibition (known as the inhibition threshold level) at the aeration basin. Th e plant m u st determ ine a headw o r k s loading protective of these two secondary treat men t u n i t s

. Using Equation 5.10, an AHL based on secondary treatm e nt inhibition can be calculated.

U-1

### Trickling Filter

Aeration Basin

Where: AHL inhab 

### = AHL based on aeration basin inhibition, lbs/day AHL inhtf 

### = AHL based on trickling filter inhibition, lbs/day C

inhab 

### = I nhibition criteria for aeration basin, m g /L C

inhtf 

### = I nhibition criteria for trickling filter, m g /L Q

potw 

### = T otal POTW flow, MGD R

prim 

### = R em oval efficiency from headworks to p r i m ary treatm e nt effluent as a de c i m a l (Se e Section 5.1.1 for calculating rem oval efficiencies) 8.34 = U nit conversion factor
The equations to calculate the AHL based on trick ling filter and aer a t i on basin inhibition includes an inhibition criteria for the trickling filter, C inhtf 

### , and aeration basin, C inhab 

### , respectively

. Both equations use the

sam e rem oval rate, R prim 

### , from the headworks to the prim ary treatm e nt effluent. R prim 

### can be determ ined by

sam pling loading at point “A,” the headworks, and point “B,” prim ary clarifier effluent (See Section 5.1 for these calculations). Q potw 

### can be determ ined through flow sam pling at point “A” as well. AHL ab 

### and the AHL tf 

### would be calculated, p ared and the m o re stringent selected.

AHL FOR 

### S LUDG E 

### D IG ESTER 

### I NHIBITION 

### This pla n t m u s t de te rm ine a he a d w o rks loa d ing prot e c ting the high-ra te s l udge dige s t e r from inhibition. Using Equation 5.12, an AHL based on sludge digester inhibition can be calculated.

Where: AHL inhhrsd 

### = AHL based on high-rate sludge digester inhibition, lbs/day C

inbhrsd 

### = H igh-rate sludge digester inhibition criteria, m g /L Q hrsd 

### = S ludge flow to high-rate sludge digester, MGD R

potw 

### = P lant rem oval efficiency from headwo rks to plant effluent (as decim a l) 8.34 = U nit conversion factor
The equation to calculate the AHL based on high-rate sl udge digester inhibition includes an inhibition criteria for the digester, C inbhrsd 

### , sludge flow to the digester, Q hrsd 

### , and an overall plant rem oval rate from headworks to plant effluent, R potw 

### . Q hrsd 

### can be determ ined sam pling flow at point “C,” the sludge wastestream from gravity thickener to digester. R potw 

### can be determ ined by sam pling at poi nt “A,” headworks before the bar screen and grit cham ber, and at point “E,” the effluent after the chlorine contact cham bers. com

by the

U-2

### AHL FOR 

### E FFLUENT 

### L IMITS 

### The plant m u st determ ine headworks loading that would lead to effluent from its chlorine contact cham bers (CCC) com p ly with NPDES Perm it lim its. Using Equation 5.5, the AHL based on NPDES Perm it lim it can be calculated.

Where:

AHL effccc 

### = AHL based on CCC effluent com p liance with NPDES, lbs/day C

npdes 

### = NPDES perm it lim it, m g /L Q

potw 

### = P OTW R

potw 

### = P lant rem oval efficiency from headwo rks to plant effluent (as decim a l) 8.34 = C onversion factor
The equation to calculate the AHL based o n CCC effl uent com p liance with NPDES includes the NPDES perm it lim it, C npdes 

### , total POTW flow, Q potw 

### , and an overall plant rem oval rate from headworks to plant effluent, R potw 

### . R potw 

### can be determ ined by sam pling loading at point “A,” screen and grit c h a m b e r , a n d a t point “E,” the effluent after the chlorine contact cham bers. Q potw 

### can be determ ined through sam pling flow at point “A” as well.

AHL FOR 

### S LUDG E 

### A PPLICATIO N 

### The plant m u st determ ine a headworks that would l ead to sludge from the belt filter press suitable for land application. on sludge land application can be calculated.

Where: AHL sabfp 

### = AHL based on com p liance with sludge application standards, lbs/day C

slgstd 

### = S ludge standard, m g /kg dry sludge PS = Percent solids of sludge leading to belt filter press Q bfp 

### = T otal sludge flow to belt filter press, MGD R

potw 

### = P lant rem oval efficiency from headwo rks to plant effluent (as decim a l) G

sldg 

### = S pecific gravity of sludge leading to filter press, kg/L 8.34 = U nit conversion factor
The equation to calculate the AHL based on belt filter press sludge com p liance with sludge standards includes the sludge lim it, C slgstd 

### ; the flow, percent solids and specific gravity of sludge leading press, Q bfp 

### , PS, and G sldg 

### , respectively ; and the overall plant rem oval rate from headworks to plant effluent, R potw 

### . R

potw 

### can be determ ined by sam pling loading at point “A , ” t h e headworks before the bar screen and grit cham ber, and at point “E,” the e f f l u e n t after the chlorine contact cham bers. Q bfp 

### , PS, and G sldg 

### , can be determ ined through sam pling point “D” the sludge waste stream to the belt filter press. flow, average, MGD

the headworks before the bar

Using equation 5.9 an AHL based

to the belt filter

U-3

### AHL FOR 

### S ECO NDARY 

### T REATMENT 

### I NHIBITION 

### At this P O T W a standard rate trickling filter, a high rate trickling filter, and rotating biological contactors (RBCs) operated in parallel provide secondary treatm e nt of the raw wast ewat er

. Each of these biological units is preceded by a different prim ary clarifier. An AHL (to prevent inhibition) should be determ ined for each of these biological unit processes because: Diagram B Raw W astewater Effluent 

### A

Chlorine Contact Chamber

Gravity Thickener 

### X X



### H

Land Application RBCs Bar Screen

Grit Chamber 

### Primary Clarifier #1 Std. Rate T rickling

Filter Secondary Clarifier #1

High Rate T rickling

Filter

Primary Digester Secondary Digester Primary Clarifier #2

Primary Clarifier #3 Secondary Clarifier #2

Secondary Clarifier #3 We t W ell

V acuum Filter 

### X

X

X

X

X

X 

### B

C

D

E

F

G

C 

### The concentration of a pollutant that could cause inhib ition at the standard rate trickling filter, high rate trickling filter, and RBCs are different.

