Diel fluctuations of high level nitrate and dissolved organic carbon concentrations in constructed wetland mesocosms

Ecological Engineering 133 (2019) 76–87

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Diel fluctuations of high level nitrate and dissolved organic carbon concentrations in constructed wetland mesocosms

T

⁎

Tiffany L. Messera, , François Birgandb, Michael R. Burchellb a

Department of Biological Systems Engineering and School of Natural Resources, University of Nebraska-Lincoln, 217 L.W. Chase Hall, P.O. Box 830730, Lincoln, NE 68583-0730, United States b Biological and Agricultural Engineering, Box 7625, North Carolina State University Campus, Raleigh, NC 27695, United States

A R T I C LE I N FO

A B S T R A C T

Keywords: Wetlands Dissolved organic carbon Nitrate-N Biogeochemistry Diel cycling

Portable in situ ultraviolet-visual spectrometers, through high frequency water quality measurements, provide new insight into biogeochemical processes occurring within dynamic ecosystems. Nitrogen and carbon cycling were observed in two distinct wetland mesocosm environments during a two-year mesocosm study. Simulated drainage water was loaded into the mesocosms across seasons with target nitrate-N levels between 2.5 and 10 mg L−1. Nitrate-N and dissolved organic carbon concentrations in the water column were measured hourly with the spectrometer and calibrated with water quality grab samples. Prominent and unique diel cycles were observed in both nitrate-N and dissolved organic carbon readings from the spectrometer, which reveal biogeochemical processes in these systems are more complicated than typically considered in empirical models. Findings support the importance of utilizing high frequency monitoring to advance current knowledge of nitrogen and carbon processes occurring in treatment wetland ecosystems.

1. Introduction Biogeochemical processes in aquatic environments, such as denitrification, are dependent on a variety of factors including O2 levels, carbon quality and availability, pH, and temperature specifically at the soil and water interface (Burchell et al., 2007; Hefting et al., 2005; Knowles, 1982; Rust et al., 2000). These conditions change quickly depending on water level, light, season, and hydrologic events (Jager et al., 2009; Jørgensen and Elberling, 2012; Liu et al., 2010; Mustafa and Scholz, 2011). As a result, nitrate-N (NO3-N) and dissolved organic carbon (DOC) concentrations have the potential to fluctuate throughout the day in estuarine and riverine environments (Birgand et al., 2011; Etheridge et al., 2014; Jollymore et al., 2012; Reynolds et al., 2016). Therefore, understanding real-time fluctuations in NO3-N and DOC concentrations is critical for fully understanding underlying biogeochemical processes occurring within aquatic environments. Diel NO3-N fluctuations have been observed in streams, estuaries, and wetlands (Cohen et al., 2012; Heffernan and Cohen, 2010; Johnson et al., 2006; Nimick et al., 2005; Roberts and Mulholland, 2007; Rode et al., 2014; Rusjan and Mikoš, 2010). Specifically in ecosystems with low NO3-N (< 2.5 mg L−1) and DOC (< 3.5 mg L−1) concentrations, NO3-N concentrations were highest during the night time and lowest during the daytime off the shore Monterey Bay, California (Johnson

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et al., 2006), in a forested stream in Tennessee (Roberts and Mulholland, 2007), and a forested stream in southwestern Slovenia (Rusjan and Mikoš, 2010). The trajectory of NO3-N concentrations back to the baseline in these waters at night has previously been described as replenishment from upstream sources. Measurements of dissolved organic matter (DOM) and DOC have been more inconsistent. DOC concentrations exhibit diel patterns in some streams with highest values in early evening and lowest values after dawn concentrations (Manny and Wetzel, 1973; Parker et al., 2010), while in other streams diel patterns are absent (Nimick et al., 2011, 2005; Walse et al., 2004). In contrast NO3-N and DOC concentrations have shown opposite diel fluctuations in systems at higher concentrations, particularly higher DOM. DOC concentrations showed as much as a 5 mg L−1 spread with lowest concentrations during afternoon hours and highest overnight during summer months in six low-flow streams adjacent to croplands (Wilson and Xenopoulos, 2012). Similar observations were observed in water dominated by biologically recalcitrant DOC, where solar radiation transformed DOM into inorganic and bioavailable forms (Vähätalo et al., 2003; Zepp, 2005). Differences in observations of DOC diel fluctuations is often attributed to highly enriched DOM waters that are impacted by direct photochemical mineralization (Bushaw et al., 1996; Moran and Zepp, 1997; Zepp et al., 1998). Concentration increases at night have been attributed to the gradient created between

Corresponding author. E-mail address: tiff[email protected] (T.L. Messer).

https://doi.org/10.1016/j.ecoleng.2019.04.027 Received 8 July 2018; Received in revised form 17 April 2019; Accepted 22 April 2019 Available online 29 April 2019 0925-8574/ © 2019 Elsevier B.V. All rights reserved.

