TREATMENT WETLANDS - CHAPTER 20 pot

Prescrip-tive criteria represent a reasonable approach to wetland siz-ing when there is a large body of preexistsiz-ing performance data for well-defined pollutants loading charts, and a

The design of subsurface flow (SSF) wetlands may be roughly

divided into two categories: sizing calculations and physical

specifications Sizing requires characterization of the

incom-ing water and regional climate as well as the goals of wetland

treatment, as discussed in Chapter 16 This chapter discusses

different sizing methodologies for SSF wetlands Chapter 21

deals with the physical considerations, including the

num-ber of cells, layout, liners, bed depth, media size, plants, and

water level control It is recognized that SSF wetlands are

not stand-alone treatment devices but rather form part of an

overall treatment process

For HSSF wetlands, primary treatment, at a minimum,

is required to remove settleable and floating solids prior to

the wetland bed This is typically provided through the use

of settling tanks Some projects may employ a higher level

of pretreatment through the use of activated sludge systems,

lagoons, or other treatment processes Vertical flow (VF)

wetlands may or may not have such preliminary treatment

stages, and biosolids systems will receive material of

differ-ent quality depending on the nature of the overall treatmdiffer-ent

process This chapter focuses on the sizing of SSF wetland

components, recognizing that this will be only one part of

the overall treatment process

To date, most SSF wetlands have been sized using

pre-scriptive criteria The most common approach has been to

tie the required wetland size to a parameter that presumably

defines the influent conditions Influent loadings,

popula-tion equivalents, detenpopula-tion time, and number of bedrooms

have all been used to create these scaling factors

Prescrip-tive criteria represent a reasonable approach to wetland

siz-ing when there is a large body of preexistsiz-ing performance

data for well-defined pollutants (loading charts), and a more

detailed understanding of the internal dynamics of the

wet-land is not necessary As a result, prescriptive criteria are

used mainly for the design of SSF wetlands that treat

domes-tic wastewater

However, for other projects, there is limited information,

and prescriptive criteria cannot be developed Pilot-scale

treatability studies may have to be conducted to measure rate

constants and temperature effects First-order modeling is

often employed to interpret these results and make

predic-tions on the anticipated performance of a full-scale wetland

Also, first-order modeling can be used to represent internal

profiles of pollutant reduction within the wetland This book

employs the P-k-C* model for performance-based wetland

design

20.1 PRESCRIPTIVE SIZING CRITERIA

Prescriptive criteria have been the most popular method for sizing SSF wetlands because these methods are quick and mathematically simple This has led to the widespread use (and sometimes misuse) of prescriptive methods

Prescriptive criteria are only reasonable when they are based on the observed performance of large numbers of wet-lands built to the same physical specifications treating the same type of wastewater under the same climatic conditions

If treatment is acceptable under a specified condition, and the data set is large enough to capture inter- and intra-system variability, it may reasonably be expected that future wet-lands operated under the same conditions will also provide acceptable treatment (and that deviations from the mean are acceptably small or infrequent) Unfortunately, prescriptive criteria have been adopted in many situations where there is insufficient data to support their use

L OADING C HARTS

Loading charts are design tools, which can be used to size wetland systems An influent loading rate is selected to pro-duce a targeted effluent concentration The wetland area is calculated from the influent mass load Recent examples include U.S EPA (2000a) and Wallace and Knight (2006) Loading charts that can be used in this manner are also included in this book They are composed, mainly, of wet-land data from Europe, North America, the United Kingdom, and New Zealand; so the vast majority of the systems repre-sented in these charts is from cool temperate climates For HSSF wetlands, these charts include

TSS: Figure 7.30(Cout versus Loadin) BOD: Figure 8.26(Cout versus Loadin) Organic N: Figure 9.18 (Cout versus Loadin) Ammonia N: Figure 9.38 (Cout versus Loadin) TKN: Figure 9.24 (Cout versus Loadin) Total N: Figure 9.30 (Cout versus Loadin) Total P: Figure 10.36 (Cout versus Loadin) Fecal coliforms: Figure 12.12 (Cout versus Cin) For VF wetlands, these charts include

TSS: Figure 7.32(Cout versus Loadin) BOD: Figure 8.31(Cout versus Loadin) TKN: Figure 9.25 (Cout versus Loadin)

Because there is considerable scatter in the loading of chart

data, selection of the wetland area will likely be an iterative

process; selection of a lower influent loading (larger wetland

area) will result in lower estimated effluent concentrations

For instance, consider the case of ammonia in Figure 20.1

(replotted from Figure 9.39)

