Quantifying the fate of wastewater nitrogen discharged to a Canadian river
Abstract
Addition of nutrients, such as nitrogen, can degrade water quality in lakes, rivers, and estuaries. To predict the fate of nutrient inputs, an understanding of the biogeochemical cycling of nutrients is needed. We develop and employ a novel, parsimonious, process-based model of nitrogen concentrations and stable isotopes that quantifies the competing processes of volatilization, biological assimilation, nitrification, and denitrification in nutrient-impacted rivers. Calibration of the model to nitrogen discharges from two wastewater treatment plants in the Grand River, Ontario, Canada, show that ammonia volatilization was negligible relative to biological assimilation, nitrification, and denitrification within 5 km of the discharge points.
Introduction
Nitrogen (N) is essential for life but can be present in the environment in excess of growth requirements due to human activities. N is a common point-source pollutant to aquatic systems from wastewater treatment plants (WWTPs). Nitrate ( ) and total ammonia nitrogen (TAN; where TAN includes both ammonia (NH3) and ammonium ( )) are the two inorganic N forms that determine the critical loads beyond which aquatic ecosystems experience eutrophication or acidification (Posch et al. 2001; Schindler et al. 2006). The fate of these inorganic N species is a key determinant in the health of ecosystems and the services they provide to humans. TAN can be both a fertilizer of and detriment to aquatic life. At elevated concentrations, NH3 is toxic to aquatic life (Canadian Council of Ministers of the Environment 2010). Similarly, elevated concentrations of degrade water quality by harming aquatic life (Canadian Council of Ministers of the Environment 2012), and those above drinking water limits can lead to adverse health effects in people (Iwanyshyn et al. 2008). Consequently, understanding the environmental fate of TAN and discharged to surface waters is important for managing of human-disturbed aquatic ecosystems.
Many processes remove N from aquatic ecosystems. By understanding the relative contributions of each process and the factors that affect their rates, the environmental fate of N loading to aquatic ecosystems can be predicted (Iwanyshyn et al. 2008). Successful nutrient mitigation strategies in larger aquatic ecosystems rely on using smaller, tractable ecosystems as realistic and replicatable systems (Schindler 1998; Dodds and Welch 2000; Withers and Lord 2002; Webster et al. 2003; Sharpley et al. 2009). The concept of nutrient spiralling in streams was developed to describe the cycling and transport of nutrients in small lotic ecosystem (Newbold et al. 1981, 1982, 1983) and is based on downstream changes in nutrient concentrations. Nutrient spiralling techniques have been improved by adding 15N-enriched compounds as a tracer and following them through different pools (e.g., Mulholland et al. 2000, 2004, 2008; Tank et al. 2000; Earl et al. 2006; Hall et al. 2009). In a similar fashion, low nutrient streams can be spiked with nutrients and changes in the nutrient pulse can be used to understand ecosystem metabolism of nutrients (e.g., Davis and Minshall 1999; Hall and Tank 2003). These studies are often restricted to short lengths of streams where the hydrology can be well characterized and to smaller systems in general. The understanding of nutrient spiralling in large impacted rivers is often confounded by a heterogeneous river morphology, frequent run-of-the-river dams, groundwater, and multiple nutrient inputs, and it consequently relies on the intensive work conducted in these smaller systems supplemented by sampling campaigns of both concentration and stable isotopes of N species. Further, observed values are a cumulative result of a plethora of contemporaneous N cycling processes with rates that change in relative importance with distance from inputs and time of day. Disentangling the relative rates of these processes in large rivers is greatly aided by the additional information supplied by stable isotopes and the development of numerical models (Denk et al. 2017).