C 

### The design and operational loadings to each of the sec ondary treatm e nt units are different and therefore loading is different.

C 

### The prim ary clarifiers m a y have different rem oval e fficiencies and therefore the pollutant concentrations to each of the secondary treatm e nt unit m a y be different.
The three equations listed below can be used to calculate secondary treatm e nt inhibition.

U-4

### Standard Trickling Filter

High-Rate Trickling Filter

RBCs

Where: AHL inhstf 

### = AHL based on standard trickling filter inhibition, lbs/day AHL inhhrtf 

### = AHL based on high-rate trickling filter inhibition, lbs/day AHL inhrbc 

### = AHL based on RBC inhibition, lbs/day C

inhstf 

### = Inhibition criteria for standard trickling filter, m g /L C

inhhrtf 

### = Inhibition criteria for high-rate trickling filter, m g /L C

inhrbc 

### = Inhibition criteria for RBC, m g /L Q

potw 

### = Total POTW flow, MGD R

prim1 

### = Re m oval efficiency from headworks to prim a r y c l a r i f i e r # 1 effluent as a decim a l (See Section 5.1.1 for calculating rem oval efficiencies) R

prim2 

### = Rem oval efficiency from headworks to pr im ary clarifier #2 effluent as a decim a l R

prim3 

### = Rem oval efficiency from headworks to pr im ary clarifier #3 effluent as a decim a l 8.34 = Unit conversion factor
Each of the AHL equations has an inhibition criteria for each secondary t r eatm e nt unit, C inhstf 

### , C inhhrtf, 

### , and C

inhrbc 

### and rem oval rates fro m th e headworks to co rresponding prim ary clarifier unit effluent, R prim1 

### , R prim2 

### , and R prim3 

### . sam pling locations “A” and “B” is used t o calculate the rem oval efficiency from headworks to the prim ary clarifier #1 effluent, R prim1 

### . sam pling locations “C” is used to calculate the rem oval efficiency from head works to prim ary clarifier #2 effluent, R prim2 

### . sam pling loca t i o n s “ A ” and “D” is used to calculate the rem oval efficiency from headworks to prim ary clarifier #3 effluent, R

prim3 

### . Q potw 

### can be determ ined at sam pling point “A.” The AHL inhstf 

### , AHL inhhrtf 

### , and AHL inhrbc 

### should be calculated, com p ared, and the m o st stringent (sm a llest) selected.

AHL FOR 

### S LUDG E 

### D IG ESTER 

### I NHIBITION 

### This plant m u st determ ine a headworks loading protecting both the prim ary and secondary sludge digesters from inhibition. on sludge digester inhi bition can be calculated.

Prim ary Digester Data from Data from “A”and Data from

Using Equation 5.12, an AHL based

U-5

### Secondary Digester

Where: AHL inbpd 

### = AHL based on prim ary digester inhibition, lbs/day AHL inbsd 

### = AHL based on secondary digester inhibition, lbs/day C

inbpd 

### = P rim a ry digester inhibition criteria, m g /L C

inbsd 

### = P rim a ry digester inhibition criteria, m g /L Q

pd 

### = Sludge flow to prim ary digester, MGD Q

sd 

### = Sludge flow to secondary digester, MGD R

potw 

### = Plant rem oval efficiency from headwo rks to plant effluent (as decim a l) 8.34 = Unit conversion factor
The equations to calculate the AHL based on sludge digester inh i b i t i on include prim ary and secondary inhibition criteria, C inbpd 

### and C inbsd 

### , sludge flow to the prim ary and secondary digesters, Q pd



### and Q sd 

### , and an overall plant rem oval rate from headworks to plant effluent, R potw 

### . Q pd 

### can be determ ined sam pling flow at point “E,” t h e s l u d g e wastestream to the prim ary digester. Q sd



### can be determ ined sam pling flow at point “F,” the sludge wastestream from the prim ary digest er to the secondary digester. R potw 

### can be determ ined by sam pling loading at point “A,” the h eadworks before the bar screen and grit cham ber, and at point “H,” the effluent after the chlorine contact cham bers. AHL inbpd 

### and AHL inbsd 

### should be c a l c u l a t e d , com p ared and the m o re stringent selected.

AHL FOR 

### E FFLUENT 

### L IMITS 

### The plant m u st determ ine headworks loading that would lead to effluent from its chlorine contact cham bers (CCC) com p ly with NPDES Perm it lim its. it lim it can be calculated.

Where: AHL effccc 

### = AHL based on CCC effluent com p liance with NPDES, lbs/day C

npdes 

### = N PDES perm it lim it, m g /L Q

potw 

### = P OTW R

potw 

### = P lant rem oval efficiency from headwo rks to plant effluent (as decim a l) 8.34 = C onversion factor
The equation to calculate the AHL based on CCC effl uent com p liance with NPDES incl u d e s t h e NPDES perm it lim it, C npdes 

### , total POTW flow, Q potw 

### , and an overall plant rem oval rate from headworks to plant effluent, R potw 

### . R potw 

### can be determ ined by sam pling at point “A,” the headworks befo re the bar screen and grit cham ber, and a t p o i n t “ H ,” the effluent after the chlorine contact cham bers. Q potw 

### can be determ ined through sam pling flow at point “A” as well. by by

Using Equation 5.5, the AHL based on NPDES Perm

flow, average, MGD

U-6

### AHL FOR 

### S LUDG E 

### A PPLICATIO N 

### The plant m u st determ ine a headworks that would lead to sludge from the vacuum filter suitab le fo r land application. Using equation 5.9 an AHL based on sludge land application can be calculated.