A TKN loading rate of 900 g/m2·yr (approximately

5 m2/PE; Ci approximately 120 mg/L) results in a predicted

effluent ammonia concentration of approximately 15 mg/L

Because of scatter in the data, this estimate of effluent

con-centration can only be considered a central tendency In other

words, approximately half of the wetlands loaded at this rate

will return ammonia concentrations greater than 15 mg/L

If the loading rate is decreased to 300 g/m2·yr

(approxi-mately 15 m2/PE) for the same influent concentration, the

probability that the wetland will produce an effluent

ammo-nia concentration equal to or less than 15 mg/L is increased

considerably This is consistent with the operational

experi-ences with HSSF wetlands, where loadings less than 600 g/

m2·yr (10 m2/PE) are necessary to return low-ammonia

efflu-ents (Geller, 1996)

It is tempting to estimate the probability that the

wet-land will not perform to this standard by counting the

num-ber of data points above the targeted performance goal For

instance, there are seven system · years above the targeted

performance level (15 mg/L at 300 g/m2·yr) out of 47 system

years at or below this loading rate, so the probability that

the wetland will meet the median effluent performance goal

is roughly 85% This predictor only describes median

per-formance over the data averaging period used to construct

the loading chart Because these data averaging periods are

often long (annual or period of record), important aspects

of system performance, such as seasonal or stochastic varia-tions in effluent concentravaria-tions, are lost More importantly, this “point counting” approach does not take into account the effect of the influent concentration ranges For instance,

many data points in Figure 20.1 are for Ci less than 20 mg/L, which is considerably lower than the 120 mg/L influent con-centration used in this example

Because the data averaging period for Figure 20.1 is annual (198 system·years of data are represented), no infor-mation on seasonal or monthly variation is contained in the chart This is a problem when the regulatory compliance interval is short (weeks or months) Some design references have attempted to address this by using shorter data-averaging periods, such as system·months (Wallace and Knight, 2006) However, tools such as those shown in Table 9.35, Chap-ter 9, can be used to assist the designer in this regard As indi-cated in the table, the 90th percentile trend multiplier on the effluent concentrations (1 9) is 1.76 Figure 20.1 indicates that at an influent loading of 300 g/m2·yr, the central tendency

in the effluent ammonia concentrations is approximately

5 mg/L If the wetland performs at or near this central ten-dency, 90% of the effluent results should be less than 8.8 mg/L (1.76 r 5 mg/L) The probability that the wetland will have poor annual and stochastic results can be crudely estimated

by multiplying the probability factors

It should be noted that the amount of data available on the performance of treatment wetlands is much less than stat-isticians are used to working with; thus the application of statistical tools to wetland performance is inherently limited

by the lack of current performance data













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FIGURE 20.1 TKN–Ammonia loading chart, with forecasted treatment performance.

Tools such as those shown in Figure 9.50, Chapter 9,

indicate that some of this variation is likely to be seasonal

There are obvious limitations to the loading chart method

First of all, there must be a functional relationship between

the inlet loading and effluent concentration Figure 7.29 in

Chapter 7 is an example where the effluent TSS

concentra-tion is essentially independent of the influent loading This is

because removal of the influent TSS occurs in only a small

region at the inlet of the HSSF wetland bed, and the overall

wetland size (and hence, loading) does not play a significant

role in determining effluent TSS concentrations Secondly,

there is considerable scatter in loading chart data, because

wetland design, climatic, and loading parameters are not

implemented in the same, exact fashion for all systems

com-prising the data set The resulting data scatter means that a

large component of the wetland design must necessarily rely

on the best professional judgment of the designer Some

per-formance features, such as the role of influent concentration

(independent of influent load), are also lost

Because loading charts require few mathematical

cal-culations, they are an attractive design tool They have also

been misused by designers who do not understand the

inher-ent limitations of the loading chart method It is important to

remember that loading charts will not predict system

perfor-mance if the physical configuration of the wetland is changed

from the parent data set, the pollutant type or concentration

changes, or the wetland is in a different climatic region For

instance, loading charts based on the BOD in domestic

waste-water will not necessarily apply to industrial effluents where

the organic compounds making up the BOD are not the same,

and they will likely have different degradation rates (k-rates)

