Section 3.0 Waste Management Unit Data

1

WMU area also impacts site layout data by defining the area of interest (AOI) and, hence, implicitly

affects other modules as well. 

3-1

3.0 Waste Management Unit Data

The 3MRA modeling system was designed to estimate potential risks from the long-term

management of hazardous and nonhazardous waste by waste management facilities. The

representative national data set focuses on Resource Conservation and Recovery Act (RCRA)

Subtitle D nonhazardous industrial waste management units (WMUs). 

The 3MRA modeling system includes 17 media-specific pollutant release, fate, transport,

exposure, and risk modules. WMU input data are used explicitly by seven of these modules: the

five source modules, the Air Module, and the Vadose Zone module.1

 These WMU inputs

describe the size and operation of the five WMU types that can be modeling by the 3MRA

modeling system: surface impoundments, aerated tanks, landfills, waste piles, and land

application units (LAUs). This section describes the derivation and use of values for the WMU

data for each of these WMU types. Data sources are described along with data collection

methodologies. 

3.1 Parameters Collected

Table 3-1 presents the WMU model inputs as required by the source and media modules. 

Three parameterization approaches were used for WMU inputs: site-specific, site-based and

national. Site-specific data on WMU area, capacity, and waste loading rates were obtained from

the Industrial D Screening Survey (Westat, 1987). Size-related WMU variables which were

derived from these Industrial D data, are referred to as site-based data in this document. All

other WMU inputs were developed on a national basis as either distributions or fixed values,

depending on potential variability and model sensitivity. 

3.2 Data Sources

The following documents are the primary data sources for the WMU data used for the

representative national data set:

# Surface impoundments, landfills, waste piles, and LAUs – Screening Survey of

Industrial Subtitle D Establishments. Draft Final Report. (Westat, 1987).

# Aerated tanks – 1986 National Survey of Hazardous Waste Treatment, Storage,

Disposal, and Recycling Facilities (TSDR) Database. (U.S. EPA, 1987). 
Section 3.0 Waste Management Unit Data

3-2

Table 3-1. WMU Inputs by Module

Model Inputs

Source Modules Media Modules`

AeratedTankLandApplicationUnitLandfillSurfaceImpoundmentWastePileAirVadose Zone

Site-Specific

area of source ! !! ! !

Setting ID (SrcType+SiteID) !

waste loading rate (dry) !

WMU type (AT, SI, LAU, WP, or LF) ! ! !! ! ! !

Site-Based

depth of WMU (0 for AT, WP) ! !! !

distance vehicle travels on active WMU surface !! !

fraction of SI occupied by sediments !

fraction organic carbon (cover soil) !

fraction surface area turbulent !

frequency of surface disturbance per month (active WMU) ! !

height (WP) !

impellers/aerators (number) !

impellers/aerators (total power) !

number of cultivations per application !

saturated hydraulic conductivity (LF cover soil) !

saturated water content (cover soil, total porosity) !

SCS curve number (WMU) ! !

soil moisture coefficient b (LF cover soil) !

source height !

spreading/compacting operations per day ! !

vehicle weight (mean) !! !

vehicles/day (mean annual) !! !

volumetric influent flow rate !

waste applications per year !

waste loading rate (dry) !

wet waste application rate !

wheels per vehicle (mean) !! !

National Tank Data (Correlated)

area of source !

depth (liquid) !

fraction surface area-turbulent !

(continued)
Table 3-1. (continued)

Section 3.0 Waste Management Unit Data

Model Inputs

Source Modules Media Modules`

AeratedTankLandApplicationUnitLandfillSurfaceImpoundmentWastePileAirVadose Zone

3-3

impellers/aerators (number) !

impellers/aerators (total power) !

volumetric flow rate (tank) !

National

biologically active solids/total solids (ratio) ! !

biomass yield ! !

depth (tilling, LAU) !

digestion (sediments) ! !

dust suppression control efficiency !! !

economic life of AT/SI ! !

fraction of unit occupied by sediments (max.) ! !

fraction vegetative cover (inactive WMU) ! !

impeller diameter ! !

impeller speed ! !

number of economic lifetimes (AT, SI) ! !

number of waste layers in a cell !

operating life (LAU, WP) ! !

optional soil cover thickness !

oxygen transfer correction factor ! !

oxygen transfer factor ! !

roughness height (inactive WMU) ! !

roughness ratio (LF waste zone surface) !

roughness ratio (till zone surface) !

saturated hydraulic conductivity (sediment layer) !

thickness of liner (or subsoil zone) !

USLE cover factor (WMU) ! !

USLE erosion control factor (WMU) ! !

vehicle speed (mean) !! ! 0

AT = aerated tank; LAU = land application unit; LF = landfill; SI = surface impoundment; USLE = universal soil loss

equation; WMU = waste management unit; WP = waste pile.

Other data sources such as literature, site visits, and vendor information were used to

supplement these sources as necessary. Table 3-2 lists the inputs for each model and includes

the source code, units, parameterization (i.e., site-specific, site-based, or national), and data

source for each input.
Section 3.0 Waste Management Unit Data

3-4

Table 3-2. WMU Data Sources

Model Input Code Units Parameterization Data Source

Landfill

area of source SrcArea m2 site-specific Ind. D Screening Survey

(Westat, 1987)

depth of source SrcDepth m site-based derived from Ind. D

distance vehicle travels on active LF cell surface mt m site-based derived from Ind. D

dust suppression control efficiency effdust unitless national U.S. EPA (1989)

fraction organic carbon (cover soil) focC mass

fraction

site-specific USSOILS (Schwarz and

Alexander, 1995)

fraction vegetative cover (inactive LF cell) veg fraction national professional judgment

frequency of surface disturbance per month

(active LF cell)

fd 1/mo site-based derived from Ind. D

number of waste layers in a cell Nly unitless site-based derived from Ind. D

optional soil cover thickness zC m national professional judgment

roughness height (inactive LF cell) zruf cm national U.S. EPA (1989)

roughness ratio (LF waste zone surface) Lc unitless national U.S. EPA (1989)

saturated hydraulic conductivity (LF cover soil) KsatC cm/h site-specific CONUS (Miller and White,

1998); Carsel and Parrish

(1988)

saturated water content (cover soil, total porosity) WCS_C volume

fraction

site-specific CONUS (Miller and White,

1998); Carsel and Parrish

(1988)

soil moisture coefficient b (LF cover soil) SMbC unitless site-specific CONUS (Miller and White,

1998); Carsel and Parrish

(1988)

spreading/compacting operations per day Nop 1/d site-based derived from Ind. D

thickness of liner (or subsoil zone) zS m national professional judgment

vehicle speed (mean) vs km/h national Overcash and Pal (1979)

vehicle weight (mean) vw Mg site-based derived from Ind. D

vehicles/day (mean annual) nv 1/d site-based derived from Ind. D

waste loading rate (dry) load Mg/yr site-based derived from Ind. D

waste zone thickness zW m site-based derived from Ind. D

wheels per vehicle (mean) nw unitless site-based derived from Ind. D

WMU type (specified LF) SrcType unitless site-specific Ind. D Screening Survey

(Westat, 1987)

Waste pile

area of source SrcArea m2 site-specific Ind. D Screening Survey

(Westat, 1987)

distance vehicle travels on WP surface mt m site-based derived from Ind. D

(continued)
Table 3-2. (continued)

Section 3.0 Waste Management Unit Data

Model Input Code Units Parameterization Data Source

3-5

dust suppression control efficiency effdust unitless national U.S. EPA (1989)

height of WP above grade zZ1WMU m site-based derived from Ind. D

operating life CutOffYr yr national professional judgment

SCS curve number (WMU) CNwmu unitless site-based derived from Ind. D

spreading/compacting operations per day Nop 1/d site-based derived from Ind. D

USLE cover factor (WMU) Cwmu unitless national Wanielista and Yousef

(1993)

USLE erosion control factor (WMU) Pwmu unitless national Wanielista and Yousef

(1993)

vehicle speed (mean) vs km/h national Overcash and Pal (1979)

vehicle weight (mean) vw Mg site-based derived from Ind. D

vehicles/day (mean annual) nv 1/d site-based derived from Ind. D

waste loading rate (dry) load Mg/yr site-specific Ind. D Screening Survey

(Westat, 1987)

wheels per vehicle (mean) nw unitless site-based derived from Ind D

WMU type (specified WP) SrcType unitless site-specific Ind. D Screening Survey

(Westat, 1987)

Land Application Unit 

area of source SrcArea m2 site-specific Ind. D Screening Survey

(Westat, 1987)

depth of tilling zZ1WMU m national literature

depth of source SrcDepth m national = tilling depth

distance vehicle travels on LAU surface mt m site-based derived from Ind. D

dust suppression control efficiency effdust unitless national U.S. EPA (1989)

fraction vegetative cover veg fraction national professional judgment

frequency of surface disturbance per month

(active LAU)

fd 1/mo site-based derived from Ind. D

number of cultivations per application fcult unitless site-based derived from Ind. D

operating life CutOffYr yr national professional judgment

roughness height zruf cm national U.S. EPA (1989)

roughness ratio (till zone surface) Lc unitless national U.S. EPA (1989)

SCS curve number (WMU) CNwmu unitless site-based derived from Ind. D

USLE cover factor (WMU) Cwmu unitless national Wanielista and Yousef

(1993)

USLE erosion control factor (WMU) Pwmu unitless national Wanielista and Yousef

(1993)

vehicle speed (mean) vs km/h national Overcash and Pal (1979)

(continued)
Table 3-2. (continued)

Section 3.0 Waste Management Unit Data

Model Input Code Units Parameterization Data Source

3-6

vehicle weight (mean) vw Mg site-based derived from Ind. D

vehicles/day (mean annual) nv 1/d site-based derived from Ind. D

waste applications per year Nappl 1/yr site-based derived from Ind. D

wet waste application rate Rappl Mg/m2

-yr site-based derived from Ind. D

wheels per vehicle (mean) nw unitless site-based derived from Ind. D

WMU type (specified LAU) SrcType unitless site-specific Ind. D Screening Survey

(Westat, 1987)

Surface Impoundment 

area of source SrcArea m2 site-specific Ind. D Screening Survey

(Westat, 1987)

biologically active solids/total solids (ratio) kba1 unitless national Tchobanoglous (1979)

biomass yield bio_yield g/g national Tchobanoglous (1979)

depth of source SrcDepth m site-specific Ind. D Screening Survey

(Westat, 1987)

depth of WMU d_wmu m site-based derived from Ind. D

digestion (sediments) k_dec 1/s national Tchobanoglous (1979)

economic life of AT/SI EconLife yr national professional judgment

fraction of SI occupied by sediments (max.) d_setpt fraction site-based derived from Ind. D

fraction surface area-turbulent F_aer fraction site-based derived from Ind. D

impeller diameter d_imp cm national U.S. EPA (1990)

impeller speed w_imp rad/s national U.S. EPA (1990)

impellers/aerators (number) n_imp unitless site-based derived from Ind. D

impellers/aerators (total power) Powr hp site-based derived from Ind. D

number of economic lifetimes NumEcon unitless national professional judgment

oxygen transfer correction factor O2eff unitless national Tchobanoglous (1979)

oxygen transfer factor J lb O2/h-hp national Tchobanoglous (1979)

saturated hydraulic conductivity (sediment layer) hydc_sed m/s national professional judgment

volumetric influent flow rate Q_wmu m3/s site-based derived from Ind. D

WMU type (specified SI) SrcType unitless site-specific Ind. D Screening Survey

(Westat, 1987)

Aerated Tank 

area of source SrcArea m2 national_correlated derived from TSDR Survey

biologically active solids/total solids (ratio) kba1 unitless national Tchobanoglous (1979)

biomass yield bio_yield g/g national Tchobanoglous (1979)

depth (liquid) d_wmu m national_correlated derived from TSDR Survey

(continued)
Table 3-2. (continued)

Section 3.0 Waste Management Unit Data

Model Input Code Units Parameterization Data Source

3-7

digestion (sediments) k_dec 1/s national Tchobanoglous (1979)

economic life of AT/SI EconLife yr national professional judgment

fraction of tank occupied by sediments (max.) d_setpt fraction national professional judgment

fraction surface area-turbulent F_aer fraction national_correlated derived from TSDR Survey

impeller diameter d_imp cm national U.S. EPA (1990)

impeller speed w_imp rad/s national U.S. EPA (1990)

impellers/aerators (number) n_imp unitless national_correlated API/CMA/SOCMA (1998)

impellers/aerators (total power) Powr hp national_correlated professional judgment

number of economic lifetimes NumEcon unitless national professional judgment

oxygen transfer correction factor O2eff unitless national Tchobanoglous (1979)

oxygen transfer factor J lb O2/h-hp national Tchobanoglous (1979)

volumetric flow rate (tank) Q_wmu m3

/s national_correlated TSDR Survey (U.S. EPA,

1987)

WMU type (specified AT) SrcType unitless site-specific specified for sites w/SI

Air

area of source SrcArea m2 site-specific Ind. D Screening Survey

(Westat, 1987)

source height SHight m site-based derived from Ind. D

SettingID (SrcType+SiteID) SettingID unitless site-specific Ind. D Screening Survey

(Westat, 1987)

WMU type (AT, SI, LAU, WP, or LF) SrcType unitless site-specific Ind. D Screening Survey

(Westat, 1987)

Vadose Zone

depth of source (0 for AT, WP) SrcDepth m site-based derived from Ind. D

WMU type (AT, SI, LAU, WP, or LF) SrcType unitless site-specific Ind. D Screening Survey

(Westat, 1987)

AT = aerated tank; LAU = land application unit; LF = landfill; SI = surface impoundment; USLE = universal soil loss

equation; WMU = waste management unit; WP = waste pile

3.2.1 Industrial D Screening Survey

The primary source of data used to characterize waste sources is the 1985 Screening

Survey of Industrial Subtitle D Establishments, referred to as the Industrial D Screening Survey

or Ind D (Westat, 1987). This survey was designed to collect information about nonhazardous

(RCRA Subtitle D) waste management practices at industrial facilities across the United States.

Data were gathered for the following land-based WMU types: surface impoundments, landfills,

waste piles, and LAUs. The representative national data set used the facility address, dimensions

of the WMUs, and annual waste volumes for the WMUs.
Section 3.0 Waste Management Unit Data

2

Industry groups as follows: (1) organic chemicals; (2) primary iron and steel; (3) fertilizer and agricultural

chemicals; (4) electric power generation; (5) plastic and resins; (6) inorganic chemicals; (7) stone, clay, glass, and

concrete; (8) pulp and paper; (9) primary nonferrous metals; (10) food and kindred products; (11) water treatment; 

(12) petroleum refining; (13) rubber and miscellaneous products; (14) transportation equipment; (15) selected

chemical and allied products; (16) textiles; and (17) leather and leather products.

3-8

The Industrial D Screening Survey collected information on land-based Ind D waste

management operations for 17 industry groups2 defined by EPA. Data from this survey have

been used to represent Ind D facility locations and WMU characteristics in a variety of RCRA

regulatory initiatives, including the 1995 HWIR proposal. Although the Industrial D data are

more than 10 years old, they are the largest consistent set of data available on Industrial D WMU

locations, dimensions, and waste volumes. Information on the survey design, response rates, and

overall data quality and completeness may be found in Westat (1987), Clickner (1988), and

Clickner and Craig (1988).

There were 15,844 total sites in the Indusrial D database. Of those, 2,850 reported that

they managed waste in a surface impoundment, landfill, waste pile, or LAU. Only 2,839 sites,

however, reported surface area, which is a required parameter for the 3MRA modeling system. 

Another 96 sites did not have address information because of confidential business information

(CBI) claims, and 67 sites were outside of the contiguous United States (25 in Alaska and 37 in

Hawaii). Some 201 sites (with a reported area and within the contiguous United States) were

randomly selected for the representative national data set. When sites with no area or no address

were selected, they were resampled. Table 3-3 shows the representativeness of the 201 sites

selected for the representative national data set compared to the entire set of 2,850 facilities in

the Industrial D Screening Survey. Section 2.0 provides general facility information, including

industry group and location, for these 201 facilities.

Table 3-3. WMU Type Distribution of 201 Sample Facilities

WMU Type 201 Sample Facilities 2,850 Industrial D Facilities

Number Percentage Number Percentage

Landfill (LF) 56 27.9 801 28.1

Land Application Unit (LAU) 28 13.9 345 12.1

Surface Impoundment (SI) 137 68.2 1,869 65.6

Waste pile (WP) 61 30.3 829 29.1

Previous EPA Composite Model for leachate migration with Transformation Products

(EPACMTP) (U.S. EPA, 1997) modeling efforts have uncovered issues associated with the

internal consistency of the Industrial D data. For example, for certain facilities, the remaining

capacity is greater than the total capacity of the unit. In other cases, depths calculated from sitespecific data are unreasonably large or small. To address such problems, questionable data have

been culled and/or replaced using procedures developed for EPACMTP (described in U.S. EPA,

1997). These replacement values were generated using random realizations from the probability

distribution of quantity and/or capacity conditioned on area. Table 3-4 lists the number of WMU

types for the entire Industrial D data set and for the subset of 201 sites used in the 3MRA
Section 3.0 Waste Management Unit Data

3-9

representative national data set, as well as the number of replacement values calculated for each

group. Table 3-5 lists the screening constraints and equation variables used to calculate

replacement values.

Table 3-4. Number of Industrial D WMUs Used for the Representative National Data Set

WMU

Type

Sample Facilities Industrial D Facilities

Selected for

Representative

National Data

Set

Waste

Quantity

Replaced

Capacity

Replaced

Manage

Waste in

WMU

Report Area

for WMU

Waste

Quantity

Replaced

Capacity

Replaced

LF 56 0 24 827 824 0 a 323

LAU 28 1 N/A 354 352 20 N/A

SI 137 4 21 1,930 1,926 57 262

WP 61 34 N/A 853 847 398 N/A

Total 201 39 45 2,850 2,839 468 571

a

 Waste quantity was missing for 6 landfills, but because they were not selected as part of the 201 facility subset, replacement

values were not calculated.

N/A = Not applicable.

Table 3-5. Screening Constraints and Replacement Values for Industrial D Data

WMU

Type Parameter Constraints

No.

Screened/

Replaced

Regression Equationa

Offset

Rangeb Slope y-Intercept

LF Capacity Missing Values 90 1.062004 4.050366 ± 0.6

2 ft<=Depthc

 <=33ft

(OR 0.5m <=Depthc

 <=10m)

233

WP Waste

Quantity

Missing Values 30 0.775536 3.874929 ± 1

Waste pile Heightd

 >=1m 368

LAU Waste

Quantity

Missing Values 8 0.840728 1.775842 ± 1

Application Ratee

 <=10,000

tons/acre-yr (or 2.24 Mg/m2

-yr)

12

SI

Waste

Quantity

Missing Values 57 0.775282 4.034241 ± 1

Capacity Missing Values 24 1.075292 3.944372 ± 0.8

1 ft <=Depthc

 <=150 ft

(or 0.3m<=Depthc

 <=46 m)

238

a In case where constraints were violated, replacement values were calculated based on a statistical regression of

known values. Each regression analysis was performed on a log scale, and the resulting best-fit line was used

as the basis for estimating replacement capacities or waste quantities as a function of unit area. b To provide a more probabilistic sampling, random numbers were generated within a limited range to offset

replacement values from the regression best-fit line.

c Unit depth = unit capacity / unit area.

d The waste pile height is a function of waste quantity and unit area (see Section 3.5.3.2). e Unit application rate = unit waste quantity / unit area.
Section 3.0 Waste Management Unit Data

3-10

In addition, the existing Industrial D database contains some zero values for waste

quantity and area that resulted from truncation of the third decimal place in the original database. 

When zero area or zero waste quantity was reported, a minimum bound of 0.005 acre (equal to

20.23 m2) or 0.005 M ton (equal to 0.005 Mg) was used (U.S. EPA, 1997).

Calculations for replacement values and other site-based model inputs are explained

further in the discussion sections for each of the WMU types. Appendix 3A shows raw data

from the Industrial D Screening Survey (including replacement values) for the 201 Industrial D

sites addressed in this analysis. This information includes the types and numbers of WMUs at

each site, the average area, the waste quantity, and the total capacity for each WMU.

3.2.2 National Survey of Hazardous Waste Treatment, Storage, Disposal, and Recycling

Facilities

The Industrial D Screening Survey (Westat, 1987) did not include tanks. Therefore, a

tanks database was developed for this analysis that compiled flow rates and tank volumes. The

primary source for these data was EPA’s National Survey of Hazardous Waste Treatment,

Storage, Disposal, and Recycling Facilities (TSDR Survey) (U.S. EPA, 1987). This

comprehensive survey requested information from 2,626 TSDR facilities concerning their 1986

hazardous waste management practices and quantities. It also included a specific questionnaire

regarding tanks used at each facility. Responses were received from 2,322 facilities. Of these,

1,700 facilities provided information on 18,773 tanks.

The TSDR Survey characterizes tanks containing hazardous (Subtitle C) wastes;

extensive data were not available on tanks used for nonhazardous waste management. EPA

believes, however, that from the perspective of basic tank design, hazardous waste tanks should

adequately represent tanks designed for treating nonhazardous wastes.

3.2.2.1 Tank Data Set. The only subset of the original TSDR Survey currently

available is for facilities that received any quantity of waste from an off-site source. This subset

of data contains information on 8,511 tanks located at 710 facilities (approximately 45 percent of

the tanks in the survey). This reduced data set was used to characterize tanks for the

representative national data set. Although it would have been preferable to use the original

complete data set, including tanks at facilities that treat only wastes generated on-site, these data

are, unfortunately, no longer available electronically. The subset data, however, include a

broad range of tank volumes – from less than 55 gal to more than 5,000,000 gal – and it is likely

that the subset data represents the range of tank volumes reported for all tanks.

Several criteria were used to guide the development of the tanks database. These criteria

were applied to the TSDR Survey data to determine which tanks should be included in the

representative national data set:

# Classification – Only aerated treatment tanks were included in the representative

national data set (see Section 3.2.2.2).

# Flow rate – Only those tanks reporting nonzero flow rates were included in the

representative national data set.
Section 3.0 Waste Management Unit Data

3

Examples of quiescent treatment tanks are clarifiers and filters (such as sand or mixedmedia filters).

3-11

# Open versus covered tanks – Only open tanks were included in the representative

national data set; closed or covered tanks were omitted because emissions from

covered tanks are likely to be significantly lower than emissions from open tanks. 

Therefore, results calculated for open tanks should also be protective for

exposures associated with covered tanks.

# Tank volume – All tanks with a volume of 55 gal or less were excluded from the

representative national data set. Inclusion of a relatively large number of smaller

volume tanks could skew the risk results in the direction of lower risk because

these smaller tanks would tend to have smaller surface areas and smaller aeration

rates (where applicable), resulting in lower emission levels. It also can be argued

that these smaller volume containers should be classified as drums and not tanks

due to their size.

Additionally, the two largest tanks (approximately 30,000,000 gal), one aerated

treatment and one nonaerated treatment, were reviewed because these tanks were

many times larger than the next largest tanks and appeared to be

nonrepresentative. The facility that owns both tanks was contacted and it was

determined that the tanks in question have volumes of 3,000,000 gal and

6,000,000 gal (Allswede, 1999), values within the range represented by the other

tanks in the database. The tank volumes for these tanks were corrected in the

representative national data set to the values provided by the facility.

3.2.2.2 Tank Classification. Industrial tanks can be used for either storage or treatment

of wastes and can be further categorized as either aerated/agitated or quiescent (i.e., not aerated

or agitated). Aeration or agitation is used in wastewater treatment systems to transfer air to the

liquid in order to improve mixing or increase biodegradation. Storage tanks are, by definition,

quiescent because they do not include aeration processes. Treatment tanks can belong to either

group.3 The 3MRA modeling system models only aerated tanks; therefore, storage tanks and any

treatment tanks identified as quiescent were not included in the representative national data set.

To determine which tanks were used for storage and which were used for aerated

treatment, process codes from the TSDR Survey were evaluated. Tanks with process codes of

either 2A (accumulation in tanks) or 2ST (storage in tanks) were classified as storage tanks. The

TSDR Survey used a broad range of treatment codes (including codes for incinerators and belt

filter presses); classification of treatment tanks was limited to those processes listed in

Appendix 3B, Table 3B-1.

The process codes were evaluated further to determine the level of aeration used for

treatment tanks. HI aeration was assigned to tanks that actively mix the liquid surface for the

purpose of aeration or that add diffused air. LO aeration was assigned to tanks that are likely to

have mixing devices used with chemical additions or other purposes. NO aeration was used for

tanks that are purposefully operated to minimize mixing or agitation (e.g., a clarifier). The
Section 3.0 Waste Management Unit Data

3-12

aeration level assignments for each process code are shown in Appendix 3B, Table 3B-1. The

treatment tanks were subsequently divided into aerated tanks (tanks designated as HI or LO

aeration) and nonaerated tanks (tanks designated as NO aeration).

The numbers of tanks included in each classification are summarized in Table 3-6. A

few tanks reported multiple process codes that included both a storage code (2A or 2ST) and a

treatment code. These tanks were classified as both storage and treatment tanks. The final tank

data set in the representative national data set consists of 624 aerated treatment tanks.

3.2.2.3 Additional Tank Data Used for Imputation. To address tank-specific data gaps

in the tanks database, additional data sources were identified. These data included information

collected in 1985 and 1986 during EPA site visits to aerated treatment systems. These systems

were selected to represent a range of aeration processes and reflect a variety of industries and

waste types. To identify candidate facilities, numerous phone contacts were made with state and

local environmental agencies. From these conversations, information on wastewater treatment

systems at 54 facilities was collected. Site visits to these facilities were then conducted, and data

on the individual tanks were provided by the facilities, including data on tank dimensions. 

Added to these data were five tanks from the TSDF background information document (U.S.

EPA, 1991). This resulted in a supplemental database of 49 tanks (13 with high aeration, 9 with

low aeration, and 27 with no aeration), presented in Appendix 3B, Table 3B-2. 

In addition to these data, several tank vendors were contacted to establish a reasonable

high end for tank capacity and depth based on design principles. As a result, a reasonable

maximum capacity for an open, partially or completely aboveground tank was defined to be

approximately 3,000,000 gal and the depth of such a tank would not be expected to exceed 10 m

(about 32 ft) (Pekar, 1999).

Table 3-6. Numbers of Tanks, by Classification

Tank Classification Number

Storage tanks 638

Aerated treatment tanks 624

High aeration 29

Low aeration 595

Nonaerated treatment tanks 273

Total 1,535

To maintain the integrity of the tank database, these site visit tanks and hypothetical

tanks were used only as a basis for imputing values and were not included in the representative

national data set. 
Section 3.0 Waste Management Unit Data

3-13

3.2.3 National Data

When site-specific data were not available, WMU inputs were either imputed from sitespecific data using national relationships or derived directly from national data. National data

sources include literature, personal communication, and best engineering judgments. Data were

collected first to derive general process diagrams for WMUs and then to develop specific

parameter value estimates for design variables. When no information could be found,

engineering calculations or judgments were used to generate design information or values for

specific parameters.

Generally, in seeking information, multiple sources were consulted in order to identify

and characterize potential variability in design aspects and specific parameter values. When the

design and operation of WMUs were not well-standardized, parameters likely to show wide

variability were flagged, and the variability was defined by distribution parameters used for the

model inputs (e.g., distribution type, minimum, maximum, mean, and standard deviation).

3.3 Methodology

The general approach for WMU data collection was to develop model facility designs

based on standard industry practices and scale the designs to the unit sizes extracted from the

Industrial D data. These designs are general descriptions of the key unit features that determine

the parameter values of interest for the source models. Based on these designs, the parameter

values have been estimated, either as fixed values or value ranges. The following steps describe

this approach:

1. Collect information to define typical WMU designs. Initially, information was

collected from the Industrial D and TSDR databases and from the literature in

order to define the typical designs for each WMU. For example, aerated tank

design features (relating to unit depth, size and number of impellers, and aeration

method) vary depending on unit size, flow rate of waste through the unit, and

materials being treated. The collected information was then used to determine the

number and general characteristics of the model facilities for each type of WMU.

2. Collect detailed data for each WMU model design. Once the general WMU

model facility designs were reviewed, information was collected about the

specific parameter values for each design. In general, multiple sources were

consulted for each parameter in order to identify and characterize typical ranges

for parameter estimates. When no information was available in the literature for a

parameter, engineering calculations were made, where possible, based on other

aspects of facility design. As a final option, engineering judgment was used as a

basis for developing data.

3.3.1 Site-Specific Data

Site-specific data describing waste management practices for surface impoundments,

landfills, waste piles, and LAUs were obtained from the Industrial D Screening Survey (Westat,

1987). Available data include the following:
Section 3.0 Waste Management Unit Data

3-14

# Surface impoundments – Total area, total 1985 waste quantity, total capacity, and

number of units; 

# Landfills – Total area, total 1985 waste quantity, total capacity, remaining

capacity, and number of units;

# Waste piles – Total area, total 1985 waste quantity, and number of units; and

# LAUs – Total area, total 1985 waste quantity, and number of units.

 WMU total area and total 1985 waste quantity were available and used for all four

WMU types. Total capacity was available only for landfills and surface impoundments. For all

parameters, average values were calculated by dividing the total values by the number of units at

a site.

In addition, site-specific data were obtained and used for landfill cover soil properties,

LAU soil properties, and soil type underlying the waste pile. These properties were obtained

from nationwide soil coverages as described in Section 7.0. Surface soil (top 20 cm) properties

were used for LAUs and waste piles, with the landfill cover soil assumed to have the average or

predominant soil properties for the vadose zone underlying the WMU.

3.3.2 Site-Based Data

For model inputs based on the site-specific data described in Section 3.3.1 (e.g., Ind D

WMU size or capacity), site-based data were derived using relationships based on the published

literature and best engineering judgment. The data were processed using a combination of

database and spreadsheet tools. As appropriate, Industrial D data were used in a consistent

fashion to other Office of Solid Waste (OSW) modeling efforts (e.g., Air

Characteristic/Industrial D modeling effort). Aspects of this approach include the following:

# When WMU dimensions (length and width) were needed, a square unit was

assumed.

# For landfills and surface impoundments, depth was calculated based on area, total

capacity, and typical waste bulk density.

# Landfill loading rates were determined based on total capacity and fixed (30-yr)

operating life.

# Annual waste loading rates were estimated for surface impoundments, waste

piles, and LAUs based on waste generation rates for 1985 (i.e., total 1985 waste

quantity).
Section 3.0 Waste Management Unit Data

3-15

3.3.3 National Tank Data (Correlated)

Because the TSDR sites could not be directly related to the sites in the Industrial D

Screening Survey, for modeling purposes, the assumption was made that if one of the

Industrial D sites contained a surface impoundment, then it also contained an aerated tank. The

3MRA modeling system randomly picks an aerated tank from the TSDR data set, referred to as

the national tank data set, using a tank index parameter (ATindex). It then correlates the data

from the selected tank to the Industrial D facility being modeled.

To ensure that the selected tank is no larger than a surface impoundment at the site, a

maximum source area (MaxSrcArea) is provided. MaxSrcArea is set equal to the source area for

the surface impoundment being modeled. Surface impoundment areas range from 13.5 to

60,705,000 m2

. Aerated tank areas range from 0.06 to 4,694 m2. The modeling system loops

through the selection process until an acceptable tank is chosen (i.e., tank area <= MaxSrcArea). 

Because the smallest tank is less than the smallest surface impoundment, a tank smaller than the

MaxSrcArea will always be available. Once a tank is selected, all the parameters for that tank

are assigned to the Industrial D site. 

3.3.4 Other National Data

When site-based data were unavailable, inputs were derived on a national basis. Each of

these inputs was defined by its type of distribution (e.g., constant, uniform, normal, lognormal). 

In addition, distribution parameters including mean, minimum, maximum, and standard

deviation were provided where applicable.

3.4 Landfill Module Inputs

This section describes the approach used to develop inputs for the Landfill Module. The

Landfill Module design is described in Section 3.4.1. Sections 3.4.2 through 3.4.4 describe the

development of input parameters for site-specific, site-based, and national data, respectively. 

