Engineered and Environmental Controls of Microbial Denitrification in

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Engineered and Environmental Controls of Microbial Denitrification in Established Bioretention Cells Lucas J Waller, Gregory K. Evanylo, Leigh-Anne Henry Krometis, Michael Strickland, Tess M Wynn-Thompson, and Brian D. Badgley Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b06704 • Publication Date (Web): 10 Apr 2018 Downloaded from http://pubs.acs.org on April 10, 2018

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Environmental Science & Technology

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Engineered and environmental controls of

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microbial denitrification in established

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bioretention cells

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Lucas J. Waller1, Gregory K. Evanylo1, Leigh-Anne H. Krometis2, Michael S. Strickland3,

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Tess Wynn-Thompson2, Brian D. Badgley1*

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Department of Crop and Soil Environmental Sciences, 2Department of Biological

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Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg,

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Virginia 24060, United States

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United States

Department of Soil and Water Systems, University of Idaho, Moscow, Idaho 83844,

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Abstract Bioretention cells (BRCs) are effective tools for treating urban stormwater, but

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nitrogen removal by these systems is highly variable. Improvements in nitrogen removal

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are hampered by a lack of data directly quantifying the abundance or activity of

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denitrifying microorganisms in BRCs and how they are controlled by original BRC

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design characteristics. We analyzed denitrifiers in twenty-three BRCs of different

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designs across three Mid-Atlantic states (MD, VA, and NC) by quantifying two bacterial

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denitrification genes (nirK and nosZ) and potential enzymatic denitrification rates within

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the soil medium. Overall, we found that BRC design factors, rather than local 1 ACS Paragon Plus Environment


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environmental variables, had the greatest effects on variation in denitrifier abundance

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and activity. Specifically, denitrifying populations and denitrification potential increased

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with organic carbon and inorganic nitrogen concentrations in the soil media and

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decreased in BRCs planted with grass compared to other types of vegetation.

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Furthermore, the top layers of BRCs consistently contained greater concentrations and

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activity of denitrifying bacteria than bottom layers, despite longer periods of saturation

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and the presence of permanently saturated zones designed to promote denitrification at

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lower depths. These findings suggest that there is still considerable potential for design

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improvements when constructing BRCs that could increase denitrification and mitigate

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nitrogen export to receiving waters.

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Introduction Bioretention cells (BRCs) are engineered soil systems designed to intercept and

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treat stormwater runoff before it infiltrates underlying soil or is discharged to nearby

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surface waters. BRCs successfully reduce many stormwater contaminants1-6 and are

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rapidly becoming one of the most popular strategies used in urban stormwater

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management. However, observed nitrogen (N) removal rates have been highly

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variable,7-9 which is likely due to the many possible N transformations that occur in

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these systems. Stormwater contains nitrogen in many forms, including particulate

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organic nitrogen (PON), dissolved organic nitrogen (DON), and dissolved inorganic

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forms such as ammonium (NH4+), nitrite (NO2-), and nitrate (NO3-).10-12 This diverse N

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speciation contributes to a high degree of complexity in N cycling. For example,

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negatively charged NOx ions (NO2- and NO3-) are highly mobile in soil and therefore

2 ACS Paragon Plus Environment

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difficult to retain. In contrast, other forms such as PON can be physically captured by

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the soil medium, but then undergo mineralization, ammonification, and nitrification

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during aerobic conditions that can further contribute NOx species during subsequent

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storms.9, 13

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Denitrification is a microbial form of anaerobic respiration that is valuable for N

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management because, when carried out to completion, it completely removes mobile

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NOx forms from soil or water by transforming them into inert nitrogen gas (N2). However,

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several underlying conditions must be present including: 1) the presence of denitrifying

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organisms; 2) an organic carbon source as an electron donor; and 3) an oxygen

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deficient environment coupled with oxidized N species to serve as alternate electron

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acceptors.14 BRC design has evolved in attempt to facilitate bacterial denitrification,

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most commonly by incorporating an internally saturated zone (ISZ) in the bottom layer

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to promote a sustained anaerobic environment. While the incorporation of an ISZ into

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BRCs can promote total nitrogen removal rates ranging from 60-80% in some studies,15-

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17

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significant difference in the reduction of N when comparing several BRCs with and

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without ISZs.

others have shown only minor reductions in N removal,18 and Hunt et al.7 found no

Bioretention research has focused on design options that optimize vegetative

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cover,12, 19, 20 soil medium composition,13, 21, 22 system capacity,23-25 and the addition of

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carbon sources15, 17, 26 to increase N removal efficiency. For instance, multiple studies

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have demonstrated enhanced N removal in BRC mesocosms containing vegetation,19,

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27, 28

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medium composition is more complicated because labile organic matter (OM) tends to

particularly with fast growing, high biomass plant species.12, 29 In contrast, soil



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leach N and phosphorus.7, 22 However, an organic carbon source for microbial activity is

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also important, so the C:N ratio may be a critical factor to consider when selecting OM

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additions.26 By contrast, fewer studies have examined the effects of environmental

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factors on N removal in bioretention systems. In those studies, N removal rates have

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been positively correlated with antecedent precipation,30 but effects of temperature can

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vary by design. While conventionally drained BRCs generally export N as temperature

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increases, BRCs containing a saturated zone show an inverse correlation between N

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export and temperature.30-32

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Despite the substantial amount of research focused on promoting nitrogen

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removal in BRCs, important knowledge gaps remain. Firstly, there are few published

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studies that evaluate broad patterns across numerous established BRCs. Second, while

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denitrification has been studied in other stormwater systems,33-35 we are aware of only

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two published studies that have quantified bacterial denitrifiers in a BRC.36, 37 Both

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reported concentrations of denitrifying genes in a single BRC but the designs were

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different, particularly considering the presence of an ISZ in the research by Willard et

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al.37 Both also reported higher abundances of denitrifying genes in the surface media

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layers compared to deeper layers, and hypothesized that saturation time and C content

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influenced the microbial denitrification population.

