WER 2008 Levine Cloth Media

8/3/2019 WER 2008 Levine Cloth Media

1/13

Pathogen and Indicator OrganismReduction Through Secondary

Effluent Filtration: Implications forReclaimed Water Production

Audrey D. Levine1*, Valerie J. Harwood2, Samuel R. Farrah3, Troy M. Scott4, Joan B. Rose5

ABSTRACT: The reduction of pathogens and indicator organisms

through secondary effluent filtration was investigated at six full-scale

treatment facilities, ranging in capacity from 0.04 to 1 m3 /s (1 to 25 mgd).

Grab samples were assayed for pathogens (cultivable enteric viruses,Giardia, a nd Cryptosporidium) and indicator organisms (coliforms,

enterococci, Clostridium perfringens, and coliphages) quarterly under peak

flow conditions from each facility over the course of 1 calendar year. Log10removals resulting from filtration averaged 0.3 to 0.8 log10 for cultivable

enteric viruses, 0.4 to 1.5 log10 for protozoan parasites, 0.01 to 3.7 log10 for

indicator bacteria, and 0.3 to 1.1 log10 for coliphages. In addition to filter

design (cloth, monomedium shallow- or deep-bed, or dual-media filters),

differences in reduction of pathogens and indicators could be attributed to

the combined effects of hydraulic loading rates, chemical addition practices,

backwashing and postbackwashing operating strategies, and the effective-

ness of upstream biological treatment and sedimentation. Water Environ.

Res., 80, 596 (2008).

KEYWORDS: reclaimed water, pathogens, wastewater filtration,

prechlorination.

doi:10.2175/106143008X266742

Introduction

With increasing use of reclaimed water for public access

applications, such as landscape irrigation, urban water uses,

wetlands and surface water augmentation, and recirculating cooling

water for buildings and other industrial applications, the effective-

ness of treatment practices for control of pathogens has become

more important. In many facilities, filtration is used between

secondary biological treatment and disinfection (either chemical or

photochemical) to reduce the concentration of suspended solids and

particle-associated microorganisms that may interfere with disin-

fection effectiveness. Because it is now recognized that some

pathogens, such as Cryptosporidium, are resistant to disinfection,

secondary effluent filtration represents a critical step to control

pathogens that are not removed by biological treatment. The

effectiveness of pathogen reduction through filtration is influenced

by filter characteristics and operating practices, microbial properties

(size, surface properties, and degree of association with other

microorganisms or particles), and water quality variables. A wide

variety of filters has been adopted for water reclamation applica-

tions, including conventional deep-bed monomedia or dual-media

filters, continuously backwashed upflow filters, disk (cloth) filters,

fuzzy filters, and membranes (Bourgeous et al., 2003; England

et al., 1994). To meet requirements for process reliability and

maintenance of flow, secondary effluent filtration typically consists

of a minimum of two filters operated in parallel, in either a declining

rate or constant rate mode. Typically, effluents from individual

filters are combined before disinfection. Direct monitoring of the

microbial characteristics of filter effluents is not widely practiced, asthe point of compliance for most reclaimed water systems is after

disinfection.

The purpose of this paper is to evaluate the role of filtration for

removal of pathogens and indicator organisms during production

of reclaimed water and the degree to which traditional models of

surface water filtration can be applied to reclaimed water filtration

scenarios. The data presented in this paper are derived from testing

conducted at six full-scale water reclamation facilities that use either

granular media filtration (anthracite and/or sand) or cloth filters

(Rose et al., 2004). Relationships between filter characteristics

(depth, hydraulic loading, and backwashing practices); water qual-

ity; and removal or persistence of viruses, protozoan pathogens,

conventional indicator bacteria, and alternative microbial indicatorsthrough full-scale secondary effluent filtration are evaluated.

Background

Individually and collectively, treatment processes provide

a combination of physical, chemical, and biological barriers to

reduce pathogen levels in reclaimed water. To verify treatment

effectiveness, disinfected effluents are routinely assayed for

bacterial indicators and sampled intermittently for viruses and/or

protozoan pathogens (U.S. EPA, 2004; York and Walker-Coleman,

2000). Coliform bacteria (either the total coliform group or

thermotolerant [fecal] coliforms) are used almost universally to

benchmark the microbiological quality of reclaimed water. In the

United States, monitoring requirements (microbial analyte[s] and

1 Office of Research and Development, U.S. Environmental ProtectionAgency, Washington D.C.

2 Department of Biology, University of South Florida, Tampa, Florida.

3 Department of Microbiology & Cell Science, University of Florida,Gainesville, Florida.

4 BCS Laboratories Inc., Miami, Florida.

5 Department of Crop and Soil Sciences, Michigan State University, EastLansing, Michigan.

* Office of Research and Development, U.S. Environmental ProtectionAgency, 1200 Pennsylvania Avenue NW 8101R, Washington D.C. 20460;e-mail: [email protected].

596 Water Environment Research, Volume 80, Number 7


2/13

frequency) and specific numerical limits are set by state and local

regulatory agencies and, typically, are based on daily monitoring of

disinfected effluents.Filtration Removal Mechanisms. Removal of microorgan-

isms through granular media filtration is governed by particle-media

interactions that result from diffusion, interception, and sedimen-

tation (Camp, 1964; Darby et al., 1991; Elimelech, 1992; Ghosh

et al., 1975; Glasgow and Wheatley, 1999; Habibian and OMelia,1975; Hall and Fitzpatrick, 2000; Iwasaki, 1937; Jegatheesan and

Vigneswaren, 2000; Kavanaugh et al., 1978; OMelia and Ali, 1978;

Stevenson, 1997; Tchobanoglous and Eliassen, 1970; Trussell and

Chang, 1999; Yao et al., 1971). The deposition of microorganisms

and other particles in filters is dependent on effective transport to the

filter media coupled with retention within the filter pores or at-

tachment to the media surface.

The single collector efficiency concept has been widely used to

evaluate the overall efficiency of filtration for removal of particles

by defining a nondimensional single collector efficiency, g, which

reflects the ratio of particles striking the filter media to the total

particle flux by incorporating the combined effects of diffusion

(gD), interception (gI), and gravity sedimentation (gG) on particle-media interactions, as defined in eq 1 (Camp, 1964; Elimelech,

1992; Habibian and OMelia, 1975; Harvey and Garabedian, 1991;

Iwasaki, 1937; Logan et al., 2001; Redman et al., 2001; Yao et al.,

1971).

g gD gI gG 0:9kT

ldpdv

!2=3

1:5dp

d

2qp qgd

2

p

18lv1

Where

g 5 overall collision frequency coefficient;

gD 5 collision frequency coefficient resulting from diffusion;gI 5 collision frequency coefficient resulting from interception,

gD;

gG 5 collision frequency coefficient resulting from gravitational

sedimentation;

k5 Boltzman constant (1.38 3 10223 J/K);

T 5 absolute temperature (K);

l 5 dynamic viscosity of water (N-s/m2);

dp 5 effective particle (microorganism) diameter (m);

d5 effective diameter of media grain (m);

v 5 water velocity 5 flowrate/surface area (m/s);

q 5 density of water (kg/m3); and

qp 5 particle (microorganism) density (kg/m3).

In this model, transport by diffusion is inversely proportional to

the media grain size (dp) and filtration velocity (v). Transport by

interception is controlled by the ratio of the microorganism size (dp)

to the grain size (d) and is independent of filtration velocity, while

transport by sedimentation is proportional to the ratio between the

particle settling velocity and the filtration velocity (v) or hydraulic

loading rate. The model does not directly incorporate water quality

variables, such as pH, ionic strength, or organic content.

A comparison of the theoretical single collector removal effi-

ciency for control of protozoa, bacteria, and viruses through sand

and anthracite media typically used for production of reclaimed

water suggests that individual bacterial cells are likely to have the

lowest removal efficiency compared with freely suspended viruses,

protozoa, or aggregates (flocs) of microorganisms and other particu-

lates (Figure 1). Essentially, removal of freely suspended viruses

and other nanoscale particles is controlled by diffusion, while re-

moval of protozoa is influenced through the combined effects of

differential sedimentation and interception. Removal of freely sus-

pended bacterial cells is influenced by the cumulative effects of

diffusion, differential sedimentation, and interception. The effective

grain size of the media affects the collector efficiency for removal of

viruses and bacteria, whereas removal of protozoa and/or microbialaggregates is more affected by hydraulic loading rates than by

media characteristics. It should also be noted that removal of bac-

teria through filtration is likely to be less sensitive to the influence of

variable hydraulic loading rates than removal of viruses or protozoa

(see eq 1).