For site-based variables, relationships between site-specific and site-based input parameters are

identified and considered to ensure that related inputs are not randomly selected in a manner that

would create physically impossible or unrealistic combinations.

Table 3-7 summarizes the data collected for Landfill Module inputs. It is organized by

site-specific and site-based data, which are extracted directly or calculated from Industrial D

Screening Survey data, and national data, which are based on relationships taken from books,

reports, and professional judgment.

3.4.1 Landfill Module Design

Landfill data collection assumes that only one type of landfill is used for disposal of

waste, (i.e., that there are no significant differences in the design of landfills depending on size

or purpose). As with all other WMU parameters except aerated tanks, average landfill

dimensions and capacity (i.e., total/number of units) are used from the Industrial D database. 

Other significant assumptions are that the landfill is excavated below ground surface, the unit
3-16

Table 3-7. WMU Data Collected for the Landfill Module Variable Units Code Value Original S

Site-Specific Data WMU type unitless SrcType specified “LF” Industrial D Screening Survey (W

area of source m2 SrcArea SrcArea = [total area]/[no. landfills] Industrial D Screening Survey (W

fraction organic carbon (cover soil) mass fraction focC depth-weighted average, entire soil column under WMU based on predominant soil texture for vadose zone soils underlying WMU (see Section 7.0) USSOILS (Schwarz and Alexansaturated hydraulic conductivity (landfill cover soil) cm/h KsatC CONUS (Miller and White, 199

(1988) saturated water content (cover soil, total porosity) volume fraction WCS_C CONUS (Miller and White, 199(1988) soil moisture coefficient b (landfill cover soil) unitless SMbC CONUS (Miller and White, 199(1978) Site-Based Data depth of source m SrcDepth capacity/[area x bulk density] (Equation LF-1) capacity (Mg) and area from Ind

(Westat, 1987); assumed waste bin m3 load Mg/yr --- capacity/30 (Equation LF-2) calculated from Industrial D Scr30-yr operating life waste zone thickness m zW zW = SrcDepth calculated from Industrial D Scr

distance vehicle travels on active landfill cell surface m mt mt = width of landfill = sqrt(SrcArea)(Equation LF-3) calculated from Industrial D Scr

square landfill vehicles per day (mean annual) 1/d nv nv = capacity/[operating life x payload x 365.25] (Equation LF-4) best professional judgment, basedata spreading and compacting operations per day 1/d Nop Nop = nv (Equation LF-5) maximum value = 2 best professional judgment frequency of surface disturbances per month (active landfill cell) 1/mo fd fd = Nop x 30 (Equation LF-6) best professional judgment 
3-17

Variable Units Code Value Original S

Table 3-7. (continued) vehicle weight (mean) Mg, m3 vw vw = payload/2 + wempty (Equation LF-7) Overcash and Pal (1979) wheels per vehicle (mean) unitless nw calculated site-specific depending on payload; 6 wheels for a small truck and 10 for a large truck best professional judgment base

Overcash and Pal (1979) and MR

number of waste layers in a cell unitless Nly SrcDepth # 1: (Table LF-8) Nly = 1 1 < SrcDepth # 2: Nly = 2 SrcDepth > 2: Nly = Integer(SrcDepth) best professional judgment National Data optional soil cover thickness m zC triangular distribution: minimum = 0.3 maximum = 0.9 mean = 0.6 best professional judgment, assu

designed to support vegetative cTchobanoglous (1993), Bagchi (

thickness of liner (or subsoil zone) m zS constant = 0 model scenario assumes an unlindust suppression control efficiency unitless effdust normal distribution: minimum = 0 maximum = 1 mean = 0.5 standard deviation = 0.3 best professional judgment base

dust suppression control efficiency unitless effdust normal distribution: minimum = 0 maximum = 1 mean = 0.5 standard deviation = 0.3 best professional judgment base

fraction vegetative cover (inactive LF cell) unitless veg normal distribution: minimum = 0.8 maximum = 1 mean = 0.9 standard deviation = 0.1 best professional judgment, assu

vegetated once unit is closed 
3-18

Variable Units Code Value Original S

Table 3-7. (continued) roughness height (inactive landfill cell) cm zruf normal distribution: minimum = 2 maximum = 4 mean = 3 standard deviation = 0.6 best professional judgment base

roughness ratio (landfill waste zone surface) unitless Lc lognormal distribution: minimum = 1E-04 maximum = 1E-03 mean = 3E-04 standard deviation = 0.304 best professional judgment base

vehicle speed km/h vs normal distribution: minimum = 20 maximum = 40 mean = 30 standard deviation = 6.1 Overcash and Pal (1979)
Section 3.0 Waste Management Unit Data

3-19

receives waste for 30 years, the landfill is capped with soil cover to establish a vegetative cover

after a cell is filled, and there is no liner.

3.4.2 Landfill Site-Specific Data

Site-specific data for landfills were obtained from the Industrial D Screening Survey

(Westat, 1987). These include total area, number of landfills at each site, total capacity,

remaining capacity, and total 1985 annual waste quantity. Average values were calculated for

use in the representative national data set by dividing the Industrial D data for each of the

parameters by the number of landfills at each site. Appendix 3A shows raw data from the

Industrial D Screening Survey for the 201 Industrial D sites included in the representative

national data set. 

3.4.2.1 Screening and Replacement of Industrial D Data. In accordance with

previous EPA modeling efforts using the Industrial D Screening Survey, landfill capacities were

screened from the Industrial D data when depth or capacity constraints were violated. 

Questionable data were screened using the following procedures (U.S. EPA, 1997):

The landfill data were screened by placing constraints on the unit depth and unit

volume to eliminate unrealistic observations. The unit depth, calculated by

dividing the unit capacity by the unit area, was constrained to be either greater

than or equal to 2 feet, or less than or equal to 33 feet. The unit depth bounds

were adopted from the previous TC rule effort.

Of the 824 landfills (reporting surface area) in the Industrial D Screening Survey, 170

had a depth less than 2 ft (0.5m) and 63 had a depth greater than 33 ft (10 m). In addition, 90

facilities were missing data on total capacity. Thus, landfill capacity was missing or screened for

323 landfills.

Landfill capacities to replace the 323 missing or removed values were estimated based on

the correlation between surface area and capacity of the remaining landfills in the Industrial D

data. The procedure used to replace values was similar to the EPACMTP methodology (U.S.

EPA, 1997):

In cases where the unit depth or remaining capacity constraints were violated, the

observed unit volume was replaced by generating a random realization from the

volume probability distribution conditioned on area assuming that the unit area

value was more likely to be correctly reported. The joint distribution was derived

from the non-missing unit area/volume pairs that met the unit depth and

remaining capacity constraints and was assumed to be lognormal. Missing values

were generated from the joint area/volume probability if both the area and volume

were missing, and from the corresponding conditional distribution if only one of

the two values was missing. Final depth values were calculated by dividing the

unit volume by the area.

First, a statistical regression of log (average capacity) versus log (average surface area)

was done on the facilities with known capacities. The regression yielded an equation for a best-
Section 3.0 Waste Management Unit Data

3-20

Figure 3-1. Correlation of total capacity to area for landfills.

fit line through the known values. This equation gave the capacity as a function of area, so the

missing or screened capacities could be estimated based on the known areas. To provide a more

probabilistic sampling of average capacities, and because the known capacities seemed to be in a

limited range (± 0.6) above and below the best-fit line, a positive or negative random number

was generated within that range and added to the calculated log (average capacity) to replace

each missing capacity with a random value that was reasonable with respect to landfill area. 

This value was then used to calculate landfill depth as described above. Figure 3-1 shows the

regression plots, including the replaced (random capacity) values, for landfills.

3.4.2.2 Cover Soil Properties. For purposes of this analysis, it was assumed that the

soil used to cover the landfill was obtained from soil at or very nearby the facility and, in many

cases, could be soil excavated to construct the landfill itself. Thus, soil properties for the vadose

zone directly underlying the landfill were used for cover soil properties. The following cover

soil parameters have been collected for use by the Landfill Module: fraction organic carbon

(focC), saturated hydraulic conductivity (KsatC), saturated water content (WCS_C), and soil
Section 3.0 Waste Management Unit Data

3-21

(LF-1)

(LF-2)

(LF-3)

(LF-4)

moisture coefficient b (SmbC). See Section 7.0 for a discussion on vadose zone soil property

data collection.

3.4.3 Landfill Site-Based Data

Site-based data are derived from Industrial D data (notably average area and average

capacity), using relationships based on the published literature and best engineering judgment.

3.4.3.1 Depth (SrcDepth). Landfill depth (SrcDepth) was calculated consistent with

previous EPACMTP modeling efforts using the Industrial D data (as described in U.S. EPA,

1997). The wet bulk density assumed for landfills was 1.8 g/cm3

.

3.4.3.2 Waste Loading Rate (load). The waste loading rate (load) is the annual quantity

of waste disposed at a landfill. Because data on the typical design life for landfills were not

available, a 30-yr operating life was assumed as a reasonable value based on professional

judgment.

3.4.3.3 Distance Vehicle Travels on Active Landfill Cell Surface (mt). Assuming a

square landfill unit configuration and assuming that a truck drives into the center of the landfill

to deliver a load of waste, the length of unpaved road on the uncovered landfill is assumed to be

equal to half the width of the landfill. The vehicle travels in and out on the road to deliver a load

of waste, so the distance a vehicle travels on the active landfill surface (mt) is equal to the

landfill width.

3.4.3.4 Average Number of Vehicles per Day (nv). The average number of vehicles

per day (nv) is calculated assuming the landfill has a 30-yr operating life and that each truck

carries a full payload.

where

payload = 35 Mg if landfill capacity >= 30,000 Mg/yr 

payload = 15 Mg if landfill capacity < 30,000 Mg/yr.
Section 3.0 Waste Management Unit Data

3-22

(LF-5)

(LF-6)

(LF-7)

3.4.3.5 Number of Spreading and Compacting Operations per Day (Nop). The

number of spreading and compacting operations (Nop) is the number of times that the whole

landfill cell area is compacted with heavy equipment. The number of loads dropped off are

equal to the average number of vehicles (nv) at the landfill. The number of spreading and

compacting operations per day is specified by the following equation (with a maximum value

of 2):

3.4.3.6 Frequency of Disturbances per Month (fd). A disturbance is defined as an

action that results in the exposure of fresh surface material. This can occur whenever material is

added to the landfill cell or the waste is compacted or moved. The frequency of disturbances

(fd) equals the number of spreading and compacting events per day (Nop) multiplied by the

number of days per month.

3.4.3.7 Vehicle Weight (vw), Payload, and Number of Wheels (nw). Two typical

truck sizes were developed for this analysis: small and large. Data on typical truck payloads and

number of wheels per truck were obtained from Overcash and Pal (1979) and Midwest Research

Institute (MRI) (1990). Data for determining the ratio of total to empty vehicle weight were

obtained from Caterpillar (1994).

A small truck is assumed to have 6 wheels and a full weight of 30 Mg (15 Mg vehicle

weight empty plus 15 Mg payload). The vehicle weight estimate is based on a payload size of 

10 m3 (roughly midrange for dump trucks in Overcash and Pal, 1979), a waste bulk density of

about 1.5 Mg/m3, and a ratio of weight loaded to weight unloaded of about 2.

A large truck is assumed to have 10 wheels and a full weight of 65 Mg (30 Mg vehicle

weight empty plus 35 Mg payload). This vehicle weight estimate is based on a payload size of

about 23 m3

 (the upper end of the dump truck sizes in Overcash and Pal, 1979), a waste density

of about 1.5 Mg/m3, and a ratio of weight loaded to weight unloaded of about 2.2.

For units managing less than 30,000 Mg/yr, a small truck is assumed. For units

managing 30,000 Mg/yr or more, a large truck is assumed. Depending on the quantity of waste

managed at the landfill, the appropriate truck payload is used in Equation LF-7.

The vehicle weight is used in particulate emission calculations. A full truck is assumed

to drive onto the unit, dump its load, and then exit empty. The vehicle weight (vw) is the

average of its weight full and empty. The weight of the vehicle is expressed as follows:
Section 3.0 Waste Management Unit Data

3-23

where

vw = vehicle weight for a small or large truck (Mg)

payload = carrying capacity of the truck (Mg)

wfull = vehicle weight when full (Mg) = wempty (Mg) + payload (Mg)

wempty = vehicle weight when empty (Mg).

The vehicle weight depends on the size of the vehicle (large or small) and the vehicle

payload. Small trucks are assumed to have a weight empty of 15 Mg, a payload of 15 Mg, and 6

wheels. Large trucks are assumed to have a weight empty of 30 Mg, a payload of 35 Mg, and 10

wheels.

In the absence of other data, the average fraction of a full load that a truck carries is

assumed to be 1 (i.e., the truck carries a full load each time because operating the truck at less

than a full load would be inefficient). A full load is considered to be a volume of waste equal to

the volume of the truck dumper (rather than waste loaded in such a way as to mound above the

sides of the truck dumper).

3.4.3.8 Number of Waste Layers in a Cell (Nly). A waste layer in the landfill is a

waste zone of uniform thickness within each landfill cell wherein initial constituent

concentrations are assumed uniform by the Landfill Module. In other words, each annual

landfill cell contains one or more uniform layers formed over time by the dumping of truck loads

of waste in the landfill cell. For this analysis, the number of waste layers (Nly) is determined as

follows:

Landfill Depth, m (SrcDepth) Number of waste layers in a cell (Nly)

(LF-8) # 11

>1 and # 22

>2 Integer (SrcDepth)

3.4.4 Landfill National Data

National data were collected for input variables when site-specific data were not

available and the variable was not correlated with other site-specific data. In most cases, a

distribution was assumed to account for nationwide variability in parameter values.

3.4.4.1 Optional Soil Cover Thickness (zC). The Industrial D landfill is assumed to

have a simple soil cover designed to establish a vegetative cover on the closed landfill, not to

limit infiltration into the landfill. This conservative assumption is consistent with the assumption

of no engineered liner under the WMU. For the Landfill Module, the minimum depth of this soil

cover is assumed to be 0.3 m and the maximum depth is 0.9 m. A triangular distribution is

assumed, with a mean of 0.6 m.
Section 3.0 Waste Management Unit Data

3-24

3.4.4.2 Thickness of Liner (or Subsoil Zone) (zS). This parameter allows a liner to be

used in the Landfill Module but was set at zero for the representative national data set, reflecting

the assumption that Industrial D landfills are unlined.

3.4.4.3 Dust Suppression Control Efficiency (effdust). Dust suppression activities

might include the watering of the landfill to reduce dust or the application of chemical dust

suppressants (U.S. EPA, 1989). A value of zero corresponds to no dust suppression activity. 

Although information was available about types of dust suppression control activities, there is no

definitive information quantifying the frequency of use or the effectiveness of these activities. 

Consequently, it is assumed that dust suppression control efficiency (effdust) has a normally

distributed value between 0 and 1, with a mean of 0.5 and a standard deviation of 0.3.

3.4.4.4 Fraction Vegetative Cover (veg). After closure of the unit, it is assumed that the

landfill is covered with vegetation. To allow for some variability in the extent of this cover, the

fraction vegetative cover (veg) is specified as a normal distribution from 0.8 to 1, with a mean of

0.9 and a standard deviation of 0.1.

3.4.4.5 Roughness Height (zruf). This factor is the height aboveground at which the

wind speed becomes zero due to obstructions (rocks, plants) on the ground surface (U.S. EPA,

1989). Roughness height (zruf) ranges from 0.1 cm to 1,000 cm for snow to urban settings. 

EPA provides some values for the roughness height for various sites in Arizona and for

industrial aggregates, as well as a chart of values for different settings. After closure, the landfill

is assumed to be similar to a grassland with a roughness height ranging from 2 cm to 4 cm,

normally distributed, with a mean of 3 cm and a standard deviation of 0.6 cm.

3.4.4.6 Roughness Ratio (Lc). This factor is the ratio of the silhouette area of the

roughness elements (>1 cm) in the soil to the total bare loose soil. Roughness ratio (Lc) can

range from 0 to 0.01 (U.S. EPA, 1989). For the representative national data set, it is assumed to

be lognormally distributed, with a minimum of 1×10-4, a maximum of 1×10-3, a mean of 3×10-4,

and a standard deviation of 0.304. Higher Lc values (>2×10-4) increase the threshold wind speed

for the onset of wind erosion (causing lower particulate emissions). Therefore, assuming the

mean and maximum Lc equal 3×10-4 and 1×10-3 is conservative with respect to particulate

emissions (but not other emission processes).

3.4.4.7 Vehicle Speed (vs). Vehicle speed (vs) is the average speed that trucks travel on

the landfill. For surface spreading, 20 to 40 km/h is a representative range (Overcash and Pal,

1979). Vehicle speed is specified as a normal distribution, with a mean of 30 km/h and a

standard deviation of 6.1 km/h.

3.5 Waste Pile Module Inputs

This section describes the approach used to develop inputs for the Waste Pile Module. 

The Waste Pile Module design is described in Section 3.5.1. Sections 3.5.2 through 3.5.4

describe the development of input parameters for site-specific, site-based, and national data,

including data sources, ranges, and assumptions. For site-based variables, relationships between

site-specific and site-based input parameters are identified and considered to ensure that related
Section 3.0 Waste Management Unit Data

3-25

inputs are not randomly selected in a manner that would create physically impossible or

unrealistic combinations. 

Table 3-8 summarizes the data collected for Waste Pile Module inputs. It is organized by

site-specific and site-based data, which are extracted directly or calculated from Industrial D

Screening Survey data, and national data, which are based on relationships taken from books,

reports, and professional judgment.

3.5.1 Waste Pile Module Design

Waste piles are essentially temporary units used for storing or accumulating waste prior

to final treatment or disposal. Waste piles are composed of solid waste materials that are dumped

into a pile and may be subsequently moved or spread. Because a waste pile is a temporary unit,

it is modeled so that the waste is deposited, remains in the waste pile for a period of time, and is

then removed and replaced with a fresh waste pile with chemical concentrations equal to that of

the original incoming waste. In this model, a waste is considered to be delivered by dump truck

to the unit location and deposited to create a pile of uniform height throughout the area of the

unit. The pile is assumed to be refreshed at least once every 5 yr, although a greater refresh

frequency may occur for Industrial D facilities with large waste generation rates and relatively

small waste pile areas.

The waste pile is assumed to be placed directly on native soil, with no compaction or

liner underneath. There is no cover (engineered or otherwise) and no control practices are

employed to limit water or wind erosion or volatile emissions from the pile.

3.5.2 Waste Pile Site-Specific Data

Site-specific data for waste piles were limited to data from the Industrial D Screening

Survey (Westat, 1987). Industrial D data included total area, number of waste piles at each site,

and total 1985 annual waste quantity (or waste loading). Average values were calculated for use

in the representative national data set by dividing the total area and total annual waste quantity

by the number of units at each site. Appendix 3A shows raw data from the Industrial D

Screening Survey (including replacement values) for the 201 Industrial D sites included in the

representative national data set. 

In some cases, the annual waste quantity was screened due to unrealistic values or

missing data. Replacement values were calculated under two conditions: (1) if surface area data

were reported for a given waste pile but the waste quantity was not provided or (2) if the

minimum waste pile height constraint of 1 m was violated when the refresh rate was once every

5 yr.

The first condition for replacing waste quantities is consistent with previous EPACMTP

efforts (U.S. EPA, 1997):

Missing volume values were replaced by random realizations from the probability

distribution of volume conditioned on area. The conditional distribution was
3-26

Table 3-8. WMU Data Collected for the Waste Pile Module Variable Units Code Value Original S

Site-Specific Variables WMU type unitless SrcType specified “WP” Industrial D Screening Survey area of source m2 SrcArea SrcArea = [total area]/[no. waste piles] Industrial D Screening Survey 

values waste loading rate (dry) Mg/yr load load = [1985 total waste quantity]/[no. waste piles] Industrial D Screening Survey 

values; missing or inconsistent the approach outlined in the EPbackground document (U.S. E

Site-Based Variables distance vehicle travels on waste pile surface (unpaved road) m mt mt = width of WP = sqrt (SrcArea) (Equation WP-1) calculated from Industrial D S

assumes a square unit and that drives into the middle of the un

height of waste pile above grade m zZ1WMU calculated using Table WP-2 best professional judgment; he

waste quantity and source area Screening Survey vehicle weight (mean) Mg, m3 vw based on facility size (Equation WP-4) small truck: weight (empty): 15 Mg payload: 15 Mg large truck: weight (empty): 30 Mg payload: 35 Mg best professional judgment, ba

Overcash and Pal (1979), MRI (1994) wheels per vehicle (mean) unitless nw based on facility size small truck: 6 large truck: 10 best professional judgment, baOvercash and Pal (1979) and M
3-27

Table 3-8. (continued) Variable Units Code Value Original S

Site-Based Variables vehicles per day (mean annual) 1/d nv nv = load/[payload × 365.25] (Equation WP-5) engineering calculation; load (

rate) calculated from Industriadata; relationship of truck size based on best professional judgbased on Overcash and Pal (19(1994) spreading/compacting operations per day 1/d Nop Nop = nv (Equation WP-6) maximum value = 2 best professional judgment SCS curve number unitless CNwmu assigned using a triangular distribution based on hydrologic soil group (see Table 3-8) best professional judgment on 

effect; site-specific hydrologic from STATSGO (USDA, 1994

National Variables dust suppression control efficiency unitless effdust normal distribution: minimum = 0 maximum = 1 mean = 0.5 standard deviation = 0.3 best professional judgment, ba

U.S. EPA (1989) operating life yr CutOffYr constant = 30 best professional judgment vehicle speed (mean) km/hr vs normal distribution: minimum = 20 maximum = 40 mean = 30 standard deviation = 6.1 best professional judgment, baOvercash and Pal (1979) USLE cover factor unitless Cwmu constant = 1 Wanielista and Yousef (1993)

USLE erosion control factor unitless Pwmu constant = 1 Wanielista and Yousef (1993)

control
Section 3.0 Waste Management Unit Data

3-28

(WP-1)

assumed to be lognormal and was derived from the non-missing unit area/volume

pairs.

The second condition (waste pile height constraint) was established because a waste pile

is supposed to be a pile with some significant height and it is supposed to be a temporary unit.

The minimum height and refresh frequency were selected based on judgment (see Sections

3.5.3.3 and 3.5.3.4). For a waste bulk density of 1.5 Mg/m3, the 1-m height and once-per-5-yr

refresh rate constraints correspond to an application rate of 0.3 Mg/m2/yr. Of the 847 facilities

reporting waste piles (and surface area), 30 facilities did not provide data on waste quantity and

368 values were screened by the minimum height and refresh rate constraint. 

To calculate replacement waste quantities, first a statistical regression of log (waste

quantity) versus log (average surface area) was performed on facilities with known quantities. 

The regression yielded an equation for a best-fit line through the known values. This equation

gave the waste quantity as a function of area, so the missing or screened waste quantities could

be estimated based on the known areas. To provide a more probabilistic sampling of average

waste quantities, and because the known quantities seemed to be in a limited range (± 1) above

and below the best-fit line (with some outliers), a positive or negative random number was

generated within that range. This random number was then added to the calculated log (average

waste quantity) to replace each missing waste quantity with a random value that was reasonable

with respect to waste pile area. Figure 3-2 shows the regression plot, including the replaced

(random waste quantity) values for waste piles.

3.5.3 Waste Pile Site-Based Data

Site-based data are derived from Industrial D data (e.g., average area, width, and height

of waste piles), using relationships based on the published literature and best engineering

judgment. In addition, the waste pile curve number (CNwmu) was derived from site-specific

soil data and general assumptions about the water-holding properties of the waste.

3.5.3.1 Distance Vehicle Travels on Waste Pile Surface (mt). Although the shape of a

waste pile is likely irregular, for purposes of this analysis, all WMUs are assumed to be square. 

It also is assumed that a truck drives into the center of the waste pile unit to deliver a load of

waste (i.e., half the width of the waste pile). The vehicle travels in and out on the unpaved 

road to deliver a load of waste, so the distance traveled (mt) equals the width of the waste pile as

follows:

3.5.3.2 Height of the Waste Pile Above Grade (zZ1WMU, SHight). Waste pile height 

varies and will affect the rate at which waste in the unit is refreshed in the Waste Pile Module. 

Site-specific waste pile height information was not available. In general, however, the height of

a waste pile is a function of the area of the unit and the annual waste quantity managed in the

unit. Discrete pile height values ranging from 1 to 10 m were assumed in order to simplify the

meteorological component of the data required for the Waste Pile Module. In particular, a

calculation was required to adjust the wind speed from the height of the measuring device to the
Section 3.0 Waste Management Unit Data

3-29

Figure 3-2. Correlation of waste quantity to area for waste piles.

Annual Waste Quantity/Surface Area

(Mg/m2

/yr)

Pile Height (m)

$0.3, <10

$10, <20

$20, <40

$40, <60

$60, <80

$80

1

2

4

6

8

10

height of the waste pile. The choice of heights was based on an interval over which the wind

velocity differences would not be too large. The extremes were selected using best professional

judgment. The relationships between pile height and the annual waste quantity per unit area were

assumed based on best judgment. The following table lists the pile heights (zZ1WMU, SHight)

as determined by the annual waste quantity to surface area ratio.
Section 3.0 Waste Management Unit Data

3-30

(WP-2)

(WP-3)

3.5.3.3 Refresh Frequency. The refresh frequency is used as a constraint for screening

data (discussed in Section 3.5.2). The pile height, area, and annual waste loading determine the

frequency of waste pile replacement, or the refresh frequency. This can be expressed as follows:

Because waste piles are considered to be temporary units, the refresh frequency should

not be too long. The minimum refresh frequency for a waste pile was assumed as once every 5

years, assuming a waste bulk density of 1.5 Mg/m3

. The Industrial D database contains

combinations of waste loading and area data that, given the method used here for determining

waste pile height, would lead to refresh frequencies of less than once every 5 yr. In these cases,

the annual waste loading data were culled from the data set and replaced with a value randomly

selected based on the distribution of valid waste pile area/annual waste loading combinations in

the Industrial D database (see Section 3.5.2).

3.5.3.4 Vehicle Weight (vw), Payload, and Number of Wheels (nw). Two typical

truck sizes were developed: small and large. Data on typical truck payloads (and number of

wheels per truck) were obtained from Overcash and Pal (1979) and MRI (1990). Data for

determining the ratio of the total vehicle weight to the weight empty were obtained from

Caterpillar (1994).

A small truck is assumed to have 6 wheels and a full weight of 30 Mg (15 Mg vehicle

weight empty plus 15 Mg payload). The vehicle weight estimate is based on a payload size of 

10 m3 (roughly midrange for dump trucks in Overcash and Pal, 1979), a waste bulk density of

about 1.5 Mg/m3, and a ratio of weight loaded to weight unloaded of about 2.

A large truck is assumed to have 10 wheels and a full weight of 65 Mg (30 Mg vehicle

weight empty plus 35 Mg payload). This vehicle weight estimate is based on a payload size of

about 23 m3

 (the upper end of the dump truck sizes in Overcash and Pal, 1979), a waste density

of about 1.5 Mg/m3

, and a ratio of weight loaded to weight unloaded of about 2.2.

The average number of vehicles used per waste application was not available from the

literature. Instead, the average number of vehicles was calculated by assigning a truck size to the

facility based on the amount of waste managed in the unit annually. For units managing less

than 30,000 Mg/yr, a small truck was assumed. For units managing 30,000 Mg/yr or more, a

large truck was assumed. For facilities managing just under 30,000 Mg/yr of waste, from

Equation WP-2 we would calculate 2,000 trucks per year, or an average of about 5.5 trucks per

day for year-round operation.

The vehicle weight (vw) is used in the particulate emission calculations. A full truck is

assumed to drive onto the unit, dump its load, pick up waste from the old pile, and then exit full. 

The vehicle weight is its weight full, expressed as follows:
Section 3.0 Waste Management Unit Data

3-31

(WP-4)

(WP-5)

where

vw = vehicle weight for a small or large truck (Mg)

payload = carrying capacity of the truck (Mg)

wempty = vehicle weight when empty (Mg).

The vehicle weight depends on the size of the vehicle (large or small) and the vehicle payload. 

Small trucks are assumed to have a weight empty of 15 Mg, a payload of 15 Mg, and 6 wheels. 

Large trucks are assumed to have a weight empty of 30 Mg, a payload of 35 Mg, and 10 wheels.

3.5.3.5 Vehicles per Day (nv). The average number of vehicles per day (nv) is

determined by the annual waste application rate and the size of the truck. The relationship can

be expressed as follows:

where

nv = number of vehicles per day (1/d)

load = waste application rate (Mg/yr)

payload = carrying capacity of the truck (Mg).

3.5.3.6 Spreading/Compacting Operations per Day (Nop). The number of spreading

or compacting operations (Nop) is the number of times that the waste pile is spread and

compacted with heavy equipment. The number of spreading and compacting operations per day

is specified by the following equation (with a maximum value of 2):

3.5.3.7 SCS Curve Number (CNwmu). Soil Conservation Survey (SCS) curve number

(CNwmu) values appropriate for waste piles could not be found in the open literature. To

estimate this parameter, a scenario was developed in which the water percolates freely into the

waste pile, with a portion of this infiltration running off once it hits the soil beneath the waste

pile. Under this scenario, CNwmu is a function of the hydrologic group (A, B, C, or D) of the

soil underlying the waste pile (as the infiltration limiting media) and the cover effect of the pile

itself. The hydrologic group of the WMU soil was obtained from State Soil Geographic

(STATSGO) data as described in Section 7.0. 

Because CNwmu is primarily subject to variability with respect to the waste pile’s cover

effect, three cover conditions were selected from USDA (1986) to define the range and typical

cover effect of the waste pile on the underlying soil as a triangular distribution. To represent 

waste piles that would tend to hold water on the soil surface, minimizing runoff and maximizing

infiltration, we selected “woods” in good hydrologic condition to define the minimum CNwmu

value for a particular hydrologic soil group. To represent more granular waste piles with
Section 3.0 Waste Management Unit Data

3-32

minimum water-holding capacity, we selected “gravel roads (including right-of-way)” to define

the maximum CNwmu value. Small-grain, contoured, and terraced row crops, with a crop

residue cover and good hydrologic condition, were used to define a moderate water-holding

condition (i.e., a waste pile on a leveled base) and the central tendency CNwmu for the triangular

distribution. These minimum, central, and maximum values are shown in Table 3-8 by

hydrologic soil groups A, B, C, and D.

To get the SCS curve number for the waste pile, the map unit with the largest area in the

WMU was used to obtain the hydrologic soil group from processed STATSGO data (see

Section 7.4.2 for more details). This hydrologic soil group was used to select the correct values

for CNwmu from Table 3-9, which are passed as a triangular distribution. 

Table 3-9. Waste Pile Curve Numbers (CNwmu), by Hydrologic Soil Group

Hydrologic Soil Group

Curve Number (CNwmu)

Minimum Central Tendency Maximum

A 30 58 76

B 55 69 85

C 70 77 89

D 77 80 91

Source 

(USDA, 1986):

Woods/Good

Table 2-2c

Small grain, contoured, and

terraced + crop residue/Good

Table 2-2b

Gravel streets and roads

Table 2-2a

3.5.4 Waste Pile National Data

3.5.4.1 Dust Suppression Control Efficiency (effdust). Dust suppression activities

could include the watering of the waste pile to reduce dust or the application of chemical dust

suppressants (U.S. EPA, 1989). A value of zero corresponds to no dust suppression activity. 

Although information was available about types of dust suppression control activities, no

information was found on how often these activities are typically employed for Industrial D

waste piles or their effectiveness. Consequently, it is assumed that dust suppression control

efficiency (effdust) has a normally distributed value between 0 and 1, with a mean of 0.5 and a

standard deviation of 0.3. A mean less than 0.5 (the value used for landfills) was selected

because it is believed to be more likely that a facility would implement only limited dust

suppression activities or that the dust suppression activities would have only limited

effectiveness for waste piles, which are not typically covered the way a landfill is.

3.5.4.2 Operating Life (CutOffYr). No information was found in the literature about

the typical operating life of a waste pile. For purposes of the representative national data set, the

operating life (CutOffYr) is assumed to be 30 yr.

