TREATMENT WETLANDS - CHAPTER 12 pot

483 12 Pathogens Pathogens are present in untreated domestic wastewaters as well as in runoff waters from animal sources. These organ- isms range from submicroscopic viruses to parasitic worms visible to the unaided eye and represent important compo- nents of water quality. Table 12.1 lists some of the most prev- alent human pathogens associated with domestic wastewater. These pathogens are divided into ve groups: viruses, bac- teria, fungi, protozoans, and helminths. The density of these organisms in raw wastewater varies geographically. Viruses are submicroscopic, nonliving particles of genetic material that are enclosed in a sheath. Viruses can- not divide and reproduce alone, but they can infect host organisms and reproduce to very large populations at the expense of the host organism. Over 100 virus types are known to occur in human feces, with minimum infective doses as low as one organism for some species. Bacteria are universally present in human feces, with normal pop- ulations of about 10 11 organisms per gram (Leclerc et al., 1977). Although most of these organisms live symbiotically with their hosts, a number of species are known human pathogens and occur with great frequency in infected indi- viduals. Human parasites derived from wastewater-related infections include protozoa and helminths. Two common protozoan parasites are Entamoeba histolytica and Giardia lamblia, which both cause diarrhea in infected humans. The phylum Aschelminthes (cavity worms) includes all parasitic worms incapable of adult life without a host organism. A number of helminths, including tapeworms and ukes, are found in infected humans and can be spread through waste- water pathways. For a summary discussion of environmen- tally transmitted pathogens, see Maier et al. (2000). The efciencies of conventional treatment technologies that reduce pathogens to noninfective levels have been stud- ied thoroughly, and wastewater treatment plants regularly add processes to accomplish necessary removals (Metcalf and Eddy Inc., 1991; Crites and Tchobanoglous, 1998). The most common add-on disinfection processes are chlorina- tion, ozonation, and ultraviolet irradiation. Because of its low cost and proven effectiveness, chlori- nation has been the disinfection method of choice for many years. However, negative side effects of efuent chlorina- tion have become apparent in the past two decades. Resid- ual free chlorine harms a variety of aquatic organisms and causes chronic and acute toxicity to microorganisms and sh. Also, when it comes in contact with organic compounds in wastewater or receiving waters, free chlorine forms trihalo- methanes and other organochlorine compounds known to be carcinogenic. These ndings have resulted in the increased use of dechlorination techniques and in the development of the ozonation and ultraviolet disinfection technologies. Animals are also a source of pathogenic organisms. Farm animals produce feces with high numbers of bacteria. Cows, sheep, pigs, and poultry produce 10 5 –10 7 fecal coli- forms per gram, and 10 6 –10 8 fecal streptococci per gram. Cats, dogs, mice, and chipmunks produce similar concentra- tions (Maier et al., 2000). Waterfowl and other wetland birds also contribute coliforms to the wetland environment and may do so in great quantities during migratory concentra- tions. Beavers, muskrats, and other warm-blooded wetland animals are also producers of enteric pathogens. Beavers have been implicated in transmission of Giardia. About half the animals in Colorado were found to be infected and shedding 10 8 cysts per animal per day. Ninety-ve percent of muskrats in this study were found to be infected with Giardia (Erlandsen, 1995). Natural treatment technologies have the potential to reduce populations of enteric pathogens because of natural die-off rates and hostile environmental conditions. Wetlands have been found to reduce pathogen populations with varying but signicant degrees of effectiveness. This chapter reviews the pathogens typically found in wastewater and describes the effect that treatment wetlands have on the pathogen popu- lations passing through them. 12.1 INDICATOR ORGANISMS AND MEASUREMENT Measurement of human pathogenic organisms in natural and wastewaters is expensive and technically challenging. Con- sequently, it has been customary to rst look for indicator organisms that are easy to monitor and correlate with popu- lations of pathogenic organisms. No perfect indicators have been found, but the coliform bacteria group has long been used as the rst choice among indicator organisms. Typical indicator organisms are listed in Table12.2. In the United States, total coliforms were the rst approved indicator organisms. These coliforms include bac- terial species that are rod-shaped, stain gram-negative, do not form spores, are facultatively anaerobic, and ferment lactose with gas production in 48 hours at a temperature of 35°C. Because many members of the group are not limited to fecal sources, methods have been developed to differen- tiate organisms of fecal origin. Fecal coliforms are sepa- rated from total coliforms by their ability to ferment lactose with gas production in 24 hours at a temperature of 44.5°C. © 2009 by Taylor & Francis Group, LLC 484 Treatment Wetlands An even narrower group, Escherichia coli, is being used more frequently as an indicator organism, because it can readily be separated from the rest of the fecal group, and because several strains are capable of causing severe human health problems. However, as these bacteria also originate in other warm-blooded animals, E. coli is not diagnostic of human fecal contamination alone. The coliform groups in general have an indicator disadvantage in that they may regrow in aquatic environments. The fecal streptococcus group is