Also, a loading chart based on a parameter such as BOD will

not predict effluent performance for another pollutant, such

as ammonia Also, as loading charts are based on inlet–outlet

data, predictions about pollutant reductions within the

wet-land (internal profiles) cannot be made

S CALING F ACTORS

Many efforts have been made to reduce the information

con-tained in a loading chart down to a single parameter—a

pre-scriptive scaling factor This is done to further simplify the

design process In general, these scaling factors attempt to

describe two aspects of system performance:

1 Median system performance at a specified inlet

condition

2 A “margin of excursion containment” that is intended

to allow for intersystem (seasonal and stochastic) as

well as intrasystem (differences in the performance

response of different wetland systems) variabilities

This margin of excursion containment is rarely defined in the

treatment wetland field; the only exception to date is Wallace

and Knight (2006)

There is often an underlying assumption that bigger is

better (larger wetland area for a given influent condition),

and this has often been used as an excuse to not rigorously collect or analyze performance data As a result, many pre-scriptive criteria are overly conservative and hinder, rather than help, the reliability of the wetland There are numerous examples of “bad” or outmoded concepts that are imbedded

in various “prescriptive rules.” Examples include making the wetland excessively large (exacerbating freezing

prob-lems in cold environments and water loss to ET in arid

cli-mates), specifying long length:width ratios (maximizing the cross-sectional loading and associated clogging problems) for HSSF wetlands, and specifying deep beds to increase the hydraulic retention time (exacerbating vertical stratification

of the flow)

Scaling Factors for HSSF Wetlands

Scaling factors provide the simplest approach to wetland design; thus it is not surprising that this is the most com-monly used design method for HSSF wetlands Examples of some existing criteria include

5 m2/PE (BOD loading equivalent to 8 g/m2·d) to produce an effluent BOD of less than 30 mg/L for primary-treated domestic wastewater

Source: (EC/EWPCA Emergent Hydrophyte

Treat-ment Systems Expert Contact Group and Water Research Centre, 1990)

3.1 cm/d

Source: (TVA, 1993)

Bed sizing q 5 m2/PE (BOD loading equivalent to

8 g/m2·d)

Source: (ATV, 1998; ÖNORM B 2505, 2005)

6 g/m2·d of inlet BOD loading to produce an efflu-ent with less than 30 mg/L BOD

Source: (U.S EPA, 2000a)

8 g/m2·d of inlet BOD loading to produce a median effluent concentration of 30 mg/L; 5 g/m2·d to pro-duce a median effluent concentration less than 25 mg/L BOD

Source: (Wallace and Knight, 2006)

10 to 13 days’ nominal hydraulic retention time

Source: (Gustafson et al., 2001)

41 L/m2·d; two to three days’ nominal hydraulic retention time

Source: (Lesikar, 1999)

28 m2 per bedroom per day; trenches 30 cm wide and 92 m long per bedroom

Source: (Iowa Department of Natural Resources,

2001)

Scaling Factors for VF Wetlands

Scaling factors have also been widely employed to size inter-mittently loaded VF wetland systems, especially in Europe Scaling factors that are currently in use are summarized in

