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Available online at www.sciencedirect.com

ScienceDirect www.journals.elsevier.com/journal-of-environmental-sciences

Methane and nitrous oxide emissions from a subtropical coastal embayment (Moreton Bay, Australia) Ronald S. Musenze1,1 , Ursula Werner1 , Alistair Grinham1,2 , James Udy3 , Zhiguo Yuan1,⁎ 1. Advanced Water Management Centre (AWMC), The University of Queensland, Brisbane, Qld 4072, Australia. E-mail: [email protected] 2. School of Civil Engineering, The University of Queensland, Brisbane, Qld 4072, Australia 3. Healthy Waterways Ltd., Brisbane, Qld 4003, Australia

AR TIC LE I N FO

ABS TR ACT

Article history:

Surface water methane (CH4) and nitrous oxide (N2O) concentrations and fluxes were

Received 10 March 2014

investigated in two subtropical coastal embayments (Bramble Bay and Deception Bay,

Revised 18 May 2014

which are part of the greater Moreton Bay, Australia). Measurements were done at 23

Accepted 14 June 2014

stations in seven campaigns covering different seasons during 2010–2012. Water–air fluxes

Available online 24 December 2014

were estimated using the Thin Boundary Layer approach with a combination of wind and currents-based models for the estimation of the gas transfer velocities. The two bays were

Keywords:

strong sources of both CH4 and N2O with no significant differences in the degree of

Greenhouse gas emissions

saturation of both gases between them during all measurement campaigns. Both CH4 and

Subtropical aquatic systems

N2O concentrations had strong temporal but minimal spatial variability in both bays.

Bay

During the seven seasons, CH4 varied between 500% and 4000% saturation while N2O varied

Methane

between 128 and 255% in the two bays. Average seasonal CH4 fluxes for the two bays varied

Nitrous oxide

between 0.5 ± 0.2 and 6.0 ± 1.5 mg CH4/(m2·day) while N2O varied between 0.4 ± 0.1 and

Uncertainty

1.6 ± 0.6 mg N2O/(m2·day). Weighted emissions (t CO2-e) were 63%–90% N2O dominated implying that a reduction in N2O inputs and/or nitrogen availability in the bays may significantly reduce the bays' greenhouse gas (GHG) budget. Emissions data for tropical and subtropical systems is still scarce. This work found subtropical bays to be significant aquatic sources of both CH4 and N2O and puts the estimated fluxes into the global context with measurements done from other climatic regions. © 2014 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.

Introduction

potentials of around 25 and 300 times that of carbon dioxide (IPCC, 2007; Myhre et al., 2013). N2O is also currently believed to be the single

Methane (CH4) and nitrous oxide (N2O) are two key potent greenhouse

most important ozone depleting substance, a position it is likely to

gases (GHGs). Both CH4 and N2O are long-lived atmospheric trace

retain for years to come (Ravishankara et al., 2009). Of great concern is

gases that remain chemically active for 8–12 years and 114–120 years,

that both CH4 and N2O concentrations in the atmosphere have

respectively (Ehhalt et al., 2001). They have respective global warming

significantly increased since industrialisation and are still on the rise

⁎ Corresponding author. E-mail addresses: [email protected] (R.S. Musenze), [email protected] (Z. Yuan). 1 Department of Civil and Environmental Engineering, College of Engineering, Design, Arts and Technology, Makerere University, P.O. Box 7062, Kampala, Uganda.

http://dx.doi.org/10.1016/j.jes.2014.06.049 1001-0742/© 2014 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.

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(IPCC, 1990; Stocker et al., 2013; Rigby et al., 2008). Yet the significance of a number of the drivers for this increase remains unclear and

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1. Materials and methods

quantitatively the strengths of the various sources and sinks are still highly uncertain (Kirschke et al., 2013).