used to conrm the ori- gins of fecal contamination. A high ratio of fecal coliform to fecal streptococcus (FC/FS  4) is regarded as an indication of human origin, whereas a low ratio (FC/FS  0.7) is indica- tive of animal pollution (Clausen et al., 1977). However, the validity of this separation has been subject to question (Gerba, 2000). Fecal streptococcus is found in the feces of humans and other warm-blooded animals including birds and mammals. These bacteria are found frequently in waters receiving fecal contamination and are not believed to multiply in natural or polluted waters and soils. Because fecal streptococci bacte- ria seem to survive longer than fecal coliforms in receiving waters, they are used as a second indicator of fecal contamina- tion. Because bacterial die-off affects the ratio, it is only appli- cable to fecal pollution within 24 hours of discharge. It is generally recognized that fecal coliforms are not suitable indicators of viral contamination in surface water receiving domestic waste because some viruses are more resistant to chlorination and environmental deactivation than bacteria (Kraus, 1977; Gersberg et al., 1987; Gerba, 2000). Bacteriophages (viruses that infect bacteria, such as coliphage MS-2) have been used as viral indicators in wetland treat- ment systems (Gersberg et al., 1987; Schuerman et al., 1989). TABLE 12.1 Some Human Pathogens Typical of Domestic Wastewater Pathogen Illness Viruses Adenovirus (31 types) Respiratory disease Enteroviruses (67 types) Diarrhea, respiratory disease, polio Hepatitis A Infectious hepatitis Norwalk agent Gastroenteritis Rotavirus Diarrhea Reovirus Gastroenteritis HIV AIDS Bacteria Campylobacter jejuni Diarrhea Escherichia coli Diarrhea Legionella pneumophila Fever, respiratory tract infections Leptospira (150 spp.) Leptospirosis Salmonella typhi Typhoid fever Salmonella (~1,700 spp.) Salmonellosis Shigella (4 spp.) Diarrhea, dysentery Vibrio spp. Cholera, diarrhea Yersinia spp. Yersiniosis Fungi Aspergillus fumigatus Aspergillosis Candida albicans Fungal infections Protozoa Balantidium coli Diarrhea, dysentery Cryptosporidium parvum Diarrhea Entamoeba histolytica Diarrhea, dysentery Giardia lamblia Diarrhea Helminths Ascaris lumbricoides Roundworm Clonorchis sinensis Bile duct infection Diphyllobothrium latum Fish tapeworm Enterobius vericularis Pinworm Fasciola hepatica Liver uke Fasciolopsis buski Intestinal uke Hymenolepis nana Dwarf tapeworm Necator americanus Hookworm Opisthorchis spp. Bile duct infection Schistosoma spp. Schistosomiasis Taenia spp. Tapeworm Trichuris trichura Whipworm Source: Data from Krishnan and Smith (1987) In Aquatic Plants for Water Treatment and Resource Recovery. Reddy and Smith (Eds.), Magnolia Pub- lishing, Orlando, Florida, pp. 855–878; Shiaris (1985) In Ecological Con- siderations in Wetlands Treatment of Municipal Wastewaters. Godfrey et al. (Eds.), Van Nostrand Reinhold, New York, pp. 243–261; Leclerc et al. (1977) In Bacterial Indicators/Health Hazards Associated with Water. Hoadley and Dutka (Eds.), American Society for Testing and Materials (ASTM), Philadelphia, pp. 23–36; Cabelli (1977) In Bacterial Indicators/ Health Hazards Associated with Water. Hoadley and Dutka (Eds.) American Society for Testing and Materials (ASTM): Philadelphia, pp. 222–238; Metcalf and Eddy Inc. (1991) Wastewater Engineering, Treatment, Dis- posal, and Reuse. Tchobanoglous and Burton (Eds.), Third Edition, McGraw-Hill, New York; Crites and Tchobanoglous (1998) Small and Decentralized Wastewater Management Systems. McGraw-Hill, New York. TABLE 12.2 Indicator Microorganisms and Their Abundance in Feces and Raw Domestic Wastewater Water Concentration (CFU/100 mL) Fecal Coliform Density (#/gram × 10 6 ) Fecal Coliforms Human 10 6 –10 8 13 Pig — 3.3 Cat — 7.9 F ecal Streptococci Human 10 4 –10 6 3 Pig — 84 Cat — 27 Clostridium perfringens 10 3 –10 5 — Pseudomonas aeruginosa 10 3 –10 5 — Salmonella 10 2 –10 4 — Cryptosporidium parvum 10–10 3 — Giardia lamblia 10 3 –10 4 — Ascaris lumbricoides 10–10 3 — Coliphages 10 2 –10 4 — Source: Data from Crites and Tchobanoglous (1998) Small and Decentral- ized Wastewater Management Systems. McGraw-Hill, New York; Maier et al. (2000) Environmental Microbiology. Academic Press, San Diego, California. © 2009 by Taylor & Francis Group, LLC Pathogens 485 Enumeration of bacteriophages is technically simpler and more rapid than enumeration of the target pathogenic viruses. Also, MS-2 is nearly the same size as enteroviruses and is more resistant to ultraviolet (UV) light, heat, and disinfec- tion than most enteric viruses. In a free water surface (FWS) study at San Jacinto, California, Chendorain et al. (1998) added cultured MS-2 virus to the wetland inuent wastewa- ter to determine die-off rates. 12.2 PATHOGEN REMOVAL PROCESSES The observed net removal of pathogenic organisms in treat- ment wetlands is the result of a variety of processes, some that remove pathogens and some that introduce patho- gens. Generally speaking, removal mechanisms are much more prevalent than reintroduction mechanisms, and most constructed wetlands demonstrate large net removals of pathogenic organisms down to a nonzero background con- centration (C*). SOLAR DISINFECTION UV radiation is a potent agent for killing bacteria in FWS wetlands. An exponential relation is commonly used to describe the inactivation in disperse batch cultures: NNe kIt   o i () (12.1) where I intensity of UV light in solution, J/m ·d 2 kk N i 2 o inactivation rate coefficient, m /J i   nnitial number of dispersed organisms survN  iiving number of dispersed organisms time,t  d However, most organisms are present in association with waste- water particulates, rather than in a dispersed phase (Emerick et al., 2000). Consequently, UV inactivation rates are lowered from shielding of the organisms by the