Where: AHL savf 

### = AHL based on com p liance with sludge application standards, lbs/day C

slgstd 

### = S ludge standard, m g /kg dry sludge PS = P ercent solids of sludge leading to vacuum filter Q vf 

### = T otal sludge flow to vacuum filter, MGD R

potw 

### = P lant rem oval efficiency from headwo rks to plant effluent (as decim a l) G

sldg 

### = S pecific gravity of sludge leading to vacuum filter, kg/L 8.34 = U nit conversion factor

The equation to calculate the AHL based on vacuum f

ilter sludge com p liance with sludge standards includes the sludge lim it, C slgstd 

### ; the flow, percent solids and specific gravity of sludge leading to the belt filter press, Q vf 

### , PS, and G sldg 

### , respectively ; and the overall plant rem oval rate from headworks to plant effluen t , R potw 

### . R

potw 

### can be determ ined by sam pling at point “A,” the h eadworks before the bar screen and grit cham ber, and at point “H,” the effluent after the chlorine contact cham bers. Q vf 

### , PS, and G sldg 

### , can be determ ined through

sam pling at point “G” the sludge waste stream to the vacuum filter.

Diagram C Raw W astewater Effluent

Landfill X 

### A

F G E 2nd Stage Aeration

Basin 3rd Stage Aeration

Basin

1st Stage Clarifier 2nd Stage Clarifier Final Clarifier Ozone T reatment

Air Flotation Thickener Primary Clarifier 1st Stage Aeration

Basin

Gravity Thickener Plate and Frame Press Sludge Digester X X X

X X X X



### B D C H

U-7

### 1 st 

### Stage Aeration Basin

2 nd 

### Stage Aeration Basin

3 rd 

### Stage Aeration Basin AHL FOR 

### S ECO NDARY 

### T REATMENT 

### I NHIBITION 

### At this POTW three activated sludge units (aerati on basins) operated in series provide secondary treatm e nt of the raw wastewater. a pollu tant entering the First Stage Aeration Basin would be different from the concentration of that pollu tan t entering the Second Stage Aeration Basin and the Third Stage Aeration Basin because of the rem oval occurring in each unit. be determ ined for each of these secondary treatm e nt units.

Where: AHL inhab1 

### = AHL based on 1 st 

### Stage Aeration Basin inhibition, lbs/day AHL inhab2 

### = AHL based on 2 nd 

### Stage Aeration Basin inhibition, lbs/day AHL inhab3 

### = AHL based on 3 rd 

### Stage Aeration Basin inhibition, lbs/day C

inhab1 

### = I nhibition criteria for 1 st 

### Stage Aeration Basin, m g /L C

inhab2 

### = I nhibition criteria for 2 nd 

### Stage Aeration Basin, m g /L C

inhab3 

### = I nhibition criteria for 3 rd 

### Stage Aeration Basin, m g /L Q

potw 

### = T otal POTW flow, MGD R

prim 

### = R em oval efficiency from headworks to prim ary treatm e nt effluent as a decim a l (See Section 5.1.1 for calculating rem oval efficiencies) R ab1 

### = R em oval efficiency from headworks to 1 st 

### Stage Aeration Basin effluent as a decim a l R ab2 

### = R em oval efficiency from headworks to 2nd 

### Stage Aeration Basin effluent as a decim a l 8.34 = U nit conversion factor
Each of the equations to calculate AHLs for secondary treatm e nt has it own inhibition criteria for each basin, C

inhab1 

### , C inhab2 

### , and C inhab3 

### and corresponding rem oval rate, R prim 

### , R ab1 

### , and R ab2 

### , respectively , for the 1 st 

### , 2 nd


### , and 3 rd 

### stage aer a t i on basins. sam pling locations “A” and “B” is used to determ ine the rem oval efficiency from headworks to prim ary clarifier effluent, R prim 

### . Data from sam pling locations “A” and “C” is used to determ ine t h e remo val efficiency from headworks to 1 st 

### stage clarifier effluent, R ab1. 

### Data from

sam pling locations “A” and t o d e te rm ine the rem oval efficien cy from headworks to 2 nd 

### stage clarifier effluent, R ab2 

### . Q potw 

### can be determ ined by sam pling at location “A.” The AHL inhab1 

### , AHL inhab2 

### , and AHL inhab3 

### should be calculated, com p ared, and the m o st stringent (sm a llest) selected. The concentration of
An AHL (to prevent inhibition) should

Data from

“D” is used

U-8

### AHL FOR 

### S LUDG E 

### D IG ESTER 

### I NHIBITION 

### This pla n t m u s t de te rm ine a he a d w o rks loa d ing prot e c ting the sludge dige s t e r from inhibition. U s ing Equation 5.12, an AHL based on sludge digester inhibition can be calculated.

Where: AHL inhsd 

### = AHL based on sludge digester inhibition, lbs/day C

inhsd 

### = S ludge digester inhibition criteria, m g /L Q

sd 

### = S ludge flow to sludge digester, MGD R

potw 

### = P lant rem oval efficiency from headwo rks to plant effluent (as decim a l) 8.34 = U nit conversion factor
The equation to calculate the AHL based on high-rate sl udge digester inhibition includes an inhibition criteria for the digester, C inhsd 

### , sludge flow to the digester, Q sd



### , and an overall plant rem oval rate from headworks to plant effluent, R potw 

### . Q sd 

### can be determ ined sam pling flow at point “E,” the sludge wastestream from air flotation thickener to the digester. R potw 

### can be determ ined by sam pling loading at point “A,” the headworks before the bar screen and grit cham ber, and at point “H ,” the effluent after the ozone treatm e nt unit.

