Greenhouse gas flux from stormwater ponds in southeastern Virginia (USA)

Anthropocene 28 (2019) 100218

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Greenhouse gas flux from stormwater ponds in southeastern Virginia (USA) A.L. Gorsky* , G.A. Racanelli, A.C. Belvin, R.M. Chambers Keck Environmental Laboratory, William & Mary, Williamsburg, VA 23187, United States

A R T I C L E I N F O

A B S T R A C T

Article history: Received 22 February 2019 Received in revised form 22 August 2019 Accepted 24 August 2019 Available online 25 August 2019

Stormwater ponds are ubiquitous features of developed landscapes of the eastern United States. Their design specifically controls the pace of water runoff from impervious cover of surrounding watersheds. Ponds accumulate organic matter that typically decomposes anaerobically in bottom sediments, and thus may be significant sources of greenhouse gases to the atmosphere (e.g., carbon dioxide (CO2), methane (CH4)). We sampled fifteen stormwater retention ponds in southeastern Virginia (USA) during summer 2018 to determine the diffusive emission of greenhouse gases with respect to measured environmental parameters. The equivalent CO2 (CO2e) flux from ponds ranged from 8.3 to 80 mmol m-2 h-1, with CH4 contributing 94%, CO2 6% and nitrous oxide less than 1% of the CO2e flux, on average. From linear mixedeffects modelling, diffusive flux of CO2 was inversely associated with pH. Maximum depth best explained diffusive flux of CH4, with surface area of secondary importance, i.e. CH4 flux was higher in smaller and more shallow ponds. With 300 stormwater ponds in the county where we conducted this study, we estimate that, during a 100-day warm season, these ponds emit 2.3  109  1.5  109 SD g C as CO2e. As small, human-constructed ponds are becoming common features of urbanizing landscapes globally, results from this study suggest that, collectively, small ponds can contribute substantially to climate forcing. Better pond designs that reduce sediment methanogenesis, however, can mitigate the hypothesized potential disservice of GHG emissions from unvegetated stormwater retention ponds. © 2019 Published by Elsevier Ltd.

Keywords: Stormwater ponds Greenhouse gas flux Methane Carbon dioxide Nitrous oxide

1. Introduction Freshwater lakes and reservoirs cover less than 4% of Earth’s non-glaciated land surface (Verpoorter et al., 2014) but are sites of intense biogeochemical activity and potential greenhouse gas (GHG) flux (Prairie et al., 2018). Interestingly, smaller bodies of water seem to contribute disproportionately to GHG emissions relative to their size (Grinham et al., 2018; Ollivier et al., 2018). Holgerson and Raymond (2016) estimated that very small ponds (<0.001 km2) represent 8.6% of lakes and ponds by area globally, but account for more than 15% of carbon dioxide (CO2) and 40% of methane (CH4) emissions. Whereas vegetated wetlands typically operate as carbon (C) sinks (Bridgham et al., 2006), small unvegetated bodies of water accumulate labile allochthonous organic C from the contributing watershed and autochthonous C from the water column (van Bergen et al., 2019). When this organic matter decomposes in bottom sediments at rates faster than

* Corresponding author. E-mail address: [email protected] (A.L. Gorsky). http://dx.doi.org/10.1016/j.ancene.2019.100218 2213-3054/© 2019 Published by Elsevier Ltd.

oxygen can be replenished, anaerobic conditions lead to both CO2 and CH4 production (Williams et al., 2013). The degree of saturation of CO2 and CH4 in small water bodies varies depending on biological activity and pond characteristics (Holgerson, 2015), which will affect diffusive gas exchange. For example, CO2 is a product of both the aerobic and anaerobic mineralization of organic matter. Because CO2 is also a reactant in photosynthesis, water column production by algae could offset the emissions of CO2 from pond sediments. Likewise, CH4 is produced anaerobically in pond sediments, but may be oxidized to CO2 prior to emission to the atmosphere (Oswald et al., 2015). Smaller impoundments tend to be more concentrated sources of CH4 emissions owing to their shallow depths and diminished opportunity for CH4 oxidation prior to atmospheric release (Lazar et al., 2014; Holgerson and Raymond, 2016; Kifner et al., 2018). With respect to its 100-year global warming potential, CH4 is roughly 28 times stronger than CO2 (IPCC, 2013). A third greenhouse gas—nitrous oxide (N2O)—is produced during denitrification under suboxic and hypoxic conditions when organic carbon oxidation is coupled with the reduction of nitrogen species (Codispoti, 2010). The global warming potential of N2O is 265 times greater than CO2 (IPCC, 2013), and N2O could be a

