Mercury methylation in stormwater retention ponds at different stages in the management lifecycle

R. J. Strickman * and C. P. J. Mitchell
University of Toronto Scarborough, Department of Physical and Environmental Sciences, 1285 Military Trail, Toronto, Ontario M4C 1A4, Canada. E-mail:

Received 17th October 2017 , Accepted 17th January 2018

First published on 18th January 2018

Stormwater retention ponds effectively manage erosion, flooding, and pollutant loadings, but are also sources of methylmercury (MeHg), a bioaccumulative neurotoxin which is produced by anaerobic aquatic microorganisms. Stormwater retention ponds have a 10–15 year working life, after which they are dredged and reflooded. In this study, we related MeHg biogeochemistry to the different stages of the management lifecycle. In a new, a dredged, and a mature stormwater retention pond, we measured MeHg and inorganic mercury (IHg) concentrations, and the potential for MeHg formation (Kmeth), during the early summer, peak summer, and fall of 2013. In our study sites, MeHg concentrations appear to be driven by mercury (Hg) methylation, indicated by significant correlations between Kmeth values and MeHg concentrations and the percent of Hg present as MeHg. Relationships between Hg variables and ancillary biogeochemistry suggest that Hg methylation is carried out by sulfate reducing bacteria, but that the process is modulated by the supply of IHg substrate, sediment total and labile organic carbon, and possibly competition with nitrate reducers. Wetlands at different points in the management lifecycle differ in terms of their MeHg biogeochemistry. The organic matter-poor new wetland had low MeHg production (mean Kmeth 0.014 per day) and sediment concentrations (mean 0.015 ng g−1), while the mature wetland both produced and accumulated MeHg about five times more actively. Methylmercury production capacity was only temporarily reduced in the reflooded sediments of the dredged wetland, which experienced rapid increases in Kmeth values from low (mean 0.015 per day) immediately after dredging, to values similar to those in the mature wetland after five months. This pattern may have been related to recolonization of the sediments with mercury methylators or increased microbial activities in response to the addition of fresh organic matter. Additional studies should focus on the applicability of these patterns to stormwater retention ponds in other areas, and particularly investigate the effects of stormwater pond dredging on their microbial ecology and MeHg biogeochemistry.

Environmental significance

Methylmercury, a bioaccumulative neurotoxin, is microbially produced in stormwater retention ponds (SRPs) but the basic mercury biogeochemistry of SRPs, and its relation to their management, is poorly understood. This limits our ability to predict and manage the production of methylmercury in these common, widely distributed structures. We found that methylmercury concentrations in SRPs we found that in SRPs were about half those in similar natural or habitat wetlands, and that their drivers of methylmercury production and concentration were subtly different. Our findings indicate that additions of organic matter to SRPs or aggressive control of nitrate pollution may inadvertently increase methylmercury production, and that dredging is unlikely to produce a long-term reduction in their methylmercury production capacity.


Stormwater retention ponds are an increasingly common water management strategy in urban and suburban landscapes1,2 because they effectively manage downstream erosion, flooding, and pollutant loadings while also providing wildlife habitat and aesthetic benefits.3 Unfortunately, in common with other wetlands, stormwater retention ponds produce methylmercury (MeHg), a bioaccumulative neurotoxin.4 Methylmercury production rates and concentrations in stormwater retention ponds are usually about half of those in wetlands managed for habitat provision or in similar natural wetlands,4–6 a pattern likely related to fundamental differences in the biogeochemistry of these systems,4 although solidly supported hypotheses for different MeHg production capacities remain elusive.

In aquatic environments, MeHg is produced by several taxonomic groups of facultative and obligate anaerobic microorganisms, including members of the sulfate reducers, iron reducers, and methanogens.7,8 Methylmercury is also degraded by both biotic and abiotic demethylation processes.9 Thus, the net MeHg accumulation capacity of an aquatic system is determined by the balance between methylation and demethylation. The biogeochemical drivers of Hg methylation and net MeHg accumulation are complex and include the supply of bioavailable inorganic mercury (IHg) and metabolic substrates for mercury (Hg) methylating microbes, the quantity and quality of sediment and dissolved organic carbon, pH, temperature, and redox status.10–13 The relative importance of these biogeochemical drivers varies among different environments14–16 and detailed biogeochemical investigations of MeHg biogeochemistry in stormwater retention ponds are lacking. This understanding is critical for guiding practical design and management strategies that further lessen MeHg production and accumulation, as well as for reducing the impact of stormwater retention ponds as sources of MeHg to downstream ecosystems.

Stormwater retention ponds differ fundamentally from other types of wetlands because they have a defined, management driven lifecycle, which lasts from five to fifteen years. This management lifecycle alters wetland biogeochemistry and may have important implications for MeHg accumulation. After initial construction, often within or nearby residential developments, stormwater retention ponds passively trap sediment and debris along with metals and other contaminants. Eventually, sediment must be removed to maintain the original design depth and functioning of the pond. In this process, the pond is drained, and sediment is removed and disposed off-site. After dredging, mulch is added to provide organic matter, and the pond is reflooded and allowed to revegetate naturally.17,18 Although moderate fluctuations in water level do not appear to affect MeHg in stormwater retention ponds,4 existing research has suggested that extreme dryout events followed by flooding may stimulate MeHg production and accumulation,19–22 likely due to aerobic regeneration of electron acceptors, such as sulfate, which later stimulates mercury methylating microbes when water levels rise.23,24 This suggests that MeHg production in stormwater retention ponds may be stimulated by dredging events. Alternatively, the removal of sediment and associated microflora, as well as accumulated IHg, may result in decreased MeHg production via limitations on the activity of mercury methylating flora or the supply of IHg substrate. Overall, a better understanding of the relationship between management stage and MeHg biogeochemistry may help reduce MeHg accumulation and impacts on sensitive species, for instance through altered timing of dredging events, but no information currently exists as to how MeHg biogeochemistry may differ between stormwater retention ponds at different stages in the management lifecycle.

The primary purpose of this study is to conduct a preliminary exploration of MeHg biogeochemistry in stormwater retention ponds at different stages in the pond management lifecycle and to generate hypotheses on the identity of the key controls on MeHg production in stormwater retention ponds. To address this topic, we conducted detailed biogeochemical surveys of a new, a mature, and a dredged stormwater retention pond over a growing season. Additionally, we compared littoral and pelagic zones within each wetland to assess spatial variation in MeHg production and concentrations in stormwater retention ponds in relation to the presence of emergent aquatic plants, nutrient and organic carbon supply, and other biogeochemical parameters.