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In this study, we address both of these knowledge gaps – uncertainty about

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denitrification controls in established systems and the lack of data that directly quantify

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denitrifiers – by investigating generalizable relationships between BRC design factors

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and microbial denitrification. Specific research questions were: 1) What are the most

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important factors controlling the abundance and activity of denitrifying bacteria in


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established BRCs and do they relate more to design or to environment? And, 2) among

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the most important factors controlling denitrifiers in BRCs, how can those relationships

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be leveraged for improved design? We conducted a comprehensive field-based survey

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of twenty-three BRCs across the mid-Atlantic region that represent different design

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specifications, climates, physiographic regions, and ages. From each BRC we analyzed

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a variety of design aspects, soil and climate characteristics, and denitrification indicators

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to identify the factors that correlated most strongly with denitrification capacity to inform

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future improvements and management strategies.

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Materials and Methods

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Site Selection & Sampling Design specifications were originally acquired for approximately 50 BRCs in the

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eastern mid-Atlantic region (Maryland, Virginia, & North Carolina) from a variety of

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published articles, public databases, and personal communications. These were

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narrowed down to twenty-three BRCs (Table S1 and Figure S1) that represent a range

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of features related to design (e.g. presence of ISZ, vegetation type, media mix

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composition, etc.) and also characteristics related to environment and locale (e.g. age,

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geographic region, mean temperature, precipitation, etc.) that we hypothesized could

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influence denitrifier abundance and activity (Table 1).

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BRCs were sampled during a one-month period in Nov/Dec 2014 during dry

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weather (i.e., following at least one week of no rainfall) to compare BRCs in a common

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functional state. Soil samples were collected as cores extending to the full depth of the



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soil medium. Triplicate cores were collected from both the front (inlet) and the rear

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(outlet) portions of each cell. To assess vertical stratification, the top 10 cm and bottom

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10 cm of media from each core were separated aseptically. The three subsamples from

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each location (i.e., top/inlet, bottom/inlet, top/outlet, bottom/outlet) were homogenized to

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provide a total of four soil samples per cell (Figure S2). The samples were stored on ice

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during transport to the laboratory and stored at -80° C for further analysis. A portion of

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the sample was refrigerated at 4° C for < 7 d for analyzing denitrification potential.

121 122 123

Denitrifier Abundance and Activity For each soil medium sample, DNA was extracted using the PowerSoil DNA

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Isolation-Kit (MOBIO Laboratories INC, CA, USA) following the manufacturer’s protocol.

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Concentrations of extracted DNA were measured using the Qubit 2.0 fluorometer

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(Invitrogen, USA) and stored at -20° C. Abundances of bacterial denitrifiers were

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measured by quantifying the copy number of two key functional genes present in the

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soil medium using quantitative polymerase chain reaction (qPCR). Target genes

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included nirK,38 which codes for a nitrite reductase that converts nitrite (NO2-) to nitric

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oxide (NO),39 and nosZ,40 which codes for a nitrous oxide reductase that converts

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nitrous oxide (N2O) to inert dinitrogen gas (N2).41 These were chosen to represent two

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key steps in the N removal process. Firstly, the conversion of NO2 to NO (nirK)

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represents the beginning of the denitrification pathway where N typically becomes

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unavailable to most organisms and will likely be removed from the system. Second, the

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conversion of N2O to N2 (nosZ) is critical because it represents the final step where

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nitrous oxide is converted to inert N2, thus preventing the production of a potent


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greenhouse gas. Gene copies for each target were estimated by comparing cycle

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threshold values to known standards of plasmids containing the target gene and

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populations corresponding to each functional gene were computed as copies per gram

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of soil medium. Standard curve R2 values for all genes were 99% or greater and

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efficiencies were similar to or higher than previous studies ranging from 90.9 – 106.4%

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for nirK 40, 42 and 83.9 – 96.5% for nosZ.43, 44 Reaction mixtures, primers, volumes, and

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thermal profiles used for qPCR are included in Table S2.

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While the number of denitrification gene copies indicates the abundance of

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denitrifying bacteria, it does not quantify actual gene expression and/or cellular activity.

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Denitrification potential, a direct measurement of denitrification enzyme activity, was

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measured by the acetylene blockage technique.45 The protocol described by Drury et

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al.46 was followed with the exception that headspace was flushed with N2. Briefly, soil

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samples were incubated in an anoxic environment after adding glucose, nitrate, and

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acetylene, and the amount of nitrous oxide was measured regularly over a 5 h period to

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determine the rate of N2O production and estimate the rate at which denitrification could

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potentially occur within a soil sample under optimal conditions. Gas samples were

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stored less than two weeks and analyzed on a gas chromatograph (Shimadzu, Kyoto,

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Japan). Due to instrument difficulties, estimates of denitrification potential are only

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presented for 48 of the 86 total BRC media samples.

156 157 158 159

Total Organic Carbon, Ammonium, and Nitrite-Nitrate Total organic carbon (TOC) and N concentrations in the BRC medium were determined from potassium sulfate (K2SO4) extracts. Extractions consisted of the



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addition of BRC medium to 0.5M K2SO4 at a 1:7 ratio, agitation using a side-arm shaker

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for four hours, and gravity filtration through a 2.5 micron, 15 cm diameter, cotton fiber

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Whatman No. 42 filter (GE Healthcare, Germany). Extracts were frozen at -20 °C prior

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to analysis. Total organic C was analyzed on an OI Model 1010 total organic carbon

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analyzer using the standard method 5301c.47 NH4+-N and NOx-N concentrations were

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determined using a Lachat QuikChem 8500 Flow Injection Analyzer following the

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QuikChem Method 10-107-04-1-L and APHA Method 4500-NO3- I.47, 48 Given that

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dissolved inorganic nitrogen (DIN) was measured as multiple species, a principle

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component analysis (PCA) was conducted on NH4+-N and NOx-N concentrations to

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calculate a single response variable that would provide simpler representation of the

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effect of soil media inorganic N. The PCA analysis explained 83% of the variation

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between nitrite-nitrate and ammonium concentrations – the principle component values

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were then used for further analysis.

