Introduction
Streams and rivers are dynamic networks that emit globally significant
quantities of carbon dioxide (CO2), methane (CH4), and nitrous
oxide (N2O) to the atmosphere. CO2 emissions via flowing waters are
equivalent to half of the annual terrestrial carbon sink
(1.2 Pg CO2-C yr-1, Cole et al., 2007; Battin et al., 2008).
Stanley et al. (2016) recently demonstrated that flowing waters are
significant CH4 sources as well, emitting approximately
28 Tg yr-1, which is equivalent to between 10 and 35 % of
emissions from wetlands globally (Bridgham et al., 2013). Approximately
10 % of global anthropogenic N2O emissions are emitted from river
networks due to nitrogen contamination of surface and groundwater (UNEP,
2013; Ciais et al., 2013). There is evidence that these N2O estimates,
based on IPCC guidelines, might be too low, given growing evidence of high
denitrification rates in small streams with high NO3- loads (Beaulieu
et al., 2011).
While much of the research on greenhouse gas (GHG) emissions from streams has
taken place in agricultural watersheds, urban-impacted river networks receive
similar N loads and have also shown elevated GHG concentrations and emissions
(e.g., Daniel et al., 2001; Beaulieu et al., 2010, 2011; Kaushal et al.,
2014a; Gallo et al., 2014). As urban land cover and populations continue to
expand, it is critical to understand the impacts on downstream waters,
including C and N loading and GHG emissions. While N2O emissions from
both urban and agricultural sources are taken into account in models based on
estimated watershed dissolved inorganic nitrogen loading (Nevison et al., 2000;
Seitzinger et al., 2000), measurements validating these estimates or
estimates of CO2 and CH4 in urban watersheds are rare. Quantifying
the variability, drivers, and sources of GHG emissions from streams will
illuminate the biogeochemical processes and potential role of urban
infrastructure on nutrient cycling, water quality, and GHG budgets.
Role of sanitary infrastructure
The form and age of stormwater and sanitary infrastructure within a
watershed can influence stream water GHG emissions in several ways. GHGs may
enter urban streams directly through buried stormwater and sanitary
infrastructure or form increased production within streams in response to
nutrient loading and/or geomorphic changes. We investigated the role of
infrastructure on GHG emissions from streams in order to evaluate these
potential drivers of heterogeneity within urban watersheds. Sanitary
infrastructure encompasses a wide array of systems to manage human waste. In
developed countries, sanitary infrastructure includes a combination of
septic systems, sanitary sewers, and sometimes combined stormwater and sanitary
sewers. Storm and sanitary sewer lines are present in areas with
medium-to-high-density development. The sanitary sewer or combined sewer network
delivers waste to centralized wastewater treatment plants (WWTPs), which
treat influent and release effluent into larger rivers or coastal zones.
Sanitary, storm, and combined sewers tend to follow stream valleys (i.e., low
points in the landscape), are often made of erodible materials such as terra
cotta or concrete, and tend to crack or develop leaks. Leaks in sanitary
sewer infrastructure can lead to chronic nutrient loading throughout stream
networks (Divers et al., 2013, Kaushal et al., 2011, 2015; Pennino et al., 2016).
Septic systems, primarily used in low-density
residential areas, are designed to settle out waste solids and leach N-rich
liquid waste into subsurface soils and groundwater. Sanitary sewer
infrastructure may influence GHG abundance and emission from streams
directly via diffusion of gases out of gravity sewer lines (Short et al., 2014) or indirectly by microbial processing along surface and subsurface
flow paths (Yu et al., 2013; Beaulieu et al., 2011). While the present study
focuses mainly on first- to third-order streams influenced by sanitary sewer
lines or septic systems, it is also worth mentioning that WWTPs are known to
be a source of CH4 and N2O in urban areas and contribute
point-source GHG loading to larger rivers and coastal areas (Beaulieu et al., 2010; Strokal and Kroeze, 2014; Alshboul et al., 2016).
Sewage leaks are likely the primary source of N2O emissions from small
urban streams (Short et al., 2014). Several studies have documented that
wastewater leakage from municipal sewers often accounts for more than 50 %
of dissolved N in urban streams (Kaushal et al., 2011; Pennino et al., 2016;
Divers et al., 2013). While sanitary sewer lines are known to leak dissolved
N, N2O losses are not accounted for in greenhouse gas budgets of the large
WWTPs that these pipes feed into. Short et al. (2014) measured intake lines
from three municipal WWTPs and estimated that N2O emissions from sewer
lines alone are on the same order of magnitude (1.7 g N2O person yr-1)
as current IPCC estimates for per capita emissions from secondary WWTPs.
Their study demonstrates the importance of constraining biogenic gas
emissions from streams, which flow alongside and may receive gaseous inputs
from aging sanitary sewer lines.
Role of stormwater infrastructure
Stormwater infrastructure varies widely across and within cities. From
stream burial in pipes to infiltration-based green infrastructure (GI)
designs, stormwater management (SWM) designs have evolved over time (Collins et al., 2010; Kaushal et al., 2014b). In Baltimore, where this study took place,
stormwater management installed prior to the 1970s consisted of
concrete-lined channels and buried streams (Baltimore County Department of
Planning, 2010). Areas developed during the 1990s and 2000s are
characterized by a more GI-based design approach, including but not limited
to upland detention ponds, infiltration basins, wetlands and bioswales.
Stream restoration projects and riparian zone protections have also been
established, restricting development within 100 m of the stream corridor for
new developments (Baltimore Department of planning, 2010).
The form of stormwater infrastructure – whether stream burial, infiltration
wetland, or restored riparian zone – may contribute to GHG saturation of
groundwater and streams. Stormwater control wetlands and riparian/floodplain
preservation may increase or decrease CH4 and N2O emissions from
streams, depending upon how watershed C and N inputs are routed along
hydro-biogeochemical flow paths. For instance, if these forms of GI are
successful at removing excess N inputs to streams, GI may reduce N2O
emissions from flowing waters. Alternatively, GI may increase both N2O
and CH4 inputs to streams and thus emissions by facilitating anaerobic
microbial metabolism (Søvik et al., 2006; VanderZaag et al., 2010). The
form of GI (i.e., stormwater control wetland vs. riparian/floodplain
preservation) may also influence GHGs due to (1) differences in water
residence time and oxygen depletion in wetland vs. floodplain soils and (2) differences in watershed-scale N removal capacity of the two different
approaches.
Variables controlling GHG production in urban watersheds
Reach-scale studies in streams across biomes have demonstrated that GHG
production and emission is sensitive to changes in nutrient stoichiometry,
organic matter quality, redox state, and temperature (e.g., Bernot et al., 2010; Kaushal et al., 2014a; Beaulieu et al., 2009; Dinsmore et al., 2009;
Baulch et al., 2011; Harrison and Matson, 2003). Several studies have shown
that infrastructure can influence solute loading and stoichiometry of
streams, which could in turn increase GHG production. For instance, Newcomer
et al. (2012) measured higher rates of N uptake and denitrification
potential in streams with restored riparian zones compared with degraded,
incised urban streams. In-stream N uptake is also consistently higher in
daylighted streams compared with streams buried in pipes (Pennino et al., 2014; Beaulieu et al., 2015). Upland or inline stormwater wetlands and
retention ponds provide additional locations for focused N removal in urban
watersheds (Newcomer Johnson et al., 2014; Bettez and Groffman, 2012). Sanitary
infrastructure (i.e., leaky sewer lines and septic systems) can also be a
source of N via leaching into groundwater (Shields et al., 2008; Kaushal et al., 2015; Pennino et al., 2016).