Site descriptions

We sampled in three stormwater retention ponds located in the City of Brampton, near Toronto in southern Ontario, Canada. All of the sites were located within 10 km of one another and received runoff only from suburban housing or construction, with no large roadways. All three stormwater retention pond study sites had a permanent pool of standing water and a single inlet and outlet. The first site, a new stormwater retention pond, had been constructed as part of a housing development approximately six months before sampling began and was hydrologically functional, but unvegetated (43°45′17.0′′N, 79°47′03.5′′W). This site was a large two-bay pond system receiving both construction and suburban runoff. Samples were collected from the smaller bay (area = 1.28 ha), which was nearest the outflow pipe. The second site, a fully mature stormwater retention pond, was fifteen years old at the time of sampling, 0.48 ha in size, and vegetated with typical emergent vegetation that was dominated by Typha latifolia (cattail) (43°46′41.1′′N, 79°43′34.3′′W). Samples at this site were collected near the single outflow point. The third site (43°42′15.7′′N, 79°46′42.4′′W) was also fifteen years old, but had recently been dredged to remove accumulated sediment and improve function. In this process, water was diverted and accumulated sediment and debris were removed, leaving the clay liner intact. Shredded bark mulch was added as an organic substrate and the system was reflooded and allowed to naturally revegetate (David Kenth, personal communication). This pond was also part of a larger multibay system (total area of 0.5 ha), with samples collected from the final bay nearest the outflow (area of 0.053 ha). The other bays in the complex had not been dredged. At the beginning of this study, the new and dredged ponds were unvegetated. Based on observations of similar sites, we anticipated that these sites would develop emergent aquatic vegetation over the course of the project. However, this did not occur, with the result that comparisons of vegetated and unvegetated sites were limited to the mature stormwater retention pond.

Sampling design

We carried out detailed biogeochemical investigations over the 2013 growing season in two spatial zones of the new, dredged, and mature stormwater retention ponds. Within each pond, we collected samples from a shallow-water littoral area at the margin of the pond and an interior, pelagic area. Each pond was sampled in early summer (late June–early July), peak summer (August), and fall (late October–early November). During sampling, multiple sediment cores were collected using 4.7 cm inner diameter transparent polycarbonate sediment core tubes, all within four meters of one another. Cores were stored on ice in coolers, and transported upright to the University of Toronto Scarborough. Sediment cores were either processed immediately, or stored at 4 °C until processing, which occurred within 48 hours. Based on previous findings of the importance of upper layers of submerged sediments to MeHg cycling (see e.g., Goulet et al., 2007),25 analyses focused on surface sediment and porewaters, with triplicate analysis of sediment physicochemical parameters at each subsite and composite analysis of at least triplicate samples for pore water parameters. In some circumstances, cores intended for the analysis of organic matter content, volumetric moisture content, and bulk density were sacrificed to yield additional porewater, with the result that some subsites at some seasons had a sample size of one rather than three (Table 1). For each zone of each pond at each season, the specific depth to which samples were collected was determined by the depth to which plant roots extended (referred to as the analytical depth). If no plants were present, samples were collected from the top two centimeters of the sediment profile. Each zone was assessed for a range of Hg and ancillary biogeochemical variables, including enriched isotope-based assays of Hg methylation potential (Kmeth).26 At vegetated sites, potential plant–Hg relationships were assessed by collecting sediment cores with rooted plants in situ, measuring aboveground and belowground biomass, and estimating the release of organic carbon from roots.
Table 1 Summary of porewater chemistry and sediment physical parameters from the new, dredged, and mature stormwater retention pond. Data are presented separately for seasons (early summer, peak summer, and fall) and zones within the pond (L: littoral, P: pelagic). Where applicable, data is summarized as mean ± standard deviation
Porewater chemistry Sediment physical parameters
Chloride (mg l−1) Nitrate (mg l−1) Sulfate (mg l−1) DOC (mg l−1) SUVA254 pH Organic matter (%) Volumetric moisture content (%) Bulk density (g cm−3)
Sample size 1 1 1 1 1 1 1–3 1–3 1–3
New Early summer L 74.96 1.23 39.89 109.17 0.22 8.09 0.6 ± 0.1 0.29 ± 0.03 1.4 ± 0.2
P 76.10 1.40 62.78 94.75 0.2 8.38 0.9 ± 0.6 0.38 ± 0.07 1.1 ± 0.3
Peak summer L 122.21 8.39 13.97 0.92 1.1 0.4 2.4
P 83.39 1.06 52.28 5.86 1.62 7.82 2.8 0.37 2.3
Fall L 96.54 2.52 80.17 164.67 0.18 8.52 0.8 0.14 0.72
P 101.34 3.22 111.55 203.87 0.12 8.34 1.3 0.18 1.0
Dredged Early summer L 340.64 9.56 33.51 49.45 5.07 8.26 14.9 ± 6.6 0.74 ± 0.48 0.7 ± 0.1
P 36.19 0.95 2.52 61.11 1.14 8.81 3.5 0.79 1.4
Peak summer L 214.91 1.26 56.82 7.38 2.3 ± 0.8 0.45 ± 0.1 2.1 ± 0.5
P 269.80 0.96 162.47 8.20 11.9 0.1 ± 0.06 0.7
Fall L 299.92 11.10 139.23 2.4 0.18 1.0
P 194.94 0.96 21.18 52.72 0.58 8.60
Mature Early summer L 67.68 1.08 4.23 180.5 0.15 8.75 6.1 ± 1.8 0.72 ± 0.1 0.6 ± 0.2
P 85.85 1.13 3.17 224 0.36 8.33 2.2 ± 0.5 0.52 ± 0.09 1.2 ± 0.1
Peak summer L 63.14 0.99 45.94 168.77 0.31 8.31 7.3 ± 1.8 0.82 ± 0.03 0.5 ± 0.2
P 31.45 0.97 34.38 23.66 0.71 8.47 5.3 ± 1.3 0.75 ± 0.09 0.6 ± 0.2
Fall L 59.14 1.10 9.74 132.47 0.26 8.09 10.4 ± 4.5 0.83 ± 0.07 0.3 ± 0.3
P 31.20 1.15 1.78 188.27 0.24 8.34 5.1 ± 0.6 0.74 ± 0.03 0.5 ± 0.0

Sample handling and Kmeth assays

Separate sediment cores were collected for the assessment of physical variables (organic matter content, volumetric moisture content/porosity, and bulk density), and for 0.5 N HCl extractable iron content, using standard methods (see Laboratory Analyses section). Sediment cores for physical variables were processed on the bench, while cores for iron analyses were extruded, sectioned to the appropriate analytical depth and homogenized in an anaerobic chamber under a N2 atmosphere. A 30–40 g sample of this homogenized sediment was collected in the glovebox, sealed in a small zipper closure bag, and kept frozen at −20 °C until analysis.

