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Review

Constructed Wetlands for Agricultural Wastewater Treatment in Northeastern North America: A Review

by
Eric R. Rozema
1,
Andrew C. VanderZaag
2,
Jeff D. Wood
3,
Aleksandra Drizo
4,
Youbin Zheng
1,
Ali Madani
1 and
Robert J. Gordon
5,*
1
School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada
2
Science and Technology Branch, Agriculture and Agri-food Canada, Ottawa, ON K1A OC5, Canada
3
Department of Soil, Water and Climate, University of Minnesota, St. Paul, MN 55108-6028, USA
4
School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University, Edinburgh EH14 4AS, UK
5
Department of Geography and Environmental Studies, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada
*
Author to whom correspondence should be addressed.
Water 2016, 8(5), 173; https://doi.org/10.3390/w8050173
Submission received: 1 March 2016 / Revised: 5 April 2016 / Accepted: 11 April 2016 / Published: 27 April 2016
(This article belongs to the Special Issue Constructed Wetlands Systems and Management)
Versions Notes

Abstract

:
Constructed wetlands (CW) are a treatment option for agricultural wastewater. Their ability to adequately function in cold climates continues to be evaluated as they are biologically active systems that depend on microbial and plant activity. In order to assess their performance and to highlight regional specific design considerations, a review of CWs in Eastern Canada and the Northeastern USA was conducted. Here, we synthesize performance data from 21 studies, in which 25 full-scale wetlands were assessed. Where possible, data were separated seasonally to evaluate the climatic effects on treatment performance. The wastewater parameters considered were five-day biochemical oxygen demand (BOD5), total suspended solids (TSS), E. coli, fecal coliforms, total Kjeldahl nitrogen (TKN), ammonia/ammonium (NH3/NH4+-N), nitrate-nitrogen (NO3−-N), and total phosphorus (TP). Average concentration reductions were: BOD5 81%, TSS 83%, TKN 75%, NH4+-N 76%, NO3−-N 42%, and TP 64%. Average log reductions for E. coli and fecal coliforms were 1.63 and 1.93, respectively. Average first order areal rate constants (ka, m·y−1) were: BOD5 6.0 m·y−1, TSS 7.7 m·y−1, E. coli 7.0 m·y−1, fecal coliforms 9.7 m·y−1, TKN 3.1 m·y−1, NH4+-N 3.3 m·y−1, NO3−-N 2.5 m·y−1, and TP 2.9 m·y−1. In general, CWs effectively treated a variety of agricultural wastewaters, regardless of season.

1. Introduction

As constructed wetland (CW) systems gain increasing acceptance as wastewater treatment technologies, a need exists for information about their design, operation and performance [1,2,3]. There are many applications for CWs ranging from the treatment of landfill leachate, domestic sewage, to the management of agricultural wastewater. It is important to consolidate the knowledge and experience gained from the many CW studies that have been conducted and summarize the regional performance and wastewater source data. Literature reviews [4,5,6,7], factsheets (e.g., [8]) and databases [1,9,10] are available for various regions and wastewater types, but, presently, a review of CW performance treating agricultural wastewater and wash water in northeastern North America does not exist. The purpose of this review is to consolidate CW research and assess their performance for agricultural applications in this region.
The climate of northeastern North America is classified as humid continental (Dfb) according to the Köppen–Geiger classification system, and the region experiences warm summers and cold winters with precipitation generally uniformly distributed throughout the year [11]. The average temperatures of Augusta, ME, Toronto, ON, and Halifax, NS, three cities in this region, are 20.8, 22.0, and 18.8 °C, respectively, for the warmest month, July, and −4.7, −2.6, and −3.6 °C, respectively, for the coldest month, February [12,13,14]. The most common agricultural systems in northeastern North America are cash crops including grains and oilseeds and beef and dairy production [15,16,17]. Runoff from crop fields, barnyards and feedlots and the discharge of contaminated process water can introduce significant amounts of unwanted nutrients and other pollutants into the environment if it is not captured and properly treated [18].
CWs are a relatively inexpensive and low-maintenance option for agricultural applications and are capable of treating a number of wastewater types [1,3]. Applications include the treatment of milkhouse wash water and farmyard runoff [19,20,21,22,23,24,25,26,27], tile drainage outflow [28,29,30,31]), aquaculture wastewater [32,33] abattoir wastewater [34], and winery process water [35]. CWs are engineered to optimize naturally occurring biological, chemical, and physical processes to treat wastewaters. However, many of these processes can be affected by temperature and as a result questions have been raised about CW ability to function year-round in cold regions.
This paper synthesizes the literature and available performance data of CWs treating agricultural wastewater in northeastern North America. The parameters included were five-day biochemical oxygen demand (BOD5), total suspended solids (TSS), E. coli, fecal coliforms, total Kjeldahl nitrogen (TKN), ammonia+ammonium-N (NH3+NH4+-N), nitrate-nitrogen (NO3−-N), and total phosphorus (TP). The average performance data of the reviewed studies are presented in Table 1 and Table 2, and a summary of the performance categorized by wetland design and season are shown in Table 3. The literature summarized was primarily peer reviewed published sources as well as graduate student dissertations. In some cases, however, when a source was brief (e.g., a conference abstract) unpublished data were requested from the authors. The geographic range was the province of Ontario and eastward in Canada and the New England states. This was intended to cover a region with a similar climate and comparable agricultural activities. Generally, indoor and laboratory experiments were not included in this review.