Stable isotope studies in rivers have shown that (i) is preferentially incorporated into the food web compared with and (ii) some TAN is lost to volatilization to the atmosphere whereas some is nitrified to (Loomer 2008; Murray 2008; Hood et al. 2014). Denitrification results in N attenuation in rivers but to a lesser extent in well-oxygenated rivers (Laursen and Seitzinger 2002, 2004; Rosamond et al. 2011, 2012). The rates of these processes change from day to night in response to the release of photosynthetic O2 into the water (Venkiteswaran et al. 2007, 2015; Wassenaar et al. 2010). δ15N values have been used to qualitatively identify anthropogenic N in coastal areas (Fourqurean et al. 1997; Fry et al. 2000; Savage and Elmgren 2004; Derse et al. 2007). Few studies have attempted to quantify the importance of these competing processes and their role in attenuation of WWTP TAN and in lotic systems though these processes set the baseline δ15N (isotopic ratios are hereafter reported as δ values) values used for benthic invertebrate and fish studies (e.g., Hood et al. 2014; Loomer et al. 2014).
Novel technical developments in the analysis of stable isotopes have allowed for improved assessment of nitrogen cycling in rivers including the use of the differences in δ15N-N2O and δ18O-N2O produced by nitrification versus denitrification (Thuss et al. 2014). Similarly, ecosystem metabolism techniques have recently been improved by the use of diel δ18O-O2 and δ13C-DIC modelling (Tobias et al. 2007; Venkiteswaran et al. 2007; Holtgrieve et al. 2010; Parker et al. 2010) leading to a greater understanding of nutrient dynamics (Fourqurean et al. 1997; Fry et al. 2000; Savage and Elmgren 2004; Murray 2008). The isotopic labelling of benthic biofilm by differing and sources has recently been described (Hood et al. 2014; Loomer et al. 2014; Peipoch et al. 2014). Here, we build on these studies by developing and testing a model that uses changes in concentrations and natural abundance (that is, not isotopically labelled) stable isotopic ratios to quantify the contributions of the various nitrogen-removal pathways in nutrient-impacted rivers. We applied this model to quantify the fate of N from the WWTP effluent discharges in a river highly impacted by both agricultural and WWTP nutrients.
The objectives of this research are to (i) quantify changes in concentrations and δ15N values of TAN and with distance downstream from WWTPs; (ii) develop a parsimonious process-based model for N cycling and the fate of WWTP N in rivers, and assess model performance with field measurements; and (iii) provide model-based estimates of the rates of nitrification, denitrification, NH3 volatilization, and N assimilation in WWTP plumes in a river impacted by both WWTP and agricultural nutrient inputs.
Methods
Field site
The Grand River, Ontario, is the largest river discharging into the Canadian side of Lake Erie (Fig. 1). Almost 1 million people live in its watershed and more than half of those rely on the river for drinking water. There are 30 wastewater treatment plants of varying sizes in the watershed where agriculture is the dominant land use (80%). We have previously studied the N and O2 cycling in the Grand River (Rosamond et al. 2011, 2012; Jamieson et al. 2013; Venkiteswaran et al. 2014, 2015). Here, we focus on two large WWTPs in the central part of the watershed that serve a combined population of about 230 000.
Fig. 1.
Ecosystems the size of the Grand River are not amenable to experimental isotope tracer additions but nevertheless afford us the opportunity to assess many of the processes resultant from the discharge of nitrogen-rich WWTP effluent. These processes include assimilation of by primary producers, nitrification of to , loss of NH3 to the atmosphere via volatilization, denitrification of , and dilution of both and . Rather than simply a point-source addition of nutrients to a pristine ecosystem, WWTP effluent in the Grand River increases nutrients in an already nutrient-rich system (Venkiteswaran et al. 2015).
The upstream Waterloo WWTP serves an urban population of approximately 120 000 and discharges a mix of and via a pipe on the west side of the river. The plume hugs that bank of the river for several kilometres downstream. At baseflow, WWTP discharge accounts for 10%–25% of river flow along this reach. The downstream Kitchener WWTP serves about 205 000 and discharges mostly via a diffuser in the middle of the river. The plume hugs the east bank of the river for several kilometres downstream before several large river bends result in lateral mixing. The river is about 50 m wide through the entire sampling area. Together, the WWTPs discharge about 900 tonnesN/year (Table S1).
In the study reach, the Grand River flows over the stony and sandy Catfish Creek till (Karrow 1974). This forms a substrate for the patchy growth dominated by the macroalga Cladophora spp. and macrophytes Myriophyllum spicatum and Stuckenia pectinatus. Their biomass (about 1 kg/m2) is greater below both WWTPs than above (Hood 2012).