In previous studies, carbon quantity and/or organic matter quality was
correlated with N uptake or removal in urban streams and wetlands (Newcomer
et al., 2012; Pennino et al., 2014; Beaulieu et al., 2015; Bettez and Groffman, 2012;
Kaushal et al., 2014c). Inverse relationships between dissolved organic carbon
(DOC) and nitrate (NO3-) concentrations have been found to persist
across a wide variety of ecosystems ranging from soils to streams to oceans
(e.g., Aitkenhead-Peterson and McDowell, 2000; Dodds et al., 2004; Kaushal and
Lewis, 2005; Taylor and Townsend, 2010). Recently, inverse relationships
between DOC and NO3- have also been reported for urban
environments ranging from groundwater to streams to river networks (Mayer et al., 2010; Kaushal and Belt, 2012; Kaushal et al., 2014c). A suite of competing
biotic processes may control this relationship, by either (1) assimilating or
reducing NO3- in the presence of bioavailable DOC or (2) by producing
NO3- regardless of DOC status (Hedin et al., 1998; Dodds et al., 2004; Kaushal and Lewis, 2005; Taylor and Townsend, 2010). The former category
includes heterotrophic denitrification, which oxidizes organic carbon to
CO2 and reduces NO3- to N2O + N2 (Knowles, 1982)
as well as assimilation of inorganic N (Wymore et al., 2015; Caraco et al., 1998;
Kaushal and Lewis, 2005). In the second category, nitrification
chemoautotrophically produces NO3- by oxidizing NH4+
and consuming CO2. Nitrification also yields N2O as an
intermediate product and has been shown to dominate N cycling processes in
low-DOC environments (Schlesinger, 1997; Taylor and Townsend, 2010; Helton et al., 2015).
Site map of headwater stream sites within Red Run and Dead Run
watersheds. Green stars signify biweekly sampling sites, and black dots
signify longitudinal sampling points sampled seasonally. Land cover
categories are colored based on the National Land Cover Database, with dark
red areas signifying dense urban land cover, light red signifying medium
urban land cover, and green colors signifying forested or undeveloped areas.
Close-up views of Dead Run and Red Run on the right represent the study
watersheds.
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In urban watersheds, denitrification is often limited by DOC due to
increased N loading and/or decreased connectivity with carbon-rich soils in
the riparian zone (Mayer et al., 2010; Newcomer et al., 2012). The C : N
stoichiometry is likely to be affected by stormwater and sanitary sewer
infrastructure designs as well (Søvik et al., 2006; Collins et al., 2010;
Kaushal et al., 2011). Stormwater wetlands may promote anoxic conditions and
increase the C : N ratio of stream water by increasing flow through carbon-rich
soils (e.g., Søvik et al., 2006; Newcomer et al., 2012). Stream burial can
reduce C : N ratios, if streams are buried in storm drains (Pennino et al., 2016; Beaulieu et al., 2014). Leaky sanitary infrastructure may additionally
reduce the C : N ratio and/or alter the form of carbon in streams (Newcomer
et al., 2012).
Study goals
The goal of the present study was to identify patterns and drivers related
to GHG dynamics in urban headwater streams draining different forms of
infrastructure (stream burial, septic systems, inline SWM wetlands, and
riparian/floodplain preservation). Although less considered compared with
nutrient loading, increased GHG emissions may be an unintended consequence
of urban water quality impairments and biogeochemical processes occurring
within and downstream of urban infrastructure. A growing body of work has
shown that nutrient and carbon loads to streams are related not only to land
cover metrics (% impervious surface, urban density, etc.) but also to urban
infrastructure (Shields et al., 2008; Kaushal et al., 2014b). Connectivity
between runoff-generating water sources (groundwater, overland flow, and shallow
subsurface flow) and urban infrastructure (sanitary sewer lines, storm
sewers, drinking water pipes, constructed wetlands, etc.) is likely to
influence nutrient export and the biogeochemical function of waterways. An
improved understanding of the relationship between infrastructure type and
biogeochemical functions is critical for minimizing unintended consequences
of water quality management, especially as growing urban populations place
greater burden on watershed infrastructure (Doyle et al., 2008; Foley et al., 2005; Strokal and Kroeze, 2014).
Summary of site characteristics including drainage area (km2),
percent impervious cover (%IC), and percent of the watershed drained by
GI stormwater best management practices (i %GI SWM drainage).
Infrastructure
Site
Drainage area
% IC
% GI SWM
Description
feature
(km2)
cover
drainage
Septic systems
RRSD
0.23
7.9
0.00
Low-density residential development with septic systems, minimal stormwater management with some stream burial.
RRSM
0.68
3.78
13.97
Floodplain preservation
RRRM
0.63
16.4
100.00
Suburban and commercial low-impact development converted from agriculture in early 2000s. Stormwater wetlands in upland + wide riparian buffer zones surround each stream and sanitary sewer infrastructure.
RRRB
0.21
22.81
54.67
Inline SWM wetlands
DRKV
0.31
39.16
100.00
Older suburban development (1950s) with GI located inline with stream channels, rather than dispersed across the landscape. Watershed is serviced by sanitary sewers.
DRGG
0.6
36.68
47.60
Stream burial
DRAL
0.26
41.9
1.10
Older suburban and commercial development (1950s) with piped headwaters upstream of the sampling point. Watershed is serviced by sanitary sewers. No management of stormwater other than the pipe network, which also contains buried streams.
DRIS
0.18
30.57
0.00
Sampling methods
Study sites
This study took place in collaboration with the Baltimore Ecosystem Study
Long-Term Ecological Research project (www.beslter.org). We
identified four categories based on distinct combinations of stormwater and
sanitary infrastructure dominating the greater Baltimore region, based on
maps of stormwater control structures, housing age, and intensive field
surveys. We then selected eight first-order streams paired across the four
categories. First-order stream sites were spread equally across two
sub-watersheds of Gwynns Falls: Dead Run and Read Run (Fig. 1).
We
have abbreviated the categories based on the dominant infrastructure feature
as follows: (1) stream burial, (2) inline stormwater management
wetlands, (3) riparian/floodplain preservation, and (4) septic systems (Table 1).
Sites in the “stream burial” category (DRAL and DIRS) drain watersheds with
streams contained in storm sewers. Sanitary infrastructure in these
watersheds is composed of aged sanitary sewer lines, installed prior to 1970
(Baltimore County Department of Planning, 2010). Streams in the “inline
stormwater management” category (DRKV and DRGG) originate in stormwater
ponds or wetlands and also flow adjacent to aging sanitary sewer lines.
Streams in the “riparian/floodplain preservation” category (RRRM and RRSM)
drain watersheds with newer development (after 2000), upland infiltration
wetlands, and 100 m wide undeveloped floodplains (Baltimore County
Department of Planning, 2010). Sanitary sewers were constructed in these
watersheds between 2000 and 2010 (Baltimore County Department of Planning,
2010). Sites in the “septic systems” category (RRSM and RRSD) drain lower
density development with stormwater management in the form of stormwater
sewer pipes (Fig. 1). All eight first-order stream sites were sampled every
2 weeks for dissolved carbon and nitrogen concentrations.
Temporal sampling of dissolved gases and stream chemistry
Headwater stream sites were sampled every 2 weeks for solutes (DOC; total dissolved nitrogen,
TDN;
humification index, HIX; and biological autochthonous inputs index, BIX) and dissolved gas (CO2, CH4 and N2O)
concentrations. Chemistry sampling took place for 2 years, between January 2013 and December 2014, and gas sampling took place between July 2013 and
July 2014. Sites were visited between 09:00 and 14:00 local time. Five
dissolved gas samples were collected per stream on each date, along an
established 20 m study reach either upstream or adjacent to the gaging station.