1.1. Physical setting and monitoring stations

A significant portion of atmospheric and stratospheric CH4 and N2O is of biogenic origin and aquatic systems are believed to be significant sources (Bastviken et al., 2011; Bouwman et al., 1995; Khalil and Rasmussen, 1993; Nevison et al., 1995; Walter et al., 2007). Oceans are estimated to contribute 13%–25% of the global N2O emissions (Bouwman et al., 1995; Nevison et al., 1995) and 1%–4% of the global CH4 emissions (Cicerone and Oremland, 1988; Karl et al., 2008) with a significant proportion of the emissions in near shore areas likely influenced by discharges from rivers and estuaries (Borges and Abril, 2011). Great uncertainty, however, surrounds these estimates mainly due to scarcity of emission measurement data (Bange et al., 1994; Cicerone and Oremland, 1988; Nevison et al., 1995). Measurement data are particularly rare from tropical and subtropical aquatic systems as most marine and coastal system studies in these areas have mainly concentrated on upwelling regions (Charpentier et al., 2010; Codispoti et al., 1992; Naqvi et al., 2009; Naqvi et al., 2000). Application of emission factors from other climatic regions to these areas during regional and global GHG emissions budgeting is likely to cause uncertainty due to neglect of spatial variability, a well known occurrence even at a small areal extent (Bange et al., 1994; Bange et al., 1998; Zhang et al., 2006). Indeed both oceanic N2O and CH4 emissions are not uniformly distributed and it is likely that previous oceanic GHG budgets that were based on open ocean measurements significantly underestimated oceanic emissions (Bange, 2006). Biologically productive coastal and estuarine areas contribute around 60% of the oceanic N2O budget (Bange et al., 1996; Seitzinger et al., 2000) and up to 75% of the global oceanic CH4 budget (Bange et al., 1994). Besides biological processes, other causes for the high N2O and CH4 concentrations and fluxes in coastal areas may include effluent discharges from wastewater treatment plants (Hashimoto et al., 1999; Zhang et al., 2006), ground water input (LaMontagne et al., 2003; Ronen et al., 1988), input from connected creeks, rivers and estuaries (Ferrón et al., 2007; Musenze et al., 2014; Scranton and McShane, 1991; Zhang et al., 2006) and gas seeps (Amouroux et al., 2002; Reeburgh et al., 1991). Another challenge to regional and global GHG emission budgeting is that existing studies on which such estimates would be based do not adequately capture temporal variability as they are often of measurements done in a single season (Borges and Abril, 2011; Zhang et al., 2004). Consequently, the lack of information on temporal variability may constitute a large source of uncertainty during global emissions budgeting. There is, therefore, a need for more studies capturing both temporal and spatial variability from systems in diverse climatic divides to support adequate evaluation of regional and global N2O and CH4 budgets. In this study we present a comprehensive assessment of CH4 and N2O concentrations and water–air emissions variability from two bays that are part of a subtropical coastal embayment in the southern hemisphere. CH4 and N2O concentration measure-

Moreton Bay is 125 km long with an area of ca. 1500 km2 and a catchment area of ca. 22,000 km2. It has an average depth of 6.8 m and a tidal range of ca. 2 m (Dennison and Abal, 1999). It is lagoon–like in setting, with a series of offshore islands that control the movement of the pacific waters into and out of the bay (Fig. 1). It is associated with over 20 major rivers and creeks including the Brisbane River, which is the longest river in South East Queensland. Some of these rivers traverse agricultural and urbanized areas and together with the creeks also receive treated domestic and industrial wastewater effluents, which they discharge into the bays. Although continuous environmental health monitoring shows a deteriorated water quality than the desired (HWPL, 2010, 2012), compared to some bays such as Jiazhou Bay (Zhang et al., 2006) and Tokyo Bay (Hashimoto et al., 1999), both Bramble Bay and Deception Bay can be considered as having relatively little anthropogenic pollution. However, Bramble Bay is affected by the activities at the port of Brisbane, which is a fast growing gateway to the sea for the state of Queensland and some of the bays' immediate surroundings to the west are gradually but steadily being cleared for human settlements. Details of Moreton Bay's physical setting have been given elsewhere (Dennison and Abal, 1999). Moreton Bay is divided into five smaller bays — Deception Bay, Bramble Bay, Central Bay, Eastern Bay and Raby Bay (Fig. 1). This study was done in Bramble Bay and Deception Bay. There are two rivers, Brisbane River and Pine River, discharging into Bramble Bay and one river, River Caboolture, discharging into Deception Bay. Monitoring was done from 13 stations in Deception Bay and 10 stations in Bramble Bay. All stations are part of the bigger monitoring network established under the Environmental Health Monitoring Program (EHMP) by the Department of Environment and Resources Management (DERM) — Queensland Government. Sampling for CH4 and N2O concentration measurements was done in October 2010 (spring-10), February 2011 (summer-11), August 2011 (winter-11), November 2011 (spring-11), February 2012 (summer-12), May 2012 (autumn-12) and August 2012 (winter-12). Physicochemical parameters including temperature, pH, turbidity, dissolved oxygen (DO), electro-conductivity (EC), nitrogen (total, ammonia and oxidised (NOx)), dissolved organic carbon (DOC) and phosphorous were monitored monthly from October 2010 for 23 months as to obtain their temporal trends. Each sampling campaign had two consecutive days, one for each bay. All sampling campaigns started 1 hr after the high tide. In summer 2011, the state of Queensland had torrential rains that caused devastating floods in over three quarters of the state. The bays received a large inflow of the floodwater from the catchments as it drained to the sea.

ments were done at 23 stations in different seasons spanning over two years to adequately capture both spatial and temporal variability. Water–air fluxes were estimated using the Thin

1.2. Sampling and analysis of dissolved CH4 and N2O concentrations and other physicochemical parameters

Boundary Layer Equation (TBLE) with a combination of wind and currents-based models for the estimation of the gas transfer velocity.

Surface water for dissolved CH4 and N2O concentration measurement was sampled from depths not exceeding 0.2 m.