particle structures. Equation 12.1 is still applied, but rate constants are much lower than for dispersed-phase populations. Emerick et al. (2000) found little variation in rates for fecal coliforms across 11 treat- ment technologies, with 0.022  k i  0.054 m 2 /J for activated sludge, trickling lter, aerated, and facultative pond efuents. The UV wavelengths that are absorbed by microorgan- isms, and hence are effective in disinfection, comprise the range 240–280 nm (Crites and Tchobanoglous, 1998). Lamps used for in-line disinfection of wastewaters are designed to provide radiation in this range. The effectiveness of the radi- ation depends on water quality factors, such as optical absor- bance and suspended solids content. Typical UV dosages are 100–850 J/m 2 to provide suitable disinfection of fecal coli- forms to the range between 1,000 and 23 CFU/100 mL (Crites and Tchobanoglous, 1998). In a natural system, such as a pond or an open-water wet- land, sunlight provides a source of ultraviolet (UV) radiation. However, the fraction of the incoming solar radiation that is in the UV range is small, typically less than 1%. Therefore, solar inactivation rates based on total solar radiation are much smaller than those determined for UV lamp sources, and these are designated k S . For example, Davies-Colley et al. (1999) present pond water data for E. coli for which k S y 0.7 m 2 /MJ, which is about 100,000 times lower than for a UV lamp. Salih (2003) determined a similar value for E. coli, k S y 1.5 m 2 /MJ. Davies-Colley et al. (1999) also demonstrated that dissolved oxygen and pH had important inuences on rates of inactivation. These authors inferred that both direct hit radiation damage and photooxidation were caused by incoming radiation. The overall solar inactivation rate for a shallow water body (pond) is further reduced by the lack of penetration of light (Mayo, 1995). The top few centimeters of a stabilization pond get the required radiation, but deeper sections do not. Accordingly, the solar inactivation rate constant is approxi- mately inversely proportional to the pond depth: k k Kh S S L  ` (12.2) where h k   water depth, m overall solar inactivati S oon rate coefficient, m /J ' intrinsic sol 2 S k  aar inactivation rate coefficient, m /J l 2 L K  iight attenuation coefficient, m 1 Mayo (1995) reported values of 0.012  hk S  0.016 m 3 /MJ for ve stabilization pond studies. Additionally, high-rate ponds (0.2–0.3 m depth and a few days’ detention) have been shown to have k S  0.0675 m 2 /MJ (Davies-Colley et al., 2003) and k S  0.083 m 2 /MJ (Craggs et al., 2004). Bacteriophages are also susceptible to UV radiation. Jolis (2002) gives a range of inactivation rate coefcients of 0.0054  k i  0.0106 m 2 /J for irradiation from UV lamp sources. This slower rate is reected in higher UV lamp dosages needed to inactivate viruses, typically six to eight times higher than for coliforms (Crites and Tchobanoglous, 1998). As for bacteria, natural systems receive solar radiation that contains small amounts of UV, which is effective in inactivation of bacterio- phages. Davies-Colley et al. (1999) provide pond data for an F-DNA phage (k S y 0.7 m 2 /MJ) and for an F-RNA phage (k S y 0.6 m 2 /MJ). The inactivation of phages by sunlight has been found to be a good rate analog for the inactivation of human pathogenic polio, echo, and coxsackie viruses (Fujioka and Yoneyama, 2002). Solar disinfection depends on the sunlight reaching and penetrating into the water column. Dense vegetation inter- cepts sunlight in the wetland environment and diminishes the potential for solar disinfection. This negative effect is putatively present for emergent and oating plant commu- nities. It may or may not be present in submersed algal or macrophyte communities where subsurface oxygenation may occur. The presence of enhanced amounts of dissolved © 2009 by Taylor & Francis Group, LLC 486 Treatment Wetlands oxygen (DO) fosters the photooxidation route of disinfection and may compensate for reduced penetration of UV radiation (Davies-Colley et al., 1999; Sun et al., 2003). However, when pond performance is compared to veg- etated wetland performance for fecal coliform reduction across large numbers of both types of systems receiving varying numbers of pathogens, little difference can be seen in the general level of reduction (Kadlec, 2005e). Other mechanisms of organism mortality and removal, beyond just solar disinfection, become operative in the wetland environment. PREDATION Most pathogens are food for nematodes, rotifers, and pro- tozoa (Decamp and Warren, 1998). Among these, rotifers and agellated and ciliated protozoa have been implicated as important contributors to the reduction of bacteria in treat- ment wetlands (Panswad and Chavalprit, 1997; Laybourn- Parry et al., 1999; Proakis, 2003; Stott and Tanner, 2005) as well as in natural aquatic systems (Menon et al., 2003). While pathogenic organisms span a wide size range (0.2–100 μm), so do the associated predator/grazing communities (Figure 12.1). They are found in secondary wastewaters in considerable numbers; for instance, Fox et al. (1981) reported up to about 50 rotifers per liter in such efuents (Figure 12.2). Proakis (2003) determined that rotifers consumed enterococci at the rate of about 600/h when the bacterial density was approximately 10 6 per 100 mL. From this rate, it was determined that rotifers, at a density of ten per mL, could suitably disinfect stormwater in a 1.2-hour detention in a marsh. The next question, then, is about concentrations of predators that can be expected in the wetland waters. Decamp and Warren (1998) found that the ciliate Para- mecium consumed more than 100 E. coli per hour when the bacterial density was approximately 10 8 per 100 mL. This was interpreted to require 20 Paramecium per mL to remove