Water in the Grand River is hard with dissolved inorganic carbon (DIC) concentrations around 50 mgC/L. Municipal drinking water, from both the Grand River and groundwater, is similarly hard, averaging 50 mgC/L of DIC or alkalinity of 400 mg/L (CaCO3 equivalent). The use of residential water softeners produces WWTP effluent high in Cl− (Region of Waterloo 2011). River dissolved organic carbon (DOC) concentrations are typically 6–8 mgC/L. The river is shallow through the sampled section, with mean depth at baseflow around 0.5 m. Typical river discharge during the sampling periods through these reaches was 2–13 m3/s (Canadian Water Survey, https://wateroffice.ec.gc.ca/). Sampling occurred during stable base-flow conditions.
Agricultural activity and some small WWTPs in the watershed result in high nutrient concentrations in the river prior to the two main WWTPs in this study. Upstream concentrations are elevated (3–4 mgN/L), whereas concentrations are low (<0.1 mgN/L) and both total phosphorus and soluble reactive phosphorus are high (70 μg/L and 10 μg/L, respectively).
Below each of the two WWTPs, eight sampling points were established based on availability of access to the river (Fig. 1). For each WWTP, the first site was immediately downstream of the effluent discharge point. The second was 100 m downstream for the Waterloo WWTP and 200 m downstream for the Kitchener WWTP. The others were about every 800–1000 m further downstream for about 5 km (Table S2). At each site, samples for , , Cl−, DOC, δ15N- , and δ15N- were collected from the centre of the plume as identified by in situ measurement of conductivity (YSI 556 MPS). Samples were collected in high-density polyethylene bottles and immediately chilled in a cooler for transport to the laboratory, filtered to 0.45 μm, and kept cold (4 °C) until analyses. Samples for and δ15N- were immediately acidified to pH 4 with HCl and frozen until analyses. In situ measurements of temperature and pH were made (YSI 556 MPS) with reported accuracy on pH and temperature of ±0.2 units and ±0.15 °C, respectively. To account for dilution of the effluent plume by river water, Cl− at these elevated concentrations was assumed to be a conservative tracer and and concentrations were adjusted accordingly.
WWTP plumes were sampled downstream of both sites twice. The plume from the Waterloo WWTP was sampled on 30 October 2007 (typical discharge 2–7 m3/s) and 1 July 2008 (typical discharge 2–4 m3/s). The plume from the Kitchener WWTP was sampled on 23 October 2007 (typical discharge 11–17 m3/s) and 18 July 2008 (typical discharge 8–11 m3/s).
Analyses
Anion concentrations were measured on a Dionex ICS-90 (Dionex, Sunnyvale, California) ion chromatograph. Precisions and detection limits of were 0.07 mgN/L (standard deviation of 15 replicates of a 5.0 mgN/L standard solution yielding 1.4% relative standard deviation) and 0.05 mgN/L, respectively. concentrations were rarely detectable. Precision of Cl− measurements was <1 mg/L (standard deviations of 12 replicates of a 20 mg/L standard solution yielded 1.0% relative standard deviation). concentrations were measured colorimetrically by the phenate colourimetric method EPA 350, (EPA 1993) on a Beckman DU500 (Beckman (Brea, California) UV/VIS spectrophotometer with a precision and detection limit of 0.005 mgN/L (0.5% relative standard deviation on a 1.0 mgN/L standard solution) and 0.015 mgN/L, respectively.
δ15N-TAN was measured via the diffusion method on acidified discs (Zhang et al. 2007). Briefly, is converted to NH3 by increasing the sample pH; NH3 is trapped in a filter pack containing a 1 cm GF/D filter, acidified with H2SO4 trapped in a polytetrafluoroethylene packet. The filter is dried and analysed for δ15N on a Carlo Erba 1108 (Thermo, Waltham, Massachusetts) elemental analyzer (EA) coupled to a Micromass Isochrom isotope-ratio mass spectrometer (IRMS). Precision of δ15N- analysis was ±0.3‰. δ15N- was measured via the AgNO3 method. Briefly, sample volumes were reduced by evaporation, was removed by barium precipitation, and was collected on anion exchange resin in a column. After being eluted from the column, AgO was added to precipitate AgNO3, which was analyzed on the same EA-IRMS as above. Precision of δ15N- was ±0.5‰. Methodological tests indicated that the AgNO3 method can capture since oxidizes rapidly to even in filtered samples (Spoelstra 2004). Since previous measurements showed there was little in this river ( was <5% of ) the results presented here can be interpreted as δ15N- .