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(Burchell et al., 2007; Kadlec et al., 2005). The plants were inundated with 4 cm of water during the growing season and were not harvested throughout the two year experimental period. A recirculation system was installed in each mesocosm to enhance mixing (the equivalent of one turnover per day) to simulate expected flow of drainage water moving through the wetlands. Twelve batch experiments were conducted between October 2012 and September 2014 to evaluate NO3-N and DOC cycling across season and at variable loading rates (Table 1). Artificial agricultural drainage water was mixed onsite in a tank and loaded into each mesocosm in one pulse application. The mesocosms were closed systems without a discharge outlet. Initial NO3-N concentrations (2.5–10 mg L−1) and water depths (18–30 cm) were purposely varied throughout each season in the batch studies to simulate concentrations commonly found in agricultural drainage water, and likely depths in the full scale restoration sites. This resulted in NO3-N loads that ranged between 0.6 g NO3N m−2 to 3.6 g NO3-N m−2. Each batch experiment lasted 7–10 days. Further details of the procedures for applying NO3-N rich drainage water to the wetland mesocosms are discussed in Messer et al. (2017a).

surface water and soil pore water following direct photochemical mineralization, which results in diffusion of DOM from the sediment back into the water column (Reddy and Delaune, 2008). Additionally, NH4+N and NO3-N production has been observed in wetland, estuary, river, and pond studies as DOM was broken down releasing NH4+-N (Bushaw et al., 1996; Rysgaard et al., 1995), followed by NO3-N concentration increases during the day as NH4+-N was nitrified near O2 release zones around roots during photosynthesis (Francoeur and Wetzel, 2003; Rysgaard et al., 1995). While these studies have provided a strong foundation for understanding carbon and nitrogen cycling in aquatic ecosystems, a critical step for improving the understanding of nitrogen and carbon cycling in treatment wetlands and their potential internal nutrient cycling can be accomplished by capturing high temporal resolution data. However, to date, few water quality studies have collected continuous measurements of water chemistry in wetland environments (Birgand et al., 2016, 2011; Etheridge et al., 2014; Hughes et al., 2016; Pellerin et al., 2012). Recent technological advancements have introduced innovative sensors that allow for nearly continuous water quality monitoring in aquatic ecosystems (Etheridge et al., 2014; Kirchner et al., 2000; Koskiaho et al., 2010; Jones et al., 2012). Portable UV–visual spectrometers are a new generation of water quality sensors that provide continuous water quality monitoring. However, the absorbance of UV and visual light varies based on the chemical nature of the compound, concentrations, and overall water chemistry (Etheridge et al., 2013). Specific compounds absorb unique ranges of wavelengths based on its chemical structure (Osburn and Morris, 2003). Therefore, changes in the absorption spectrum can be associated to changes in concentrations of identified compounds such as NO3-N (Crumpton et al., 1992; Johnson and Coletti, 2002; Olsen, 2008) and DOC (Coble, 2007; Rochelle-Newall and Fisher, 2002). However, these absorption spectrums vary based on the water chemistry in different aquatic environments (Fichot and Benner, 2011). This study investigated two distinct pulse flow treatment wetland mesocosm systems receiving NO3-N and comprised of organic rich soils, providing a first look at hourly NO3-N and DOC concentration fluctuations at higher concentrations than have been published to date in treatment wetlands. Therefore, the goal of this work was to improve our understanding of nitrogen and carbon cycling within pulse flow treatment wetland environments by collecting continuous hourly NO3-N and DOC measurements within the water column of two distinct wetland environments using real time, high frequency UV–Vis during various pulsed NO3-N loading events.

2.2. Instrumentation Hourly NO3-N and DOC concentrations were measured from the six wetland mesocosms using a S::CAN Spectro::Lyser™ automatic UV–Vis spectrophotometer probe (S::CAN Measuring Systems-Vienna, Austria) connected to a Multiplexor Pumping System (MPS). The UV–Vis spectrometer measured absorbance from 220.0 to 742.5 nm at 2.5 nm intervals from which the instrument calculated decadal absorption coefficients (m−1). Instrumental software produced a turbidity compensated spectrum of absorption coefficients, the first derivative of the raw spectrum, and the first derivative of the turbidity compensated spectrum for every raw spectrum collected during the experiments. The MPS (Birgand et al., 2016) included: 12 in-line three-way solenoid valves and a manifold, a peristaltic pump fitted with a H-bridge for current inversion, 0.32 cm polypropylene tubing, a 4 mm quartz flowthrough cuvette (Starnacells® model 46-Q-4), a storage tubing spool, and programmable Arduino microcontroller (Arduino, www.arduino. cc). The spectrophotometer probe has been successfully used to measure NO3-N and DOC concentrations in the water column in previous wetland studies (Birgand et al., 2016; Etheridge et al., 2014). The spectrometer probe was connected to the MPS, which allowed measurements of NO3-N and DOC from all mesocosms at hourly intervals (Fig. 2; Birgand et al., 2016). The microcontroller was programmed to switch on a peristaltic pump and open one dedicated solenoid valve at a time for water quality sampling of each mesocosm. The entire system was flushed with the new incoming sample 15 s prior to each reading. Samples were sent to the 4 mm quartz cuvette positioned between the measurement window of the spectrometer for analysis, and flushed sample water was stored in the storage spool above the spectrometer. Once the spectrometer reading was completed, the peristaltic pump would reverse the pumping direction and purge the system by routing water back into the sampled mesocosm. Each sample period took approximately 3 min from start to finish. The cuvette was serviced at least every 48 h, which was imperative to ensure data was not affected by biological and chemical fouling. During the service the cuvette was soaked in oxalic acid solution for up to 10 min before being rinsed with deionized water. Lastly, data stored on the spectrometer was offloaded to a field computer (Acer Inc., Taiwan) during service times. While attempts were made to complete an experiment during the winter months, only the growing season was evaluated using the UV–Vis spectrometer due to complications with freezing in the multiplexor system.