Gas samples were collected at 0, 5, 10, 15, and 20 m from the fixed starting
point of the study reach. Samples were collected by submerging a 140 mL
syringe with a three-way Luer lock and pulling 115 mL of stream water into the
syringe. We added 25 mL of ultra-high purity helium to the syringe in the
field and then shook the syringes vigorously for 5 min to promote equilibration
of gases between aqueous and gas phases. After equilibration, 20 mL of the
headspace was immediately transferred into a pre-evacuated glass vial capped
with a screw-top rubber septum (LabCo Limited, Lampeter, UK) and then transported
to the laboratory, where samples were stored at room temperature for up to
4 weeks prior to analyses. Water temperature and barometric pressure
during the equilibration were recorded in the field. We collected three
helium headspace blanks by injecting 25 mL of helium into pre-evacuated vials
in the field.
We collected stream water samples in a 250 mL high-density polyethylene
bottles, one sample per site. One duplicate sample was collected on
each sampling date, and the site for duplicate sample collection rotated
among the sampling dates. Dissolved oxygen (DO) concentration and pH were
measured at the upstream end of each study reach using a handheld YSI 550-A
dissolved oxygen meter (YSI Inc., Yellow Springs, OH) and an Oakton handheld
pH meter (Oakton Instruments, Vernon Hills, IL).
Longitudinal sampling of dissolved gases
Longitudinal surveys were conducted in June 2012, March 2014, and December 2014 in Red Run and Dead Run. Longitudinal sampling started at the outlet of
each major tributary (Dead Run or Red Run) and extended every 500 m
upstream to include the four biweekly sampled headwater sites in each
watershed (Fig. 1). During spring and fall months, solute and gas samples
were collected along all major tributaries (> 5 % main stem
flow) as well as every 500 m along the main stem of Dead Run and Red Run.
Minor tributaries (< 5 % of main stem flow) were not sampled.
Stream discharge was measured at each sampling point using a Marsh-McBirney
Flo-Mate handheld velocity meter (Marsh-McBirney Inc., Frederick, MD, USA).
We used cross-sectional measurements of stream velocity and water depth to
calculate instantaneous discharge at each sampling site. We measured
velocity and depth at a minimum of 10 points at each cross section in order
to properly characterize flow across the channel. Discharge data were
provided by USGS when sampling sites were co-located with a USGS gaging
station (US Geological Survey, 2017).
We calculated the watershed contributing area above each sampling point and
flow length from each sampling point to the watershed outlet using the
“Hydrology”
toolbox in ArcMap 10. Sampling locations were designated pour points in the
hydrology tools workflow. Because sampling points were always co-located
with road crossings, we were able to acquire the latitude and longitude of
sampling sites using Google Earth software (Google Inc., 2009). Watersheds
were delineated using a 2 m resolution digital elevation model (DEM; Baltimore County Government,
2002). We first corrected the DEM for spurious depressions using the
“Fill” tool in the ArcMap10.0 Hydrology toolbox. Next, we calculated flow
direction for each pixel of this filled DEM raster. We then used the “Flow
Accumulation” tool to evaluate the number of pixels contributing to each
downstream pixel. After ensuring that each pour point was co-located on the
map streams (i.e., areas with flow accumulation > 500 pixels), we
used the “Watershed” tool to delineate the pixels draining into each sampled
location.
Laboratory methods
Dissolved gas concentrations
Samples of headspace equilibrated gas concentrations (CO2, CH4,
and N2O) were stored at room temperature for up to 1 month in airtight
exetainer vials and transported to the EPA National Risk Management Research
Laboratory, Cincinnati, Ohio, for analysis. Concentrations of CO2,
CH4, and N2O were measured using a Bruker 450 (Bruker, Billerica, MA,
USA) gas chromatograph equipped with a methanizer, flame ionization
detector, and electron capture
detector. Instrument detection
limits were 100 ppb for N2O, 10 ppm for CO2, and 0.1 ppm for
CH4.
Solute concentrations
Water samples were transported on ice to the University of Maryland, College
Park, and filtered using pre-combusted 0.7 µm glass fiber filters
within 24 h. A Shimadzu TOC analyzer (Shimadzu Scientific, Kyoto, Japan)
was used to measure total dissolved nitrogen and dissolved organic
carbon. The non-purgeable organic carbon (NPOC) method was utilized
for DOC, despite potential underestimation of volatile compounds because the
NPOC method is insensitive to variations in dissolved inorganic carbon (DIC; Findlay et al., 2010). TDN
was measured on the same instrument using the “TDN” method, which consists
of high-temperature combustion in the presence of a platinum catalyst.
Nitrate (NO3-) concentrations were measured via colorimetric
reaction using a cadmium reduction column (Lachat method 10-107- 04-1-A) on
a Lachat flow injection analyzer (Hach, Loveland, CO).
DOM characterization
Filtered water samples were analyzed for optical properties in order to
characterize dissolved organic matter (DOM) sources. After filtering (0.7 µm GF/F filter grade),
samples were stored in amber glass vials at
4 ∘C for a maximum of 2 weeks prior to analyses. The detailed
methodology for optical properties and fluorescence indices can be found in
Smith and Kaushal (2015), and numerous other studies have followed a similar
filtration and storage procedure (Singh et al., 2014, 2015;
Huguet et al., 2009; Dubnick et al., 2010; Gabor et al., 2014). Fluorescently
active DOM constitutes a wide range of lability. While some highly labile
compounds may break down within hours of sample collection, more
recalcitrant forms can remain stable for months. The 2-week window is a
convention meant to facilitate comparisons between sites, rather than a
biologically based limit to storage (R. Gabor and S. Duan, personal communication, 2017). Briefly, fluorescence and absorbance
properties of DOM were measured in order to evaluate the relative abundance
of terrestrial and aquatic sources to the overall DOM pool.
A FluoroMax-4 Spectrofluorometer (Horiba Jobin Yvon, Edison, NJ, USA) was
used to measure the emission spectra of samples in response to a variety of
excitation wavelengths. Excitation–emission matrices (EEMs) were used for
characterizing indices of terrestrial vs. aquatic DOM sources. The
humification index is defined as the ratio of emission intensity of
the 435–480 nm region of the EEM to the emission intensity of the 300–345 nm
region of the EEM at the excitation wavelength of 254 nm (Zsolnay et al., 1999;
Ohno, 2002). HIX varies from 0 to 1, with higher values signifying
high-molecular-weight DOM molecules characteristic of humic terrestrial
sources. Lower HIX indicates DOM of bacterial or aquatic origin (Zsolnay et al., 1999). The biological autochthonous inputs index is defined as the ratio of
fluorescence intensity at the emission wavelength 380 nm to the intensity
emitted at 430 nm at the excitation wavelength of 310 nm (Huguet et al., 2009).
Lower BIX values (< 0.7) represent terrestrial sources, and higher
BIX values (> 0.8) represent algal or bacterial sources (Huguet
et al., 2009).
Calculations
Dissolved gas concentrations were calculated using Eqs. (1)–(3). First, we
used Henry's law to convert measured mixing ratios (ppmv) to the molar
concentration of each gas in the headspace vial [Cg] (µmol L-1)
following Eq. (1):
[C]=PVRT,
where P is pressure (1 atm), V is the measured partial pressure of the gas
of interest (ppmv), R is the universal gas constant
(0.0821 L atm mol1 K-1), and T is the temperature of a water sample (Kelvin) during
headspace equilibration. We used Henry's law and a temperature-corrected
Bunsen solubility coefficient to calculate [Caq], which is the concentration of
residual gas remaining in water following headspace equilibration (Eq. 2;
Stumm and Morgan, 1981):
[Caq]=V⋅Bp⋅BunsenRT,
where V is measured gas mixing ratio (ppmv), Bp is the barometric pressure
(atm), and Bunsen is the solubility coefficient in the vessel
(L L-1 at 1 atm).