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Fig. 1 – Location map of Bramble Bay and Deception Bay. Dashed lines are boundary demarcations for the bays and polygons for the different sampling stations. Inset is the map of Australia. Triplicate samples of 7 mL each were injected into pre-evacuated Exetainers (Labco, High Wycombe, UK) using a syringe and needle with the exetainer vacuum providing the driving suction. All samples were filtered through 0.22 μm sterile PES filters (Merck Millipore, Kilsyth, Victoria, Australia) during injection into the exetainers. Samples were then kept in a dark icebox and transported back to the laboratory for analysis or storage (at 4 or −20°C; maximum storage time one month prior to analysis). Exetainer headspace pressure balancing to atmospheric level was done with grade 5.0 ultra high purity (UHP) nitrogen (BOC Industrial gases, Australia) in an inflatable glove bag by piercing

the septum with a gauge 23 needle (Terumo Corporation, Springfield, SA, Australia). Headspace CH4 and N2O equilibration with the dissolved phase was done by vortexing for at least 30 sec and leaving the samples to equilibrate for at least 30 min prior to commencement of gas analysis. Headspace CH4 and N2O analysis was done simultaneously with a 7890A Gas Chromatography (GC) system complete with an autosampler (Agilent technologies, Santa Clara, CA, USA). CH4 was analysed using a Flame Ionisation Detector (FID) while N2O was analysed using a micro-Electron Captured Detector (ECD). The detectors were maintained at 250 and 350°C for FID and ECD respectively. The

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GC is fitted with two stainless steel (6 m long, 1/8 in. od) packed columns (HayeSep Q 80/100). The air flow is maintained at 21 mL/min. The make-up gas is Ultra High Purity (UHP) nitrogen (BOC Gases, Linde group) certified by the National Association of Testing Authorities (NATA) to be of 99.99% purity. Calibration was made by dilution of two mixed standards having concentrations of 50.53 ±0.51 ppm N2O and 249.8 ± 1.3 ppm CH4 and a second one having 0.5 ± 0.01 N2O and 1.8 ± 0.02 CH4 all in nitrogen (BOC Gases) with NATA certification to 95% confidence. The standard error on standards measurements was 0.4 ± 0.2% and 2.5 ±1.1% for N2O and CH4 respectively. Reproducibility for dissolved gas measurements was tested on samples from the Brisbane river estuary with samples from the same station used for the tidal influence study as described above. The standard error on 15 replicate samples was 1.7% and 3.0% for N2O and CH4 respectively. CH4 and N2O concentrations in the liquid phase (Cobs) were determined from the measured headspace gas concentrations by backward calculations using Henry's Law (e.g., Alberto et al., 2000; Guisasola et al., 2008). The exact volume of the liquid sample was determined by gravimetrically measuring the exetainers. To correct for possible exetainer background CH4 and N2O contamination, we determined exetainer residual air volumes by measuring the average vacuum in each of the used exetainer batches. We then corrected the dissolved phase concentrations by assuming background residual air mixing ratios of 1800 and 325 ppbv for CH4 and N2O, respectively. To measure physicochemical water parameters (pH, DO, EC, temperature and turbidity), we used a multi-parameter probe YSI 6920 Sonde (YSI incorporated, Ohio, USA). These parameters were measured both in surface water and as water column profiles at each of the measurement stations. The probe was calibrated before measurements and corrected for any drifting at the end of each measurement day. Water depth was measured with sonar depth sounders installed on the research boats and verified with the YSI probe during water column measurements by lowering the probe through the water column until the probe guard touched the bottom sediments. Salinity was determined from the relationship with conductivity and temperature using algorithms of the practical salinity scale and the international state of seawater (UNESCO, 1983). Chlorophyll-a was analysed as a constituent component of phytoplanktonic biomass in accordance with the standard methods for testing of water and wastewater (American Public Health Association (APHA), 1998). Chlorophyll-a samples were collected by filtering a known volume of surface water through a Whatman 1 μm glass microfibre filter paper at pressures not exceeding half atmospheric levels as to ensure non-disruption of chloroplasts that would potentially lead to degradation of the chlorophyll. Excess moisture in the filter papers was removed by blotting and the filter papers kept in screw capped polypropylene tubes with 0.01 g magnesium carbonate, which was used as a buffer during the extractions. Samples were immediately wrapped in aluminium foil and placed on ice in a dark, insulated container to lower the sample temperature and prevent chlorophyll degradation. All extractions were done with 90% acetone and the chlorophyll-a concentration was determined using the monochromatic method following determination of absorbance at 663 and 750 nm (Lorenzen, 1967).