Table 15.1, in Chapter 15

•

•

•

•

•

•

•

•

Cautions Regarding the Use of Scaling Factors

These summaries are by no means a complete list of all the

scaling factors existing in the treatment wetland field

Obvi-ously, these examples bear little functional relationship to

each other; all are clearly artifacts of the available data,

reg-ulatory situation, and climatic conditions where they were

developed The designer is thus cautioned that extrapolating

these types of prescriptive rules beyond their local

regula-tory jurisdiction will likely result in inappropriate wetland

sizing; also, many of the physical specifications contained

in these prescriptive rules are obsolete and have not

with-stood the test of modern data analysis in the treatment wet-

land field

E MPIRICAL E QUATIONS

Empirical equations are another means of predicting system

performance These equations are developed based on a

pre-existing data set (similar to those summarized in the loading

charts presented in this book) This data set is then analyzed

to determine a mathematical relationship that has the highest

level of statistical correlation It is important to note that this

method makes no attempt to describe the internal dynamics

of the wetland system and is utterly dependent on the input–

output data set being analyzed Recent examples of this

method are included in Crites et al (2006) and Figure 8.27,

Chapter 8, of this book

Empirical equations can best be thought of as a

sliding-scale “rule of thumb.” All the limitations inherent in the

load-ing chart method and scalload-ing factors also apply to empirical

equations As a result, designers are cautioned that applying

empirical equations to situations not representative of the

parent data set will often result in inappropriate designs

20.2 PERFORMANCE-BASED WETLAND SIZING

In performance-based design, the effects of degradation rate

coefficients (k-rates), temperature (Q-factors), and the

com-bined effects of internal hydraulics and pollutant

weather-ing (PTIS) are considered discretely The great advantage of

this method is that it allows prediction of treatment wetland

performance across different hydraulic and temperature

regimes

Historically, early HSSF designs were based on first-order

modeling with the assumption of plug flow (EC/EWPCA

Emergent Hydrophyte Treatment Systems Expert Contact

Group and Water Research Centre, 1990) In the realm of

proprietary wetland designs, this method was extended to

waste-specific k-rates and pollutant weathering for soil-media

HSSF wetlands (Kickuth, 2002), although these results were

not reported to the international scientific community

The first-order approach was largely abandoned in

Europe in favor of simple scaling factors because the

major-ity of HSSF wetlands were implemented for small flows of

domestic wastewater under similar climatic conditions

In North America, implementation of HSSF wetlands was less rapid than in Europe However, there has been an expanding interest in using HSSF wetlands for applications other than domestic wastewater treatment, resulting in the continued development of sizing models using first-order kinetics (Kadlec and Knight, 1996)

This book includes a performance-based approach to

HSSF wetland sizing using the first-order P-k-C* model

Conceptually, this is a tank-in-series (TIS) model using

a relaxed parameter, P, to account for both hydraulic and weathering effects The P-k-C* model is discussed in detail

in Chapter 6 This performance-based approach is in contrast to most

of the currently existing wetland manuals, which advocate a prescriptive loading approach for HSSF wetland design In this book, loading charts are proposed as a means to check the relative conservativeness of a proposed wetland sizing

The P-k-C* model has a number of advantages for HSSF

wetland sizing, which include Loading charts cannot predict internal profiles of pollutant reduction, but this is easily done through first-order modeling

Differences in hydraulic efficiency or pollutant weathering can be explicitly addressed This is especially important when designing full-scale wetlands based on pilot system data, which may have different hydraulic properties

The first-order approach allows the modeling of sequential removals, such as for nitrogen

Effects of a nonzero background concentration,

C*, may be explicitly addressed.

The procedure outlined here is for a constant influent flow situation Different influent flow rates may need to be consid-ered to deal with daily or seasonal peaking factors Different seasons may need to be considered so that the design-limiting (“bottleneck”) period of the year is identified and analyzed The design-limiting period depends on the goals of the project and the climate of the project location For instance, for proj-ects in very cold climates, operation of the wetland without freezing during the winter months may be a design priority In

hot, arid climates, water loss due to evapotranspiration (ET)

may be a major concern

B ASIC A PPLICATION OF THEP- K -C*

M ODEL TO HSSF W ETLANDS

The simplest situation is where there are no gains of water

from precipitation or losses of water due to ET or infiltra-tion This can be easily described using the P-k-C* model

(Figure 17.1, Chapter 17) and Equation 6.57 in Chapter 6, restated here as Equation 20.1:

¤

¦

¥

³ µ

*

1 1

1

•

•

•

•

k modified first-order areal rate constant, m/d

modified first-order volumetric rat

V

apparent number of TIS

1

P

An example is detailed in Table 20.1 This hypothetical

exam-ple is configured for a small HSSF wetland (100 PE) with a

BOD loading of 40 g/PE and a flow rate of 200, L/PE (Ci 200

mg/L) A k-rate of 45 m/yr is selected based on the

distribu-tion shown in Table 8.7 Chapter 8 The wetland is assumed to

hydraulically function as eight tanks in series (see Table 6.2,

Chapter 6); a reduced value of P 4 has been selected here

to account for weathering of the BOD mixture as it passes

through the wetland The background concentration, C*, has

been estimated at 8 mg/L based on Figure 8.27, Chapter 8

Equation 20.1 can be solved directly, or the calculation

can be completed one tank at a time using a computer

spread-sheet, as shown in Table 20.1

W ATER B UDGET E FFECTS

If there is a net gain or loss of water from the wetland, effluent

concentrations will be different from those predicted by

Equa-tion 20.1 For HSSF wetlands operating under hot, arid

condi-tions, water loss from ET may be a significant design concern,

and the effects of the water budget should be considered

The annual water budget forms the basis for a first approach

to understanding pollutant reductions and the area required

Under the assumption of a constant water level, the flows from

the wetland may be computed from the influent flow rate and

meteorological data from the vicinity of the project site

Calcu-lation procedures have been established in Chapter 2

The inlet hydraulic loading will be increased by

rain-fall (y0.5–1.5 m/yr) and decreased by ET (y0.5–1.5 m/yr)