Model setup
To interpret patterns in the data, a dynamic model (hereafter NANNO: nitrate, ammonia, nitrite, nitrous oxide) was developed to describe the dynamics of TAN, , , N2O and their δ15N values (eight states, in total). The model was implemented in R (R Core Team 2016) using the simecol (Petzoldt and Rinke 2007) package. Five processes were modelled: volatilization of NH3, two-step nitrification ( → and → ), denitrification ( → N2O), and biological assimilation of (Fig. 2). We note that complete ammonia oxidation (comammox) by a single organism has recently been identified (van Kessel et al. 2015) and assume that any such contribution is negligible or encompassed by the model structure. In the model, N2O produced by denitrification is allowed to accumulate rather than being further reduced to N2; this choice was made because the N2O:N2 ratio produced during denitrification varies widely and once nitrogen is removed from the TAN and pools, it is very unlikely to return to those pools especially in a system where N is in excess. Similarly, the biological assimilation of was not included given that is in excess. Metabolic costs suggest is the preferred source of nitrogen over for phytoplankton and aquatic plants (Mariotti et al. 1982; Yoneyama et al. 1991; Collier et al. 2012) and that cycling of is rapid (Mulholland et al. 2000). Isotopic evidence suggests this is also true for macrophytes in the Grand River (Hood 2012; Hood et al. 2014). Each process is associated with isotopic fractionation (ε, where ε = α − 1 and α = Rproduct/Rreactant). Fractionation factors for physical processes are typically known with greater precision than biological ones. Since the Grand River is very well buffered, average measured pH values were used for TAN speciation calculations. Model parameters and commonly reported values are summarized in Table 1.
Table 1.
| Parameter | Description | Typical value or range | Units | Reference |
|---|---|---|---|---|
| kge | Gas exchange coefficient | 0.0001–0.0006 | t−1, m−1 | Jamieson et al. 2013; Venkiteswaran et al. 2015 |
| knit1 | Nitrification rate constant: to | 0–0.01 | t−1, m−1 | — |
| knit2 | Nitrification rate constant: to | 0–0.01 | t−1, m−1 | — |
| kdenit | Denitrification rate constant | 0–0.001 | t−1, m−1 | — |
| kamup | Ammonium uptake rate constant | 0–0.01 | t−1, m−1 | — |
| αNH3NH4 | Equilibrium isotope fractionation factor between NH3 and | 1.047 | Unitless | Hermes et al. 1985; Li et al. 2012 |
| αge | Kinetic isotope fractionation factor for gas exchange of NH3 | 0.995–1.000 | Unitless | Thode et al. 1945; Kirshenbaum et al. 1947; Norlin et al. 2002 |
| αnit1 | Isotope fractionation factor for nitrification: to | 0.990–1.000 | Unitless | Gammons et al. 2010 |
| αnit2 | Isotope fractionation factor for nitrification: to | 0.990–1.000 | Unitless | Gammons et al. 2010 |
| αdenit | Isotope fractionation factor for denitrification: to N2O | 0.985–1.000 | Unitless | Sebilo et al. 2003 |
| αamup | Isotope fractionation for uptake | 0.973–1.000 | Unitless | Delwiche and Steyn 1970; Mariotti et al. 1981; Hoch et al. 1992; Fogel and Cifuentes 1993 |
| pH | Ph | 7.5–8.5 | Unitless | Measured in situ |
| Temp | Temperature | 10–20 | °C | Measured in situ |
| pKa | Acid dissociation constant for | 9.4 | Unitless | Calculated (Olofsson 1975) |
Note: Ranges were provided as constraints within which the model’s fitting routine could search for possible values.