85

Samples for the analysis of total nitrogen, dissolved organic carbon (DOC) and dissolved phosphorous concentrations were collected with well-rinsed 60 mL syringes. Nitrogen and phosphorous measurements were done on a monthly basis throughout the study period but DOC was measured only during November 2010 to June 2011. Except for total nitrogen, all samples were filtered with 0.45 μm PES filters (Merck Millipore, Kilsyth, Victoria, Australia) into clean collection bottles well rinsed with the same sample water. All samples were kept in a dark icebox and transported back to the lab within 2 hr of sampling for analysis or storage. Dissolved gas, nitrogen and phosphorous samples were stored at either 4 or − 20°C before testing. All DOC samples were stored at 4°C. Phosphorus, nitrate (NO−3), nitrite (NO−2) and ammonium (NH+4) concentrations were analysed with an automated LACHAT 8000QC flow injection analyser (FIA) (Lachat instruments, Colorado, USA). Total nitrogen was analysed using colorimetry in accordance with standard methods for the examination of water and wastewater (American Public Health Association (APHA), 1998). DOC was analysed by high temperature oxidation and non-dispersive infrared detection (NDIR) using an Elementar HighToc2 system (Elementar Analysensysteme GmbH, Hanau, Germany) by the Queensland Health Scientific Services Laboratories.

1.3. CH4 and N2O saturation The degree of N2O and CH4 saturation in the surface waters was determined from the observed respective Cobs values and the corresponding atmospheric equilibrium concentrations (Csat) (Eq. (1)). To determine Csat, we used the coefficients of Yamamoto et al. (1976) and Weiss and Price (1980) with assumed atmospheric mixing ratios of 1800 and 325 ppbv for CH4 and N2O, respectively. %N2 O or CH4 saturation ¼ ðC obs =C sat Þ  100%

ð1Þ

1.4. Flux estimations CH4 and N2O fluxes (F (mg/(m2·day))) were estimated from Cobs (mg/m3), Csat (mg/m3) and the gas transfer velocity, k (m/day), using the Thin Boundary Layer Equation (Eq. (2)): F ¼ kðC obs −C sat Þ:

ð2Þ

In coastal areas, k is influenced by various factors (UpstillGoddard, 2006) but wind and currents are likely the major controls in its parameterisation in bays (Hahm et al., 2006). A combination of wind-based models and currents-based models would thus be the best approach in estimating k in bays. We used the model of Wanninkhof (1992) (wind-based) and O'Connor and Dobbins (1958) (currents-based) to estimate k (e.g., Borges et al., 2004; Borges and Abril, 2011; Musenze et al., 2014). To estimate the likely uncertainty in the emissions due to the used k estimation models, we also estimated fluxes by using combinations of other wind-based models and current-based models that included Liss and Merlivat (1986) (hereafter LM86), Nightingale et al. (2000) (hereafter N00), Raymond and Cole (2001) (hereafter RC01), Ro and Hunt (2006) (hereafter RH06), Owens et al. (1964) (hereafter OW64), Langbein and Durum (1967) (hereafter LD67),

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and Wilcock (1984) (hereafter W84) (see Eqs. S4–S12, e.g., Musenze et al., 2014). Wind data were obtained from the Bureau of Meteorology's Redcliffe Jetty automatic wind station (station No. 040958, see Fig. 2), which is situated in the middle of the two bays. Records were taken every 10 min and averaged at 30 min intervals as to minimise uncertainty in k estimates due to wind speed variability. This allowed the determination of the gas transfer velocity due to wind, kwind (Eq. (3)) (Wanninkhof, 1992) for the entire study period with a very fine resolution of 30 min. kwind ¼ 0:39ðSc =660Þ−0:5 ð ln ð10=Z 0 Þ= ln ðZ=Z 0 ÞÞ

2

Z 0

T

U 2Z dt

ð3Þ

where, Uz (m/sec) is the measured wind speed at height Z (m) above the roughness height Zo (m). We assumed neutral conditions and stability of the boundary layer for which the typical value for Zo is 2 × 10− 4 m (Clark et al., 1994; Large and Pond, 1981). SC (Schmidt number) for CH4 and N2O was determined from its dependence on the in-situ temperature and salinity using the formulations of Wanninkhof (1992) (Eqs. S(1)–S(3)). Current data at 20 min intervals were obtained using the CSIRO RWQM3 real time model (CSIRO, 2010). Current data were available for each of the measurement stations covering three seasons (6th July 2011 to 29th February 2012), viz; winter 2011, spring 2011, and summer 2011. We assumed that these data were representative for similar seasons for which data were not available. We averaged the currents for summer and winter to obtain the currents for autumn at the respective measurement stations. The gas transfer velocity due to currents, kcurrents, was determined as in Eq. (4) (O'Connor and Dobbins, 1958), with a resolution of 20 min. kcurrents ¼ 1:829ðSc =600Þ−0:5

Z

T

w0:5 h

−0:5

1.5. Statistical analyses ð4Þ

dt

0

where, w (cm/sec) is the current velocity and h (m) is the water depth at the measurement station. SC was determined as explained above.