AHL FOR 

### E FFLUENT 

### L IMITS 

### The plant m u st determ ine headworks loading that would lead to effluent from its ozone treatm e nt unit (OTU)

com p ly with N P D E S P e r m it lim its. it lim it can be calculated.

Where: AHL effotu 

### = AHL based on OTU effluent com p liance with NPDES, lbs/day C

npdes 

### = N PDES perm it lim it, m g /L Q

potw 

### = P OTW R

potw 

### = P lant rem oval efficiency from headwo rks to plant effluent (as decim a l) 8.34 = C onversion factor
The equation to calculate the AHL based on OT U e f f l u e n t c o m p liance with NPDES includes the NPDES perm it lim it, C npdes 

### , total POTW flow, Q po t w 

### , and an overall plant rem oval rate from headworks to plant effluent, R potw 

### . R potw 

### can be determ ined by sam pling loading at point “A,” screen and grit cham ber, and at point “H,” the effluent after the OTU. Q potw 

### can be determ ined through

sam pling flow at point “A” as well. by

Using Equation 5.5, the AHL based on NPDES Perm

flow, average, MGD

the headworks before the bar

U-9

### AHL FOR 

### S LUDG E 

### A PPLICATIO N 

### The plant m u st determ ine a headworks that would lead to sludge from the plate and fram e press (PFP) suitable for land application. Using equation 5.9 an AHL based on sludge land application can be calculated.

Where: AHL sapfp 

### = AHL based on com p liance with sludge application standards, lbs/day C

slgstd 

### = S ludge standard, m g /kg dry sludge PS = P ercent solids of sludge leading to PFP Q

pfp 

### = T otal sludge flow to PFP, MGD R

potw 

### = P lant rem oval efficiency from headwo rks to plant effluent (as decim a l) G

sldg 

### = S pecific gravity of sludge leading to PFP, kg/L 8.34 = U nit conversion factor
The equation to calculate the AHL based on PFP sludge com p liance with sludge standards includes the sludge lim it, C slgstd 

### ; the flow, percent so lid s an d specific gravity of sludge leading to the PFP, Q pfp 

### , PS, and G sldg 

### , respectively ; and the overall plant rem ova l rate from headworks to plant effluent, R potw 

### . R potw 

### can be determ ined by sam pling loading at point “A,” the h eadworks before the bar screen and grit cham ber, and at point “H,” the effluent after the OTU. Q pfp 

### , PS, and G sldg 

### , can be determ ined through sam pling flow at point “E” the sludge waste stream to the PFP.

U-10

# A PPENDIX 

# V

- D OMESTIC 

# P OLLUTANT 

# L OADINGS

Re s i de ntia l/Comme rc ia l Trunk line Monitoring Da ta

Pollutant Number of Detectio n s Minimum Number of Co n cen tratio n Samples Maximu m Co n cen tratio n (mg/L) A v erag e Co n cen tratio n (mg/L)

0.088 0.007 0.216 0.42 0.11 0.008 0.007 0.006

1.2 0 .034 0.74 0.14 0.37 0.082 0 .27 3.4 2.04 0.058 0 .031 0.031 0 .161 0.087 0.054 0.002

1.6 0 .047 30.2 0 .7 0.7 1.052 0.019 1.28 0.231 0.115

0.3

0.255 0.989

28.8 Arsenic Barium Boron Cadmium Chromium (III) Chromium (T ) Copper Cy anide F l uoride Iron Lead Lithium Manganese Mercury Nickel Phosphate T o tal Phosphorous Silver Z i nc

Chloroform 1,1-Dichloroethene 1,1-Dichloroethane T r ans-1,2-Dichloroet hene F l uoranthene Methy l ene Chloride Phenols Bis (2-ethy l hexy l ) Phthalate Py rene T e trachloroethene 1,2,4-T r ichlorobenzene

T o tal BHC 4,4-DDD T o tal Endosulfan (mg/L) INORGANICS 140

3 4 361

1 311 603

7 2 18 433

2 3 218 313

2 1 181 636 205 0.0004 3 0.04 4 0 .1 538 0.00076

2 < 0.005 522 < 0.001 607 0.005

7 0.01 2 0 .24 18 0.0002 540 0.001

2 0 .03 3 0 .04 235 < 0.0001 540 < 0.001

2 27.4 1 0.7 224 0.0007 638 0.01

ORGANICS 21 2 1 1 2 7 2 5 2 5 1 30 < 0 .002 2 9 0 .005 2 8 0 .026 2 8 0 .013 5 0.00001 3 0 0 .00008 2 0 .00002 5 0 .00002 3 0.00001 2 9 0 .00001 3 < 0 .002
PEST ICIDES 3 3 3 3 0 .001 3 0.00026 3 0 .002 0.069 0.008 0.026 0.013

< 0 .001 0.055 0.00003

0.022

< 0 .005 0.037 0.035 0.009 0.007 0.026 0.013 0.001 0.027 0.000025

0.006 0.0002 0.014 0.013

0.001 0.001 0.0004 0.0003 0.002 0.002



### V-1

### Source: U. S

. EPA’s Supplemental Manual on the Development and Implementation of Local Discharg e Limitations Under the Pretreatment Programs , May 1991. “ P o l l u t a n t l e v e ls reported below specified detection lim it were considered in the data analy s is and, for the purpose of statistical analy s is, were considered equal to the detection lim it.”