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substantial contributor to GHG flux from small impoundments. For example, N2O gas emissions were high in headwater streams draining urban areas (Beaulieu et al., 2011); small urban ponds receive runoff high in nitrogen and their organic sediments are often oxygen-depleted, suggesting they might also be denitrification hotspots (Bettez and Groffman, 2012). McPhillips and Walter (2015), however, found that denitrification potential and CH4 flux were both high in two wet stormwater ponds, but N2O fluxes were relatively low. More recently, Blaszczak et al. (2018) determined the rates of N2O production from sediments of 64 urban stormwater ponds across the U.S. were in fact no greater than rates from freshwater bodies receiving drainage from undeveloped landscapes. Stormwater retention ponds are small impoundments designed specifically to manage water flows in developing urban and suburban watersheds. In the densely populated eastern United States, where annual rainfall ranges from 75 to 150 cm (Ingram et al., 2013), we estimate that wet retention ponds number in the tens to hundreds of thousands. For example, some 300 ponds have been constructed in James City County, Virginia, where 80,000

people live (F. Geisler, JCC Stormwater Division) (Fig. 1). Applying that pond density to the eastern U.S. where over 100 million people live (U.S. Census 2010) yields over 300,000 ponds. Although the actual number of ponds currently may be far fewer, with ongoing land development stormwater ponds may become common, convergent features of urbanized landscapes (Steele et al., 2014; Saulnier-Talbot and Lavoie, 2018). The retention feature of stormwater ponds provides opportunity for water quality improvement prior to discharge downstream (Tixier et al., 2011). Sediments and sorbed phosphorus eroded and transported into ponds can settle out in the low-velocity water. Dissolved nitrogen (N) and phosphorus concentrations may be decreased when these nutrients are assimilated into organic matter by algae and littoral zone vegetation. Additionally, denitrification can transform and remove N, especially from ponds in urban areas that receive elevated influent concentrations of N (Collins et al., 2010). Other stormwater control measures may provide better opportunities for C and N processing (e.g., bioretention swales, constructed wetlands—see Søvik et al., 2006), but wet retention ponds historically have been the best

Fig. 1. (A.) Location of Williamsburg/James City County in eastern Virginia (USA). (B.) Spatial distribution of wet stormwater retention ponds in Williamsburg/James City County. (C.) Aerial photograph of our fifteen study sites in Williamsburg/James City County (coordinates in Table 1).

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management practice (BMP) chosen by developers in urban and suburban settings (Collins et al., 2010). Stormwater ponds provide important ecosystem services like carbon sequestration, biodiversity support, and cultural services including education and aesthetics (Moore and Hunt, 2012), but Burgin et al. (2013) question whether the ecosystem services provided by stormwater retention ponds might also include “disservices”, i.e., the negative contribution of excess GHG emissions to climate forcing. To test for hypothesized disservices of suburban stormwater ponds in southeastern Virginia, we used the floating static chamber method and examined three research questions: (1) What are the daytime diffusive fluxes of CH4, CO2, and N2O to the atmosphere during summer? (2) What are the relative contributions of each GHG to global warming potential, i.e., equivalent CO2 (CO2e flux)? (3) What water quality parameters and/or pond characteristics are associated with GHG flux? Based on prior research, we hypothesized that greenhouse gas flux would

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be dominated by methane and would be positively correlated with temperature and negatively correlated with pond size. Results of this work can be used in part to evaluate the design and use of human-made ponds that are becoming common aquatic environments in the age of the Anthropocene (Saulnier-Talbot and Lavoie, 2018). 2. Methods 2.1. Site description We conducted the study in the city of Williamsburg and the adjacent James City County, Virginia during June through August 2018. Williamsburg/James City County is located on the Virginia Coastal Plain and lies within the Chesapeake Bay watershed. Working closely with the county, we selected 15 unvegetated stormwater retention ponds from an online database of almost 300

Table 1 Physical, chemical and biological characteristics of stormwater ponds; means with ranges in parentheses. Pond

Latitude; Longitude

SA (ha)

Max. Depth (m)

P to A Ratio

Water Temp. ( C)

pH

Sp. Cond. (mS/cm)

TSS (mg/L)

NO3- (mM)

NH4+ (mM)

DIP (mM)