To test for differences amongst the different seasonal measurements, we pooled the respective station percent N2O and CH4 saturation values into groups representing the different measurement seasons. We then analysed for homogeneity

Spring 2010

Spring 2011

Autumn 2012

Summer 2010

Summer 2011

Winter 2012

0

a

-2 Depth below suface (m)

Depth below suface (m)

0

The resultant gas transfer velocity was then determined as k = kwind + kcurrents (e.g. Borges et al., 2004; Chu and Jirka, 2003; Upstill-Goddard, 2006). To determine the fluxes for the investigated seasons from measured surface water concentrations, we assumed that the measured CH4 and N2O concentrations were representative of the respective measurement campaigns' seasonal average surface water CH4 and N2O concentrations. The average seasonal fluxes at the measurement stations were determined by integrating Eq. (2) over the season using the outcomes of Eqs. (3) and (4) as determined from the high temporal resolution wind and current data. Daily average fluxes were obtained by dividing the total flux by the number of days in the season. To obtain the total emissions from the study area, we divided the bays into polygons around the measurement stations by using the mid-points between neighbouring stations (Figs. 2 and 3). The respective polygon areas were determined using ImageJ 1.45s (Wayne Rashband, National Institute of Health, Bethesda, MD, USA). The total emission from the area was then obtained by multiplying the station flux with its corresponding polygon area. To determine the average seasonal fluxes for the respective bays, we divided the total emissions with the respective total bay area. This approach of area weighting caters for spatial variability of fluxes within the bay. To determine the average annual emissions from the respective bays, we averaged the emissions from similar seasons for seasons measured more than once during the study, before summing with data from other seasons. To determine the weighted emissions (t CO2-e), we multiplied the average seasonal or annual CH4 and N2O emissions by 25 and 300, respectively (Myhre et al., 2013). Total emissions (t CO2-e) were then determined as a sum of the weighted emissions.

-4 -6 -8

-4 -6 -8

-10

-10

-12

-12 Temperature (°C)

b

-2

DO (mg/L)

Fig. 2 – Water column temperature (a) and DO (b) as measured at station 3 in Bramble Bay. Measurements were done on the same day and time as for the surface water CH4 and N2O sampling during spring 2010, summer 2011, spring 2011, summer 2012, autumn 2012 and winter 2012.

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Chlorophyll-a (μg/L)

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30

Spring

25

a

Summer

Autumn

Winter

Spring

Summer

Autumn

Winter

20 15 10 5 0

Total nitrogen-N (mg/L)

b

Oct-10 Nov-10 Dec-10 Jan-11

Feb-11 Mar-11 Apr-11 May-11 Jun-11 Jul-11

Aug-11 Sep-11 Oct-11 Nov-11 Dec-11 Jan-12

Feb-12 Mar-12 Apr-12 May-12 Jun-12 Jul-12

Aug-12

Month of monitoring

Fig. 3 – Trends of chlorophyll-a concentration (a) and total nitrogen concentrations (b) in Bramble Bay and Moreton Bay. Error bars are standard deviations on replicate measurements from the different measurement stations (n = 10 and 13 in Bramble Bay and Deception Bay, respectively). (Sept–Nov is spring, Dec–Feb is summer, Mar–May is autumn, Jun–Aug is winter).

of variances amongst the groups using the Bartlett Chi-square statistic prior to parametric analysis of variance (ANOVA) with post-hoc tests. Upon finding heterogeneity, we would perform non-parametric One Way Analysis of Variance using the Kruskal– Wallis statistic. To analyse for differences between the levels of saturation in the two bays, we pooled together all the seasonal saturation values into two groups for the respective bays for N2O and CH4 separately. To understand the influence of the various physicochemical factors on the observed degree of N2O and CH4 saturation, we pooled the respective N2O and CH4 percent saturation values from the different seasons together with the corresponding physicochemical parameters from their measurement stations. We then performed a series of regression analyses for the different factors. To verify whether discharges from estuaries connected to the bay have an impact on the observed degree of N2O and CH4 saturation in the bays, we determined average seasonal percent N2O and CH4 saturation values in Bramble Bay and related them with the respective seasonal percent N2O and CH4 saturation values measured within the Brisbane River mouth before discharging into Bramble Bay as reported by Musenze et al. (2014). All tests were performed at the 0.05 significance level using StatPlus®mac:2009 5.8.3.8 (AnalystSoft Inc., Alexandria, VA, USA).

2. Results 2.1. Physicochemical water parameters The water column was always well mixed with only minor stratification during spring (Fig. 2a). The water column was also always well oxygenated but DO decreased from the surface towards the bottom in summer 2011 (Fig. 2b). Table 1 summarises other measured physicochemical water parameters. Salinity was remarkably low in summer 2011 compared to other seasons. Chlorophyll-a concentrations were closely similar in the two bays and peaked in summer 2011 (Fig. 3a). Total nitrogen (Fig. 3b),

organic nitrogen (results not shown), total phosphorous (Fig. S1) and reactive phosphorous (results not shown) showed strong seasonality being highest in summer and lowest in winter in both bays. NH+4–N and NOx–N concentrations were low (data not shown) and often close to our measurement's detection limit (0.002 mg/L). Dissolved organic carbon (DOC) was between 2-9 mg/L and was highest in summer 2011 (Fig. 4). Some parameters e.g. pH showed minimal spatial and temporal variability over the study period varying between 8.1 ± 0.0 and 8.3 ± 0.1.