3.5.4.3 Vehicle Speed (vs). The mean speed that trucks travel on the waste pile unit is

assumed to range from 20 to 40 km/h, based on information in Overcash and Pal (1979) for
Section 3.0 Waste Management Unit Data

3-33

LAUs. The vehicle speed (vs) is specified as normally distributed, with a mean of 30 km/h and a

standard deviation of 6.1 km/h, again based on Overcash and Pal (1979).

3.5.4.4 USLE Cover Factor (Cwmu). For the representative national data set, it was

assumed that waste piles are bare. Therefore, the universal soil loss equation (USLE) cover

factor (Cwmu) for waste piles was fixed at a value of 1, indicating no cover (Wanielista and

Yousef, 1993).

3.5.4.5 USLE Erosion Control Factor (Pwmu). For the representative national data set,

it was assumed that no erosion control practices are implemented for the waste piles. Therefore,

the USLE erosion control factor (Pwmu) for waste piles was fixed at a value of 1, indicating no

erosion control (Wanielista and Yousef, 1993).

3.6 LAU Module Inputs

This section describes the approach used to develop inputs for the LAU Module. The

LAU Module design is described in Section 3.6.1. Sections 3.6.2, 3.6.3, and 3.6.4 describe the

development of input parameters for site-specific, site-based, and national data, respectively. 

For site-based variables, relationships between site-specific and site-based input parameters are

identified and considered to ensure that related inputs are not randomly selected in a manner that

would create physically impossible or unrealistic combinations. 

Table 3-10 summarizes the data collected for LAU Module inputs. It is organized by

site-specific and site-based data, which are extracted directly or calculated from Industrial D

Screening Survey data, and national data, which are derived based on relationships taken from

books, reports, and professional judgment.

3.6.1 LAU Module Design

3.6.1.1 Unit Configuration. Land treatment generally involves the application of wastes

to an agricultural plot of land in either a liquid or semisolid form, tilling the wastes into the soil,

and treatment through the biological degradation of the hazardous constituents in the soil zone. 

Typical land treatment unit designs include single plot, progressive plot, and rotating plot

arrangements (U.S. EPA, 1989).

In single plot designs, wastes are spread uniformly over the available area. This design is

typically used when applications occur only during one season or on a few specific occasions

during the year. In progressive plot designs, the treatment unit is divided into smaller treatment

cells, with only a single cell active at a given time. The other cells remain fallow and may be

vegetated. Rotating plot designs also involve the division of the unit into smaller cells, with

cells being used sequentially. The time frame during which waste is applied to a cell depends on

the waste treatment requirements; however, rotation among cells is fast enough that all cells are

essentially active.

For the LAU Module, the single plot design was used and the entire LAU is modeled as a

single unit. Application of the module to the single plot facility design is straightforward

because this design assumes that waste is applied uniformly across the entire unit area. A 
3-34

Table 3-10. WMU Data Collected for the LAU Module Variable Units Code Value Data Sourc

Site-Specific Variables WMU type unitless SrcType specified “LAU” Industrial D Screening Survey (Westaarea of source m2 SrcArea SrcArea = [total area]/[no. LAUs] Industrial D Screening Survey (Westa

Site-Based Variables distance vehicle travels on LAU surface m mt mt = width of LAU = sqrt(SrcArea) (Equation LAU-1) calculated from Industrial D Screening 

square unit wet waste application rate Mg/m2- yr Rappl Rappl = waste quantity/SrcArea(Equation LAU-2) calculated using data from Industrial 

vehicle weight Mg vw vw = payload/2 + wempty (Equation LAU-3) small truck: weight (empty): 15 Mg payload: 15 Mg large truck: weight (empty): 30 Mg payload: 35 Mg best professional judgment; based on i

Overcash and Pal (1979), MRI (1990)small or large truck based on Industria

wheels per vehicle (mean) unitless nw small truck: 6 large truck: 10 best professional judgment; based on i

Overcash and Pal (1979) and MRI (19based on Industrial D annual waste qu

vehicles per day (mean annual) 1/d nv nv = [SrcArea x Rappl]/[payload × 365.25] (Equation LAU-4) engineering calculation; relationship o

application rate based on best professidesigns based on Overcash and Pal (1(1994) waste applications per year 1/yr Nappl assigned using table LAU-5 best professional judgment; based on U(1983), and Reed and Crites (1984) 
3-35

Table 3-10. (continued) Variable Units Code Value Data Sourc

Site-Based Variables (continued) frequency of cultivation unitless fcult calculated using table LAU-6 best professional judgment; based on q

per unit area and information from ER(1989) frequency of surface disturbances per month (active LAU) 1/mo fd fd = Nappl x fcult/12 (Equation LAU-7) calculated from the number of applicafrequency of cultivation per applicatio

SCS curve number unitless CNwmu triangular distribution assigned based on hydrologic soil group CN range for row crops, meadow (US

on best professional judgment; hydrolfrom STATSGO (see Section 7.0) National Variables depth of source, depth of tilling m SrcDepth, zZ1WMU constant = 0.2 m best professional judgment, based on i

al. (1983), ER&T (1983), Martin et al(1996b). dust suppression control efficiency unitless effdust normal distribution: minimum = 0 maximum = 1 mean = 0.5 standard deviation = 0.3 best professional judgment; based on i

(1989) fraction vegetative cover fraction veg normal distribution: minimum = 0.8 maximum = 1 mean = 0.9 standard deviation = 0.1 best professional judgment; assuming 

operation and after closure 
3-36

Table 3-10. (continued) Variable Units Code Value Data Sourc

National Variables (continued) operating life yr CutOffYr constant = 40 best professional judgment roughness height cm zruf normal distribution: minimum = 2 maximum = 4 mean = 3 standard deviation = 0.6 best professional judgment; based on v

for grassland roughness ratio unitless Lc lognormal distribution: minimum = 1 E-04 maximum = 1 E-03 mean = 3 E-04 standard deviation = 0.304 best professional judgment; based on i

(1989) vehicle speed km/h vs normal distribution: minimum = 20 maximum = 40 mean = 30 standard deviation = 6.1 best professional judgment; based on i

and Pal (1979) mode of aggregate size distribution mm constant = 5 conservative value from Soil Screenin

1996a) USLE cover factor unitless Cwmu constant = 0.08 Wanielista and Yousef (1993); assume

USLE erosion control factor unitless Pwmu constant = 0.50 Wanielista and Yousef (1993); assume
Section 3.0 Waste Management Unit Data

3-37

rotating plot unit also could be modeled essentially in the same manner as a single plot

operation. For the rotating plot facility, each cell would be assumed to be of equal size and

operated in an identical manner, and each cell could be considered to be active throughout the

year. The total waste quantity managed in the unit would be divided equally among all cells. 

Each cell would be modeled as receiving the same number of waste applications per year. This

approach reduces the model of the rotating plot to that of a single plot design.

Modeling of a progressive plot facility would need to be based on application of the LAU

Module to a series of waste cells. For the progressive plot facility, each cell could be assumed to

be used sequentially. The facility could be divided into a certain number of equally sized cells,

where each cell would be used for a percentage of the total operating life of the facility. For

example, if the facility was divided into 10 cells and operated for 40 yr, each cell would be

considered to be active for 4 yr. The operation of each cell would be considered identical. 

During the time a cell was not being used, that cell would be fallow and would be assumed to be

growing vegetation. For modeling, it would be necessary to track how many cells had been used

and to model the contaminant transport from each. For example, in a 3-cell unit operating 30 yr

and modeled for 30 yr, the first cell operating would have 30 yr of contaminant transport, the

second cell 20 yr, and the last cell 10 yr. The progressive plot facility would effectively reduce

the area of the active unit, for modeling purposes.

Because of the difficulties in applying the LAU Module to this situation and the lack of

additional module inputs that would be needed, development of input parameters based on the

progressive plot facility design was not pursued. Applying the LAU Module in this manner

would be problematic because the location of the individual cells relative to each other, and to

the local watershed, would need to be known to determine the appropriate transport of

contaminants from runoff/erosion. Further, the number of plots into which a given site would be

divided, and the frequency with which plots would be used, are likely to be site-specific

variables dictated by factors such as the rate of metals buildup expected in the soil. No

information was found in the literature on the typical frequency of plot rotation or size. 

Consequently, for a progressive plot model, these parameters would have to be assumed inputs

for the model.

3.6.1.2 Waste Application. The assumed method of waste delivery to the unit is

important for calculating the particulate emissions due to vehicles traveling over the surface of

an LAU. The waste application method in LAUs depends on the solid content of the waste

(Overcash and Pal, 1979). Application methods can be divided between those for semiliquid

materials (up to 15 percent solids) and those for solid or low-moisture materials (>15 percent

solids). Semiliquid systems are generally applied using mobile tanks. Mobile tanks may either

be tractor-hauled or truck-mounted. For the representative national data set, truck-mounted

tanks are assumed to deliver any liquid or sludge wastes. Truck-mounted tanks range from 5,000

to 22,500 L, with a typical size of 9,500 L (Overcash and Pal, 1979). For low-moisture

materials, truck-trailers transport and spread waste at the site. The parameter selection for the

representative national data set includes model unit designs based on these waste application

methods.

Liquid wastes (<8 percent solids, particle size <2.5 cm) are generally applied by either

surface irrigation (tank truck) or sprinkler irrigation. Although the application of wastes to
Section 3.0 Waste Management Unit Data

3-38

LAUs at industrial facilities may occur through surface or sprinkler irrigation, these application

methods are not the basis for the development of the LAU Module. Therefore, in the parameter

selection approach, we have not attempted to simulate these types of unit designs.

3.6.2 LAU Site-Specific Data

Site-specific data for LAUs were obtained from the Industrial D Screening Survey

(Westat, 1987). This includes total area, number of LAUs at each site, and total 1985 annual

waste quantity. Average values were calculated for use in the representative national data set by

dividing the total area and the total annual waste quantity by the number of units at each site. 

Appendix 3A shows raw data from the Industrial D Screening Survey for the 201 Industrial D

sites included in the representative national data set. 

In some cases, the LAU annual waste quantity was screened due to unrealistic values or

missing data. Replacement values were calculated under two conditions: (1) if surface area data

were reported for a given LAU but the waste quantity was not provided or (2) if the calculated

application rate (based on waste quantity and area) exceeded the upper bound set at 10,000

tons/acre-yr (which is equivalent to 2.24 Mg/m2

-yr). Questionable data were screened following

EPACMTP methodology (U.S. EPA, 1997):

The land application managed waste data were screened by constraining unit

application rate to be less than 10,000 tons/acre/year to eliminate unrealistic

values. The application rate was calculated by dividing the waste managed in

1985 by the site acreage. (The upper bound was derived by assuming a maximum

application rate of 200 dry tons/acre/year with a 2% solids content.)

Of the 352 LAUs (reporting surface area), 8 facilities did not provide data on annual

waste quantity. Waste quantities were replaced for 12 other facilities because the calculated

application rate exceeded the upper bound. The procedure to replace values was consistent with

EPACMTP methodology (U.S. EPA, 1997):

Missing and screened values were replaced by random realizations from the joint

area/volume probability distribution or the corresponding marginal distributions

depending on where both or only one of either the waste volume or area

observation was missing or screened. The joint distribution was assumed to be

lognormal and was derived from the non-missing unit area/volume pairs that met

the application rate constraint.

In order to calculate replacement values for the screened and missing annual waste

quantities, first a statistical regression of log (average 1985 waste quantity) versus log (average

surface area) was performed on the facilities with a known annual waste quantity. The

regression yielded an equation for a best fit line through the known values. This equation gave

the waste quantity as a function of the area, so the missing or screened waste quantities could be

estimated based on the known areas. To provide a more probabilistic sampling of average waste

quantities, and because the known quantities seemed to be in a limited range (± 1) above and

below the best-fit line (with some outliers), a positive or negative random number was generated

within that range. This random number was then added to the calculated log (average waste
Section 3.0 Waste Management Unit Data

3-39

Figure 3-3. Correlation of waste quantity to area for LAUs.

quantity) to replace each missing waste quantity with a random value that was reasonable with

respect to LAU area. Figure 3-3 shows the regression plot, including the replaced (random

waste quantity) values for Industrial D LAUs.

3.6.3 LAU Site-Based Data

Site-based data are derived from Industrial D data (e.g., average area and width of the

LAU), using relationships based on the published literature and best engineering judgment.

3.6.3.1 Distance Vehicle Travels on LAU Surface (mt). The representative national

data set assumes that WMUs are square. In reality, the shape of LAUs is likely to be dictated by

the available land; however, because data on length/width ratios in LAUs are absent from the

literature, assuming a square unit is reasonable and allows the various models to be applied

appropriately to the LAU. The distance traveled over the LAU (mt) by the truck is assumed to

be equal to the width of the WMU, as given by Equation LAU-1:
Section 3.0 Waste Management Unit Data

3-40

(LAU-1)

(LAU-2)

(LAU-3)

3.6.3.2 Wet Waste Application Rate (Rappl). The wet waste application rate (Rappl) is

calculated as the average annual quantity of waste managed at the facility divided by the average

LAU area as follows:

3.6.3.3 Vehicle Weight (vw), Payload, and Number of Wheels (nw). Two typical

truck sizes were developed for this analysis: small and large. Data on typical truck payloads

(and number of wheels per truck) were obtained from Overcash and Pal (1979) and MRI (1990). 

Data for determining the ratio of total to empty vehicle weight were obtained from Caterpillar

(1994).

A small truck is assumed to have 6 wheels and a full weight of 30 Mg (15 Mg vehicle

weight empty plus 15 Mg payload). The vehicle weight estimate is based on a payload size of 

10 m3 (roughly midrange for tank trucks in Overcash and Pal, 1979), a waste bulk density of

about 1.5 Mg/m3, and a ratio of weight loaded to weight unloaded of about 2.

A large truck is assumed to have 10 wheels and a full weight of 65 Mg (30 Mg vehicle

weight empty plus 35 Mg payload). This vehicle weight estimate is based on a payload size of

about 23 m3 (the upper end of the tank truck sizes in Overcash and Pal, 1979), a waste density of

about 1.5 Mg/m3, and a ratio of weight loaded to weight unloaded of about 2.2.

The average number of vehicles used per waste application was not available from the

literature. Instead, the average number of vehicles was calculated by assigning a truck size to the

facility based on the amount of waste managed in the unit annually (i.e., waste quantity). For

units managing less than 30,000 Mg/yr, a small truck is assumed. For units managing 30,000

Mg/yr or more, a large truck is assumed. Depending on the quantity of waste managed at the

LAU, the appropriate truck payload is used in Equation LAU-4.

The vehicle weight is used in the particulate emission calculations. A full truck is

assumed to drive onto the unit, dump its load while driving across the unit, and then exit empty. 

The vehicle weight is the average of its weight full and empty. The weight of the vehicle

depends on the size of the vehicle (large or small) and the vehicle payload and is expressed as

follows:

where

vw = vehicle weight for a small or large truck (Mg)

payload = carrying capacity of the truck (Mg)
Section 3.0 Waste Management Unit Data

3-41

(LAU-4)

wfull = vehicle weight when full (Mg) = wempty (Mg) + payload (Mg)

wempty = vehicle weight when empty (Mg).

3.6.3.4 Vehicles per Day (nv). The average number of vehicles per day (nv) is

determined by the average area of the LAU, the average annual waste application rate (Rappl

from Equation LAU-2), and the carrying capacity (or payload) of the truck. The relationship is

expressed as follows:

3.6.3.5 Waste Applications per Year (Nappl). The following table shows how to

determine the number of waste applications per year (Nappl) based on the annual waste quantity

managed at the LAU. It was developed to keep the estimated number of applications at less than 

100 per yr, based on information about typical applications per year found in U.S. EPA (1989),

Environmental Research & Technology (ER&T) (1983), and Reed and Crites (1984). According

to U.S. EPA (1989), the range for refineries is typically 2 to 52 applications per yr.

Annual Waste Quantity

(Mg/yr) Waste Applications per Year, Nappl (1/yr)

<1500 Waste quantity/15

>=1,500, <15,000 Waste quantity/150 (LAU-5)

>=15,000, <150,000 Waste quantity/1,500

>=150,000 Waste quantity/15,000

3.6.3.6 Frequency of Cultivation (fcult). The frequency of cultivation (fcult) is

determined based on the quantity of waste applied per unit area. In general, more cultivation per

waste application is required for waste applied at a greater quantity per unit area (ER&T, 1983). 

According to U.S. EPA (1989), there are 1 to 5 cultivation events per application. The frequency

of cultivation is calculated based on the following table, which was developed based on best

professional judgment.

Annual Waste Application Rate

(Mg/m2

/yr)

Frequency of Cultivation, fcult

(cultivations per waste application)

<0.01 1

>=0.01, <0.1 2 (LAU-6)

>=0.1, <1.0 3

>=1.0, <10.0 4

>=10.0 5
Section 3.0 Waste Management Unit Data

3-42

(LAU-7)

3.6.3.7 Frequency of Surface Disturbances per Month (fd). A disturbance is defined

as an action that results in the exposure of fresh surface material. This would occur whenever

material is added to the surface or the surface is tilled. The frequency of surface disturbances per

month (fd) equals the number of applications of waste per month multiplied by the number of

cultivation events per application, as follows:

where

fd = frequency of surface disturbances per month (1/mo)

Nappl = number of waste applications per year (1/yr) (from table LAU-5)

fcult = average frequency of cultivation of the plot, per application (from table

LAU-6).

3.6.3.8 SCS Curve Number (CNwmu). The Soil Conservation Survey (SCS) curve

number for the LAU (CNwmu) was based on the site-specific hydrologic soil group and the

assumption that during operation and post-closure, the LAU would be actively cultivated with

row crops, pasture, or meadow. To represent the potential variability in CNwmu under

cultivation, a triangular distribution, based on three cover conditions, was selected from USDA

(1986) and used to define the range and typical cover effect of agricultural cultivation. "Meadow

– continuous grass, protected from grazing and generally mowed for hay" was used to represent

conditions leading to minimum runoff and maximum infiltration, or minimum CNwmu, for a

particular hydrologic soil group. To represent maximum CNwmu, or maximum runoff and

minimum infiltration, straight row crops in poor hydrologic condition were used. Contoured row

crops, with a crop residue cover and good hydrologic condition were used as the central

tendency CNwmu for the triangular distribution, to represent a moderate water holding

condition. These minimum, central and maximum values are shown in Table 3-10 by hydrologic

soil groups A, B, C, and D. 

To get the SCS curve number for the LAU, the map unit with the largest area in the

WMU was used to obtain the hydrologic soil group from processed STATSGO data (see Section

7.4.2 for more details). This hydrologic soil group was used to select the correct values for

CNwmu from Table 3-11, which were sent to the LAU Module as the parameters of a triangular

distribution.

3.6.4 LAU National Data

3.6.4.1 Depth of Tilling (SrcDepth, zZ1WMU). The till depth is generally a function of

the equipment used to conduct the tilling operation. Because this variable was sent both in the

site layout (SrcDepth) and LAU (zZ1WMU) data groups, it was necessary to fix the value to

ensure that the same value would be used for both variables. A tilling depth of 0.20 m was

selected as a typical till depth based on a literature review (Brown et al., 1983; ER&T, 1983;

Martin et al., 1986; U.S. EPA, 1996b). The limited range for this variable found in these data

sources also supports the use of a constant value.
Section 3.0 Waste Management Unit Data

3-43

Table 3-11. LAU Curve Numbers (CNwmu), by Hydrologic Soil Group

Hydrologic Soil

Group

Curve Number (CNwmu)

Minimum Central Tendency Maximum

A 30 64 72

B 58 74 81

C 71 81 88

D 78 85 91

Source 

(USDA, 1986):

Meadow

Table 2-2c

Contoured row crops +

crop residue/Good

Table 2-2b

Straight row

crops/Poor

Table 2-2b

3.6.4.2 Dust Suppression Control Efficiency (effdust). Dust suppression activities

might include watering the LAU to reduce dust or applying chemical dust suppressants (U.S.

EPA, 1989). A value of zero corresponds to no dust suppression activity. Although information

was available about types of dust suppression control activities, no information was found on

how often these activities are typically employed or their effectiveness. Consequently, it is

assumed that dust suppression control efficiency (effdust) has a normally distributed value

between 0 and 1, with a mean of 0.5 and a standard deviation of 0.3. The mean value for dust

suppression control efficiency for the LAU was set higher than that for the waste pile because

the application of liquid waste in the LAU can suppress dust.

3.6.4.3 Fraction Vegetative Cover (veg). During LAU operation, fraction vegetative

cover (veg) is assumed to be between 0.8 and 1, reflecting operation of the unit as agricultural

cropland. The fraction vegetative cover is specified as a normal distribution, with a mean of 0.9 

and a standard deviation of 0.1.

3.6.4.4 Operating Life (CutOffYr). For the representative national data set, LAU

operating life was set at a constant value of 40 yr.

3.6.4.5 Roughness Height (zruf). This factor is the height aboveground at which the

wind speed becomes zero (U.S. EPA, 1989). The range is 0.1 to 1,000 cm for snow to urban

settings. U.S. EPA (1989) provides some values for the roughness height for various sites in

Arizona and for industrial aggregates, as well as a chart of values for different settings. The

roughness height for a closed LAU is assumed to be normally distributed from 2 to 4 cm

(corresponding to grassland), with a mean of 3 cm and a standard deviation of 0.6 cm.

3.6.4.6 Roughness Ratio (Lc). This factor is the ratio of the silhouette area of the

roughness elements (>1 cm) in the soil to the total bare loose soil. The roughness ratio (Lc) can

range from 0 to 0.01 (U.S. EPA, 1989). Lc was assumed to be lognormally distributed, with a

minimum of 1×10-4, a maximum of 1×10-3, a mean of 3×10-4, and a standard deviation of 0.304. 

Higher Lc values (>2×10-4) increase the threshold wind speed for the onset of wind erosion

(which means lower particulate emissions due to wind erosion). Therefore, assuming a value for

Lc of 1×10-4 is conservative with respect to particulate emissions.
Section 3.0 Waste Management Unit Data

3-44

3.6.4.7 Vehicle Speed (vs). Vehicle speed is the mean speed that trucks travel on the

LAU. For surface spreading, a range of 20 to 40 km/h is representative (Overcash and Pal

1979). Vehicle speed (vs) is assumed to have a normal distribution from 20 to 40 km/h with a

mean of 30 km/h and a standard deviation of 6.1 km/h.

3.6.4.8 Mode of Aggregate Size Distribution (asdm). This parameter is the mode value

of the size of soil aggregates in an LAU. Because little data were available on this parameter, a

conservative value of 0.5 mm is assumed for this parameter based on the Soil Screening

Guidance: Technical Background Document (U.S. EPA, 1996a).

3.6.4.9 USLE Cover Factor (Cwmu) and Erosion Control Factor (Pwmu). For the

representative national data set, it was assumed that the LAU is maintained as cropland. 

Therefore, the USLE cover factor (Cwmu) was fixed at a value of 0.08 and the erosion control

factor (Pwmu) was fixed at a value of 0.50 (Wanielista and Yousef, 1993).

3.7 Surface Impoundment Module Inputs

This section describes the approach used to develop inputs for the Surface Impoundment 

Module. The module design is described in Section 3.7.1. Sections 3.7.2 through 3.7.4 describe

the development of input parameters for site-specific, site-based, and national data, respectively. 

Relationships between site-specific and site-based input parameters are identified and considered

to ensure that related inputs are not randomly selected in a manner that would create physically

impossible or unrealistic combinations.

Table 3-12 summarizes the data collected for Surface Impoundment Module inputs. It is

organized by site-specific and site-based data, which are extracted directly or calculated from

Industrial D Screening Survey data, and national data, which are derived based on relationships

taken from books, reports, and professional judgment.

3.7.1 Surface Impoundment Module Design

A surface impoundment is an excavation or diked area typically used for the treatment,

storage, or disposal of liquids or sludges containing free liquids. Liquids and solids typically

separate in a surface impoundment by gravity settling. Liquids from surface impoundments are

removed by draining, evaporation, or flow through an outlet structure. Accumulated solids are

removed by dredging during impoundment operation or at the time of closure.

There are more than 180,000 surface impoundments in the United States (Hartley, 1992). 

Nearly 30,000 are used by industry, including chemical manufacturers, food processors, oil

refineries, primary and fabricated metals, paper plants, and other commercial facilities.

For the representative national data set, surface impoundments were categorized

depending on the waste composition, waste generation rate, and purpose of impoundment. 

Based on their purpose, the three generic impoundment types are storage, disposal, and

treatment. Table 3-13 summarizes the distribution of surface impoundment applications.
3-45

Table 3-12. WMU Data Collected for the Surface Impoundment Module Variable Units Code Value Data S

Site-Specific Variables WMU type unitless SrcType specified “SI” Industrial D Screening Sarea of source m2 SrcArea SrcArea = [total area]/[no. surface impoundments] Industrial D Screening S

average values Site-Based Variables depth of source m d_wmu, SrcDepth capacity/[SrcArea × bulk density] (Equation SI-1) derived from Industrial D assumed bulk density of 

depth constraint 0.3 to 4

volumetric influent flow rate m3/s Q_wmu Q_wmu = waste quantity/[365.25 (d/y) x 86,400 (s/d) x bulk density](Equation SI-2) derived from Industrial D 

assumed bulk density of 

fraction of surface impoundment occupied by sediments fraction d_setpt d_wmu < 1.5: d_setpt = 0.2 d_wmu >= 1.5 and <5: d_setpt = (d_wmu - 1.2)/d_wmu d_wmu >= 5: d_setpt = 0.76 best professional judgme

(1979) impellers/aerators (number) unitless n_imp SrcArea > 1,600 m2: n_imp = integer(SrcArea/1,600) + 1 SrcArea < 1,600 m2: n_imp = integer(SrcArea/160) + 1 U.S. EPA (1990); maximimpellers/aerators is 66 (
3-46

Table 3-12. (continued) Variable Units Code Value Data S

Site-Based Variables impellers/aerators (total power) hp Powr SrcArea > 1,600 m2: Powr = n_imp x 75 SrcArea < 1,600 m2: Powr = n_imp x 7.5 U.S. EPA (1990); total p

exceed 5,000 hp turbulent area m2 — turbulent area = n_imp x 5.22 x Powr U.S. EPA (1990); assum

area fraction surface area turbulent fraction F_aer F_aer = turbulent area/SrcArea National Variables biologically active solids/total solids (ratio) unitless kba1 uniform distribution: minimum = 0.7 maximum = 0.9 Tchobanoglous (1979) biomass yield g/g bio_yield uniform distribution: minimum = 0.4 maximum = 0.8 Tchobanoglous (1979) digestion (sediments) 1/s k_dec uniform distribution: minimum = 4.6 E-07 maximum = 8.7 E-07 Tchobanoglous (1979) economic life of a tank/surface impoundment yr EconLife constant = 50 best professional judgme

impeller diameter cm d_imp constant = 61 U.S. EPA (1990) impeller speed rad/s w_imp constant = 126 U.S. EPA (1990) number of economic lifetimes unitless NumEcon constant = 1.0 best professional judgme
3-47

Table 3-12. (continued) Variable Units Code Value Data S

National Variables oxygen transfer correction factor unitless O2eff uniform distribution: minimum = 0.80 maximum = 0.85 Tchobanoglous (1979) oxygen transfer factor lb O2/h-hp J constant = 3 Tchobanoglous (1979) saturated hydraulic conductivity (sediment layer) m/s hydc_sed uniform distribution: minimum = 1 E-9 maximum = 1 E-6 best professional judgme
Section 3.0 Waste Management Unit Data

3-48

Table 3-13. Distribution of Surface Impoundment Applications

Storage

Percentage

Disposal

Percentage

Treatment

Percentage

Agricultural 55 26 19

Municipal 5 31 64

Industrial 17 31 52

Mining 18 27 56

Oil and gas 29 67 4

Source: Hartley, 1992.

3.7.2 Surface Impoundment Site-Specific Data

Site-specific data for surface impoundments were obtained from the Industrial D

Screening Survey (Westat, 1987). The data include total area, number of surface impoundments

at each site, total capacity, and total 1985 annual waste quantity. Average values were

calculated for use in the representative national data set by dividing each of the parameters by

the number of units at each site. Appendix 3A shows raw data from the Industrial D Screening

Survey (including replacement values) for the 201 Industrial D sites included in the

representative national data set. 

In accordance with previous EPA modeling efforts using the Industrial D Screening

Survey, surface impoundment capacities were screened from the Industrial D data when either

the capacity was missing or the depth constraint was violated. As with landfills, the unit depth

was calculated by dividing the unit capacity by the unit area. The depth constraint was described

by EPACMTP documentation as follows (U.S. EPA, 1997):

The surface impoundment volume data were screened by constraining the

calculated unit depth to be between one and 150 feet in order to eliminate

unrealistic values.

Some 1,926 Industrial D surface impoundment facilities reported surface area. Missing

waste quantity values were replaced for 57 of these facilities. Replacement capacity values were

calculated for 262 of the facilities with either missing or screened capacities. The procedures to

replace waste quantity and capacity are conditioned on area, as described for other WMU types,

and are consistent with EPACMTP methodology (U.S. EPA, 1997).

In order to calculate replacement values for the screened and missing annual waste

quantities, first a statistical regression of log (average annual waste quantity) versus log (average

surface area) was performed on the facilities with known quantities. The regression yielded an

equation for a best fit line through the known values. This equation gave the waste quantity as a

function of area, so the missing or screened waste quantities could be estimated based on the

known areas. To provide a more probabilistic sampling of average waste quantities, and because
Section 3.0 Waste Management Unit Data

3-49

Figure 3-4. Correlation of waste quantity to area for surface impoundments.

the known quantities seemed to be in a limited range (± 1) above and below the best-fit line

(with some outliers), a positive or negative random number was generated within that range. 

This random number was then added to the calculated log (average waste quantity) to replace

each missing waste quantity with a random value that was reasonable with respect to the surface

impoundment area. Figure 3-4 shows the regression plot, including the replaced (random waste

quantity) values, for surface impoundments.

To calculate replacement values for capacity, first a statistical regression of log (average

total capacity) versus log (average surface area) was performed on the facilities with known

capacities. The regression yielded an equation for a best-fit line through the known values. This

equation gave the capacity as a function of area, so the missing or screened capacities could be

estimated based on the known areas. To provide a more probabilistic sampling of average

capacities, and because the known capacities seemed to be in a limited range (± 0.8) above and

below the best-fit line (with some outliers), a positive or negative random number was generated

within that range and added to the calculated log (average total capacity) to replace each missing

capacity with a random value that was reasonable with respect to the surface impoundment area. 
Section 3.0 Waste Management Unit Data

3-50

Figure 3-5. Correlation of total capacity to area for surface impoundments.

(SI-1)

This value was then used to calculate depth as described above. Figure 3-5 shows the regression

plots, including the replaced (random capacity) values, for surface impoundments.

3.7.3 Surface Impoundment Site-Based Data

Surface impoundment variables related to site-specific surface impoundment area,

capacity, or annual waste quantity were calculated as described below.

3.7.3.1 Depth of Surface Impoundment (d_wmu, SrcDepth). Surface impoundment

depth (d_wmu or SrcDepth) was calculated consistent with previous EPA modeling efforts using

the Industrial D capacity and area data (U.S. EPA, 1997). The bulk density used for surface

impoundment was 1.0 g/cm3

. A range of 0.3 to 46 m (1 to 150 ft) was set as the depth constraint.
Section 3.0 Waste Management Unit Data

3-51

(SI-2)

3.7.3.2 Volumetric Influent Flow Rate (Q_wmu). Volumetric influent flow rate

(Q_wmu) is calculated based on the annual waste quantity from the Industrial D Screening

Survey and an assumed bulk density of 1.0 Mg/m3. Values ranged from 1×10-20 to 10 m3/s. It

was calculated as follows:

3.7.3.3 Fraction of Unit Occupied by Sediments (d_setpt). To calculate the fraction of

unit occupied by sediments (d_setpt), the following formula was developed based on best

professional judgment, as follows:

For depths less than 1.5 m, d_setpt = 0.2

For depths less than 5 m, d_setpt = (depth - 1.2)/(depth)

For depths greater that 5 m, d_setpt = 0.76 (maximum value).