However, there may be seasonal imbalances These amounts are important if the wetland is to have a very low hydrau-lic loading, or correspondingly, a long detention time Net

evapotranspiration (ET  P) has two effects: lengthening of

detention time and concentration of dissolved constituents

The inverse is true in net precipitation environments (P 

ET ) The use of an average flow rate compensates for altered

detention time but not for dilution or concentration There-fore, it may be prudent to consider worst-case seasonal con-ditions when calculating water-budget effects on the wetland treatment performance

Pollutant mass balances are conducted on a cells-in-series basis (see Figure 20.2) Results of the overall water mass balance are apportioned to the cells according the chosen number of TIS For the first unit in the series (Equation 6.66

in Chapter 6, restated here as Equation 20.2),

Q1QiA P1( ET I) (20.2) where

A ET

1

2 area of the first segment (tank), m e

 vvapotranspiration, m/d infiltration, m/d

I P

precipitation, m/d inlet flow rate, m / i

3

outlet flow rate from segment #1, m /d 1

3

The additional data input requirements for the water mass balances are

1 Inflow (Qi)

2 Precipitation (P)

3 Evapotranspiration (ET)

4 Infiltration (I)

5 Area (A)

TABLE 20.1

Estimated Pollutant Reduction Using a First-Order (P-k-C*) Model for Constant Flows (P  ET )

Flow rate, Q 20 m 3 /d Volume per tank 14.3 m 3

Area, A 500 m 2 Influent flow, Qi 20.0 m 3 /d

Porosity, E 0.38 Effluent flow, Qo 20.0 m 3 /d

Bed depth 0.30 m Average flow, Qavg 20.0 m 3 /d

Ci 200 mg/L Effluent mass load 551 g/d

k 45 m/yr HRT based on Qavg 2.85 d

k 0.123 m/d HRT based on PTIS 2.85 d

Concentration, C mg/L 200.0 116.4 69.2 42.6 27.5 27.5

The example of Table 20.1 is continued forward to

exam-ine a situation where there is a net loss of water because

of ET Results of the hydraulic changes are summarized in

Table 20.2 For ease of comparison, the parameter P has been

kept at P  4 (even though a higher value could be justified in

the absence of pollutant weathering)

As seen in Table 20.2, there is a net change in the flow

rate, hydraulic loading, and detention time as water flows

through the wetland reaction segments, because in this

par-ticular example, approximately 25% of the water flow is lost

through the system Averaging the inlet and outlet flows does

not fully account for these effects

P OLLUTANT M ASS B ALANCES

If there is a net gain or loss of water within the system, the

flow in each reaction compartment is different, and it becomes

necessary to calculate contaminant removals on a mass basis,

as concentration alone no longer adequately describes system

performance

This computation is then repeated for the remaining

seg-ments, in each case using the outlet concentrations and flows

from the preceding segment The wetland outlet concentra-tion is that exiting from the final reacconcentra-tion segment The mass output is calculated from the effluent flow rate and output concentration

As the flow rate is different for each reaction segment, Equation 20.1 cannot be used to directly calculate the result Instead, calculations must be carried out sequentially for each reaction segment (tank) This is most easily done using a com-puter spreadsheet

Comparison of Table 20.1 with Table 20.2 illustrates that the effluent concentrations predicted by these methods

are almost identical Water is lost to ET, which concentrates

pollutants within the wetland However, the volume reduc-tion results in lower hydraulic loading rates (longer detenreduc-tion times), so a greater degree of treatment also occurs within the wetland reactor Use of average flow accounts for only the longer detention and not the evaporative concentration Reviewing the system performance from a mass load basis reveals a very different picture In the example of Table 20.1, the assumption is of no water loss, so the effluent mass load is 27.5 g/m3r 20 m3/d, or 551 g/d In the example

of Table 20.2, the effluent flow has been reduced by 24%, so the effluent mass load is 27.6 g/m3r 15.2 m3/d, or 420 g/d

and Iterations

Parameter Selection and Calculation

Secondary Considerations

Set influent flow and concentrations

Establish target effluent concentrations

Set inflow and seepage

Determine precipitation, ET, and temperatures

Select k-rates for targeted parameters

Select P-values for targeted parameters

Select C* values for targeted parameters

Estimate wetland area

Check proposed wetland site against loading charts

Assess seasonal impacts of plant biomass cycling

Finalize wetland area

Assess the regulatory compliance interval, and select appropriate trend multipliers (1 + Ψ)

Adjust k-rate

if necessary

Adjust k-rate

if necessary

Adjust k-rate

if necessary

Check proposed mass demands of the system against biogeochemical constraints (e.g., oxygen transfer)

Adjust k-rate

if necessary

FIGURE 20.2 Design process flow-chart for HSSF wetlands.