These calculations suggest that the use of bacterial indicators may

provide a conservative assessment of filter performance for removal

of microorganisms, if bacteria are present as discrete particles (not

associated with other particulate matter), and disinfectant chemicals

are not added upstream. From a process design perspective, this

model suggests that smaller grain size media would affect removal

of freely suspended viruses and other nanoparticles, whereas lower

hydraulic loading rates would be more effective at improvingremoval efficiency for protozoan pathogens. In addition, the use of

upstream coagulation to aggregate freely suspended viral and bac-

terial particles should be designed in conjunction with optimization

of hydraulic loading rates. This model does not address the devel-

opment of headloss through filter runs, the interplay of media size

and hydraulic loading rates on headloss development, or potential

effects of coagulant chemicals and water quality on headloss and

filter run length.

While the collector efficiency concept describes the likelihood

of particles colliding with filter media, the net removal depends

on retention within the filter. Factors that are likely to influence

attachment include the net surface charge on the filter media and

microbial surfaces; media properties (type, size, and depth);hydraulic loading rates; upstream chemical use (oxidants and/or

coagulants); water quality variables; flow control; and backwashing

and post-backwashing practices. The characteristics of secondary

effluent that might affect filter and microbial surface characteristics

include pH, ionic strength, temperature, particle characteristics, and

characteristics of dissolved organics (concentration, molecular size,

and charge density). High ionic strength and residual organics act

to decrease the electric double layer surrounding microorganisms,

particles, and filter media, thereby increasing the potential for

particle attachment (Brown and Abramson, 2006; Elimelech and

OMelia, 1990; Hsu and Huang, 2002; Hsu et al., 2001). Ionic

strength varies with total dissolved solids levels in reclaimed water,

which can range from 200 to 2500 mg/L, depending on the char-

acteristics of the service area source water quality, water usage

patterns, stormwater effects (infiltration and inflow), and sources of

dissolved solids from industrial discharges and point-of-use water

treatment discharges. The presence of residual organics may affect

adsorption to filter media or may serve to improve transport through

filtration (Foppen et al., 2006).

Typically, secondary effluent contains active biomass that

consists of microorganisms that are either freely dispersed or

entrapped in microbial agglomerates (flocs) that can colonize the

filter media. During a filter run, the buildup of accumulated material

within the filter coupled with the development of biofilms can serve

to augment filter efficiency; however, a side effect of particle

accumulation and biofilm formation is an increase in specific

velocities within pores, resulting in pore blockage, channeling

Levine et al.

July 2008 597


3/13

effects, higher effective hydraulic loading rates, and particle remo-bilization (Ahmad and Amirtharajah, 1998; Hall and Fitzpatrick,

2000; Hozalski and Bouwer, 1998). Some particles/pathogens may

be released during filtration, depending on the duration of the filter

run, hydraulic inconsistencies, and effectiveness of upstream treat-

ment (Ahmad and Amirtharajah, 1998; Darby et al., 1991; Glasgow

and Wheatley, 1999; Jegatheesan and Vigneswaran, 2000; Jiminex

et al., 2000).

Backwashing and postbackwashing practices vary with filter

designs and plant operating practices. Backwashing operations are

designed to dislodge and scour accumulated particles from filter

media, by fluidizing the media and applying a combination of high-

velocity water with or without air scour (Ahmad and Amirtharajah,

1998; Hall and Fitzpatrick, 2000; Hozalski and Bouwer, 1998).

Operating variables, such as backwashing frequency and duration,

degree of fluidization, backwash source water (chlorinated or non-

chlorinated), degree to which air scour is used and monitored, and

approach for returning a filter to service following backwashing, can

affect the efficiency of subsequent filtration cycles. In general, back-

washing is not designed to remove biofilms (Wang et al., 1995).

Particle/pathogen release may occur following backwashing, re-

sulting from either remnant particles (Amburgey et al., 2004) or

hydrodynamic inconsistencies in the filter media (Glasgow and

Wheatley, 1999).

Modeling of filtration effectiveness for reclaimed water applica-

tions is complicated by the heterogeneity of the particles in

secondary effluent coupled with the lack of tools to quantify the

effects of particle accumulation and filter porosity on interstitial

fluid velocities (Redman et al., 2001). One approach that has beenwidely used is to assume that pathogen deposition through filtration

can be modeled as pseudo-first-order removal, as shown in eq 2,

where Cis the number concentration of particles or microorganisms

entering a unit volume of the filter, k is a filter coefficient, and L

is depth (Brown and Abramson, 2006; Camp, 1964; Harvey and

Garabedian, 1991; Iwasaki, 1937; Reddi, 1997; Trussell and Chang,

1999). The filter coefficient, k, reflects the composite effects of filter

media characteristics, hydrodynamic factors, and water quality

variables.

dC

dL kC 2

Where

C5 number concentration of microorganisms (#/100 mL or

#/100 L),

L5 depth from surface of filter (m), and

k 5 filter coefficient (m21).

Under steady-state conditions, the concentration of microorganisms

in the filter effluent can be estimated as follows:

C Coekz 3

Where

Co 5 concentration of particles or microorganisms applied to the

surface of the filter (#/100 mL or #/100 L), and

z 5 filter depth (m).

Figure 1Predicted removal efficiency of viruses, bacteria, and protozoa based on hydraulic loading rates (1.0 to

3.4 L/s) and media size (0.3, 0.5, and 1 mm) typical of secondary effluent filtration. Calculations based on eq 1.

Assumed sizes were viruses 2 lm.

Levine et al.



4/13

This model suggests that, for a given filter coefficient, k, filter

efficiency should increase with depth. However, in practice, filter

depth and filtration run length are limited by headloss and other

practical constraints. Another way of increasing filter performance

is to increase the effective filter coefficient, k, through upstream

chemical addition of coagulants, flocculants, and/or oxidants.Review of Pathogen Removal Through Surface Water

Filtration in the Context of Water Reclamation. Pathogen re-moval through filtration has been studied extensively in the produc-

tion of drinking water from surface water sources (Amburgey et al.,

2004, 2005; Bustamante et al., 2001; Edzwald et al., 2000; Emelko

et al., 2003; Harrington et al., 2003; Hsu and Huang, 2002; Logan

et al., 2001; Nasser et al., 1995; Nieminski and Ongerth, 1995;

Patania et al., 1995; Swertfeger et al., 1999; Xagoraraki et al., 2004).

In potable water systems, it hasbeen observed that removal ofGiardia

cysts and Cryptosporidium oocysts is influenced by the degree of

filter maturation; use of coagulant chemicals (Koivunen et al., 2003;

Mosher and Hendricks, 1986; Patania et al., 1995); ionic strength;

pH; zeta potential (Bustamante et al., 2001; Hsu and Huang, 2002);

and filter grain size (Logan et al., 2001; Stevenson, 1997). In general,

the surface charge of cysts and oocysts is negative under neutral andbasic pH conditions, with the zeta potential for Cryptosporidium

approximately double that ofGiardia at neutral pH (Hsu and Huang,

2002; Ongerth and Pecoraro, 1996), suggesting that removal of

Cryptosporidium through filtration maynot parallel Giardia removal,

particularly under low-ionic-strength and/or alkaline pH conditions.

While reclaimedwaterfiltration is similar to surface water filtration, in

principle, limited data are available on the relative roles of pH, ionic

strength, and organic matter in reclaimed water applications. Because

indicator organisms (coliform bacteria and coliphages) are used in

conjunction with indirect measures of microbial concentrations

(suspended solids and turbidity) to assess the performance of re-

claimed water filtration, it is important to identify the appropriate

parameters that should be monitored to facilitate assessment andprediction of pathogen removal through filtration.Comparison of Regulatory Approaches for Surface Water

Filtration and Reclaimed Water Production. Because of con-

cerns about the protozoan pathogens Giardia and Cryptosporidium

in water supplies, the U.S. Environmental Protection Agency

(Washington, D.C.) (U.S. EPA) has implemented rules that mandate

that surface-water-treatment facilities include robust filtration sys-

tems that meet stringent turbidity requirements and achieve at least

a 2-log reduction of protozoan pathogens (40 CFR 141.721 [U.S.