To obtain sufficient porewater for analyses, cores from each subsite (at least n = 3) were pooled to yield one sample per position. These cores were transferred to an anaerobic chamber, where overlying water was discarded. Cores were extruded to the appropriate analytical depth for that subsite, and homogenized. Porewater was vacuum-extracted from this sediment through acid-washed 0.45 μm filters. Porewater samples were collected for dissolved organic carbon measurement and major anion analysis, and stored in the dark at 4 °C until analysis.

Mercury variables (total mercury, THg; MeHg; percent of THg present as MeHg, % MeHg; and potential methylation rate constant, Kmeth) and sediment nutrient analyses (sediment nitrate and ammonium) were obtained from the same sediment core (mercury/nutrient core). In vegetated subsites, a living, rooted cattail plant was maintained in the core for the duration of the Kmeth incubation. To assess the potential rate at which IHg was transformed to MeHg, isotopically enriched (94.3%) inorganic 200Hg (as 200HgCl+ equilibrated with site pore water for one hour) was added to cores using a gastight syringe to pierce through one cm-spaced septa, and the production of isotopically enriched Me200Hg was assessed after a set time period, as per Hintelmann & Evans (1997).27 It should be emphasized that Kmeth is based on the methylation of newly added isotopic Hg(II), which is more bioavailable to methylating microbes than ambient Hg; thus, methylation rate constants are more representative of the potential, rather than the actual ability of the microbial community to methylate Hg.26 Injection amounts were calculated to only introduce an “excess” 200Hg concentration in the sediment that approximately matched the estimated background ambient THg levels in these sites.4,6 To allow the plants in vegetated cores to maintain metabolic activity, the aboveground parts of rooted plants were illuminated under fluorescent grow lights during incubations. The sediment cores themselves were wrapped in black plastic to shield them from light, and incubated at 22 °C for 19 hours. At the conclusion of the incubations, cores were extruded and sediment samples collected from the surface of the core to the analytical depth of that subsite. In vegetated cores, plant root biomass was gently separated from sediment, leaving the plant intact. Subsequent analyses using these plant parts are described below in Laboratory Analyses. All sediments were homogenized thoroughly, and 30–40 g samples collected for the analysis of sediment nutrients (nitrate and ammonium) and Hg variables. These samples were stored at −20 °C until analysis.

Laboratory analyses

THg and MeHg quantification. All Hg analyses were conducted on freeze-dried samples. Methylmercury concentrations (including simultaneous measurement of ambient MeHg concentrations and “excess” Me200Hg concentrations) were determined using isotope dilution-gas chromatography-inductively coupled plasma mass spectrometry (ID-GC-ICPMS) according to Hintelmann and Evans (1997)28 and Hintelmann and Ogrinc (2002)29 and as further explained in ESI Text S1. To measure THg concentrations, 0.2–0.3 g of homogenized sediment was nitric acid digested until vapors became colorless. Total mercury digestates were diluted with deionized water and oxidized with 0.5 ml BrCl, followed by automated reduction via SnCl2 reaction, capture on dual gold traps, and thermal desorption with an Tekran 2600 Hg analyzer that was hyphenated to the ICP-MS for Hg (including excess 200Hg) quantification. Inorganic Hg in each sample was estimated as THg concentration minus MeHg concentration. The quality of Hg determinations was carefully monitored and these data are available in Table S1. Several MeHg concentrations fell below the detection limit; in these cases, half of the detection limit value was used in statistical analyses.
Iron. Sediment concentrations of 0.5 N HCl-extractable Fe(II) were assessed by complexation with ferrozine-HEPES and measurement of UV absorbance at 562 nm, using a UV spectrophotometer. Reduction of all iron present with hydroxylamine allowed the subsequent quantification of total Fe, with the difference between total Fe and Fe(II) attributable to Fe(III) concentrations.30
Sediment nutrients. Sediments for nitrate and ammonium analysis were extracted in 30 ml of 2 M KCl, agitated at 130 rpm for thirty minutes, and filtered through Whatman no. 2 filter paper. Following extraction, sediment nitrate-N and ammonium-N were assessed colorimetrically using a Lachat QuickChem 8500 flow injection autoanalyzer.
Physical parameters. Moisture content, bulk density, and organic matter were determined from one to three cores at both zones in each pond per season. Sediment water content was assessed by drying cores for 24 h at 105 °C, and bulk density calculated as the quotient of dry sediment mass and sediment volume. Moisture content was calculated volumetrically, which also provided a measurement of porosity in these saturated sediments. The organic matter (OM) content of the core, expressed as a percentage of the dry sediment mass, was then determined using the loss-on-ignition method by ashing at 550 °C for four hours.
Porewater analyses. Porewater was assessed for pH, concentrations of major anions (sulfate, nitrate and chloride), dissolved organic carbon (DOC), and specific UV absorbance (SUVA254). Porewater pH was measured on the same day as porewater collection using a daily calibrated pH electrode. Major anions (sulfate, nitrate and chloride) were assessed on a Perkin Elmer ion chromatograph with autosupressor (Alltech ERIS 1000HP). Dissolved organic carbon in porewaters was measured with a Shimadzu TOC-VCSH analyzer. Aliquots of samples collected for DOC analysis were transferred to a clear 1 cm diameter quartz cell, and the UV absorbance (in inverse meters, m−1) was determined at 254 nm on an Agilent 8453 UV-vis spectrophotometer at the University of Toronto TRACES facility. To obtain the SUVA254 value for each sample, which conveys an estimate of DOC aromaticity,31 this absorbance was corrected for the DOC content of the porewaters and reported in l mg−1 m−1.
Plant analyses. Following mercury incubations, plants were carefully removed from the cores and washed copiously with tap water to remove sediment. In order to estimate the release of organic carbon into the rhizosphere, roots were submerged in 100–1000 ml of DI water, and the plant maintained under fluorescent growlights for 19 hours.32 This water was filtered through a 0.2 μm syringe filter and analyzed for DOC concentrations and SUVA254 using the same methods applied to porewaters. After these incubations, the plant was separated into above and belowground biomass and oven-dried at 104 °C for 24 h to collect dry biomass weights. Using the concentration of DOC in the incubation water, the total mass of carbon exuded by each plant was calculated and normalized to the plant size.32
Statistical analyses. Variables were assessed for normality using qq plots and frequency distribution histograms, and transformed to approximate a normal distribution as necessary. Relationships between ancillary biogeochemical variables and Hg variables were assessed using a Pearson correlation matrix at α ≤ 0.05. Homogeneity of variance was tested using Levene's test. Differences among seasons and ponds were assessed using a two-way or one-way ANOVA with White's adjustment for heterogeneity of variance as necessary.33 Statistical analysis was conducted with R version

Results and discussion

Mercury in stormwater retention ponds

Inorganic Hg levels in these stormwater retention ponds ranged from 2–45.8 ng g−1. Although this study did not investigate the loadings of incoming IHg, the concentrations of IHg in these ponds were approximately one third to one half of the concentrations found in other stormwater retention ponds around the Greater Toronto Area.4 In comparison to other stormwater wetlands or retention ponds for which both MeHg and IHg data are available, our study sites also had relatively low burdens of IHg. These sites, which are distributed throughout North America, range between mean IHg concentrations of 20 to c. 300 ng g−1 (Table S2). The watersheds surrounding these ponds were small and did not contain high-traffic roadways, suggesting that relatively low runoff inputs of IHg explain this pattern.