V-2

# A PPENDIX 

# W

- B EST 

# M ANAGEMENT 

# P RACTICES 

# M INI 

# -C ASE 

# S TUDIES

POTWs can im plem ent best m a nagem e nt practice (BMP) program s to gain control ove r w a s t e w ater discharg e s f r o m c o mme rcial sources. By developing a less form al source control program with em phasis on source control, education, BMPs, as-needed inspec tions, and individual or “general” perm its, POTWs can gain additional control over uncontrolled wastewater discharges from com m e rcial sources. Source control program s should place em phasis on certain specific pollutant s of concern. For exam ple, silver and m e rcury are often of great concern to POTWs because of NPDES perm it requirem e nts.

The com m e rcial sources of wastewater that are addressed by BMP-based source control tend to have lower pollutant concentrations and loadings than other m o re traditional industrial facilities regulated by the traditional pretreatm e nt program

. Taken as a group, however, num erous uncontrolled com m e rcial establishm ents m a y represent a significant source co ntrol opportunity that can lead to m easurable im provem e nt in environm ental quality

. Several pretreatm e nt program s have docum ented p o l l u t a nt reductions after the im plem entation of BMP and source reduction program s. Several BMP/source control program s im plem ented at several POTWs are sum m a rized in the f o llowing p a ragraphs to illustrate a variety of approaches that have been taken to utilize BMPs for the contro l o f

com m e rcial sources of wastewater. Program s reviewed include: E ast Bay Municipal Utility District (EBMUD) in Oakl an d , California; Metropolitan W astewater in San Diego, California; Seattle M e t r opolitan/King County in Washington State; W e s t e r n L a ke S upe r i or S a ni t a r y D i s t r i c t , D u l u t h , M i nne s o t a ; and the Connecticut Departm e nt of Environm enta l Protection (CT DEP), Hartford, Connecticut.

E AST 

### B AY 

### M UNICIPAL 

### D ISTRICT 

### (EBMUD), O AK LAND 

### , CA

EBMU D ha s be e n is s u ing pollution pre v e n tion pe rm its (PPPs) s i nc e 1988. EBMU D be ga n the i r PPP program in response to tighter air em ission standard s and m o re stringent NPDES perm it requirem e nts. As a result, EBMUD had to augm ent headworks sam pling and analy s is program s for the developm ent and local lim its. Based on attentive tracking of regulatory requirem e nts over the past 10 y ears, EBMUD has sequentially identified POCs, identified com m e rcial u sers contributing those pollutants, and developed PPPs with best m a nagem e nt practice requirem e nts.

When EBMUD recognizes a pollutant of concern, they follow step-wise procedures. First, inform ation on businesses and com m e rcial activities that m a y be contribu ting the targeted POC is collected and refined. Next, EBMUD engages in an outreach program and works wi t h t h e businesses to define BMPs. Finally , EBMUD issues the PPP and evaluates com p liance. Gene rally , there is one sect o r o f business activity that is responsible for the specific pollutant. For exam ple, silver is linked to the photo finishers. EBMUD works with representatives of the com m e rcial sector to research and develop BMPs that will effectively m i nim i ze the pollutant in effluent wastewater.

EBMUD believes that the m o st im portant aspect of a successful PPP program is education, outreach, and user
awareness com b ined with an enfor ceable perm it. When conducting outreach, EBMUD gets in touch with each user to confirm the nature of their business and the processes they use at the business that can contribute wastewater and pollutants of concern. The establishm ent is asked to participate in educational wo rk sh ops to review EBMUD' s concerns, review and refine pre lim inary m e thods to prevent pollutant releases and lay the groundwork for successful com m unicati o n s an d u nderstanding of the problem and the solution. Users are introduced to the perm itting and enforcem ent process in a non-threatening forum

. EBMUD’ s inventory of user s i s b a sed o n water supply account inform ati on. Whenever a user opens a water account, it is

W-1

### autom a tically characterized for wastewater and source control purposes. A revenue collection sy stem ($3.25 per com m e rcial account per m onth) assures adequate funding for the PPP program

.
EBMUD staff researches ideas for generalized BMPs th rough discussions with the business sector and State trade group associations. Each of the EBMUD staff tracks one or m o re business sectors and are responsible for knowing current pollution control m easures and tr ends. During the research phase of BMP developm ent, each user that is a potential contributor of a POC is sent a letter to notify them that EBMUD is working on a PPP and reviewing the anticipated tim e fram e for perm it issuance.

The PPP includes sim ilar BMPs for each per m i t t ee. Som e exam ples include silver recovery canisters connected in series to optim ize rem oval of silver with a required logging procedure that assures canisters are changed at the appropriate frequency to elim inate br eak through. EBMUD PPPs vary in length from two to 8 pages and are issued in industry group batches for a duration of 5 y ears. In the adm i nistration of their PPP program , EBMUD conducts vis its /ins p e c tions of the pe rm itte e s , provide s follow up education, distributes fact sheets, procedures and posters illustrating acceptable wast e and wastewater disposal procedures. During the life of a 5-y ear perm it, EBMUD tries to inspect the perm ittee at least once or twice. Based on the findings of the initial visit, and the degree to which the pe rm ittee is im plementing the prescribed BMPs, the follow up frequency is set. When EBMUD fails to get c ooper a t i on from businesses, they initiate enforcem ent actions. As an exam ple, EBMUD sought and obtained a $27,000 penalty from a dry cleaner.
A partial list of the businesses for which EBMUD has developed BMPs and PPP perm its includes:

• P hotofinishers

• B o a t Y a r d s
• D ry Cleaners

• A uto Repairs

• P rint Shop

• R adiator Repair Shops

• F urniture Stripping

M E T R OPOL IT A N 

### W ASTEWATER 

### , S AN 

### D IEGO 

### , CA
Metropolitan Wastewater issues sector-specif i c B M P disch arge authorizations to com m e rcial custom ers. These authorizations require:

• S pecific pollution prevention m easures.

• A n initial certification of com p liance.

• O n-going sem i-annual “rem inder” certifications for businesses to dem onstrate fam iliarity with their pollution prevention m easures.