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Data Analysis Vegetation in the BRCs surveyed in this study was broadly classified into three

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types: grassed, landscaped, and overgrown. Grassed BRCs (n=4) were planted with

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sod, did not contain a mulch layer, and did not contain forb, shrub, or woody plant

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growth. Landscaped BRCs (n=12) contained a combination of herbaceous plants,

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shrubs, woody plant species, were typically mulched, and were well-maintained to

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prevent volunteer species from overgrowing the planted vegetation. Overgrown BRCs

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(n=7) had not been well maintained and, although they contained some of the original

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planted species, were primarily dominated by dense volunteer vegetation. To


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investigate the effect of the original composition of the soil medium, BRCs were divided

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into two categories: mixes comprised of ≤ 50% sand (n=8) and those with ≥ 80% sand

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(n=11). The original media mixes of the remaining four BRCs were unknown. It is

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important to note that denitrification potential estimates were available from only one

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BRC with a medium ≤ 50% sand, so only four samples from one BRC were available for

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that comparison. Measurements of denitrifying gene abundances, however, were

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available for all BRCs.

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Linear regression models were used to investigate the relationship between

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denitrification response variables and the design and environmental data collected on

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BRCs. Multi-model inference using Akaike’s Information Criterion (AIC), a model

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selection approach, was used to identify which models were most important in

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predicting the denitrifying response variables.49 Akaike’s Information Criterion

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incorporates a likelihood function that is corrected for the number of variables included

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in a particular model, which allows for the determination of variables that most influence

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the observed data and prevent continual addition of an increasing number of variables

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to a model. We used a corrected variation of the AIC (AICc), which is a second order

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formula recommended for analyses when the number of parameters is not large in

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comparison to the number of samples.49 The result is an AICc value, with lower values

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identifying more probable models. This approach has become increasingly popular in

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understanding ecological processes and dynamics among datasets from which specific

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variables cannot be isolated,50-52 which is a common problem in field-based surveys.53

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The final model selection results were then produced by averaging the top

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models for the denitrification response variables. Top models included in the averaging



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were identified as having ∆AICc of < 10, with ∆AICc equaling the difference between the

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AICc value for the model in question and AICc value of the best model for a given

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response variable, as proposed by Burnham and Anderson.49 The reported values for

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each factor represent a relative variable importance (RVI), which are calculated by

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summing the AIC weights across the top models and range from 0 to 1. Variables with

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RVI approaching one have a greater model weight or appear more often in the top

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models or both.

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To further reduce the risk of identifying spurious correlations that can result from

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random data dredging, the model selection approach was used in a structured

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hypothetical manner to evaluate the relative importance of design and environmental

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variables. Variables that are environmentally influenced and those that are fixed at the

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time of design and construction were initially separated and analyzed independently to

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reduce complexity (Table 1). After identifying the top models for each category

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(environmental and design), the predictor variables from only the top models in each

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category were then combined and analyzed together. The most important variables

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were determined by those with the greatest RVI value and variables that had an RVI of

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> 80% of the top RVI value. The intent of this approach was to eliminate elements which

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indicated a marginal effect on the predictor variable and ultimately determine if the most

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influential variables were environmental in nature or could be manipulated by design.

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Predictor variables were transformed using a log or square root transformation to

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reduce non-normal distributions in the dataset. This provided a better representation of

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the gradients within our data and allowed for the identification of fewer top models with

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higher confidences. Following the model selection process, Wilcoxon non-parametric


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tests (P < 0.05) and continuous variable regression ANOVA (P < 0.05) were used to

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identify significant differences and examine relationships between categorical and

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continuous variables for each denitrification response variable. Model selection

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analyses were carried out using the MuMIn package54 in R55 and statistical significance

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tests were determined using JMP.56

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Results and Discussion

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Model Selection Results

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The primary objective of this research was to identify which factors or

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combination of factors most strongly correlated with denitrification in established BRCs

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and to determine whether those factors can be manipulated in future BRC designs to

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potentially increase nitrogen removal rates. Given that numerous potential predictor

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variables were measured (Table 1), it is not possible to present relationships between

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denitrifiers and all measured predictor variables. Therefore, we used the model

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selection process to identify which factors were the best predictors of denitrification

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gene abundance and denitrification potential as response variables. This initial analysis

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allowed us to focus the remainder of the discussion on the factors with the most

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potential for facilitating increased denitrification.

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The results of the complete multi-model inference analysis of all possible

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measured predictor variables (Table 1) identified soil inorganic N, vegetation type, soil

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TOC, medium composition, soil medium depth, and sampling depth to be the most

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important variables affecting denitrification in the surveyed BRCs (Table 2). While this

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still encompasses several factors that could have an impact, it is important to note that,



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from a water quality management and engineering perspective, each of these variables

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can be manipulated by BRC design. This is a key finding because it results from a

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uniquely comprehensive examination of many established BRCs to show that original

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design factors have lasting and dominant effects on BRC function compared to local

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environmental variables.

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While denitrification potential is representative of complete denitrification, the

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denitrification genes quantified in this study represent different steps in the

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denitrification pathway. It’s important to recognize that the denitrification response

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variables correlated most strongly with different factors (Table 2), suggesting that

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individual steps in the denitrification pathway are affected differently. For example,

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inorganic N, sampling depth, and vegetation type significantly affect all of the measured

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response variables, suggesting they have broad control over the complete denitrification

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pathway. In contrast, TOC was a strong predictor of nirK abundance and denitrification

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potential, but it did not correlate well with nosZ abundance, which seemed to respond

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more strongly to total BRC depth and medium composition. This distinction is critical

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because nosZ codes for the enzyme that converts nitrous oxide (a potent greenhouse

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gas) to inert N2, suggesting that some design factors may affect two key impacts of

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bioretention systems – dissolved nitrogen removal and greenhouse gas emission – in

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different ways. In the following sections, we further describe each of these important

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factors independently and discuss how they could potentially be used to improve BRC

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design.