Fig. 2.
To simplify modelling efforts, the common time-for-distance substitution was made, using Cl− as a conservative tracer, and and concentrations were adjusted accordingly. Additionally, to avoid requiring river surface area for gas exchange with the atmosphere, all rate constants, including the gas exchange coefficient, were modelled with units of per time. First-order rate kinetics were used for nitrification (Dinçer and Kargı 2000; Chen et al. 2006), biological assimilation (MacIsaac and Dugdale 1969; D’Elia and DeBoer 1978), and denitrification (Dinçer and Kargı 2000). Sensitivity of model outputs to these parameters is described in Supplementary Material 1. NH3 gas exchange was modelled with the thin-boundary layer equation (Denmead and Freney 1992) assuming the atmospheric NH3 concentration was negligible (ppb-range concentration (Finlayson-Pitts and Pitts 1986; Mészáros 1992)). Ranges of potential gas exchange coefficients can be estimated from river channel geometry (Raymond et al. 2012) and converted for use with NH3 via Schmidt number scaling (Jähne et al. 1987), for example, at 20 °C the Schmidt number for NH3 in freshwater is 585 (Kreith 2000).
For the Grand River, the gas exchange coefficient for O2 (0.1–0.5 m/h) has been estimated for its length with focus on the areas including the WWTPs (Jamieson et al. 2013; Venkiteswaran et al. 2015). These values were converted for use with NH3 recognizing that because of differences in solubility between NH3 and O2 the air-side and water-side resistances are very different (Blomquist et al. 2006; Johnson 2010). Measured river depths at all sampling sites were 0.5–1.0 m. River velocities are measured at several sites by the Water Survey of Canada and modelled at other locations by the Grand River Conservation Authority (Veale and Cooke 2016). Modelled river velocities were 0.2–0.4 m/s for typical flow conditions (M.J. Anderson, personal communication, 2015). Gas exchange coefficients were converted from units of vertical distance per time to units of per distance by dividing by river depth and multiplying by river velocity. This produces a range of kge values for NH3 of 0.0001–0.0006 m−1 downstream of the WWTPs.
An initial best-fit solution for each set of field data was found by allowing the model to find a combination of rate constants (≥0), isotopic fractionation factors (between the lowest literature α values (i.e., the strongest values) and 1), and initial values that minimized the sum of squared errors between field data and model output.
Results
Field measures of N concentrations and isotopes in WWTP plumes
Waterloo
On both dates, TAN concentrations declined from 5–7 mgN/L to ≪1 mgN/L by the 5 km sampling point, although the rate of decline was much faster on 1 July 2008 than on 30 October 2007. Nitrate response in the plumes was different on both dates: on 30 October 2007, there was a gradual decline in but on 1 July 2008 there was an increase of >1 mgN/L. Together, these results suggest different fates for N in the Waterloo plume on each date.
On 30 October 2007, δ15N-TAN values increase rapidly from 12‰ to 30‰ in the first 500 m before concentrations became too low for analyses. On 1 July 2008, only the initial sample had an adequate TAN concentration for δ15N-TAN analysis but TAN persisted at a greater distance downstream than on 30 October 2007. On both dates, the δ15N- values declined from 16‰ to 11‰ within the first 1 km, and then rose gradually.
Kitchener
TAN concentrations downstream of the Kitchener WWTP declined to <1 mgN/L over the 5.5 km sampling transect from initial values of 14 mgN/L and 4 mgN/L on 23 October 2007 and 18 July 2008. Nitrate concentrations increased substantially on 23 October 2007, from 0.8 mgN/L to 5.9 mgN/L, and to a lesser degree on 18 July 2008 from 1.3 mgN/L to 3.1 mgN/L.
δ15N-TAN values increased on both days, by 22‰ on 23 October 2007 and by 12‰ on 18 July 2008 over the 5.5 km. Unlike at Waterloo, the δ15N- values changed only by around 3‰; decreasing on 23 October 2007 and increasing on 18 July 2008.