1

37.298913  N; 76.795748  W

0.39

2.00

0.112

26.8

6.72

168.67

3.0

0.36

0.00

2.48

2

37.304075  N; 76.772000  W

10.56

2.95

0.038

(26.0-28.0) 26.3

(6.29-7.02) 7.54

(160.0-172.5) 130.80

(0.7-7.5) 6.2

(0.04-0.92) 0.22

0.00

(1.47-3.50) 0.84

3

37.305028  N; 76.778653  W

1.26

2.00

0.060

(26.1-26.7) 25.7

(7.08-7.80) 6.53

(126.0-136.7) 183.70

(1.3-11.9) 2.8

(0-0.46) 0.67

0.02

(0.43-1.61) 0.77

4

37.284201  N; 76.777052  W

0.40

2.00

0.102

(25.0-26.8) 27.8

(6.24-6.89) 6.67

(114.1-307.2) 51.97

(0.6-4.9) 4.4

(0.63-0.71) 0.50

(0-0.05) 0.00

(0.66-0.90) 0.46

5

37.289494  N; 76.770059  W

3.18

2.50

0.043

(27.0-29.2) 27.7

(6.33-7.02) 6.50

(49.4-55.1) 106.33

(2.5-6.4) 1.7

(0.04-1.18) 0.35

0.00

(0.38-0.52) 0.44

6

37.259865  N; 76.737618  W

0.56

1.60

0.077

(26.7-29.2) 25.9

(6.46-6.58) 7.05

(105.0-107.2) 139.90

(0.3-4.1) 43.3

(0.04-0.59) 1.21

0.00

(0.33-0.57) 0.77

7

37.271205  N; 76.71649  W

0.07

1.45

0.242

(22.0-29.7) 22.0

(6.90-7.20) 6.96

(131.7-148.1) 442.17

(22.4-73.6) 15.0

(0.76-1.60) 1.07

1.63

(0.52-1.14) 2.43

8

37.243615  N; 76.710369  W

2.39

4.00

0.066

(20.4-23.4) 25.2

(6.66-7.15) 6.95

(243.5-590.0) 186.70

(7.5-28.0) 0.3

(0.13-2.31) 0.20

(0-3.31) 0.00

(1.04-5.11) 0.39

9

37.301159  N; 76.795262  W

2.07

2.75

0.061

(23.1-26.8) 26.5

(6.88-7.09) 7.11

(181.7-190.7) 74.80

(0.2-0.4) 1.1

(0.04-0.38) 0.88

0.03

(0.33-0.52) 1.06

10

37.241350  N; 76.700654  W

2.75

4.50

0.067

(26.3-26.7) 24.1

(6.90-7.32) 6.88

(68.2-81.4) 262.23

(0.6-1.8) 40.3

(0.17-1.26) 0.17

(0-0.1) 0.00

(0.43-1.89) 0.44

11

37.270421  N; 76.717210  W

0.19

3.95

0.071

(23.0-24.7) 20.7

(6.73-6.98) 6.99

(259.1-264.1) 420.87

(0.2-60.4) 4.2

(0.08-0.34) 8.60

9.67

(0.28-0.61) 1.04

(20.1-21.0)

(6.84-7.29)

(366.6-466.8)

(3.0-5.8)

(0-18.61)

(0.57-1.51)

0.02

0.80

12

37.307190  N 76.779884  W

1.45

2.50

0.063

26.1

6.45

119.40

5.4

(0.0416.93) 0.28

13

37.251110  N; 76.683591  W

7.53

1.80

0.038

(25.3-26.9) 24.3

(6.34-6.52) 6.81

(111.6-129.0) 315.27

(2.0-7.1) 2.6

(0.04-0.46) 1.75

(0-0.05) 0.00

(0.52-1.04) 0.55

14

37.227535  N; 76.722106  W

6.77

4.75

0.061

(22.8-25.7) 24.7

(6.70-6.88) 7.46

(259.1-362.0) 237.35

(1.2-4.0) 9.0

(0.04-2.69) 0.57

0.00

(0.38-0.71) 0.52

15

37.294015  N; 76.784604  W

1.16

2.50

0.059

(24.3-25.0) 26.0

(7.37-7.54) 8.02

(229.8-244.9) 122.30

(1.8-19.6) 5.7

(0.46-0.71) 0.22

0.13

(0.43-0.66) 0.52

(25.8-26.3)

(7.78-8.45)

(116.4-128.9)

(0.7-8.8)

(0.04-0.55)

(0-0.36)

(0.38-0.61)

4

A.L. Gorsky et al. / Anthropocene 28 (2019) 100218

pond sites, based on access and permission (Fig. 1). Using aerial imagery, we determined the surface area and the perimeter of each pond and measured maximum depth at each site (Table 1). The ponds were situated either off state highways (n = 2), on a college campus (n = 2), or in suburban housing complexes (n = 11). All ponds were surrounded predominately by suburban land cover types, including impervious surfaces, houses, lawns and golf courses (Fig. 1C). 2.2. Data collection and instrumentation To address our first research question, we used four floating static chambers to measure diffusive GHG flux from the ponds to the atmosphere (Soumis et al., 2004; Teiter and Mander, 2005; McPhillips and Walter, 2015; Lazar et al., 2014). We constructed the static chambers from an inverted 5-gallon plastic bucket. The total headspace above the water within the chambers measured 9.1 L. We added Styrofoam pool floats to the outside of the chambers for stability in the water. We covered the outside of the chambers with aluminum foil to reduce heat exchange, and a small fan was installed in the headspace to ensure uniform gas mixing. We inserted an airtight rubber stopper into each of two 1.8-cm diameter holes drilled into the top of the chamber. One stopper allowed for the insertion of the wires of the battery-powered fan and the other held the sampling valve port. We sampled gas flux at each pond on three separate dates during the three-month study during the hours of 8:00 to 11:00 am. In addition to gas sampling, we measured water temperature and specific conductance at approximately 10 cm depth from the side of the canoe with a YSI-30 hand-held multimeter (YSI Inc., Yellow Springs, OH). We anchored the canoe at approximately the maximum depth of the pond. The four static chambers were attached to the canoe by a 1.5 m rope and placed on either side of the bow and stern, so they were relatively free floating during the sampling period (Supplementary Fig. 1). After turning on the fans and waiting fifteen minutes for the chambers to reach equilibrium, gas samples were collected sequentially from the chambers with a 10 mL plastic syringe equipped with a two-way luer-lock valve at times 0, 20, 40, and 60 min (Collier et al., 2014; Lazar et al., 2014). In addition, a liter of surface water was collected at each anchor site for supplementary analysis of water chemistry. All samples, including the gas syringes and bottles of surface water were kept on ice in a cooler and brought back to the lab for immediate analysis. We analyzed gas samples on a greenhouse gas analyzer (GC2014; Shimadzu Scientific Instruments) equipped with a flame ionization detector and an electron capture detector for CH4, CO2, and N2O concentrations. For each of the three gases, the