2.2. Surface water N2O and CH4 concentrations Both Bramble Bay and Deception Bay were supersaturated with N2O in the surface water relative to atmospheric equilibrium levels (Fig. 5). The degree of saturation between the two bays was not significantly different (p > 0.05). For the entire study period, N2O saturation ranged between 128% and 255% (8.0–18.9 nM) in the two bays. In the respective bays, the degree of N2O saturation was generally uniform with minimal spatial variability. However, there were significant differences amongst the degree of saturation measurements from the different seasons (Hdf,n = 48.16,70, p < 0.001 and Hdf,n = 67.16,88, p < 0.001 for Bramble and Deception Bay respectively). Overall, N2O saturation increased from spring 2010 through summer 2011, following flooding in the catchments, to winter 2011. It then decreased through the seasons to low values measured in autumn and winter 2012 (Fig. S2a, b). N2O saturation (average ± SD) was in the range (141 ± 9)%–(215 ± 19)% in Bramble Bay and 137 ± 5%–202 ± 11% in Deception Bay. Both bays were also supersaturated with CH4 during all measurement campaigns (Fig. 6). Like N2O, the degree of CH4 saturation in both bays was not significantly different (p > 0.05). For the entire study period, CH4 ranged between 510% and 4000% saturation (12–81 nmol/L) in the two bays. Similarly, there was minimal spatial variability of the degree of saturation within the respective bays for most of the measurement campaigns with

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Table 1 – Bramble Bay and Deception Bay physicochemical water parameters. Site Bramble Bay

Deception Bay

Monitoring campaign 4 Oct 10 11 Feb 2011 8 Aug 2011 7 Nov 2011 17 Feb 2012 18 May 2012 13 Aug 2012 5 Oct 2010 14 Feb 2011 9 Aug 2011 8 Nov 2011 20 Feb 2012 21 May 2012 16 Aug 2012

a

Temperature (°C) 20.9 26.7 18.3 25.0 26.2 19.4 15.9 22.1 27.3 17.7 25.4 28.8 19.3 17.7

± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.5 0.1 0.5 0.23 0.3 0.6 0.4 0.9 0.5 0.5 0.6 0.6 0.3 0.7

Salinity b 33.5 26.5 34.5 33.4 30.1 33.3 33.2 33.1 25.4 33.9 35.9 27.6 31.0 33.8

± ± ± ± ± ± ± ± ± ± ± ± ± ±

Dissolved Oxygen - DO (mg/L)

1.5 2.0 0.7 5.9 2.2 0.8 0.6 1.3 1.4 1.0 0.3 1.9 1.0 0.7

7.5 7.3 7.8 7.3 6.9 6.7 8.2 7.2 6.8 7.9 6.6 7.1 7.6 7.8

± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.3 0.3 0.1 0.9 0.3 0.4 0.0 0.2 0.3 0.2 0.1 0.6 0.3 0.2

Turbidity (NTU) 4.3 7.4 4.0 9.0 9.0 4.0 0.7 5.3 15.7 1.6 11.1 11.5 4.0 2.0

± ± ± ± ± ± ± ± ± ± ± ± ± ±

2.8 3.6 3.3 3.4 3.8 1.1 0.6 3.6 9.4 2.8 14.4 14.2 2.1 1.9

Values are average ± SD based on 1-day measurements in the indicated months (n = 10 in Bramble Bay and n = 13 in Deception Bay). a Oct and Nov is spring, Aug is winter, Feb is summer and May is autumn. b Salinity is in practical salinity units (psu) normalized to a temperature of 25°C.

spatial variability being generally lower in Bramble Bay than in Deception Bay (Fig. S2c, d). However, in some seasons, elevated concentrations were found in some near shore stations, especially closer to rivers' and creeks' mouths (e.g. Stations 1, 5, 6, 7 and 18) creating a weak west–east saturation gradient in the bays. Considering all measurements, there is no clear strong seasonal pattern. Within the respective bays, there were significant differences amongst the seasonal percent CH4 saturation levels measured with variability majorly contributed by the summer-12 and winter-12 measurements (Hdf,n = 46.86,69, p < 0.001 and Hdf,n = 48.36,88, p < 0.001) for Bramble Bay and Deception Bay respectively. CH4 saturation (Average ± SD) varied between (700 ± 190)% and (2690 ± 160)% in Bramble Bay and 970 ± 290 and (2830 ± 560)% in Deception Bay. The observed degree of CH4 saturation positively correlated with temperature (p < 0.05, r2 = 0.4). It did, however,

negatively correlate with salinity (p < 0.001, r2 = 0.4) but to varying levels in the different seasons (e.g., p < 0.001, r2 = 0.8; Bramble Bay, winter-11). Relationships with the other measured parameters were weak.