The difference in the predicted effluent mass load can

be approximated by the ratio a  (P–ET)/qi, which is the

atmospheric augmentation or deficit The fractional error in

a first-order-model prediction of mass load is approximately

rates, the effluent mass load will be overpredicted by about

25% unless flow corrections are made If the wetland gains

water through precipitation, the inverse is true It should be

noted that averaging the flow will not completely address

this problem Predicted effluent mass loads for the flow

assumptions presented in Tables 20.1–20.3 are summarized

in Table 20.4

I NTERCONNECTED P OLLUTANTS

Some pollutants have sequential degradation processes and

thus require a linkage of the mass balances between the

parent and daughter processes A classic example of this is

nitrogen: nitrogen species interconvert, thereby linking the mass balances for organic, ammonia, and oxidized nitro-gen It is sometimes possible to disconnect these species, as, for instance, in the case of wetlands that receive nitrate but little or no organic or ammonia nitrogen However, in many cases it is necessary to account for the (internal) production

of ammonia from organic sources—the incoming water or the decomposition of wetland necromass—and the internal production of oxidized nitrogen The reaction sequence has been presented in Equation 17.8, Chapter 17 (restated here as Equation 20.3) A simple presumed chemistry is

In a simplified version of this analysis, uptake and return from biomass is not included The effects of biogeochemical cycling will be explored in a latter part of the design pro-cess In the case of nitrogen, the three mass balances become

TABLE 20.2

Example of BOD Reduction under Variable Flows (ET  P)

Flow rate, Q 20 m 3 /d Volume per tank 14.25 m 2

Precipitation, P 0.5 mm/d Area per tank 125 m 2

ET 10 mm/d Influent flow, Qi 20.00 m 3 /d

Infiltration 0.02 mm/d Effluent flow, Qo 15.24 m 3 /d

PTIS (system) 4 Average flow, Qavg 17.62 m 3 /d

Area, A 500 m 2 Effluent mass load 420 g/d

Porosity, E 0.38 Nominal HLR 0.0400 m/d

Bed depth 0.30 m HLR based on Qavg 0.0352 m/d

Ci 200 mg/L HLR based on PTIS 0.0341 m/d

Precipitation m 3 /d 0.063 0.063 0.063 0.063 0.063 —

Infiltration m 3 /d 0.003 0.003 0.003 0.003 0.003 —

Concentration, C mg/L 200.0 120.5 72.3 43.9 27.6 27.6

TABLE 20.3

Effluent Pollutant Mass Loads (Based on the P-k-C* Model) under Different Flow Scenarios

Method of Calculation

Predicted Effluent Flow Rate (m 3 /d)

Predicted Effluent Concentration (mg/L)

Predicted Effluent Mass Load (g/d)

P-k-C*, based on constant flow 20 27.5 551

P-k-C*, based on average flow 17.6 27.6 486

P-k-C*; P-ET corrections based on PTIS 15.2 27.6 420

linked, and the tank equations are

Q C1 O1(Q Cin O,in) (I A C1 O1) k A CO 1( O1 CO*) (20.4)

k

A

1 A 1 in A,in 1 1 A 1 A 1 A

O

A

k

1 N 1 in N,in 1 N 1 N 1 N 1 N

A

A

where

C

C

O

A

organic N concentration, mg/L

ammonia

 NN concentration, mg/L

oxidized N concent

N

organic N rate coefficient,

O

ammonia N rate coefficient, m/d

o

A

N

k

k

 xxidized N rate coefficient, m/d

and the subscript “in” denotes parameters associated with the

influent flow for each nitrogen form

These may be rearranged to solve the outlet concentrations

for each tank:

O

in O,in O

*

O 1

1

¤

¦

³

A

in A,in A A

*

A 1

1

¤¤

¦

¥

³ µ

´ (20.8)

N

*

N 1

¤¤

¦

¥

³ µ

´ (20.9)