2. Methods 2.1. Mesocosm setup Batch run experiments were completed in a covered, single gable greenhouse near North Carolina State University using six large wetland mesocosms (3.5 m long × 0.9 m wide × 0.75 m deep), which were constructed and planted 16 months prior to the initial experiment (Fig. 1; Messer et al., 2017a). Atmospheric temperature was not controlled in the greenhouse to allow for seasonal assessments. Soils were excavated directly from two distinctly different future wetland restoration sites: Deloss [fine-loamy, mixed, semiactive, thermic Typic Umbraquults], referred to as WET-Min, and Scuppernong [loamy, mixed, dysic, thermic Terric Haplosaprists], referred to as WET-Org. Total carbon content in the wetland substrates were 22.5–27.6% in the WET-Org mesocosms and 3.4–4.0% in the WET-Min mesocosms. WETMin and WET-Org mesocosms were planted with a monoculture of softstemmed bulrush (Schoenoplectus tabernaemontani) as described in Messer et al. (2017a). In summary, soft-stem bulrush was chosen based on the plants rapid above and below ground biomass establishment and previous use to evaluate NO3-N removal in wetland mesocosms

2.3. Water sampling and analytical methods Grab water quality samples for laboratory analysis were collected 77

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Flow

Flow

WET-Min 1

WET-Org 1

Flow

Flow

WET-Min 2

WET-Org 2

Flow

Flow

WET-Org 3

WET-Min 3

Fig. 1. Schematic of wetland mesocosm experimental setup with Multiplexor Pumping System. Table 1 Summary of wetland mesocosm batch studies (Adapted from Messer et al. (2017a)). Season

Fall Fall Fall Fall Spring Spring Summer Summer Summer Summer Summer Summer †

Date

10/16-10/26/12 9/24-10/4/13 10/15-10/25/13 9/2-9/9/14 5/28-6/7/13 5/27-6/6/14 7/2-7/12/13 8/6-8/16/13 8/20-8/27/13 6/13-6/20/14 7/22-8/1/14 8/12-8/19/14

Experiment Period

Water Depth Prior to Loading

Days

cm

10 10 10 7 10 10 10 10 7 7 10 7

4 4 4 −5† 4 4 4 4 4 4 4 4

Water Depth After Loading

Target NO3-N −1

18 30 18 20 18 30 30 30 30 18 30 18

Target NO3-N Load

mg L

g N m−2

5 10 2.5 2.5 2.5 2.5 2.5 5 2.5 2.5 10 5

0.9 3.6 0.6 0.9 0.6 0.9 0.9 2.0 0.9 0.6 3.6 0.9

Negative value indicates water level below wetland surface.

using a Mettler Toledo™ SevenGo™ pH portable field meter (Columbus, OH), while DO was measured using a YSI ProPlus Quatro cable (Yellow Springs, OH) connected to a Pro Series Polarographic DO sensor. Soil redox potential was measured using pre-constructed platinum-tipped probes (described by Wafer et al. (2004)), installed in replicates of 5 at a soil depth of 5 cm within each mesocosm, connected to an Accumet AP63 portable pH/mV meter (Fisher Scientific ®, Pittsburgh, Pa) and a portable KCl-saturated Ag/AgCl reference electrode (Jensen Instruments, Tacoma, WA). Water temperature was measured hourly using a using 8k HOBO pendant temperature sensors (Onset Computer Corporation, Bourne, MA). Water chemistry grab sample concentrations were adjusted based on the changes in the water depth to account for evapotranspiration, which was measured on a stage gage in each mesocosm during each grab sampling event. Further details regarding the description of the overall experimental setup are described in Messer et al. (2017a).