EPA, 2006]). For example, under the Long-Term 2 Enhanced Sur-

face Water Treatment Rule enacted in 2006, at least 95% of the

combined filter effluent turbidity measurements must be less than

or equal to 0.15 NTU (40 CFR 141.727) (U.S. EPA, 2003). While

turbidity levels do not correspond to pathogen concentrations, the

rule is based on conclusive evidence from a wide array of surface

water filtration studies, which demonstrated that turbidity levels

below 0.2 NTU are likely to correspond to low or nondetectable

levels of Giardia and Cryptosporidium (Edzwald et al., 2000;

Emelko et al., 2005; Xagoraraki et al., 2004). In contrast, turbidity

(or suspended solids) requirements for water reclamation facilities

are based on monitoring of the disinfected effluent, with typical

turbidity limits ranging from 2 to 5 NTU or suspended solids limits

of 5 mg/L, depending on permit requirements (U.S. EPA, 2004;

York and Walker-Coleman, 2000). Thus, surface-water-filtration

practices cannot be directly extrapolated to reclaimed water produc-

tion, as a result of the order of magnitude differences in turbidity

limits and other confounding variables.

Role of Filtration in Reclaimed Water Production. In

reclaimed water production, filtration is intended to provide for

removal of microorganisms and associated turbidity, as a pretreat-

ment to disinfection. Disinfection systems are designed to inactivate

pathogens that are not removed through upstream processes. His-

torically, wastewater treatment facilities have relied on the use of

chlorine as a disinfectant. However, because of increasing concerns

about the presence of disinfection byproducts in treated wastewaterand the need to dechlorinate effluents that are discharged to

receiving waters, there has been a shift towards the use of UV and

other alternative disinfectants. In addition, some pathogens are re-

sistant to chlorine. As disinfection systems become more stream-

lined, the role of filtration becomes more important for pathogen

control. In addition, pathogen removal through filtration is even

more critical for control of pathogens that are resistant to chemical

or photochemical disinfection.

Methodology

The reduction of pathogens through filtration was assessed

through sampling of six wastewater treatment facilities under peak-flow conditions. A summary of the characteristics of the treatment

facilities is given in Table 1. The filters tested in this project

included granular media (deep-bed and shallow-bed) and cloth

filters. Prechlorination was applied in one of the facilities (S11);

cationic polyelectrolyte was applied upstream of filtration in another

(A48); and a third facility used alum in the secondary clarifier

(A24S48), while the remaining facilities did not use any upstream

chemicals during the testing program. Backwashing practices varied

among the facilities, but four of the six facilities used chlorinated

effluent on a routine basis, as detailed in Table 1. A minimum of

four sampling events were conducted at each facility, over a 1-year

period, to assess pathogen and indicator concentrations associated

with untreated wastewater and effluents from biological treatment(postsedimentation), filtration, and disinfection. For each sample

event, filter operating data were compiled, including hydraulic

loading rates, backwashing history, and operational anomalies.

Routine water quality monitoring data, such as the 5-day test for

carbonaceous biochemical oxygen demand (C-BOD5), total sus-

pended solids (TSS), turbidity, ammonia, and phosphorus were

obtained from each facility; however, all facilities did not monitor all

parameters on the same schedule as the microbial sampling program.

A side-by-side comparison of the effects of prechlorination on

removal of indicator organisms was conducted over a 10-hour filter

run at the facility that prechlorinates (S11). In addition, intensive

testing of a single filter run, to evaluate the consistency of removal

of bacterial indicators, was conducted at three of the facilities

(S11, A48, and A24S48). The effect of backwashing on protozoan

pathogen concentrations (Giardia and Cryptosporidium) in filter

effluents immediately before and after putting a filter back in service

was evaluated at two facilities (A48 and A24S48).

Nine independent measurements of microbiological water quality

were used to assess filtration performance. A summary of the charac-

teristics of the indicator organisms (coliform bacteria, enterococci,

Clostridium perfringens, and coliphages) and pathogens (cultivable

enteric viruses, Giardia, and Cryptosporidium) tested in the influent

and effluent from each filter is shown in Table 2. The frequency

with which each of the tested microorganisms was detected in each

filter effluent is also reported in Table 2.

All microbial assays were carried out as previously reported

(Harwood et al., 2005; Rose et al., 2004). One-liter grab samples

Levine et al.

July 2008 599


5/13

were collected for analysis of the bacterial and viral indicators,

while the parasite and virus samples were collected and concen-

trated by pumping up to 100 L of water through appropriate filters.

All chlorinated samples were dechlorinated by adding Na2S2O3to a final concentration of 0.001% immediately after collection.

Cartridge filters (1MDS, Cuno Inc., Meriden, Connecticut) were used

for collection and concentration of viruses, while Pall-Gelman

Envirochek HV capsule filters (Pall Gelman Laboratory, Ann Arbor,

Michigan) were used for collection and concentration of parasites.

Bacteria were quantified using membrane filtration (47 mm;

0.45-lm pore size). When necessary, samples were diluted with

phosphate-buffered saline, and all were filtered in triplicate. Total

coliforms and fecal coliforms (FC) were enumerated according to

Standard Methods (APHA et al., 1998)total coliforms on mEndo-

LES agar at 378C and fecal coliforms on mFC agar at 44.58C.

Enterococcus spp. were enumerated using method 1600 (U.S. EPA,

1996) on mEI agar at 418C. Clostridium perfringens was enumer-

ated on m-CP plates (Acumedia Manufacturers Inc., Baltimore,

Maryland) using anaerobic gas packs (BBL GasPak, Beckton

Dickinson, Cockeysville, Maryland) at 458C for 24 hours, followed

by exposure to ammonium hydroxide fumes. Colonies that turned

red or dark pink were enumerated as Clostridium perfringens

(C. perfringens) (Bisson and Cabelli, 1979).

Coliphages were analyzed by the agar overlay method of Adams

(1959) using two E. coli host strains in separate assaysE. coli HS

(pFamp) R (ATCC #700891) and E. coli C3000 (ATCC #15597)

and were enumerated as plaque-forming units (PFU)/100 mL.

Viruses were concentrated by filtration followed by organic floccu-

lation (U.S. EPA, 1996). Cultivable enteric viruses were detected by

the observation of cytopathic effects on recently passed (,4 days)

Buffalo Green Monkey, Rhabdosarcoma, and MA-104 cells.

Giardia and Cryptosporidium were concentrated by filtration with

Table 1Comparison of preliminary treatment, primary treatment, secondary treatment and filtration at wastewater

reclamation facilities included in this study.*

Filter label Cloth S11 A48 SU40 A30S10 A24S48

Average capacity,

m3 /s (mgd)

0.04 to 0.11

(0.9 to 2.6)

0.61 to 0.71

(13.9 to 16.2)

0.42 to 0.45

(9.6 to 10.3)

0.06 to 0.09

(1.3 to 2.1)

0.5 to 1.1

(11 to 25)

0.06 to 0.1

(1.3 to 2.4)

Preliminary/primary

treatment

Grit removal Grit removal Grit removal

Primaryclarifier

Grit removal

Equalizationbasin

Grit removal Grit removal

Biological treatment

Process type Activated

sludge

Activated

sludge

Activated

sludge

Biological

nutrient

removal

Activated

sludge

Biological

nutrient

removal

Mean cell residence

time (days) 6 to 8 3.5 to 6 1.6 to 2.7 8.7 to 13.3 3 to 5 8 to 16

Filtration

Filter type Cloth Monomedium

automatic

backwashing

filter

Monomedium Continuously

backwashed

upflow filter

Dual media Dual media

Filter media(depth)

Fabric(3.8 mm)

Sand (0.3 m) Anthracite(1.2 m)

Sand (1.2 m) Anthracite (0.8 m)Sand (0.25 m)