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Vertical Stratification as a Denitrification Control


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Overall, indicators of denitrification activity were significantly elevated in the

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topmost 10 cm of BRCs with nirK abundance, nosZ abundance, and denitrification

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potential rates respectively averaging 5.7, 3.6, and 23 times higher than in the

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bottommost 10 cm (Figure 1). These results are similar to recent studies that also found

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lower concentrations of denitrifying bacteria in the deeper layers of single BRCs,36, 37

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suggesting that this is a widespread phenomenon. The current paradigm is that lower

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layers of BRCs, particularly those with ISZs, facilitate greater denitrification because

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they stay saturated (and thus anaerobic) for longer periods of time. However,

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measurements of proportionally greater denitrification in the upper layers suggests that

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large fractions of many BRCs may not be performing as designed.

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We hypothesize that, over time, particulate organic matter carried in stormwater

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runoff accumulates primarily in the surface layer of the medium. This results in limited

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amounts of available N and C in the deeper layers, which restrict denitrifying

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populations. In our samples, mean N and C concentrations were 5.0 and 2.9 times

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higher, respectively, in the top 10 cm compared to the bottom 10 cm of soil medium and

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only one of the 5 BRCs with an ISZ documented the addition of a carbon source to the

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saturated zone. Furthermore, denitrification gene abundances and denitrification

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potential were actually lower in the bottom 10 cm samples of BRCs that included an ISZ

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(Figure S3) compared to conventionally drained designs, casting doubt on whether

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ISZs, which are currently recommended in some states,57, 58 promote denitrification in

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the lower layers as they are currently designed.

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Higher abundances of denitrification genes and rates of denitrification activity in the surface layers, where aerobic conditions are expected to dominate, suggest that



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substantial amounts of denitrification may occur either in anaerobic microsites or during

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very brief inundation periods following each storm event. Additionally, degradation of

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plant matter and the accumulation of particulate OM in the upper layers of BRCs may

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result in the persistence of denitrification “hot spots” within the system that could explain

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the higher denitrification rates in the upper layers of BRCs, as has been seen in other

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soil environments.59 Further research on how denitrifier abundance and activity are

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affected by C, N, and oxygen at different time scales and in different soil

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microenvironments will help determine how to best promote nitrogen removal

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throughout the entire volume of the BRC. Particularly in these systems, due to common

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size and volume constraints and the mobile nature of NOx species, exploiting the entire

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vertical profile of BRCs must be a vital strategy for reducing the export of stormwater

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nutrients.

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Vegetation as a Denitrification Control Relative to other factors, vegetation type was a strong predictor of microbial

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denitrification. Specifically, denitrifying populations and activity were significantly

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decreased in grassed BRCs (Figure 2), where mean nirK abundance, nosZ abundance,

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and denitrification potential were respectively 5.5, 2.5, and 27 times lower than in BRCs

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that were categorized as landscaped or overgrown. An important caveat exists in our

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data because many BRCs that were classified as grassed were also constructed with

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media containing a high sand content. As a result, there is some confounding of these

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two effects in this study, making it difficult to separate causation from correlation.

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However, there are clear biological mechanism that could be responsible for such an


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effect. First, grassed BRCs do not contain a mulch layer that could capture organic N.

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Using BRC mesocosms containing a 2.5 cm mulch layer and shrub vegetation, Davis et

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al.13 found that the majority (> 50%) of total Kjeldahl nitrogen (TKN) removal occurred in

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the top 20 cm of the soil medium, suggesting significant sorption potential in the mulch

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layer and captured organic matter. In our data, ammonium (NH4-N) was 3.0 and 4.3

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times higher while nitrite-nitrate (NO2-NO3-N) was 13.7 and 13.6 times higher in the soil

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medium of landscaped and overgrown BRCs, respectively, than in grassed BRCs. Once

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sorbed onto mulch and organic matter, mineralization of organic N can fuel

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denitrification and potentially a greater abundance of denitrifiers in BRCs containing

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herbaceous and woody vegetation types. However, given that our data represent only a

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single point in time, additional work is required to understand the balance among the

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amount of nitrogen available in the soil medium, denitrifier abundance, and the rate at

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which N may be leached, denitrified, or otherwise lost from the system.

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A second important aspect of grassed BRCs is the dense rooting systems that

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may compete with bacteria for available N between saturation events. For example,

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Passeport et al.31 found grassed BRCs could remove N as efficiently as BRCs

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containing “traditional” vegetative cover (trees, shrubs, & mulch) and Payne et al.60

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observed that > 97% of nitrate in BRC soil columns was plant assimilated. Considering

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nitrification occurs in the shallow aerobic zones of BRCs, vegetation with high nutrient

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accumulation potential and dense rooting systems in these shallow layers may capture

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and reduce the export of nitrified products. Multiple studies have confirmed the ability of

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high biomass plant species, particularly those with deep rooting structures, to assimilate

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a significant portion of stormwater N in BRC mesocosms.61-63 This is a critical



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consideration especially if, as discussed in the previous section, BRCs are

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underperforming with regard to microbial denitrification in deeper layers. In that case, in

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addition to deep rooting plant species, engineering improvements that facilitate

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denitrification from the deeper layers could be an important factor for maximizing N

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removal in bioretention systems. It is also important to remember that plant assimilation

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only works as a N removal mechanism when plant biomass is captured and removed,

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so improved denitrification would be a significant advantage in BRCs that are not well

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maintained. Overall, both plant and microbial processes can clearly be important for N

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removal and increased ecological understanding of these systems is required to

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facilitate complimentary mechanisms to achieve significant improvements in total

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reductions.