Calculations of the Bunsen coefficient were based on Weiss (1974) for
CO2, Weiss (1970) for N2O, and Yamamoto et al. (1976) for CH4.
The final stream water concentration [Cstr] was then calculated
using mass balance of these two pools, described in Eq. (3), where
Vaq and Vg were the volumes of water and gas
respectively in a water sample with helium headspace.
[Cstr]=Caq⋅Vaq+Cg⋅VgRT
Because gas solubility is temperature dependent, it was useful to display
gas concentrations as the percent saturation, or the ratio of the measured
dissolved gas concentration to the equilibrium concentration. To determine
gas saturation, the equilibrium concentration, [Ceq], was calculated
based on water temperature, atmospheric pressure, and an assumed value for
the current atmospheric mixing ratios of each gas following Eq. (2). We
obtained current ratios for CO2 from The Keeling Curve (Scripps
Institution of Oceanography, 2017) and N2O and CH4 from the NOAA
Earth Systems Research Laboratory (NOAA ESRL, 2017;
Dlugokencky,
2017). The saturation ratio is defined as a ratio [Cstr] / [Ceq], and excess (i.e., xsCO2) is described as a mass difference
([Cstr]-[Ceq]). Supersaturation is the condition when the
saturation ratio is greater than 1 or gas excess (i.e., xsCO2) is
greater than 0.
Apparent oxygen utilization
Apparent oxygen utilization (AOU) is defined as the difference between the O2
concentrations (µM) at equilibrium with the atmosphere vs. ambient
measured O2 concentrations in the stream. A positive value of AOU
represents net oxygen consumption conditions along the
soil–groundwater–stream flow path, while a negative AOU (µM) represents
net O2 production within the stream. Because aerobic respiration and
photosynthesis couples CO2 production and O2 consumption, we can
assume that AOU is equivalent to the CO2 produced/consumed along the
same flow path (Richey et al., 1998). Under aerobic conditions, respiration of
organic matter consumes O2 and produces CO2 at approximately a
1:1
molar ratio (Schlesinger, 1997). Therefore, 1 mol of AOU should result in 1 mol of xsCO2
(measured minus equilibrium CO2 concentration).
This ratio
was then used, with an offset to 1.2:1 to account for differences in
diffusion constants for the two gases (Stumm and Morgan, 1981; Richey et al., 1988), to determine the proportion of CO2 produced by aerobic
respiration. When CO2 concentrations are greater than AOU, the
difference between measured CO2 and AOU (xsCO2-AOU) represents
additional sources from either anaerobic respiration or abiotic sources. We
split our analysis of CO2 into these two categories (AOU and
xsCO2-AOU) in order to determine whether patterns in CO2 saturation
were solely represented by aerobic respiration or other processes and sources as
well.
Greenhouse gas emissions
We calculated the gas flux rate using Eq. (4), where FGT is the flux
(g m-2 d-1) of a given gas (G) at ambient temperature (T) and d is
water depth (m). KGT (d-1) is the re-aeration coefficient for a
given G at ambient T. Measured and equilibrium gas concentrations
[Cstr] and [Ceq] were calculated following Eqs. (3) and (4) and then
converted to units of g m-3.
FGT=KGT⋅d⋅([Cstr]-[Ceq])
We modeled KGT for each site and sampling date using the energy
dissipation model (Tsivoglou and Neal, 1976). The energy dissipation model
predicts K from the product of water velocity (V, m d-1), water
surface gradient (S), and the escape coefficient, Cesc
(m-1;
Eq. 5).
K=Cesc⋅S⋅V
Cesc is a parameter related to additional factors other than
streambed slope and velocity that affect gas exchange, such as streambed
roughness and the relative abundance of pools and riffles. The Cesc
value used in this study was derived from 22 measurements of K, made using
the SF6 gas tracer method, carried out across a range of flow
conditions in four streams within 5 km of our study sites and reported in
Pennino et al. (2014). Cesc was calculated as the slope of the
regression of K vs. S⋅V from data in Pennino et al. (2014) and was assumed to be representative
of our headwater stream sites in Dead Run and
Red Run.
We calculated Cesc to be 0.653 m-1 (n=22, r2= 0.42, p=0.001). The 95 % confidence interval of this Cesc based on measured
K20,O2 values was ±0.359 m-1, which corresponds to
±55 % of a given gas flux estimate. This estimate of Cesc from these
nearby sites was assumed to be representative of the eight stream reaches
investigated in this study. Given the moderate range of uncertainty in
Cesc, as well as additional uncertainties associated with slope
estimation and relating Cesc to different stream sites, gas flux
estimates must be interpreted with caution.
Measurements of K were converted to K for each GHG (as well as
O2 for general comparisons) by multiplying by the ratio of their
Schmidt numbers (Stumm and Morgan, 1981). K measured at ambient temperature
(KT)
was converted to K (d-1 at 20 ∘C (K20) following Eq. (6).
K20=KT1.0421T-20
In order to compare re-aeration rates across sites and prior studies, we
calculated the gas transfer velocity, k600, which is defined as
K20,O2 multiplied by water depth, with units of m d-1.
We estimated S of headwater streams with GHG sampling sites by measuring the
change in elevation along the stream above and below stream gaging stations.
We determined the latitude and longitude of the stream gage, which was
co-located with GHG sampling sites in Red Run and Dead Run using a Trimble
GeoXH handheld 3.5G edition GPS unit (10 cm accuracy). We then plotted this
location atop a 1 m resolution lidar-based DEM (Baltimore County Government,
2002) in ArcMap 10. Using low points in the DEM to represent the stream
channel, we then selected one point above and one point below the stream
gaging station and measured the distance between these two points along the
stream channel with the “Measure” tool. We calculated S based on the change
in elevation divided by distance. The slope measurement reach overlapped
with, but did not coincide exactly with, the gas sampling reach in order to
ensure measurable differences in elevation. We followed the same protocol
to estimate S for reaches in Pennino et al. (2014), except, rather than
estimating points above and below a gaging station, we determined the change
in elevation over the specific reach where SF6 injections took place.
Pennino et al. (2014) provided data on the latitude and longitude of their
SF6 injection reaches.
Pennino et al.'s (2014) measurements of V during gas injections ranged from
0.02 to 0.15 m s-1. V measured at headwater gaging stations in our
sites ranged from undetectable to 0.34 m s-1. In order to avoid
extrapolation, we limited our estimation of gas fluxes to sampling sites and
dates with V in the range measured by Pennino et al. (2014). These
conditions corresponded to 37 measurements in total, which were spread unevenly across the
four headwater sites with complete rating curves (DRAL, DRKV, RRRB, DRGG). K
estimates were restricted to 5 dates at DRAL, 18 dates at DRKV, 11 dates
at RRRB, and 3 dates at DRGG.