Anthracite(0.6 m)

Sand (1.2 m)

Media grain

size (mm)

Nominal Pore

size: 0.01

0.55 to 0.65 1.4 0.9 Anthracite

Sand: 0.65

Anthracite: 2

Sand: 1.8

Uniformity

coefficient

1.5 Anthracite: 1.3

Sand: 1.35

Chemical use None Prechlorinate Cationic

polyelectrolyte

None None Alum added to

secondary

clarifier

Hydraulic loading

rate, L/s (gpm/ft2)

1.0 to 3.9

(1.5 to 5.8)

0.4 to 0.7

(0.6 to 1)

0.7 to 1.2

(1.1 to 1.8)

0.7 to 1.2

(1.2 to 2)

1.5 to 3.9

(2.2 to 5.8)

0.9 to 1.0

(1.3 to 1.5)

(L/d) 545 857 1300 Sand: 384 Anthracite: 300

Sand: 667

Volume per

backwash,

L/filter (gal/filter)

4542 (1200) 11 355 (3000) 90 840 (24 000) 8% of flow 946 250

(250 000)

227 100

(60 000)

Backwashing

frequency (hours)

72 to 84 Automatic

(daily)

48 Continuous 48 to 168 48 to 168

Backwash

water source

Filtered

effluent

Chlorinated

effluent

(6 mg/L

as Cl2)

Chlorinated

filter effluent

(30 mg/L

as Cl2)

Final effluent

(unchlorinated);

shock

chlorinated

periodically

Chlorinated effluent

(3 to 6 mg/L);

shock chlorinated

33/year

Chlorinated

effluent

(5 mg/L

as Cl2)

* Based on operating data during sampling.

Levine et al.



6/13

Pall-Gelman Envirochek HV filters and processed by immunomag-

netic separation (Dynal Inc., Burlington, Ontario, Canada) and

immunofluorescent antibody detection (method 1623, U.S. EPA,

2005). Concentrated samples were enumerated by fluorescent

microscopic counts (Giardia and Cryptosporidium) (U.S. EPA,

1995, 1996) and by a viability assay (Cryptosporidium only) (Slifko

et al., 1999).

Data were sorted by filter type and microorganism concentration.

Microbial data were log10-transformed before data analysis. In cases

where values were below detection limits, the detection limit was

used for statistical analyses. The performance of the individual

filters was evaluated based on calculating microbial persistence

through filtration (C/Co) and log10 reduction (log10 Co 2 log10 C).

The validity of using parametric statistics to evaluate

Table 2Comparison of characteristics of indicator organisms and pathogens tested in this study.

Indicator/pathogen

Example species

or description Cell wall Shape Size (lm)

Frequency of detection in filter effluents (%)

Cloth S11 A48 SU40 A30S10 A24S48

Bacterial indicators used in this study

Total coliform Escherichia, Klebsiella,

Citrobacter,Enterobacter

Gram-negative,

non-spore-forming

Rod 0.5 to 2.0 80 100 100 100 100 100

Fecal coliform Escherichia, some

Klebsiella spp.,

and other

thermotolerant

coliforms

Gram-negative,

non-spore-

forming

Rod 0.5 to 2.0 60 0 50 100 100 75

Enterococci Enterococcus

faecalis,

Enterococcus

faecium

Gram-positive,

non-spore-

forming

Cocci 0.5 to 1.0 80 75 50 100 100 100

Clostridium

perfringens

Highly heat-resistant,

opportunistic

pathogen,

producesenterotoxin

Gram-positive,

spore-forming

Rod 0.6 to 1.3 3

2.4 to 19.0

40 100 75 75 70 100

Coliphages Viruses that infect

E. coli and other

coliform bacteria

0.025 to 0.20

Coliphages

that infect

E. coli 700891

(Famp)

Male-specific (F1)

coliphages, can

only replicate when

bacterial host cell

is in logarithmic

growth phase

at .308C

No cell wall,

coat protein

protects

nucleic acid

Icosahedral

capsid

0.025 40 100 100 100 14 60

Coliphages

that infect

E. coli 15597

Male-specific (F1)

and somatic

coliphages thatinfect E.coli

15597 (ATTC*)

No cell wall,

coat protein

protectsnucleic acid

Icosahedral

capsid

0.025 40 100 100 100 14 40

Cultivable enteric

viruses

Genus within the

family Picornaviridae

includes poliovirus,

coxsackievirus,

echovirus, hepatitis

A virus;

single-strand (ss)

RNA genome

No cell

wall, Non-

enveloped

protein coat

Icosahedral

capsid

0.025 to 0.030 60 20 80 25 67 60

Protozoan

parasites

Complex life cycle.

Zoonotic

(animal to human)

transmissionGiardia

intestinalis

Flagellated protozoan,

Phylum Mastigophora

Ovoid cyst ;8.5 3 10.0 100 100 83 100 50 100

Cryptosporidium

parvum

Coccidian protozoan,

Phylum Apicomplexa

Ovoid oocysts 4.0 to 6.0 100 80 67 50 67 67

* ATTC 5 American Type Culture Collection.

Levine et al.

July 2008 601


7/13

log10-transformed microbial data was tested using the Kolmogorov-

Smirnov test for normality, with an alpha value of 0.05, using

Graphpad Prism statistical software (Graphpad 2000, GraphPad

Software Inc., San Diego, California). Statistically significant

differences among filters were identified using either analysis ofvariance (ANOVA) or the Kruskal Wallis test (nonparametric).

Post-tests included Tukey, Newman-Kuels, and Bonferroni for

normally distributed data or the Dunns test for nonparametric

analyses. Pairwise comparisons of filter performance were con-

ducted using either t-tests (normally distributed data) or the Mann-

Whitney test (nonparametric).

Comparison of Filtration Performance

To facilitate comparison of the filters, they were grouped into two

categories. Group I includes filters that either use chemicals directly

upstream of filtration (S11 [chlorine] and A48 [polymer]) or are

influenced by surface filtration mechanisms (CLOTH), while group

II includes deep-bed filters that contain sand (SU40, A30S10, andA24S48 [alum used in secondary clarifier]). Differences among the

filters were evaluated based on influent concentrations and removal

of cultivable enteric viruses, protozoan parasites (Giardia and

Cryptosporidium), and bacterial indicators. Detailed analyses were

conducted on group II filters to assess the effects of hydraulic

loading rate and sand depth on reduction of pathogens.

A summary of the water quality associated with sampling of each

facility is given in Table 3. As shown, C-BOD5 values were

typically below 5 mg/L for most of the facilities, except A48. TheTSS levels were typically below 4 mg/L. Nitrogen (ammonia,

nitrite, and nitrate) and phosphorus levels varied with the extent of

nutrient removal.

The overall performance of the individual treatment facilities for

reduction of pathogens and indicators was assessed by comparing

the average log10 reduction for removal of each microbial parameter

through each filter, as shown in Table 4. A different pattern was

observed for removal of pathogens compared with the indicator

organisms. The highest log10 removal was associated with the filter

that has the deepest layer of sand, hydraulic loading rates ranging

from 30 to 36 m3/m2 min (1.3 to 1.5 gpm/ft2), and uses coagulationduring secondary sedimentation (A24S48). Filtration without

prechlorination achieved average cultivable enteric virus removalsof 0.3 to 0.8 log10 and parasite removals of 0.4 to 1.5 log10. In

contrast to surface-water-filtration systems designed to provide at

least a 2-log reduction of protozoan pathogens (40 CFR 141.721),

the log reductions associated with the filters tested in this project

Table 3Summary of water quality data for each filter effluent. Filter descriptions are given in Table 1.