355 356 357

Soil Nutrients as a Denitrification Control Extractable TOC concentrations in the BRC soil medium samples ranged from

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below detection to 1.02 mg/g dry soil with a mean value of 0.16 mg/g dry soil. All

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denitrification response variables had a statistically significant linear correlation with

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total organic carbon (Figure 3). These findings are different than those of Chen et al.,36

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who found significant effects of organic matter on a different nitrite reductase gene, nirS,

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but not on nirK or nosZ gene abundances. Biologically, a relationship between TOC and

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denitrifiers is expected considering that most are heterotrophs that utilize organic

364

carbon as an electron donor. Although our results indicate that increased organic matter

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could facilitate more denitrifiers, from a water treatment perspective, the benefits of

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increased denitrification could be negated if the added organic matter is also a source of


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leachable carbon and nutrients.11 However, there is evidence that some organic carbon

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sources can fuel the microbial community but not contribute to nutrient leaching. For

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example, Peterson et al.26 had success with the addition of woodchips and found

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optimal N removal rates in gravel bioretention soil columns using 5 mm woodchips at a

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quantity of 4.5% of the soil medium by mass. Presumably, the high C:N ratio of

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woodchips resulted in minimal N leaching and a slow decomposition rate that

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continuously supplied the microbial community with organic carbon.

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Denitrification potential and denitrifying gene abundances also all had significant

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correlations with inorganic soil N (Figure 3). Extractable ammonium concentrations

376

ranged from below detection to 136 µg/g dry soil with a mean of 12.9 µg/g dry soil.

377

Extractable nitrite – nitrate (NO2--NO3-) concentrations ranged from below detection to

378

38.2 µg/g dry soil with a mean of 6.27 µg/g dry soil. This particular effect represents

379

something of a design paradox since stimulating denitrification by adding N would

380

clearly be counterproductive. However, the N cycle is biologically complex and the

381

amount of inorganic N available to denitrifiers is a function of not just total nitrogen in

382

the medium but also other factors, including how quickly N is mineralized from

383

stormwater inputs, decomposition rates of organic N in the soil medium, and turnover

384

from plant and microbial biomass. Furthermore, after mineralization, the mobility of N

385

species and their availability to denitrifiers is affected by whether they are subsequently

386

nitrified during aerobic conditions. Relating these complex dynamics to BRC design is

387

particularly challenging because stormwater N generally cannot be controlled, so

388

increasing N removal efficiency in these systems will most benefit from optimization of



Page 18 of 36

389

organic matter in the starting soil media, promoting beneficial microbial transformations,

390

and increasing vegetative uptake.

391

On average, denitrifying gene abundances and potential denitrification rates were

392

higher in the BRCs containing low sand media (i.e., higher starting amounts of organic

393

matter), where they respectively averaged 1.1, 2.9, and 1.5 times higher nirK, nosZ, and

394

denitrification potential than in BRCs with high sand contents (Figure 4). Due to the low

395

sample size available for denitrification potential within BRCs with low sand content, the

396

relationship between denitrification potential and media mix serve only as a preliminary

397

assessment and will benefit from future studies. Organic carbon and nitrite-nitrate

398

concentrations also had higher averages in the low sand media mixes, but these

399

differences were not statistically significant in our data set. While increasing nutrient

400

availability via organic matter in the media mix may have the ability to enhance the

401

denitrifying microbial community, there are practical limitations to this strategy. For

402

example, low sand mixes are prone to compaction and clogging,64 are typically more

403

expensive,65 and have been shown to leach DON and phosphorus.9, 22 Constructing

404

BRCs with high sand mixtures and supplementing with organic carbon sources of more

405

favorable C:N ratios or N forms, particularly in specific underperforming areas of the

406

BRC, may be the most feasible design options that facilitate infiltration and optimize N

407

removal through denitrification.

408 409

Denitrifying groups nosZ vs. nirK

410

Differences between abundances of nirK and nosZ genes are particularly

411

important because a lack of nosZ activity is an indicator of incomplete denitrification that


Page 19 of 36


412

could result in the emission of nitrous oxide, a potent greenhouse gas.66 As

413

demonstrated by the RVI data based on the model selection analysis (Table 2), the

414

abundance of the nirK nitrite reductase gene was most affected by sample depth and

415

vegetation type. TOC and inorganic N were also included in the top models but by

416

comparison were less important. For nosZ abundance, however, inorganic N and

417

sample depth were the most important variables followed by total soil medium depth,

418

vegetation type, and medium composition, respectively. Interestingly, unlike nirK and

419

denitrification potential, the model selection results identified medium composition and

420

soil medium depth to be important factors affecting nosZ gene populations specifically.

421

Relative to each other, mean nirK and nosZ gene abundances were weakly

422

correlated (Figure 5A), but on a per sample basis, nirK abundances were over an order

423

of magnitude higher than nosZ (Figure 5B). To our knowledge, relationships between

424

nitrous oxide emissions and denitrifying gene abundances have not been studied in

425

BRCs. However, the nirK/nosZ gene ratio is positively correlated with nitrous oxide

426

emissions in other soil environments.67-69 Additionally, nosZ gene transcription can be

427

suppressed at low oxygen concentrations, while nirK expression generally exhibits a

428

higher oxygen tolerance.70, 71 Given that we found the highest denitrification activity in

429

the upper (i.e., frequently aerobic) layers of soil medium, these relationships suggest

430

incomplete denitrification is highly likely and that nitrous oxide is potentially being

431

emitted from many BRCs. And while N2O emissions in BRCs are estimated to be

432

equivalent to that of other urban landscape features,72, 73 future design

433

recommendations should seek to minimize nitrous oxide emissions. To address this



Page 20 of 36

434

topic, quantifying nitrous oxide flux, rather than simply quantifying N removal from these

435

systems, should be included when possible in future research and monitoring.