about 2 × 10 6 E. coli per 100 mL in eight hours’ detention. This concentration of Paramecium was comfortably within the observed concentration range in the SSF wetland from which the protozoa were taken. Heterotrophic nanoagellates (HNAN) were found to dominate the protozoan community in both lagoons and grass lters treating sewage (Laybourn-Parry et al., 1999). Ciliates contributed to only about 10% of the grazer popu- lations. The grass lters harbored more numbers (approxi- mately 10 8 per 100 mL) of these bacterial predators than the lagoons (approximately 10 7 per 100 mL). The overall effect of grazing on bacterial numbers in aquatic systems was quantied by Menon et al. (2003). Grazing by protozooplankton was responsible for more than 90% of the overall mortality rate of both fecal and autochthonous bacteria in the river Seine. For example, whereas in the presence of pro- tozoa the overall rst-order volumetric mortality coefcient was 34 × 10 −3 h −1 , it was 2 × 10 −3 h −1 in the absence of protozoa. Grazing is generally higher at higher temperatures (Laybourn-Parry et al., 1999; Menon et al., 2003), but temperature coefcients for wetland environments are not available. The aforesaid studies indicate a strong poten- tial for pathogen removal through predation in constructed wetlands. They do not, unfortunately, allow quantication of this removal mechanism. Whereas predation/grazing is an important removal mechanism (especially in FWS wetlands), it is not the only removal mechanism. Settling and ltration also play important roles in pathogen reduction, and these removal mechanisms have a cumulative effect in treatment wetland systems. SETTLING AND FILTRATION A measurable proportion of wastewater microorganisms are found either associated with particulates or as aggregates of FIGURE 12.1 Pathogen and predator size chart. Pathogen size class Consumer’s preferred food size class Removal processes* Size (µm) 0.1 *Note: Filtration processes are system dependent. 1 10 100 Physical Settling Bacteria Microcrustaceans Rotifers Larger Organisms Predation Filtration* Protozoa Helminth Ova Protozoa © 2009 by Taylor & Francis Group, LLC Pathogens 487 many organisms. For the activated sludge process, the frac- tion of particles with associated coliform ranges from 1% to 24% (Loge et al., 2002). This fraction declines with the residence time (mean cell residence time) of organics in the activated sludge process. The loss of coliform organisms inside the activated sludge process has been speculatively attributed to predation by protozoans (Loge et al., 2002). Organisms associated with particles are far less susceptible to UV or solar disinfection, presumably because of shielding effects (Emerick et al., 2000). Bacteria may clump together to form aggregates. These are highly amorphous and porous, but the internal voids are apparently lled with exopolymeric material (Li and Yuan, 2002). Sedimentation of these particles contributes to their removal from the water column, with removals of 25–75% due to gravitational sedimentation in conventional treatment plants (Metcalf and Eddy Inc., 1991). Tests of algal settling ponds produced 40% removal of E. coli by sedimentation (Davies-Colley et al., 2003). In the wetland environment, submersed plant parts and their associated biolms form “sticky traps” for particles, including all sizes of microorganisms (see Chapter 7). These biolms are capable of trapping considerable numbers of organisms (Flood and Ashbolt, 2000; Stott and Tanner, 2005). Such biolms are enhanced in quantity by larger quantities of submersed surfaces and exposure to light. There may be an optimal plant density that allows light and provides the neces- sary surfaces for biolm growth. MORTALITY AND REGROWTH Bacteria, protozoa, helminths, and viruses typically do not survive longer than about 30 days in freshwater environ- ments and about 50 days in soil environments (Crites and Tchobanoglous, 1998). Similar survivals might therefore be predicted for wetlands, but there are many site-specic factors and processes which may materially increase or decrease survival. A part of the overall removal of pathogens consists of the death or inactivation of organisms for reasons other than radiation damage or predation. This has been called the dark death rate (Khatiwada and Polprasert, 1999b). Mortailty has been found to be approximately 25% of the overall removal rate in ponds (Craggs et al., 2004), with a similar fraction in a Typha FWS wetland in Thailand (Khatiwada and Polprasert, 1999b). However, care must be taken to separate sedimenta- tion and ltration from mortality in this dark death rate. For instance, Khatiwada and Polprasert (1999b) estimated that only 6.5% of the overall fecal coliform removal rate was due to temperature-modulated death of organisms. There is no denitive study of the temperature effect on mortality of bacteria in wetlands. Estimates of temperature FIGURE 12.2 (a) Rotifer Keratella spp. 200 µm. (b) Rotifer Lepadella spp. 100 µm. (c) Protozoan Discophrya spp. 80 µm. (d) Protozoan Vorticella spp. 65 µm. (From Fox et al. (1981) Sewage Organisms: A Color Atlas. Lewis Publishers, Boca Raton, Florida. Reprinted with permission.) (a) (b) (c) (d) © 2009 by Taylor & Francis Group, LLC 488 Treatment Wetlands coefcients (Q) for ponds range from 1.000 (no effect) to 1.070 (strong effect). Very low temperatures may result in freezing conditions. Ice formation followed by a thaw drastically reduces the pop- ulations of pathogens in water. For instance, Torrella et al. (2003) found that only 1.2% of fecal coliforms survived four days of freezing, but more coliphages survived. However, care must be taken in enumeration, because organisms may be viable after freezing, but unculturable in routine tests (Parker and Martel, 2002). Further, many bacteria survive the