Parameter (units)

Cloth S11 A48 A30S10 SU40 A24S48

Average Range Average Range Average Range Average Range Average Range Average Range

C-BOD5 (mg/L) 2.4 1.2 to 3.2 2.8 2.0 to 4.0 7.5 5.9 to 9.1 2.7 2.3 to 3.4 2.0 ,2 to 2 2.0 ,2 to 2

TSS (mg/L) 0.8 0.4 to 1.1 1 ,1 to 2 1.2 0.2 to 2.7 0.7 0.3 to 1.2 2.7 0.8 to 4.4 1.1 ,0.5 to 1.2

Ammonia-nitrogen(mg/L) 10.9 8.3 to 13.5 8.5 4.6 to 15.3 15.8 11 to 21.9 6.4 2.4 to 10.5 0.2 0.09 to 0.2 0.5 0.1 to 1.2

Nitrite-nitrogen 1

nitrate-nitrogen

(mg/L as nitrogen) 0.4 0.2 to 0.5 3.8 1.3 to 6.9 4.6 3.0 to 6.0 1.5 1.2 to 1.7 5.2 3.9 to 7.3 1.0 0.2 to 2.4

Orthophophate (mg/L

as phosphorus) 5.3 4.3 to 6.3 0.7 0.56 to 0.9 0.9 0.4 to 1.3 0.2 0.1 to 0.3

Table 4Summary of log10 reductions of indicators and pathogens through each filtration system.

Microorganism

Cloth S11 A48 A30S10 SU40 A24S48

Ave ra ge Ran ge Averag e Ran ge Ave rag e Ran ge Av era ge Ra nge Ave rag e Ran ge Av era ge Ra nge

Indicators

Total coliform 3.7 2.3 to 6.2 3.8 0.4 to 5.7 1.7 0.9 to 2.2 0.2 21.1 to 1 1.8 0.2 to 4.5 3.1 2.8 to 3.3

Fecal coliform 3.0 1.9 to 4.3 4.2 0.3 to 5.6 2.5 2.3 to 2.9 0.1 21.3 to 1.1 0.9 20.4 to 2.5 2.6 1.7 to 3.4

Enterococci 2.4 0.8 to 4.5 3.1 1 to 4.8 1.4 0 to 2.7 0.01 20.5 to 0.4 0.8 0.1 to 1.6 1.3 1 to 2.5

Clostridium

perfringens 1.0 0 to 2.8 2.0 1.4 to 2.8 0.4 20.3 to 0.8 0.7 20.3 to 2.1 0.3 0 to 0.6 0.8 0.4 to 1.3

Coliphage 1 0.6 20.1 to 1.8 1 0.6 to 1.4 1.3 0.1 to 5.5 1.3 0 to 2.9 0.6 0 to 1.1 0.3 0 to 1

Coliphage 2 0.4 20.1 to 1 0.3 20.1 to 0.9 0.4 20.4 to 1.3 0.8 20.1 to 2 0.3 21 to 1.5 0.2 0 to 0.6

Pathogens

Cultivable

enteric viruses 0.5 0.3 to 0.8 1.2 0.8 to 1.8 0.8 0.3 to 1.4 0.3 0 to 0.9 0.6 0.2 to 1.3 0.7 20.3 to 1.3

Giardia (total) 0.5 20.4 to 1.3 0.7 20.4 to 1.3 1.3 0.4 to 3.2 0.8 0 to 1.7 0.7 20.1 to 1.4 1.5 0.3 to 2.6

Cryptosporidium

(total) 0.4 0.1 to 0.6 1.0 0.7 to 1.4 0.4 22 to 2.3 0.1 20.3 to 0.6 0.6 0 to 1.3 1.3 0.5 to 2.2

Cryptosporidium

(infectious) 0.5 0.3 to 0.7 1.0 0.7 to 1.2 0.8 0.5 to 1.1 0.3 0.3 to 0.5 0.8 0.3 to 1.7 1.1 0.6 to 1.3

Levine et al.



8/13

were typically below 2-log, and, in some cases, higher concen-

trations were observed in filter effluents than influents, resulting in

negative log reductions.

In general, log10 reduction of bacteria (coliforms, enterococci,

and Clostridium) was 2- to 9-fold greater than log10 reduction of

pathogens, suggesting that, in contrast to Figure 1, the use of

bacterial indicators as a monitoring tool may overpredict pathogen

reduction. The highest overall removal of the indicator organisms

was associated with S11, the prechlorinated filter (cumulative log10reduction of indicators of 13.7); this filter ranked second (after

A28S48) for cumulative pathogen reduction. The cloth filter, which

performs as a depth filter, had the second highest log10 reduction

of the tested indicators (cumulative log10 reduction of indicators

of 10.6) and the lowest reduction of protozoan pathogens. Among

the other filters, the two filters that used coagulants either during

secondary sedimentation (A28S48) or upstream of filtration (A48)

had greater log10 reduction of indicators and pathogens than the

filters with no chemical pretreatment (A30S10 and SU40). In

general, the weakest removal of indicators and pathogens was

associated with the filter with the shallowest bed of sand and higher

hydraulic loading rates (1.5 to 3.9 L/s [2.2 to 5.8 gpm/ft2]), which

did not apply upstream chemicals (A30S10).Comparison of Pathogen Removal Through Deep-Bed Filters

that Contain Sand. Further analysis of data collected fromfacilities that operated deep-bed filters that contain sand (SU40,

A30S10, and A24S48) was conducted to evaluate the filter

collection efficiency, g, and filter coefficient, k, for cultivable

enteric viruses, Giardia, and Cryptosporidium (Figure 2). Based on

theoretical values of collection efficiency (Figure 1), one would

expectg values for protozoan pathogens to be higher than observed

values for viruses, as a result of differences in particle size and

dominant removal mechanisms. In addition, variations in g values

among the filter types may reflect differences in apparent particle

size, as a result of association with larger particles. The collection

efficiency, g, was significantly higher for Giardia and Cryptospo-

ridium associated with the dual-media filter A30S10 compared with

the other filters (p , 0.05), perhaps because of the lower hydraulic

loading rates. It is important to note that, even though the collection

efficiency was high for A30S10, the overall log10 reduction was

lower than that of the other filters (see Table 4). In contrast, the filter

coefficients, k, were more variable and were not significantly

different among the filters.

Relationships between filter coefficients, k, and hydraulic loading

rates for cultivable enteric viruses, Giardia, and Cryptosporidium

are shown in Figure 3 (for hydraulic loading rates below 1.4 L/s [2

gpm/ft2]). Even though the range of hydraulic loading rates that

were tested in this project was controlled by operating parameters at

the individual facilities (as opposed to systematic variation through

experimental design), filter coefficients were inversely correlated to

the hydraulic loading rate (p , 0.05) for the tested pathogens (i.e.,

increased removal was associated with lower hydraulic loading

Figure 2Comparison of calculated filter collection efficiency, g, and filter coefficients, k, for removal of cultivable

enteric viruses (VIR), Giardia (GIA), and Cryptosporidium (CRY) through three deep-bed granular media filters. Filter

descriptions are given in Table 1.

Figure 3Relationship between filter coefficients, k, and

hydraulic loading rates for cultivable enteric viruses,

Giardia, and Cryptosporidium, for pooled data from

A24S10, SU40, and A24S48, with hydraulic loading rates

below 0.0015 m/s (2 gpm/ft2). Correlation coefficients, R2,

were 0.8 for enteroviruses and 0.6 for Giardia and

Cryptosporidium. The slopes of the regression lines were

similar for all tested pathogens (2300 s/m).

Levine et al.

July 2008 603


9/13

rates). Conversely, there was no significant correlation between

hydraulic loading rates and removal of the bacterial indicators

(coliforms, enterococci, and C. perfringens), as predicted by eq 1

and Figure 1. In addition, microbial sampling for this project was

conducted under daily peak-flow conditions, which correspond to

higher hydraulic loading rates. Additional sampling would be

needed to account for the effects of daily and seasonal variations in

flowrates (and loading rates) on removal of pathogens and

indicators.

The data were also evaluated to determine if there were anyrelationships between the depth of filtration media and log10reduction of indicators and pathogens from deep-bed filters operated

without upstream chemical addition. For Giardia and Cryptospo-

ridium, weak but significant correlations were observed. Increased

depth was associated with improved log10 reduction, which ranged

from approximately 0.005 log10 removal for Giardia (total) to

approximately 0.01 log10 removal for Cryptosporidium (total) per

incremental centimeter of sand. While these correlations were

statistically significant, these results suggest that it would require an

additional 100 cm (3.3 ft) of sand to provide filtration capacity for

an additional 0.5 log10 reduction of Giardia and 1 log10 reduction

of Cryptosporidium. The additional headloss associated with this

increased depth makes this concept impracticable for control ofprotozoan pathogens, except in the context of groundwater recharge

and soil-aquifer treatment. Sand depth did not have a significant

effect on removal of cultivable enteric viruses or indicators (p .