436 437 438

Implications for Future Design Nitrogen removal continues to be a challenging facet of bioretention

439

performance. This research suggests that denitrifying bacteria are largely influenced by

440

the design of BRCs rather than local environmental factors, and that nitrogen removal

441

efficiency can potentially be enhanced by simple design alterations. Specifically, these

442

alterations include constructing BRCs with media mixes that provide the microbial

443

community with non-leachable nutrients, creating synergic relationships between high

444

biomass vegetation and denitrifiers, and by encouraging denitrification in the lower

445

layers by supplementing with C sources. Further research on denitrification in BRCs is

446

needed to determine if these findings can be used reliably to reduce net N export from

447

these systems at a large scale.

448 449

Acknowledgments

450

The authors would like to thank the Science Museum of Virginia, Dr. Allen Davis, the

451

North Carolina Department of Transportation, and the Montgomery County, MD DEP for

452

supplying BRC design information that was critical to this research. The authors would

453

also like to thank the Virginia Tech Institute for Critical Technology and Applied Science

454

for the funding that made this research possible.

455 456

Supporting Information


Page 21 of 36


457

Supporting information is available for this manuscript which includes bioretention cell

458

locations and sources of design information, qPCR thermal profiles and reaction

459

mixtures, sampling map, sampling locations within bioretention cells, gene abundances

460

in bottom 10cm of BRCs with and without ISZs, number of BRCs classified within

461

categorical variables, and mean and range data for continuous variables collected on

462

BRCs. This information is available free of charge via the Internet at http://pubs.acs.org.

463 464

Corresponding Author Contact

465 466 467 468 469 470

Address: RB1880 Suite 1129, Room 1121 Blacksburg, Virginia 24061 E-Mail: [email protected]

471 472 473



474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518

Page 22 of 36

References (1) Li, H.; Davis, A. P., Water quality improvement through reductions of pollutant loads using bioretention. J. Environ. Eng. 2009, 135, (8), 567-576. (2) Sun, X.; Davis, A. P., Heavy metal fates in laboratory bioretention systems. Chemosphere 2007, 66, (9), 1601-1609. (3) Randall, M. T.; Bradford, A., Bioretention gardens for improved nutrient removal. Water Qual. Res. J. Can. 2013, 48, (4), 372-386. (4) DeBusk, K. M.; Wynn, T. M., Storm-water bioretention for runoff quality and quantity mitigation. J. Environ. Eng. 2011, 137, (9), 800-808. (5) DiBlasi, C. J.; Li, H.; Davis, A. P.; Ghosh, U., Removal and fate of polycyclic aromatic hydrocarbon pollutants in an urban stormwater bioretention facility. Environ. Sci. Technol. 2009, 43, (2), 494-502. (6) Davis, A. P., Field performance of bioretention: Water quality. Environ. Eng. Sci. 2007, 24, (8), 1048-1064. (7) Hunt, W. F.; Jarrett, A. R.; Smith, J. T.; Sharkey, L. J., Evaluating bioretention hydrology and nutrient removal at three field sites in North Carolina. J. Irrig. Drain. Eng. 2006, 132, (6), 600-608. (8) Hsieh, C. H.; Davis, A. P., Evaluation and optimization of bioretention media for treatment of urban storm water runoff. J. Environ. Eng. 2005, 131, (11), 1521-1531. (9) Hatt, B. E.; Fletcher, T. D.; Deletic, A., Hydrologic and pollutant removal performance of stormwater biofiltration systems at the field scale. J. Hydrol. 2009, 365, (3-4), 310-321. (10) Taylor, G. D.; Fletcher, T. D.; Wong, T. H. F.; Breen, P. F.; Duncan, H. P., Nitrogen composition in urban runoff - implications for stormwater management. Water Res. 2005, 39, (10), 1982-1989. (11) Li, L.; Davis, A. P., Urban stormwater runoff nitrogen composition and fate in bioretention systems. Environ. Sci. Technol. 2014, 48, (6), 3403-3410. (12) Read, J.; Wevill, T.; Fletcher, T.; Deletic, A., Variation among plant species in pollutant removal from stormwater in biofiltration systems. Water Res. 2008, 42, (4), 893-902. (13) Davis, A. P.; Shokouhian, M.; Sharma, H.; Minami, C., Water quality improvement through bioretention media: Nitrogen and phosphorus removal. Water Environ. Res. 2006, 78, (3), 284-293. (14) Philippot, L.; Hallin, S.; Schloter, M., Ecology of denitrifying prokaryotes in agricultural soil. Adv. Agron. 2007, 96, 249-305. (15) Kim, H.; Seagren, E. A.; Davis, A. P., Engineered bioretention for removal of nitrate from stormwater runoff. Water Environ. Res. 2003, 75, (4), 355-367. (16) Lucas, W. C.; Greenway, M., Hydraulic response and nitrogen retention in bioretention mesocosms with regulated outlets: part II--nitrogen retention. Water Environ. Res. 2011, 83, (8), 703-713. (17) Zinger, Y.; Fletcher, T. D.; Deletic, A.; Blecken, G. T.; Viklander, M. In Optimisation of the nitrogen retention capacity of stormwater biofiltration systems. Novatech 2007: 6th International Conference on Sustainable Techniques and Strategies in Urban Water Management, Lyon, France, June 25-28, 2007; Groupe de Recherche Rhone-Alpes sur les Infrastructures et l’Eau: Villeurbanne, France, 2007.