time-course concentrations were plotted and fitted to a linear curve, under the assumption that gas diffusion into or out of the ponds was constant and gas would therefore accumulate in or be removed from the chamber headspace in a linear fashion. These regression equations typically fit the data quite well, accounting for >80% of the gas concentration increase over time (r2 > 0.80). For instances when the regression equation had an r2 value  0.80, the flux for that gas in that chamber was not calculated and was omitted from further analysis (Lazar et al., 2014). Departures from linear gas accumulation could have occurred due to evasion of gas bubbles in the chamber, incomplete mixing of the gases in the chamber, or sampling/analytical error. For N2O only, when flux could not be calculated because initial and final concentrations were similar, we assigned a value of zero for the N2O flux in those chambers (Lazar et al., 2014). To convert trace gas concentrations from volume to mass, we then used the Ideal Gas Law, with a barometric pressure of 1 atm and a headspace volume of 9.1 L, to express GHG flux in units of mg m 2 h 1 (Collier et al., 2014). To address our second research question, fluxes of each gas were then converted to CO2e by multiplying the CH4 and N2O flux by 28 and 265, respectively (IPCC, 2013). We could then compare the relative contributions of each GHG to warming potential. Surface water samples collected each day of sampling were measured for pH at 25  C using a combined glass and reference electrode, with a two-point calibration performed each day of use. Water samples were then filtered through pre-weighed glass fiber filters (pre-combusted Whatman GF/C, nominal 1 mm). After oven drying at 60  C, the filters were re-weighed to determine total suspended solids (TSS). Filtrates were analyzed spectrophotometrically for dissolved inorganic phosphate (DIP), ammonium (NH4) and nitrate (NO3) using established methods (Parsons et al., 1984). Results of chemical analyses are included in pond descriptions (Table 1). 2.3. Statistical analysis All statistical analyses were conducted in R (R v. 3.3.1, The R Core Team, 2017). We first averaged the replicate samples to calculate CH4 and CO2 flux per sampling day at each pond (n = 42). There were three ponds that had one sampling day that did not have acceptable flux rates from any of the four replicate samples. To address our third research question, i.e., to assess the relationships between gas flux and environmental variables, we created linear mixed effects models with pond as a random effect using the “lme4” package (Bates et al., 2014). We modeled separately flux rates for CH4 and CO2 (but not N2O; see results). The environmental variables we included were: pH, TSS, water temperature (temp),

Table 2 Pearson correlation coefficients between the log-transformed CH4 and CO2 and environmental variables. SA is Surface Area; Ratio is Perimeter:SA Ratio.

pH TSS Temp logCond logDIP logNO3 logNH4 logDepth logSA logRatio logCH4 logCO2

pH

TSS

1

0.04 1

Significance: *p < 0.05. Bold: values of r between 0.7 and 1.