from the recirculation system on days 0, 1, 2, 3, 5, 7, and in 7 of 12 batch run experiments on day 10 to calibrate the spectrometer. All water quality samples were filtered through 0.45 μm filters and refrigerated until samples were submitted to the water quality laboratories. All samples were analyzed for NO3-N, while samples from days 0, 5, and 7 or 10 were analyzed for DOC. NO3-N samples were evaluated at the Soil Science Environmental and Agricultural Testing Laboratory (SSC-EATS Lab) in Raleigh, NC with a Quikchem 8000 (Lachat, Milwaukee, WI) using the cadmium reduction method (EPA Method 353.2). DOC grab samples were completed in the North Carolina State University Biological and Agricultural Engineering Environmental Analysis Test Service Laboratory (BAE EAL) in Raleigh, NC using a Teledyne Techmar Apollo 9000 combustion TOC Analyzer (Mason, OH), which used the high-temperature combustion method (EPA Method 415.1 and 9060A). Photosynthetic radiation rates were collected from a State Climate Office weather site approximately 1 mile from the study location (REED). Other water quality parameters measured daily during grab sampling times included water pH, dissolved oxygen (DO), soil redox potential, water temperature, and water depth. Water pH was measured

2.4. Partial least square regression Data adjustments needed due to interference and noise common in 78

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Spectro:: Lyser Probe

Storage Spool

4 mm Cuvette Arduino Microcontroller

Peristaltic Pump Solenoid Valve Manifold 12 Different Sampling Source Tubing Data Offload

Fig. 2. S::CAN Spectro::Lyser™ automatic UV–Vis spectrometer probe field setup with Multiplexor Pumping System.

3. Results

spectrometers were performed using procedures established by Etheridge et al. (2014). In summary, spectrometer absorbance readings were converted into NO3-N and DOC concentrations by fitting NO3-N and DOC concentrations from grab samples analyzed using traditional EPA methods in R Studio (RStudio Team, 2015) with Partial Least Square Regression (PLSR) (Mevik et al., 2011). PLSR is a chemometric technique that has been used to reduce the dimension of absorption spectra measurements from hundreds of wavelengths to fewer components that correlate with the analyte concentrations. The optimum number of PLSR components for NO3-N and DOC was determined as the lowest number of components for which the root mean square error of concentration prediction (RMSEP) was at its minimum value. NO3-N spectrometer readings were fit to grab sample data concentrations collected for each individual batch experiment and mesocosm, while DOC readings from the spectrometer were calibrated using all DOC grab sample concentrations from the study for each treatment (WET-Min or WET-Org) in one calibration simulation. The DOC calibrations were fewer in comparison to the NO3-N calibrations since fewer grab samples were collected for DOC. Impacts of evapotranspiration on individual mesocosm water depth was considered; however, in the event that had impacted the measured concentrations both DOC and NO3-N concentrations would have increased and decreased simultaneously, which was not observed.

3.1. Water quality summary Water quality laboratory results (used for calibrating the UV–Vis spectrometer) and in situ measurements of environmental conditions in the mesocosms during each of the experimental runs are summarized in Tables 2 and 3. In summary, pH values, DO concentrations, and NO3-N concentrations decreased over each experimental period, while DOC concentrations gradually increased, specifically in the WET-Org mesocosms. pH, DOC, and water temperature appeared to be favorable for denitrification in both the WET-Min and WET-Org mesocosms in every experiment. Soil redox potential values at the 5 cm soil depth remained below 220 mV throughout the experiments, which additionally indicated that soil conditions were favorable for denitrification (Fielder et al., 2007; Patrick, 1960). Average daily water temperatures remained above 16 °C.

3.2. Calibration results Calibration results for the UV–Vis spectrometer NO3-N and DOC concentrations using PLSR in the R statistical framework and laboratory-measured water samples are shown in Tables 4 and 5. The WETMin and WET-Org wetland treatments had high correlations between spectrometer readings and laboratory-measured NO3-N concentrations (R2 > 0.87 in 12 of 12 batch studies for WET-Min mesocosms and 11 of 12 batch studies for the WET-Org mesocosms). This verified the spectrometer could be used to make reliable hourly measurements in each the wetland mesocosms. Similarly, DOC concentrations had high correlations (R2 > 0.76) between the spectrometer readings and laboratory-measured DOC concentrations. The R2 were slightly lower likely due to the reduced DOC grab samples that were available for DOC calibrations compared to the NO3-N calibrations and large DOC concentration changes through the study periods, particularly in the WETOrg systems (Table 3).

2.5. Goodness of fit indicators The coefficient of determination (R2) and standard error of regression (SER) was calculated by comparing lab-measured and predicted values for each treatment and batch experiment dataset for NO3-N. However, due to more limited sampling, R2 and SER were calculated by comparing lab-measured and predicted values for each treatment over the entire study for DOC. The R2 was used to determine goodness of fit, while the SER represented the standard deviation of the concentrations above and below the regression line.