0.05). No other significant correlations were observed between

depth and microbial removal (total depth or anthracite depth).Comparisons Between Indicator Organisms and Pathogens

in Filter Effluents. Coliphages have been widely used as

indicators for viral pathogens because of their similarity in size.

To evaluate the validity of this relationship for the filtration data

collected in this project, ANOVA tests were conducted to compare

log10 reductions and C/Co for coliphages and cultivable enteric

viruses for all filters tested in this project. No significant differences

were observed for the individual organisms among the filters;

therefore, the data were pooled for further comparisons. A

comparison of log10 reductions of coliphages and cultivable enteric

viruses is given in Figure 4 in a boxplot format. In general, higher

log10 reductions were associated with coliphages than cultivable

enteric viruses. In addition, coliphages and cultivable enteric viruses

did not display consistent patterns of detect versus nondetect (see

Table 2). The highest frequency of nondetects for cultivable enteric

viruses (80%) was associated with S11, while the highest frequency

of nondetects for coliphages (86%) was associated with A30S10.

These discrepancies may be the result of the characteristics of the

wastewater associated with each facility coupled with removalmechanisms upstream of filtration, resulting from differences in the

biological treatment system.

To test the efficacy of using coliphage concentrations to predict

cultivable enteric virus concentrations, filter effluent concentrations

of cultivable enteric viruses are shown as a function of coliphage

concentrations in Figure 5. The data are weakly correlated (R2 5

0.4); however, in approximately 44% of the cases where cultivable

enteric viruses were detected, coliphages were not detected (blue

squares to the left of coliphage detection limit line). Thus, for this

study, the absence of coliphages did not correspond to the absence

of viruses. Perhaps this relationship could be improved if larger

volume assays for phage were used (1 L versus 10 mL). Improve-

ments in coliphage detection limits might provide an opportunity forrevisiting this issue. However, because the sources of enteric viruses

in wastewater differ from the sources of coliphages, the use of

coliphages as a viral indicator should be verified for individual

facilities.Effect of Prech lorin ation on Removal of In dicator

Organisms. To evaluate the effect of prechlorination, two filters

at the facility that prechlorinates (S11) were isolated and operated in

parallel. One filter received secondary effluent amended with

approximately 2 mg/L chlorine, and the second filter received

nonchlorinated secondary effluent. Hydraulic loading rates were

similar for the two filters. Grab samples were collected at 2-hour

intervals and evaluated for selected indicators. A comparison of

C/Co for fecal coliforms, enterococci, coliphages, and C. perfrin-

gens with and without prechlorination is shown in Figure 6.

Figure 4Comparison of log10 reduction of coliphages and cultivable enteric viruses using pooled data from six full-scale filters.

Levine et al.



10/13

Prechlorination resulted in significantly greater removal of fecal

coliforms and enterococci (p , 0.05); however, prechlorination did

not have a significant effect on removal ofC. perfringens, probably

because it is less susceptible to chlorine than the other bacterial

indicators (Sobsey, 1989). Minimal removal of coliphages occurredwith or without prechlorination. These results suggest that these

more resistant organisms may be better indicators for assessing

filtration efficacy.Effect of Backwashing Practices on Removal of Protozoan

Pathogens. To evaluate the effects of backwashing practices on

removal of protozoan pathogens, samples were collected before and

after backwashing of two filtersA48 and A24S48 (Figure 7). In

each case, chlorinated filter effluent was used as a source of

backwash water, and the filters were backwashed and returned to

service using normal protocols. For both filters (A48 and A24S48),the concentrations of Giardia and Cryptosporidium increased

following backwashing. There was almost a 1 log10 increase

associated with the dual media deep-bed filter (A24S48). While the

cost of pathogen testing precluded additional testing of this issue

in the context of this project, similar findings have been reported

for filters used to process drinking water, where the presence of

remnant particles or the lack of filter ripening can result in

pathogen breakthrough following backwashing (Ahmad and

Amirtharajah, 1998; Amburgey et al., 2004, 2005; Darby et al.,

1991; Emelko et al., 2003; Hall and Fitzpatrick, 2000; Hozalski and

Bouwer, 1998; Jegatheesan and Vigneswaran, 2000; Logan et al.,

2001). Optimization of backwashing procedures by using extended

terminal subfluidization backwash and/or more gradual postback-washing startup procedures, such as delayed start or slow start, have

been suggested for improving the consistency of microbial removal

through filtration (Amburgey et al., 2004).Water Quality Variables. This study was based on evaluation

of full-scale wastewater reclamation facilities that produce re-

claimed water that is used for nonpotable public access reuse. In

general, all of the facilities produce a high-quality effluent with

BOD5 and TSS values below 10 mg/L (see Table 3). Two of the

facilities are designed for biological nutrient removal (SU40 and

A24S48). Water quality data from each facility were evaluated to

determine if there were any relationships between reduction of

pathogens and indicators through filtration and secondary effluent

characteristics. Removal of indicator bacteria, coliphages, cultivable

enteric viruses, and Giardia by filtration were not significantly

Figure 5Comparison of cultivable enteric virus concentrations and coliphage concentrations in pooled data from filter

effluents tested in this project. n represents detected levels of cultivable enteric viruses, s represents samples where

enteroviruses are not detected (plotted as detection limit). The line is an exponential fit to the data. The correlation

coefficient, R2, was 0.4 in both cases (p< 0.05). Detection limits for coliphages ranged from 5 to 10 PFU/100 mL. MPN 5

most probable number.

Figure 6Comparison of persistence of fecal coliforms,

enterococci, coliphages, and C. perfringensthrough S11

filters operated with and without prechlorination (sam-

pling conducted every 120 minutes over a 10-hour filter

run). The first box in each section represents the

prechlorinated filter (labeled as prechlorine) and the

second box (unshaded) represents the filter that received

secondary effluent without upstream chlorination (non-

prechlorinated).

Levine et al.

July 2008 605


11/13

correlated to any of the monitored water quality parameters (p .

0.05). However, effluent concentrations of Cryptosporidium were

weakly correlated to effluent BOD5, TSS, ammonia, and phosphate

(i.e., increased removal was associated with higher water quality)

(linear correlation coefficients, R2, were 0.2 [p 5 0.02] for BOD5,

0.6 [p5 0.0002] for TSS, and 0.3 [p5 0.03] for NH3-N and PO4-P).

Summary and RecommendationsGranular media filtration of secondary effluent is widely used for

production of reclaimed water. Key process variables include the

filter media characteristics (i.e., depth and particle size), use of

upstream chemicals (oxidants and/or coagulants), hydraulic loading

rates, and backwash practices. In this study, data from six different

full-scale wastewater reclamation facilities were compared to

evaluate filter performance. Because of the nature of the grab

sampling conducted in this project and differences in operating

parameters of the individual filters, it was not possible to sample

each filter under similar operating conditions, thus introducing

confounding variables that may affect data interpretation. For

example, the facility that uses prechlorination (S11) also had the

lowest hydraulic loading rates and smallest media, making itdifficult to separate the effects of prechlorination and hydraulic

loading rate under routine plant operations.

Filtration of secondary effluent differs from current practices that

have been adopted for filtration of surface water. Key differences

include the degree to which upstream chemical amendments are

used, turbidity limits, and filter backwashing and postbackwashing

protocols. While the higher ionic strength and organic content of

secondary effluents may serve to enhance particle removal through

filtration, the log10 reduction of pathogens observed through this

study was significantly lower than values reported for surface water

filtration. Based on the data from this study and others, some degree

of pathogen reduction was observed through all of the filters that

were tested; however, the results suggest that optimization offiltration operations (including pretreatment, loading rates, and

backwashing practices) can improve pathogen reduction through

filtration and thereby improve the overall microbial safety of re-

claimed water. In addition, because of differences in source charac-

teristics and removal mechanisms through biological treatment and

filtration, removal patterns associated with traditional bacterial

indicators (coliforms) and coliphages do not correspond to reduc-

tion of protozoan pathogens and cultivable enteric viruses. Adop-

tion of alternative monitoring approaches and extrapolation of design

and operation strategies from surface-water filtration may result in

more robust filtration systems for the production of reclaimed water.