Statistical analyses
Role of infrastructure and seasonality
A linear mixed effects modeling approach was used to determine the
significant drivers of each gas across streams in different headwater
infrastructure categories. Due to uncertainties in the gas flux parameters,
GHG saturation ratios were used rather than GHG emissions to compare spatial
and temporal patterns across sites. Mixed effects modeling was carried out
using R (R Core Team, 2014) and the “nlme” package (Pinheiro et al., 2012) following
guidance outlined in Zurr et al. (2009). Separate mixed effects models were
used to detect the role of infrastructure category and date on each response
variable. Response variables included saturation ratios for each gas
(CO2, N2O, and CH4), solute concentrations (DOC, DIC, TDN,
and NO3-), and organic matter source indices (HIX and BIX). Fixed effects
were “infrastructure category” and “sampling date” as well as an
interaction term for the two. The effect of a random intercept for “site”
was included in each model. The statistical assumptions of normality and
equal variances were validated by inspecting model residuals. When
necessary, variances were weighted based on infrastructure category to
remove heteroscedasticity in model residuals (Zuur et al., 2009). The
assumption of temporal independence was examined by testing for temporal
autocorrelation in each response variable. This test was performed using the
function “corAR1”, which is part of the package “nlme” in R. The
significance of random effects, weighting variances, and temporal
autocorrelation was tested by comparing Akaike information criterion (AIC)
scores for models with and without each of these attributes. Additionally,
pairwise ANOVA tests were run to determine whether each additional level of
model complexity significantly reduced the residual sum of squares. Final
model selection was based on meeting model assumptions, minimizing the AIC
value, and minimizing the residual standard error. Pairwise comparisons among
infrastructure categories were examined using the Tukey HSD post-hoc test
(“lsmeans” package, Lenth, 2016) for each response variable where “infrastructure
category” had a significant effect. Where “infrastructure category” did not
have a significant effect on a response variable after incorporating “site”
as a random effect, a separate set of linear models was run with “site” and
“date” as main effects rather than “infrastructure category”. The role of
“site” was evaluated in these cases to determine the degree to which
site-specific factors overwhelmed the effect of infrastructure category.
Role of environmental variables on gas saturation
A stepwise linear regression approach was used to examine the role of
multiple environmental variables on CO2, N2O, and CH4 saturation across sites and dates. Predictor variables were selected via a backward
stepwise procedure, using the “Step” function in R. This involves first
running a model that includes all potential driving factors and then running
sequential iterations of that model after removing one variable at a time
until the simplest and most robust combination of predictors was achieved.
Model fit at each step was evaluated using the AIC score. Parameters that
did not reduce AIC when comparing models were removed until the model had
the best fit with the minimum number of factors. The initial list of
potential drivers included temperature, DO, DOC, TDN, DIC, HIX, and the BIX.
Prior to the stepwise regression, we calculated the variance inflation
factor (VIF) for each response variable to test for multicolinearity. VIF > 3 was
the cut off for assessing multicolinearity. All variables
in this study were below the VIF > 3 threshold (Zuur et al., 2010).
Analysis of covariance (ANCOVA) was carried out to determine whether
relationships among gases (CO2 vs. N2O and CO2 vs. CH4) and
solutes (log of DOC : NO3- ratio) varied systematically across
infrastructure categories. ANCOVA involved comparing two generalized least
squares models. The first linear model included an interaction term between
one of the predictor variables (i.e., DOC or CO2) and infrastructure
category to predict the response variable (N2O or CH4). The second
was a linear model with the same two independent variables but no
interaction term. When infrastructure category had a significant influence
on both the intercept (first model) and slope (second model) of a
relationship, this refuted the null hypothesis that infrastructure category
had no influence on a relationship.
Because we used three separate models to evaluate variations in three GHG
concentrations (for across infrastructure categories, continuous variables,
and ANCOVA), we used a Bonferroni correction for the 95 % confidence
level. We determined the new confidence level by dividing the 95 % level
(0.05) by the number of models used on all gases across headwater stream
sites (6). This new p value (0.008) was then used to determine significance
rather than 0.05.
Longitudinal variability in gas saturation
We analyzed longitudinal data using multiple linear regressions in order to
evaluate whether patterns observed in headwater sites were representative of
the broader stream network. We compiled data from four surveys – Red Run
and Dead Run in spring and fall – and used a stepwise linear regression
approach to determine the significant drivers for each gas (Table 6).
Covariates included the log of drainage area above each point, watershed (Red
Run vs. Dead Run), season (spring vs. fall), DOC concentration, DIC
concentration, TDN concentration, log of discharge, location (tributary vs.
main stem), DOC : TDN molar ratio, a TDN by drainage area interaction term,
and a DOC by drainage area interaction term. We used the stepAIC function
in R to determine the optimal model formulation, selecting the model with
minimum AIC.
Summary of results (main effects p values) from mixed effects models
examining the role of infrastructure typology and date on the following
response variables: CO2, N2O, and CH4 saturation ratios; TDN
and DOC concentrations (mg L-1), BIX, and HIX (unitless).
Main effects
CO2
CH4
N2O
TDN
DOC
BIX
HIX
DOC : NO3-
Infrastructure typology p value
0.496
0.298
0.488
0.068
0.200
0.441
0.020
< 0.008∗
Date p value
0.957
< 0.008∗
< 0.008∗
0.086
0.387
0.155
0.765
0.492
Date by infrastructure typology Interaction p value
< 0.008∗
< 0.008∗
< 0.008∗
0.114
0.978
0.490
0.899
0.894
∗ indicate variables that are significant at the 0.008 level.
Mean with standard error in parentheses of GHG saturation ratios,
TDN and DOC concentrations (mg L-1), BIX values, and HIX values for each
site.
Infrastructure
Site
CO2
CH4
N2O
TDN
DOC
BIX
HIX
DOC : NO3-
typology
Septic systems
RRSD
52.9
14.9
28.0
6.40
0.76
0.89
0.74
0.06
(1.1)
(0.5)
(0.7)
(0.20)
(0.12)
(0.02)
(0.01)
(0.01)
RRSM
13.5
25.6
5.9
3.49
1.40
0.70
0.782
0.27
(0.5)
(1.5)
(0.2)
(0.13)
(0.25)
(0.02)
(0.015)
(0.04)
Riparian/floodplain
RRRM
6.6
207.3
1.7
0.59
2.89
0.67
0.85
12.16
preservation
(0.3)
(36.2)
(0.04)
(0.08)
(0.27)
(0.01)
(0.02)
(3.45)
RRRB
9.6
103.6
3.6
0.35
1.58
0.716
0.85
9.24
(0.4)
(8.6)
(0.1)
(0.02)
(0.18)
(0.01)
(0.01)
(2.43)
Inline SWM
DRKV
28.1
50.8
19.1
2.52
2.65
0.75
0.86
2.38
(1.0)
(8.5)
(0.6)
(0.16)
(0.24)
(0.01)
(0.003)
(0.67)
DRGG
16.3
225.8
7.9
1.16
5.32
0.73
0.83
8.72
(1.1)
(31.9)
(0.4)
(0.07)
(0.60)
(0.02)
(0.01)
(2.23)
Stream burial
DRAL
7.9
11.3
5.1
2.68
2.64
0.81
0.83
1.42
(0.3)
(0.6)
(0.2)
(0.09)
(0.37)
(0.01)
(0.01)
(0.40)
DRIS
22.6
78.4
10.7
2.42
2.51
0.79
0.82
1.82
(1.0)
(5.8)
(0.5)
(0.09)
(0.27)
(0.01)
(0.01)
(0.44)
Results
Effect of infrastructure on water quality and DOC : NO<inline-formula><mml:math id="M230" display="inline"><mml:mrow><mml:msubsup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn><mml:mo>-</mml:mo></mml:msubsup></mml:mrow></mml:math></inline-formula>
ratios
We detected significant differences among TDN, NO3-, and DOC : NO3- ratios across infrastructure categories (Table 2). TDN
concentrations ranged from 0.12 to 8.7 mg N L-1 (Table 3). Pairwise
comparisons yielded significantly higher TDN concentrations in sites in the
typology of “septic systems”, compared with the “inline SWM wetlands”
typology, and sites in the “riparian/floodplain preservation” typology.