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Table 2 Summary of environmental conditions in WET-Min mesocosms.* Season

Batch Experiment Date

Average Daily Water Temperature (°C)

Water Depth (cm)

pH

DO (mg L−1)

Redox Potential at 5 cm Depth (mV)

NO3-N (mg L−1)

DOC (mg L−1)

Fall

10/16-10/26/12 9/24-10/4/13 10/15-10/25/13 9/2-9/9/14

17 21 16 27

18 30 18 20

8.2–6.8 6.8–6.5 6.7–6.8 7.3–6.4

8.72–4.91 9.71–5.80 10.05–9.76 7.38–3.24

4–79 106–(−)41 44–83 281–113

5.01–0.91 12.46–2.84 2.55–0.04 3.22–0.05

4.94–6.46 5.03–5.46 6.35–6.61 7.48–7.38

Spring

5/28-6/7/13 5/27-6/6/14

25 25

18 30

6.6–6.5 7.4–6.0

9.77–3.11 9.83–1.27

223–108 88–49

2.68–0.01 2.73–0.05

11.92–7.50 8.07–7.10

Summer

7/2-7/12/13 8/6-8/16/13 8/20-8/27/13 6/13-6/20/14 7/22-8/1/14 8/12-8/19/14

27 27 25 26 26 25

30 30 30 18 30 18

6.4–6.3 6.1–6.6 6.5–6.5 7.1–6.1 7.6–6.2 7.0–6.3

10.56–3.08 9.10–5.94 9.37–5.27 8.25–2.00 7.88–1.55 8.36–2.11

100–143 38–30 (−)93–50 44–(−)8 116–31 106–46

2.24–0.02 5.20–0.04 2.75–0.27 2.5–0.05 12.04–0.05 5.21–0.05

12.54–10.50 19.94–15.53 17.26–13.63 8.78–5.27 21.94–13.17 7.66–7.07

* Values for DO, redox potential, NO3-N, and DOC are the average values at the beginning (Day 0, left) and end of the experiment (Day 7/10, right). Table 3 Summary of environmental conditions in WET-Org mesocosms.* Season

Batch Experiment Date

Average Daily Water Temperature (°C)

Water Depth (cm)

pH

DO (mg L−1)

Redox Potential at 5 cm Depth (mV)

NO3-N (mg L−1)

DOC (mg L−1)

Fall

10/16-10/26/12 9/24-10/4/13 10/15-10/25/13 9/2-9/9/14

17 21 16 27

18 30 18 20

7.5–4.8 6.3–5.8 6.7–6.2 6.9–6.0

9.34–5.12 9.93–4.53 9.65–9.55 7.36–2.24

74–164 61–(−)34 47–89 149–95

5.48–0.99 12.56–4.47 3.00–0.14 3.22–0.05

16.32–49.63 15.01–40.93 15.65–42.90 12.96–20.94

Spring

5/28-6/7/13 5/27-6/6/14

25 25

18 30

5.7–4.8 6.7–5.5

9.95–2.95 9.85–1.01

227–185 112–62

2.16–0.01 2.94–0.05

28.67–54.25 21.60–40.24

Summer

7/2-7/12/13 8/6-8/16/13 8/20-8/27/13 6/13-6/20/14 7/22-8/1/14 8/12-8/19/14

27 27 25 26 26 25

30 30 30 18 30 18

6.1–5.4 6.7–5.7 6.4–5.8 7.0–5.5 7.7–5.9 6.6–5.8

11.5–3.07 9.10–4.43 9.11–4.49 7.69–1.77 7.74–1.26 8.14–1.52

108–120 54–87 (−)43–125 77–11 147–93 123–97

2.33–0.07 6.07–0.16 3.19–0.27 2.81–0.05 12.29–1.01 5.08–0.05

29.51–79.94 35.20–58.57 29.22–40.12 20.73–46.37 17.16–25.55 22.84–19.29

* Values for DO, redox potential, NO3-N, and DOC are the average values at the beginning (Day 0, left) and end of the experiment (Day 7/10, right). Table 4 Summary of the multiple regression results used in calibrating NO3-N concentration UV–Vis readings to analytical chemistry results. Note: SER = standard error of regression. Season

Batch Experiment Date

WET-Min R

WET-Org

2

SER

Components

R2

SER

Components

Fall

10/16-10/26/12 9/24-10/4/13 10/15-10/25/13 9/2-9/9/14

87.43 98.66 97.81 94.58

0.621 1.023 0.346 0.357

2 4 5 2

89.34 97.21 95.55 97.77

0.641 0.931 0.956 0.279

2 6 4 3

Spring

5/28-6/7/13 5/27-6/6/14

99.88 97.29

0.221 0.254

8 2

Unable to Calibrate 99.30 0.850

8

Summer

7/2-7/12/13 8/6-8/16/13 8/20-8/27/13 6/13-6/20/14 7/22-8/1/14 8/12-8/19/14

98.14 99.86 99.59 99.84 99.72 90.71

0.189 0.115 0.180 0.0611 0.353 0.684

3 5 7 3 4 2

99.74 98.04 99.70 99.20 94.61 99.04

10 3 7 4 3 5

3.3. Nitrate-N and dissolved organic carbon concentration dynamics

Table 5 Summary of the multiple regression results used in calibrating DOC concentration UV–Vis readings to analytical chemistry results. Note: SER = standard error of regression. Mesocosm