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(35) Bettez, N. D.; Groffman, P. M., Denitrification Potential in Stormwater Control Structures and Natural Riparian Zones in an Urban Landscape. Environ. Sci. Technol. 2012, 46, (20), 10909-10917. (36) Chen, X.; Peltier, E.; Sturm, B. S.; Young, C. B., Nitrogen removal and nitrifying and denitrifying bacteria quantification in a stormwater bioretention system. Water Res. 2013, 47, (4), 1691-700. (37) Willard, L. L.; Wynn-Thompson, T.; Krometis, L. H.; Neher, T. P.; Badgley, B. D., Does it pay to be mature? Evaluation of bioretention cell performance seven years postconstruction. J. Environ. Eng. 2017, 143, (9), 04017041. (38) Henry, S.; Baudoin, E.; Lopez-Gutierrez, J. C.; Martin-Laurent, F.; Brauman, A.; Philippot, L., Quantification of denitrifying bacteria in soils by nirK gene targeted realtime PCR. J. Microbiol. Methods 2004, 59, (3), 327-335. (39) Braker, G.; Zhou, J. Z.; Wu, L. Y.; Devol, A. H.; Tiedje, J. M., Nitrite reductase genes (nirK and nirS) as functional markers to investigate diversity of denitrifying bacteria in Pacific northwest marine sediment communities. Appl. Environ. Microbiol. 2000, 66, (5), 2096-2104. (40) Henry, S.; Bru, D.; Stres, B.; Hallet, S.; Philippot, L., Quantitative detection of the nosZ gene, encoding nitrous oxide reductase, and comparison of the abundances of 16S rRNA, narG, nirK, and nosZ genes in soils. Appl. Environ. Microbiol. 2006, 72, (8), 5181-5189. (41) Canfield, D. E.; Glazer, A. N.; Falkowski, P. G., The evolution and future of Earth's nitrogen cycle. Science 2010, 330, (6001), 192-196. (42) Regan, K.; Kammann, C.; Hartung, K.; Lenhart, K.; Muller, C.; Philippot, L.; Kandeler, E.; Marhan, S., Can differences in microbial abundances help explain enhanced N2O emissions in a permanent grassland under elevated atmospheric CO2? Glob. Change Biol. 2011, 17, (10), 3176-3186. (43) Towe, S.; Albert, A.; Kleineidam, K.; Brankatschk, R.; Dumig, A.; Welzl, G.; Munch, J. C.; Zeyer, J.; Schloter, M., Abundance of microbes involved in nitrogen transformation in the rhizosphere of Leucanthemopsis alpina (L.) Heywood grown in soils from different sites of the Damma glacier forefield. Microb. Ecol. 2010, 60, (4), 762770. (44) Harter, J.; Krause, H. M.; Schuettler, S.; Ruser, R.; Fromme, M.; Scholten, T.; Kappler, A.; Behrens, S., Linking N2O emissions from biochar-amended soil to the structure and function of the N-cycling microbial community. ISME J. 2014, 8, (3), 660674. (45) Smith, M. S.; Tiedje, J. M., Phases of denitrification following oxygen depletion in soil. Soil Biol. Biochem. 1979, 11, (3), 261-267. (46) Drury, C. F.; Myrold, D. D.; Beauchamp, E. G.; Reynolds, W. D., Denitrification Techniques for Soils. In Soil Sampling and Methods of Analysis, Second Edition; Carter, M. R.; Gregorich, E. G., Eds. CRC Press: Boca Raton, FL, 2007; pp 471-493. (47) Federation, W. E., Standard Methods for the Examination of Water and Wastewater. American Public Health Association (APHA): Washington, DC, 2005. (48) Lachat Instruments. Determiniation of Nitrate/Nitrite by Flow Injection Analysis. Low Flow Method. QuikChem Method 10-107-04-1-L. Lachat Instruments: Loveland, CO, 2007.


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(49) Burnham, K. P.; Anderson, D. R., Model selection and multimodel inference: a practical information-theoretic approach. Springer Science & Business Media: 2003. (50) Symonds, M. R. E.; Moussalli, A., A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike's information criterion. Behav. Ecol. Sociobiol. 2011, 65, (1), 13-21. (51) Richards, S. A.; Whittingham, M. J.; Stephens, P. A., Model selection and model averaging in behavioural ecology: the utility of the IT-AIC framework. Behav. Ecol. Sociobiol. 2011, 65, (1), 77-89. (52) Johnson, J. B.; Omland, K. S., Model selection in ecology and evolution. Trends Ecol. Evol. 2004, 19, (2), 101-108. (53) Richards, S. A., Testing ecological theory using the information-theoretic approach: Examples and cautionary results. Ecology 2005, 86, (10), 2805-2814. (54) Bartoń, K. MuMIn: Multi-Model Inference, R package version 1.40.4; 2018. (55) R Core Team. R: A language and environment for statistical computing., R Foundation for Statistical Computing: Vienna, Austria, 2017. (56) JMP, Version 13; SAS Institute Inc: Cary, NC, 2007. (57) NCDENR. Stormwater Best Management Practices Manual. North Carolina Department of Environment and Natural Resources: Raleigh, NC, 2007. (58) ARC. Georgia Stormwater Management Manual – Vol. 2: Technical Handbook. Atlanta Regional Commission: Atlanta, GA, 2016. (59) Parkin, T. B., Soil microsites as a source of denitrification variability. Soil Sci. Soc. Am. J. 1987, 51, (5), 1194-1199. (60) Payne, E. G.; Fletcher, T. D.; Russell, D. G.; Grace, M. R.; Cavagnaro, T. R.; Evrard, V.; Deletic, A.; Hatt, B. E.; Cook, P. L., Temporary storage or permanent removal? The division of nitrogen between biotic assimilation and denitrification in stormwater biofiltration systems. PLoS One 2014, 9, (3), e90890. (61) Cording, A. Evaluating stormwater pollutant removal mechanisms by bioretention in the context of climate change. Dissertation, The University of Vermont, Burlington, VT, 2016. (62) Glaister, B. J.; Fletcher, T. D.; Cook, P. L. M.; Hatt, B. E., Interactions between design, plant growth and the treatment performance of stormwater biofilters. Ecol. Eng. 2017, 105, 21-31. (63) Rycewicz-Borecki, M.; McLean, J. E.; Dupont, R. R., Nitrogen and phosphorus mass balance, retention and uptake in six plant species grown in stormwater bioretention microcosms. Ecol. Eng. 2017, 99, 409-416. (64) Hatt, B. E.; Fletcher, T. D.; Deletic, A., Hydraulic and pollutant removal performance of fine media stormwater filtration systems. Environ. Sci. Technol. 2008, 42, (7), 2535-2541. (65) Davis, A. P.; Hunt, W. F.; Traver, R. G.; Clar, M., Bioretention technology: Overview of current practice and future needs. J. Environ. Eng. 2009, 135, (3), 109-117. (66) Zumft, W. G., The biological role of nitric oxide in bacteria. Arch. Microbiol. 1993, 160, (4), 253-264. (67) Philippot, L.; Andert, J.; Jones, C. M.; Bru, D.; Hallin, S., Importance of denitrifiers lacking the genes encoding the nitrous oxide reductase for N2O emissions from soil. Glob. Change Biol. 2011, 17, (3), 1497-1504.