2. Constructed Wetland Design

The most common CW designs are surface flow (SF), horizontal subsurface flow (H-SSF) and vertical subsurface flow (V-SSF). However, in the relatively small region of northeastern North America there are no standardized design criteria. Research at the University of Vermont’s Constructed Wetland Research Center (CRWC) continues to investigate H-SSF systems, while experiments at the Bio-Environmental Engineering Centre (BEEC) in Bible Hill, Nova Scotia have primarily focused on SF systems. It was suggested [2] that SSF are better suited to Canadian climatic conditions because of their ability to insulate microbial communities from cold winter air temperatures, while Ducks Unlimited endorse SF systems because they are more similar to natural wetlands [42]. The majority of the studies included in this review used SF designs and there were only a few SSF. Therefore, it is not possible to make a conclusion based on the performance data presented in this paper. Many of the CWs considered were designed for the treatment of high solids wastewater from livestock or aquaculture operations. However, different designs can be better suited for the removal of different contaminants found in agricultural wastewater so it may be beneficial to incorporate hybrid designs to take advantage of the strengths of each design.

2.1. Vegetation

Many studies have compared plant species for treatment performance [33,43,44,45,46]. Although there is no conclusive species with unanimous acceptance, Typha sp. (cattails) tend to be the most commonly used in this region [39,47]. However, it may be best to consider what wetlands plants are found within the area of construction to allow natural succession to determine the species composition after establishment.

2.2. Aeration

The effects of artificial aeration have been examined in a number of experiments, and it generally seems to enhance CW performance [26,44,48,49,50]. Aeration can increase dissolved oxygen (DO) in a CW system and stimulate organic matter decomposition and plant and microbial respiration, especially during the non-growing season when plant root zones are dormant [26,44,48]. Artificial aeration also induces mechanical mixing and engages stagnant zones to increase active wetland volume, further enhancing performance [49,51].
Nitrification (oxidization of NH4+-N to NO2−-N and then to NO3−-N) is a biologically driven process that is also affected by DO concentrations. Nitrification requires >2 mg·L−1 DO but CWs generally have DO concentrations of <1 mg·L−1 [26] therefore artificial aeration has the potential to enhance nitrification rates. In a greenhouse mesocosm study, aeration increased nitrification rates by 43% resulting in better NH4+-N treatment [48]. A study [26] compared two similarly loaded parallel SF CWs, one that was aerated and one was not. The year-round performance of both the systems proved to be similar but the mass reduction of NH4+-N in the aerated system was 87%, compared to 78% in the non-aerated system. However, it was concluded that the additional treatment was not significant enough to justify the cost and operation of the aeration system. Another study [44] concluded that aeration increased the removal efficiencies of TSS, TKN, and the chemical oxygen demand (COD) and suggest that CWs should be aerated if the costs of aeration outweigh the costs of reduced treatment efficiencies.