Temp 0.14 0.07 1

logCond

logDIP

logNO3

logNH4

logDepth

logSA

logRatio

logCH4

logCO2

0.09 0.17 0.73* 1

0.05 0.11 0.21 0.25 1

0.07 0.09 0.46* 0.34* 0.62* 1

0.12 0.05 0.15 0.17 0.18 0.08 1

0.22 0.06 0.02 0.05 0.37* 0.35* 0.37* 1

0.18 0.11 0.24 0.10 0.45* 0.50* 0.03 0.55* 1

0.13 0.16 0.30 0.21 0.52* 0.58* 0.07 0.40* 0.90* 1

0.12 0.09 0.15 0.06 0.43* 0.11 0.10 0.66* 0.58* 0.43* 1

0.63* 0.25 0.34* 0.43* 0.23 0.12 0.26 0.23 0.29 0.33* 0.25 1

A.L. Gorsky et al. / Anthropocene 28 (2019) 100218

conductance (cond), DIP, NO3, NH4, maximum depth (depth), pond surface area (SA), and pond perimeter to area ratio (P to A Ratio). We evaluated GHG fluxes and environmental variables for normality using the Shapiro-Wilk test and the following variables were log-transformed: CH4, CO2, depth, SA, P to A Ratio, cond, DIP, NO3, and NH4. We then scaled and centered each variable before producing a Pearson correlation matrix (Table 2) using the “Hmisc” package in R (Harrell and Dupont, 2017). To address collinearity, we identified highly correlated predictors (|r|  0.70), and selected the more directly measured variable for all future analyses (Dormann et al., 2013). We then built linear mixed models with pond as a random effect by screening the environmental variables individually on the basis of AICc (Supplementary Table 1), which adjusts for small sample sizes (Burnham et al., 2011). All univariate models that performed better than the null (that is, had lower AICc) were exhaustively combined (when possible) into a new set of candidate models, which were again ranked by fit according to their AICc value. We did not consider interactive terms in any model because we could not justify the behavior biologically. This conservative model selection approach was used to reduce the risk of reporting spurious models that had been overfit to our small dataset (Anderson, 2008). The validity of each model was assessed by plotting the normalized residuals against the

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fitted values and all covariates to visually confirm homoscedasticity (Zuur et al., 2009). Using the “MuMIn” package in R, we calculated the marginal (proportion of variance explained by fixed factors) and conditional (proportion of variance explained by both the fixed and random factors) r2 values to assess model fit (Barton, 2014). We then examined the 95% confidence interval (CI) of each β estimate; the variables for which the interval did not include zero were identified as important predictors. 3. Results 3.1. Pond and water characteristics The ponds varied in size, depth, and shape with the average  SD surface area of 2.71  3.13 ha and an average maximum depth of 2.75  1.07 m (Table 1). The perimeter to surface area ratio ranged from 0.038 to 0.242. Water temperature during the three-month study ranged from 20.1  C to 29.7  C, with a mean of 25.3  2.0  C. Mean water pH for all ponds was 6.95  0.43, without much variation within a pond across the sampling season. Other water chemistry varied by pond but most ponds had undetectable levels of NO3 and low TSS, NH4 and DIP during the study.

Fig. 2. Boxplots of diffusive flux rates of carbon dioxide (A), methane (B) and nitrous oxide (C). The horizontal line inside the box indicates the medians, while, the lower and upper boundaries of the boxes indicate the 25th and 75th percentiles, respectively. Outlier points extend >1.5 times beyond the interquartile range.

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3.2. Greenhouse gas diffusive flux rates For each gas species, the number of chambers that met the acceptable criterion for flux calculation (r2 > 0.80) varied. For CO2 and CH4, 78% and 85%, respectively, of chambers met the acceptable criterion. For N2O, only 32% of chambers met the acceptable criterion and an additional 60% were given a flux value of zero, since the concentration of N2O essentially remained unchanged throughout the sampling period. The mean CO2 flux rate across all fifteen ponds was 62.5 mg m 2 h 1, and across all sampling chambers ranged from 60.3 to 294.0 mg m 2 h 1 (Fig. 2a). The mean CH4 flux rate was 15.1 mg m 2 h 1, and across all sampling chambers ranged from 0.3 to 76.3 mg m 2 h 1 (Fig. 2b). The N2O flux rates averaged 4.2  10-3 mg m 2 h 1 with a range of 2.9  10 2 to 5.0  10 2 mg m 2 h1 (Fig. 2c). The 100-year global warming potential of the ponds was assessed by converting the fluxes of N2O and CH4 into CO2 equivalents (CO2e). Based on the average flux rates, the three gases were first converted into mmol m 2 h 1 and then multiplied by the corresponding strength of warming potential of 28 for CH4 and 265 for N2O (IPCC, 2013). Across all ponds, the mean GHG flux rate expressed in CO2 equivalents was 26.7 mmol m 2 h 1 (Fig. 3); on average, CH4 made up 94% of the diffusive GHG flux in CO2 equivalents. 3.3. Modeling environmental predictors The Pearson correlation matrix showed CO2 flux had a significant positive correlation with water conductance and pond perimeter to area ratio and a negative correlation with pH and water temperature (Table 2). CH4 flux was positively correlated with DIP and perimeter to area ratio and negatively correlated with depth and surface area. We also observed collinearity between predictor variables. Water temperature and specific conductance were significantly negatively correlated (r = 0.73); we excluded conductance from further model development. Likewise, surface area and perimeter to area ratio were significantly negatively correlated (r = 0.90), so we excluded perimeter to area ratio in model development.