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(68) Li, S.; Song, L.; Jin, Y.; Liu, S.; Shen, Q.; Zou, J., Linking N2O emission from biochar-amended composting process to the abundance of denitrify (nirK and nosZ) bacteria community. AMB Express 2016, 6, (37), 1-9. (69) Li, S.; Song, L.; Gao, X.; Jin, Y.; Liu, S.; Shen, Q.; Zou, J., Microbial abundances predict methane and nitrous oxide fluxes from a windrow composting system. Front. Microbiol. 2017, 8, 409. (70) Bergaust, L.; Shapleigh, J.; Frostegard, A.; Bakken, L., Transcription and activities of NOx reductases in Agrobacterium tumefaciens: the influence of nitrate, nitrite and oxygen availability. Environ. Microbiol. 2008, 10, (11), 3070-81. (71) Dalsgaard, T.; Stewart, F. J.; Thamdrup, B.; De Brabandere, L.; Revsbech, N. P.; Ulloa, O.; Canfield, D. E.; DeLong, E. F., Oxygen at nanomolar levels reversibly suppresses process rates and gene expression in anammox and denitrification in the oxygen minimum zone off northern Chile. Mbio 2014, 5, (6), e01966-14. (72) McPhillips, L.; Goodale, C.; Walter, M. T., Nutrient leaching and greenhouse gas emissions in grassed detention and bioretention stormwater basins. J. Sust. Water in the Built Environ. 2018, 4, (1), 04017014. (73) Grover, S. P. P.; Cohan, A.; Sen Chan, H.; Livesley, S. J.; Beringer, J.; Daly, E., Occasional large emissions of nitrous oxide and methane observed in stormwater biofiltration systems. Sci. Total Environ. 2013, 465, 64-71.


Page 27 of 36


674

Figure and Table Captions

675

Table 1. Designation of design and environmental factors used as predictor variables in

676

the model selection analyses to predict denitrifier abundance and activity (response

677

variables).

678 679

Table 2. Model averaging results for predicting nirK abundance, nosZ abundance, and

680

denitrification potential. RVI values are presented on a scale from 0 to 1. Variables with

681

higher values (i.e. closest to 1) are considered to have a greater effect on the response

682

variable. Cells that do not contain an RVI value indicates that the explanatory variable

683

was not included in the top models for the denitrification response variable.

684 685

Figure 1. Mean denitrifying gene abundances and denitrification potential in top 10 cm

686

vs. the bottom 10 cm of BRC soil media samples.

687 688

Figure 2. Mean values for nirK abundance, nosZ abundance, and denitrification

689

potential for all soil medium samples from BRCs with grassed (n=15), landscaped

690

(n=48), and overgrown (n=24) vegetation schemes.

691 692

Figure 3. Linear regressions for total organic carbon (TOC) and inorganic N

693

concentrations in the soil medium vs. nirK abundance, nosZ abundance, and

694

denitrification potential across all BRC medium samples. All regressions are statistically

695

significant (p < 0.05).

696



697

Figure 4. Mean values for nirK abundance, nosZ abundance, and denitrification

698

potential in all soil medium samples from BRCs with known media mixes containing ≤

699

50% sand (n=32) and ≥ 80% sand (n=40).

Page 28 of 36

700 701

Figure 5. Linear regression of nosZ vs nirK gene abundances in each sample. Panel A

702

represents correlation between the abundances of each gene and Panel B presents the

703

same data on equally scaled axes to compare relative abundance. The dotted line

704

represents a 1:1 ratio of nosZ to nirK that has been presented as an ‘ideal’ ratio to

705

prevent nitrous oxide emission.


Page 29 of 36

706


Table 1. Design Factors

Environmental Factors

Vegetation (Grassed vs. Landscaped vs. Overgrown)

Soil Medium Total Organic Carbon

BRC:Catchment Surface Area Ratio

Region (Piedmont vs. Coastal Plain)

Presence/Absence of Saturated Zone

BRC Age

Soil Medium Composition

Soil Medium pH

(≤ 50% sand vs. ≥ 80% sand) Soil Medium Depth

Mean Annual Temperature & Precipitation

Sample Depth (Top vs Bottom)

Soil Medium Ammonium & Nitrite-Nitrate

707



708

Page 30 of 36

Table 2.

DIN

Sampling Depth

Vegetation Type

TOC

nirK

0.46

0.96

0.78

0.64

Denitrification Potential

1.00

0.82

1.00

1.00

nosZ

1.00

1.00

0.91

Soil Medium Composition

Soil Medium Depth

0.85

0.97

709


Page 31 of 36

710


Figure 1.

711



712

Page 32 of 36

Figure 2.

713


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714


Figure 3.

715



716

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Figure 4.

717


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718


Figure 5.

719 720



TOC Art 84x47mm (225 x 225 DPI)

ACS Paragon Plus Environment

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Engineered and Environmental Controls of Microbial Denitrification in

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