Sites in the “stream burial” typology fell within the mid-range of TDN
concentrations and were not different from any other category. DOC
concentrations varied widely from 0.19 to 16.89 mg L-1 but were not
significantly predicted by infrastructure typology (Table 2). DOC : NO3- ratios varied over 4 orders of magnitude, from 0.02 to 112
(Fig. 2). Infrastructure typology was a significant predictor of DOC : NO3-, with the lowest ratios in sites with septic systems and
highest in sites with riparian/floodplain preservation (Fig. 2). Pairwise
comparisons showed no difference in DOC : NO3- ratios between in
the inline SWM wetland and complete stream burial typologies, however (Fig. 2).
Boxplot of molar DOC : NO3- ratio across sites in
watersheds with differing infrastructure typologies. The median of each
dataset is signified by the middle horizontal line for each category. Boxes
signify the range between the first and third quartiles (25th and 75th
percentiles). Vertical lines extend to the minimum and maximum points in the
dataset that are within 1.5 times the interquartile range. Points signify
data that fall above or below this range. Letters represent significant (p < 0.01) differences between infrastructure typologies for
DOC : NO3- across all sampling dates, determined using a linear
mixed effects model.
![]()
Effect of urban infrastructure on DOM quality
Measurements of HIX ranged from 0.30 to 0.90, while BIX ranged from 0.40 to
1.15 across all sites and sampling dates in headwater streams. Streams
draining septic system infrastructure had significantly lower HIX values
than any other infrastructure typology. BIX values showed no significant
pattern across infrastructure typologies (Table 2).
Effect of urban infrastructure on gas concentrations
Mixed effects models did not detect significant influence of infrastructure
typology alone on CO2, CH4, and N2O saturation in streams.
There was, however, a significant interaction effect between sampling date
and infrastructure typology on the saturation ratios of all three gases
(Table 2). This indicated that sampling date was important to GHG saturation
for some infrastructure typologies or that the effect of infrastructure is
dependent upon sampling date. The second set of linear models, which used
site rather than infrastructure category as a main effect, yielded
significant differences across all sites for N2O (Fig. 3). Similarly,
for CO2, there were significant differences in 25 out of 28 pairwise
comparisons. Pairwise comparisons across sites for CH4 saturation were
significant in 23 out of 28 cases. These patterns suggest that site-specific
effects overwhelmed the role of infrastructure categories on GHG saturation.
Boxplot of CO2, CH4, and N2O saturation ratios
across stream sites in varying infrastructure categories. Letters denote
significant pairwise differences across streams for a given gas from linear
mixed effects models with “Watershed” as a main effect. Boxes signify the
range between the first and third quartiles (25th and 75th
percentiles). Vertical lines extend to the minimum and maximum points in the
dataset that are within 1.5 times the interquartile range. Points signify
outliers outside of 1.5 times the interquartile range.
![]()
Effect of environmental variables on gas concentrations
Stepwise model parameter selection yielded several variables that correlate
with each GHG saturation ratio (Table 4). TDN was the strongest predictor of
N2O saturation, followed by DO. The final model for N2O
(r2= 0.78) also included temperature, HIX, BIX, %SWM, and
DOC : NO3-. CO2 saturation had a similar pattern of predictors
and nearly identical model fit (r2= 0.78). The DOC : NO3- ratio
was the strongest predictor of CH4 saturation followed by DO and
temperature. HIX, %IC, and %SWM were also related to CH4
saturation, but TDN and BIX were not.
Main effects, model coefficients, adjusted r2, and overall
model p value for stepwise regression models examining the relationship
between continuous variables and GHG saturation ratios. The model
coefficient is the main effect of each parameter, and the absolute value of
this coefficient signifies the relative contribution of each predictor. A ∗
indicates the predictor with the greatest influence for each response
variable (CO2, CH4, and N2O). Rows with “n.a.”
indicate that the predictor variable was not retained in the final model.
CO2
CH4
N2O
Predictor
Coefficient
Coefficient
Coefficient
TDN
1.08∗
n.a.
1.10∗
Temperature
-0.22
0.25
-0.26
DO
-0.46
-0.27
-0.37
HIX
0.09
-0.15
0.13
BIX
0.11
n.a.
0.15
%IC
n.a.
-0.16
0.14
%SWM
0.18
0.16
0.31
log(DOC : NO3-)
0.32
0.55∗
0.19
Overall model fit
Adjusted r2
0.78
0.5
0.78
P value
< 0.008∗
< 0.0008∗
< 0.008∗
Covariance among GHG abundance and C : N stoichiometry
AOU ranged from -180.9 to 293.9 across all sites and sampling dates;
however,
AOU was only negative (net oxygen production along surface and subsurface
flow paths) in 6 % of samples, or 43 out of 691 measurements. N2O was
significantly but weakly correlated with AOU (p < 0.008, r2 = 0.12) and
strongly correlated with xsCO2-AOU (p < 0.008,
r2= 0.87). The log of CH4 saturation ratio was very weakly correlated
with AOU (p < 0.008, r2= 0.01) as well as xsCO2-AOU
(p < 0.008, r2= 0.07). The relationships between xsCO2-AOU and both N2O and CH4 saturation ratios were also
significantly different between categories (Fig. 4). There was an overall
negative relationship between DOC and NO3-, with a significant
interaction with infrastructure category (Fig. 4c; ANCOVA, p value < 0.008).
Scatterplots of (a) N2O saturation vs. xsCO2-AOU
(µM),
(b) CH4 saturation vs. anaerobic CO2, and (c) relationships between
NO3- and DOC. Lines denote significant (p < 0.01)
correlations among gas or solute concentrations, which vary by
infrastructure category.
![]()
Longitudinal variability in CO2 (a–b), CH4 (c–d), and
N2O (e–f) saturation ratios from spring and fall synoptic surveys of
Dead Run and Red Run. Dotted lines denote tributaries to each watershed,
while straight lines denote the main stem sites.
![]()
Longitudinal patterns in GHG saturation
Spatial variability in GHG saturation was examined in order to evaluate
whether concentrations measured in tributaries were consistent between
headwaters and the larger third-order watersheds of Red Run and Dead Run
respectively (Fig. 5). Multiple linear regressions yielded a set of distinct
controlling factors on saturation of each gas. The optimal models for
CO2 and N2O were similar and included the log of drainage area,
TDN concentration, log of discharge, and TDN × discharge interaction term.
The CO2 model also included the DOC : TDN molar ratio. The optimal model for
CH4 saturation was slightly different and included the log of drainage
area, season (spring vs. fall), DOC concentration, and DOC : TDN molar ratio
(Table 6). TDN concentration was not included in the optimal model for
CH4. Watershed location (tributary vs. main stem) was not included in
the final model for any of the three gases.
Greenhouse gas emissions
GHG emission rates were sensitive to differences in modeled k600.
Despite having medium-to-low gas saturation ratios compared with other
sites, DRKV had the highest GHG emission rates on all dates. This is due in
part to having the highest slope (0.10 m m-1) and thus the highest modeled
k600 (m d-1). Our 37 estimates of k600 ranged from 2.4 to
122.6.1 m d-1. Site-averages for
k600 varied from 5.39 ± 0.73 to 28.0 ± 7.0 m d-1. The median value for all k600
estimates was 13.24 m d-1. This range of values and site-averaged
values extends beyond that measured by Pennino et al. (2014) of 0.5 to
9.0 m d-1. The discrepancy between Pennino et al.'s (2014) k600
measurements is driven by differences in channel gradient. Gradients in the
present study ranged from 0.01 to 0.1, while Pennino's ranged from 0.001 to
0.016 m d-1. Channel gradient (S) is also the parameter with the
greatest uncertainty, thus warranting cautious interpretation of our gas
emission estimates.