In the mixed-effects models relative to the null model, diffusive flux rate of CO2 was best explained by pH, which carried 95% of the AICc weight. pH was negatively correlated with CO2 flux (β = 0.55, Lower 95% CI = 0.83, Upper 95% CI = 0.27). In this top model, pH (fixed effect) explained 32% of the variance, and between-pond variation explained an additional 38%. Diffusive flux of CH4 was best explained by maximum depth, with surface area being of secondary importance (Table 3). Depth and surface area were both negatively correlated with CH4 flux. In the top model, however, depth emerged as the only predictor. The fixed effect of depth explained 39% of the variance, with an additional 26% explained by between-pond variation. Visual inspection of residual plots from all top models did not show any obvious deviations from homoscedasticity or normality. 4. Discussion Greenhouse gas emissions from stormwater ponds in our study were dominated by carbon dioxide and methane flux, with little nitrous oxide flux detected. Our sampling methodology allowed us to assess diffusive flux but not ebullitive flux of greenhouse gases, and thus may yield a conservative estimate of total atmospheric GHG exchange. For example, recent studies found that CH4 ebullition dominated CO2e flux from urban ponds and lakes (Natchimuthu et al., 2014; Grinham et al., 2018; van Bergen et al., 2019). During summer, diffusive CH4 flux from all ponds in our study was always positive and comprised the majority of CO2e flux (Fig. 3). For two ponds the average CO2 flux was negative, indicating that rates of photosynthesis exceeded respiration during the intervals sampled. Our study of stormwater ponds examined flux only during summer and only during the day; we would expect greater variation in gas flux and relative contributions to warming potential both diurnally and throughout the year, as has been documented in prior studies (Lazar et al., 2014; van Bergen et al., 2019). For our study, other environmental characteristics including pond water temperature and perimeter to area ratio were correlated with CO2 flux (Table 2), but in the mixed-effects model, pH was the single variable that best explained the diffusive flux of

Fig. 3. Mean greenhouse gas flux rates expressed in CO2 equivalents (CO2e) for CO2 and CH4. N2O not included owing to less than 1% contribution to CO2e flux.

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Table 3 Linear mixed effects models for predicting CH4 flux, with pond as a random effect. CIs represent the profiled likelihood CIs for the slope (β). Model

k

AICc

DAICc

AICc Wt

R2 Marg

R2 Cond

Depth Depth + SA

4 5

98.32 98.89

0 0.58

0.46 0.34

0.39 0.48

0.65 0.68

SA null

4 3

100.29 104.04

1.97 5.72

0.17 0.03

0.32 0.00

0.67 0.66

CO2. Differences in CO2 flux are not driven by pH; instead, changes in pH likely are a consequence of CO2 dynamics. The dissolution of carbon dioxide into freshwater generates carbonic acid that dissociates into free hydrogen and bicarbonate ions, thereby lowering pH. Inversely, removal of CO2 via photosynthesis decreases hydrogen ion activity and increases pH in the water column. The two ponds with the highest measured pH (Ponds 2 and 15) also had a net negative flux of CO2, i.e., flux from the atmosphere and into the water, to be taken up by algal photosynthesis (Fig. 2a). Prior studies have seen similar patterns of negative and positive CO2 flux at different times of day or seasons (Hamilton et al., 1994; Roulet et al., 1997; Torgersen and Branco, 2008), although most freshwater lakes tend toward supersaturation with CO2 and thus are a net source of carbon to the atmosphere (Cole et al., 1994). We thought perhaps that warmer temperatures would be associated with greater rates of decomposition and CO2 production and flux, but the total range in temperature we measured may have been too small to detect an effect (less than 10 degrees C; Table 1), and temperatures did not increase throughout the period of study. Further, warmer temperatures associated with increased solar radiation could stimulate both algal photosynthesis in the water column and benthic decomposition, leading to either a negative correlation between temperature and CO2 flux (Natchimuthu et al., 2014) or no correlation at all (van Bergen et al., 2019). In contrast, both of these studies of urban ponds reported that CH4 production and flux were positively associated with temperature, indicating increased anaerobic oxidation of organic matter under warmer conditions (Johansson et al., 2004; McPhillips and Walter, 2015). The delivery and subsequent anaerobic decomposition of organic matter derived from allochthonous sources is thought to drive methane production (Grinham et al., 2018). Depending on surrounding land use, organic matter could include deciduous leaf fall (Gasith and Hasler, 1976), woody branches, grass clippings (Monaghan et al., 2016), dissolved organic carbon (del Giorgio et al., 1997), or runoff from other allochthonous sources (van Bergen et al., 2019), all of which could contribute organic C for methanogenesis. For the linear mixed-effects model, depth was negatively correlated with CH4 emissions (Table 3). From a group of small ponds in New England, the inverse relationship between water column CH4 concentration and depth was attributed in part to dilution during high water events (Holgerson, 2015; Kifner et al.,