ConclusionsBased on the data derived from this project, filter loading rates,

use of upstream chemical addition, backwashing practices, and

water quality of the secondary effluent can influence the efficiency

of granular media filtration for removal of pathogens from sec-

ondary effluent. The major conclusions are as follows:

(1) The use of traditional models of single collector efficiency and

filter coefficients can be applied to provide a qualitative

comparison of pathogen removal from granular media filtration

of secondary effluent; however, supplemental water quality data

and operations information are needed to optimize filter

performance.

(2) Higher rates of removal through granular media filtration were

observed for conventional bacterial indicators compared with

pathogens, suggesting that the use of bacterial indicators is notan effective predictor of filtration performance for pathogen

reduction.

(3) The suite of microbiological indicators tested in this project

did not correlate to concentrations, removal (log reduction),

or presence/absence of cultivable enteric viruses, Giardia, or

Cryptosporidium in the filtered secondary effluents tested in

this project. Coliphage removal or concentrations did not

correspond to removal or concentrations of cultivable enteric

viruses.

(4) For deep-bed filters that contained sand and were not

prechlorinated, removal of pathogens was inversely propor-

tional to hydraulic loading rates and directly proportional to the

depth of sand.

(5) The use of prechlorination resulted in improved removal of

microorganisms that are susceptible to chlorine inactivation

(coliforms and enterococci) but had minimal effect on chlorine-

resistant bacteria, such as the endospore-forming Clostridium

perfringens.

(6) The return of filters to service directly following backwashing

may result in higher effluent concentrations of Giardia and

Cryptosporidium until filter ripening occurs. The extent of the

increase varies with filter characteristics (media, depth, and

upstream chemical use). Continuous backwash filters may not

experience these performance fluctuations.

(7) Water quality variables that were correlated to removal of

Cryptosporidium through granular media or cloth filtration

include effluent concentrations of BOD5, TSS, ammonia

Figure 7Comparison of (a) concentrations of total

Cryptosporidiumand total Giardiain filter effluents before

and after backwashing from two full-scale filtersA48

and A24S48, and (b) ratio of effluent to influent concen-

tration (C/Co) of total Cryptosporidium and total Giardiain

filter effluents before and after backwashing from two full-

scale filters.

Levine et al.



12/13

nitrogen, and phosphate. No statistically significant water

quality relationships were associated with removal of the other

microorganisms tested in this project (cultivable enteric viruses,

Giardia, and indicator organisms).

(8) For the filters tested in this project, removal of conventional

indicator organisms through filtration tended to exhibit different

patterns than those observed for viral and protozoan pathogens,

suggesting that alternative monitoring practices may be neededto assess and manage microbial health risks associated with

reclaimed water applications.

Credits

This study was funded by the Water Environment Research

Foundation (Alexandria, Virginia) as project 00-PUM2T. Personnel

from wastewater reclamation facilities in Phoenix, Arizona; Santa

Barbara, California; and Eustis, St. Petersburg, and Hillsborough

County, Florida assisted with sample collection and data compila-

tion. Vasanta Chivulaka, Molly McLaughlin, Stefica Depovic, and

Angela Gennaccaro conducted microbiological testing for the

project.Submitted for publication November 12, 2006; revised manuscript

submitted September 9, 2007; accepted for publication December

11, 2007.

The deadline to submit Discussions of this paper is October 15,

2008.

References

Adams, M. H. (1959) Bacteriophages ; Interscience Publishers: New

York.

Ahmad, R.; Amirtharajah, A. (1998) Detachment of Particles During Filter

Backwashing. J. Am. Water Works Assoc., 90 (12), 7485.

Amburgey, J. E.; Amirtharajah, A.; Brouckaert, B. M.; Spivey, N. C. (2004)

Effect of Washwater Chemistry and Delayed Start on Filter Ripening.

J. Am. Water Works Assoc., 96 (1), 97110.

Amburgey, J. E.; Amirtharajah, A.; York, M. T.; Brouckaert, B. M.; Spivey,

N. C.; Arrowwood, M. J. (2005) Comparison of Conventional and

Biological Filter Performance for Cryptosporidium and Microsphere

Removal. J. Am. Water Works Assoc., 97 (12), 7791.

American Public Health Association; American Water Works Association;

Water Environment Federation (1998) Standard Methods for the

Examination of Water and Wastewater, 20th ed.; American Public

Health Association: Washington, D.C.

Bisson, J. W.; Cabelli, V. J. (1979) Membrane Filter Enumeration for

Clostridium Perfringens. Appl. Environ. Microbiol., 37 (1), 5566.

Bourgeous, K. N.; Riess, J.; Tchobanoglous, G.; Darby, J. L. (2003)

Performance Evaluation of a Cloth Media Filter for Wastewater

Reclamation. Water Environ. Res., 75 (6), 532538.Brown, D. G.; Abramson, A. (2006) Collision Efficiency Distribution of

a Bacterial Suspension Flowing Through Porous Media and Implica-

tions for Field Scale Transport. Water Res., 40, 15911598.

Bustamante, H. A.; Shanker, S. R.; Pashley, R. M.; Karaman, M. E. (2001)

Interaction Between Cryptosporidium Oocysts and Water Treatment

Coagulants. Water Res., 35 (13), 31793189.

Camp, T. R. (1964) Theory of Water Filtration. ASCE J. Sanit. Eng. Div.,

90 (SA4), 130.

Darby, J. L.; Lawler, D. F.; Wilshusen, T. P. (1991) Depth Filtration of

Wastewater: Particle Size and Ripening. J. Water Pollut. Control Fed.,

63 (2), 228238.

Edzwald, J. K.; Tobiason, J. E.; Parento, L. M.; Kelley, M. B.; Kaminski,

G. S.; Dunn, H. J.; Galant, P. B. (2000) Giardia and Cryptosporidium

Removals by Clarification and Filtration Under Challenge Conditions.


Elimelech, M. (1992) Predicting Collision Efficiencies of Colloidal Particles

in Porous Media. Water Res., 26 (1), 18.

Elimelech, M.; OMelia, C. R. (1990) Kinetics of Deposition of Colloidal

Particles in Porous Media. Environ. Sci. Technol., 24, 15281536.

Emelko, M. B.; Huck, P. M.; Coffey, B. M. (2005) A Review of

Cryptosporidium Removal by Granular Media Filtration. J. Am. Water

Works Assoc., 97 (12), 101115.

Emelko, M. B.; Huck, P. M.; Douglas, I. P. (2003) Cryptosporidium and

Microsphere Removal During Late-in-Cycle Filtration. J. Am. WaterWorks Assoc., 95 (5), 173182.

England, S. K.; Darby, J. L.; Tchobanoglous, G. (1994) Continuous-

Backwash Upflow Filtration for Primary Effluent. Water Environ. Res.,

60 (2), 145152.

Foppen, J. W.; Okletey, S.; Schijven J. F. (2006) Effect of Goethite Coating

and Humic Acid on the Transport of Bacteriophage PRD1 in Columns

of Saturated Sand. J. Contam. Hydrol., 85 (34), 287301.

Ghosh, M.; Jordan, T. A.; Porter, R. L. (1975) Physicochemical Approach to

Water and Wastewater Filtration. ASCE J. Sanit. Eng. Div., 101 (EE1),

7186.

Glasgow, G. D. E.; Wheatley, A. D. (1999) Observations of Rapid Flow

Rate Disturbances in Drinking Water Filters and Their Effect on Solids

Removal. J. Water SRT-Aqua, 48, 257262.

Habibian, M. T.; OMelia, C. R. (1975) Particles, Polymers, andPerformance in Filtration. ASCE J. Environ. Eng. Div., 101 (EE4),

567583.

Hall, D.; Fitzpatrick, C. S. B. (2000) Suspension Concentration Profiles

During Rapid Gravity Filter Backwashing. Chem. Eng. J., 80, 197201.

Harrington, G. W.; Xagoraraki, I.; Assavasilavasukul, P.; Standridge, J. H.