2.3. Water–air fluxes Water–air fluxes are driven by the gas transfer coefficient, k, and the concentration gradient between surface water and the atmosphere. The determination of k incorporated the influences of both wind and currents. There was a strong seasonality of k. It was highest in summer and lowest in winter with wind being a stronger driver for k than currents (Fig. 7). Summer and spring had stronger winds than winter and autumn (Fig. S3). N2O fluxes were low in spring 2010 but peaked in summer 2011 before decreasing through the seasons up to winter 2012

10 Bramble bay Deception bay

DOC (mg/L)

8 6 4 2 0 Month of monitoring

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in both Bramble Bay (Fig. 8a) and Deception Bay (Fig. 8b). Estimated fluxes at the different measurement stations varied between 0.3 and 3.0 mg N2O/(m2·day) while area-weighted fluxes for the different measurement campaigns for both bays varied between 0.4 ± 0.1 and 1.6 ± 0.6 mg N2O/(m2·day) with minimal differences between the averages of the two bays. Fluxes correlated more strongly with the degree of N2O saturation (p < 0.001, r2 = 0.59) than with changes in the gas transfer velocity (p < 0.001, r2 = 0.21). CH4 fluxes showed strong temporal variability (Fig. 8c, d). Temporal flux patterns were similar to those of the gas transfer velocity as driven by wind (Fig. 7) and were more pronounced than the patterns for the degree of surface water CH4 saturation (see Fig. S1c, d). Summer seasons had the highest and winter the lowest fluxes. Estimated fluxes at the different measurement stations for both bays varied between 0.3 and 9.2 mg CH4·m−2·d−1 while area-weighted fluxes for the different measurement campaigns for both bays varied between 0.5 ± 0.2 and 6.0 ± 1.5 mg CH4·m− 2·d− 1. CH4 fluxes correlated strongly with the degree of surface water CH4 saturation (p < 0.001, r2 = 0.86) and the gas transfer velocity across the water–air interface (p < 0.001, r2 = 0.73).

2.4. Total emissions from each bay Total emissions (t CO2-e) were N2O dominated in both bays. The contribution of N2O (average (min–max)) was 83 (63–90)% and 81 (64–90)% in Bramble Bay and Deception Bay, respectively. Consequently, the emission patterns would be similar to that of the N2O fluxes. Emissions varied between 3.9 and 17.3 t CO2-e/day in Bramble Bay and 4.4 and 19.3 t CO2-e/day in Deception Bay indicating likelihood of significant inter-seasonal emissions variability. All metadata supplementary to this article are available online at http://dx.doi. org/10.1594/PANGAEA.832905.

3. Discussions 3.1. Bays are a strong source of both N2O and CH4 Our study found the bays supersaturated with both N2O and CH4 in all seasons during which measurements were done. Data from tropical and subtropical systems is still scarce (Naqvi et al., 2009) but Oudot et al. (1990) reported the degree of surface water N2O saturation of 123%–132% in the tropical Atlantic Ocean close to the West African coast of Senegal. These tropical measurements are on the lower tail end of our observed degree of N2O saturation (128%–255%), which is also generally higher than that found in temperate bays. Bange et al. (1996) evaluated N2O saturation in coastal waters, marginal seas and coastal upwelling regions from various studies that investigated diverse marine systems. They reported surface water N2O saturation (average (min–max)) of 109 (102–118)% and 176 (108–442)% for coastal waters and marginal seas, and coastal upwelling regions, respectively. With the