Overall, IHg concentrations were highest in the mature, undredged pond (26.86 ± 12.36 ng g−1), probably reflecting the accumulation of ongoing inputs35,36 and sorption to organic matter, which comprised 5.7% of the sediment mass (Table 1). In the new pond, much lower organic matter content (mean of 1.1%) and the short depositional history of the site likely explain the low IHg levels (6.37 ± 2.49 ng g−1, Fig. 1). In the recently dredged pond, marked spatiotemporal variability in IHg concentrations among sampling subsites and across seasons (Fig. 1 and Table S4) resulted in a mean IHg burden intermediate between the new and the mature pond (16.35 ± 13.28 ng g−1). Although IHg in the mature and dredged ponds appeared to vary across seasons (Fig. 1), these differences were not significant (F2,45 = 2.54, p = 0.09), and probably represent fine-scale spatial variability in IHg between sampling subsites rather than a meaningful change in IHg deposition between seasons.

image file: c7em00486a-f1.tif
Fig. 1 Distribution of mercury variables over early summer, peak summer, and fall in a new, a dredged, and a mature pond. Figure displays (a) inorganic mercury, (b) methylmercury, (c) percentage of mercury present as methylmercury (% MeHg) and (d) potential methylation rate constants (Kmeth).

Methylmercury levels were similar to those found in other stormwater retention ponds around the Greater Toronto Area4 and ranged from <dl–2.48 ng g−1, with an overall mean of 0.55 ng g−1 (Tables S2 and S4). The MeHg levels of the study sites were also comparable to sediment MeHg concentrations in other stormwater retention ponds in North America, which ranged between 0.5 and 1.5 ng g−1 (Table S2). These data are available for relatively few stormwater retention ponds, however, which are located in the Southeastern and Southwestern USA.37 The overall mean of our study sites was markedly less than the mean MeHg burdens of sediments in similar nearby created habitat wetlands (1.5–8 ng g−1)6 and natural wetlands (1.2–3 ng g−1; Table S3).5,6 While no significant differences in MeHg concentrations were observed in any pond across the growing season (F2,45 = 1.76, p = 0.18), MeHg concentrations differed among ponds (F2,45 = 19.59, p < 0.001, Fig. 1). The newly constructed pond had the lowest levels of MeHg (0.15 ± 0.11 ng g−1), the mature pond had the highest (0.79 ± 0.41 ng g−1) and values in the dredged pond were intermediate (0.68 ± 0.83 ng g−1). This pattern, though based on a small sample size, contrasts intriguingly with previous work comparing MeHg dynamics across ponds, reservoirs and wetlands of different ages. These reports, which derive from environments as diverse as beaver ponds,38 constructed habitat wetlands,6 and created reservoirs36 consistently found that MeHg accumulation was enhanced in younger wetlands and subsequently declined.

This disparity between previous work and our findings in stormwater retention ponds may result from differences in the biogeochemistry and microbiology of the pond sediment. Methylmercury production, as assessed through isotope incubations, was the most important correlate of MeHg concentrations (p < 0.01, r = 0.80, n = 46; Fig. 2a) and % MeHg (p < 0.01, r = 0.76, n = 50) in sediment, suggesting that the development and activity of the Hg methylating microflora helped to modulate MeHg levels in stormwater retention ponds at different stages in the management cycle. Although demethylation was not measured, the strong correlation between MeHg concentrations and Kmeth values suggests that methylation, rather than demethylation, was the stronger control on MeHg in these systems. Low Kmeth values in the new pond (0.001–0.034 per day) suggest that the Hg methylating microflora was less active or less abundant than that of the mature pond, which had a greater potential for producing MeHg (Kmeth = 0.027–0.090 per day; Fig. 1). Newly created ponds are often less active in terms of other microbially mediated biogeochemical processes, such as nitrogen cycling,39 suggesting that the sediments in the new stormwater retention pond had not yet been abundantly populated by typical wetland Hg methylators or that their activity was depressed by suboptimal conditions. It is likely that the higher mercury methylation capacity of the mature pond was related to its more developed microbial community, which had probably become relatively similar to that of a natural wetland, a pattern of ecological development that has been observed in other constructed wetlands for different microbial guilds.40 In the dredged pond, Kmeth values were very low during the early summer, but subsequently rebounded over the growing season to levels more similar to those found in the mature pond (Fig. 1), explaining the concurrent increases in MeHg concentrations. Over the same time period, MeHg production in the new pond remained low, suggesting that the rebound in MeHg production in the dredged pond was the result of increased activity or abundance of Hg methylating microbiota, rather than a response to broader seasonal changes in temperature or precipitation. We hypothesize that this rapid recovery may have been due to recolonization of the dredged pond from pockets of undredged sediment, or perhaps to degradation of the organic matter added during flood-up. Previous work has found that wetland microflora, including the mercury methylating subset, are resilient to other types of disturbance,41,42 supporting this interpretation.

image file: c7em00486a-f2.tif
Fig. 2 Relationships between selected mercury and biogeochemical variables. Results of Pearson correlations, obtained from transformed data, are displayed in the upper left corner. MeHg was square root transformed. Porewater nitrate was reciprocal cube transformed in analyses, although untransformed data is displayed in this graph for clarity.

In addition to this reduced activity or abundance of mercury methylators, the supply of IHg for methylation may have contributed to the relatively low MeHg concentrations in the new pond. Both MeHg and IHg concentrations were low in the new pond relative to the mature pond (Fig. 1) and we identified a significant overall correlation between MeHg and IHg concentrations (p < 0.02, r = 0.68; n = 50). These findings, which concur with earlier work in other nearby stormwater retention ponds,4 suggest that the availability of IHg substrate is important for regulating the production of MeHg in these systems. Although IHg is a necessary prerequisite for the formation of MeHg, the controls on which fraction of IHg is truly bioavailable to methylators are complex and incompletely understood. In general, simple measurements of the total IHg concentration of an environment usually do not capture this bioavailable fraction.13 The fact that our data identifies a relationship between IHg and MeHg concentrations implies that the fraction of IHg which was bioavailable for methylation was unusually consistent between and within these stormwater retention ponds, and that IHg concentration was an important control on MeHg concentrations in these systems.