Metropolitan Wastewater covers a variety of sectors with this program

. General perm its are issued to film processing and dry cleaners. The photo processing BMPs are based on the Code of Managem e nt Practice for Silver Dischargers (AMSA 1996) developed by the S ilver Council in concert with the Association of Metropolitan Sewerage Agencies and EPA. Metropolitan Wastewater conduc ted workshops to review silver control BMPs with their users. Boat repair y a rds or dry docks are required to subm it their own custom ized BMPs which are incorporated into a perm it. Food estab lishm ent discharge perm its are also issued and require grease rem oval equipm ent, operation and m a intenance, and com p liance with general and specific discharge prohibitions. Auto repair shops that have steam -clean ing operations are required to have a sum p for all O&G

W-2

### wastewater from steam cleaning operations. San Diego has initiated a m o re aggressive program to enforce grease trap cleaning particularly at food establishm en ts. They have discovered that excessive am ounts of grease buildup contribute to dry weather flows into Sa n Diego and Mission Bay

. Analy tical/Research Labs are required to im plem ent a solvent certification program th at is very sim ilar to the total toxic organic (TTO) certification program for m e tal finishers and electroplaters.
San Diego has a 301(h) waiver for their wastewater treatm e nt facility , allowing con d itional discharge of wastewater without full secondary treatm e nt. One condition of the waiver requires the City to reexam ine their local lim its ev er y y ear and reassess loadings from a ll sources (dom estic, SIU, and non-SIU contributions). The IU is considered a "contributor" of a pollutant of concern (presently one of six heavy m e tals) if the user has one of the six m e tals in its effluent at a concentr ation that is two standard deviations above the average dom estic concentration. Once the IU is deem ed to be a “contributor” of a pollutant of concern, wastewater flow is evaluated to determ ine whether the load is significant enough to be assigned to the “allocated” versus “non-allocated” load for their local lim its accounting proce dures. When the load is sign i f i can t , the user is included in the allocated portion of headworks load calcu lations and the user is required to com p ly with local lim its. Users with m i nor concentrations or loadings of pollutants of concern are not required to com p ly with local lim its. However, they m a y still be required to com p ly with BMPs and general perm it requirem e nts.

S EATTLE 

### M E T R OPOL IT A N 

### /K ING 

### C O UNTY 

### , W ASH I NG TO N 

### Seattle Metropolitan /King County (Seattle Metro) has a very large and active pollution prevention program that has acquired a great deal of inform ation on dental m e rcury source control. Dental facilities in the Seattle Metro collection sy stem are subject to the m e rcury local lim its. After strong lobby ing by the Dental Association against m a ndatory BMPs, dental facilities currently have latitude in controlling m e rcury through BMPs. Seattle Metro has developed a variet y of tools to control m e rcury , including:

• A certified list of the vendors and technologies that are able to achieve a 90% reduction in m e tals.

• V ideot a p e o n mercury and silver source cont rol from dental offices entitled “Am a lgam Waste Conference.”

• A dental facility waste m a nagem e nt poster a nd a booklet entitled, “Waste Managem e nt Guidelines for Dental Facilities.”

• R ecords on the am ount of am algam that is being reclaim e d by recy clers as a m eans of tracking the success of their education efforts.

• V oucher program that gives $500 to dental offices to obtain one of the approved m e tal rem oval units identified in the list above.

• E ducational m a terials (posters, videos, booklets).

W ESTERN 

### L AK E 

### S UPERIO R 

### S ANITARY 

### D ISTRICT 

### (WLSSD), D ULUTH 

### , M INNESO TA 

### With support from the Great Lakes Protection Fund , th e WLSSD conducted a two-y ear Mercury Zero Discharge Project to exam ine the sources of m e rcury to its wastewater treatm e nt plant and to determ ine how to reduce or elim inate those sources. This project included cooperative initiatives with industries known to be discharging m e rcury , progr a m s a i me d a t specific uses of m e rcury , a m onitoring program to identify additional sources and a public awareness cam paign. In addition to these external program s, WLSSD also

exam ined its own facilities and practices.

W-3

### WLSSD has authored the Blueprint for Mercury Elimination, Mercury Reduction Project Guidance for Wastewater Treatment Plants , March, 1997. Selected for an AMSA National Environm ental Achievem ent Award for excellence in Public Info rm ation & Education, the docum ent exam ines sources of m e rcury in the environm ent, reviews contributions to the w a s t e w a t e r c o lle c tion s y s t e m , a nd give s e x a m ple s of s u ccess stories

on m e rcury source reduction. Appendices to the doc um ent pro v i d e useful "how to" references for im plem en t i n g a s ource reduction program , such as a sam ple news release for a m e rcury reduction project,

sam ple letters to m e rcury contributors, telephone surv ey form s to interview possible contributors, survey form s for hospitals and dental offices.

C O NNECTICUT 

### D EPARTMENT O F 

### E NVIRO NMENTAL 

### P RO TECTIO N 

### (CT DEP), H ARTFO RD 

### , C O NNECTICUT 

### In 1992, CT DEP began a Statewide general perm it program

. The program established requirem e nts for industries that were not SIUs, regulated by CT D EP’s State-run pretreatm e nt perm itting program , but were a potential source of concern for POTWs. The ge neral permitting program is “self im plem enting” and expects com m e rcial establishm ents to be m a de aware of general perm it program requirem e nts by the local Town officials, the State, or through consultants. Thus, the general perm itting program avoids the resource intensive individual perm itting of traditional program s. The program works in the following m a nner. An IU assesses their eligibility for a general perm it (versus a traditional pretreatm e nt perm it). CT DEP encourages i ndustries to determ ine eligibility for a general perm it, as the perm itting process is quicker and less costly for th e IU and CT DEP. Each general perm it identifies BMPs that m u st be followed by each perm it holder. CT DEP conducts selective auditing and enforcem ent of general perm it holders, and facilities that m a y have failed to register fo r a general perm it. By publicizing the enforcem ent actions and penalties, industries are m a de aware of their duties to have a perm it and com p ly with the BMPs, record keeping, m onitoring, and wh ere applicable, effluent lim its. General perm its developed by CT DEP include the following sectors:

1. Constructions and Operati on of Certain Recy cling Facilities 2. Car Wash Wastewater 3. Dom estic Sewage of 50,000 gallons per day or 5% of the POTW Design Flow 4. Groundwater Contam ination Recovery Sy stem s 5. Hy drostatic Pressure Testing 6. Minor Boiler Blowdown 7. Minor Non-Contact Cooling Water 8. Minor Photographic Processing 9. Minor Tum b ling and Cleaning of Parts Wastewater 10. Storm Water Associated with Industrial Activities 11. Storm Water and Dewatering W astewaters - Construction Activities 12. Vehicle Service Floor Drain and Car Wash Wastewater 13. Storm Water Associated with Com m e rcial Activities 14. Minor Printing and Publishing Wastewater 15. Water Treatm e nt Wastewater - Com m e rcial 16. Food Processing Wastewater 17. Public Swim m i ng Pool Backwash 18. Water Softening/Treatm e nt Unit Wastewater-Individual Hom es (under developm ent)

W-4

# A PPENDIX 

# X

- R EGION 

# 1, R EASSESSMENT OF 

# T ECHNICALLY 

# B ASED 

# I NDUSTRIAL 

# D ISCHARGE 

# L IMITS 

# C HECKLIST

Attachment A.
EPA - New England
Reassessment of Technically Based Industrial Discharge Limits

Under 40 CFR 122.21(j)(4), all Publicly Owned Tr eatm e nt Works (POTWs) with approved Industrial Pretreatm e nt Program s (IPPs) shall provide the following inform ation to the Director: a written evaluation of the need to revise local industrial discharge lim its under 40 CFR 403.5(c)(1).

Below is a form designed by the U.S. Environm ental Protection Agency (EPA - New England) to assist POTWs with approved IPPs in evaluating whethe r their existing Technically Based Local Lim its (TBLLs) need to be recalculated. The form allo ws the perm ittee and EPA to evaluate and com p are pertinent inform ation used in previous TBLLs cal culations against present conditions at the POTW.
Please read direction below before filling out form.

ITEM I.

* I n Colum n (1), list what y our POTW’s influent flow rate was when y our existing TBLLs were calculated. In Colum n (2), list y our POTW’ s pr esent influent flow rate. Your current flow rate should be calculated using the POTW' s average da ily flow rate from the previous 12 m onths.

* I n Colum n (1) list what y our POTW’s SIU fl ow rate was when y our existing TBLLs were calculated. In Colum n (2), list y our POTW’ s present SIU flow rate.

* I n Colum n (1), list what dilution ratio and/or 7Q10 value was used in y our old/expired NPDES perm it. In Colum n (2), list what dilution ratio and/or 7Q10 value is presently being used in y our new/reissued NPDES perm it.
The 7Q10 value is the lowest seven day average fl ow rate, in the river, over a ten-y ear period. The 7Q10 value and/or dilution ratio used by EPA in y our new NPDES perm it can be found in y our NPDES perm it “Fact Sheet.”

* I n Colum n (1), list the safety factor, if any , that was used when y our existing TBLLs were calculated.

* I n Colum n (1), note how y our biosolids we re m a naged when y our existing TBLLs were calculated. In Colum n (2), note how y our PO TW is presently disposing of its biosolids and how y our POTW will be disposing of its biosolids in the future.

ITEM II.

* L ist what y our existing TBLLs are - as they appear in y our current Sewer Use Ordinance (SUO).

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### ITEM III.

* I dentify how y our existing TBLLs are allocat ed out to y our industrial com m unity

. Som e pollutants m a y be allocated differently than others, if so please explain.

ITEM IV.

* S ince y our existing TBLLs were calculated, identify the following in detail:
(1) i f y our POTW has experienced any upsets, inhibition, interference or pass-through as a result of an industrial discharge.
(2) i f y our POTW is presently violating an y of its current NPDES perm it lim itations - include toxicity

.
ITEM V.

* U sing current sam pling data, list in Colum n (1) the average and m a xim u m am ount of pollutants (in pounds per day ) received in the POTW' s influe nt. Current sam pling data is defined as data obtained over the last 24 m onth period.
All influent data collected and analy zed m u st be in accordance with 40 CFR 136. Sam p ling data collected should be analy zed using the lowest po ssible detection m e thod(s), e.g., graphite furnace.

* B ased on y our existing TBLLs, as presented in Item II., list in Colum n (2), for each pollutant the Maxim u m Allowable Industrial Headwork Loadi ng (MAIHL) values derived from an applicable environm ental criteria or standard, e.g., water quality , sludge, NPDES, inhibition, etc. For each pollutant, the MAIHL equals the calculated Maxim u m Allowable Headwork Loading (MAHL)

m i nus the POTW's dom estic loading source(s). For m o re inform ation, please see p. 3-28 in EPA’s Guidance Manual on the Development and Implementation of Local Limits Under the Pretreatment Program, 12/87

.
ITEM VI.

* U sing current sam pling data, list in Colum n (1) the average and m a xim u m am ount of pollutants (in m i crogram s per liter) present in y our POTW’ s e ffluent. Current sam pling data is defined as data obtained during the last 24-m onth period.
All effluent data collected and analy zed m u st be in accordance with 40 CFR 136. Sam p ling data collected should be analy zed using the lowest po ssible detection m e thod(s), e.g., graphite furnace.

* L ist in Colum n (2A) what the Water Quality Standards (WQS) were (in m i crogram s per liter) when y our TBLLs were calculated, please note wh at hardness value was used at that tim e. Hardness should be expressed in m illigram per liter of calcium carbonate.