For depths less than 1.5 m, it is assumed the unit is cleaned whenever more than 20 percent of

the unit depth is filled with sediment. For depths greater than 5 m, it is assumed the unit is

cleaned whenever more than 76 percent of the unit is filled with sediment. For depths between

1.5 and 5 m, the set point is calculated as the percentage required to keep at least 1.2 m of clean

depth. These values are based on engineering judgment and Tchobanoglous (1979).

3.7.3.4 Impellers/Aerator Number (n_imp), Total Horsepower (Powr), and Fraction

Surface Area Turbulent (F_aer). It is assumed that a one hp motor will agitate an area of 

5.22 m2 (U.S. EPA, 1990). Based on this assumption, we can calculate the total turbulent area

using a 75- or 7.5-hp motor (turbulent area = ~392 m2

 for 75-hp motor). Typically, the turbulent

area for a surface impoundment is about 25 percent of the total area of the unit (U.S. EPA,

1990). Therefore, a 75-hp motor will aerate about 25 percent of a 1,600 m2

 surface impoundment

(392/1,600 = ~0.25). The number of impellers is taken to be equal to the number of motors. Two

motor sizes were assumed: 7.5 hp and 75 hp. The smaller motor was assumed for smaller

impoundments (area <1,600 m2

), while the larger motor was assumed for larger impoundments

(area >=1,600 m2

).

# The number of impellers (n_imp) is calculated as follows:

If source area >1,600 m2

, then n_imp = Integer [source area (m2)/1,600 m2] + 1.

If source area <1,600 m2, then n_imp = Integer [source area (m2)/160 m2] + 1.

# Total impeller power (Powr, hp) = n_imp × power per impeller (hp).

If source area >1,600 m2, then power per impeller = 75 hp.

If source area <1,600 m2, then power per impeller = 7.5 hp.

# Assuming 5.22 m2/hp turbulent, turbulent area (m2) = Powr (hp) × 5.22 (m2/hp).

# Fraction turbulent area (F_aer) = turbulent area (m2)/source area (m2).
Section 3.0 Waste Management Unit Data

3-52

Total power is assumed not to exceed 5,000 hp. From the above calculations, therefore, the

maximum number of impellers/aerators is 66 (4,950 hp).

3.7.4 Surface Impoundment National Data

Noncorrelated surface impoundment variables included in the national data tables are

described below.

3.7.4.1 Ratio of Biologically Active Solids to Total Solids (kba1). Values for the ratio

of biologically active solids to total solids were assumed to be uniformly distributed between 0.7

and 0.9. The range is based on data from Tchobanoglous (1979).

3.7.4.2 Biomass Yield (bio_yield). Values for biomass yield were assumed to vary

uniformly, from 0.4 to 0.8 g/g. This is based on a range of 0.4 to 0.8 mg VSS/mg of biological

oxygen demand for a 5-day test (BOD5), reported by Tchobanoglous (1979). 

3.7.4.3 Digestion Rate (Sediments) (k_dec). Values for sediment digestion rate were

assumed to be uniformly distributed between 4.6×10-7 s-1 and 8.7×10-7 s-1. This is based on a

range of 0.04 to 0.075 d-1 given for the parameter Kd (the endogenous decay coefficient) in

Table 9-7 of Tchobanoglous (1979).

3.7.4.4 Economic Life of Surface Impoundment (EconLife). The economic life of a

surface impoundment was assumed to be 50 yr.

3.7.4.5 Impeller Diameter (d_imp). The impeller diameter was fixed at 61 cm, based

on U.S. EPA (1990). 

3.7.4.6 Impeller Speed (w_imp). The value for impeller speed was fixed at 126 rad/s. 

This is based on data from U.S. EPA (1990).

3.7.4.7 Number of Economic Lifetimes (NumEcon). The number of economic

lifetimes was fixed at 1.

3.7.4.8 Oxygen Transfer Correction Factor (O2eff). Values for the oxygen transfer

factor were assumed to be uniformly distributed between 0.80 and 0.85. The recommended

range is based on data from Tchobanoglous (1979).

3.7.4.9 Oxygen Transfer Factor (J). Values for the oxygen transfer factor were fixed

at 3.0 lb O2/hp h. This is the median of the recommended range of 2.0 and 4.0 O2/hp h based on

data from Tchobanoglous (1979).

3.7.4.10 Saturated Hydraulic Conductivity of Sediment Layer (hydc_sed). The

value of this parameter was assumed to be uniformly distributed between 1×10-9 and 1×10-6 m/s,

based on engineering judgment that the sediment layer will be similar to a compacted soil.
Section 3.0 Waste Management Unit Data

3-53

3.8 Aerated Tank Module Inputs

This section describes the approach used to develop inputs for the Aerated Tank Module. 

The Aerated Tank Module design is described in Section 3.8.1. Section 3.8.2 describes the use

of tank data from the TSDR Survey (U.S. EPA, 1987) to derive correlated model input

variables. Section 3.8.3 describes the collection of other, noncorrelated, nationally specified

input parameters. These data were derived based on relationships taken from books, reports, and

professional judgment. Table 3-14 summarizes the data collected for Aerated Tank Module

inputs.

3.8.1 Aerated Tank Module Design

Tanks are used to treat and/or store a variety of waste materials. Tanks may be enclosed,

covered, or open to the atmosphere. This analysis focused on treatment tanks that were open to

the atmosphere and had some agitation of material contained in the tank. Two levels of agitation

(or aeration) were considered. Tanks that actively mix the liquid surface for the purpose of

aeration (transferring oxygen from the atmosphere into the liquid) were classified as HI aeration

because these units have a high degree of turbulence by design. Tanks that are likely to have

mixing devices used with chemical additions or other purposes were classified as LO aeration. 

These tanks have convective currents and some degree of turbulence that increases volatile

losses, but they are not designed specifically to enhance air-liquid mass transfer. Key parameters

for any tank design are tank capacity and flow rate (which fixes the hydraulic retention time). 

For treatment tanks, the number, type, and power input to the aerators/mixers are important

design parameters. For biological treatment tanks, the concentration of active biomass and the

overall oxygen transfer rate (which is dependent on the aerator characteristics as well as the

surface area to volume ratio) are also key design parameters. 

Treatment tanks can be elevated (bottom of tank above ground level), on the ground

(bottom of tank at ground level), or in-ground (bottom of tank below ground level). As the tank

volume increases, so does the likelihood that the tank is on or in the ground. The elevation of

the tank with respect to the ground level impacts the release height of the volatile emissions and

the subsequent dispersion characteristics.

As described in Section 2.0, an aerated tank is assumed to be present at every facility that

has a surface impoundment for the representative national data set. This is based on the

assumption that surface impoundments are an indication that a facility manages liquid wastes

and that tanks are likely to be present at such facilities. During the execution of the 3MRA

modeling system, tanks are randomly selected for placement at aerated tank sites from a

correlated data set of model tanks developed from the 624-tank TSDR database described in

Section 3.2.2. The following section (Section 3.8.2) describes how this data set was developed.
3-54

Table 3-14. WMU Data Collected for the Aerated Tank Module Variable Units Code Value Data So

Site-Specific (Correlated) Variables aerated tank index unitless ATIndex uniform distribution from 1 to 624 (number of tanks in national tank data set) volumetric flow rate (tank) m3/s Q_wmu converted from gallons TSDR Survey (U.S. EPA, 1

depth (liquid) m d_wmu calculated using tank capacity and HI/LO aeration designations; uses a random variation on calculated depths (Equation AT-1 and Section 3.8.2.3) derived from TSDR Survey area of source m2 SrcArea calculated based on tank volume and projected tank depth (d_wmu) derived from TSDR Survey fraction surface area- turbulent fraction F_aer assigned depending on HI/LO aeration designation HI aeration, normal distribution: minimum = 0 maximum = 1 mean = 0.75 standard deviation = 0.1 LO aeration, normal distribution: minimum = 0.2 maximum = 0.8 mean = 0.50 standard deviation = 0.2 derived from TSDR Survey 
3-55

Table 3-l4. (continued) Variable Units Code Value Data So

Site-Specific (Correlated) Variables impellers/aerators (total power) hp Powr assigned depending on HI/LO aeration designation (minimum = 0.25) HI aeration, normal distribution: 90 percent between 80 and 150 hp per million gallons of tank volume LO aeration, normal distribution: 90 percent between 15 and 45 hp per million gallons of tank volume Tchobanoglous (1979), Ad

(1974) and Water Pollution (WPCF, 1988); minimum tminimum size of commercifor containers holding 55 g

impellers/aerators (number) unitless n_imp Powr #25 hp: n_imp = 1 25 hp < Powr < 80 hp: randomly pick 1 or 2 (equal probability) Powr $80 hp: Integer (Powr/[random number between 60 and 100]) Adams and Eckenfelder (19

Control Federation (WPCF

National Variables biologically active solids/total solids (ratio) unitless kba1 uniform distribution: minimum = 0.7 maximum = 0.9 Tchobanoglous (1979) biomass yield g/g bio_yield uniform distribution: minimum = 0.4 maximum = 0.8 Tchobanoglous (1979) digestion (sediments) 1/s k_dec uniform distribution: minimum = 4.6E-07 maximum = 8.7E-07 Tchobanoglous (1979) 
3-56

Table 3-l4. (continued) Variable Units Code Value Data So

National Variables economic life of AT/SI yr EconLife constant = 20 best professional judgment fraction of tank occupied by sediments (max.) fraction d_setpt constant = 0.3 best professional judgment impeller diameter cm d_imp constant = 61 U.S. EPA (1990) impeller speed rad/s w_imp constant = 126 U.S. EPA (1990) number of economic lifetimes unitless NumEcon constant = 2.5 best professional judgment oxygen transfer correction factor unitless O2eff constant = 0.83 Tchobanoglous (1979) oxygen transfer factor lb O2/h-hp J constant = 3.0 Tchobanoglous (1979)
Section 3.0 Waste Management Unit Data

3-57

(AT-1)

3.8.2 Aerated Tank Correlated Data

Correlated tank data include size-related variables directly extracted or derived from the

TSDR database described in Section 3.2.2. These variables are passed to the system as a data set

of 624 model tanks. Correlated tank data and related parameters used in their derivation (such as

aeration level, tank size, and tank capacity) are presented in Appendix 3B-3. As described in

Section 3.3.3, when the 3MRA modeling system models an aerated tank site, one of these tanks

is randomly selected and placed at the site. To avoid placing unreasonably large tanks at sites

with small surface impoundments, tank selection is constrained so that the selected tank is no

larger than the average surface impoundment area at the facility in question (i.e., the data set is

resampled when this occurs).

3.8.2.1 Aerated Tank Index (ATIndex) and Maximum Source Area (MaxSrcArea).

The ATIndex and MaxSrcArea are used by the 3MRA modeling system site definition processor

in the selection of a tank to model at a facility. ATIndex is an integer with a uniform distribution

from 1 to 624 that is randomly sampled to provide the tank to be modeled at a site. Each

correlated tank variable is indexed on the ATIndex to allow the system to pick a consistent set of

correlated values.

MaxSrcArea is simply the average surface impoundment area (SrcArea) for the site in

question (see Section 3.7.2). 

3.8.2.2 Tank Throughput (Q_wmu). Values for tank throughput in the original TSDR

Survey database were in units of gallons per year. These values were converted into units of

cubic meters per second (m3/s) based on the assumption of 8,766 h/yr operation.

3.8.2.3 Preliminary Tank Depth. The preliminary tank depth is used to estimate tank

depth for the model as described in Section 3.8.2.4.

In 1985 and 1986, Research Triangle Institute (RTI) conducted numerous site visits to

aerated treatment systems. Data extracted from site visit test reports is presented in Appendix

3B, Table 3B-2. Based on these data and the addition of a cubic 55-gal tank and a 3-milliongal/32-ft-deep tank (based on vendor information), the following log expression was derived:

Using this expression, 55 gal tanks (the smallest tanks in the database) are approximately

4.4 ft tall and have a diameter less than 18 in. Consequently, for small tanks, a cubed-shaped

tank was used as the central tendency value (i.e., depth = capacity0.333). At a tank size of

approximately 10 m3, Equation AT-1 predicts approximately cubed-shaped tanks. Therefore,

Equation AT-1 is used for tanks greater than 10 m3. 

The largest tank in the LO aeration category is 25,000 m3

, and the projected depth for this

tank is 6.6 m (22 ft), which is acceptable for mixing tanks. The largest tank in the HI aeration

category is 114,000 m3

, and the projected depth for this tank is 8.1 m (27 ft). In evaluating the
Section 3.0 Waste Management Unit Data

3-58

depths of mechanically aerated tanks, however, the maximum depth was 6.1 m (20 ft). Even this

depth appeared to be an outlier compared to the other HI aeration, mechanically aerated tanks. 

Data were available for eight tanks (two of which were the TSDF model tanks). The other seven

tanks all had depths ranging from 3.35 m (11 ft) to 4.88 m (16 ft). The mid-range of the latter

depths is approximately equivalent to a 1,000-m3 tank as calculated using Equation AT-1. 

Therefore, for HI aeration tanks greater than 1,000 m3

, a random (maximum) depth is set from

the expected range of depths for HI aeration units.

3.8.2.4 Estimating Tank Depth (d_wmu) from Tank Capacity. In the following

calculation algorithms the nomenclature random#(X to Y) indicates that a random number was

selected based on a normal distribution, with 90 percent of the values falling within the range of

X to Y.

Estimating depth for LO aeration tanks, nonaerated tanks, and storage tanks:

# If tank capacity is <10 m3, depth = random#(0.8 to 1.2)*capacity0.333.

# If tank capacity is >=10 m3, use Equation AT-1 and then apply a random variation

on the calculated depth [i.e., random#(0.8 to 1.2)].

Estimating depth for HI aeration tanks:

# If tank capacity is <10 m3, depth = random#(0.8 to 1.2)*capacity0.333.

# If 10 m3 <= tank capacity <=1,000 m3, use Equation AT-1 and then apply a

random variation [i.e., random#(0.8 to 1.2)].

# Above 1,000 m3, use random#(3.5 to 4.8) and then apply a random variation on

the calculated depth [i.e., random#(0.8 to 1.2)].

3.8.2.5 Surface Area (SrcArea). Tank surface area values were calculated, based on the

database value for tank volume and the projected values for tank depth.

3.8.2.6 Source Height (SHight). Because the heights of the tanks in the TSDR database

were not provided, source height for tanks (SHight) was randomly generated for each tank in the

624-tank data set. Based on professional experience, it was assumed that tank heights do not

exceed 20 m, that some tanks (especially larger ones) are buried beneath the ground surface, and

that there can be some freeboard height (0.5 m aboveground) for every tank. 

Given this perspective, SHight was randomly selected once for each of the 624 tanks in

the data set from a uniform distribution ranging from 0 to 20 m. If the randomly selected value

was less than the tank depth (d_wmu) + 0.5 m, the value was placed in the data set. Otherwise,

SHight was set to d_wmu + 0.5 m.

3.8.2.7 Fraction Surface Area Turbulent (F_aer). Three aeration levels were assumed

to exist: (1) HI aeration when agitation is used for biological treatment or other high-energy

processes, (2) LO aeration for mixing processes, and (3) NO aeration for complete quiescence. 
Section 3.0 Waste Management Unit Data

3-59

Aeration categories (HI/LO/NO) were assigned according to the waste treatment classification,

as shown in Appendix 3B, Table 3B-3. The following aeration distributions were developed

based on best engineering judgment:

# The HI aeration was assumed to follow a normal distribution, centered on a

fraction turbulent area of 0.75, with a standard deviation of 0.1 and no values

above 1.0.

# The LO aeration was assumed to follow a normal distribution, centered on a

fraction turbulent of 0.50, with a standard deviation of 0.2 and no values above

0.8 or below 0.2.

3.8.2.8 Total Aerator Horsepower (Powr). Total aerator horsepower was calculated

based on the following algorithm:

# The HI aeration power was assumed to follow a normal distribution, with 90

percent of the values falling between 80 and 150 hp per million gals of tank

volume (Tchobanoglous, 1979).

# The LO aeration power was assumed to follow a normal distribution, with 90

percent of the values between 15 and 45 hp per million gals of tank volume. This

estimate was based on references that indicate surface aerators used for mixing

typically have power levels of 15 to 20 hp/million gal (Adams and Eckenfelder,

1974) and that minimum surface aerator power to mix activated sludge tanks is 20

to 30 hp/million gal (WPCF, 1988). The upper value of 30 hp was multiplied by

1.5, with a resultant value of 45 hp/million gal, to provide values above the

minimum.

# Set the minimum Total Aerator Power to 0.25 hp, if the estimated value is less

than 0.25 hp based on the minimum size of commercially available mixers for

containers holding 55 gal or more.

3.8.2.9 Number of Impellers/Aerators (n_imp). The number of impellers/aerators

(n_imp) is determined based on the total aerator horsepower as follows:

# For Total Power #25 hp, n_imp = 1.

# For 25 hp < Total Power <80 hp, randomly pick 1 or 2 (equal probability).

# For Total Power $80 hp, randomly pick a number between 60 and 100 (uniform

distribution) and then divide the Total Power by the random number and round up

to the next integer.

3.8.3 Aerated Tank National Data

Noncorrelated tank variables included in the national data tables for the HWIR modeling

system are described below.
Section 3.0 Waste Management Unit Data

3-60

3.8.3.1 Biologically Active Solids/Total Solids Ratio (kba1). Values for the ratio of

biologically active solids to total solids were assumed to be uniformly distributed between 0.7

and 0.9. The range is based on data from Tchobanoglous (1979).

3.8.3.2 Biomass Yield (bio_yield). Values for biomass yield are assumed to vary

uniformly from 0.4 to 0.8 g/g. This is based on a range of 0.4 to 0.8 mg VSS/mg BOD5 reported

by Tchobanoglous (1979).

3.8.3.3 Digestion (k_dec). Values for digestion of sediments are assumed to vary

uniformly from 4.6×10-7 to 8.7×10-7, based on data from Tchobanoglous (1979).

3.8.3.4 Economic Life of AT (EconLife). The economic life of an aerated tank is

assumed to be 20 yr.

3.8.3.5 Fraction of Tank Occupied by Sediments (d_setpt). The maximum fraction of

the tank occupied by sediments is set equal to 0.3.

3.8.3.6 Impeller Diameter (d_imp). The impeller diameter was assumed to have a fixed

value of 61 cm, based on U.S. EPA (1990). 

3.8.3.7 Impeller Speed (w_imp). The value for impeller speed was assumed to be fixed

at 126 rad/s. This is based on data from U.S. EPA (1990).

3.8.3.8 Number of Economic Lifetimes (NumEcon). The number of economic

lifetimes for an aerated tank is fixed at 2.5. Combined with an EconLife value of 20 yr, this

places a tank at each facility with a surface impoundment for the assumed lifetime of the surface

impoundment (50 yr).

3.8.3.9 Oxygen Transfer Correction Factor (O2eff). The value for the oxygen transfer

factor was fixed at 0.83, based on data from Tchobanoglous (1979).

3.8.3.10 Oxygen Transfer Factor (J). Values for the oxygen transfer factor were

assumed to be fixed at 3.0 lb O2/hp h. This is the median of the recommended range of 2.0 and

4.0 lb O2/hp h based on data from Tchobanoglous (1979).

3.9 Quality Assurance/Quality Control

For the Industrial D data collection effort, manual calculations or data entry were

checked 100 percent against their original references. Automated data extraction (or data

processing) was checked for accuracy by checking approximately 10 percent of the values. 

Checks were conducted across WMU types to ensure that calculations in the database were

correct.

Quality Assurance/quality control (QA/QC) of national data collection was based on a

combination of internal and external review checks and collection of data from multiple sources

for each parameter of interest. As described in Section 3.2.3, data for design parameters were

generally sought from multiple sources. This helped to ensure that the data collected were
Section 3.0 Waste Management Unit Data

3-61

representative of standard industry practice. When no standard industry practice existed and

parameter values were found to be highly variable, using multiple data sources aided the

characterization of parameter variability, which then became a consideration in the model unit

designs.

Quality of the data sources was reviewed, and all data elements were entered into a

database along with an indicator of the data source and the general estimated quality. Data

quality was ranked as follows:

# In general, more recent literature sources were considered of higher quality than

older sources.

# Standard reference texts were generally considered of higher quality than

information collected by telephone or from the Internet.

# Engineering calculations were considered of lower quality than data from the

literature.

# Engineering judgment was considered the lowest quality source.

Internal reviews consisted of senior engineering reviews of individual parameter values

for realism and reviews of overall model system designs to ensure that parameter estimates

within the model were internally consistent. For example, for an aerated tank, impeller

parameter estimates, aerator requirements, flow rates, and biomass yield data should be

consistent for a given facility design. External reviews of model facility designs and parameter

estimates were also conducted to ensure that these were representative of typical industry

practices.

Quality assurance was conducted to ensure that an adequate QC methodology was in

place and correctly implemented and recorded. QA/QC records for WMU data processing can

be provided on request.

3.10 Issues and Uncertainties

3.10.1 Age/Accuracy of Industrial D Data

For the nationally collected data, one issue of concern was the availability of recent data

on actual Industrial D units (the Industrial D data is more than 10 years old). Although

significant data can be found on Subtitle C waste management units (e.g., permit records, design

requirements), recent Industrial D data are not compiled or readily available. In spite of its age,

the Industrial D Screening Survey represents the largest consistent set of data available on

facility locations and WMU dimensions. The 201-facility sample was selected from the survey

to represent the types and geographical locations of WMUs at which nonhazardous wastes might

be disposed. At some of the 201 facilities, there probably have been WMU additions or closures

since the survey was conducted. We consider this approach of basing the assessment on actual

WMU, land use, and population data, however, to be preferable to developing and evaluating

hypothetical exposure scenarios. 
Section 3.0 Waste Management Unit Data

3-62

Another issue that was identified during use of the Industrial D data were whether to

revisit the methodology used to screen out questionable entries in the Industrial D database. For

consistency, it was decided to use the methodology from previous EPACMTP modeling efforts,

as described in U.S. EPA (1997).

3.10.2 Underrepresentation of Highly Aerated Tanks

The tank database appears to underrepresent highly aerated tanks. This is probably due

to a disproportionate number of aerated biological treatment systems being operated at facilities

that only process on-site waste. This under representation introduces some uncertainty into the

analysis, the result of which is that risks from highly aerated tanks may be underestimated.

3.11 References

Adams, C. E. and W. W. J. Eckenfelder (eds.). 1974. Process Design Techniques for Industrial

Waste Treatment. Enviro Press, Inc., Nashville, TN.

Allswede, Sharon. 1999. "1986 Survey Corrections." Memorandum to Mark A. Bahner

(Research Triangle Institute) from Sharon Allswede (Dow North America Michigan

Environmental Operations), Midland, MI. March 25.

Bagchi, Amalendu. 1990. Design, Construction, & Monitoring of Sanitary Landfill. John Wiley

& Sons, Inc., New York, NY.

Carsel, R. F., and R. S. Parrish. 1988. Developing joint probability distributions of soil water

retention characteristics. Water Resources Research, 24(5):755-769. May.

Caterpillar, Inc. 1994. Caterpillar Performance Handbook. Caterpillar Inc., Peoria, IL. pp. 9-2.

Clapp, R. B., and G. M. Hornberger. 1978. Empirical equations for some soil hydraulic

properties. Water Resources Research, 14:601-604.

Clickner, Robert. 1988. "Sampling weights for the industrial Subtitle D screening survey."

Memorandum to Zubair Saleem (U.S. Environmental Protection Agency) from Robert

Clickner (Westat, Inc.), Rockville, MD. July 28.

Clickner, Robert P., and Jim Craig. n.d. Using Business Establishment Size in an

Environmental Survey (Unpublished).

Environmental Research & Technology (ER&T), Inc. 1983. Land Treatment Practices in the

Petroleum Industry. The American Petroleum Institute. Environmental Research &

Technology, Inc., Concord, MA. June.

Goldberg, Elliot (ed.). 1997. Handbook of Downstream Processing. 1st Edition. Chapman &

Hall, London, UK.
Section 3.0 Waste Management Unit Data

3-63

Hartley, Robert P. 1992. Surface Impoundments: Design, Construction and Operation. Noyes

Data Corporation, Park Ridge, NJ.

Martin, J. P., Ronald C. Sims, and John Matthews. 1986. Review and evaluation of current

design and management practices for land treatment units receiving petroleum wastes.

Hazardous Waste & Hazardous Materials, 3(3):261-280.

McBean, Edward A., Frank A. Rovers, and Grahame J. Farquhar . 1995. Solid Waste Landfill

Engineering and Design. Prentice Hall PTR, Englewood Cliffs, NJ.

Midwest Research Institute (MRI). 1990. Special Management Standards for Municipal Waste

Combustion (MWC) Ash. U.S. Environmental Protection Agency, Municipal Solid Waste

Program. Midwest Research Institute, Falls Church, VA. June 29.

Miller, D. A., and R. A. White. 1998. A Conterminous United States Multilayer Soil

Characteristics Dataset for Regional Climate and Hydrology Modeling. Website at

http://www.essc.psu.edu/soil_info/index.cgi?soil_data&index.html.

Overcash, M. R., and D. Pal . 1979. Design of Land Treatment Systems for Industrial Wastes -

Theory and Practice. Ann Arbor Science Publishers, Inc., Ann Arbor, MI. pp. 509-512.

Pekar, Zachary. 1999. "Personal Communication with Kendall Smith, Liquid Sales Manager for

AO Smith Industrial." Information on the size and volume of large above-ground

treatment/storage tanks. Memorandum from Zachary Pekar (Research Triangle Institute),

Research Triangle Park, NC. March 16.

Reed, Sherwood C., and Ronald W. Crites. 1984. Handbook of Land Treatment Systems for

Industrial and Municipal Wastes. Noyes Publications, Park Ridge, NJ. 219.

Reynolds, Tom D., and Paul A. Richards. 1996. Unit Operations and Processes in

Environmental Engineering. 2nd Edition. PWS Publishing Company, Boston, MA.

Schwarz, G. E. and R. B. Alexander. 1995. State Soil Geographic (STATSGO) Data Base for

the Conterminous United States. Edition 1.1. Open-File Report 95-449. U.S. Geological

Survey, Reston, VA. Website at

http://water.usgs.gov/GIS/metadata/usgswrd/ussoils.html. September 1.

Seaber, P. R., F. P. Kapinos, and G. L. Knapp. 1987. Hydrologic Unit Maps. U.S. Geological

Survey Water-Supply Paper 2294. U.S. Government Printing Office, Washington, DC.

pp. 1-13.

Tchobanoglous, George . Cerra, Frank J. and J. W. Maisel (eds.). 1979. Wastewater

Engineering: Treatment, Disposal, Reuse. 2nd Edition. McGraw-Hill Book Company,

New York, NY.

Tchobanoglous, George, Hilary Theisen, and Samuel Vigil. 1993. Integrated solid waste

management: engineering principles and management issues. In: McGraw-Hill Series in
Section 3.0 Waste Management Unit Data

3-64

Water Resources and Environmental Engineering. P.H.King and R.Eliassen (eds.).

McGraw-Hill, Inc., New York, NY.

Treybal, Robert E. 1980. Mass-transfer operations. In: McGraw-Hill Chemical Engineering

Series. J.J.Carberry, J.R.Fair, M.S.Peters, W.R.Schowalter, and J.Wei (eds.). 3rd Edition.

McGraw-Hill, Inc., New York, NY.

U.S. EPA (Environmental Protection Agency). 1983. Hazardous Waste Land Treatment.

EPA/530/SW-83/874. U.S. Environmental Protection Agency, Office of Research and

Development, Cincinnati, OH. April.

U.S. EPA (Environmental Protection Agency). 1984. Evaluation and Selection of Models for

Estimating Air Emissions from Hazardous Waste Treatment, Storage, and Disposal

Facilities. EPA/450/3-84-020. U.S. Environmental Protection Agency, Office of Air

Quality Planning and Standards, Research Triangle Park, NC. December.

U.S. EPA(Environmental Protection Agency). 1987. 1986 National Survey of Hazardous Waste

Treatment, Storage, Disposal, and Recycling Facilities (TSDR) Database. U.S.

Environmental Protection Agency, Office of Solid Waste, Washington, DC.

U.S. EPA (Environmental Protection Agency). 1989. Hazardous Waste TSDF (Treatment,

Storage, and Disposal Facilities). Fugitive Particulate Matter Air Emissions Guidance

Document. EPA/450/3-89/019. U.S. Environmental Protection Agency, Office of Air

Quality Planning and Standards, Research Triangle Park, NC. May.

U.S. EPA (Environmental Protection Agency). 1990. Background Document for the Surface

Impoundment Modeling System (SIMS) Version 2.0. EPA-450/4-90-019b. U.S.

Environmental Protection Agency, Emission Standards Division, Office of Air Quality

Planning and Standards, Research Triangle Park, NC. September.

U.S. EPA (Environmental Protection Agency). 1991. Design, construction, and operation of

hazardous and non-hazardous waste surface impoundments. EPA/530/SW-91/054. U.S.

Environmental Protection Agency, Risk Reduction Engineering Laboratory, Waste

Minimization, Destruction, and Disposal Research Division, Cincinnati, OH. June.

U.S. EPA (Environmental Protection Agency). 1991. Hazardous Waste TSDF - Background

Information for Proposed RCRA Air Emission Standards. Volume 1, Chapters 1-8 and

Appendices A-C. (Draft EIS). EPA-450/3-89-023a. U.S. Environmental Protection

Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC.

June.

U.S. EPA (Environmental Protection Agency). 1994. 1:250,000 Scale Quadrangles of

Landuse/Landcover GIRAS Spatial Data in the Conterminous United States: Metadata.

Office of Information Resources Management, Washington, DC. Website at

http://www.epa.gov/ngispgm3/nsdi/projects/giras.htm.
Section 3.0 Waste Management Unit Data

3-65

U.S. EPA (Environmental Protection Agency). 1996a. Soil Screening Guidance Technical

Background Document. EPA/540/R/95/128. U.S. Environmental Protection Agency,

Office of Solid Waste and Emergency Response, Washington, DC. May.

U.S. EPA (Environmental Protection Agency). 1996b. Technical Support Document for the

Round Two Sewage Sludge Pollutants. EPA-822-R-96-003. U.S. Environmental

Protection Agency, Office of Water, Washington, DC. August.

U.S. EPA (Environmental Protection Agency). 1997. EPA’s Composite Model for Leachate

Migration with Transformation Products. EPACMTP: User’s Guide. U.S. Environmental

Protection Agency, Office of Solid Waste, Washington, DC.

USDA (Department of Agriculture). 1986. Urban Hydrology for Small Watersheds. TR-55.

U.S. Department of Agriculture, Engineering Division, Soil Conservation Service,

Washington, DC. pp. 2-5. June.

USDA (Department of Agriculture). 1994. State Soil Geographic (STATSGO) Data Base. Data

use information. Miscellaneous Publication Number 1492. U.S. Department of

Agriculture, Natural Resources Conservation Service, Fort Worth, TX. December.

Wanielista, M. P., and Y. A. Yousef. 1993. Stormwater Management. John Wiley & Sons, Inc.,

New York, NY. pp. 399-410.

Westat, Inc. 1987. Screening Survey of Industrial Subtitle D Establishments. Draft Final

Report. U.S. Environmental Protection Agency. Westat, Inc., Rockville, MD.

December 29.

WPCF, and ASCE. 1988. Aeration, A Wastewater Treatment Process. Manual of Practice -

No.FD-13. Water Pollution Control Federation, Alexandria, VA.
Section 3.0 Waste Management Unit Data

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Appendix 3A WMU Data

3-67

Appendix 3A

WMU Data

Table 3A-1. WMU Data from the Industrial D Screening Survey . . . . . . . . . . . . . . . . . . . . . 3-69
Appendix 3A WMU Data

3-68

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Appendix 3A WMU Data

3-69

Table 3A-1. WMU Data from the Industrial D Screening Survey

Landfill Industrial D Data Landfill Calculated FRAMES Inputs

IDNum

Total

LF

Depth (m) Average

surface

area

(acres)

Average

surface

area (m2) Average

capacity

(Mg)

Capacity

replaced?