Spatial variability in Hg biogeochemistry within stormwater retention ponds

One of the aims of this project was to observe the relationship between MeHg biogeochemistry and the developing plant community in stormwater retention ponds over the course of a growing season. In diverse natural and managed wetlands,43–45 the presence and size of plants has been found to increase MeHg production and accumulation by up to 699%.46 This effect is likely related to stimulation of Hg methylators through the release of labile organic carbon44,46,47 or evapotranspiration-driven concentration of MeHg in the rhizosphere,48 and suggests a potential tradeoff between the many benefits provided by aquatic vegetation in artificial wetlands49 and their potential to stimulate MeHg production.46 In our study, although sample numbers were limited to only the mature pond, no significant correlations were found between plant biomass or estimated exudation of organic carbon (Table S5), and any Hg variable in the mature pond (p = 0.17–0.99, n = 7). Additionally, no differences in MeHg concentrations, Kmeth, or % MeHg were found between the vegetated littoral and unvegetated pelagic zones in the mature wetland (F1,16 = 1.15–2.05, p = 0.17–0.30). These results were based on Kmeth assays conducted with living plants in situ, a unique approach which closely linked MeHg production with the presence of aquatic vegetation. This overall lack of influence from plants on MeHg production contrasts with the established consensus on the stimulatory effect of plants on MeHg production,47 but is similar to recent work in other stormwater retention ponds, that found no consistent relationship between the presence of plants and MeHg production or concentrations.4 While the reasons for this disparity are not clear, the bulk of evidence indicates that plant-mediated stimulation of Hg methylation does not occur in stormwater retention ponds to the same degree observed in other systems.

Marked spatial variability in MeHg production and concentrations, unrelated to the presence of vegetation, was observed in the dredged pond. While mean MeHg concentrations in the dredged pond were similar to those in the mature pond (Table S4), a detailed examination of data from individual sampling subsites reveals that the majority of MeHg concentrations were overall very low and similar to those found in the new pond. Superimposed on this pattern were two “hot spots” of ten- to twenty-fold greater MeHg production, leading to disproportionately high MeHg concentrations and % MeHg values (Fig. 2). While the reasons for the increased MeHg production at these two subsites cannot be conclusively determined, it is notable that these two sampling subsites had significantly elevated porewater sulfate concentrations relative to the other samples from the same pond (Fig. 3d). As observed in other systems, increased concentrations of porewater sulfate may have stimulated the activity of sulfate reducing mercury methylators, resulting in higher rates of mercury methylation and greater net MeHg accumulation.50–52 The source of this high porewater sulfate may be related to the dredging process, which exposed previously deeper buried sediments to oxygen, possibly regenerating sulfate from reduced sulfur species.20,21 These initial deposits of reduced sulfur were likely distributed unevenly over the basin of the pond.

image file: c7em00486a-f3.tif
Fig. 3 Spatial distribution of (a) methylmercury, (b) percent of mercury present as methylmercury (percent MeHg or % MeHg), (c) potential methylation rate constants (Kmeth) and (d) porewater sulfate concentrations between the littoral and pelagic zones over the early summer (ES), peak summer (PS), and fall (Fall) of a new, a dredged, and a mature pond. Methylmercury, % MeHg, and Kmeth values are summarized with Tukey's boxplots; porewater sulfate measurements were measured only once per zone at each season, and are indicated with dots.

Biogeochemical controls on MeHg in stormwater retention ponds

There are two lines of evidence suggesting that sulfate reducing bacteria were involved in Hg methylation in these stormwater retention ponds, in concurrence with existing work in other stormwater retention ponds,37 but existed in conditions that are suboptimal for mercury methylation. First, the concurrence of elevated MeHg concentrations, Kmeth, % MeHg and porewater sulfate in the dredged pond supports the possible involvement of sulfate reducers in the production of MeHg (Fig. 3). Second, a significant negative relationship was identified between porewater nitrate and Kmeth (p = 0.01, r = −0.60, n = 18; Fig. 2b). Nitrate has been previously linked with reduced MeHg production in a lake hypolimnion53 and a subsurface streambed bioreactor, wherein nitrate reducing bacteria diverted carbon and energy flows from sulfate reducers, thus inhibiting Hg methylation.54 Nitrate additions have also been used as a successful remediation strategy to reduce MeHg production in mercury-contaminated Onondaga Lake in New York State.55 Porewater nitrate concentrations in the stormwater retention ponds were indeed elevated (2.7 ± 3.2 mg l−1) compared to other natural wetlands in Southern Ontario (0.0081 mg l−1).56 From the available evidence, there is preliminary support for the hypothesis that Hg methylation in our study sites was carried out by SRB that were limited by competition with nitrate reducers, rather than the availability of sulfate. This would explain the lack of significant correlations between porewater sulfate and Kmeth, % MeHg, or MeHg values (p = 0.685–0.910, n = 17). The sulfate concentrations found at the two “hot spots” in the dredged pond were high in relation to the background sulfate usually found in created wetlands in Ontario (150 mg l−1versus 1–10 mg l−1),57 which may have allowed the sulfate reducing bacteria to overcome the competitive inhibition they faced from nitrate reducers.58 Elevated nitrate in stormwater retention ponds,59,60 and its concurrent competitive depression of sulfate reducers, may thus help explain the relatively low MeHg concentrations in stormwater retention ponds in comparison to other artificial wetlands.4,6

Organic matter accumulation in these three stormwater retention ponds appears important in helping to modulate MeHg biogeochemistry in two ways. Firstly, sediment organic matter likely scavenged IHg and held it sorbed to sulfur-containing thiol functional groups on soil organic matter,61 as suggested by the significant, positive relationship between organic matter and IHg (p < 0.01, r = 0.74, n = 33; Fig. 2c). Given the importance of IHg substrate in governing MeHg production in these ponds, and other stormwater retention ponds in Southern Ontario,4 the low capacity of the new stormwater retention pond to have trapped incoming IHg would thus limit MeHg production. Secondly, higher organic matter content in the mature pond and, to a lesser extent, the dredged pond sediments, compared to the new pond, may have directly stimulated MeHg production62 by supporting a more abundant and more active microbial community.63 Supporting this interpretation, we identified a significant, positive, although relatively weak relationship between sediment organic matter and MeHg (p = 0.031, r = 0.38, n = 33; Fig. 2d).