Equation 20.7 represents the degradation of organic nitrogen Equations 20.8 and 20.9 contain extra production terms from ammonification in the ammonia balance and from nitrification in the oxidized nitrogen balance The three must be solved sequen-tially—20.7, followed by 20.8, followed by 20.9 This can be done through an expanded process of the computer spreadsheet illustrated in Table 20.3, with one sequence of calculations for organic nitrogen, another sequence of calculations for ammonia, and a final sequence of calculations for oxidized nitrogen

More than One Parameter

Multi-parameter wetland sizing has been previously sum-marized inTables 17.4 and 17.5, Chapter 17, for FWS sys-tems; the same mathematical process can be applied to SSF wetlands

20.3 ACCOMPLISHING PERFORMANCE-BASED SIZING FOR HSSF WETLANDS

The procedure explored here analyzes the effect of changing the wetland area (or equivalently, detention time) on the fore-casted effluent concentrations of contaminants It is pre-sumed that the designer will conduct the design calculations via a computer spreadsheet so that design changes may be easily explored as an iterative process

The general performance-based sizing algorithm has been previously discussed in Chapter 15 for FWS wetlands

A modified version, more specific to the typical needs and priorities for SSF wetlands, is presented here:

Establish the design basis

1 Set inlet concentrations

2 Set target effluent concentrations (regulatory lim-its and exceedance factors)

TABLE 20.4

BOD Removal Based on Performance-Based Criteria; Constant Flow Assumption

Flow rate, Q 20 m 3 /d Volume per tank 18.5 m 3

Area, A 650 m 2 Influent flow, Qi 20.0 m 3 /d

Porosity, E 0.38 Effluent flow, Qo 20.0 m 3 /d

Bed depth 0.30 m Average flow, Qavg 20.0 m 3 /d

Ci 200 mg/L Effluent mass load 399 g/d

k 0.123 m/d HRT based on PTIS 3.71 d

Concentration, C mg/L 200.0 103.9 55.9 31.9 20.0 20.0

3 Set inflow and seepage (typically zero if synthetic

liners are used)

4 Determine precipitation, ET, and temperature for

critical seasons and targeted outflow rates

Parameter selection and primary sizing calculations

5 Select k-rates for the targeted parameters.

6 Assess the regulatory compliance interval, and

select appropriate trend multipliers (1 9)

7 Select the PTIS value Since P incorporates both

the hydraulic efficiency (number of TIS) and

pol-lutant weathering effects, the selected P-value may

not be the same for all targeted parameters

8 Select C* parameters for the targeted parameters.

9 Adjust the wetland area until design goals are

met

10 Check the proposed wetland size against loading

charts (if available) to assess the relative

conserva-tiveness of the design for the targeted parameters

11 Check the proposed mass demands of the system

against biogeochemical constraints (e.g., oxygen

transfer)

12 Adjust the wetland area until design goals are met

Secondary design considerations

13 Assess the seasonal impact of plant biomass

cycling (potentially important if nitrogen is a

tar-geted parameter)

14 Modify the wetland area, if necessary, to meet

secondary considerations

This is necessarily an iterative process, and the designer may

have to iterate at several stages in the overall design process,

as illustrated in Figure 20.2

Of necessity, performance-based wetlands require more

mathematical effort than scaling rules, which is easily accom-

plished using spreadsheets and judicious selection of design

parameters This book provides a variety of resources to

assist the designer in using performance-based design tools

These include:

k-rate, C*, PTIS

Organic Nitrogen Table 9.12

TKN Tables 9.15,9.33

Total nitrogen Table 9.19

Ammonia (nitrification) Tables 9.25, 9.33

Nitrate (denitrification) Table 9.39

Temperature effects (O-factors)

Organic nitrogen Table 9.13

Ammonia (nitrification) Table 9.32

cur-rently available data

BOD Tables 8.11,8.12

Total phosphorus Table 10.16

C ONSERVATISM IN D ESIGN

Part of the inherent challenge of using a general design model

(such as the P-k-C* model) is that there are so many

vari-ables that can be adjusted by the designer For instance, the designer can introduce conservatism into the design model at many stages, including:

Choosing a k-rate at the lower range of the

fre-quency distribution Incorporating effluent multipliers for seasonal variations (see Table 8.13, Chapter 8, for example)

Choosing a lower value of P (lower hydraulic

effi-ciency and/or greater effect of pollutant weathering)