Average

waste

quantity

(Mg)

Vehicles

per day

(1/d)

Vehicle

weight

(Mg)

Wheels

per

vehicle

Distance

vehicle

travels

on LF

(m)

Frequency

of surface

disturbance

per month

(1/mo)

Spreading/

compacting

operations

per day (1/d)

Waste

loading

rate

(dry,

Mg/yr)

Waste

zone

thickness

(m)

No.

waste

layers

in a

cell

SrcDepth SrcArea nv vw nw mt fd Nop load zW Nly

0114001

0130207

0131104

0131207

0131508 1 4.67594 110 445170 3746855 0 140507 8.25952 50.7 10 667.211 60 2 124895 4.675935 5

0136703

0220102

0221207

0223504 1 2.69 0.5 2023.5 9797.8 0 136.1 0.04968 24 6 44.9833 1.490273 0.0496758 326.59 2.690004 3

0224002

0231002 1 0.50947 3.3 13355.1 12247.2 0 1020.6 0.06209 24 6 115.564 1.8628337 0.0620945 408.24 0.509468 1

0231106 1 0.53136 15 60705 58060.8 0 3011.9 0.29437 24 6 246.384 8.8312115 0.2943737 1935.4 0.531357 1

0231407 1 1.17521 7.1 28733.7 60782.4 0 14515.2 0.30817 24 6 169.51 9.2451745 0.3081725 2026.1 1.175205 2

0231610

0231911

0231914

0232305

0232313 3 2.49073 33.3333 134900 604800 0 5911.8 3.06639 24 6 367.287 60 2 20160 2.490734 2

0232402 1 6.61257 11.3 45731.1 544320 0 16547.3 2.75975 24 6 213.848 60 2 18144 6.612568 7

0232415

0232501 1 7.37858 0.8 3237.6 43000 0 0.005 0.21801 24 6 56.8999 6.5404213 0.218014 1433.3 7.378579 7

0232705

0233601

0233603 1 1.07627 2 8094 15680.4 -1 13608 0.0795 24 6 89.9667 2.3850403 0.0795013 522.68 1.076274 2

0234904 1 1.86805 4 16188 54432 0 9072 0.27598 24 6 127.232 8.2792608 0.2759754 1814.4 1.86805 2

0235301 1 6.34689 100 404700 4623455 -1 1262.8 10.1919 50.7 10 636.16 60 2 154115 6.34689 6

0312301

0314202 1 0.7279 0.2 809.4 1060.49 -1 1.5 0.00538 24 6 28.45 0.1613028 0.0053768 35.35 0.727895 1

0321802

0331006

0331902

0332104

0332707 1 0.74257 5 20235 27046.7 -1 13.6 0.13713 24 6 142.25 4.1138835 0.1371295 901.56 0.742573 1

0332811

0430108

0430412 1 1.44921 5 20235 52784.5 -1 72.6 0.26762 24 6 142.25 8.0286638 0.2676221 1759.5 1.449207 2

0431912 1 7.82897 137 554439 7813232 -1 9353232 17.2234 50.7 10 744.607 60 2 260441 7.828967 8

0432011 1 0.64761 2 8094 9435.19 -1 108.9 0.04784 24 6 89.9667 1.4351187 0.0478373 314.51 0.647612 1

0432106

0432716

0433201 1 6.19311 3.61 14609.7 162863 -1 175634 0.82573 24 6 120.87 24.771895 0.8257298 5428.8 6.193114 6

0433204

0433404 2 3.1088 13.5 54634.5 305726 0 161028 1.55006 24 6 233.74 46.501848 1.5500616 10191 3.108805 3

0433408

0434505

0434804

0435510

0436007

0436108

(continued)
Appendix 3A WMU Data

Table 3A-1. (continued)

Landfill Industrial D Data Landfill Calculated FRAMES Inputs

IDNum

Total

LF

Depth (m) Average

surface

area

(acres)

Average

surface

area (m2) Average

capacity

(Mg)

Capacity

replaced?

Average

waste

quantity

(Mg)

Vehicles

per day

(1/d)

Vehicle

weight

(Mg)

Wheels

per

vehicle

Distance

vehicle

travels

on LF

(m)

Frequency

of surface

disturbance

per month

(1/mo)

Spreading/

compacting

operations

per day (1/d)

Waste

loading

rate

(dry,

Mg/yr)

Waste

zone

thickness

(m)

No.

waste

layers

in a

cell

SrcDepth SrcArea nv vw nw mt fd Nop load zW Nly

3-70

0530901

0531301

0531502

0531702 2 0.84911 22 89034 136080 0 798.35 0.68994 24 6 298.386 20.698152 0.6899384 4536 0.849114 1

0531902

0534504

0613402

0620401

0620604

0621603

0621902

0622902

0625002 1 2.17057 0.34 1375.98 5376 0 299.4 0.02726 24 6 37.0942 0.8177048 0.0272568 179.2 2.170574 2

0625501

0631701

0631903 1 0.79703 20 80940 116122 0 36.3 0.58875 24 6 284.5 17.662423 0.5887474 3870.7 0.797035 1

0632003 1 0.69594 3.4 13759.8 17236.8 0 5806.1 0.08739 24 6 117.302 2.6217659 0.0873922 574.56 0.69594 1

0632606 3 4.37714 22.9567 92905.6 731989 -1 5140.8 3.71125 24 6 304.804 60 2 24400 4.377137 4

0632608 1 0.6427 9.99 40429.5 46771.2 0 10.9 0.23713 24 6 201.071 7.1140315 0.2371344 1559 0.642699 1

0634001

0635301

0713618

0713705 2 4.40545 5 20235 160460 -1 2903.05 0.81355 24 6 142.25 24.406377 0.8135459 5348.7 4.40545 4

0715007

0715216

0716701

0720506

0720803 1 6.52385 3 12141 142571 -1 22.7 0.72285 24 6 110.186 21.685441 0.722848 4752.4 6.523851 7

0721305

0722107

0722503

0722505

0722705 1 1.58142 0.07 283.29 806.4 0 121 0.00409 24 6 16.8312 0.1226557 0.0040885 26.88 1.581418 2

0723607

0724206

0724301

0724804

0724909

0730407 1 1.88509 2.87 11614.9 39411.1 -1 272.2 0.19982 24 6 107.772 5.9945467 0.1998182 1313.7 1.885087 2

0730502

0730914 1 1.99259 2 8094 29030.4 0 206.8 0.14719 24 6 89.9667 4.4156057 0.1471869 967.68 1.992587 2

0731111

0731405

0731411

0731412

0731501

0731507

0731514 1 2.29914 65 263055 1088640 0 14515.2 2.39979 50.7 10 512.889 60 2 36288 2.299139 2

0731703 1 1.83142 0.34 1375.98 4536 0 90.7 0.023 24 6 37.0942 0.6899384 0.0229979 151.2 1.831422 2

0732110 1 4.30496 4.5 18211.5 141120 0 3774 0.71549 24 6 134.95 21.464705 0.7154902 4704 4.304963 4

0732405

0732510

(continued)
Appendix 3A WMU Data

Table 3A-1. (continued)

Landfill Industrial D Data Landfill Calculated FRAMES Inputs

IDNum

Total

LF

Depth (m) Average

surface

area

(acres)

Average

surface

area (m2) Average

capacity

(Mg)

Capacity

replaced?

Average

waste

quantity

(Mg)

Vehicles

per day

(1/d)

Vehicle

weight

(Mg)

Wheels

per

vehicle

Distance

vehicle

travels

on LF

(m)

Frequency

of surface

disturbance

per month

(1/mo)

Spreading/

compacting

operations

per day (1/d)

Waste

loading

rate

(dry,

Mg/yr)

Waste

zone

thickness

(m)

No.

waste

layers

in a

cell

SrcDepth SrcArea nv vw nw mt fd Nop load zW Nly

3-71

0733203

0733210 1 0.61858 3 12141 13518.3 -1 7257.6 0.06854 24 6 110.186 2.0561646 0.0685388 450.61 0.618577 1

0733302

0733404 1 0.71266 5.7 23067.9 29591.3 -1 0.005 0.15003 24 6 151.881 4.5009254 0.1500308 986.38 0.712663 1

0733501

0733606

0734604

0735309 3 1.38734 6.66667 26980 67374.7 0 1372.9 0.3416 24 6 164.256 10.247887 0.3415962 2245.8 1.387339 2

0826707

0830601 2 1.75846 10 40470 128097 0 13608 0.64946 24 6 201.172 19.483862 0.6494621 4269.9 1.758458 2

0830903 1 0.50922 45 182115 166925 0 5080.3 0.84632 24 6 426.749 25.389733 0.8463244 5564.2 0.509217 1

0831102

0831406 2 0.80395 36 145692 210833 0 25054.2 1.06894 24 6 381.696 32.06834 1.0689447 7027.8 0.803954 1

0831904 1 3.7361 40 161880 1088640 0 54432 2.39979 50.7 10 402.343 60 2 36288 3.736101 4

0832304 1 1.59118 293 1185771 3396194 0 504908 7.48653 50.7 10 1088.93 60 2 113206 1.591179 2

0832510

0832903

0832904

0832909

0833001 2 1.66049 180 728460 2177280 0 108864 4.79957 50.7 10 853.499 60 2 72576 1.660489 2

0833007 1 2.79912 50 202350 1019525 -1 3810.2 2.24743 50.7 10 449.833 60 2 33984 2.799125 3

0834009

0923004

0930205

0930301 1 1.5656 2.8 11331.6 31933.4 0 544.3 0.16191 24 6 106.45 4.8571602 0.1619053 1064.4 1.565602 2

0930702 4 0.67636 23.875 96622.1 117632 -1 6804 0.59641 24 6 310.841 17.892161 0.5964054 3921.1 0.676358 1

0932103 1 1.11851 4 16188 32591.5 -1 181.4 0.16524 24 6 127.232 4.9572546 0.1652418 1086.4 1.118506 2

0932507

0932509 2 7.16666 20.405 82579 1065269 -1 96163.2 2.34827 50.7 10 287.366 60 2 35509 7.166663 7

0932903 1 0.55195 75 303525 301553 0 12236 1.5289 24 6 550.931 45.867108 1.5289036 10052 0.551947 1

0933704

1010805

1012203 1 3.26705 0.5 2023.5 11899.6 -1 27.2 0.06033 24 6 44.9833 1.8099585 0.0603319 396.65 3.267049 3

1013209

1014805 1 7.5742 30 121410 1655250 -1 18.1 3.64882 50.7 10 348.439 60 2 55175 7.574199 8

1015510 1 1.18822 0.03 121.41 259.67 -1 9.1 0.00132 24 6 11.0186 0.0394966 0.0013166 8.6557 1.188216 2

1023705

1031503

1031507

1032715

1032802 1 3.34048 16 64752 389345 -1 3774 1.97401 24 6 254.464 59.2204 1.9740133 12978 3.340476 3

1033107

1033114

1033202

1033602

1034005

1034210

1034406

1034805

1035117

1035405

1035508

(continued)
Appendix 3A WMU Data

Table 3A-1. (continued)

Landfill Industrial D Data Landfill Calculated FRAMES Inputs

IDNum

Total

LF

Depth (m) Average

surface

area

(acres)

Average

surface

area (m2) Average

capacity

(Mg)

Capacity

replaced?

Average

waste

quantity

(Mg)

Vehicles

per day

(1/d)

Vehicle

weight

(Mg)

Wheels

per

vehicle

Distance

vehicle

travels

on LF

(m)

Frequency

of surface

disturbance

per month

(1/mo)

Spreading/

compacting

operations

per day (1/d)

Waste

loading

rate

(dry,

Mg/yr)

Waste

zone

thickness

(m)

No.

waste

layers

in a

cell

SrcDepth SrcArea nv vw nw mt fd Nop load zW Nly

3-72

1120904

1122705

1131103

1131802

1133902

1134405

1212301

1221704

1223404

1230111

1230206

1230517

1230919

1231101

1231705

1233101

1235205

1236637

1236652

1236732

1236810

1236820

1331103

1333001

1333701

1415407

1421506

1430107 2 2.17581 33.65 136182 533350 -1 2721.6 2.70413 24 6 369.028 60 2 17778 2.175811 2

1430404

1430602

1431515

1434022

1434802

1435317 1 1.22999 7.5 30352.5 67199.9 0 1633 0.34071 24 6 174.22 10.221294 0.3407098 2240 1.22999 2

1522504

1530605 1 5.09655 13 52611 482642 -1 2721.6 2.44704 24 6 229.371 60 2 16088 5.096549 5

1530808

1532401

1621808

1630106 1 0.75281 4.5 18211.5 24677.7 0 4644.9 0.12512 24 6 134.95 3.7535478 0.1251183 822.59 0.752812 1

1630401

1631701

1632106

1632703 1 3.18078 2 8094 46341.4 -1 4.5 0.23496 24 6 89.9667 7.0486603 0.2349553 1544.7 3.18078 3

1633404

1633405

1635404

1721603

(continued)
Appendix 3A WMU Data

Table 3A-1. (continued)

3-73

Waste Pile Industrial D Data Waste Pile Calculated FRAMES Inputs

IDNum

Total

WP

Average surface

area (acres)

Average

surface

area (m2)

Average

waste

quantity

(Mg)

Waste

quantity

replaced?

Height of

waste pile

above grade

(m)

Distance

vehicle

travels on

WP (m)

Vehicles

per day

(1/d)

Vehicle

weight (Mg)

Wheels per

vehicle

Spreading/

compacting

operations per

day

Waste loading

rate (dry,

Mg/yr)

SrcArea zZ1WMU mt nv vw nw Nop load

0114001 2 0.025 101.175 2968.068 yes 4 10.0586 0.45145 33 6 0.4514515 2968.07

0130207 1 0.11 445.17 11249.3 4 21.0991 1.71105 33 6 1.7110503 11249.3

0131104

0131207

0131508 1 0.08 323.76 4536 2 17.9933 0.68994 33 6 0.6899384 4536

0136703

0220102 1 1.2 4856.4 2449.4 1 69.6879 0.37256 33 6 0.3725607 2449.4

0221207 1 0.005 20.235 453.6 4 4.49833 0.06899 33 6 0.0689938 453.6

0223504 1 6.5 26305.5 153240.2 yes 1 162.19 10.134 71.4 10 2 153240

0224002 1 0.04 161.88 128.4 1 12.7232 0.01953 33 6 0.01953 128.4

0231002

0231106

0231407

0231610

0231911 3 5.33333 21584 60480 1 146.915 3.99964 71.4 10 2 60480

0231914

0232305 1 0.52 2104.44 2720.692 yes 1 45.8742 0.41382 33 6 0.4138249 2720.69

0232313 1 1.55 6272.85 34083.02 yes 1 79.2013 2.25397 71.4 10 2 34083

0232402

0232415 1 0.06 242.82 81648 10 15.5827 5.39952 71.4 10 2 81648

0232501 1 0.16 647.52 1576.505 yes 1 25.4464 0.23979 33 6 0.2397909 1576.51

0232705

0233601

0233603

0234904

0235301

0312301

0314202

0321802 1 1.38 5584.86 30437.59 yes 1 74.7319 2.01289 71.4 10 2 30437.6

0331006

0331902 1 0.45 1821.15 2269.775 yes 1 42.6749 0.34524 33 6 0.3452392 2269.78

0332104 3 0.23 930.81 316.1739 yes 1 30.5092 0.04809 33 6 0.0480909 316.174

0332707 2 75 303525 158760 1 550.931 10.4991 71.4 10 2 158760

0332811 1 0.005 20.235 107 1 4.49833 0.01628 33 6 0.016275 107

0430108 1 0.21 849.87 659.6076 yes 1 29.1525 0.10033 33 6 0.1003282 659.608

0430412

0431912

0432011

0432106

0432716

0433201

0433204

0433404

0433408

0434505

0434804

0435510

0436007

0436108

0530901

0531301

0531502

0531702

0531902

0534504

0613402

0620401

0620604

0621603

0621902

0622902

0625002

(continued)
Appendix 3A WMU Data

Table 3A-1. (continued)

Waste Pile Industrial D Data Waste Pile Calculated FRAMES Inputs

IDNum

Total

WP

Average surface

area (acres)

Average

surface

area (m2)

Average

waste

quantity

(Mg)

Waste

quantity

replaced?

Height of

waste pile

above grade

(m)

Distance

vehicle

travels on

WP (m)

Vehicles

per day

(1/d)

Vehicle

weight (Mg)

Wheels per

vehicle

Spreading/

compacting

operations per

day

Waste loading

rate (dry,

Mg/yr)

SrcArea zZ1WMU mt nv vw nw Nop load

3-74

0625501

0631701

0631903

0632003

0632606

0632608

0634001

0635301

0713618

0713705

0715007 1 0.5 2023.5 1590.265 yes 1 44.9833 0.24188 33 6 0.2418837 1590.26

0715216 1 0.005 20.235 562.8561 yes 4 4.49833 0.08561 33 6 0.085612 562.856

0716701

0720506 1 0.1 404.7 1546.653 yes 1 20.1172 0.23525 33 6 0.2352503 1546.65

0720803

0721305 1 0.17 687.99 4536 1 26.2296 0.68994 33 6 0.6899384 4536

0722107 1 0.06 242.82 3692.677 yes 2 15.5827 0.56167 33 6 0.5616666 3692.68

0722503

0722505

0722705

0723607

0724206 1 2 8094 2721.6 1 89.9667 0.41396 33 6 0.413963 2721.6

0724301 1 0.01 40.47 1088.6 4 6.3616 0.16558 33 6 0.1655791 1088.6

0724804 1 0.005 20.235 30.22716 yes 1 4.49833 0.0046 33 6 0.0045976 30.2272

0724909 1 0.02 80.94 571.8954 yes 1 8.99667 0.08699 33 6 0.0869869 571.895

0730407

0730502 2 0.61 2468.67 33763.27 yes 2 49.6857 2.23282 71.4 10 2 33763.3

0730914 6 0.01333 53.96 756 2 7.34575 0.11499 33 6 0.1149897 756

0731111

0731405

0731411 1 0.01 40.47 499 2 6.3616 0.0759 33 6 0.0758993 499

0731412 1 3 12141 13644.38 yes 1 110.186 2.07535 33 6 2 13644.4

0731501

0731507 20 0.12 485.64 1723.68 1 22.0372 0.26218 33 6 0.2621766 1723.68

0731514

0731703

0732110

0732405 1 0.01 40.47 1134 4 6.3616 0.17248 33 6 0.1724846 1134

0732510 1 0.37 1497.39 2721.6 1 38.6961 0.41396 33 6 0.413963 2721.6

0733203

0733210

0733302 1 0.09 364.23 124.1385 yes 1 19.0848 0.01888 33 6 0.0188818 124.138

0733404 1 0.09 364.23 136.1151 yes 1 19.0848 0.0207 33 6 0.0207035 136.115

0733501

0733606 1 50 202350 174800 yes 1 449.833 11.5598 71.4 10 2 174800

0734604 2 0.0025 10.1175 18.15 1 3.1808 0.00276 33 6 0.0027607 18.15

0735309

0826707

0830601

0830903

0831102 1 4 16188 27659.75 yes 1 127.232 4.20713 33 6 2 27659.7

0831406

0831904

0832304 1 0.12 485.64 81179.9 10 22.0372 5.36856 71.4 10 2 81179.9

0832510 1 0.01 40.47 1360.8 4 6.3616 0.20698 33 6 0.2069815 1360.8

0832903 3 16.6667 67450 492625.3 yes 1 259.711 32.5781 71.4 10 2 492625

0832904

0832909

0833001

0833007

0834009

(continued)
Appendix 3A WMU Data

Table 3A-1. (continued)

Waste Pile Industrial D Data Waste Pile Calculated FRAMES Inputs

IDNum

Total

WP

Average surface

area (acres)

Average

surface

area (m2)

Average

waste

quantity

(Mg)

Waste

quantity

replaced?

Height of

waste pile

above grade

(m)

Distance

vehicle

travels on

WP (m)

Vehicles

per day

(1/d)

Vehicle

weight (Mg)

Wheels per

vehicle

Spreading/

compacting

operations per

day

Waste loading

rate (dry,

Mg/yr)

SrcArea zZ1WMU mt nv vw nw Nop load

3-75

0923004 1 0.005 20.235 77.69577 yes 1 4.49833 0.01182 33 6 0.0118177 77.6958

0930205 1 0.2 809.4 429.3907 yes 1 28.45 0.06531 33 6 0.0653115 429.391

0930301 1 0.25 1011.75 3175.2 1 31.808 0.48296 33 6 0.4829569 3175.2

0930702

0932103 1 2.07 8377.29 14453.58 yes 1 91.5275 2.19843 33 6 2 14453.6

0932507 1 0.25 1011.75 396.287 yes 1 31.808 0.06028 33 6 0.0602764 396.287

0932509 1 0.57 2306.79 9072 1 48.0291 1.37988 33 6 1.3798768 9072

0932903

0933704

1010805

1012203 1 0.5 2023.5 4481.975 yes 1 44.9833 0.68172 33 6 0.681721 4481.97

1013209

1014805

1015510

1023705

1031503

1031507

1032715

1032802

1033107

1033114

1033202

1033602

1034005 1 0.4 1618.8 1763.243 yes 1 40.2343 0.26819 33 6 0.2681943 1763.24

1034210

1034406

1034805

1035117

1035405

1035508

1120904

1122705

1131103

1131802

1133902

1134405

1212301

1221704 1 0.18 728.46 3216.37 yes 1 26.99 0.48922 33 6 0.489219 3216.37

1223404

1230111

1230206

1230517

1230919 1 0.04 161.88 2348.422 yes 2 12.7232 0.3572 33 6 0.3572017 2348.42

1231101

1231705

1233101

1235205 1 0.23 930.81 907.2 1 30.5092 0.13799 33 6 0.1379877 907.2

1236637

1236652

1236732 1 0.005 20.235 49.9 1 4.49833 0.00759 33 6 0.0075899 49.9

1236810 3 0.00167 6.745 7.566667 1 2.59711 0.00115 33 6 0.0011509 7.56667

1236820

1331103

1333001

1333701 4 0.0125 50.5875 54.82949 yes 1 7.11249 0.00834 33 6 0.0083397 54.8295

1415407 1 0.005 20.235 1048.087 yes 6 4.49833 0.15942 33 6 0.159417 1048.09

1421506 2 0.115 465.405 276.7 1 21.5732 0.04209 33 6 0.0420869 276.7

1430107

1430404

1430602

1431515

1434022

(continued)
Appendix 3A WMU Data

Table 3A-1. (continued)

Waste Pile Industrial D Data Waste Pile Calculated FRAMES Inputs

IDNum

Total

WP

Average surface

area (acres)

Average

surface

area (m2)

Average

waste

quantity

(Mg)

Waste

quantity

replaced?

Height of

waste pile

above grade

(m)

Distance

vehicle

travels on

WP (m)

Vehicles

per day

(1/d)

Vehicle

weight (Mg)

Wheels per

vehicle

Spreading/

compacting

operations per

day

Waste loading

rate (dry,

Mg/yr)

SrcArea zZ1WMU mt nv vw nw Nop load

3-76

1434802

1435317

1522504

1530605

1530808

1532401 2 0.0025 10.1175 119.3817 yes 2 3.1808 0.01816 33 6 0.0181583 119.382

1621808

1630106 1 0.02 80.94 51 1 8.99667 0.00776 33 6 0.0077572 51

1630401 1 0.07 283.29 2038.266 yes 1 16.8312 0.31003 33 6 0.3100261 2038.27

1631701

1632106

1632703

1633404

1633405

1635404

1721603

(continued)
Appendix 3A WMU Data

Table 3A-1. (continued)

3-77

Land Application Unit Industrial D Data Land Application Unit Calculated FRAMES Inputs

IDNum

Total

LAU

Average

surface area

(acres)

Average

surface

area (m2)

Average

waste

quantity

(Mg)

Waste

quantity

replaced?

Wet waste

application

rate

(Mg/m2-yr)

Vehicles

per day

(1/d)

Vehicle

weight

(Mg)

Wheels

per

vehicle

Waste

applications

per Year

(1/yr)

Distance

vehicle

travels on

LAU (m)

Frequency of

surface

disturbance

per month

(1/mo)

Number of

cultivations

per

application

SrcArea Rappl nv vw nw Nappl mt fd fcult

0114001

0130207

0131104

0131207

0131508

0136703 2 67.5 273172.5 13208.7 0.0483528 3.01361 21 6 88.057667 522.6591 14.676278 2

0220102

0221207

0223504 2 5 20235 217.75 0.0107611 0.04968 21 6 14.516667 142.2498 2.4194444 2

0224002

0231002

0231106

0231407

0231610

0231911

0231914

0232305

0232313

0232402

0232415

0232501

0232705

0233601

0233603

0234904

0235301

0312301 2 10 40470 37.75 0.0009328 0.00861 21 6 2.5166667 201.1716 0.2097222 1

0314202

0321802

0331006

0331902

0332104 1 100 404700 509477 1.2588992 50.5388 43.8 10 33.9651 636.1604 11.3217 4

0332707

0332811

0430108

0430412

0431912

0432011

0432106

0432716

0433201

0433204

0433404

0433408

0434505

0434804

0435510 1 6.5 26305.5 1.4 5.322E-05 0.00032 21 6 1 162.1897 0.0833333 1

0436007

0436108

0530901 1 5 20235 4126 0.2039041 0.94136 21 6 27.506667 142.2498 6.8766667 3

0531301

0531502

0531702

0531902

0534504

0613402

0620401

0620604

0621603

0621902

0622902

0625002

(continued)
Appendix 3A WMU Data

Table 3A-1. (continued)

Land Application Unit Industrial D Data Land Application Unit Calculated FRAMES Inputs

IDNum

Total

LAU

Average

surface area

(acres)

Average

surface

area (m2)

Average

waste

quantity

(Mg)

Waste

quantity

replaced?

Wet waste

application

rate

(Mg/m2-yr)

Vehicles

per day

(1/d)

Vehicle

weight

(Mg)

Wheels

per

vehicle

Waste

applications

per Year

(1/yr)

Distance

vehicle

travels on

LAU (m)

Frequency of

surface

disturbance

per month

(1/mo)

Number of

cultivations

per

application

SrcArea Rappl nv vw nw Nappl mt fd fcult

3-78

0625501 1 33 133551 7547.8 0.0565162 1.72206 21 6 50.318667 365.4463 8.3864444 2

0631701 1 1 4047 39.6 0.009785 0.00903 21 6 2.64 63.61604 0.22 1

0631903

0632003

0632606

0632608

0634001

0635301

0713618

0713705

0715007

0715216

0716701

0720506

0720803 3 1.33333 5396 3.03333 0.0005621 0.00069 21 6 1 73.45747 0.0833333 1

0721305

0722107

0722503

0722505

0722705

0723607

0724206

0724301

0724804

0724909

0730407

0730502

0730914

0731111

0731405

0731411

0731412

0731501

0731507

0731514

0731703

0732110

0732405

0732510

0733203

0733210

0733302

0733404

0733501

0733606

0734604

0735309

0826707

0830601

0830903

0831102

0831406

0831904 1 300 1214100 754780 0.6216786 74.8723 43.8 10 50.318667 1101.862 12.579667 3

0832304

0832510

0832903

0832904

0832909

0833001

0833007

0834009

0923004

(continued)
Appendix 3A WMU Data

Table 3A-1. (continued)

Land Application Unit Industrial D Data Land Application Unit Calculated FRAMES Inputs

IDNum

Total

LAU

Average

surface area

(acres)

Average

surface

area (m2)

Average

waste

quantity

(Mg)

Waste

quantity

replaced?

Wet waste

application

rate

(Mg/m2-yr)

Vehicles

per day

(1/d)

Vehicle

weight

(Mg)

Wheels

per

vehicle

Waste

applications

per Year

(1/yr)

Distance

vehicle

travels on

LAU (m)

Frequency of

surface

disturbance

per month

(1/mo)

Number of

cultivations

per

application

SrcArea Rappl nv vw nw Nappl mt fd fcult

3-79

0930205

0930301

0930702

0932103

0932507

0932509

0932903

0933704

1010805 1 0.5 2023.5 192.5 0.0951322 0.04392 21 6 12.833333 44.98333 2.1388889 2

1012203

1013209

1014805

1015510

1023705

1031503 2 31.5 127480.5 1235.95 0.0096952 0.28199 21 6 82.396667 357.0441 6.8663889 1

1031507

1032715

1032802 1 60 242820 207565 0.8548081 20.5899 43.8 10 13.837633 492.7677 3.4594083 3

1033107

1033114

1033202 3 5 20235 505.6 0.0249864 0.11535 21 6 33.706667 142.2498 5.6177778 2

1033602 1 230 930810 2903 0.0031188 0.66233 21 6 19.353333 964.785 1.6127778 1

1034005 1 13 52611 62269.3 1.1835795 6.17696 43.8 10 41.512867 229.3709 13.837622 4

1034210 1 40 161880 170 0.0010502 0.03879 21 6 11.333333 402.3431 0.9444444 1

1034406

1034805

1035117

1035405

1035508 1 58.68 237478 108236 0.4557707 10.7367 43.8 10 72.157 487.3171 18.03925 3

1120904

1122705

1131103

1131802 1 5 20235 754.8 0.0373017 0.17221 21 6 50.32 142.2498 8.3866667 2

1133902 1 4 16188 36.6 0.0022609 0.00835 21 6 2.44 127.2321 0.2033333 1

1134405 8 25 101175 61.0125 0.000603 0.01392 21 6 4.0675 318.0802 0.3389583 1

1212301

1221704

1223404

1230111

1230206

1230517

1230919

1231101 1 5 20235 439.9 0.0217396 0.10037 21 6 29.326667 142.2498 4.8877778 2

1231705 10 14 56658 6415.63 0.1132343 1.46375 21 6 42.770867 238.0294 10.692717 3

1233101

1235205

1236637

1236652

1236732

1236810

1236820

1331103

1333001 1 115 465405 17895.8 0.0384521 4.083 21 6 11.930533 682.206 1.9884222 2

1333701

1415407

1421506

1430107

1430404

1430602

1431515

1434022

1434802

(continued)
Appendix 3A WMU Data

Table 3A-1. (continued)

Land Application Unit Industrial D Data Land Application Unit Calculated FRAMES Inputs

IDNum

Total

LAU

Average

surface area

(acres)

Average

surface

area (m2)

Average

waste

quantity

(Mg)

Waste

quantity

replaced?

Wet waste

application

rate

(Mg/m2-yr)

Vehicles

per day

(1/d)

Vehicle

weight

(Mg)

Wheels

per

vehicle

Waste

applications

per Year

(1/yr)

Distance

vehicle

travels on

LAU (m)

Frequency of

surface

disturbance

per month

(1/mo)

Number of

cultivations

per

application

SrcArea Rappl nv vw nw Nappl mt fd fcult

3-80

1435317

1522504 1 0.02 80.94 6.89828 yes 0.0852271 0.00157 21 6 1 8.996666 0.1666667 2

1530605

1530808

1532401

1621808 1 30 121410 86799.7 0.7149304 8.61031 43.8 10 57.866467 348.4394 14.466617 3

1630106

1630401

1631701 1 114 461358 5903.9 0.0127968 1.347 21 6 39.359333 679.2334 6.5598889 2

1632106 1 27 109269 31134.7 0.2849363 3.08848 43.8 10 20.756467 330.5586 5.1891167 3

1632703

1633404

1633405

1635404

1721603

(continued)
Appendix 3A WMU Data

Table 3A-1. (continued)

3-81

Surface Impoundment Industrial D Data Surface Impoundment Calculated FRAMES Inputs

IDNum

Total

SI Depth (m)

Average

surface

area

(acres)

Average

surface

area (m2)

Average

capacity

(Mg)

Capacity

replaced?

Average

waste

quantity

(Mg)

Waste

quantity

replaced?