Patterns of organic matter content may also help explain the unusually low MeHg production and accumulation in young stormwater retention ponds in comparison to other types of new created wetlands. In Ontario, created habitat wetlands are most frequently created by flooding upland soils, resulting in a pulsed availability of organic carbon as submerged terrestrial vegetation and carbon stores decay.36,64,65 In contrast, stormwater retention ponds are usually created by excavation, and are isolated from surrounding soils with a clay liner.17 Reflecting this, we have previously observed increasing organic matter from young to old stormwater retention ponds4 as a result of the gradual accumulation of organic carbon from decaying plant matter and potentially, allochthonous inputs. In the current study, we observe that MeHg concentrations climb as stormwater retention ponds age, most likely reflecting the initial paucity and then increasing availability of Hg substrate over time as it sorbs to organic matter ligands, which may also fuel the metabolism of the wetland microbiota.

The quality of sediment organic carbon, estimated using sediment ammonium as a proxy measure,66 may also regulate MeHg production and accumulation. Sediment ammonium was significantly, positively and in most cases, strongly correlated with IHg (p < 0.01, r = 0.71, n = 36), MeHg (p < 0.01, r = 0.66, n = 36), Kmeth (p = 0.010, r = 0.44, n = 36) and % MeHg (p = 0.047, r = 0.33, n = 36) (Fig. 4). While ammonium has not been commonly identified as a potential explanatory factor for MeHg production and accumulation, positive relationships between ammonium and trophic accumulation of Hg in lake fish67 and concurrent increases in sediment ammonification and MeHg concentrations in rice paddy soil66 have been observed. Liu et al. (2016)66 ascribed the relationship between sediment ammonification and MeHg to stimulation of microbial metabolism as the microflora degraded newly added labile carbon, and concurrently converted the labile nitrogen to ammonium.68 We suggest that a similar process was occurring in the stormwater retention ponds as high-quality organic detritus was broken down, resulting in the production of ammonium, which readily sorbs to cation exchange sites in sediment.69 Supporting this interpretation, sediment ammonium and organic matter content were strongly and positively related to one another (p < 0.01, r = 0.71, n = 33).

image file: c7em00486a-f4.tif
Fig. 4 Relationships between sediment ammonium concentrations and (a) methylmercury, (b) inorganic mercury, (c) percent of mercury present as methylmercury (% MeHg) and (d) potential methylation rate constants (Kmeth). Results of Pearson correlations, obtained from transformed data, are displayed in the upper left corner. Sediment ammonium, and inorganic mercury were natural log transformed; % MeHg was log transformed, and Kmeth and MeHg variables were square root transformed.


The ability of these stormwater retention ponds to both produce and accumulate MeHg was relatively low, representing about half of the values in similar small, natural wetlands or wetlands managed for other purposes.4–6 This relatively low production of MeHg appears to be a general feature of stormwater wetlands, with similar patterns of low MeHg in stormwater ponds in the Southeastern and Southwestern USA.37 Our findings represent the only study to directly examine the impact of management activities on the MeHg biogeochemistry of selected stormwater ponds. Our intensive investigations, while directed at a small number of ponds, suggest that management activities do impact MeHg cycling. Our work also generates several hypotheses for the mechanisms behind these impacts. Future work should assess the applicability of our findings to stormwater ponds in other geographic areas and climates.

Stormwater retention ponds at different stages in the management lifecycle differed in their ability to produce and accumulate MeHg, and in the amount of IHg accumulated in their sediments. The newly constructed stormwater retention pond, while hydrologically functional, had extremely low concentrations of IHg and produced very little MeHg, while the mature pond sediment had approximately five times higher MeHg production and concentrations. This pattern differs from previously studied natural wetlands and reservoirs, where MeHg production is high when the wetland is new, and subsequently declines. This inconsistency was likely due to both low availability of IHg substrate in new stormwater retention ponds and to the reduced activity of Hg methylating microbiota as a result of limitations in sediment organic matter quantity and quality. Based on these findings, we caution that the amendment of stormwater retention ponds with organic matter, a management intervention which can improve several wetland functions such as denitrification and cation binding,70,71 may also inadvertently stimulate MeHg production. We also speculate that the relatively low MeHg production and concentrations in stormwater retention ponds may be related to competitive inhibition of sulfate-reducing Hg methylators by nitrate reducers. Nitrate is a common contaminant in stormwater retention ponds in Ontario,56 and in other climates,72 suggesting that this effect may be widespread and help to moderate the MeHg production capacity of stormwater retention ponds. In concurrence with the findings of Strickman and Mitchell (2017)4 we observed no relationship between plants and MeHg production and accumulation, and thus suggest that plants have little to no influence on MeHg production in these systems.

While dredging, a common management tool in mature stormwater retention ponds, appeared to be related to temporarily reduced MeHg production and accumulation, the effect was shortlived, and was accompanied by increased spatial variability. Methylmercury production, concentrations, and % MeHg in the dredged pond were very low immediately after the completion of the dredging process, but subsequently rebounded over the growing season to levels comparable to the mature, undredged pond. Although stormwater retention ponds in this geographic area appear to be sites of only limited MeHg production and accumulation, these observations may be of concern to managers of constructed wetlands in areas with known Hg or MeHg contamination problems.20,73,74

Conflicts of interest

There are no conflicts to declare.


The authors would like to thank Scott Patrick and Haiyong (Planck) Huang for invaluable support in the field and laboratory. Kristine Ciardullo of the City of Brampton helped secure access to study sites; David Kenth of the City of Brampton was of great assistance in locating appropriate stormwater retention ponds and providing background on the realities of practical stormwater retention pond management. This project was funded by a Natural Sciences and Engineering Research Council Discovery Grant (Canada).