β

i

Lower 95% CI

0.62 0.45 0.35 0.58 –

Upper 95% CI

0.93 0.78 0.68 0.94

0.30 0.13 0.01 0.21

–

–

2018). Generally, however, water in smaller, more shallow ponds is in closer and more extensive contact with benthic organic matter, allowing methane to diffuse quickly from the benthos to the atmosphere (Holgerson and Raymond, 2016). Greater depth presumably allows some labile organic matter to oxidize aerobically in the water column before falling into anoxic sediment, thereby decreasing organic matter loading to the benthos. Methane produced in anaerobic bottom sediments may diffuse more directly to the atmosphere in shallow ponds, whereas in deeper ponds the opportunity for CH4 oxidation in an aerobic water column is enhanced (Miller and Oremland, 1988; Prairie et al., 2018). Indeed, Stadmark and Leonardson (2005) measured CH4 oxidation in the water column of ponds as shallow as 1.5 m. The depths of our 15 ponds ranged from 1.45 to 4.75 m, so we suspect that methane oxidation in the water column of our deeper ponds accounts in part for the observed inverse relationship between depth and CH4 flux. Methane emissions also were negatively correlated with pond surface area (Table 2). Ponds with small surface area tend to have larger edge to water volume ratios (Holgerson and Raymond, 2016). All of our ponds were relatively shallow (<5 m), so the negative correlation between surface area and CH4 flux may have been related to delivery of organic matter from the surrounding uplands, which would be more concentrated in smaller ponds. With greater pond surface area, the sediment density of organic matter from terrestrial sources would be less, with concomitantly lower rates of organic matter mineralization and methane production. Relative to the fixed effect variables of pH, depth and surface area, between-pond variation accounted for 38% and 26% of the modeled diffusive flux of CO2 and CH4, respectively. The large contribution of the pond random effect to the flux of both gases suggests other, unmeasured environmental features contributed to variation. For example, anaerobic respiration would generate CO2 and CH4 (Torgersen and Branco, 2008), but we did not measure oxygen concentration in either the water column or sediments. Also, Kifner et al. (2018) found that the flux of both gases was positively correlated with water column alkalinity, demonstrating how the degree of anaerobic respiration between ponds could be incorporated into a fixed effect variable. More difficult environmental features to measure would be the groundwater contributions to CO2 flux (Peacock et al., 2019) or the amounts of allochthonous carbon that have been associated with both CO2 flux

Table 4 Average fluxes of methane, carbon dioxide and nitrous oxide from urban water bodies. D = Diffusive flux only; D + E = Diffusive plus ebullitive flux. All flux rates converted to mg m 2 d-1 for comparison. Water Source

Flux

CH4

Urban Lakes, Australia Urban Ponds, Sweden Urban Pond, Sweden Urban Pond, Netherlands Stormwater Basins, USA Stormwater Channel, Canada Urban Stormwater Basins, Canada Stormwater Ponds, USA

D+E D D+E D+E D D D D

129 30 160 120 66 45 314 362

CO2

N2O

Reference

0.012 0.036 0.042 0.101

Grinham et al. (2018) Peacock et al. (2019) Natchimuthu et al. (2014) van Bergen et al. (2019) McPhillips and Walter (2015) D’Acunha and Johnson (2019) Badiou et al. (2019) This study

752 48 950 4100 40 1500

8

A.L. Gorsky et al. / Anthropocene 28 (2019) 100218

(van Bergen et al., 2019) and CH4 flux (Grinham et al., 2018). Future studies of stormwater ponds might include some of these additional variables to improve flux model performance. Similar to recent studies (Lazar et al., 2014; Blaszczak et al., 2018), the flux of N2O was never large, and despite the relative intensity of N2O as a greenhouse gas, the CO2e flux from N2O was very small relative to CO2 and CH4. We anticipated that suburban stormwater ponds might receive dissolved N as runoff from the surrounding watershed and thus be a potential site for elevated denitrification, but surface water nitrate concentrations typically were quite low (Table 1). Either conditions for N2O production were poor (i.e., low substrate availability or elevated stressor levels), anoxic conditions allowed for full denitrification to N2 gas, or other nitrogen transformation processes dominate in these ponds, such as dissimilatory nitrate reduction to ammonium or nitrate assimilation by primary producers. Also, since many of our incubations did not yield a measurable increase in N2O over the hour-long measurement period, we suspect that longer incubation times for N2O flux are needed to overcome the low signal:noise ratio we often encountered. We compared our average GHG flux rates with prior flux studies completed in urban bodies of water, including lakes, ponds, stormwater basins and stormwater channels (Table 4). Our measured rates are slightly high relative to prior studies, many of which were completed in climates where cooler temperatures may slow rates of anaerobic decomposition and GHG release (e.g., van Bergen et al., 2019). Average methane flux ranged from 30 mg m 2 d 1 (Peacock et al., 2019) to 362 mg m 2 d 1 (this study), and carbon dioxide flux ranged from 40 mg m 2 d 1 (Badiou et al., 2019) to 4100 mg m 2 d 1 (D’Acunha and Johnson, 2019). None of four studies found N2O flux to be a large contributor to overall GHG flux (Table 4), supporting the conclusion of Blaszczak et al. (2018) that urban stormwater ponds are unlikely to be a problematic source of N2O to the atmosphere. From the 15 ponds in our study, the average CO2e emissions were 642  425 SD mmol m 2 d 1. In James City County, from a survey of 96 ponds the average size is~1 ha (Montagna et al., 2009). We obtained a rough estimate of flux from the 300 ponds in the county by assuming the average flux is representative of flux across the entire average surface area and throughout the warm season (~100 days). Recognizing these sources of uncertainty, we estimated the CO2e flux from stormwater ponds in James City County is roughly 2  108 moles of CO2e during the warm season, or 2.3  109  1.5  109 SD g C as CO2e. For comparison, dairy cows emit methane at a rate of ~2.4  106 g yr-1 as CO2e (Boadi et al., 2004). Thus, average CO2e emissions from stormwater ponds in this county during the warm season are roughly equivalent to the annual methane emissions from a herd of almost 1000 dairy cows. Because James City County is just one of 96 counties and 36 independent cities in Virginia, we suspect the GHG flux from stormwater ponds across the state is much more substantial. Given that Virginia has only two natural lakes, the addition of thousands of stormwater ponds has fundamentally changed the way that water flows across the landscape and the extent to which small lentic systems now serve as a source of GHG to the atmosphere. 5. Conclusions With respect to our stated research questions, we found that (1) summertime diffusive flux of GHG from stormwater ponds to the atmosphere was largely comprised of carbon dioxide and methane, with little measureable nitrous oxide flux. (2) Methane dominated diffusive CO2e flux from unvegetated stormwater ponds, accounting for 94% of the average total CO2e flux, with carbon dioxide accounting for 6% and nitrous oxide less than 1%. (3) Regarding environmental variables, carbon dioxide flux was negatively associated with surface water pH, and methane flux was negatively