Fraction of

SI occupied

by

sediments

Depth of

SI (m)

Number of

impellers/

aerators

Total

power to

impellers

(HP)

Fraction

surface area

turbulent

Volumetric

influent flow

rate (m3/s)

SrcDepth SrcArea d_setpt d_wmu n_imp Powr F_aer Q_wmu

0114001

0130207

0131104 6 4.303853 0.02167 87.685 377.3833 0 138376 0 0.72118 4.3039 1 3.87273 0.23055 0.004385

0131207 3 1.261004 0.22333 903.83 1139.733 0 91831.6 0 0.2 1.261 1 101.11 0.58395 0.00291

0131508 11 11.15607 2.54545 10301.45 114923.8 0 77193.4 0 0.76 11.156 4 441.679 0.22381 0.002446

0136703 1 2.027213 1.38 5584.86 11321.7 0 37739 0 0.408054 2.0272 5 508.532 0.47531 0.001196

0220102

0221207

0223504

0224002

0231002

0231106

0231407

0231610 1 1.046454 0.02 80.94 84.7 0 1067.3 0 0.2 1.0465 1 10.4212 0.67208 3.38E-05

0231911

0231914 4 2.494109 0.895 3622.065 9033.825 0 1.3E+07 0 0.518866 2.4941 4 427.011 0.61539 0.411083

0232305

0232313 1 3.60514 0.13 526.11 1896.7 0 10119.1 0 0.667142 3.6051 1 35.8389 0.35559 0.000321

0232402 8 4.627534 0.665 2691.255 12453.88 0 688737 0 0.740683 4.6275 3 304.909 0.59141 0.021825

0232415 2 2.55745 0.015 60.705 155.25 0 14061.6 0 0.530783 2.5574 1 3.22078 0.27695 0.000446

0232501

0232705 2 1.219447 6.5 26305.5 32078.15 0 6302413 0 0.2 1.2194 31 2914.93 0.57843 0.199711

0233601 1 1.882876 0.01 40.47 76.2 0 76.2 0 0.362677 1.8829 1 4.68005 0.60365 2.41E-06

0233603

0234904

0235301

0312301 1 0.597762 0.03 121.41 72.57429 -1 75.5 0 0.2 0.5978 1 17.239 0.74119 2.39E-06

0314202

0321802 4 3.743168 0.25 1011.75 3787.15 0 120204 0 0.679416 3.7432 2 70.1915 0.36214 0.003809

0331006 3 3.252969 1.14667 4640.56 15095.6 0 21385.4 0 0.631106 3.253 4 359.353 0.40422 0.000678

0331902 6 1.154902 1.24667 5045.26 5826.783 0 83654.8 0 0.2 1.1549 6 417.782 0.43225 0.002651

0332104 3 0.951547 1.79667 7271.11 6918.8 0 471738 0 0.2 0.9515 9 775.544 0.55677 0.014948

0332707

0332811

0430108 1 2.092732 0.14 566.58 1185.7 0 43203.6 0 0.426587 2.0927 1 38.936 0.35872 0.001369

0430412 2 2.70295 0.345 1396.215 3773.9 0 943.45 0 0.556041 2.703 1 137.468 0.51395 2.99E-05

0431912 1 5.98075 400 1618800 9681638 0 463579 0 0.76 5.9808 54 5000 0.01612 0.01469

0432011

0432106 4 2.331295 10 40470 94347.5 0 9072000 0 0.485265 2.3313 33 2279.27 0.29399 0.287474

0432716 6 4.557928 100 404700 1844594 0 43545.6 0 0.736723 4.5579 67 5000 0.06449 0.00138

0433201 6 2.682873 58 234726 629740 0 3673.27 0 0.552718 2.6829 65 5000 0.11119 0.000116

0433204 7 6.956878 57.3429 232066.5 1614459 0 88586.4 0 0.76 6.9569 62 5000 0.11247 0.002807

0433404 9 5.469069 18.4556 74689.63 408482.7 0 3944774 0 0.76 5.4691 81 5000 0.34945 0.125002

0433408 6 11.2739 87.5333 354247.4 3993750 0 35252.2 0 0.76 11.274 60 5000 0.07368 0.001117

0434505 1 1.830849 0.9 3642.3 6668.5 0 167 0 0.344566 1.8308 5 545.721 0.78211 5.29E-06

0434804 1 3.562519 145 586815 2090540 0 42094.1 0 0.66316 3.5625 64 5000 0.04448 0.001334

0435510 2 6.684962 25 101175 676351 0 1887327 0 0.76 6.685 67 5000 0.25797 0.059806

0436007 2 0.740663 48 194256 143878.3 0 18098.7 0 0.2 0.7407 53 5000 0.13436 0.000574

0436108 1 7.514657 176 712272 5352480 0 2630880 0 0.76 7.5147 53 5000 0.03664 0.083368

0530901 3 1.877477 0.45 1821.15 3419.167 0 926.1 0 0.360844 1.8775 2 198.396 0.56867 2.93E-05

0531301 12 1.853496 0.63667 2576.59 4775.7 0 1016.43 0 0.352575 1.8535 2 213.685 0.43291 3.22E-05

0531502 2 0.559427 0.025 101.175 56.6 0 33965.1 0 0.2 0.5594 1 5.26862 0.27183 0.001076

0531702

0531902 7 2.57809 0.02429 98.28429 253.3857 0 37739 0 0.534539 2.5781 1 12.6577 0.67227 0.001196

0534504 1 0.62186 0.06 242.82 151 0 176.589 -1 0.2 0.6219 1 13.2526 0.2849 5.6E-06

0613402 2 9.538046 0.12 485.64 4632.057 -1 37.75 0 0.76 9.538 1 29.9191 0.32159 1.2E-06

0620401 1 0.932518 1 4047 3773.9 0 20379.1 0 0.2 0.9325 4 365.531 0.47148 0.000646

0620604 1 1.16262 300 1214100 1411537 0 49060.7 0 0.2 1.1626 56 5000 0.0215 0.001555

0621603 1 8.048887 0.39 1578.33 12703.8 0 237001 0 0.76 8.0489 1 126.099 0.41705 0.00751

0621902 2 0.420322 0.48 1942.56 816.5 0 299.4 0 0.2 0.4203 1 144.708 0.38886 9.49E-06

(continued)
Appendix 3A WMU Data

Table 3A-1. (continued)

Surface Impoundment Industrial D Data Surface Impoundment Calculated FRAMES Inputs

IDNum

Total

SI Depth (m)

Average

surface

area

(acres)

Average

surface

area (m2)

Average

capacity

(Mg)

Capacity

replaced?

Average

waste

quantity

(Mg)

Waste

quantity

replaced?

Fraction of

SI occupied

by

sediments

Depth of

SI (m)

Number of

impellers/

aerators

Total

power to

impellers

(HP)

Fraction

surface area

turbulent

Volumetric

influent flow

rate (m3/s)

SrcDepth SrcArea d_setpt d_wmu n_imp Powr F_aer Q_wmu

3-82

0622902 1 1.081173 0.69 2792.43 3019.1 0 754.8 0 0.2 1.0812 5 394.738 0.7379 2.39E-05

0625002 2 5.853484 0.23 930.81 5448.481 -1 145.15 0 0.76 5.8535 1 83.7068 0.46943 4.6E-06

0625501

0631701

0631903 7 0.546951 71.4286 289071.4 158108 0 571476 0 0.2 0.547 63 5000 0.09029 0.018109

0632003 5 6.873601 6.954 28142.84 193442.6 0 101736 0 0.76 6.8736 14 1265.59 0.23475 0.003224

0632606 5 1.215414 36.732 148654.4 180676.7 0 471738 0 0.2 1.2154 64 5000 0.17558 0.014948

0632608 5 1.972041 0.424 1715.928 3383.88 0 92.9 0 0.391493 1.972 1 144.83 0.44058 2.94E-06

0634001 1 0.480356 14 56658 27216 0 9072 0 0.2 0.4804 81 5000 0.46066 0.000287

0635301 1 1.656721 0.8 3237.6 5363.8 0 6.8 0 0.275678 1.6567 3 340.828 0.54952 2.15E-07

0713618 2 7.206173 0.07 283.29 2041.437 -1 1500.15 0 0.76 7.2062 1 34.1251 0.6288 4.75E-05

0713705 1 0.911589 2 8094 7378.4 0 63945.9 0 0.2 0.9116 13 1155.59 0.74527 0.002026

0715007

0715216

0716701 1 3.160548 5.96 24120.12 76232.8 0 381.2 0 0.620319 3.1605 41 3149.01 0.6815 1.21E-05

0720506

0720803 1 3.264146 0.02 80.94 264.2 0 15095.6 0 0.632369 3.2641 1 12.042 0.77661 0.000478

0721305

0722107

0722503 1 2.118853 0.02 80.94 171.5 0 76.2 0 0.433656 2.1189 1 4.11365 0.2653 2.41E-06

0722505 1 0.739139 2.3 9308.1 6879.977 -1 152.5 0 0.2 0.7391 10 687.077 0.38531 4.83E-06

0722705

0723607 1 1.092826 0.3 1214.1 1326.8 0 3458.3 -1 0.2 1.0928 1 94.3365 0.4056 0.00011

0724206 2 2.802076 4 16188 45360 0 4536 0 0.571746 2.8021 13 1286.58 0.41487 0.000144

0724301

0724804

0724909

0730407 2 6.170117 0.14 566.58 3495.865 -1 2.25 0 0.76 6.1701 1 44.8629 0.41333 7.13E-08

0730502 2 0.443036 0.38 1537.86 681.3278 -1 1.3 0 0.2 0.443 2 191.758 0.65089 4.12E-08

0730914 2 0.784861 0.3 1214.1 952.9 0 57.15 0 0.2 0.7849 2 54.1861 0.23297 1.81E-06

0731111 2 1.998306 0.07 283.29 566.1 0 200017 0 0.399491 1.9983 1 25.6057 0.47182 0.006338

0731405 1 1.657799 0.45 1821.15 3019.1 0 27.2 0 0.276149 1.6578 1 105.074 0.30118 8.62E-07

0731411

0731412 1 1.412611 45.45 183936.2 259830.3 -1 22680 0 0.2 1.4126 76 5000 0.1419 0.000719

0731501 4 1.724851 0.015 60.705 104.7071 -1 13.2 0 0.304288 1.7249 1 3.92049 0.33712 4.18E-07

0731507 2 2.406243 11.82 47835.54 115104 0 1041.6 0 0.501297 2.4062 51 4877.82 0.53229 3.3E-05

0731514 7 2.453995 8.14286 32954.14 80869.29 0 754780 0 0.511001 2.454 52 3288.39 0.52089 0.023918

0731703

0732110

0732405

0732510

0733203 4 1.523818 0.2575 1042.103 1587.975 0 125 0 0.212505 1.5238 1 117.215 0.58714 3.96E-06

0733210 1 0.494234 18 72846 36003 0 1000.1 0 0.2 0.4942 50 4376.02 0.31358 3.17E-05

0733302 3 0.724101 0.07667 310.27 224.6667 0 259.2 0 0.2 0.7241 1 12.6393 0.21264 8.21E-06

0733404

0733501 1 4.758418 0.07 283.29 1348.012 -1 9.1 0 0.747815 4.7584 1 34.6801 0.63903 2.88E-07

0733606 1 12.22724 11 44517 544320 0 22680 0 0.76 12.227 58 5000 0.58629 0.000719

0734604

0735309 3 3.991179 0.33333 1349 5384.1 0 125797 0 0.699337 3.9912 2 151.078 0.5846 0.003986

0826707 1 0.646421 0.01 40.47 26.16064 -1 2.7 0 0.2 0.6464 1 2.71906 0.35072 8.56E-08

0830601

0830903 4 3.759886 14.075 56961.53 214168.8 0 1390682 0 0.680841 3.7599 43 3142.22 0.28796 0.044068

0831102

0831406 1 2.130921 8 32376 68990.7 0 5717.5 0 0.436863 2.1309 29 2034.52 0.32803 0.000181

0831904 10 1.16658 80 323760 377691.9 0 1132170 0 0.2 1.1666 52 5000 0.08062 0.035876

0832304

0832510 3 2.623233 1.83667 7432.99 19498.47 0 500042 0 0.542549 2.6232 4 312.778 0.21966 0.015845

0832903 3 2.767227 40.3333 163229 451691.8 0 13608 0 0.566353 2.7672 55 5000 0.1599 0.000431

0832904 2 2.534016 0.92 3723.24 9434.75 0 94.35 0 0.526443 2.534 3 256.058 0.35899 2.99E-06

0832909 2 1.39064 0.745 3015.015 4192.8 0 4139.1 0 0.2 1.3906 4 369.892 0.64041 0.000131

0833001

(continued)
Appendix 3A WMU Data

Table 3A-1. (continued)

Surface Impoundment Industrial D Data Surface Impoundment Calculated FRAMES Inputs

IDNum

Total

SI Depth (m)

Average

surface

area

(acres)

Average

surface

area (m2)

Average

capacity

(Mg)

Capacity

replaced?

Average

waste

quantity

(Mg)

Waste

quantity

replaced?

Fraction of

SI occupied

by

sediments

Depth of

SI (m)

Number of

impellers/

aerators

Total

power to

impellers

(HP)

Fraction

surface area

turbulent

Volumetric

influent flow

rate (m3/s)

SrcDepth SrcArea d_setpt d_wmu n_imp Powr F_aer Q_wmu

3-83

0833007 1 1.714187 2.72 11007.84 18869.5 0 1320.9 0 0.29996 1.7142 8 615.524 0.29189 4.19E-05

0834009 1 6.391418 2.01 8134.47 51990.8 0 2103.3 0 0.76 6.3914 12 1000.26 0.64188 6.66E-05

0923004

0930205 1 3.2638 1 4047 13208.6 0 113217 0 0.63233 3.2638 3 289.846 0.37386 0.003588

0930301 1 1.882995 10 40470 76204.8 0 3810.2 0 0.362717 1.883 83 5000 0.64492 0.000121

0930702 9 5.053792 45.5556 184363.3 931734 0 88704 0 0.76 5.0538 73 5000 0.14157 0.002811

0932103 3 4.310886 2.16667 8768.5 37800 0 302400 0 0.721635 4.3109 5 515.144 0.30667 0.009582

0932507 4 1.659123 0.425 1719.975 2853.65 0 44.35 0 0.276726 1.6591 4 260.108 0.78941 1.41E-06

0932509 4 3.343123 3.2775 13264.04 44343.33 0 1487.68 0 0.641054 3.3431 9 588.811 0.23172 4.71E-05

0932903 7 1.120923 110.714 448060.7 502241.4 -1 629615 -1 0.2 1.1209 55 5000 0.05825 0.019951

0933704 1 1.520396 0.92 3723.24 5660.8 0 18.9 0 0.210732 1.5204 8 535.962 0.75142 5.99E-07

1010805

1012203 8 1.787326 1.5 6070.5 10849.96 0 14152.1 0 0.328606 1.7873 3 290.887 0.25013 0.000448

1013209 2 0.498137 0.25 1011.75 503.9906 -1 264.15 0 0.2 0.4981 1 129.535 0.66832 8.37E-06

1014805

1015510

1023705 1 2.210711 4.64 18778.08 41512.9 0 294364 0 0.457188 2.2107 14 1383.05 0.38447 0.009328

1031503

1031507 9 3.038619 17.7778 71946.67 218618.5 0 1677.29 0 0.605084 3.0386 64 5000 0.36277 5.32E-05

1032715 3 1.272039 7.33333 29678 37751.57 0 314492 0 0.2 1.272 24 1977.38 0.3478 0.009966

1032802 1 1.311353 16 64752 84912.7 0 180015 0 0.2 1.3114 58 5000 0.40308 0.005704

1033107 2 0.466259 1 4047 1886.95 0 60005 0 0.2 0.4663 2 186.272 0.24026 0.001901

1033114 1 2.260649 3.3 13355.1 30191.2 0 1037823 0 0.469179 2.2606 14 1461.86 0.57138 0.032887

1033202

1033602 6 2.306754 1.58333 6407.75 14781.1 0 94347.5 0 0.479788 2.3068 5 515.703 0.42011 0.00299

1034005

1034210 5 1.279623 1.8 7284.6 9321.54 0 52834.6 0 0.2 1.2796 8 848.348 0.60791 0.001674

1034406 2 1.51931 7.5 30352.5 46114.85 0 131143 0 0.210168 1.5193 32 3066.09 0.5273 0.004156

1034805 1 5.616281 0.44 1780.68 10000.8 0 200017 0 0.76 5.6163 3 242.582 0.71112 0.006338

1035117 1 1.748456 0.4 1618.8 2830.4 0 291345 0 0.31368 1.7485 1 118.041 0.38064 0.009232

1035405 3 2.206769 0.21 849.87 1875.467 0 37739 0 0.456219 2.2068 1 119.019 0.73103 0.001196

1035508 4 4.554747 1.8375 7436.363 33870.75 0 38493.8 0 0.736539 4.5547 3 385.108 0.27033 0.00122

1120904 1 1.299677 5.74 23229.78 30191.2 0 109443 0 0.2 1.2997 37 2953.88 0.66377 0.003468

1122705 2 16.43275 0.03 121.41 1995.1 0 14520.1 0 0.76 16.433 1 11.907 0.51194 0.00046

1131103 1 1.523818 1.03 4168.41 6351.9 0 721.2 0 0.212505 1.5238 7 585.746 0.73352 2.29E-05

1131802

1133902

1134405

1212301 2 5.84781 0.575 2327.025 13608 0 13608 0 0.76 5.8478 2 254.33 0.57051 0.000431

1221704 2 1.551355 0.03 121.41 188.35 0 1008.28 -1 0.226483 1.5514 1 9.12361 0.39227 3.2E-05

1223404 6 0.485294 0.04833 195.605 94.92595 -1 0.03333 0 0.2 0.4853 1 13.0165 0.34737 1.06E-09

1230111 3 2.026844 1.37667 5571.37 11292.3 0 185976 0 0.407947 2.0268 8 556.003 0.52094 0.005893

1230206 4 0.987369 1.0625 4299.938 4245.625 0 707606 0 0.2 0.9874 7 591.178 0.71767 0.022423

1230517 12 1.759468 0.57417 2323.653 4088.392 0 43242.6 0 0.317976 1.7595 1 113.887 0.25584 0.00137

1230919

1231101 2 1.165641 1 4047 4717.35 0 349086 0 0.2 1.1656 6 508.467 0.65584 0.011062

1231705 4 3.861116 1.9925 8063.648 31134.68 0 468907 0 0.689209 3.8611 6 665.508 0.43082 0.014859

1233101 1 3.067509 0.38 1537.86 4717.4 0 26.8 0 0.608803 3.0675 2 205.226 0.6966 8.49E-07

1235205 1 2.497794 0.28 1133.16 2830.4 0 15095.6 0 0.519576 2.4978 1 78.2748 0.36058 0.000478

1236637 1 0.560909 0.02 80.94 45.4 0 181.4 0 0.2 0.5609 1 9.62998 0.62106 5.75E-06

1236652 1 1.709909 0.03 121.41 207.6 0 100 0 0.298208 1.7099 1 14.221 0.61143 3.17E-06

1236732

1236810

1236820 1 9.591611 4 16188 155269 0 27216 0 0.76 9.5916 13 1391.37 0.44866 0.000862

1331103 3 1.060322 0.26667 1079.2 1144.3 0 42.3333 0 0.2 1.0603 1 66.755 0.32289 1.34E-06

1333001

1333701

1415407

1421506

1430107 2 1.146998 0.5 2023.5 2320.95 0 275.5 0 0.2 1.147 1 153.433 0.39581 8.73E-06

1430404 1 0.910099 0.41 1659.27 1510.1 0 1377474 0 0.2 0.9101 1 77.5044 0.24383 0.04365

(continued)
Appendix 3A WMU Data

Table 3A-1. (continued)

Surface Impoundment Industrial D Data Surface Impoundment Calculated FRAMES Inputs

IDNum

Total

SI Depth (m)

Average

surface

area

(acres)

Average

surface

area (m2)

Average

capacity

(Mg)

Capacity

replaced?

Average

waste

quantity

(Mg)

Waste

quantity

replaced?

Fraction of

SI occupied

by

sediments

Depth of

SI (m)

Number of

impellers/

aerators

Total

power to

impellers

(HP)

Fraction

surface area

turbulent

Volumetric

influent flow

rate (m3/s)

SrcDepth SrcArea d_setpt d_wmu n_imp Powr F_aer Q_wmu

3-84

1430602 2 1.119002 0.25 1011.75 1132.15 0 49060.7 0 0.2 1.119 1 121.519 0.62696 0.001555

1431515 20 0.427149 0.007 28.329 12.10072 -1 566.085 0 0.2 0.4271 1 4.29039 0.79056 1.79E-05

1434022 1 2.176098 0.03 121.41 264.2 0 943.5 0 0.448554 2.1761 1 8.66214 0.37243 2.99E-05

1434802 1 0.883617 0.15 607.05 536.4 0 2145.2 0 0.2 0.8836 0 89.4877 0.7695 6.8E-05

1435317

1522504

1530605 6 4.054426 1.91667 7756.75 31449.17 0 377390 0 0.704027 4.0544 11 1167.63 0.78577 0.011959

1530808 4 0.8605 0.225 910.575 783.55 0 30191.2 0 0.2 0.8605 1 37.3749 0.21426 0.000957

1532401

1621808 2 2.069267 1.5 6070.5 12561.49 -1 32502.7 0 0.420085 2.0693 5 509.375 0.43801 0.00103

1630106 16 3.242903 0.79438 3214.836 10425.4 0 161806 0 0.629961 3.2429 5 341.184 0.55399 0.005127

1630401

1631701 2 1.873679 0.54 2185.38 4094.7 0 634232 0 0.359549 1.8737 1 110.611 0.26421 0.020098

1632106 1 1.92332 4 16188 31134.7 0 31134.7 0 0.376079 1.9233 30 2093.15 0.67496 0.000987

1632703

1633404 1 2.127032 2 8094 17216.2 0 245304 0 0.435834 2.127 11 1003.99 0.64749 0.007773

1633405 2 9.114597 0.155 627.285 5717.45 0 0.0025 0 0.76 9.1146 1 68.2371 0.56784 7.92E-11

1635404 1 8.504571 0.5 2023.5 17209 0 250021 0 0.76 8.5046 1 111.812 0.28844 0.007923

1721603 2 0.932493 0.25 1011.75 943.45 0 25002.1 0 0.2 0.9325 2 139.714 0.72084 0.000792
Appendix 3B Aerated Tank Data

3-85

Appendix 3B

Aerated Tank Data

Table 3B-1. TSDR Survey Wastewater Treatment Codes Used in Identifying 

Treatment Tanks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-87

Table 3B-2. Summary of Tank Information Collected by EPA on Aerated Wastewater 

Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-88

Table 3B-3. Correlated Tank Data from TSDR Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-89
Appendix 3B Aerated Tank Data

3-86

This page intentionally left blank
Appendix 3B Aerated Tank Data

3-87

Table 3B-1. TSDR Survey Wastewater Treatment Codes Used 

in Identifying Treatment Tanks

Process Code/Process Level Aeration Process Code/Process Aeration Level

Equalization Filtration

1WT Equalization LO 34WT Diatomaceous earth NO

Cyanide oxidation 35WT Sand NO

2WT Alkaline chlorination LO 36WT Multimedia NO

3WT Ozone LO 37WT Other filtration NO

4WT Electrochemical LO Sludge dewatering

5WT Other cyanide oxidation LO 38WT Gravity thickening NO

General oxidation (including disinfection) Air flotation

6WT Chlorination LO 43WT Dissolved air flotation HI

7WT Ozonation LO 44WT Partial aeration HI

8WT UV radiation LO 45WT Air dispersion HI

9WT Other general oxidation LO 46WT Other air flotation HI

Chemical precipitation Oil skimming

10WT Lime LO 47WT Gravity separation NO

11WT Sodium hydroxide LO 48WT Coalescing plate separation NO

12WT Soda ash LO 49WT Other oil skimming NO

13WT Sulfide LO Other liquid-phase separation

14WT Other chemical precipitation LO 50WT Decanting NO

Chromium reduction 51WT Other liquid phase-separation NO

15WT Sodium bisulfite LO Biological treatment

16WT Sulfur dioxide LO 52WT Activated sludge HI

17WT Ferrous sulfate LO 54WT Fixed film – rotating contactor LO

18WT Other chromium reduction LO 57WT Anaerobic NO

19WT Complexed metals treatment LO 58WT Other biological treatment HI

Emulsion breaking Other wastewater treatment

20WT Thermal NO 60WT Neutralization LO

21WT Chemical LO 61WT Nitrification LO

22WT Other emulsion breaking LO 62WT Denitrification LO

Evaporation 63WT Flocculation and/or coagulation NO

31WT Solar NO 64WT Settling (clarification) NO

Fuel blending 66WT Other wastewater treatment LO

1FB Fuel blending LO Other Processes

1TR Other treatment LO
3-88

Table 3B-2. Summary of Tank Information Collected by EPA on Aerated Wastewater Trea

Company Type of Unit Type of Aerator Aeration Level Volume (m3) Area (m2) Depth (m) Total hp # Aer. d_imp (cm) w_imp (rad/s) Q_air (m3/s) O2 transfer rate (lb/hp-h) h

T

V

Rielly Tar Aerated lagoon Mechanical SI 3,369 2,109 1.60 30 2 14.0 367 Texaco, IL Bubbling pit Diffused HI 453 74 6.10 2.17 EWR So eq. basin Mechanical LO 240 109 2.21 30 2 106.7 7.1 EWR No eq. basin Mechanical LO 191 84 2.29 20 1 152.4 5.9 Summit Mixing tank Mechanical LO 68 9 7.32 1.5 1 182.9 1.0 Leaman Mixing tank Mechanical LO 112 34 3.35 3 1 Leaman Aeration tank Mechanical HI 112 34 3.35 7.5 1 Texaco, TX Aerated lagoon Mechanical SI 45,425 1,800 28 LNVA Eq. basin Mechanical LO 41,261 11,241 3.66 150 5 121.9 7.1 LNVA Aeration tank Mechanical HI 41,261 11,241 3.66 900 9 259.1 188.5 3.0 LNVA Aux. Aer. tank Mechanical HI 21,804 4,459 4.88 450 6 259.1 125.7 3.0 Neches Aeration tank Mechanical HI 26,546 5,806 4.57 900 6 274.3 Shell, IL Aerated lagoon Mechanical SI 24,975 5,853 4.27 270 6 41.9 123.0 Sun, OK Aeration tank Mechanical HI 3,367 910 3.70 150 2 50 123.6 3.0 Plant A3 Aeration tank Diffused HI 5,764 1,051 5.49 2.35 Plant A2 Aeration tank Diffused HI 5,830 3.78 Plant A2 Aeration tank Mechanical HI 1,211 Plant A1 Eq. basin Mechanical LO 681 200 3.40 Plant A1 Aeration tank Diffused HI 1,666 159 10.46 0.80 Amoco Aeration tank Mechanical HI 5,678 931 6.10 300 3 EI DuPont Aeration tank Diffused HI 4,542 618 7.36 Mobay Aeration tank Diffused HI 3,785 730 5.19 Borg-Warner Aeration tank Diffused HI 6,814 4.72 TSDF -T01G Aerated trt. tank Mechanical HI 108 27 4.00 7.5 TSDF -T01H Aerated trt. tank Mechanical HI 1,600 430 3.70 120 TSDF -T01A Treatment tank LO 30 13 2.40 TSDF -T01B Treatment tank LO 76 26 2.70 TSDF -T01C Treatment tank LO 800 65 12.00 Average 146.2 105.4 

Median 137.2 123.0 
Appendix 3B Aerated Tank Data

3-89

Table 3B-3. Correlated Tank Data from TSDR Survey

Tank

Index

Aeration

Level

Size

(gal)

Capacity

(m3

)

Throughput

(m3

/s ) Depth (m)

Surface

Area (m2)

Source

Height

(m)

Fraction

Aerated

Total

Aerator

Horsepower

Number of

Impellers/

Aerators

ATIndex Q_wmu d_wmu SrcArea SHight F_aer Powr n_imp

1 HI 300 1.14 8.15E-05 1.03 1.11 1.53 0.691 0.250 1

2 HI 400 1.51 1.62E-03 1.25 1.21 0.90 0.633 0.250 1

3 HI 850 3.22 2.40E-07 1.28 2.51 1.78 0.929 0.250 1

4 HI 1,000 3.79 7.20E-07 1.44 2.62 1.94 0.787 0.250 1

5 HI 3,200 12.11 2.24E-04 2.90 4.18 3.40 0.691 0.259 1

6 HI 5,125 19.40 1.26E-04 2.47 7.87 2.97 0.800 0.847 1

7 HI 5,880 22.26 1.49E-03 2.16 10.31 2.66 0.834 0.922 1

8 HI 10,200 38.61 1.63E-04 3.27 11.81 3.77 0.558 1.031 1

9 HI 10,200 38.61 1.70E-03 2.66 14.51 3.16 0.761 1.133 1

10 HI 15,000 56.78 8.15E-05 3.26 17.39 1.40 0.783 1.544 1

11 HI 13,900 52.62 3.51E-04 2.90 18.16 3.40 0.647 1.447 1

12 HI 20,000 75.71 1.02E-03 2.97 25.51 3.47 0.762 2.357 1

13 HI 30,370 114.96 8.40E-04 4.01 28.65 0.80 0.922 2.129 1

14 HI 21,000 79.49 3.56E-03 2.76 28.79 3.26 0.630 2.250 1

15 HI 31,300 118.48 5.97E-03 3.21 36.94 3.71 0.707 3.331 1

16 HI 196,000 741.94 1.70E-03 4.87 152.50 5.10 0.785 18.478 1

17 HI 225,000 851.71 2.40E-06 3.58 237.62 4.08 0.811 22.185 1

18 HI 415,600 1,573.21 3.92E-03 3.70 424.73 4.20 0.765 32.900 1

19 HI 640,000 2,422.65 2.06E-01 4.16 582.44 4.66 0.651 48.541 1

20 HI 640,000 2,422.65 2.06E-01 3.39 714.37 3.89 0.745 58.856 2

21 HI 1,800,000 6,813.72 8.64E-02 5.17 1,319.02 5.67 0.898 273.136 4

22 HI 3,000,000 11,356.19 9.15E-01 5.54 2,049.16 6.04 0.706 330.106 4

23 HI 6,000,000 22,712.39 9.15E-01 4.80 4,734.60 5.30 0.849 829.287 9

24 HI 4,624,000 17,503.68 4.71E-01 5.06 3,455.96 5.56 0.655 649.233 7

25 HI 4,624,000 17,503.68 4.71E-01 3.77 4,644.58 4.27 0.732 555.704 9

26 HI 4,624,000 17,503.68 4.71E-01 3.46 5,054.12 0.60 0.585 317.706 4

27 HI 8,200,000 31,040.27 3.88E-01 3.68 8,430.73 4.18 0.810 890.808 12

28 HI 8,200,000 31,040.27 3.29E-01 3.67 8,452.59 4.17 0.713 1,164.902 13

29 HI 3,000,000 11,356.19 9.15E-01 3.38 3,361.45 1.50 0.720 263.739 3

30 LO 120 0.45 5.15E-04 0.90 0.50 1.40 0.342 0.250 1

31 LO 100 0.38 1.34E-04 0.74 0.51 1.24 0.582 0.250 1

32 LO 150 0.57 2.75E-05 1.04 0.55 1.54 0.760 0.250 1

33 LO 100 0.38 8.30E-05 0.69 0.55 1.19 0.553 0.250 1

34 LO 100 0.38 8.30E-05 0.69 0.55 1.19 0.393 0.250 1

35 LO 110 0.42 1.78E-07 0.75 0.55 1.25 0.506 0.250 1

36 LO 120 0.45 5.15E-04 0.76 0.60 1.26 0.678 0.250 1

37 LO 100 0.38 1.80E-08 0.63 0.60 1.13 0.797 0.250 1

38 LO 150 0.57 2.40E-07 0.92 0.62 1.42 0.631 0.250 1

39 LO 150 0.57 2.40E-07 0.90 0.63 1.40 0.786 0.250 1

40 LO 125 0.47 1.49E-08 0.72 0.65 1.22 0.287 0.250 1

41 LO 200 0.76 2.55E-04 1.14 0.66 1.64 0.368 0.250 1

42 LO 200 0.76 5.48E-04 1.09 0.69 0.80 0.292 0.250 1

43 LO 150 0.57 2.40E-07 0.80 0.71 1.30 0.518 0.250 1

44 LO 180 0.68 1.80E-07 0.95 0.72 1.45 0.659 0.250 1

45 LO 215 0.81 1.64E-06 1.08 0.75 1.58 0.471 0.250 1

46 LO 150 0.57 1.50E-05 0.72 0.79 1.22 0.319 0.250 1

47 LO 200 0.76 2.55E-04 0.91 0.83 1.41 0.274 0.250 1

48 LO 230 0.87 1.64E-06 1.01 0.86 1.51 0.200 0.250 1

49 LO 185 0.70 5.48E-04 0.81 0.86 1.31 0.640 0.250 1

(continued)
Table 3B-3. (continued)