3. Recognized Challenges

3.1. Cold Climate Considerations

Colder temperatures can affect the treatment efficiencies of CWs, but certain design considerations mitigate this issue. The use of SSF versus SF helps to limit freezing because the water surface is not exposed to the atmosphere [2]. However, from the data presented in this review both SSF and SF wetlands have also been found effective during winter (Table 3). Two studies [23,39] examined the year-round performance of SF CWs in Atlantic Canada and found that even with the seasonal fluctuations, SF CWs performed well and were suitable water treatment options. Steps can be taken to further improve winter performance of CWs, such as allowing snow and dead vegetation to accumulate on the surface of the wetland to help insulate the system [2,49] and supplemental aeration can prevent freezing [49].
Two loading schedules were compared [25] to determine which would result in better overall treatment: continuous year-round loading versus storing the wastewater during the winter and loading the CW only during the summer. It was found that continuous, year-round, loading was the superior option, as it performed better than the seasonally loaded system [25]. The performance of a V-SSF treating winey process water was monitored over six years [35], and it was found that there was no difference in the seasonal performance for the treatment of COD, TSS, TKN, NH4+-N, and fecal coliforms. The CW consistently met effluent discharge requirements throughout the six years of monitoring [35].
The data synthesized in this review of 21 studies (Table 1, Table 2 and Table 3) also suggest that CWs are a suitable option for year-round agriculture wastewater treatment in the cold climate of northeastern North America and this will be addressed in further detail in this paper.

3.2. Phosphorous Management

Soil phosphorus (P) adsorption capacity has been identified as the limiting factor in CW treatment of agricultural wastewater, and it is suggested that research into better substrates for P removal be pursued [52]. Research on CWs with standard substrates (soil and/or gravel) shows that temporary P treatment can be possible, but it can fluctuate significantly depending on the hydrology of the system [24,53]; however, eventually adsorption sites become saturated and treatment performance decreases [54]. A comprehensive assessment of a 4-cell SF system at a 30-head dairy farm considered the P adsorption capacity of the wetland soils [20,53,54]. Initially, the wetland proved capable of P removal (~86% concentration reduction; Table 2), but, over time, the P adsorption capacity decreased, and the wetland’s lifespan with respect to P management was estimated to be eight years [20,54].
In eastern Canada and the northeastern USA, the most commonly researched approach to improve CW P management has been post-wetland treatment filters [32,55,56]. Many studies on this topic have taken place in northeastern North America [55,57,58,59,60]. Bench-scale experiments have involved columns filled with electric arc furnace (EAF) slag [55], sedimentary vs. igneous apatites [57], serpentinite [58], and various combinations of EAF slag, granite and limestone, of three different sizes (fine: 2–5 mm, medium: 5–10 mm, coarse: 10–20 mm) [59]. The latter study retrofitted the outlet of a 28 m2 H-SSF CW providing tertiary treatment at an aquaculture operation with pilot-scale (300 L) columns containing the best combination (a first column containing medium slag, fine granite, and medium limestone, followed by a second column containing only slag) [47]. From these studies, it was determined that, with appropriate substrate selection, P removal can be possible and EAF emerged as a highly effective and readily available substrate (a by-product from steel manufacturers in Quebec). EAF has a P retention capacity of up to 2.2 g·kg−1, which can equate to P reductions ranging from 75% to 100% [56,58,61]. These materials will inevitably reach their P retention limit and need to be exchanged, but this was taken into account by choosing readily available and affordable materials.

4. Treatment Performance

4.1. Areal Rate Constant

Despite its limitations [62], the area-based first-order model (Equation (1)) has become the most widely used representation of CW removal kinetics [63]:
k a = − q ln [ C o u t − C * C i n − C * ]
where ka is the first order area-based plug flow rate constant (m·y−1), q is the hydraulic loading rate (m·y−1), Cout is the outlet concentration (mg·L−1 or CFU 100 mL−1), Cin is the inlet concentration (mg L−1 or CFU 100 mL−1), and C* is the background concentration (mg·L−1 or CFU 100 mL−1). Of the plug flow assumptions required for the use of this model [64], the most inaccurate is the assumption that inflow and outflow are equal [23]. External hydrologic factors (surface flow into or out of the CW, precipitation, and ET) play important roles in either concentrating [44,65] or diluting [21,23,24,66] wetland effluent, which can skew treatment efficiency calculations. An adjusted first-order rate constant, ka, has been proposed [23] using the ratio of outflow to inflow to eliminate concentration and dilution effects, according to the following modified equation:
k a = − q ln [ C o u t ( Q o u t Q i n ) − C * C i n − C * ]
where Q o u t Q i n is the ratio of outflow to inflow (dimensionless). When the required data were available, Equation (2) was used to generate rate constants for the purposes of comparison and discussion (Table 1 and Table 2). Most studies did not provide background concentration values (C*), and they were therefore assumed to be zero as the wastewaters considered here were high strength and the C* values would be minimal compared to Cin.