Selecting a high concentration for C* (limiting the

level of treatment the wetland can achieve) Choosing a higher Q-factor (more temperature sen-sitivity); this is a conservative design assumption in cold climates

A designer who selects the most conservative value for each

of these parameters will have an overly conservative design that ignores the many thousands of successful SSF wetland systems operating around the world The current state-of-the-art design knowledge requires judicious selection of design parameters The recommended approach in this book

is to select median parameters for P, k, C*, and Q, predict the

system performance, and adjust using variability tables (such

as those presented in Tables 8.13, 9.35, and 10.16) Of course,

if the physical configuration of the wetland is different than these parent data sets, then designers must use their best pro-fessional judgment to select appropriate design parameters, because existing performance data is unlikely to represent the altered wetland configuration

Fortunately, loading charts (such as those presented in

Figures 7.30, 8.26, 9.18, 9.24, 9.30, 9.38, 10.36, and 12.12) provide a simple means to check the relative conservatism

of a wetland design against the body of knowledge accumu-lated from wetland systems already in operation This iter-ative check against loading chart data is also described in

Figure 20.3 Let us consider the example wetland previously described

in Tables 20.1 and 20.2; the wetland sizing will be determined through system performance rather than a prescriptive scal-ing factor In Table 20.1, the HSSF wetland size was set at

5 m2/PE, which is a scaling factor commonly used in Europe (ATV, 1998) In this example, we will assume that the inlet flow of 20 m3/d and inlet BOD concentration of 200 mg/L remain the same and the wetland must achieve a 90% BOD

mass load reduction We will further assume that the k-rate

•

•

•

•

•

(45 m/yr) and PTIS (P 4) parameters remain the same as

the previous examples for ease of comparison

M OST B ASIC C ASE : C ONSIDERATION OF C ONCENTRATION

R EDUCTION O NLY , N O C HANGE IN F LOW

If it is assumed that there is no change in flow, then a 90%

reduction in mass load corresponds to a 90% reduction in

concentration Equation 20.1 can be used (with some

alge-braic manipulation) to directly calculate the area required,

or a spreadsheet-based approach, such as the one presented

inTable 20.1, can be adopted The spreadsheet can be

manu-ally iterated by the designer, or the iteration process can be

automated using functions such as the Solver™ routine in

Microsoft Excel™ An example of this spreadsheet-based

approach (for the assumption of no change in flow) is shown

in Table 20.4 The assumption of C*  8 mg/L has been

retained from the previous example

Table 20.4 summarizes the manual iteration of Table 20.1

In just three manual iterations, a solution of 650 m2 (6.5 m2/

PE) is arrived at This is a prediction of the median annual

performance of the wetland under a constant-flow situation

(P  ET).

S ECOND C ASE : P OLLUTANT R EDUCTIONS

UNDER V ARIABLE F LOW

If precipitation and ET are not equal, there will be

differ-ences between the calculations for concentration

reduc-tion and mass load reducreduc-tion The spreadsheet example of

Table 20.2 has been utilized here to iterate a solution of 90%

mass load reduction for the high ET season previously

con-sidered Results are summarized in Table 20.5 The spreadsheet of Table 20.5 was manually iterated (three times) to converge on a wetland size of 515 m2 (5.15 m2/PE) to achieve the required mass load reduction on median annual basis

R OLE OFC*IN P OLLUTANT R EDUCTION

The pollutant removal rate, k, and the background concen-tration, C*, are independent parameters of the wetland under

consideration However, they are mathematically linked in the sizing process To use the analogy of descending in an

elevator through a building, k describes how rapidly you drop, and C* determines what floor you arrive at A high value of k implies a rapid drop from the initial inlet con-centration, and a low level of C* implies that the wetland

can remove pollutants to low concentrations (if sized large enough)

Let us consider the case of Table 20.5, with the altered

assumption that C* is now 20 mg/L, not 8 mg/L This

situation could easily occur if there is a significant por-tion of BOD that is from nondomestic sources Olive mill processing wastewater and landfill leachates are two

exam-ples where a higher level of C* may be justified The results

are summarized in Table 20.6, which indicates that the wetland must be 700 m2 (7.0 m2/PE) to achieve the design goal of 90% mass load reduction on a median annual basis (even though the effluent concentration leaving the wetland









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FIGURE 20.3 Comparison of the P-k-C* model against existing performance data based on Table 20.2.

FIGURE 20. 3 Comparison of the P-k-C* model against existing performance data based on Table 20. 2.