Appendix 3B Aerated Tank Data

Tank

Index

Aeration

Level

Size

(gal)

Capacity

(m3

)

Throughput

(m3

/s ) Depth (m)

Surface

Area (m2)

Source

Height

(m)

Fraction

Aerated

Total

Aerator

Horsepower

Number of

Impellers/

Aerators

ATIndex Q_wmu d_wmu SrcArea SHight F_aer Powr n_imp

3-90

50 LO 235 0.89 1.44E-08 0.99 0.90 1.10 0.551 0.250 1

51 LO 200 0.76 1.64E-06 0.83 0.91 1.33 0.622 0.250 1

52 LO 400 1.51 2.40E-05 1.64 0.92 2.14 0.730 0.250 1

53 LO 210 0.79 1.44E-08 0.84 0.94 1.34 0.311 0.250 1

54 LO 210 0.79 1.44E-08 0.82 0.96 1.32 0.584 0.250 1

55 LO 200 0.76 1.20E-07 0.76 0.99 1.26 0.268 0.250 1

56 LO 250 0.95 1.46E-06 0.93 1.01 1.43 0.716 0.250 1

57 LO 350 1.32 6.72E-07 1.28 1.04 1.78 0.591 0.250 1

58 LO 350 1.32 6.72E-07 1.23 1.07 0.50 0.316 0.250 1

59 LO 300 1.14 6.10E-05 1.05 1.08 1.55 0.371 0.250 1

60 LO 350 1.32 6.72E-07 1.22 1.08 1.72 0.407 0.250 1

61 LO 400 1.51 1.40E-04 1.32 1.15 1.82 0.414 0.250 1

62 LO 350 1.32 6.72E-07 1.14 1.16 1.64 0.461 0.250 1

63 LO 350 1.32 6.72E-07 1.08 1.23 1.58 0.567 0.250 1

64 LO 350 1.32 6.72E-07 1.06 1.24 1.56 0.253 0.250 1

65 LO 450 1.70 9.31E-06 1.37 1.25 1.87 0.367 0.250 1

66 LO 450 1.70 2.24E-04 1.30 1.31 1.80 0.669 0.250 1

67 LO 550 2.08 2.15E-06 1.56 1.33 2.06 0.443 0.250 1

68 LO 500 1.89 7.65E-05 1.41 1.34 1.91 0.557 0.250 1

69 LO 475 1.80 4.80E-07 1.33 1.35 1.70 0.800 0.250 1

70 LO 530 2.01 1.68E-04 1.42 1.41 1.92 0.430 0.250 1

71 LO 400 1.51 2.40E-05 1.07 1.42 0.50 0.771 0.250 1

72 LO 500 1.89 1.45E-07 1.33 1.42 0.50 0.285 0.250 1

73 LO 450 1.70 2.24E-04 1.17 1.46 1.67 0.310 0.250 1

74 LO 500 1.89 1.09E-03 1.29 1.47 1.79 0.480 0.250 1

75 LO 550 2.08 1.67E-06 1.42 1.47 1.92 0.441 0.250 1

76 LO 550 2.08 2.15E-06 1.40 1.49 1.90 0.521 0.250 1

77 LO 500 1.89 3.60E-07 1.26 1.51 1.76 0.800 0.250 1

78 LO 500 1.89 1.67E-06 1.26 1.51 1.76 0.415 0.250 1

79 LO 600 2.27 7.48E-07 1.50 1.51 2.00 0.482 0.250 1

80 LO 600 2.27 2.57E-03 1.50 1.51 2.00 0.232 0.250 1

81 LO 435 1.65 1.20E-07 1.08 1.52 1.58 0.551 0.250 1

82 LO 500 1.89 7.80E-06 1.24 1.53 1.74 0.495 0.250 1

83 LO 500 1.89 1.10E-04 1.23 1.54 1.73 0.633 0.250 1

84 LO 475 1.80 1.67E-06 1.17 1.54 0.50 0.472 0.250 1

85 LO 636 2.41 2.56E-03 1.53 1.57 2.03 0.594 0.250 1

86 LO 500 1.89 7.20E-07 1.20 1.58 1.70 0.200 0.250 1

87 LO 430 1.63 2.40E-10 1.03 1.59 1.53 0.256 0.250 1

88 LO 600 2.27 1.71E-03 1.43 1.59 1.93 0.683 0.250 1

89 LO 500 1.89 2.40E-05 1.16 1.64 1.66 0.284 0.250 1

90 LO 500 1.89 6.00E-07 1.15 1.65 1.65 0.800 0.250 1

91 LO 500 1.89 3.68E-05 1.14 1.66 0.80 0.589 0.250 1

92 LO 500 1.89 1.09E-03 1.13 1.67 1.63 0.800 0.250 1

93 LO 327 1.24 1.44E-08 0.73 1.71 1.23 0.667 0.250 1

94 LO 500 1.89 3.60E-06 1.11 1.71 1.61 0.200 0.250 1

95 LO 670 2.54 2.77E-06 1.48 1.71 1.98 0.384 0.250 1

96 LO 600 2.27 7.48E-07 1.32 1.73 1.82 0.210 0.250 1

97 LO 500 1.89 2.71E-04 1.10 1.73 1.60 0.455 0.250 1

98 LO 600 2.27 5.33E-04 1.30 1.74 1.80 0.346 0.250 1

(continued)
Table 3B-3. (continued)

Appendix 3B Aerated Tank Data

Tank

Index

Aeration

Level

Size

(gal)

Capacity

(m3

)

Throughput

(m3

/s ) Depth (m)

Surface

Area (m2)

Source

Height

(m)

Fraction

Aerated

Total

Aerator

Horsepower

Number of

Impellers/

Aerators

ATIndex Q_wmu d_wmu SrcArea SHight F_aer Powr n_imp

3-91

99 LO 500 1.89 4.68E-05 1.07 1.77 1.57 0.235 0.250 1

100 LO 600 2.27 5.62E-04 1.24 1.83 1.74 0.733 0.250 1

101 LO 650 2.46 1.09E-03 1.34 1.83 0.60 0.271 0.250 1

102 LO 670 2.54 4.08E-06 1.37 1.86 1.87 0.714 0.250 1

103 LO 600 2.27 7.48E-07 1.22 1.86 1.72 0.273 0.250 1

104 LO 500 1.89 1.29E-06 1.00 1.90 1.50 0.589 0.250 1

105 LO 800 3.03 2.13E-05 1.59 1.91 2.09 0.358 0.250 1

106 LO 1,000 3.79 5.62E-05 1.96 1.93 2.46 0.564 0.250 1

107 LO 900 3.41 4.80E-07 1.74 1.96 0.50 0.623 0.250 1

108 LO 600 2.27 2.57E-03 1.15 1.97 1.65 0.363 0.250 1

109 LO 750 2.84 9.00E-06 1.42 2.00 1.92 0.258 0.250 1

110 LO 500 1.89 8.16E-04 0.94 2.01 1.44 0.535 0.250 1

111 LO 600 2.27 5.49E-04 1.12 2.02 1.62 0.200 0.250 1

112 LO 800 3.03 1.86E-03 1.49 2.03 1.99 0.569 0.250 1

113 LO 790 2.99 1.64E-06 1.47 2.04 1.97 0.446 0.250 1

114 LO 1,000 3.79 3.60E-03 1.85 2.05 2.35 0.598 0.250 1

115 LO 636 2.41 1.62E-04 1.17 2.05 1.67 0.220 0.250 1

116 LO 800 3.03 4.42E-03 1.47 2.06 1.97 0.466 0.250 1

117 LO 1,000 3.79 1.01E-06 1.82 2.08 2.32 0.431 0.250 1

118 LO 800 3.03 2.13E-05 1.45 2.09 1.95 0.289 0.250 1

119 LO 1,160 4.39 1.50E-05 2.09 2.10 2.59 0.305 0.250 1

120 LO 1,000 3.79 1.20E-05 1.79 2.11 2.29 0.671 0.250 1

121 LO 748 2.83 2.69E-05 1.32 2.15 1.82 0.519 0.250 1

122 LO 748 2.83 2.69E-05 1.32 2.15 1.82 0.257 0.250 1

123 LO 675 2.56 1.38E-04 1.14 2.24 1.64 0.444 0.250 1

124 LO 1,008 3.82 3.92E-03 1.65 2.31 2.15 0.611 0.250 1

125 LO 1,041 3.94 9.60E-07 1.68 2.35 2.18 0.702 0.250 1

126 LO 934 3.54 9.23E-05 1.50 2.36 2.00 0.237 0.250 1

127 LO 1,000 3.79 1.10E-04 1.59 2.38 2.09 0.363 0.250 1

128 LO 1,000 3.79 3.60E-03 1.54 2.46 2.04 0.505 0.250 1

129 LO 1,000 3.79 6.69E-03 1.53 2.47 2.03 0.511 0.250 1

130 LO 1,041 3.94 7.50E-06 1.57 2.50 2.07 0.547 0.250 1

131 LO 800 3.03 4.29E-05 1.21 2.51 1.71 0.200 0.250 1

132 LO 1,000 3.79 6.90E-06 1.50 2.53 2.00 0.524 0.250 1

133 LO 1,200 4.54 6.00E-07 1.78 2.55 2.28 0.780 0.250 1

134 LO 1,000 3.79 3.60E-03 1.46 2.59 1.96 0.800 0.250 1

135 LO 1,100 4.16 1.38E-04 1.59 2.63 2.09 0.297 0.250 1

136 LO 1,400 5.30 5.76E-02 2.01 2.64 0.90 0.200 0.250 1

137 LO 1,100 4.16 9.02E-04 1.57 2.65 2.07 0.200 0.250 1

138 LO 1,500 5.68 6.00E-05 2.13 2.67 2.63 0.663 0.250 1

139 LO 1,000 3.79 4.08E-07 1.42 2.67 1.92 0.203 0.250 1

140 LO 1,000 3.79 2.88E-06 1.41 2.68 1.91 0.260 0.250 1

141 LO 1,000 3.79 2.32E-06 1.40 2.70 1.90 0.624 0.250 1

142 LO 1,050 3.97 1.10E-04 1.47 2.71 1.97 0.234 0.250 1

143 LO 1,000 3.79 1.08E-03 1.37 2.75 1.87 0.646 0.250 1

144 LO 1,400 5.30 6.52E-04 1.92 2.77 2.42 0.520 0.250 1

145 LO 1,000 3.79 1.58E-05 1.36 2.79 1.86 0.334 0.250 1

146 LO 1,500 5.68 1.62E-04 2.01 2.82 2.51 0.450 0.250 1

147 LO 1,615 6.11 1.35E-03 2.12 2.88 2.62 0.287 0.250 1

(continued)
Table 3B-3. (continued)

Appendix 3B Aerated Tank Data

Tank

Index

Aeration

Level

Size

(gal)

Capacity

(m3

)

Throughput

(m3

/s ) Depth (m)

Surface

Area (m2)

Source

Height

(m)

Fraction

Aerated

Total

Aerator

Horsepower

Number of

Impellers/

Aerators

ATIndex Q_wmu d_wmu SrcArea SHight F_aer Powr n_imp

3-92

148 LO 950 3.60 1.08E-03 1.25 2.88 1.75 0.393 0.250 1

149 LO 1,500 5.68 6.24E-03 1.95 2.91 2.45 0.684 0.250 1

150 LO 1,400 5.30 1.20E-08 1.81 2.93 0.50 0.773 0.250 1

151 LO 1,269 4.80 2.07E-02 1.63 2.95 2.13 0.368 0.250 1

152 LO 1,200 4.54 1.89E-04 1.53 2.97 2.03 0.496 0.250 1

153 LO 1,800 6.81 2.30E-07 2.23 3.05 2.73 0.524 0.250 1

154 LO 2,000 7.57 6.24E-03 2.48 3.06 2.98 0.552 0.250 1

155 LO 1,270 4.81 4.37E-03 1.55 3.11 2.05 0.540 0.250 1

156 LO 1,800 6.81 2.30E-07 2.18 3.12 2.68 0.592 0.250 1

157 LO 1,350 5.11 4.80E-07 1.64 3.12 2.14 0.742 0.250 1

158 LO 1,800 6.81 2.30E-07 2.16 3.15 2.66 0.200 0.250 1

159 LO 1,870 7.08 3.15E-03 2.23 3.17 2.73 0.627 0.250 1

160 LO 1,800 6.81 2.30E-07 2.15 3.17 2.65 0.528 0.250 1

161 LO 1,450 5.49 1.33E-04 1.72 3.19 2.22 0.292 0.250 1

162 LO 1,000 3.79 1.45E-03 1.17 3.23 1.67 0.378 0.250 1

163 LO 2,000 7.57 6.40E-06 2.29 3.30 2.79 0.491 0.250 1

164 LO 1,650 6.25 2.76E-03 1.86 3.36 0.60 0.295 0.250 1

165 LO 1,800 6.81 1.44E-05 2.02 3.38 2.52 0.352 0.250 1

166 LO 1,600 6.06 1.24E-03 1.79 3.39 2.29 0.427 0.250 1

167 LO 1,400 5.30 3.54E-05 1.56 3.40 1.40 0.545 0.250 1

168 LO 1,800 6.81 1.73E-04 1.99 3.42 2.49 0.529 0.250 1

169 LO 2,150 8.14 9.01E-04 2.35 3.46 2.85 0.790 0.250 1

170 LO 1,800 6.81 2.30E-07 1.96 3.48 2.30 0.542 0.250 1

171 LO 1,500 5.68 6.15E-03 1.58 3.59 2.08 0.289 0.250 1

172 LO 2,550 9.65 5.48E-04 2.65 3.64 3.15 0.273 0.250 1

173 LO 2,000 7.57 1.82E-03 2.05 3.69 2.55 0.495 0.250 1

174 LO 1,800 6.81 2.30E-07 1.82 3.74 2.32 0.312 0.250 1

175 LO 2,000 7.57 1.80E-05 1.97 3.84 1.90 0.350 0.250 1

176 LO 3,000 11.36 6.24E-06 2.94 3.86 3.44 0.538 0.250 1

177 LO 2,000 7.57 1.09E-03 1.95 3.88 2.45 0.493 0.250 1

178 LO 2,000 7.57 1.53E-04 1.95 3.89 2.45 0.726 0.250 1

179 LO 2,000 7.57 1.44E-06 1.93 3.92 2.43 0.599 0.250 1

180 LO 2,000 7.57 4.80E-07 1.93 3.92 2.43 0.443 0.250 1

181 LO 2,290 8.67 4.99E-05 2.19 3.97 2.69 0.416 0.250 1

182 LO 2,000 7.57 2.19E-02 1.90 3.99 2.40 0.501 0.250 1

183 LO 2,100 7.95 2.16E-06 1.99 4.00 2.49 0.286 0.250 1

184 LO 2,000 7.57 2.40E-05 1.88 4.03 2.38 0.323 0.250 1

185 LO 2,000 7.57 1.39E-05 1.86 4.06 2.36 0.800 0.250 1

186 LO 2,300 8.71 1.35E-03 2.13 4.08 2.63 0.548 0.250 1

187 LO 2,200 8.33 4.42E-03 2.03 4.10 1.30 0.436 0.250 1

188 LO 3,000 11.36 4.80E-05 2.76 4.11 3.00 0.276 0.250 1

189 LO 1,800 6.81 2.16E-06 1.62 4.21 2.12 0.639 0.250 1

190 LO 2,000 7.57 2.44E-05 1.79 4.23 2.29 0.546 0.250 1

191 LO 2,500 9.46 2.80E-02 2.22 4.26 2.72 0.684 0.250 1

192 LO 2,800 10.60 5.61E-06 2.48 4.28 1.20 0.552 0.250 1

193 LO 2,150 8.14 9.01E-04 1.88 4.33 2.38 0.478 0.250 1

194 LO 2,500 9.46 3.43E-03 2.17 4.36 2.67 0.306 0.250 1

195 LO 2,000 7.57 4.80E-06 1.69 4.47 1.20 0.321 0.250 1

196 LO 3,230 12.23 6.67E-04 2.69 4.55 2.60 0.350 0.250 1

(continued)
Table 3B-3. (continued)

Appendix 3B Aerated Tank Data

Tank

Index

Aeration

Level

Size

(gal)

Capacity

(m3

)

Throughput

(m3

/s ) Depth (m)

Surface

Area (m2)

Source

Height

(m)

Fraction

Aerated

Total

Aerator

Horsepower

Number of

Impellers/

Aerators

ATIndex Q_wmu d_wmu SrcArea SHight F_aer Powr n_imp

3-93

197 LO 3,000 11.36 1.44E-06 2.49 4.57 2.99 0.507 0.250 1

198 LO 2,400 9.08 6.67E-04 1.96 4.64 2.46 0.800 0.250 1

199 LO 2,500 9.46 1.44E-05 2.03 4.66 2.53 0.480 0.250 1

200 LO 3,000 11.36 1.01E-02 2.43 4.67 2.93 0.465 0.250 1

201 LO 2,000 7.57 1.15E-04 1.62 4.67 1.90 0.593 0.250 1

202 LO 2,500 9.46 2.80E-02 2.02 4.68 1.10 0.453 0.250 1

203 LO 2,500 9.46 1.38E-07 2.02 4.68 2.52 0.474 0.250 1

204 LO 3,100 11.73 1.33E-02 2.50 4.69 3.00 0.470 0.250 1

205 LO 3,000 11.36 1.09E-03 2.42 4.69 2.92 0.561 0.250 1

206 LO 2,220 8.40 1.35E-03 1.79 4.70 2.29 0.555 0.250 1

207 LO 3,000 11.36 4.80E-05 2.38 4.76 2.88 0.355 0.250 1

208 LO 2,400 9.08 1.73E-04 1.89 4.79 0.50 0.527 0.250 1

209 LO 2,550 9.65 5.48E-04 2.01 4.81 0.50 0.678 0.250 1

210 LO 3,800 14.38 7.32E-06 2.94 4.89 3.44 0.574 0.250 1

211 LO 3,300 12.49 2.55E-04 2.54 4.92 3.04 0.675 0.250 1

212 LO 3,000 11.36 3.66E-05 2.31 4.92 2.81 0.472 0.250 1

213 LO 2,700 10.22 2.64E-06 2.08 4.92 2.58 0.592 0.250 1

214 LO 3,000 11.36 1.01E-02 2.30 4.93 0.60 0.623 0.250 1

215 LO 3,000 11.36 1.44E-06 2.29 4.96 2.79 0.555 0.250 1

216 LO 2,400 9.08 5.22E-03 1.81 5.01 2.31 0.380 0.250 1

217 LO 3,100 11.73 4.96E-03 2.33 5.04 2.83 0.472 0.250 1

218 LO 4,000 15.14 1.36E-06 2.99 5.06 3.49 0.434 0.250 1

219 LO 3,300 12.49 2.55E-04 2.47 5.06 2.97 0.746 0.250 1

220 LO 2,400 9.08 1.40E-04 1.79 5.09 2.29 0.603 0.250 1

221 LO 3,100 11.73 1.98E-05 2.30 5.10 2.80 0.441 0.250 1

222 LO 3,800 14.38 6.14E-06 2.81 5.12 3.31 0.322 0.250 1

223 LO 3,400 12.87 1.90E-03 2.50 5.14 3.00 0.482 0.250 1

224 LO 3,100 11.73 5.01E-03 2.25 5.21 0.90 0.514 0.250 1

225 LO 3,100 11.73 1.98E-05 2.24 5.23 2.74 0.330 0.250 1

226 LO 3,500 13.25 1.09E-02 2.50 5.31 3.00 0.608 0.250 1

227 LO 3,384 12.81 2.07E-02 2.40 5.33 2.90 0.754 0.250 1

228 LO 3,000 11.36 1.08E-04 2.13 5.34 2.63 0.617 0.250 1

229 LO 2,500 9.46 3.43E-03 1.76 5.37 2.26 0.360 0.250 1

230 LO 4,000 15.14 1.50E-05 2.80 5.40 3.30 0.512 0.250 1

231 LO 3,000 11.36 1.82E-03 2.09 5.44 0.50 0.800 0.250 1

232 LO 3,000 11.36 7.20E-06 2.09 5.44 2.59 0.527 0.250 1

233 LO 3,000 11.36 1.08E-04 2.06 5.51 2.56 0.475 0.250 1

234 LO 3,000 11.36 6.24E-03 2.05 5.53 2.55 0.712 0.250 1

235 LO 2,200 8.33 4.42E-03 1.50 5.54 0.90 0.665 0.250 1

236 LO 4,000 15.14 2.11E-04 2.71 5.59 3.21 0.255 0.250 1

237 LO 4,100 15.52 8.17E-03 2.77 5.60 3.27 0.515 0.250 1

238 LO 4,000 15.14 1.36E-06 2.67 5.68 3.17 0.200 0.250 1

239 LO 3,350 12.68 2.49E-01 2.21 5.73 2.71 0.304 0.250 1

240 LO 4,200 15.90 9.86E-03 2.73 5.83 3.23 0.455 0.250 1

241 LO 4,200 15.90 9.86E-03 2.73 5.83 3.23 0.257 0.250 1

242 LO 2,000 7.57 1.90E-03 1.27 5.96 1.77 0.514 0.250 1

243 LO 3,400 12.87 8.17E-03 2.15 5.98 2.65 0.376 0.250 1

244 LO 4,320 16.35 9.23E-05 2.73 5.98 3.23 0.200 0.250 1

245 LO 4,000 15.14 4.71E-05 2.49 6.08 2.99 0.631 0.250 1

(continued)
Table 3B-3. (continued)

Appendix 3B Aerated Tank Data

Tank

Index

Aeration

Level

Size

(gal)

Capacity

(m3

)

Throughput

(m3

/s ) Depth (m)

Surface

Area (m2)

Source

Height

(m)

Fraction

Aerated

Total

Aerator

Horsepower

Number of

Impellers/

Aerators

ATIndex Q_wmu d_wmu SrcArea SHight F_aer Powr n_imp

3-94

246 LO 5,000 18.93 1.92E-05 3.11 6.09 3.61 0.622 0.250 1

247 LO 4,000 15.14 1.50E-05 2.47 6.13 2.30 0.273 0.250 1

248 LO 4,000 15.14 1.80E-05 2.47 6.13 2.97 0.469 0.250 1

249 LO 3,500 13.25 7.75E-05 2.16 6.14 2.66 0.493 0.250 1

250 LO 4,000 15.14 6.04E-05 2.45 6.17 2.95 0.439 0.250 1

251 LO 3,600 13.63 4.29E-04 2.19 6.21 2.69 0.200 0.250 1

252 LO 4,200 15.90 9.86E-03 2.53 6.28 3.03 0.526 0.250 1

253 LO 3,945 14.93 1.53E-04 2.37 6.31 2.87 0.800 0.250 1

254 LO 3,600 13.63 4.29E-04 2.15 6.33 2.65 0.217 0.250 1

255 LO 4,600 17.41 1.38E-03 2.72 6.39 3.22 0.575 0.250 1

256 LO 4,000 15.14 1.36E-06 2.34 6.48 2.84 0.800 0.250 1

257 LO 4,000 15.14 6.24E-03 2.32 6.53 2.82 0.425 0.250 1

258 LO 4,700 17.79 2.18E-05 2.70 6.59 1.60 0.251 0.250 1

259 LO 4,000 15.14 1.36E-06 2.29 6.61 2.79 0.369 0.250 1

260 LO 3,445 13.04 4.20E-03 1.93 6.74 2.43 0.321 0.250 1

261 LO 4,000 15.14 5.61E-06 2.24 6.77 2.74 0.795 0.250 1

262 LO 4,300 16.28 1.40E-02 2.40 6.78 2.90 0.586 0.250 1

263 LO 4,400 16.66 3.60E-03 2.41 6.90 2.91 0.290 0.250 1

264 LO 5,000 18.93 1.23E-04 2.74 6.91 3.24 0.552 0.250 1

265 LO 4,500 17.03 9.36E-06 2.44 6.99 2.94 0.573 0.250 1

266 LO 5,000 18.93 3.00E-05 2.70 7.02 2.10 0.200 0.250 1

267 LO 3,780 14.31 1.38E-07 2.02 7.10 2.52 0.433 0.250 1

268 LO 4,200 15.90 4.68E-07 2.20 7.24 2.70 0.295 0.250 1

269 LO 5,800 21.96 4.80E-04 3.02 7.27 3.50 0.200 0.250 1

270 LO 5,000 18.93 2.76E-06 2.59 7.30 3.09 0.347 0.250 1

271 LO 5,800 21.96 2.52E-02 3.00 7.31 3.50 0.620 0.250 1

272 LO 4,700 17.79 2.42E-05 2.42 7.34 2.92 0.238 0.250 1

273 LO 5,000 18.93 1.23E-04 2.56 7.40 3.06 0.381 0.250 1

274 LO 4,000 15.14 9.60E-05 2.04 7.41 2.54 0.273 0.250 1

275 LO 5,800 21.96 2.52E-02 2.93 7.49 3.43 0.800 0.274 1

276 LO 5,400 20.44 1.84E-04 2.68 7.62 3.18 0.304 0.250 1

277 LO 5,500 20.82 8.11E-03 2.70 7.70 3.20 0.467 0.250 1

278 LO 4,320 16.35 9.23E-05 2.11 7.74 2.60 0.694 0.250 1

279 LO 4,320 16.35 9.23E-05 2.10 7.78 2.60 0.610 0.250 1

280 LO 4,500 17.03 1.82E-03 2.19 7.79 0.50 0.427 0.250 1

281 LO 5,600 21.20 3.15E-04 2.67 7.94 1.90 0.338 0.250 1

282 LO 5,000 18.93 4.99E-05 2.38 7.95 0.50 0.536 0.250 1

283 LO 5,000 18.93 7.41E-05 2.34 8.10 2.84 0.654 0.254 1

284 LO 6,000 22.71 1.07E-02 2.78 8.18 3.28 0.784 0.250 1

285 LO 5,000 18.93 7.41E-05 2.31 8.20 0.90 0.403 0.250 1

286 LO 5,000 18.93 4.37E-03 2.27 8.34 2.77 0.402 0.250 1

287 LO 7,500 28.39 1.78E-05 3.34 8.50 3.84 0.413 0.250 1

288 LO 5,000 18.93 4.68E-05 2.21 8.56 2.71 0.323 0.250 1

289 LO 5,000 18.93 1.92E-05 2.21 8.58 2.71 0.445 0.250 1

290 LO 7,100 26.88 3.53E-06 3.12 8.61 3.62 0.460 0.250 1

291 LO 4,500 17.03 6.79E-03 1.97 8.66 2.47 0.403 0.250 1

292 LO 5,000 18.93 2.76E-06 2.18 8.69 2.68 0.492 0.250 1

293 LO 6,000 22.71 4.80E-06 2.56 8.86 3.06 0.411 0.250 1

(continued)
Table 3B-3. (continued)

Appendix 3B Aerated Tank Data

Tank

Index

Aeration

Level

Size

(gal)

Capacity

(m3

)

Throughput

(m3

/s ) Depth (m)

Surface

Area (m2)

Source

Height

(m)

Fraction

Aerated

Total

Aerator

Horsepower

Number of

Impellers/

Aerators

ATIndex Q_wmu d_wmu SrcArea SHight F_aer Powr n_imp

3-95

294 LO 6,000 22.71 8.01E-04 2.51 9.05 3.01 0.466 0.275 1

295 LO 6,000 22.71 8.01E-04 2.50 9.07 3.00 0.561 0.250 1

296 LO 6,390 24.19 4.64E-03 2.65 9.11 3.15 0.624 0.250 1

297 LO 6,850 25.93 5.04E-03 2.80 9.25 3.30 0.349 0.250 1

298 LO 4,700 17.79 2.42E-05 1.92 9.27 2.42 0.316 0.250 1

299 LO 8,000 30.28 2.27E-05 3.27 9.27 3.77 0.442 0.283 1

300 LO 4,000 15.14 2.74E-05 1.63 9.28 2.13 0.750 0.250 1

301 LO 6,500 24.61 4.77E-03 2.60 9.47 3.10 0.483 0.250 1

302 LO 7,000 26.50 1.24E-03 2.77 9.56 0.70 0.598 0.250 1

303 LO 4,000 15.14 1.80E-04 1.56 9.72 2.06 0.494 0.250 1

304 LO 7,500 28.39 1.87E-05 2.91 9.77 3.41 0.251 0.250 1

305 LO 7,000 26.50 8.20E-05 2.64 10.05 3.14 0.613 0.250 1

306 LO 9,000 34.07 7.74E-04 3.36 10.15 3.86 0.545 0.280 1

307 LO 7,500 28.39 1.78E-05 2.77 10.25 3.27 0.347 0.250 1

308 LO 8,000 30.28 2.68E-02 2.86 10.58 3.36 0.581 0.348 1

309 LO 7,000 26.50 9.72E-06 2.51 10.58 3.01 0.479 0.250 1

310 LO 7,500 28.39 1.46E-05 2.68 10.58 3.18 0.734 0.302 1

311 LO 7,680 29.07 2.41E-03 2.74 10.60 3.24 0.533 0.279 1

312 LO 8,500 32.18 4.48E-03 3.02 10.67 3.52 0.302 0.306 1

313 LO 6,171 23.36 1.60E-01 2.18 10.72 2.68 0.695 0.250 1

314 LO 7,000 26.50 2.93E-03 2.47 10.74 2.97 0.445 0.250 1

315 LO 7,000 26.50 1.09E-02 2.46 10.77 2.96 0.627 0.250 1

316 LO 6,800 25.74 2.81E-03 2.38 10.83 1.00 0.583 0.250 1

317 LO 7,500 28.39 1.73E-03 2.61 10.88 3.11 0.620 0.250 1

318 LO 7,000 26.50 1.87E-05 2.43 10.92 2.93 0.456 0.300 1

319 LO 5,800 21.96 4.80E-04 2.01 10.94 2.51 0.368 0.250 1

320 LO 9,000 34.07 7.74E-04 3.11 10.95 3.61 0.303 0.327 1

321 LO 7,500 28.39 1.87E-05 2.59 10.96 3.09 0.541 0.311 1

322 LO 7,000 26.50 2.93E-03 2.41 11.00 2.91 0.516 0.250 1

323 LO 6,800 25.74 2.81E-03 2.33 11.06 1.50 0.765 0.250 1

324 LO 7,500 28.39 6.55E-06 2.56 11.07 3.06 0.538 0.285 1

325 LO 7,500 28.39 1.46E-05 2.55 11.12 3.05 0.478 0.314 1

326 LO 8,000 30.28 1.60E-01 2.70 11.23 3.20 0.738 0.250 1

327 LO 11,200 42.40 1.34E-03 3.75 11.29 4.25 0.553 0.250 1

328 LO 9,000 34.07 2.88E-03 2.99 11.38 3.49 0.347 0.250 1

329 LO 9,000 34.07 1.48E-05 2.96 11.53 2.20 0.658 0.336 1

330 LO 9,300 35.20 1.98E-05 3.05 11.55 3.55 0.498 0.284 1

331 LO 7,500 28.39 6.55E-06 2.45 11.58 2.95 0.351 0.250 1

332 LO 7,500 28.39 1.78E-05 2.44 11.64 2.94 0.780 0.292 1

333 LO 8,000 30.28 9.95E-04 2.59 11.70 3.09 0.800 0.250 1

334 LO 9,000 34.07 1.51E-02 2.88 11.83 2.90 0.800 0.303 1

335 LO 9,000 34.07 1.51E-02 2.87 11.87 3.37 0.768 0.374 1

336 LO 10,000 37.85 9.60E-04 3.11 12.17 3.61 0.369 0.417 1

337 LO 9,100 34.45 1.46E-03 2.82 12.20 3.32 0.565 0.282 1

338 LO 9,900 37.48 3.03E-03 3.06 12.25 3.56 0.466 0.360 1

339 LO 8,000 30.28 7.52E-05 2.46 12.29 2.96 0.470 0.424 1

340 LO 10,000 37.85 7.82E-05 3.08 12.30 3.58 0.578 0.423 1

341 LO 8,000 30.28 6.79E-05 2.45 12.35 2.95 0.536 0.250 1

342 LO 9,000 34.07 1.48E-05 2.74 12.45 3.24 0.567 0.261 1

343 LO 9,000 34.07 1.51E-02 2.72 12.51 3.22 0.570 0.389 1

(continued)
Table 3B-3. (continued)