Model development: effect of N cycling processes on coupled N concentrations and isotopes
The coupling of concentrations and isotopes in a simple process-based model shows that the various N cycling processes result in different patterns at the river scale. These results suggest the model may reproduce the variety of expected patterns from each process in the model. Additionally, as we describe next, the dynamic features of each process are sufficiently distinct that we would expect the model to be identifiable. That is, we would expect to arrive at a tight estimate of the kinetic parameters given a sufficiently rich field data set. If this were not the case, then there would be less likelihood that a unique model solution describing TAN, , , N2O and their δ15N values could be found.
We could include many processes or only a few but the field data inform us as to what processes we must include because the isotopic effects are very different for the different processes. Both NH3 degassing and biological assimilation result in expected and rapid increases in δ15N values, but increases in δ15N-TAN have different values with similar decreased in TAN because of the difference in isotopic fractionation between the two processes. Nitrification alone produces a transient but small increase in (<20% of DIN (dissolved inorganic nitrogen, DIN = TAN + ) at its greatest) as is oxidized to . There is a characteristic initial dip in δ15N- that must be a function of the initial δ15N values and isotopic fractionation (αnit1 and αnit2) and may be exploitable as an identifier of significant nitrification. Denitrification alone, unlikely in a WWTP plume in a shallow oxygenated river during the day, results in a clear decline in associated with an increase in δ15N- and no changes in TAN or δ15N-TAN. The model predicts a corresponding increase in N2O and a transient dip in δ15N-N2O values that depends on initial conditions and the strength of αdenit.
An initial structural identifiability analysis (Bellman and Åström 1970) (see Supplementary Material 1) of the model dynamics and outputs confirmed that with the available data, it would not be possible to separately estimate the rates at which the equilibrated TAN pool (NH3 and ) is lost through gas exchange and biological assimilation processes. This conclusion is valid regardless of the quality of the concentration data and δ15N values (see also Supplementary Material 1). However, the gas exchange coefficient can be independently quantified.
Consequently, we simplified the model by fixing from independent sources the gas exchange coefficient as quantified in these areas of the Grand River (kge (Jamieson et al. 2013; Venkiteswaran et al. 2015)) and its isotope fractionation factor as quantified in laboratory experiments (αge (Thode et al. 1945; Kirshenbaum et al. 1947; Norlin et al. 2002).
We fit the resulting model separately to the four field data sets and then, in each case, applied uncertainty analysis as described in Methods. The results varied, but from this preliminary analysis (results not shown) we discovered that in every case the available data were not sufficient to provide accurate estimates of the eight free parameters. In particular, the knit2 and αnit2 parameters could not be well-estimated from any of the data sets. Consequently, we reduced the model further, by removing and instead describe a single-step nitrification process (knit1 and αnit1) where is oxidized to ; this is justified given that concentrations are low compared with and TAN and not accumulating. The resulting system has six states: TAN, , N2O, δ15N-TAN, δ15N- , and δ15N-N2O. Results of fitting and uncertainty analysis, as described in Methods, are shown in Tables S3–S6.
In the case of the Kitchener 2007 data set (Table S6), the k parameters for nitrification and denitrification all appear to be reasonably well constrained. The α estimates are less confident. Two of the best-fit α value estimates, αdnit and αamup, are at the bounds of the range of α allowed (Table 1; (0.975, 1)), suggesting the data provide minimal useful information about their values. Moreover, while two of the sensitivities are not unreasonably low, the confidence intervals are considerably larger than the search space, which has a width of only 2.5% (25‰).
For the Kitchener 2008 best fit, the trend in certainty is similar but the data constrain the parameter estimates to a lower degree. Data from Waterloo (2007 and 2008, Tables S4 and S5) provide even less ability to constrain the rates likely because the system behaviour is not as dynamic, i.e., the range in δ15N values is small despite the change in concentration being large.
Discussion
The process-based NANNO model was able to reproduce the observed dynamics in concentrations and the δ15N values of TAN and (Tables S3–S6). Results from two seasons, with different proportional fates of N processing, at two different WWTPs with different TAN: ratios in their effluent indicate a good degree of coherence between model results and field data (Figs. 3–6 and Tables S3–S6). Additionally, the shapes of the curves (increases, decreases, and plateaus) were all generally reproducible by the model. The model was least successful in reproducing behaviour when there were increases in concentration without a change in δ15N- . This scenario suggests nitrification where the new has the same δ15N- as the extant .