snow- making process. Less than one log loss was measured for fecal streptococcus due to snow formation and melting (Parker et al., 2000). To complicate matters even more, some indicator organ- isms are capable of regrowing and multiplying in an aquatic environment. All members of the coliform group have been observed to regrow in natural surface water (Gerba, 2000). Such regrowth is fostered by high concentrations of organic matter and by elevated temperatures. This phenomenon is recognizable when disinfected wastewater is introduced into treatment wetlands, and bacterial populations increase along the d irection of ow (Figure 12.3). REINTRODUCTION Indicator organisms can be produced by sources other than incoming wastewaters. This is particularly true for the coli- form group, which may originate from many different warm- blooded animals that frequent wetlands. As an example, consider the performance of the Orlando Easterly FWS Wet- lands (Figure 12.4). Incoming fecal coliforms are very low, but animal (bird) populations are high, especially near the outlet. Background geometrical annual averages range from 50 to 142 per 100 mL. Monthly values show an exceedance frequency of 40% of the permit limit of 100 CFU/100 mL. The ve-year median fecal coliform was 64 CFU per 100 mL and FS was 106 CFU per 100 mL. The ratio FC/FS was 0.6; thus, the presumption may be made that the source of con- tamination is animal rather than human (criterion  FC/FS  0.7). In this case, the treatment wetland has been measured to have very large bird populations, which also supports the concept of reintroduction of pathogens from avian popula- tions (U.S. EPA, 1993a; U.S. EPA, 1993d). These naturally-caused bacterial populations are gen- erally low, but they may be variable and seasonally high because of wildlife activity patterns. Total and coliform bacteria have been measured in natural wetlands that receive no wastewater. For example, Fox et al. (1984) measured between 109 and 456 CFU/100 mL of fecal coliforms in cypress wetlands in Florida that were not receiving any exter- nal water inputs. Fecal coliform concentration in lake water passing through a wetland in Montreal, Quebec, increased from 40 to 110 CFU/100 mL (Vincent, 1992). Because nat- ural sources of coliforms and fecal streptococcus bacteria are found in all wetlands open to wildlife, outow indicator bacteria populations in treatment wetlands cannot be consis- tently reduced to near zero unless disinfection is used. Subsurface ow (SSF) wetlands provide much less wildlife habitat than FWS systems; however, their use by wildlife cannot FIGURE 12.3 Longitudinal proles of total residual chlorine (TRC) and E. coli through Tres Rios Hayeld, Arizona, wetland H1. 0.00 0.50 1.00 1.50 2.00 2.50 3.00 Inlet Splitter Inlet DZ DZ1 DZ2 DZ3 DZ4 DZ5 H1 EFF TRC (mg/L) 0 200 400 600 800 1,000 1,200 1,400 E. coli (#/100 mL) TRC E. coli FIGURE 12.4 Fecal coliforms entering and leaving the Orlando Easterly Wetland, in Florida during 1993–1997. The incoming wastewater is disinfected, with only a few detections of the indica- tor organisms. The balance of the nondetect samples are plotted as half the detection limit of 1.0 per 100 mL. (Unpublished data from city of Orlando.) 0.1 1 10 100 1,000 0 1224364860 Months FC (#/100 mL) Inlet Outlet © 2009 by Taylor & Francis Group, LLC Pathogens 489 be excluded. For instance, a HSSF wetland in Arizona, fed with disinfected potable water, produced on an average, efuent con- centrations of 130 total coliforms (CFU/100 mL) and 22.3 fecal coliforms (CFU/100 mL). This reintroduction of indicator organ- isms was attributed to wildlife use of the system. Because wild- life use of SSF wetlands has been observed on many occasions (Wallace et al., 2001), it is to be expected that these treatment wetlands will also deliver a nonzero background concentra- tion (C*) for indicator organisms. However, wildlife use is so restricted that backgrounds are likely of little consequence. 12.3 FECAL COLIFORM REMOVAL IN FWS WETLANDS The most common indicator organism group is the fecal coli- form group and consequently the wetland database is larg- est for this group. Further, several attempts have been made to extract quantitative generalities about the removal of this indicator group in FWS treatment wetlands. FIRST-ORDER REMOVAL MODELS The complexity of the suite of processes responsible for reducing indicator bacteria in FWS wetlands is considerable, with the many interacting processes described previously. Although some attempts have been made to combine these into a single removal model (Mayo, 1995; Bahlaoui et al., 1998; Khatiwada and Polprasert, 1999b), data have been insuf- cient to provide robust calibrations of combination models. Information on light exposure and penetration, vegetative cover, and predator density is typically lacking. Therefore, at this point in the evolution of treatment wetland technology, only very simple global removal models may be considered. Model Structure First-order models have been used to describe reductions of indicator bacterial populations in lagoons and wetlands: Fi rst-order (volumetric) CC CC k N N o i V   ¤ ¦ ¥ ³ µ ´  ¤ ¦ ¥ ³ µ ´  * * 1 T (12.3) First-order (areal) CC CC k Nh N o i A   ¤ ¦ ¥ ³ µ ´  ¤ ¦ ¥ ³ µ ´  * * 1 T (12.4) Plug-ow limit CC CC k q o i A   ¤ ¦ ¥ ³ µ ´  ¤ ¦ ¥ ³ µ ´ * * exp (12.5) where C C i o inlet concentration, #/100mL outlet co   nncentration, #/100mL * background concentrC  aation, #/100mL water depth, mh  k k A V areal rate coefficient, m/d volumetric   rate coefficient, d number of TIS nom 1 T N   iinal detention time, d The rst question that arises is whether to use Equation 12.3 or Equation 12.4, the former being volume-specic and the later being area-specic. In the situation of unvegetated systems, von Sperling and Mascarenhas (2005) provide an unequivo- cal