List in Colum n (2B) the current WQSs or “Chronic Gold Book” values for each pollutant

m

u ltiplied by the dilution ratio used in y our new/ reissued NPDES perm it. For exam ple, with a dilution ratio of 25:1 at a hardness of 25 m g /L - calcium carbonate (copper’ s chronic WQS equals 6.54 ug/L) the chronic NPDES perm it lim it for copper would equal 156.25 ug/L.

X-2

### ITEM VII.

* I n Colum n (1), list all pollutants (in m i crogram s per liter) lim ited in y our new/reissued NPDES perm it. In Colum n (2), list all pollutant s lim ited in y our old/expired NPDES perm it.

ITEM VIII.

* U sing current sam pling data, list in Colum n (1) the average and m a xim u m am ount of pollutants in y our POTW' s biosolids. Current data is defined as data obtained during the last 24 m onth period. Results are to be expressed as total dry weight.
All biosolids data collected and analy zed m u st be in accordance with 40 CFR 136.

In Colum n (2A), list current State and/or Federa l sludge standards that y our facility ’s biosolids

m

u st com p ly with. Also note how y our POTW currently m a nages the disposal of its biosolids. If y our POTW is planing on m a naging its biosolids di fferently , list in Colum n (2B) what y our new biosolids criteria will be and m e thod of disposal.
In general, please be sure the units reported are correct and all pertinent inform ation is included in y our evaluation. If y ou have any questions, please contact y our pretreatm e nt representative at EPA - New England.

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## --------- ----------- --------- -----------

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--------- ----------- --------- -----------

--------- ----------- --------- ----------- REASSESSMENT OF TECHNICALLY BASED LOCAL LIMITS (TBLLs)

POTW Name & Address :

_____________________________________________ NPDES PERMIT # :

_________________________________________________ Date EPA approved current TBLLs :

________________________________ Date EPA approved current Sewer Use Ordinance :

__________________
ITEM I. In Column (1), list the conditions that existed when your current TBLLs were calculated. In Column (2), list current conditions or expected conditions at your POTW.

Column (1) Column (2)

EXISTING TBLLs

POTW Flow (MGD)

SIU Flow (MGD)

Dilution Ratio or 7Q10 (from NPDES Permit)
Safety Factor
Biosolids Disposal Method(s)

POLLUTANT PRESENT CONDITIONS

N/A

NUMERICAL LIMIT (mg/L) or (lb/day) ITEM II. EXISTING TBLLs

NUMERICAL LIMIT
(mg/L) or (lb/day) POLLUTANT

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------- ---------- --------- ITEM III. Note how your existing TBLLs, listed in Item II., are allocated to your Significant Industrial Users (SIUs), i.e., uniform concentration, contributory flow, mass proportioning, other. Please specify by circling.

ITEM IV. Has your POTW experienced any upsets, inhibition, interference or pass-through from industrial sources since your existing TBLLs were calculated?
If yes, explain.

_________________________________________________________________

____________________________________________________ Has your POTW violated any of its NPDES permit limits and/or toxicity test requirements?
If yes, explain.

_________________________________________________________________

____________________________________________________
ITEM V. Using current POTW influent sampling data fill in Column (1). In Column (2), list your Maximum Allowable Headwork Loading (MAHL) values used to derive your TBLLs listed in Item II. In addition, please note the Environmental Criteria for which each MAHL value was established, i.e., water quality, sludge, NPDES, etc.

Column (1) Column (2) Pollutant Influent Data Analyses MAHL Values Criteria Maximum Average (lb/day) (lb/day) (lb/day)

Arsenic

--------- --------- Cadmium

--------- --------- Chromium

--------- --------- Copper

--------- --------- Cyanide

--------- --------- Lead

--------- --------- Mercury

--------- --------- Nickel

--------- --------- Silver

--------- --------- Zinc

--------- --------- Other (List)

--------- ---------

--------- ---------

--------- ---------

X-5

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---------- ----------- ---------- ----------- ITEM VI. Using current POTW effluent sampling data, fill in Column (1). In Column (2A) list what the Water Quality Standards (Gold Book Criteria) were at the time your existing TBLLs were developed. List in Column (2B) current Gold Book values multiplied by the dilution ratio used in your new/reissued NPDES permit. Columns Column (1) (2A) (2B) Pollutant Effluent Data Analyses Water Quality Criteria Maximum Average (Gold Book) From TBLLsToday (ug/L) (ug/L) (ug/L) (ug/L)

Arsenic *Cadmium *Chromium

---------- --------- *Copper Cyanide *Lead Mercury *Nickel Silver *Zinc Other (List) ---------

---------

---------

---------

---------

---------

---------

---------

---------

---------

---------

--------- *Hardness Dependent (mg/L - CaCO3)

ITEM VII. In Column (1), identify all pollutants limited in your new/reissued NPDES permit. In Column (2), identify all pollutants that were limited in your old/expired NPDES permit.

Column (1) NEW PERMIT Pollutants Limitations (ug/L) Column (2) OLD PERMIT Pollutants Limitations (ug/L)

X-6

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--------- --------- --------- ITEM VIII. Using current POTW biosolids data, fill in Column (1). In Column (2A), list the biosolids criteria that was used at the time your existing TBLLs were calculated. If your POTW is planing on managing its biosolids differently, list in Column (2B) what your new biosolids criteria would be and method of disposal.

Columns Column (1) (2A) (2B) Pollutant Biosolids Data Analyses Biosolids Criteria

Arsenic Cadmium Chromium Copper Cyanide Lead Mercury Nickel Silver Zinc Molybdenum Selenium Other (List) Average (mg/kg) From TBLLs New (mg/kg)

---------

---------

---------

---------

---------

---------

--------­ (mg/kg)

X-7