4.2. Wetland Treatment Performance

The performance of the 25 reviewed wetlands is discussed, and, when appropriate, compared to the Livestock Wastewater Treatment Database [1]. They synthesized agricultural treatment wetland performance data throughout the USA. This allows us to compare treatment performance of wetlands in the cold climate of northeastern North America with aggregated data from systems across different climates of the USA. Along with the areal rate constants, the percentages of concentration reductions (CR) or log reductions (LR) are presented (Table 1 and Table 2). A summary of the performance data separated by wetland design and season is presented in Table 3. The majority of studies found in the literature only present data in CR, so data were presented similarly here to allow for easy comparisons. CR was calculated using:
C R = C i n − C o u t C i n × 100 %
The mean CRs were calculated by taking the mean of the CRs from the available data for each parameter. The standard error of the mean was also calculated by dividing the standard deviation by the square root of the sample size. Standard error is presented with the means in the text and tables.

4.3. BOD5

The mean (± standard error) BOD5 influent and effluent concentrations were 1134 ± 267 mg L−1 and 157 ± 55 mg L−1, respectively. There was inter-site variation due to the different wastewater characteristics and the uniqueness of each CW. The mean CR of BOD5 was 81% ± 3.9%, and the rate constant was 6.1 ± 1.4 m·y−1. The mean influent and effluent concentrations were higher than those reported by some [1], but the CRs were similar. Overall, CWs are a viable option for the removal BOD5, and, if designed properly, removal efficiencies of 99% can be possible (Table 1) even with influent concentrations >1000 mg·L−1, regardless of season [27].

4.4. Total Suspended Solids

The mean influent and effluent TSS concentrations were 1153 ± 237 mg·L−1 and 157 ± 55 mg·L−1, respectively. The mean TSS CR was 83% ± 3.3% and the rate constant was 7.7 ± 2.1 m·y−1. The data were similar to other studies [1]. Seasonality has no clear effect on TSS removal and year round performance is satisfactory (Table 3). In general, CWs are known to efficiently remove suspended solids [3], and these data reinforce that knowledge.

4.5. Nitrogen

The removal of TKN, NH4+-N, and NO3−-N were assessed when considering CW N management. The mean influent and effluent concentrations for TKN were 184 ± 20 mg·L−1 and 50 ± 12 mg·L−1, respectively, with a mean CR of 75% ± 4.4% and a ka of 3.1 ± 0.6 m·y−1. For NH4+-N, the mean influent and effluent concentrations were 88 ± 18 mg·L−1 and 15 ± 5 mg·L−1, the average CR was 76% ± 4.8%, and the rate constant 3.3 ± 0.6 m·y−1. The TKN and NH4+-N removals were higher than expected. N removal by CWs is known to decrease in lower temperatures [67], but the treatment of efficiencies of TKN and NH4+-N in the reviewed wetlands were actually higher than the efficiencies reported in warmer climates [1].
The mean influent and effluent NO3−-N concentrations were 5.3 ± 2.3 mg·L−1 and 5.7 ± 3.4 mg·L−1. The removal efficiencies of NO3−-N were usually lower than the other forms of N. NO3−-N removal occurs through denitrification, which requires anaerobic conditions and a carbon source for the denitrifying bacteria. CWs can be designed to meet those demands and can be quite effective for NO3−-N removal [68]; however, NO3−-N was not a top priority for many of the wetlands included in this review, and this is reflected in the treatment data (low influent concentrations and CR) as seen in Table 2.

4.6. Phosphorus

The mean influent and effluent concentrations of TP were 39 ± 7 mg·L−1 and 13 ± 3 mg·L−1, respectively. The mean CR was 64% ± 3.9%, and the mean ka value was 2.9 ± 0.8 m·y−1. Although some of the systems appeared to be rather successful at removing P (i.e., CRs > 80%; Table 2), the age of the wetland must be taken into account. Phosphorus removal will often be higher in the first few years of operation with decreases over time as the soil adsorption sites become saturated [54]. In six years of monitoring a study, [35] reported that TP removal decreased with time and recommended that additional TP treatment may be necessary. The length of most of the studies included in this review was relatively short (average ~12 months), but it is clear that P treatment is substrate dependant and that, over time, P removal will decrease as a function of loading.