Combined Batch Experiment Dates

R2

SER

Components

WET-Org WET-Min

10/16/12-8/19/2014 10/16/12-8/19/2014

76.21 93.98

6.651 0.9192

4 11

0.281 0.342 0.192 0.308 0.823 0.294

The hourly concentrations measured with the UV–Vis spectrometer probe (Figs. 3 and 4) showed trends at the 5-day scale, and variations above and below that trend at the daily scale. NO3-N concentrations tended to decrease over the 5-day period. DOC concentrations tended to increase for the WET-Org mesocosms, but showed no clear trend for the WET-Min mesocosms. The diel variations suggest that it is possible to capture the overall 5-day trends from daily sampling provided samples are obtained at fixed times, which was done and previously reported by 80

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Fig. 3. Hourly NO3-N concentrations from the S::CAN Spectro::Lyser™ and photosynthetic radiation rates for 3.6 g m−2 N load batch run completed in a. Summer 2014 (R2 = 94.61 (organic) and 99.72 (mineral)) and b. Fall 2013 (R2 = 97.21 (organic) and 98.66 (mineral)). *Breaks in data were due to multiplexor malfunction during the experiments.

samples of more or less concentrated water was collected and analyzed by the UV–vis spectrometer. Therefore, slight variation in the performance of the recirculation pumps or differences in internal hydraulics influenced by plants stems likely contributed to the observed noise in NO3-N concentrations for the WET-Org mesocosm in Fig. 3b, and DOC concentrations for the WET-Min mesocosm in Fig. 4b. NO3-N concentrations had a general decease in both mesocosm treatments (137–603 mg m2 day−1), while DOC had a general increase only in WET-Org (141–1129 mg m2 day−1) throughout the 7–10 day experiments. Unlike many reports, diel peaks and troughs were observed to be 4–5× less or more than the general decreases and increases and less in absolute concentration increase or decrease from a generally stable concentration. DOC diel concentration variations, in many cases, exhibited absolute concentration daily troughs, while the NO3-N diel concentrations exhibited absolute peaks. For example, DOC concentrations would be highest at sunrise and

Messer et al. (2017a). However, the hourly data suggests that samples taken at dawn seem to best represent the long term trend (Figs. 3 and 4). Data from most mesocosms appeared ‘smooth’ and closely aligned with visible hourly trends (Fig. 3a). However, others generated ‘noisier’ data, where consecutive concentration readings would increase and decrease around a general concentration rate of change. Distinguishing noise from actual variations has to be developed through experience. However, noise can be distinguished as an erratic sudden increase or decrease in concentrations, while actual variations exhibited more of a systematic pattern. We have previously observed similar erratic noise in mesocosms monitored using essentially the same system (Birgand et al., 2016). In this current study, as in the one reported in 2016, some mesocosms exhibited a noisy signal, while adjacent ones did not. We hypothesize here, as before, that most of the noise observed in some of the mesocosms reflected imperfect mixing of water, where variable 81

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Fig. 4. Hourly DOC concentrations from the S::CAN Spectro::Lyser™ and photosynthetic radiation rates for 3.6 g m−2 N load batch run completed in a. Summer 2014 and b. Fall 2013. *Breaks in data were due to multiplexor malfunction during the experiments.

again. NO3-N dynamics in both the WET-Min and WET-Org exhibited these tendencies, just at differing magnitudes. Specifically, NO3-N concentrations in the WET-Org systems periodically increased by as much as 0.04–0.08 mg L−1 h−1 in batch experiments during the midday period in contrast to the overall gradual decrease of 0.01–0.05 mg L−1 h−1 observed overall in the batch experiments. Similarly, periodic smaller increases in NO3-N concentrations in the WET-Min systems were observed during midday, as concentrations increased by 0.02–0.05 mg L−1 h−1, compared to the overall decreases observed that ranged from 0.02 to 0.08 mg L−1 h−1 during the batch experiments. Penton et al., (2013) made similar observations as those seen in this study in a flooded agroecosystem (taro field) in Hawaii, reporting NO3-N concentrations occasionally increased during a 6-hour lag period after sunrise. Investigation during this study suggested the changes in NO3-N followed two tendencies. During some periods, NO3-N concentrations did not exhibit an increase during the day (e.g., Fig. 6c), but a

consistently decrease until approximately 2–3 PM, then sharply increase until the middle of the night, and finally plateau until the following morning (Figs. 5 and 6). DOC concentrations in the WET-Org systems periodically decreased by as much as 0.50 mg L−1 h−1 in batch experiments during the midday period in contrast to the overall gradual increase of 0.01–0.33 mg L−1 h−1 (average 0.10 mg L−1 h−1) observed overall in the batch experiments, while the WET-Min DOC concentrations decreased by as much as 0.15 mg L−1 h−1 in batch experiments during the midday period in contrast to the relatively stable concentrations observed overall in the batch experiments (average 0.01 mg L−1 h−1). In contrast, NO3-N concentrations typically showed a small decrease or plateau period at the beginning of the day as solar radiation began to rise. However, NO3-N concentrations, in many cases, either increased or the rate of observed removal slowed around midday during the time solar radiation reached its peak. After daylight dissipated and until the following morning, observed NO3-N removal rates increased once 82