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Although process-based models provide several advantages over purely empirical ones, the goodness of fit and performance of such models depends on their particular application (Saloranta et al. 2003). The variables in this underdetermined model system were constrained in several ways. The variables in this underdetermined model system were constrained in several ways, but the available data were insufficient to identify a unique best-fit model parameterization within the constrained parameter space. The local sensitivity of specific behaviours on the resulting modelling parameters can only be confidently assessed once best-fit model solutions have been developed (Moore and Doherty 2005). This issue is typical of ecosystem models, which nevertheless are often used to predict the fate of nitrogen, oxygen, and biological oxygen demand in rivers (e.g., the Grand River Simulation Model that is used by the local Grand River Conservation Authority when making decisions about nutrient loads and river health).
In all four cases, N is lost from the river downstream of the WWTPs. Rates for each N process can be summarized by their rate constants (Tables S3–S6) but are better compared as the mass of N transformed by each process (Table 2). In three of four cases, NH3 loss via volatilization was much lower than loss via update or nitrification (Table 2) (Chen 2013; Jamieson et al. 2013; Chen et al. 2014; Venkiteswaran et al. 2015). Thus N from WWTP effluent largely remained in and was transformed with the Grand River in these three cases where river pH values were 7.6–8.4, well below the pKa value of 9.4, and with high rates of community metabolism (Chen 2013; Jamieson et al. 2013; Chen et al. 2014; Venkiteswaran et al. 2015).
Table 2.
| Waterloo WWTP 2007-10-30 | Waterloo WWTP 2008-07-01 | Kitchener WWTP 2007-10-23 | Kitchener WWTP 2008-07-18 | |
|---|---|---|---|---|
| NH3 Volatilization | 2.3 | 0.11 | 0.02 | 0.12 |
| uptake | 0.16 | 0.37 | 5.0 | 1.9 |
| Nitrification | 2.1 | 7.3 | 8.4 | 3.0 |
| Denitrification | 3.0 | 4.1 | 0.30 | 0.41 |
Note: Reported as X mgN/L or mass of N transformed by each process in the river reaches under study. WWTP, wastewater treatment plant.
In both Waterloo cases, denitrification played a modest role in reducing N concentrations (Table 2). N2O concentrations in and fluxes from the Grand River are high downstream of these WWTPs (Rosamond et al. 2011, 2012; Venkiteswaran et al. 2014). More detailed sampling of N2O and its δ15N values may provide additional constraints to improve the model fit.
Nitrification played a moderate role in N cycling in all four cases. There were no clear correlations between nitrification rates and rates of other N processes suggesting that predictions about the fate of N in the Grand River cannot be simply derived from other components of ecosystem metabolism. Where measurable, concentrations and δ15N values may provide additional information to the model by constraining nitrification.
The δ15N values of benthic periphyton and invertebrates (Loomer 2008; Loomer et al. 2014) as well as macrophtes (Hood 2012; Hood et al. 2014) are often used as indicators of different N sources and N pollution because they form the base of the food web. Interpreting these data requires an ability to understand and predict the fate of large isotopically distinct N sources like WWTP effluent since the δ15N values measured in biota ultimately depend on the source of N and isotopic fractionation during biological assimilation. Moreover, macrophytes integrate N over a much longer time scale than the effluent-plume travel time or diel variability (Hood et al. 2014; Loomer et al. 2014).
There are several key model parameters that are insufficiently characterized, such as isotopic fractionation during biological assimilation of both TAN and , preferential use of different N species, and release of TAN and . The variability in isotopic fractionation during biological assimilation is large and varies nonlinearly with concentration (Hoch et al. 1992; Pennock et al. 1996; Yoneyama et al. 2001). This poses a vexing problem at the ecosystem scale since the isotopic enrichment—concentration relationship varies between species and both concentrations and species vary within ecosystems.