4.7. Pathogens

The capacity of CWs to remove pathogens was assessed by using measurements of E. coli or fecal coliforms, which are common indicator organisms used to diagnose fecal contamination. Ten of the 25 wetlands were monitored for E. coli and six were monitored for fecal coliforms. The mean influent E. coli density was 1.85 × 1011 ± 8.32 × 1010 CFU 100 mL−1 and the mean effluent density was 1.71 × 1010 ± 1.15 × 1010 CFU 100 mL−1. For fecal coliforms the mean influent and effluent densities were 7.59 × 105 ± 3.35 × 105 CFU 100 mL−1 and 705 ± 487 CFU 100 mL−1. The mean log reductions for E. coli and fecal coliforms were 1.63 ± 0.2 and 1.93 ± 0.3, respectively. Mean ka values were 7.0 ± 1.5 m·y−1 for E. coli and 9.7 ± 1.4 m y−1 for fecal coliforms.
In general, CWs are an effective technology for pathogen removal in cold climates [2,40], and the data support this (Table 1). However, special care needs to be given to make sure the effluent water meets regulatory standards for discharge as human health can be at risk. Although mean treatment results appear to be satisfactory, month-to-month or even day-to-day fluctuations in effluent concentrations could result in health risks, and the resulting discharge limits are therefore very strict.

5. Conclusions

Constructed wetlands are suitable for agricultural wastewater treatment in the cold climate of northeastern North America. We found that CWs are an excellent option for the treatment of BOD5, TSS, E. coli, fecal coliforms, TKN, and NH4+-N without significant decreases in performance during the winter months. Some of the other findings are specific to cold climates and some will apply to all CW design:
  • Aeration can increase DO and improve treatment performance (specifically NH4+-N removal) in certain cases, but the benefits need to outweigh the costs
  • Continuous loading throughout the year results in better treatment performance compared to storing the wastewater and loading it only during summer months
  • Phosphorous removal remains one of the main weaknesses of CWs, but there is much promising research being conducted on different adsorptive materials that could be used in or in conjunction with CW systems
  • It is crucial to properly characterize the wastewater before designing a CW and to consider the maximum loading possible rather than relying on averages
  • There is no one CW design (SF, H-SSF, and V-SSF) that is the most effective for agricultural wastewater, but, rather, each design has strengths and weaknesses so hybrid designs may prove to be the most practical
  • More research is needed to increase the understanding of CW hydrology and the effects of the various hydrological inputs and outputs on treatment performance and the determination of areal rate constants
It is also worth noting that the availability of performance data from full-scale commercial systems is still limited. Most of the data comes from university run projects, but it would be useful to have access to data from commercial systems. Collaborations between academic researchers and industry members have the potential to greatly increase the knowledge base and to increase the economic return from CWs by improving the technology and finding more applications for it.
Considerable research is being conducted in northeastern North America and CWs are being accepted as a viable solution to the water management issues facing the agricultural sector. Even though there are still research needs, CWs should be considered an option for current agricultural wastewater applications. Many have adopted this view, and, as a result, there are a large amount of full scale, functional CWs found throughout North America, and the world, that are being used to treat various types of wastewater.

Acknowledgments

The authors would like to thank the Natural Sciences and Engineering Research Council (NSERC), the Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA), the Nova Scotia Department of Agriculture, the Ontario Agricultural College of the University of Guelph and Wilfrid Laurier University for their support in the preparation of this review article.

Conflicts of Interest

The authors declare no conflict of interest.