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Fig. 5. Zoomed in hourly NO3-N and DOC concentrations from the S::CAN Spectro::Lyser™ for a) 3.6 g N m−2 batch run, b) 2.0 g N m−2 batch run, and c) 3.6 g N m−2 batch run completed in the one WET-ORG mesocosm. *Shaded areas indicate period of sunset to sunrise at the research sites.

concentration peaks are observed in the early mornings and troughs in late afternoons (Cohen et al., 2012; Heffernan and Cohen, 2010; Johnson and Tank, 2009; Manny and Wetzel, 1973; Nimick et al., 2011; Roberts and Mulholland, 2007; Rusjan and Mikoš, 2010). Previous studies have identified these diel cycles in open stream systems to be a result of assimilation of NO3-N by primary producers dependent on light conditions, temperature, and autotrophic and heterotrophic activities, but all had lower NO3-N (< 2.5 mg L−1) and DOC (< 3.5 mg L−1) concentrations compared to this study (Kent et al., 2005; Nimick et al., 2011; Roberts and Mulholland, 2007; Rusjan and Mikoš, 2010). In our study, diel variations cannot be described as well in terms of concentration peaks and troughs, but rather as deviation from a general trend, i.e., NO3-N decrease or DOC increase, which were observed regardless of season (see Tables 2 and 3 and typology Fig. 7). The main reason is our mesocosms were closed systems where the water was recirculated within each mesocosm. As a result, all NO3-N removed

diminution of the removal rate, followed in the next night by a decrease in concentration at a rate similar to that observed the previous night (Fig. 7a, dotted green line). As a result, NO3-N concentrations the following day were higher than they would have been had the removal rates from the previous night been maintained (Fig. 7a, black dotted line). However, during other days (e.g., Fig. 6b), NO3-N production exceeded removal resulting in concentration increases, but the rate of change at the end of the day was much higher, and such the concentrations appeared to reach the level predicted by the rate of change from the previous day (Fig. 7a, red line). Such dual behavior was also observable for DOC, where much higher production than the overall concentration rate of change was observed at the end of day (Fig. 7b). 4. Discussion Diel patterns in NO3-N concentrations observed in this study are rather different from those observed in previous studies, where 83

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Fig. 6. Zoomed in hourly NO3-N and DOC concentrations from the S::CAN Spectro::Lyser™ for a) 3.6 g N m−2 batch run, b) 2.0 g N m−2 batch run, and c) 3.6 g N m−2 batch run completed in the one WET-Min mesocosm. *Shaded areas indicate period of sunset to sunrise at the research sites.

NH4+ generated from photo-oxidized DOC. A previous 15N tracer study originally aimed at tracing NO3-N fate in these same mesocosms (Messer et al., 2017a,b,c) helped form these hypotheses. During the previous 15N enrichment study NO3-N removal processes were evaluated, where the driving removal processes included uptake by macrophytes as well as denitrification (Messer et al., 2017b). In our case, we hypothesize that the sharp NO3-N increases indicate that nitrification produced more NO3-N than was removed from the water column. Additionally, the inhibition of benthic denitrification by increased oxygen production during the day could further decrease observed daytime NO3-N removal rates. Previous studies have observed NH4+ will typically be oxidized during the day in periods with strong light penetration and increased water temperature, DO, and pH (Johnson and Coletti, 2002; Laursen and Seitzinger, 2004; Harrison et al., 2005; Harris and Smith, 2009), followed by consumption of NO3N by denitrifying bacteria at night (Harris and Smith, 2009). The diel fluctuation of denitrification in other studied systems were often driven

from the water column was removed by denitrification or assimilation (Messer et al., 2017b); hence, the several day tendency towards NO3-N concentration decrease. Similarly, due to diagenetic processes the organic matter in the sediments (both WET-Min and WET-Org) produced DOC which, when refractory may accumulate over time in the water column (although this effect may be counteracted by DOC removal processes). Majority of high frequency nitrogen and carbon data has been completed in open stream systems (Cohen et al., 2012; Heffernan and Cohen, 2010; Johnson and Tank, 2009; Manny and Wetzel, 1973; Nimick et al., 2011; Roberts and Mulholland, 2007; Rusjan and Mikoš, 2010). However, our system was a closed wetland mesocosm system, which likely reflects the differing balance in the dominating processes observed in our systems. While identifying biogeochemical processes was not the objective of this study, we hypothesize NO3-N production processes included 1) nitrification of upward diffusing NH4+ from the sediment associated with O2 exuded by roots, and 2) nitrification of 84