answer: no difference was found in doubling the depth (80 cm versus 40 cm) and thus doubling the detention time for E. coli removal. The implications are that k V is inversely proportional to depth and, hence, the areal rate coefcient in Equation 12.4 is constant. The same inverse proportion is found for the Arcata, California, pilot data, in which six pairs of emergent FWS wetland cells were run at two depths and different hydraulic loadings (data from Gearheart et al., 1983). Thus, for both ponds and wetlands, there is strong evi- dence that the areal rate coefcient (k A ) is constant, whereas the volumetric rate coefcient (k V ) decreases with increasing depth. In this book, the areal rate coefcient will be used and presumed constant over some modest depth range, such as 20–80 cm. However, FWS volumetric rate coefcients, when they are reported elsewhere, may be easily converted to an areal basis simply by dividing by the free water depth. A far more serious question unique to pathogen removal is the internal pattern of the ow (number of tanks in series, N) to be assigned to the system for which k is to be deter- mined or used. For other measures of wetland water quality improvement, such as biochemical oxygen demand (BOD), total suspended solids (TSS), and nutrient removal, reduc- tions are rarely greater than 90–95% (less than one log 10 removal). For pathogens, reductions often reach 99.99% (two to three log 10 removal) or more. This is precisely the region of performance in which reductions are extremely sensitive to the number of tanks-in-series (NTIS) value (see Chapter 6). Equation 12.4 has been graphed for the case of a negligibly small C* (C*  C o ) in Figure 12.5. FIGURE 12.5 Reductions according to a rst-order model in the high removal range. $ $ $ $ $ $ $           #"Dakt/h   $  "! N N N N N% © 2009 by Taylor & Francis Group, LLC 490 Treatment Wetlands The Damköhler number (Da  k A T/h) embodies the intrin- sic rate of removal together with the detention time and depth. Independent of these factors, the wetland may have differing degrees of hydraulic efciency (see Chapter 2), as indicated by different values of N. Figure 12.5 indicates that the number of log 10 reductions increases steeply with number of tanks in series (N) at any given detention time (Damköhler number). The underlying phenomenon is hydraulic short-circuit- ing. Small elements of ow can carry enough organisms to the outlet, without treatment, to jeopardize the high levels of removal that prevail for the remainder (majority) of the ow. Conversely, if a wetland exhibits performance of, say, two log 10 reduction, the rate coefcient is extremely sensi- tive to whether the NTIS value is 4, 8, or ∞ (plug ow); the computed areal rate coefcient (k A ) changes by approx- imately 100% if N  4 as opposed to N  ∞ (plug ow) (Figure 12.5). Data from waste stabilization ponds provides conrma- tion of this phenomenon. Lloyd et al. (2003) studied fecal coliform removal in a sequence of pond modications, each designed to improve the hydraulic efciency. The levels of contamination were in the range of 10 4 –10 6 fecal coliforms per 100 mL, outlet to inlet; thus, in this instance, C* is negli- gible. The results may be summarized as follows: Open pond, aspect 4:1 90.26% 1.01 log Chan 10 () nnelized, aspect 36:1 96.03% 1.42 log Chan 10 () nnelized, aspect 36:1, wind break 98.13% 1.7(88log Batch reduction (plug-flow equival 10 ) eent) 99.7% (2.6 log 10 ) Although tracer tests were not performed to determine the number of tanks in series, it is clear that the increasing aspect ratio and decreasing wind mixing had very large effects on the performance of the pond. These results correspond nicely to the theoretical projections of Figure 12.5 for a Damköhler number of about 6.0. From these considerations, it may be concluded that the reduction of pathogens in FWS wetlands is critically dependent on the internal ow patterns. Longitudinal Profiles Irrespective of the degree of hydraulic efciency in a particu- lar wetland, global rst-order models forecast a decline in pathogens through the wetland, provided the incoming lev- els exceed the regrowth and reintroduction potentials. This theory corresponds to observations for a number of wetlands, su ch as the Byron Bay, Australia, FWS system (Figure 12.6). There is a drop of about two log 10 over the rst third of that system, followed by a plateau at levels of 300–2,000 fecal coliforms per 100 mL, depending upon the year in question. Avian use of the outlet portions of the system may account for the plateaus. For most FWS wetland systems, complete elimination of fecal coliforms has not been achieved, and therefore the inclusion of a C* value in the rst-order model is a necessity. Because of residual indicator bacteria populations in all wetlands, bacteria removal efciency is a function of the inow bacteria population. Removal efciency is typically high at high-inow populations but declines to negative ef- ciencies when inow populations are lower than the in situ bacteria production and addition rates. For instance, at the Titusville, Florida, wetland treatment system, inow to the wetland contains essentially no indicator bacteria because of a high level of pretreatment and disinfection (less than 1 CFU/100 mL); however, the wetland outow contains small numbers of fecal coliforms (63 CFU/100 mL) and fecal strep- tococci (170 CFU/100 mL), presumably because of wetland bird use (unpublished data from Titusville, Florida). I m plications for Design There are two important points to be kept in mind when extracting information from the literature for use in design: (1) literature die-off rates are often computed using the plug- ow equivalent of Equations 12.3 and 12.4, and (2) the use of that plug-ow formulation in design in an extrapolation mode is extremely