The mass and δ15N of river biomass are difficult to capture in the parsimonious NANNO model structure; model fitting may be improved if the release of TAN and by biomass contributes significantly to river N relative to WWTP effluent (Loomer et al. 2014). Nitrogen assimilation and release rates can be estimated with nutrient spiralling techniques but this analysis often conflates TAN and . It is therefore difficult to discern which N form is used, which is released, and how these results apply to a river with more than 100 km of upstream nutrient inputs. The degree of importance, if any, to dissolved organic N mineralization or N release from microbes and macrophytes in the nutrient-replete WWTP plumes is unknown.
Understanding the ecosystem effects of changes in nitrogen sources, such as altering WWTPs to produce only instead of to improve river O2 concentrations requires knowledge about which N enters the base of the foodweb via primary producers and consumers. In cases where δ15N- and δ15N-TAN values are far enough apart, or one is changing while the other is constant, the use of each by primary producers and consumers may be teased apart. Biological assimilation is associated with little to no isotopic fractionation (Mariotti et al. 1981; Yoneyama et al. 1998, 2001) and in the WWTPs’ effluent plumes in the Grand River δ15N- values do not vary as much as δ15N-TAN values. In such scenarios, response to increasing δ15N-TAN may be observable as a concomitant increase in the δ15N of primary producers and consumers (Hood et al. 2014; Loomer et al. 2014).
Since O2, N, and phosphorus cycles are strongly linked, improving the understanding of nitrogen processes allows previous work on O2 and phosphorus cycling in the Grand River (Barlow-Busch et al. 2006; Venkiteswaran et al. 2014, 2015) to be extended to process-based biogeochemical models that incorporate multiple elements and their isotopes. Components that may be added to NANNO to improve constraints on nitrogen processes include δ18O- values. However, recent work has demonstrated that predicting the δ18O values of nitrogenous species is more complicated than originally thought because there are poorly understood abiotic factors that alter the δ18O value of and as well as multiple pathways to produce N2O (Buchwald and Casciotti 2010; Casciotti et al. 2010; Snider et al. 2010, 2012, 2013, 2015; Buchwald et al. 2012). Nevertheless, there are opportunities to produce a more constrainable model.
Conclusions
We have presented a process-based isotopic model of key nitrogen species for use in nutrient plumes in rivers. The NANNO model successfully reproduced observed dynamics in TAN and concentrations and their δ15N values including seasonal differences in the way N species were processed. The ability to model these processes is a key step to making predictions about how improvements in WWTP effluent will affect receiving waters.
Acknowledgements
Canada’s Natural Science and Engineering Research Council funded the field research under STPGP 336807-06 and STPGP 381058-09. Grand River Conservation Authority provided assistance with field data. A portion of this was work performed while under the sponsorship of the International Atomic Energy Agency Coordinated Research Project F32007. Field and laboratory assistance was provided by Richard Elgood, Neus Otero, Marilla Murray, Sarah Sine, David Snider, and Madeline Rosamond. We thank Thomas Petzoldt for the ease with which the simecol package can be used and Hadley Wickham and Winston Chang for their work on and documentation of ggplot2. Data and code are available as part of the NANNO package at doi:https://doi.org/10.5281/zenodo.2647564.
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FACETS
Volume 4 • Number 1 • June 2019
Pages: 315 - 335
Editor: Tom A. Al
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Received: 30 July 2018
Accepted: 24 March 2019
Version of record online: 22 July 2019
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© 2019 Venkiteswaran et al. This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
Data Availability Statement
All relevant data are within the paper and in the Supplementary Material. Data and code are available as part of the NANNO package at DOI: https://doi.org/10.5281/zenodo.2647564.
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JJV and SLS conceived and designed the study.
JJV and SLS performed the experiments/collected the data.
All analyzed and interpreted the data.
All contributed resources.
All drafted or revised the manuscript.
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The authors have declared that no competing interests exist.
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Jason J. Venkiteswaran, Sherry L. Schiff, and Brian P. Ingalls. 2019. Quantifying the fate of wastewater nitrogen discharged to a Canadian river. FACETS.
4(1): 315-335. https://doi.org/10.1139/facets-2018-0028
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