  1. S. V. Smith, W. H. Renwick, J. D. Bartley and R. W. Buddemeier, Sci. Total Environ., 2002, 299, 21–36 CrossRef CAS PubMed.
  2. M. Scholz and B. Lee, Int. J. Environ. Stud., 2005, 62, 421–447 CrossRef.
  3. P. Malaviya and A. Singh, Crit. Rev. Environ. Sci. Technol., 2012, 42, 2153–2214 CrossRef CAS.
  4. R. J. Strickman and C. P. J. Mitchell, Environ. Pollut., 2017, 221, 326–334 CrossRef CAS PubMed.
  5. T. He, J. Lu, F. Yang and X. Feng, Sci. Total Environ., 2007, 386, 53–64 CrossRef CAS PubMed.
  6. K. A. Sinclair, Q. Xie and C. P. J. Mitchell, Environ. Pollut., 2012, 171, 207–215 CrossRef CAS PubMed.
  7. C. C. Gilmour, M. Podar, A. L. Bullock, A. M. Graham, S. D. Brown, A. C. Somenahally, A. Johs, R. A. Hurt, K. L. Bailey and D. A. Elias, Environ. Sci. Technol., 2013, 47, 11810–11820 CrossRef CAS PubMed.
  8. M. Podar, C. C. Gilmour, C. C. Brandt, A. Soren, S. D. Brown, B. R. Crable, A. V. Palumbo, A. C. Somenahally and D. A. Elias, Sci. Adv., 2015, 1, e1500675 Search PubMed.
  9. C.-C. Lin, N. Yee and T. Barkay, Environ. Chem. Toxicol. Mercury, 2012, 155–191 CAS.
  10. M. Ravichandran, Chemosphere, 2004, 55, 319–331 CrossRef CAS PubMed.
  11. J. Munthe, R. A. Drew Bodaly, B. A. Branfireun, C. T. Driscoll, C. C. Gilmour, R. Harris, M. Horvat, M. Lucotte and O. Malm, AMBIO: J. Hum. Environ., 2007, 36, 33–44 CrossRef CAS.
  12. I. Tjerngren, T. Karlsson, E. Björn and U. Skyllberg, Biogeochemistry, 2011, 108, 335–350 CrossRef.
  13. H. Hsu-Kim, K. H. Kucharzyk, T. Zhang and M. A. Deshusses, Environ. Sci. Technol., 2013, 2441–2456 CrossRef CAS PubMed.
  14. C. P. J. Mitchell and C. C. Gilmour, J. Geophys. Res.: Biogeosci., 2008, 113, G00C04 Search PubMed.
  15. I. Lehnherr, V. L. St. Louis and J. L. Kirk, Environ. Sci. Technol., 2012, 46, 10523–10531 CrossRef CAS PubMed.
  16. M. E. Little, N. M. Burgess, H. G. Broders and L. M. Campbell, Environ. Sci. Technol., 2015, 49, 2059–2065 CrossRef CAS PubMed.
  17. Ministry of the Environment, Stormwater management planning and design manual, Toronto, Ontario, 2003 Search PubMed.
  18. J. Drake and Y. Guo, Can. Water Resour. J., 2008, 33, 351–368 CrossRef.
  19. M. S. Gustin, P. V. Chavan, K. E. Dennett, S. Donaldson, E. Marchand and G. Fernanadez, Appl. Geochem., 2006, 21, 2023–2035 CrossRef CAS.
  20. S. Feng, Z. Ai, S. Zheng, B. Gu and Y. Li, Water, Air, Soil Pollut., 2014, 225, 1929 CrossRef.
  21. C. J. Oswald and S. K. Carey, Environ. Pollut., 2016, 213, 628–637 CrossRef CAS PubMed.
  22. C. S. Eckley, T. P. Luxton, J. Goetz and J. McKernan, Environ. Pollut., 2017, 222, 32–41 CrossRef CAS PubMed.
  23. J. K. Coleman Wasik, C. P. J. Mitchell, D. R. Engstrom, E. B. Swain, B. A. Monson, S. J. Balogh, J. D. Jeremiason, B. A. Branfireun, S. L. Eggert, R. K. Kolka and J. E. Almendinger, Environ. Sci. Technol., 2012, 46, 6663–6671 CrossRef CAS PubMed.
  24. J. K. Coleman Wasik, D. R. Engstrom, C. P. J. Mitchell, E. B. Swain, B. A. Monson, S. J. Balogh, J. D. Jeremiason, B. A. Branfireun, R. K. Kolka and J. E. Almendinger, J. Geophys. Res.: Biogeosci., 2015, 120, 1697–1715 CrossRef CAS.
  25. R. R. Goulet, J. Holmes, B. Page, L. Poissant, S. D. Siciliano, D. R. S. Lean, F. Wang, M. Amyot and A. Tessier, Geochim. Cosmochim. Acta, 2007, 71, 3393–3406 CrossRef CAS.
  26. H. Hintelmann, K. Keppel-Jones and R. D. Evans, Environ. Toxicol. Chem., 2000, 19, 2204–2211 CrossRef CAS.
  27. H. Hintelmann and R. D. Evans, Fresenius. J. Anal. Chem., 1997, 358, 378–385 CrossRef CAS.
  28. H. Hintelmann, R. Falter, G. Ilgen and R. D. Evans, Fresenius. J. Anal. Chem., 1997, 358, 363–370 CrossRef CAS.
  29. H. Hintelmann and N. Ogrinc, ACS Symp. Ser., 2002, 835, 321–338 CrossRef.
  30. E. Viollier, P. W. Inglett, K. Hunter, A. N. Roychoudhury and P. Van Cappellen, Appl. Geochem., 2000, 15, 785–790 CrossRef CAS.
  31. J. L. Weishaar, G. R. Aiken, B. A. Bergamaschi, M. S. Fram, R. Fujii and K. Mopper, Environ. Sci. Technol., 2003, 37, 4702–4708 CrossRef CAS PubMed.
  32. R. K. Miya and M. K. Firestone, J. Environ. Qual., 2001, 30, 1911–1918 CrossRef CAS PubMed.
  33. J. G. MacKinnon and H. White, J. Econom., 1985, 29, 305–325 CrossRef.
  34. R Core Team, R Found. Stat. Comput., Vienna Austria,
  35. J. Liebens, Environ. Geol., 2001, 41, 341–351 CrossRef CAS.
  36. V. L. St. Louis, J. W. M. Rudd, C. A. Kelly, R. A. Drew Bodaly, M. J. Paterson, K. G. Beaty, R. H. Hesslein, A. Heyes and A. R. Majewski, Environ. Sci. Technol., 2004, 38, 1348–1358 CrossRef CAS PubMed.
  37. R. J. Strickman and C. P. J. Mitchell, in Multifunctional Wetlands, ed. N. Nagabhatla and C. D. Metcalfe, Springer International Publishing, Cham, 2018, pp. 207–240 Search PubMed.
  38. V. Roy, M. Amyot and R. Carignan, Environ. Sci. Technol., 2009, 43, 5605–5611 CrossRef CAS PubMed.
  39. K. L. Wolf, C. Ahn and G. B. Noe, Wetlands, 2011, 31, 699–712 CrossRef.