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Fig. 7. Schematic of typical a. NO3-N and b. DOC diel trends over time observed in the wetland mesocosms utilizing the UV–Vis spectrometer probe. *Shaded areas indicate period of sunset to sunrise.

might under- or overestimate systems that do not have the same internal contributions. Thus, access to high frequency replicated data opens the possibility to conduct experiments to obtain the necessary data to calibrate more universal process-based models.

by photosynthesis of benthic microalgae, which allowed increased daytime O2 production inhibiting NO3-N removal by denitrification during the day (Pind et al., 1997; Risgaard et al., 2018). However, in our study a definitive determination of these processes were not possible as hourly DO, NH4-N, and microalgae were not regularly measured and should be considered for future studies. For water column DOC, we hypothesize that the driving removal process included photooxidation; while the production process included upward diffusion from the sediment from organic matter diagenesis. Tendencies in concentrations were observed in both the WET-Min and WET-Org mesocosms throughout the growing season. However, larger troughs were observed in the WET-Org, which was expected given the higher organic matter in those specific wetland soils. The main difference with our study compared to previous studies is the overall organic nature of the sediment substrate in the mesocosms (even for the mesocosms labeled ‘WET-Min’). Our observations and proposed processes may reflect the specific organic nature of our mesocosm substrates, as has been discussed in past publications (Messer et al., 2017a,b,c). We hypothesize DOC troughs correspond to the strong photooxidation of diagenetically produced DOC. Previous studies have reported DOC concentration increases and decreases changed synchronously at the end of day (Bushaw et al., 1996; Risgaard et al., 2018; Vahatalo and Zepp, 2005). The sharper than normal (e.g., night-time) DOC increase at the end of day suggests that there were very active DOC releasing processes that also favored NO3-N removal processes and NH4-N production as organic matter broke down. These observations are consistent with a previous report where DOC has been observed to decrease during the daytime due to solar radiation (Zepp et al., 1998), which was rather different from previous reports in open systems where typically DOC increased during the day and decreased at night (Kaplan and Bott, 1982; Manny and Wetzel, 1973). Metabolic processes likely caused these diel effects, with heterotrophic organisms consuming DOC at night (Kuserk et al., 1984), while autotrophic processes excreted labile DOC during the daytime (Forget et al., 2009; Kiss, 1996; Spencer et al., 2007). Future studies are needed to definitively determine the biogeochemical processes that impacted peaks and troughs in NO3-N and DOC concentrations observed in this study. However, observations from this study support that natural wetlands have an internal source of both nitrogen and carbon, in addition to upland contributions. Findings from this study suggests current empirical modeling methods for NO3-N removal ignores the internal contribution mechanisms, which could explain reported variable removal rate constants in similar wetlands systems. In particular, empirical models, when obtained in systems having large internal contributions of nitrogen and carbon like ours

5. Conclusions High frequency water quality monitoring is a major step for enhancing the current understanding of biogeochemical processes occurring in aquatic environments. The monitoring scheme presented in this work enabled two distinct wetland environments to be monitored simultaneously. Based on observations in this study, grab sampling campaigns are recommended to collect samples early in the morning, during periods where biogeochemical processes are expected to be more stable by minimizing sunlight inconsistencies (i.e., cloudy). Diel observations reveal that the apparent functioning of these systems is more complicated than just denitrification for NO3-N and diagenetic production for DOC. The apparent trends are a combination of balanced processes, which would be desirable to eventually model to more accurately predict NO3-N and DOC removal and production in wetland systems. Future high frequency sampling and enrichment studies are needed to better understand these biogeochemical processes. Further, high frequency replicated concentration data are certainly imperative to unveil and better quantify biogeochemical processes occurring in wetlands, and necessary to establish robust process-based models for these important natural and treatment systems. Declaration of interests The authors declare that they have no known competing financial interests. Acknowledgements The project was supported with funds provided by the Clean Water Management Trust Fund, United States (Grant ID 2010-415), North Carolina Sea Grant, United States (Grant ID 12-11-W:R/MG-1215), and the Water Resources Research Institute, United States (Grant ID 13-03W). Additionally, the research described in this article was funded in part by the United States Environmental Protection Agency (EPA) under the Science to Achieve Results (STAR) Graduate Fellowship Program (Grant ID FP17483). EPA has not officially endorsed this publication and the views expressed herein may not reflect the views of the EPA. Special thanks to Maggie Rabiipour, Mary Curtis, Dr. Randall Etheridge, Kathleen Bell, L.T. Woodlief, and Nicole Mathis for field assistance. 85

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