dangerous, because it creates large over- estimates of reductions. Data from 28 FWS systems, totaling 47 wetland years, was used to estimate fecal coliform areal rate coefcients (k A ) for a presumed C*  40 CFU/100 mL. These systems were selected for having inlet fecal coliform concentrations of at least 1,000 CFU/100 mL, thus eliminating systems with low inuent fecal coliform concentrations, whether due to pretreatment disinfec- tion or other factors. The hydraulic parameter N, representing the number of tanks in series, was relaxed to the more general case of P, which accounts not only for the nonideal hydrau- lics but also treatment effects as well (P  N), as discussed in Chapter 6. Because the critically important PTIS values were not known, three different assumptions were made: PTIS (plug flow; known to be wrong, but i c nncommonuse) TIS 3 (a modest degree of deP  pparture from both plug flow and complete miixing) TIS 1 (complete mixing)P  The resulting annual areal rate coefcients were Pk P TIS s.e. 85 17 m/yr median 40 m/yr) T A c o  o ( IIS 3 s.e. 288 73 m/yr median 83 m/yr) TI A oo k P ( SS 1 s.e. 16,300 9,500 m/yr median 1,030 A oo k (mm/yr) It is seen that the presumed degree of nonideal ow has a very large effect on the calibrated areal rate coefcients, as has been theoretically explained previously. This wide dis- parity exists for each individual wetland as well as for the means and medians of the N  28 dataset. © 2009 by Taylor & Francis Group, LLC Pathogens 491 The danger of extrapolation is seen in an example for which we assume C*  0 for simplicity. Suppose that the given wet- land is producing 1.0 log 10 reduction at a hydraulic loading rate of 10 cm/d and the wetland hydraulics can be characterized by PTIS  3. The plug ow k A  84 m/yr (areal die-off coefcient) (Equation 12.5), and the 3TIS k A  126 m/yr (Equation 12.4). It is desired to improve performance by lowering the hydraulic loading rate to 2 cm/d. According to the plug ow model (Equa- tion 12.5), performance should increase to 5.0 log 10 reduction. But, according to the 3TIS model (Equation 12.4), performance would increase to only 2.49 log 10 reduction. Thus, even though the plug ow rate coefcient is typically considered to be “con- servative,” it is in error by more than a factor of 300. It is concluded that rst-order modeling is not useful in design unless the hydraulic characterization of the wetland is taken into account. Nonetheless, it is appropriate to explore the rates of reduc- tion of pathogens in FWS systems, and here the indicator fecal coliforms is used to identify the intersystem variabil- it y. Table 12.3 shows the frequencies of reductions and areal k-values for a set of FWS wetlands that are in the mode of removal, i.e., they receive a load of organisms at or above the expected background, here taken to be 40 CFU/100 mL. For a presumed PTIS  3, the median reduction is 1.54 log 10 , and the median k  83 m/yr. Seasonal and Stochastic Effects There are no pronounced seasonal or temperature effects for pathogen removal in the FWS treatment wetland datasets that are currently available. Computation of cosine trends in outlet concentrations typically produces a low amplitude, about 12% in log 10 , with the annual maximum in September (Table12.4). Ten years’ data from Estevan, Saskatchewan, showed an increase in fecal coliforms in the wetland (0.26 log 10 increase), but a decrease in total coliforms (2.01 log 10 reduction). However, the seasonal trend for both displays higher summer values (Figure 12.7). Temperature Coefficients There are conicting potential effects of temperature on pathogen reduction. Cold temperatures are inimical to TABLE 12.3 Annual Reduction of Fecal Coliforms in FWS Wetlands Stipulations 1. Annual averages are used in calculations. 2. For k-value calculations, the following P-k-C* parameters are selected: a. C*  40 #/100 mL b. P  3 TIS 3. Ranges of variables: HLR (cm/d) log 10 FC In (CFU/100 mL) log 10 FC Out (CFU/100 mL) Mean 3.8 5.21 3.28 Median 4.6 4.76 3.25 Max 24.3 6.89 5.43 Min 0.2 3.02 1.14 Results (N  47 wetland∙years for 23 wetlands). Percentile log 10 Reduction (CFU/100 mL) Rate Constant (m/yr) 0.05 −0.16 11 0.10 0.17 27 0.20 0.64 43 0.30 1.00 49 0.40 1.31 68 0.50 1.54 83 0.60 1.77 115 0.70 1.96 177 0.80 2.26 321 0.90 2.72 856 0.95 3.12 1,365 FIGURE 12.6 Average annual longitudinal proles of fecal coliforms in the Byron Bay, Australia, treatment wetlands. (Unpublished data from Byron Bay operations.) 100 1,000 10,000 100,000 1 , 000 , 000 0.0 0.2 0.4 0.6 0.8 1.0 Fractional Distance through Wetland Cell FC (#/100 mL) 1990–91 1991–92 1992–93 © 2009 by Taylor & Francis Group, LLC 492 Treatment Wetlands TABLE 12.4 Annual Cyclic Trends and Trend Multipliers for the log 10 of Concentration (CFU/100 mL) of Pathogenic Bacteria in the Effluent from FWS Wetlands System Years log 10 Mean log 10 Amplitude Yearday Maximum R 2 Excursion Frequency 80% 90% 95% 99% Fecal Coliforms Estevan, Saskatchewan 10 0.46 1.55 230 0.493 1.00 1.62 1.93 2.37 Imperial, California 4 2.00 0.17 223 0.247 1.01 1.21 1.30 1.36 Brawley, California 4 1.99 0.19 234 0.203 1.00 1.24 1.32 1.50 Boggy Gut, North Carolina 5 2.67 0.08 266 0.082 0.98 1.16 1.25 1.33 Kohukohu, New Zealand a 10 2.87 0.11 265 0.434 0.98 1.23 1.29 1.31 Bennett, Colorado 4 2.59 0.17 176 0.170 1.06 1.23 1.34 1.36 Delta, Colorado 4 2.52 0.11 166 0.057 1.02 1.24 1.33 1.38 La Veta, Colorado 4 2.84 0.11 247 0.233 0.98 1.11 1.20 1.27 Ouray, Colorado 10 2.88 0.12 231 0.095 1.04 1.21 1.33 1.35 Silt, Colorado 4 2.60 0.28 142 0.299 1.01 1.23 1.44 1.53 Augusta, Georgia 5 1.16 0.08 315 0.027 0.96 1.36 1.53 1.76 Total Coliforms Estevan, Saskatchewan 10 2.62 0.29 217 0.664 1.03 1.19 1.29 1.39 Imperial, California 4 3.02 0.07 211 0.068 1.06 1.11 1.20 1.26 Brawley, California 4 3.17 0.02 318 0.010 1.01 1.11 1.23 1.28 E. coli