Summary of gas flux estimations for the four sites with continuous
flow data. Average, standard error (SE), and number of measurements (n)
are listed for CO2 (g C m-2 d-1), CH4
(mg C m-2 d-), N2O (mg N m-2 d-), and predicted k600
(m d-1).
Infrastructure typology
Site
Parameter
Minimum
Maximum
Mean
SE
n
Stream burial
DRAL
CO2
2.37
23.12
11.51
6.12
5
Inline SWM
DRGG
53.28
548.01
134.55
30.18
3
Inline SWM
DRKV
3.39
23.81
10.30
1.74
18
Floodplain preservation
RRRB
0.61
5.51
2.55
1.10
11
Stream burial
DRAL
CH4
7.71
23.67
14.09
4.88
5
Inline SWM
DRGG
2.27
1339.62
102.51
75.57
3
Inline SWM
DRKV
3.26
62.98
16.80
5.29
18
Floodplain preservation
RRRB
2.19
12.11
6.69
2.19
11
Stream burial
DRAL
N2
2.13
24.21
12.33
6.43
5
Inline SWM
DRGG
60.45
565.17
149.63
33.91
3
Inline SWM
DRKV
1.90
8.61
5.14
0.79
18
Floodplain preservation
RRRB
2.57
16.98
7.03
2.63
11
Stream burial
DRAL
k600
3.84
19.20
10.97
4.47
5
Inline SWM
DRGG
12.82
122.59
28.02
7.06
3
Inline SWM
DRKV
2.40
8.89
5.39
0.73
18
Floodplain preservation
RRRB
2.57
13.91
6.45
2.33
11
Covariates and model fit parameters for linear models describing
drivers of gas saturation ratios (CO2, CH4, and N2O) from
longitudinal surveys of Dead Run and Red Run. X's denote that a given
parameter was used in the final model while dashes (–) denote that parameters were not
used.
Covariates tested
CO2 sat. ratio
CH4 sat. ratio
N2O sat. ratio
Log of drainage area (km2)
X
X
X
Watershed (Dead Run vs. Red Run)
–
–
–
Season
–
X
X
DOC (mg L-1)
–
X
–
DIC (mg L-1)
–
–
–
TDN (mg L-1)
X
–
X
Log of Q (m3 s-1)
X
–
X
Location (tributary vs. main stem)
–
–
–
DOC : TDN molar ratio
X
X
–
TDN x log of drainage area interaction
X
–
X
DOC x log of drainage area interaction
–
–
–
Model AIC
336.85
542.14
263.59
Overall model r2
0.789
0.153
0.795
Overall model p value
< 0.008
0.0082
< 0.008
Site-average CO2 emissions ranged from 6.4 ± 2.3 g C m-2 d-1 at DRAL (±standard error) to 134 ± 30.2 at DRKV.
Mean emission rates for DRGG and RRRB were 11.5 ± 6.1 and 10.3 ± 1.7 respectively. Site-average CH4 emissions ranged from 2.6 ± 1.1
at DRAL to 102.5 ± 75.6 mg C m-2 d-1 at DRKV. N2O
emissions ranged from 5.1 ± 0.8 at RRRB to 149 ± 33.9 mg N m-2 d-1 at DRKV. The full range of values and standard errors for
fluxes are listed in Table 5.
Discussion
Overview
This study showed strong relationships between urban water quality and GHG
saturation across streams draining different forms of urban infrastructure.
N2O and CO2 saturation was correlated with nitrogen
concentrations but did not differ between infrastructure typologies. DOC : NO3- did differ among the four infrastructure categories, however
(Table 2). While infrastructure categories did not show a significant
predictor of GHG saturation in streams, the gradients in DOC : NO3-
found across all categories were strongly correlated with GHG saturation.
Stoichiometric variation may thus serve as a predictor of GHG saturation
downstream where land cover and infrastructure does not. While direct GHG
loading to streams from leaky sanitary and/or stormwater infrastructure
may play a role, the strongest predictors of GHGs in this study were
continuous/environmental variables (i.e., TDN and DOC concentrations, DO,
temperature), rather than categorical (infrastructure category).
Relationships between anaerobic xsCO2-AOU
and N2O saturation
further suggest that anaerobic metabolism contributes to N2O production
along hydrologic flow paths (Fig. 4).
C : N stoichiometry as an indicator of microbial metabolism
By comparing various forms of infrastructure, results from this study
support a growing understanding of the biogeochemical consequences of
expanded hydrologic connectivity in urban watersheds. Strong inverse
relationships between DOC and NO3- present across all
infrastructure categories (Fig. 4c) suggest that organic carbon availability
modulates inorganic nitrogen loading to streams. DOC availability has been
shown to control NO3- concentrations across terrestrial and
aquatic ecosystems through a variety of coupled microbial processes (Hedin
et al., 1998; Kaushal and Lewis, 2005; Taylor and Townsend, 2010).
Additionally, the average DOC : NO3- ratio (i.e., the slope of this
relationship) varied significantly across categories. Variation in this
relationship is likely driven by a combination of differential N loading
across categories as well as different capacities for microbial N uptake and
removal.
We speculate that the location of infrastructure on the landscape may affect
the relative importance of direct anthropogenic loading vs. microbial
processes on DOC : NO3- ratios of stream water. For instance we
found high concentrations of NO3- and low DOC in streams
draining septic systems. Much of this excess NO3- is likely from
septic plumes, but the lack of DOC may be the result of microbial C
mineralization along subsurface flow paths. On the other end of the spectrum,
there were very low NO3- and TDN concentrations
in streams draining watersheds in the
floodplain preservation category, which were also newly developed. In this
case, the higher C : N may have been driven by lower N leakage rates as well
as improved ecological function of the preserved floodplain wetlands to
remove any N that does enter the groundwater from stormwater or sewage
leaks.
Understanding the spatial variability in N2O concentrations, as well as
the processes responsible for N2O production and NO3- removal
in watersheds, is useful for informing watershed management. The relationship
between N2O and CO2 can provide insight into production mechanisms
because nitrification consumes CO2 while denitrification simultaneously
produces N2O and CO2. We found a strong positive relationship
between N2O saturation and CO2 concentrations, suggesting that
denitrification was the primary source of N2O (Fig. 5c). By contrast,
very low DOC : NO3- ratios (Fig. 2) in stream water with the highest
N2O saturation (Fig. 3a) suggest that nitrification was the dominant
process at these sites. Taylor and Townsend (2010) suggest that the ideal
DOC : NO3- stoichiometry for denitrification is 1:1 and that
persistent conditions below that are more ideal for nitrification. DOC : NO3- was consistently below 1 in streams in septic system
infrastructure, suggesting that in-stream denitrification would be carbon
limited. We measured DOC : NO3- consistently above 1 at sites in
riparian/floodplain preservation typology, suggesting NO3- was
limiting for in-stream denitrification in this infrastructure category.
Conversely, the mean stoichiometric ratio was consistently near 1 in sites
with inline SWM wetlands and stream burial, suggesting that denitrification
may be occurring within the stream channel at these sites. While DOC : NO3- stoichiometry in watersheds with septic systems appeared more
favorable for nitrification, the positive xsCO2-AOU vs. N2O
relationships in these streams suggest that these gases were produced
anaerobically (by denitrification). One possible explanation for this
discrepancy is that the N2O and CO2 observed in the stream were
produced under stoichiometric conditions more favorable for denitrification
along groundwater flow paths prior to emerging in the stream channel.