Appendix 3B Aerated Tank Data

Tank

Index

Aeration

Level

Size

(gal)

Capacity

(m3

)

Throughput

(m3

/s ) Depth (m)

Surface

Area (m2)

Source

Height

(m)

Fraction

Aerated

Total

Aerator

Horsepower

Number of

Impellers/

Aerators

ATIndex Q_wmu d_wmu SrcArea SHight F_aer Powr n_imp

3-96

344 LO 6,850 25.93 5.04E-03 2.07 12.53 2.57 0.579 0.250 1

345 LO 10,000 37.85 1.20E-04 3.01 12.57 3.51 0.758 0.260 1

346 LO 10,000 37.85 4.05E-04 2.99 12.64 3.30 0.297 0.354 1

347 LO 10,000 37.85 4.08E-05 2.98 12.69 3.48 0.234 0.316 1

348 LO 7,680 29.07 2.41E-03 2.27 12.83 2.77 0.463 0.273 1

349 LO 9,000 34.07 2.67E-02 2.62 13.00 3.12 0.477 0.250 1

350 LO 10,000 37.85 1.50E-03 2.91 13.00 3.41 0.719 0.338 1

351 LO 8,000 30.28 9.95E-04 2.32 13.04 2.82 0.578 0.278 1

352 LO 10,000 37.85 4.08E-05 2.88 13.13 3.38 0.710 0.250 1

353 LO 10,000 37.85 2.19E-02 2.86 13.25 3.36 0.204 0.420 1

354 LO 11,000 41.64 3.42E-04 3.14 13.27 3.64 0.510 0.250 1

355 LO 10,000 37.85 6.60E-05 2.85 13.28 3.35 0.384 0.250 1

356 LO 10,885 41.20 2.70E-03 3.09 13.34 3.59 0.427 0.314 1

357 LO 9,000 34.07 1.51E-02 2.55 13.38 3.05 0.372 0.276 1

358 LO 10,000 37.85 4.08E-05 2.79 13.59 3.29 0.577 0.336 1

359 LO 12,000 45.42 1.50E-04 3.33 13.65 3.83 0.466 0.313 1

360 LO 9,000 34.07 1.51E-02 2.49 13.66 2.99 0.438 0.250 1

361 LO 9,300 35.20 1.98E-05 2.56 13.77 0.50 0.750 0.250 1

362 LO 9,000 34.07 7.74E-04 2.47 13.77 2.97 0.442 0.250 1

363 LO 12,000 45.42 2.40E-03 3.28 13.87 3.78 0.592 0.399 1

364 LO 10,000 37.85 9.12E-06 2.72 13.94 3.22 0.408 0.250 1

365 LO 10,000 37.85 7.82E-05 2.69 14.06 3.10 0.772 0.273 1

366 LO 10,000 37.85 1.09E-02 2.68 14.11 3.18 0.708 0.479 1

367 LO 12,800 48.45 3.74E-04 3.40 14.26 3.90 0.553 0.440 1

368 LO 10,000 37.85 4.08E-05 2.65 14.26 3.15 0.527 0.307 1

369 LO 10,000 37.85 9.72E-06 2.65 14.29 3.15 0.564 0.363 1

370 LO 10,000 37.85 9.60E-04 2.64 14.35 3.14 0.550 0.446 1

371 LO 10,000 37.85 9.60E-04 2.63 14.39 3.13 0.707 0.250 1

372 LO 10,000 37.85 4.80E-04 2.61 14.48 3.11 0.315 0.331 1

373 LO 12,600 47.70 1.51E-02 3.24 14.72 3.74 0.617 0.250 1

374 LO 10,000 37.85 1.50E-03 2.57 14.74 3.07 0.690 0.250 1

375 LO 10,000 37.85 3.81E-03 2.56 14.76 3.06 0.515 0.250 1

376 LO 11,000 41.64 3.42E-04 2.80 14.86 3.30 0.631 0.250 1

377 LO 10,000 37.85 5.45E-03 2.52 15.05 3.02 0.200 0.250 1

378 LO 12,000 45.42 5.06E-03 2.99 15.18 3.49 0.279 0.689 1

379 LO 10,640 40.28 2.70E-03 2.65 15.20 3.15 0.648 0.370 1

380 LO 15,000 56.78 1.19E-03 3.72 15.28 4.22 0.645 0.639 1

381 LO 13,400 50.72 2.80E-02 3.31 15.32 3.81 0.685 0.250 1

382 LO 12,000 45.42 1.50E-04 2.96 15.35 3.46 0.459 0.250 1

383 LO 11,360 43.00 2.70E-03 2.79 15.43 3.29 0.700 0.410 1

384 LO 12,000 45.42 5.52E-03 2.91 15.59 3.41 0.367 0.250 1

385 LO 15,000 56.78 4.66E-03 3.62 15.67 3.20 0.476 0.479 1

386 LO 12,500 47.32 1.50E-04 3.02 15.68 3.52 0.662 0.250 1

387 LO 12,800 48.45 3.74E-04 3.08 15.74 3.58 0.279 0.299 1

388 LO 8,000 30.28 1.50E-05 1.92 15.78 2.42 0.321 0.336 1

389 LO 13,500 51.10 8.08E-03 3.21 15.90 3.71 0.689 0.312 1

390 LO 8,700 32.93 1.09E-05 2.07 15.90 2.57 0.225 0.254 1

391 LO 10,000 37.85 4.05E-04 2.34 16.16 2.84 0.615 0.564 1

392 LO 12,000 45.42 6.24E-05 2.81 16.18 3.31 0.325 0.450 1

(continued)
Table 3B-3. (continued)

Appendix 3B Aerated Tank Data

Tank

Index

Aeration

Level

Size

(gal)

Capacity

(m3

)

Throughput

(m3

/s ) Depth (m)

Surface

Area (m2)

Source

Height

(m)

Fraction

Aerated

Total

Aerator

Horsepower

Number of

Impellers/

Aerators

ATIndex Q_wmu d_wmu SrcArea SHight F_aer Powr n_imp

3-97

393 LO 12,000 45.42 3.86E-04 2.79 16.27 3.29 0.736 0.359 1

394 LO 11,000 41.64 3.42E-04 2.55 16.36 3.05 0.453 0.340 1

395 LO 8,500 32.18 4.48E-03 1.94 16.57 2.44 0.200 0.250 1

396 LO 12,500 47.32 1.50E-04 2.81 16.84 3.31 0.588 0.513 1

397 LO 12,000 45.42 1.57E-01 2.65 17.17 3.15 0.487 0.284 1

398 LO 12,000 45.42 1.25E-03 2.63 17.24 3.13 0.800 0.506 1

399 LO 12,000 45.42 1.44E-06 2.63 17.24 3.13 0.764 0.342 1

400 LO 11,200 42.40 1.34E-03 2.45 17.31 2.95 0.745 0.399 1

401 LO 11,200 42.40 1.34E-03 2.44 17.39 2.94 0.495 0.250 1

402 LO 12,000 45.42 1.19E-03 2.61 17.39 3.11 0.445 0.347 1

403 LO 10,000 37.85 1.09E-03 2.18 17.40 2.68 0.205 0.250 1

404 LO 15,000 56.78 2.85E-05 3.25 17.46 3.75 0.800 0.551 1

405 LO 12,000 45.42 3.86E-04 2.60 17.47 3.10 0.405 0.387 1

406 LO 12,000 45.42 6.24E-05 2.58 17.59 3.08 0.458 0.357 1

407 LO 11,000 41.64 3.42E-04 2.36 17.68 2.86 0.538 0.286 1

408 LO 15,000 56.78 1.37E-02 3.18 17.88 3.68 0.496 0.464 1

409 LO 15,000 56.78 2.19E-03 3.16 17.96 3.66 0.498 0.264 1

410 LO 11,200 42.40 1.34E-03 2.34 18.09 2.84 0.739 0.373 1

411 LO 11,500 43.53 4.80E-06 2.37 18.36 2.87 0.442 0.250 1

412 LO 14,490 54.85 9.37E-03 2.98 18.41 2.70 0.200 0.691 1

413 LO 13,500 51.10 8.08E-03 2.77 18.44 3.27 0.499 0.343 1

414 LO 12,000 45.42 1.44E-01 2.44 18.61 2.94 0.282 0.329 1

415 LO 16,000 60.57 1.44E-03 3.24 18.68 3.74 0.646 0.250 1

416 LO 12,000 45.42 1.25E-03 2.42 18.78 2.92 0.286 0.250 1

417 LO 14,950 56.59 2.70E-03 3.01 18.79 3.51 0.505 0.608 1

418 LO 12,800 48.45 3.74E-04 2.57 18.83 3.07 0.515 0.379 1

419 LO 13,500 51.10 9.23E-05 2.70 18.90 3.20 0.200 0.351 1

420 LO 15,000 56.78 3.86E-04 2.98 19.03 3.48 0.461 0.349 1

421 LO 13,400 50.72 2.80E-02 2.65 19.14 3.15 0.493 0.389 1

422 LO 15,000 56.78 3.42E-04 2.95 19.27 3.45 0.600 0.411 1

423 LO 14,950 56.59 2.70E-03 2.91 19.43 3.41 0.557 0.648 1

424 LO 13,400 50.72 2.80E-02 2.61 19.43 3.11 0.451 0.312 1

425 LO 15,000 56.78 1.19E-03 2.91 19.51 3.41 0.200 0.620 1

426 LO 13,400 50.72 4.99E-05 2.56 19.84 3.06 0.780 0.250 1

427 LO 15,000 56.78 3.42E-04 2.86 19.85 3.36 0.784 0.400 1

428 LO 14,000 53.00 2.94E-04 2.65 19.96 2.20 0.512 0.289 1

429 LO 12,000 45.42 3.86E-04 2.27 20.03 2.77 0.628 0.455 1

430 LO 15,000 56.78 3.42E-04 2.80 20.29 3.30 0.657 0.544 1

431 LO 15,000 56.78 7.24E-07 2.80 20.29 1.10 0.469 0.452 1

432 LO 15,000 56.78 3.42E-04 2.79 20.35 3.29 0.465 0.513 1

433 LO 13,000 49.21 4.82E-03 2.41 20.43 2.91 0.443 0.310 1

434 LO 20,000 75.71 1.85E-04 3.70 20.44 4.20 0.800 0.399 1

435 LO 12,800 48.45 3.81E-04 2.35 20.66 2.85 0.563 0.349 1

436 LO 15,000 56.78 1.19E-03 2.72 20.88 3.22 0.455 0.372 1

437 LO 15,000 56.78 3.42E-04 2.71 20.94 3.21 0.654 0.260 1

438 LO 17,000 64.35 2.52E-05 3.05 21.11 3.55 0.613 0.404 1

439 LO 20,000 75.71 1.85E-04 3.52 21.53 4.02 0.660 0.781 1

440 LO 20,000 75.71 5.42E-04 3.50 21.66 4.00 0.283 0.581 1

441 LO 15,500 58.67 4.46E-04 2.69 21.80 3.19 0.513 0.609 1

(continued)
Table 3B-3. (continued)

Appendix 3B Aerated Tank Data

Tank

Index

Aeration

Level

Size

(gal)

Capacity

(m3

)

Throughput

(m3

/s ) Depth (m)

Surface

Area (m2)

Source

Height

(m)

Fraction

Aerated

Total

Aerator

Horsepower

Number of

Impellers/

Aerators

ATIndex Q_wmu d_wmu SrcArea SHight F_aer Powr n_imp

3-98

442 LO 13,400 50.72 2.80E-02 2.32 21.87 2.82 0.416 0.297 1

443 LO 20,000 75.71 3.60E-05 3.45 21.91 3.95 0.356 0.431 1

444 LO 20,000 75.71 1.14E-04 3.44 22.00 3.94 0.572 0.528 1

445 LO 15,000 56.78 2.26E-05 2.56 22.21 3.06 0.200 0.550 1

446 LO 15,000 56.78 1.50E-05 2.54 22.34 3.04 0.321 0.250 1

447 LO 15,000 56.78 1.19E-03 2.51 22.64 3.01 0.232 0.345 1

448 LO 20,000 75.71 1.14E-04 3.33 22.72 3.83 0.250 0.250 1

449 LO 20,000 75.71 2.06E-01 3.30 22.93 3.80 0.375 0.457 1

450 LO 15,000 56.78 1.50E-03 2.46 23.05 2.96 0.687 0.535 1

451 LO 15,000 56.78 8.15E-04 2.46 23.08 2.96 0.744 0.576 1

452 LO 20,000 75.71 6.24E-03 3.25 23.30 3.75 0.503 0.801 1

453 LO 20,000 75.71 1.85E-04 3.21 23.56 3.71 0.320 0.538 1

454 LO 21,600 81.76 3.00E-04 3.44 23.79 3.94 0.452 0.697 1

455 LO 24,000 90.85 1.80E-05 3.79 23.99 4.29 0.525 0.480 1

456 LO 20,000 75.71 1.06E-03 3.15 24.02 3.65 0.496 0.329 1

457 LO 20,000 75.71 5.42E-04 3.14 24.11 3.64 0.538 0.570 1

458 LO 15,000 56.78 4.66E-03 2.35 24.20 2.85 0.529 0.250 1

459 LO 15,000 56.78 1.50E-04 2.33 24.33 1.70 0.569 0.582 1

460 LO 21,000 79.49 3.02E-07 3.25 24.46 3.75 0.253 0.665 1

461 LO 15,000 56.78 2.23E-05 2.30 24.68 2.80 0.800 0.250 1

462 LO 20,000 75.71 5.04E-03 3.03 24.99 3.53 0.550 0.614 1

463 LO 21,000 79.49 1.44E-03 3.17 25.06 3.20 0.519 0.563 1

464 LO 20,000 75.71 1.85E-04 3.02 25.11 3.52 0.372 0.289 1

465 LO 20,000 75.71 3.60E-05 3.00 25.23 3.50 0.408 0.808 1

466 LO 21,000 79.49 3.02E-07 3.15 25.25 3.65 0.611 0.720 1

467 LO 20,000 75.71 5.04E-03 2.99 25.28 3.49 0.402 0.286 1

468 LO 20,000 75.71 3.79E-05 2.97 25.46 2.30 0.554 0.636 1

469 LO 20,000 75.71 1.06E-03 2.86 26.51 3.36 0.501 0.495 1

470 LO 20,000 75.71 3.57E-04 2.84 26.66 3.34 0.581 0.871 1

471 LO 23,540 89.11 2.70E-03 3.28 27.13 3.78 0.200 0.742 1

472 LO 26,930 101.94 5.06E-06 3.74 27.24 2.50 0.308 1.269 1

473 LO 20,000 75.71 5.04E-03 2.78 27.25 3.28 0.800 0.766 1

474 LO 25,000 94.63 9.00E-04 3.43 27.57 3.93 0.500 0.975 1

475 LO 20,000 75.71 1.14E-04 2.71 27.90 1.10 0.557 0.790 1

476 LO 23,000 87.06 1.46E-02 3.07 28.37 3.57 0.370 0.719 1

477 LO 20,948 79.30 7.32E-05 2.78 28.52 3.28 0.800 0.730 1

478 LO 21,000 79.49 4.48E-03 2.78 28.59 3.28 0.429 0.717 1

479 LO 25,000 94.63 5.46E-03 3.25 29.14 3.75 0.548 1.079 1

480 LO 15,000 56.78 1.78E-05 1.94 29.25 2.44 0.339 0.331 1

481 LO 25,000 94.63 5.96E-02 3.24 29.25 2.80 0.394 0.832 1

482 LO 14,360 54.36 6.79E-03 1.85 29.42 2.35 0.558 0.499 1

483 LO 25,000 94.63 5.46E-03 3.19 29.70 1.50 0.284 0.891 1

484 LO 24,000 90.85 1.46E-02 3.03 29.98 3.53 0.555 0.739 1

485 LO 20,000 75.71 5.42E-04 2.52 30.07 3.02 0.530 0.547 1

486 LO 25,000 94.63 9.00E-04 3.11 30.44 3.61 0.424 0.532 1

487 LO 20,000 75.71 4.80E-06 2.48 30.58 2.98 0.528 0.864 1

488 LO 30,000 113.56 1.09E-05 3.71 30.58 4.21 0.580 1.345 1

489 LO 26,930 101.94 5.06E-06 3.30 30.89 3.80 0.200 0.250 1

490 LO 21,000 79.49 4.50E-04 2.56 31.09 3.06 0.537 0.537 1

(continued)
Table 3B-3. (continued)

Appendix 3B Aerated Tank Data

Tank

Index

Aeration

Level

Size

(gal)

Capacity

(m3

)

Throughput

(m3

/s ) Depth (m)

Surface

Area (m2)

Source

Height

(m)

Fraction

Aerated

Total

Aerator

Horsepower

Number of

Impellers/

Aerators

ATIndex Q_wmu d_wmu SrcArea SHight F_aer Powr n_imp

3-99

491 LO 20,000 75.71 5.42E-04 2.34 32.29 2.84 0.739 0.649 1

492 LO 20,000 75.71 3.79E-05 2.34 32.32 2.84 0.567 0.446 1

493 LO 26,000 98.42 1.87E-03 2.96 33.28 0.50 0.541 0.641 1

494 LO 30,000 113.56 4.65E-04 3.40 33.42 3.90 0.546 1.330 1

495 LO 25,000 94.63 2.87E-03 2.83 33.49 3.33 0.650 0.895 1

496 LO 26,000 98.42 1.86E-05 2.92 33.71 3.42 0.421 0.452 1

497 LO 26,930 101.94 5.06E-06 3.02 33.80 1.90 0.327 0.660 1

498 LO 22,000 83.28 1.36E-03 2.46 33.90 2.96 0.800 0.630 1

499 LO 35,000 132.49 5.51E-05 3.81 34.77 4.31 0.432 0.910 1

500 LO 25,000 94.63 9.00E-04 2.71 34.95 3.21 0.751 0.492 1

501 LO 26,930 101.94 5.06E-06 2.91 35.05 3.41 0.800 1.295 1

502 LO 28,000 105.99 3.23E-03 3.01 35.21 3.51 0.436 1.147 1

503 LO 33,000 124.92 1.07E-01 3.53 35.41 4.03 0.554 1.352 1

504 LO 29,000 109.78 5.04E-03 3.08 35.66 3.58 0.597 0.933 1

505 LO 20,000 75.71 1.30E-05 2.11 35.83 2.61 0.527 0.804 1

506 LO 34,000 128.70 9.26E-04 3.51 36.72 4.01 0.478 0.729 1

507 LO 33,000 124.92 2.25E-03 3.32 37.64 3.82 0.518 1.312 1

508 LO 37,300 141.20 5.57E-06 3.74 37.77 4.24 0.359 1.200 1

509 LO 21,200 80.25 5.14E-03 2.12 37.77 2.62 0.598 0.688 1

510 LO 25,000 94.63 2.87E-03 2.47 38.30 2.97 0.388 1.021 1

511 LO 28,000 105.99 1.06E-03 2.74 38.65 3.24 0.652 1.054 1

512 LO 34,000 128.70 3.86E-06 3.33 38.67 3.83 0.584 1.395 1

513 LO 35,000 132.49 4.66E-05 3.42 38.74 1.20 0.272 1.142 1

514 LO 30,000 113.56 3.84E-03 2.93 38.76 3.43 0.468 0.721 1

515 LO 26,930 101.94 5.06E-06 2.62 38.89 3.12 0.524 1.169 1

516 LO 26,930 101.94 5.06E-06 2.60 39.27 1.40 0.800 0.909 1

517 LO 24,300 91.99 1.44E-06 2.31 39.85 2.81 0.784 0.589 1

518 LO 35,000 132.49 2.49E-03 3.28 40.39 3.78 0.541 0.793 1

519 LO 25,000 94.63 9.00E-04 2.34 40.47 2.84 0.473 0.788 1

520 LO 36,000 136.27 1.80E-05 3.34 40.84 0.50 0.293 1.847 1

521 LO 40,000 151.42 7.64E-05 3.69 41.00 0.50 0.204 1.180 1

522 LO 37,300 141.20 5.57E-06 3.43 41.18 3.93 0.495 1.070 1

523 LO 41,000 155.20 2.79E-03 3.75 41.36 4.25 0.705 1.458 1

524 LO 41,000 155.20 2.28E-03 3.72 41.70 4.22 0.449 1.925 1

525 LO 40,000 151.42 2.96E-02 3.61 41.92 4.11 0.358 0.988 1

526 LO 40,465 153.18 6.76E-03 3.49 43.89 3.99 0.523 1.385 1

527 LO 40,000 151.42 2.16E-05 3.40 44.50 3.90 0.389 1.925 1

528 LO 40,933 154.95 2.57E-01 3.46 44.79 3.96 0.684 1.428 1

529 LO 50,000 189.27 7.44E-04 4.12 45.96 4.62 0.403 1.820 1

530 LO 30,000 113.56 7.20E-03 2.46 46.12 2.96 0.691 0.836 1

531 LO 40,000 151.42 2.87E-05 3.28 46.20 3.78 0.453 1.625 1

532 LO 40,000 151.42 2.96E-02 3.27 46.24 3.77 0.200 0.560 1

533 LO 37,000 140.06 1.07E-01 3.01 46.55 3.51 0.458 1.599 1

534 LO 47,300 179.05 2.05E-03 3.80 47.10 4.30 0.641 1.775 1

535 LO 41,000 155.20 2.79E-03 3.17 48.95 3.67 0.307 1.204 1

536 LO 37,300 141.20 5.57E-06 2.88 49.00 3.38 0.626 0.906 1

537 LO 46,000 174.13 2.28E-03 3.55 49.04 1.30 0.598 0.788 1

538 LO 41,000 155.20 2.79E-03 3.11 49.97 3.61 0.640 1.530 1

539 LO 47,000 177.91 1.07E-01 3.50 50.79 0.90 0.449 1.674 1

(continued)
Table 3B-3. (continued)

Appendix 3B Aerated Tank Data

Tank

Index

Aeration

Level

Size

(gal)

Capacity

(m3

)

Throughput

(m3

/s ) Depth (m)

Surface

Area (m2)

Source

Height

(m)

Fraction

Aerated

Total

Aerator

Horsepower

Number of

Impellers/

Aerators

ATIndex Q_wmu d_wmu SrcArea SHight F_aer Powr n_imp

3-100

540 LO 48,600 183.97 6.67E-04 3.60 51.09 4.10 0.650 1.772 1

541 LO 55,000 208.20 7.08E-03 4.00 52.08 4.50 0.616 1.779 1

542 LO 46,000 174.13 2.41E-03 3.33 52.31 3.83 0.720 1.205 1

543 LO 40,000 151.42 7.64E-05 2.87 52.78 3.37 0.630 1.196 1

544 LO 40,000 151.42 2.96E-02 2.87 52.81 3.37 0.440 1.743 1

545 LO 54,000 204.41 1.63E-02 3.84 53.20 4.34 0.710 0.761 1

546 LO 55,000 208.20 2.72E-03 3.85 54.11 4.35 0.389 1.328 1

547 LO 55,000 208.20 2.72E-03 3.83 54.38 4.33 0.697 1.706 1

548 LO 50,000 189.27 2.68E-02 3.46 54.70 3.96 0.442 1.064 1

549 LO 55,000 208.20 2.81E-03 3.78 55.14 4.28 0.492 1.905 1

550 LO 37,300 141.20 5.57E-06 2.56 55.18 3.06 0.200 1.039 1

551 LO 61,500 232.80 1.34E-03 4.17 55.84 4.67 0.344 2.283 1

552 LO 55,000 208.20 2.81E-03 3.72 55.97 4.22 0.402 2.169 1

553 LO 57,000 215.77 2.24E-02 3.81 56.61 4.31 0.562 1.701 1

554 LO 50,000 189.27 4.80E-04 3.31 57.10 3.81 0.290 1.585 1

555 LO 61,500 232.80 1.34E-03 3.98 58.50 4.48 0.800 1.552 1

556 LO 48,500 183.59 4.50E-04 3.08 59.61 3.58 0.800 1.670 1

557 LO 50,000 189.27 4.02E-02 3.08 61.37 3.58 0.800 1.387 1

558 LO 50,000 189.27 2.72E-02 3.05 62.01 3.55 0.361 1.073 1

559 LO 61,500 232.80 1.34E-03 3.65 63.70 4.15 0.498 1.344 1

560 LO 50,000 189.27 8.77E-05 2.96 63.90 3.46 0.626 2.093 1

561 LO 48,500 183.59 4.50E-04 2.82 65.11 3.10 0.640 2.705 1

562 LO 67,000 253.62 5.80E-03 3.69 68.79 0.90 0.249 2.977 1

563 LO 65,000 246.05 7.84E-06 3.53 69.77 1.10 0.200 1.722 1

564 LO 65,000 246.05 1.05E-02 3.51 70.17 4.01 0.229 2.586 1

565 LO 46,000 174.13 5.04E-04 2.44 71.34 2.30 0.385 1.307 1

566 LO 48,000 181.70 3.47E-03 2.51 72.51 3.01 0.342 1.253 1

567 LO 80,000 302.83 5.04E-03 4.11 73.67 3.70 0.378 3.230 1

568 LO 80,000 302.83 5.04E-03 3.92 77.17 4.42 0.633 1.738 1

569 LO 55,000 208.20 2.81E-03 2.66 78.37 3.16 0.800 1.723 1

570 LO 65,000 246.05 7.84E-06 3.13 78.53 3.63 0.406 2.017 1

571 LO 80,000 302.83 8.07E-03 3.79 79.95 4.29 0.533 3.450 1

572 LO 80,000 302.83 5.04E-03 3.65 82.88 1.00 0.612 3.799 1

573 LO 75,830 287.05 4.37E-03 3.41 84.11 3.91 0.479 2.570 1

574 LO 72,000 272.55 2.42E-05 3.14 86.88 3.64 0.354 0.473 1

575 LO 100,000 378.54 2.89E-04 4.30 88.03 4.80 0.297 2.237 1

576 LO 87,000 329.33 1.07E-01 3.57 92.35 0.60 0.574 1.206 1

577 LO 125,000 473.17 3.00E-03 4.70 100.68 5.20 0.781 4.376 1

578 LO 117,800 445.92 9.02E-04 4.32 103.30 4.82 0.505 4.034 1

579 LO 120,000 454.25 4.96E-03 4.36 104.29 2.90 0.289 2.480 1

580 LO 80,000 302.83 8.07E-03 2.89 104.65 2.90 0.259 2.951 1

581 LO 92,400 349.77 3.22E-03 3.22 108.70 3.72 0.781 3.220 1

582 LO 120,000 454.25 7.20E-05 4.13 110.05 4.63 0.200 3.337 1

583 LO 92,400 349.77 3.22E-03 3.17 110.23 3.67 0.711 2.796 1

584 LO 120,000 454.25 7.56E-03 4.01 113.18 0.50 0.385 3.602 1

585 LO 100,000 378.54 2.89E-04 3.31 114.29 3.80 0.401 3.772 1

586 LO 119,700 453.11 1.51E-02 3.77 120.17 4.27 0.553 3.545 1

587 LO 100,000 378.54 2.68E-02 3.10 122.12 3.60 0.504 3.415 1

588 LO 110,000 416.39 1.89E-02 3.40 122.59 3.90 0.550 3.623 1

(continued)
Table 3B-3. (continued)

Appendix 3B Aerated Tank Data

Tank

Index

Aeration

Level

Size

(gal)

Capacity

(m3

)

Throughput

(m3

/s ) Depth (m)

Surface

Area (m2)

Source

Height

(m)

Fraction

Aerated

Total

Aerator

Horsepower

Number of

Impellers/

Aerators

ATIndex Q_wmu d_wmu SrcArea SHight F_aer Powr n_imp

3-101

589 LO 119,700 453.11 1.51E-02 3.59 126.25 4.09 0.358 2.031 1

590 LO 103,600 392.17 1.05E-02 3.06 128.25 3.56 0.598 3.176 1

591 LO 150,000 567.81 1.55E-03 3.96 143.23 4.46 0.391 4.456 1

592 LO 200,000 757.08 4.71E-01 4.70 161.09 5.20 0.326 2.832 1

593 LO 210,000 794.93 1.89E-02 4.88 162.74 4.30 0.678 5.052 1

594 LO 200,000 757.08 4.71E-01 4.61 164.14 5.00 0.404 9.990 1

595 LO 225,000 851.71 2.86E-04 4.88 174.58 5.38 0.674 6.577 1

596 LO 200,000 757.08 4.71E-01 4.30 176.20 4.80 0.757 2.150 1

597 LO 135,000 511.03 2.68E-02 2.74 186.84 3.24 0.469 2.278 1

598 LO 250,000 946.35 4.88E-05 4.71 200.88 5.21 0.437 5.826 1

599 LO 250,000 946.35 4.88E-05 4.67 202.57 5.17 0.586 4.592 1

600 LO 250,000 946.35 2.44E-02 4.58 206.53 3.00 0.585 5.402 1

601 LO 187,000 707.87 8.64E-02 3.36 210.47 2.20 0.670 1.963 1

602 LO 300,000 1,135.62 2.67E-02 4.87 233.15 5.37 0.601 4.749 1

603 LO 250,000 946.35 4.88E-05 3.98 238.02 4.48 0.606 8.768 1

604 LO 320,000 1,211.33 6.00E-04 4.68 258.69 1.50 0.354 7.695 1

605 LO 300,000 1,135.62 8.17E-04 4.31 263.38 4.81 0.513 12.948 1

606 LO 320,000 1,211.33 6.07E-04 4.38 276.61 2.80 0.466 11.313 1

607 LO 420,000 1,589.87 1.07E-01 5.74 276.85 6.24 0.551 8.451 1

608 LO 312,000 1,181.04 1.45E-03 4.25 278.17 4.75 0.315 14.275 1

609 LO 395,300 1,496.37 6.96E-04 5.29 282.79 5.79 0.559 9.977 1

610 LO 395,300 1,496.37 6.96E-04 5.15 290.36 5.65 0.420 14.353 1

611 LO 320,000 1,211.33 6.07E-04 4.12 294.27 4.62 0.794 8.523 1

612 LO 450,000 1,703.43 6.15E-03 5.23 325.81 5.73 0.665 10.896 1

613 LO 415,000 1,570.94 1.86E-03 4.74 331.54 4.50 0.200 6.073 1

614 LO 460,000 1,741.28 1.07E-01 4.89 355.95 5.39 0.717 17.351 1

615 LO 543,300 2,056.61 6.26E-03 5.30 388.02 5.80 0.648 7.178 1

616 LO 500,000 1,892.70 5.40E-02 4.59 412.37 5.09 0.638 11.484 1

617 LO 900,000 3,406.86 3.18E-05 5.73 594.96 6.23 0.200 21.511 1

618 LO 543,300 2,056.61 6.26E-03 3.46 595.02 3.96 0.692 11.471 1

619 LO 900,000 3,406.86 2.72E-02 4.51 755.52 5.01 0.567 9.136 1

620 LO 1,500,000 5,678.10 2.03E-02 6.29 902.31 0.50 0.380 24.253 2

621 LO 1,500,000 5,678.10 8.35E-03 5.51 1,031.00 6.01 0.648 39.801 2

622 LO 1,500,000 5,678.10 2.03E-02 5.13 1,107.74 5.63 0.330 35.589 1

623 LO 3,000,000 11,356.19 4.02E-02 6.18 1,837.68 6.68 0.744 55.158 1

624 LO 6,720,000 25,437.88 2.36E-03 7.03 3,616.24 1.10 0.200 183.260 2
Appendix 3B Aerated Tank Data

3-102

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