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Table
Table 1. Mean influent and effluent concentrations, concentration reductions (CR, %), and areal adjusted rate constants (ka, y−1) for five-day biochemical oxygen demand (BOD5) and total suspended solids (TSS). Mean log reductions (LR) for E. coli, and fecal coliforms.
Table 1. Mean influent and effluent concentrations, concentration reductions (CR, %), and areal adjusted rate constants (ka, y−1) for five-day biochemical oxygen demand (BOD5) and total suspended solids (TSS). Mean log reductions (LR) for E. coli, and fecal coliforms.
StudyProv./StateaCW TypeArea (m2)Waste Water SourceStudy Length (mo)BOD5 (mg L−1)TSS (mg L−1)E. coli (CFU/100 mL)Fecal Coliforms (CFU/100 mL)
inoutCR %kainoutCR %kaInoutLRkainoutLRka
[34]NSSF58.5abattoir247044494.06.81143966.02.59.00 × 104882.0113.46.00 × 10531381.2811.0
[36] dairy
yr. 1 GS bONSF4620 71522285.73.5------------
yr. 2 GS 71031981.43.1------------
yr. 3 GS 7892078.02.8------------
yr. 4 GS 7992178.92.8------------
[37]PESF1520dairy32195517890.9-82819176.9-----1.82 × 1045731.50-
[22]cONSF4620dairy ----------------
[31]NSSF512tile drain15--------122420.467.7----
[29] dairy
yr. 1 NGSdMESF690 4----16785197.038.7--------
yr. 2 NGS 4----14015196.412.1--------
[20] GSNSSF1022dairy47365892.1-------------
[23]NSSF100dairy3817473498.17.014505596.25.9----2.17 × 10531501.848.3
[28] GSMESF690tile drain5----770036895.218.1--------
[38] dairy
site 1MESF360 112174139136.0-132357656.5---------
site 2 NGSSF270 42810125255.4-130072044.6---------
[30] tile drain
yr. 1 GSQCSF1215 7----------------
yr. 2 GS 6----------------
yr. 3 GS 4----------------
yr. 4 GS 6----------------
[26] dairy
aeratedNSSF100 2016664697.24.025377896.93.84.20 × 10527972.185.9----
non-aeratedNSSF100 2016665396.83.725378596.73.64.20 × 10538692.045.4----
[27] dairy
site 1 GSONVSSF72 610222.799.74.1259542883.51.061364.53.135.1----
site 1 NGS 512313.099.84.213564.699.73.912046.42.273.6----
site 2 GSONVSSF72 69061997.918.663315675.43.32.32 × 104308.91.8821.3----
site 2 NGS 511289.499.224.15469981.85.11287457.10.451.0----
site 3 GSONVSSF72 611644.099.7-95132765.6-1.53 × 10446.52.52-----
stie 3 NGS 58634095.3-3171595.3-29.13.80.88-----
[19]NSSF1022dairy2491131865.1-41012469.7---------
[35] winery
GSONVSSF404 36----3322.798.0-7.66 × 103241.60-3.34 × 1043431.56-
NGS 36----1782.997.7-4050--1.87 × 1051172.52-
[39,40] dairy
wetland 1NSSF100 1714911898.8-7163994.6-----7438212.55-
wetland 2NSSF100 1714917.699.5-7162197.1-----7438242.49-
[41] dairy
yr. 1 GSONSF4620 73415185.13.44638082.73.2--------
yr. 2 GS 71495464.11.9907714.70.4--------
[21] dairy
yr. 1NSHSSF200 11875026397.021.510633297.021.52.34 × 1061.53 × 1051.1818.7----
yr. 2 9126321583.011.319225697.123.66.32 × 1045.94 × 1040.03−1.2----
[25] dairy
yr. 1 GS eNSSF100 643315863.53.643315863.53.61.43 × 10122.05 × 10110.846.8----
yr. 1 NGS eNSSF100 64335786.9-4335786.9---------
yr. 1 GS fNSSF100 643326438.90.585814583.12.87.52 × 10111.4 × 10101.736.6----
yr. 1 NGS fNSSF100 643316262.70.285813484.31.55.43 × 10111.17 × 10110.671.0----
yr. 2 GS eNSSF100 62722575.40.82722575.40.82.75 × 10114.99 × 1091.747.0----
yr. 2 NGS eNSSF100 62727971.0-2727971.0---------
yr. 2 GS fNSSF100 62721993.02.28772197.63.11.46 × 10113.58 x1082.615.0----
yr. 2 NGSfNSSF100 62723388.01.38774095.41.95.61 × 10111.10 × 1092.714.0----
[24]NSSF100dairy48----------------
Mean113415781.16.0115313382.97.71.85 × 10111.71 × 10101.637.07.59 × 1057051.939.7
Standard Error26754.73.91.423729.33.32.18.32 × 10101.15 × 10100.21.53.35 × 1054870.31.4
a Nova Scotia (NS), Quebec (QC), Prince Edward Island (PE), Ontario (ON), and Maine (ME); b Growing season (May–October); c The CW was in its eighth year of operation; d Non growing season (November–April); e The CW was loaded seasonally; f The CW was loaded continuously.
Table
Table 2. Mean influent and effluent concentrations (mg L−1), concentration reductions (CR, %), and areal adjusted rate constants (ka, m·y−1) for total Kjeldahl nitrogen (TKN), ammonium (NH+4-N), nitrate (NO−3-N), and total phosphorous (TP).
Table 2. Mean influent and effluent concentrations (mg L−1), concentration reductions (CR, %), and areal adjusted rate constants (ka, m·y−1) for total Kjeldahl nitrogen (TKN), ammonium (NH+4-N), nitrate (NO−3-N), and total phosphorous (TP).
StudyProv./StateaCW TypeArea (m2)Waste Water SourceStudy Length (mo)TKNNH4+-NNO3−-NTP