associated with pond depth and surface area. These results are consistent with prior studies showing that small, shallow ponds are sites of organic matter accumulation and subsequent anaerobic decomposition (Holgerson and Raymond, 2016; Peacock et al., 2019) with little opportunity for methane oxidation prior to evasion. From a management standpoint, we suggest that construction of larger, deeper stormwater ponds would allow for CH4 oxidation in the water column and reduce diffusive flux to the atmosphere. Although carbon sequestration via burial could be viewed as an offset to GHG emissions from ponds (Prairie et al., 2018), sediment accumulation in stormwater ponds is rapid, so that maintenance dredging at regular intervals is needed to remove the organic-rich, anaerobic sediments and thereby reduce the source of methane production. In addition, Badiou et al. (2019) suggest that vegetated stormwater retention basins have lower GHG emissions relative to unvegetated basins such as those in our study. We conclude that the hypothesized, potential disservice of GHG emissions from unvegetated stormwater retention ponds (Burgin et al., 2013) can be mitigated by larger, deeper pond designs that reduce sediment methanogenesis. The scope of this conclusion is potentially worldwide, as freshwater “anthropohydrocosms” such as stormwater ponds are created for urban planning projects globally (Saulnier-Talbot and Lavoie, 2018). Declaration of Competing Interest None. Acknowledgments Funding provided by Virginia Environmental Endowment grant 18-04 to RMC. Thanks to James City County Stormwater Personnel D.E. Cook and F. Geissler for logistics support, to Project Maintenance Manager R. Lee for access to Ford’s Colony ponds, to S. Mason for statistical guidance, and to W&M graduate C. May for designing and building the flux chambers. Thanks to two anonymous reviewers who greatly improved the manuscript. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.ancene.2019.100218. References Anderson, D.R., 2008. Model Based Inference in the Life Sciences: a Primer on Evidence, 1st ed. Springer, New York. Badiou, P., Page, B., Ross, L., 2019. A comparison of water quality and greenhouse gas emissions in constructed wetlands and conventional retention basins with and without submerged macrophyte management for storm water regulation. Ecol. Eng. 127, 292–301. Barton, K., 2014. Package ‘MuMIn’. R Package Version 1.0-7. . Bates, D., Maechler, M., Bolker, B., Walker, S., 2014. lme4: linear mixed effects models using Eigen and S4. R package version 1, 0–7. Beaulieu, J.J., Tank, J.L., Hamilton, S.K., et al., 2011. Nitrous oxide emission from denitrification in stream and river networks. Proc. Natl. Acad. Sci. U. S. A. 44, 7527–7533. Bettez, N.D., Groffman, P.M., 2012. Denitrification potential in stormwater control structures and natural riparian zones in an urban landscape. Environ. Sci. Technol. 46, 10909–10917. Blaszczak, J.R., Steele, M.K., Badgley, B.D., Heffernan, J.B., Hobbie, S.E., Morse, J.L., Rivers, E.N., Hall, S.J., Neill, C., Pataki, D.E., Groffman, P.M., Bernhardt, E.S., 2018. Sediment chemistry of urban stormwater ponds and controls on denitrification. Ecosphere 9, e02318. Boadi, D., Benchaar, C., Chiquette, J., Massé, D., 2004. Mitigation strategies to reduce enteric methane emissions from dairy cows: update review. Can. J. Anim. Sci. 84, 319–335. Bridgham, S.D., Megonigal, J.P., Keller, J.K., Bliss, N.B., Trettin, C., 2006. The carbon balance of North American wetlands. Wetlands 26, 889–916. Burgin, A.J., Lazar, J.G., Groffman, P.M., Gold, A.J., Kellogg, D.Q., 2013. Balancing nitrogen retention ecosystem services and greenhouse gas disservices at the landscape scale. Ecol. Eng. 56, 26–35.

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