Imperial, California 4 1.86 0.14 224 0.202 1.04 1.19 1.25 1.31 Brawley, California 4 1.58 0.25 233 0.382 1.01 1.18 1.33 1.37 Brighton, Ontario 6 2.10 0.12 53 0.039 0.94 1.49 1.62 1.66 Median 4.0 2.61 0.12 231 0.19 1.01 1.22 1.31 1.36 Mean 5.9 2.39 0.24 232 0.22 1.01 1.23 1.36 1.46 Note: The multipliers that represent the frequency of excursions from the trend are also shown. For instance, the 99th percentile median log 10 of concentration is 1.36 times higher than the trend. If the outlet trend value is 1,000 per 100 mL, then one time out of twenty the outlet may reach 1,000 1.36  12,022 per 100 mL. Trend multiplier is (1 + 9); see Equation 6.61. a Date adjusted 182 days FIGURE 12.7 Folded time series of log 10 fecal and total coliforms at the Estevan, Saskatchewan, FWS wetlands. The wetlands operate during the unfrozen season. Note a midsummer peak in both. Fecal coliforms showed an increase of 0.26 log 10 , whereas total coliforms decreased 2.01 log 10 . (Unpublished data from city of Estevan.) 0 1 2 3 4 5 6 100 130 160 190 220 250 280 310 340 Yearday Coliforms (log 10 #/100 mL) FC Data FC Trend TC Data TC Trend © 2009 by Taylor & Francis Group, LLC [...]... are: Escherichia coli (2.66-log), Clostridium perfringes (2.80-log), Enterococci (2.30-log), Salmonella (3.84-log), and fecal streptococci (2.10-log) It should be noted that data in Table 12. 24, similar to Table 12. 23, do not represent a guarantee of treatment performance, because the data on which the table is based is extremely limited 12. 8 PARASITE AND VIRUS REMOVAL IN SSF WETLANDS PARASITES Parasites... HSSF wetlands is summarized in Table 12. 26 Data from the 32 systems listed in Table 12. 26 indicate that the global reduction of viruses is on the order of 1.47log For different virus classifications, this is broken down as coliphage (2.52-log), somatic coliphage (1.14-log), F coliphage (0.41-log), MS-2 (1.78-log), and enterovirus (4.00-log) It should be noted that the data presented in Table 12. 26... not imply a guarantee of treatment performance Compared to FWS or HSSF wetlands, considerably less is known about the fate and transport of viral organisms in VF wetlands Barrett et al., (2001) indicate that pulse-loaded unsaturated flow VF wetlands can reduce viral organisms on the order of 2-1 og This can be subdivided into somatic coliphage (2.70-log) and F coliphage (1.30-log) A similar study treating... Wetland Treatment Systems Database (Project 01-CTS-5; Final Report by Wallace and Knight, 2006) Compiled by J Nivala and R Clarke Water Environment Research Foundation (WERF): Alexandria, Virginia.) A number of HSSF wetlands have a median of 1.002 (Table 12. 21) However, six systems display high values ranging from 1.043 to 1.073 The disparity in temperature effects is illustrated in Figures 12. 16 and 12. 17... to monitor analytically, they have not received as much study as bacterial indicator populations Existing research indicates that wetlands are generally hostile environments for viruses (Table 12. 12) 498 Treatment Wetlands TABLE 12. 8 Reduction of Fecal Streptococcus in FWS Wetlands Site Name OEW, Florida OEW, Florida OEW, Florida OEW, Florida OEW, Florida OEW, Florida OEW, Florida OEW, Florida OEW,... greater than 90% at a number of geographically distinct sites WILDLIFE PATHOGENS Wildlife pathogens are also potentially associated with treatment wetlands as they are with natural wetlands Avian 500 Treatment Wetlands TABLE 12. 10 Reduction of Miscellaneous Bacteria in FWS Wetlands Site Name Listowel, Ontario Listowel, Ontario Listowel, Ontario Listowel, Ontario Listowel, Ontario Duplin, North Carolina... these two wetlands are nearly log-linear (Figure 12. 13) © 2009 by Taylor & Francis Group, LLC FIGURE 12. 13 Reduction of fecal coliforms at Richmond, New South Wales Both wetlands were long and narrow (L:W 25), and tracer test results indicated NTIS 20 (Data from Bavor et al (1988) Treatment of secondary effluent Report to Sydney Water Board, Sydney, NSW, Australia.) However, as noted in Chapters 2... and treatment wetlands (Friend, 1985) Conditions that favor transmission are exposed mudflats together with stressed and overcrowded bird populations The nematode Eustronglydes ignotus has been identified as a potential threat to wading birds that feed and nest near nutrient-rich sites However, extensive research on the fish that are intermediate hosts have shown no potential vector in Florida wetlands. .. 1997) © 2009 by Taylor & Francis Group, LLC 12. 6 FECAL COLIFORM REMOVAL IN SSF WETLANDS The most common indicator group is fecal coliforms; consequently, the performance database for SSF wetlands is largest for this indicator group Global removals (input–output data), for example, in HSSF and VF wetlands, are shown in Table 12. 13 For a larger database of 130 wetlands or operating conditions, the median... 3.78 1.53 3.70 2.65 0.52 3.43 1.81 2.00 4.19 3.32 1.55 5.40 5.76 3.14 3.26 2.40 2.00 Treatment Wetlands VF VF VF VF VF VF VF Outlet log10 (CFU/100 mL) 6.04 6.05 System Name and Location Pathogens 503 TABLE 12. 14 Effect of the Presence of Vegetation on the Reduction of Fecal Coliforms in Side-by-Side Studies in HSSF Wetlands log10 Reduction System Name and Location Phragmites Audlem, United Kingdom Audlem, . tanks-in-series (NTIS) value (see Chapter 6). Equation 12. 4 has been graphed for the case of a negligibly small C* (C*  C o ) in Figure 12. 5. FIGURE 12. 5 Reductions according to a rst-order. Existing research indi- cates that wetlands are generally hostile environments for viruses (Table 12. 12). © 2009 by Taylor & Francis Group, LLC 498 Treatment Wetlands TABLE 12. 8 Reduction of. PATHOGENS Wildlife pathogens are also potentially associated with treat- ment wetlands as they are with natural wetlands. Avian TABLE 12. 9 Reduction of E. coli in FWS Treatment Wetlands Site Name System