  40. C. P. Duncan and P. M. Groffman, J. Environ. Qual., 1994, 23, 298 CrossRef.
  41. S. D. Allison and J. B. H. Martiny, Proc. Natl. Acad. Sci. U. S. A., 2008, 105, 11512–11519 CrossRef CAS PubMed.
  42. R. J. S. Strickman, R. R. Fulthorpe, J. K. Coleman Wasik, D. R. Engstrom and C. P. J. Mitchell, Sci. Total Environ., 2016, 556–557, 1289–1296 CrossRef PubMed.
  43. J. Canário, M. Caetano, C. Vale and R. Cesário, Environ. Sci. Technol., 2007, 41, 7376–7382 CrossRef.
  44. L. Windham-Myers, M. Marvin-Dipasquale, D. P. Krabbenhoft, J. L. Agee, M. H. Cox, P. Heredia-Middleton, C. Coates and E. Kakouros, J. Geophys. Res., 2009, 114, G00C05 CrossRef.
  45. N. Regier, B. Frey, B. Converse, E. Roden, A. Grosse-Honebrink, A. G. Bravo and C. Cosio, PLoS One, 2012, 7, e45565 CAS.
  46. L. Windham-Myers, M. Marvin-DiPasquale, C. A. Stricker, J. L. Agee, L. H. Kieu and E. Kakouros, Sci. Total Environ., 2014, 484, 308–318 CrossRef CAS PubMed.
  47. C. Cosio, R. Flück, N. Regier and V. I. Slaveykova, Environ. Toxicol. Chem., 2014, 33, 1225–1237 CrossRef CAS PubMed.
  48. P. A. M. Bachand, S. M. Bachand, J. A. Fleck, C. N. Alpers, M. Stephenson and L. Windham-Myers, Sci. Total Environ., 2014, 472, 957–970 CrossRef CAS PubMed.
  49. H. Brix, Water Sci. Technol., 1997, 35, 11–17 CrossRef CAS.
  50. C. C. Gilmour, E. A. Henry and R. Mitchell, Environ. Sci. Technol., 1992, 26, 2281–2287 CrossRef CAS.
  51. J. D. Jeremiason, D. R. Engstrom, E. B. Swain, E. A. Nater, B. M. Johnson, J. E. Almendinger, B. A. Monson and R. K. Kolka, Environ. Sci. Technol., 2006, 40, 3800–3806 CrossRef CAS PubMed.
  52. S. Åkerblom, K. Bishop, E. Björn, L. Lambertsson, T. Eriksson and M. B. Nilsson, Geochim. Cosmochim. Acta, 2013, 102, 1–11 CrossRef.
  53. S. G. Todorova, C. T. Driscoll, D. A. Matthews, S. W. Effler, M. E. Hines and E. A. Henry, Environ. Sci. Technol., 2009, 43, 6572–6578 CrossRef CAS PubMed.
  54. R. Shih, W. D. Robertson, S. L. Schiff and D. L. Rudolph, J. Environ. Qual., 2011, 40, 1586 CrossRef CAS PubMed.
  55. D. A. Matthews, D. B. Babcock, J. G. Nolan, A. R. Prestigiacomo, S. W. Effler, C. T. Driscoll, S. G. Todorova and K. M. Kuhr, Environ. Res., 2013, 125, 52–60 CrossRef CAS PubMed.
  56. A. S. Chiandet and M. A. Xenopoulos, Urban Ecosyst., 2016, 4 DOI:10.1007/s11252-016-0559-8.
  57. D. Fortin, R. Goulet and M. Roy, Geomicrobiol. J., 2000, 17, 221–235 CrossRef CAS.
  58. D. R. Lovley and M. J. Klug, Appl. Environ. Microbiol., 1983, 45, 187–192 CAS.
  59. J. N. Galloway, J. D. Aber, J. W. Erisman, S. P. Seitzinger, R. W. Howarth, E. B. Cowling and B. J. Cosby, Bioscience, 2003, 53, 341–356 CrossRef.
  60. G. D. Taylor, T. D. Fletcher, T. H. F. Wong, P. F. Breen and H. P. Duncan, Water Res., 2005, 39, 1982–1989 CrossRef CAS PubMed.
  61. U. Skyllberg, P. R. Bloom, J. Qian, C.-M. Lin and W. F. Bleam, Environ. Sci. Technol., 2006, 40, 4174–4180 CrossRef CAS PubMed.
  62. B. Meng, X. Feng, G. Qiu, Z. Li, H. Yao, L. Shang and H. Yan, Environ. Toxicol. Chem., 2016, 35, 191–199 CrossRef CAS PubMed.
  63. M. Schallenberg and J. Kalff, Ecology, 1993, 74, 919–934 CrossRef.
  64. B. D. Hall, V. L. S. Louis, K. R. Rolfhus, R. A. Bodaly, K. G. Beaty, M. J. Paterson and K. A. P. Cherewyk, Ecosystems, 2005, 8, 248–266 CrossRef CAS.
  65. M. Mailman, L. Stepnuk, N. Cicek and R. A. Drew Bodaly, Sci. Total Environ., 2006, 368, 224–235 CrossRef CAS PubMed.
  66. Y.-R. Liu, J.-X. Dong, L.-L. Han, Y.-M. Zheng and J.-Z. He, Environ. Pollut., 2016, 209, 53–59 CrossRef CAS PubMed.
  67. J. B. Glover, M. E. Domino, K. C. Altman, J. W. Dillman, W. S. Castleberry, J. P. Eidson and M. Mattocks, Ecotoxicology, 2010, 19, 781–795 CrossRef CAS PubMed.
  68. J. Vymazal, Sci. Total Environ., 2007, 380, 48–65 CrossRef CAS PubMed.
  69. I. Phillips, Commun. Soil Sci. Plant Anal., 1999, 30, 1–20 CrossRef CAS.
  70. G. L. Bruland, C. J. Richardson and W. L. Daniels, Wetlands, 2009, 29, 1153–1165 CrossRef.
  71. K. A. Ballantine, J. Lehmann, R. L. Schneider and P. M. Groffman, Ecol. Appl., 2015, 25, 215–225 CrossRef PubMed.
  72. R. H. Kadlec and S. Wallace, Treatment wetlands, CRC Press, Boca Raton, FL, 2nd edn, 2009 Search PubMed.
  73. D. G. Rumbold and L. E. Fink, Environ. Monit. Assess., 2006, 112, 115–135 CrossRef CAS PubMed.
  74. J. Stamenkovic, M. S. Gustin and K. E. Dennett, Wetlands, 2005, 25, 748–757 CrossRef.


Electronic supplementary information (ESI) available: One text file with supporting information on methods, Q/A parameters for mercury analyses, and supplementary results. See DOI: 10.1039/c7em00486a
Current Address: University of Washington, Department of Civil and Environmental Engineering, 201 More Hall, 3760 E. Stevens Way NE, Seattle, Washington 98195.

This journal is © The Royal Society of Chemistry 2018