inOutCR %kainoutCR %kainoutCR %kainoutCR %ka
[34]NSSF58.5abattoir241232183.04.7686.6845.40.10.96-9.4-3.10.5881.03.7
[36] dairy
yr. 1 GS bONSF4620 71012476.52.7--------174.374.72.5
yr. 2 GS 7792766.52.0--------209.153.61.5
yr. 3 GS 7702170.42.3--------178.748.81.3
yr. 4 GS 7943167.12.1--------187.756.31.6
[37]PESF1520dairy324027880.6-2974684.5-1.71.417.6-338.773.6-
[22]cONSF4620dairy12633151.0-157.252.0-1.31.24.7-211337.9-
[31]NSSF512tile drain15--------6.72.267.29.2----
[29] dairy
yr. 1 NGS dMESF690 4------------2.60.4682.115.2
yr. 2 NGS 4------------5.50.5190.82.4
[20] GSNSSF1022dairy43013588.5-3171894.4-4.80.981.3-436.385.5-
[23]NSSF100dairy382371992.04.11881492.64.33.70.683.8-0.2377.180.81.6
[28] GSMESF690tile drain5------------221.792.313.6
[38] dairy
site 1MESF360 112632389.5---------81757.4-
site 2 NGSSF270 4352369−4.8-18013027.8-6496−50.0-----
[30] tile drain
yr. 1 GSQCSF1215 7--------3.12.89.7-915341.9-
yr. 2 GS 6--------2.92.127.6-452838.2-
yr. 3 GS 4--------3.93.023.1-924452.4-
yr. 4 GS 6--------4.43.031.8-825829.2-
[26] dairy
AeratedNSSF100 203012292.72.62371593.62.84.11.465.90.5509.081.91.4
non-aeratedNSSF100 203013090.02.12372489.72.14.10.782.91.4508.682.71.4
[27] dairy
site 1 GSONVSSF72 6692.596.42.1290.797.62.40.52.3--2351792.61.6
site 1 NGS 5701.897.42.4210.299.13.10.35.9--1271489.01.3
site 2 GSONVSSF72 6873461.40.5472253.2−0.70.15.8--321166.61.4
site 2 NGS 51129.191.910.1565.590.29.00.27.6--321263.00.8
site 3 GSONVSSF72 6419.477.2-101.288.0-0.12.7--693450.4-
stie 3 NGS 5381365.6-4.44.24.5-1.00.5--633446.8-
[19]NSSF1022dairy241835371.0-1835371.0-3.80.976.3-286.078.6-
[35] winery
GSONVSSF404 3692.20.4588.7-2.180.1872.7-0.012.03--5.00.1795.9-
NGS 3613.90.0498.8-0.910.0298.2-0.160.83--2.730.2371.0-
[39,40] dairy
wetland 1NSSF100 171731193.5-1478.194.5-2.40.676.4-444.091.0-
wetland 2NSSF100 171733.897.8-1471.698.9-2.50.485.7-442.295.0-
[41] dairy
yr. 1 GSONSF4620 71452483.23.21075.395.15.4111.090.94.3191333.20.8
yr. 2 GS 7----132.184.33.41.01.1-9.7-0.01171134.20.9
[21] dairy
yr. 1NSHSSF200 111823780.010.91072874.19.1178.749.44.4781086.713.7
yr. 2 9583638.01.5622954.03.43.12.326.00.4131024.00.2
[25] dairy
yr. 1 GS eNSSF100 632716549.52.5231247.82.3----5.12.942.02.0
yr. 1 NGS eNSSF100 6-72---3.1----------
yr. 1 GS fNSSF100 63337577.52.2123.471.31.8----2.61.445.40.7
yr. 1 NGS fNSSF100 631711464.00.2108.513.6−1.1----2.12.03.3-1.3
yr. 2 GS eNSSF100 63076977.43.1101.881.43.4----2.10.4479.03.2
yr. 2 NGS eNSSF100 6-41---0.5----------
yr. 2 GS fNSSF100 63091395.72.65.30.295.92.7----1.10.1586.51.7
yr. 2 NGS fNSSF100 63443091.21.53.70.684.31.1----0.880.1089.21.3
[24]NSSF100dairy48------------489.779.81.0
Mean18450.374.53.187.614.675.53.35.35.741.62.53913.164.32.9
Standard Error20.412.44.20.618.04.54.80.62.33.49.01.26.92.73.90.8
a Nova Scotia (NS), Quebec (QC), Prince Edward Island (PE), Ontario (ON), and Maine (ME); b Growing season (May–October); c The CW was in its eighth year of operation; d Non growing season (November–April); e The CW was loaded seasonally; f The CW was loaded continuously.
Table
Table 3. Mean (± standard error) concentration reductions (CR, %), for five-day biochemical oxygen demand (BOD5), total suspended solids (TSS), total Kjeldahl nitrogen (TKN), ammonium (NH+4-N), nitrate (NO−3-N), and total phosphorous (TP). Mean (± standard error) log reductions (LR) for E. coli, and fecal coliforms. The treatment performance is categorized by wetland design, surface flow (SF) or sub-surface flow (SSF) and season, growing season (GS) or non-growing season (NGS).
Table 3. Mean (± standard error) concentration reductions (CR, %), for five-day biochemical oxygen demand (BOD5), total suspended solids (TSS), total Kjeldahl nitrogen (TKN), ammonium (NH+4-N), nitrate (NO−3-N), and total phosphorous (TP). Mean (± standard error) log reductions (LR) for E. coli, and fecal coliforms. The treatment performance is categorized by wetland design, surface flow (SF) or sub-surface flow (SSF) and season, growing season (GS) or non-growing season (NGS).
SFSSFGSNGS
BOD576.3 ± 4.6696.5 ± 2.0076.0 ± 7.0482.3 ± 6.05
TSS76.9 ± 5.4189.1 ± 3.7369.5 ± 9.5486.4 ± 4.94
TKN72.3 ± 5.4079.5 ± 6.2176.9 ± 3.5472.0 ± 13.9
NH+4-N76.7 ± 5.5973.2 ± 9.3680.2 ± 5.1959.7 ± 16.0
NO−3-N42.0 ± 9.9537.7 ± 11.736.4 ± 13.9-
TP62.8 ± 4.6868.6 ± 7.3859.0 ± 4.9766.9 ± 10.6
E. coli1.7 ± 0.251.5 ± 0.342.0 ± 0.251.4 ± 0.46
Fecal coliforms1.9 ± 0.262.0 ± 0.48--