(2003) Effect of Filtration Conditions on Removal of Emerging

Pathogens. J. Am. Water Works Assoc., 95 (12), 95104.

Harvey, R. W.; Garabedian, S. P. (1991) Use of Colloid Filtration Theory in

Modeling Movement of Bacteria Through a Contaminated Sandy

Aquifer. Environ. Sci. Technol., 25 (1), 178185.

Harwood, V. J.; Levine, A. D.; Scott, T. M.; Chivukula, V.; Lukasik, J.;

Farrah, S. R.; Rose, J. B. (2005) Validity of the Indicator Organism

Paradigm for Pathogen Reduction in Reclaimed Water and Public

Health Protection. Appl. Environ. Microbiol., 71 (6), 31633170.

Hozalski, R. M.; Bouwer, E. J. (1998) Deposition and Retention of Bacteria

in Backwashed Filters. J. Am. Water Works Assoc., 90 (1), 7185.

Hsu, B.-M.; Huang, C. (2002) Influence of Ionic Strength and pH on

Hydrophobicity and Zeta Potential of Giardia and Cryptosporidium.

Colloids Surf. A, 201, 210206.

Hsu, B.-M.; Huang, C.; Pan, J. R. (2001) Filtration Behaviors of Giardia

and Cryptosporidium Ionic Strength and pH Effects. Water Res.,

35 (16), 37773782.

Iwasaki, T. (1937) Some Notes on Sand Filtration. J. Am. Water Works

Assoc., 29 (10), 15911602.

Jegatheesan, V.; Vigneswaran, S. (2000) Transient Stage Deposition of

Submicron Particles in Deep Bed Filtration Under Unfavorable

Conditions. Water Res., 34 (7), 21192131.

Jiminex, B.; Chavex, A.; Leyva, A.; Tchobanoglous, G. (2000) Sand and

Synthetic Medium Filtration of Advanced Primary Treatment Effluentfrom Mexico City. Water Res., 14 (2), 473480.

Kavanaugh, M. C.; Toregas, G.; Chung, M.; Pearson, E. A. (1978)

Particulates and Trace Pollutant Removal by Depth Filtration. Prog.

Water Technol., 10 (5/6), 197213.

Koivunen, J.; Siitnonen, A.; Heinonen-Tanski, H. (2003) Elimination of

Enteric Bacteria in Biological-Chemical Wastewater and Tertiary

Filtration Units. Water Res., 37, 690698.

Logan, A. J.; Stevik, T. K.; Siegrist, R. L.; Ronn, R. M. (2001) Transport

and Fate of Cryptosporidium parvum Oocysts in Intermittent Sand

Filters. Water Res., 35 (18), 43594369.

Mosher, R. R.; Hendricks, D. W. (1986) Rapid Rate Filtration of Low

Turbidity Water Using Field-Scale Pilot Filters. J. Am. Water Works

Assoc., 78 (12), 4251.

Nasser, A.; Weinberg, D.; Dinoor, N.; Fattal, B.; Adin, A. (1995) Removal

of Hepatitis A Virus (HAV), Poliovirus, and MS2 Coliphage by

Levine et al.

July 2008 607


13/13

Coagulation and High Rate Filtration. Water Sci. Technol., 31 (56),

6368.

Nieminski, E. C.; Ongerth, J. E. (1995) Removing Giardia and

Cryptosporidium by Conventional Treatment and Direct Filtration.


OMelia, C. R.; Ali, W. (1978) The Role of Retained Particles in Deep Bed

Filtration. Prog. Water Technol., 10 (5/6), 167182.

Ongerth, J. E.; Pecoraro, J. P. (1996) Electrophoretic Mobility of

Cryptosporidium Oocysts and Giardia Cysts. J. Environ. Eng., 122 (3),228231.

Patania, N. L.; Jacangelo, J. G.; Cummings, L.; Wilczak, A.; Riley, K.;

Oppenheimer, J. (1995) Optimization of Filtration for Cyst Removal;

AWWA Research Foundation: Denver, Colorado.

Reddi, L. N. (1997) Particle Transport in Soils: Review of Significant

Processes in Infrastructure Systems. J. Infrastruct. Syst., 3 (2), 78

86.

Redman, J. A.; Grant, S. B.; Olson, T. M.; Estes, M. K. (2001) Pathogen

Filtration, Heterogeneity and the Potable Reuse of Wastewater.

Environ. Sci. Technol., 35 (9), 17981805.

Rose, J. B.; Farrah, S. R.; Harwood, V. J.; Levine, A. D.; Lukasik, J.;

Menendez, P.; Scott, T. M. (2004) Reduction of Pathogens, Indicator

Bacteria, and Alternative Indicators by Wastewater Treatment and

Reclamation Processes, 00-PUM-2T; Water Environment Federation:Alexandria, Virginia.

Slifko, T. R.; Huffman, D. E.; Rose, J. B. (1999) A Most-Probable-Number

Assay for Enumeration of Infectious Cryptosporidium Parvum Oocysts.

Appl. Environ. Microbiol., 65 (9), 39363941.

Sobsey, M. D. (1989) Inactivation of Health-Related Microorganisms in

Water by Disinfection Processes. Water Sci. Technol., 21, 179195.

Stevenson, D. G. (1997) Flow and Filtration Through Granular MediaThe

Effect of Grain and Particle Size Dispersion. Water Res., 31 (2),

310322.

Swertfeger, J.; Metz, D. H.; DeMarco, J.; Braghetta, A.; Jacangelo, J. A.

(1999) Effect of Filter Media on Cyst and Oocyst Removal. J. Am.

Water Works Assoc., 91 (9), 90100.

Tchobanoglous, G.; Eliassen, R. (1970) Filtration of Treated Sewage

Effluent. ASCE J. Sanit. Eng. Div., 96 (SA2), 243265.

Trussell, R. R.; Chang, M. (1999) Review of Flow Through Porous Media

as Applied to Head Loss Through Filters. J. Environ. Eng., 125 (11),

9981006.

U.S. Environmental Protection Agency (2004) Guidelines for Water Reuse,

EPA-625/R-04-108; U.S. Environmental Protection Agency: Wash-

ington, D.C.

U.S. Environmental Protection Agency (1995) ICR Protozoan Method for

Detecting Giardia Cysts and Cryptosporidium Oocysts in Water by

a Fluorescent Antibody Procedure, EPA-814/B-95-003; U.S. Environ-

mental Protection Agency: Washington, D.C.

U.S. Environmental Protection Agency (2003) Long Term 2 Enhanced

Surface Water Treatment Rule Toolbox Guidance Manual, EPA-815/

D-03-009; U.S. Environmental Protection Agency: Washington, D.C.

U.S. Environmental Protection Agency (2005) Method 1623: Cryptospo-

ridium and Giardia in Water by Filtration/IMS/FA, EPA-815/R-05-

002; U.S. Environmental Protection Agency: Washington, D.C.

U.S. Environmental Protection Agency (1996) Microbial Laboratory

Manual. ICR: Information Collection Rule. U.S. Environmental

Protection Agency: Cincinnati, Ohio.

U.S. Environmental Protection Agency (2006) Ultraviolet Disinfection

Guidance Manual for the Final Long-Term 2 Enhanced Surface Water

Treatment Rule, EPA-815/R-06-007; U.S. Environmental ProtectionAgency: Washington, D.C.

Wang, J. Z.; Summers, R. S.; Miltner, R. J. (1995) Biofiltration

Performance: Part 1, Relationship to Biomass. J. Am. Water Works

Assoc., 87 (12), 5563.

Xagoraraki, I.; Harrington, G. W.; Assavasilavasukul, P.; Standridge, J. H.

(2004) Removal of Emerging Waterborne Pathogens and Pathogen

Indicators by Pilot Scale Conventional Treatment. J. Am. Water Works

Assoc., 96 (5), 102113.

Yao, K.; Habibian, M. T.; OMelia, C. R. (1971) Water and Waste Water

Filtration: Concepts and Applications. Environ. Sci. Technol., 5 (11),

11051112.

York, D. W.; Walker-Coleman, L. (2000) Pathogen Standards for Reclaimed

Water. Water Environ. Technol., 12 (1), 5861.

Levine et al.


Documents

WER 2008 Levine Cloth Media