Denitrification occurring along groundwater flow paths may draw down the DOC
concentration as it is converted to CO2; however, the initial N load in
septic plumes may be too high to noticeably decline. Pabich et al. (2001)
documented this phenomenon, in which DOC concentrations in a septic plume
were quite high (> 20 mg L-1) in the upper part of the
plume and declined exponentially, resulting in a very low DOC : NO3-
ratio at depth.
Overall, the relationships between CH4 and CO2 were much weaker
and more variable than the relationships between CO2 and N2O
(Fig. 4). While CO2 and CH4 are sometimes correlated in wetlands
and rivers with low oxygen (Richey et al., 1998), this was not the case for
our study sites. Instead, CO2 and N2O were highly coupled,
suggesting prevalence of NO3- as a terminal electron acceptor over
CO2.
Effects of infrastructure on N<inline-formula><mml:math id="M434" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>O saturation and emissions
The present study documents some of the highest N2O concentrations
currently reported in the literature for streams and rivers, ranging from
0.009 to 0.55 µM, with a median value of 0.07 µM and mean of
0.11 µM N2O-N. This range of concentration is greater than that
reported for headwater agricultural and mixed land use streams in the
Midwestern United States (0.03–0.07 µM, Werner et al., 2012; 0.03
to 0.15 µM, Beaulieu et al., 2008). A similar range of dissolved
N2O concentrations was reported for macrophyte-rich agriculturally
influenced streams in New Zealand (0.06 to 0.60 µM, Wilcock and
Sorrell, 2008). The only report of higher dissolved N2O concentrations
in streams is from a subtropical stream receiving irrigation runoff,
livestock waste, and urban sewage (saturation ratio maximum of 60 compared with
47 in this study; Harrison et al., 2005).
Average daily N2O emissions were high, ranging from 5.1 to 149.6 mg N2O-N m2 d-1. Our value rates fall on the high end compared
with numerous studies of N2O emission from urban and agriculturally
influenced waterways, including agricultural drains in Japan (maximum = 179 mg N m2 d-1; Hasegawa et al., 2000) or the Humber Estuary,
UK (maximum = 121 mg N m2 d-1; Barnes and Owens, 1998). When the highest site
(DRKV) is removed, these average daily fluxes remain high (range from 5.1 to
12.3 mg N m2 d-1) compared with estimates reported for nitrogen-enriched agricultural and mixed land use streams in the Midwestern U.S. from
Beaulieu et al. (2008) (mean = 0.84 and maximum = 6.4 mg N2O-N m2 d-1). Laursen and Seitzinger (2004) reported higher maximum
rates (20 mg N m2 d-1) to our overall median N2O emission
rates (13.8 mg N m2 d-1) and the maximum daily rates measured in
tropical agricultural streams in Mexico (mean = 1.2 maximum = 58.8 mg N2O-N m2 d-1; Harrison and Matson, 2003). While our measured
N2O saturation ratios were highly correlated solute concentrations and
redox conditions (Table 4), emission rates were sensitive to the gas transfer
velocity (k600), which varied by 2 orders of magnitude in our study
(Table 6), and fell within the range of values estimated by Raymond et
al. (2012).
Correlations between TDN and N2O concentrations in this study highlight
the role of urban N loading on GHG production along urban flow paths, which
include groundwater, within pipes, and along the stream networks (Tables 3
and 4). While urban streams receive a mixture of different N sources
including fertilizer, wastewater, and atmospheric deposition (e.g., Kaushal et al., 2011; Pennino et al., 2016), the location of aging gravity sewers
adjacent to stream channels is likely to influence the relative importance
of sewage on N and N2O loading to streamwater. While this source of
N2O emission is likely a small portion of the global budget, gaseous
losses of N can contribute a significant portion of watershed-scale N budgets,
which are relevant to nutrient management (Gardner et al., 2016). N2O
emissions from uncollected human waste (i.e., leaky sanitary sewer lines,
septic system effluent, dug pits) are largely unmeasured globally (Strokal
and Kroeze, 2014; UNEP, 2013) and warrant further study in the context of
watershed management as well as local GHG accounting. Direct emissions from
wastewater treatment plants are well documented (Foley et al., 2010;
Townsend-Small et al., 2011; Strokal and Kroeze, 2014; UNEP, 2013); however, the
upstream losses of N2O from delivery pipes into streams and rivers are
not well documented (Short et al., 2014). Short et al. (2014) measured N2O
concentrations in WWTP influent in Australia and determined that sanitary
sewers are consistently supersaturated with N2O, with concentrations
in excess of equilibrium by as much as 3.5 µM. Average daily sewer pipe
xsN2O concentrations were 0.55 µM, which is nearly identical to the
maximum xsN2O measured in the present study (0.54 µM). While
wastewater only contributes a portion of excess N in urban streams, further
accounting for this source is necessary to improve municipal N2O
budgets.
Synoptic surveys of N2O saturation in Red Run and Dead Run in this
study provide evidence that the entire network is a net source of N2O
(Fig. 5). N2O saturation shows a significant decline with increasing
drainage area (Table 6, Fig. 5), suggesting that emissions outpace new
sources to the water column. Variability in gas concentration headwater
sites and along the third-order stream networks is largely explained by
a combination of discharge and/or drainage area as well as N concentrations
and C : N stoichiometry in streamwater.
Effects of infrastructure on CH<inline-formula><mml:math id="M491" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">4</mml:mn></mml:msub></mml:math></inline-formula> saturation and emissions
Methane was consistently supersaturated across all streams in this study
and varied significantly across headwater infrastructure categories. The
highest CH4 saturation ratios were measured in sites with riparian
reconnection (RRRM and RRRB) followed by streams draining inline SWM
wetlands (DRKV and DRGG; Fig. 3 as with CO2). CH4 saturation was
negatively correlated with DO; however, CH4 was positively correlated
with DOC : NO3-. CO2 and N2O, by contrast, were more
strongly and positively correlated with TDN (Table 4). These patterns
suggest that, along with redox conditions, carbon availability may modulate
CH4 production as well.
CH4 concentrations in our study ranged from 0.06 to
6.08 µmol L-1, which is equivalent to the mean ±standard deviation of concentrations
reported by a meta-analysis by Stanley et al. (2016). The saturation ratio (3.0
to 2157) fell within the lower range of previously measured values in
agricultural streams in Canada (saturation ratio of 500 to 5000; Baulch et al., 2011a). Mean daily CH4 emissions estimates in this study ranged from
2.6 to 103.5 mg CH4-C m2 d-1 and are comparable to
measurements in agricultural streams of New Zealand (Wilcock and Sorrel,
2008; 17–56 mg CH4-C m2 d-1) and southern Canada (20–172 mg C m2 d-1, Baulch et al., 2011); however, these studies also measured
ebullitive (i.e., bubble) fluxes, whereas the present study only examined
diffusive emissions. Stanley et al. (2016) reported the average of all
current CH4 emission rates to be 98.7 mg CH4-C m2 d-1, with a minimum of -125.3 and a maximum of 5194 overall. While the
CH4 emission estimates in the present study have a large margin of
uncertainty due to the nature of estimating gas flux parameters as well as
the lack of ebullitive flux measurements, our sites were consistently
sources to the atmosphere throughout the year at both headwater sites
(Fig. 3) and throughout third-order drainage networks (Fig. 5b).
Differences in CH4 abundance across infrastructure categories, as well
as the negative relationship between CH4 saturation and TDN, suggest
that CH4 may increase if TDN declines with the addition of stormwater
wetlands and floodplain reconnection in urban areas.