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Rozema, E.R.; VanderZaag, A.C.; Wood, J.D.; Drizo, A.; Zheng, Y.; Madani, A.; Gordon, R.J. Constructed Wetlands for Agricultural Wastewater Treatment in Northeastern North America: A Review. Water 2016, 8, 173. https://doi.org/10.3390/w8050173

AMA Style

Rozema ER, VanderZaag AC, Wood JD, Drizo A, Zheng Y, Madani A, Gordon RJ. Constructed Wetlands for Agricultural Wastewater Treatment in Northeastern North America: A Review. Water. 2016; 8(5):173. https://doi.org/10.3390/w8050173

Chicago/Turabian Style

Rozema, Eric R., Andrew C. VanderZaag, Jeff D. Wood, Aleksandra Drizo, Youbin Zheng, Ali Madani, and Robert J. Gordon. 2016. "Constructed Wetlands for Agricultural Wastewater Treatment in Northeastern North America: A Review" Water 8, no. 5: 173. https://doi.org/10.3390/w8050173

APA Style

Rozema, E. R., VanderZaag, A. C., Wood, J. D., Drizo, A., Zheng, Y., Madani, A., & Gordon, R. J. (2016). Constructed Wetlands for Agricultural Wastewater Treatment in Northeastern North America: A Review. Water, 8(5), 173. https://doi.org/10.3390/w8050173

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