exclusion of estuarine systems and river plumes, Bange (2006) on reviewing several studies found N2O saturation in European coastal waters to be (113 ± 21)%, which is lower than the N2O saturation found in our investigated subtropical coastal waters. Zhang et al. (2010) reviewed various studies that included estuarine and riverine plumes. With the exclusion of some data points from the Scheldt, Colne and Humber estuaries, the reported degree of surface water N2O saturation was either below or comparable to our reported degree of N2O saturation. Indeed our reported N2O saturation is at the lower tail end of measurements from some anthropogenically impacted bay and estuarine systems (e.g., Jiazhou Bay and Tokyo Bay; 53%–1630%, Humber estuary and Tees estuary; 140%–6500% N2O saturation) receiving wastewater effluent that enriches their nitrogen content (Barnes and Upstill-Goddard, 2011; Hashimoto et al., 1999; Zhang et al., 2006). Whereas some of the impacted bays were partly sinks for atmospheric N2O, the investigated bays were net N2O sources all year round. The estimated fluxes from the two bays (0.3–3.0 mg N2O/(m2·day)) are higher than estimates from open ocean systems (0.05–0.08 mg/(m2·day), Oudot et al., 2002) and a number of bays (e.g., Jiazhou Bay (0.11–1.7 mg/(m2·day)), Zhang et al., 2006; Bay of Bengal (0.03 mg/(m2·day)), Naqvi et al., 1994) but within range of fluxes from subtropical anthropogenically impacted bays (e.g., Tokyo Bay 0.07–6.7 mg/(m2·day), Hashimoto et al., 1999). This confirms bays as an important source of oceanic N2O (e.g. Bange et al., 1996). Fluxes being within the range of estimates from more N2O saturated bays is likely a result of exposure to stronger winds that enhance the gas transfer velocity in the studied bays. Additionally, previous emission studies in bays including the impacted bays, did not consider the impact of currents, which though not the dominant control of the gas transfer velocity, makes a significant contribution. It is, therefore, likely that previous studies, which have largely not considered the influence of currents on the gas transfer velocity, especially in shallow coastal systems (depth < 10 m), underestimated fluxes (Raymond and Cole, 2001). Our measurements found the two bays supersaturated with CH4 during all the measurement seasons, showing that these bays are a net source of atmospheric CH4 all-year-round. The range of CH4 saturation and fluxes (510%–4000%, 0.3–9.2 mg CH4/(m2·day)) suggests that these subtropical bays could be a stronger CH4 source than most temperate coastal waters, oligotrophic transitional and even some upwelling coastal regions. The degree of CH4 saturation in coastal waters in different climatic regions is widely ranging (e.g., Bange et al., 1994). CH4 saturation in some tropical aquatic systems including rivers has also been reported as broad (e.g., 1496%–51,843%, Koné et al., 2010) while CH4 saturation for a number of coastal systems has been shown to vary between undersaturation to supersaturation (Borges and Abril, 2011). An evaluation of oligotrophic, transitional and upwelling (including tropical and subtropical) coastal systems similarly shows low CH4 saturation in the surface waters ranging between undersaturation (sinks) to supersaturation (sources) (e.g., 86%–438%, −0.008–0.221 mg/(m2·day); Bange et

Fig. 6 – Spatial–temporal variability of the degree of CH4 saturation (in % relative to atmospheric equilibrium) in Bramble Bay and Deception Bay. 100% is atmospheric equilibrium level. Solid dots are locations of measurement, stations numbers are station identifiers and dashed lines are station area boundaries.

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al., 1994). Although some coastal and shelf temperate systems are, partly or entirely, sinks of atmospheric CH4 at some point in the year (e.g. Cynar and Yayanos, 1992; Scranton and McShane, 1991), they generally have higher CH4 saturation and fluxes (e.g., 95%–42,000%, 0.06–968 mg/(m2·day), in Bange et al., 1994; Zhang et al., 2004) compared to the open ocean. Our results also suggest that subtropical bays could be significant GHG sources. Though the higher N2O and CH4 saturation values and fluxes could partly be attributed to the higher temperatures in the subtropics compared to temperate regions (Xing et al., 2005) as the influence of temperature on organic matter mineralisation and biogas production is known to be strong (Ferrón et al., 2007), the influence of other factors such as nutrient loading (especially nitrogen and carbon), external inputs such as from the connected estuaries and creeks amongst others, could as well have been contributory (Ferrón et al., 2007; Scranton and McShane, 1991; Zhang et al., 2006). The impact of these factors needs to be further investigated. Considering all the seven measurement campaigns, N2O dominated emissions (81%) with seasonal contributions of between 63% and 90%. The dominance of N2O highlights the need for further investigations to identify its source(s) as to direct efforts for emission reduction and possibilities of reducing nitrogen availability in the bays if N2O is predominantly produced within the bays.

3.2. There is minimal spatial but strong temporal variability Our results showed minimal spatial variability for both N2O and CH4 saturation and fluxes. Though spatial patterns were not pronounced for both N2O and CH4, some near-shore stations especially under the direct influence of rivers and creeks had elevated concentrations compared to inner bay stations during some of the measurement campaigns. This suggests that the connected estuaries and creeks could be making significant direct and/or indirect inputs of both CH4 and N2O into the bays. Rivers and estuaries are considered to be stronger GHG sources than bays and generally coastal areas with N2O saturation, for instance, ranging between 101% and 2550% (Bange et al., 1996) and CH4 saturation ranging between 580% and 420,000% (Upstill-Goddard et al., 2000). CH4 and N2O concentrations in the Brisbane River estuary discharging into Bramble Bay were measured during the same period as this study (Musenze et

al., 2014). Percent CH4 saturation in the estuary mouth ranged between 1340% and 6060% and was positively correlated with CH4 saturation in Bramble Bay (p < 0.05, r2 = 0.7). Similarly, N2O saturation in the estuary's mouth (140%–270%) also positively correlated with the observed N2O saturation in the bay if the summer 2011 (with severe flooding) measurements were excluded (p = 0.05, r2 = 0.7). The degree of saturation for both CH4 and N2O in the estuary's mouth being higher than the respective degrees of saturation in the bays and the strong positive correlations strongly suggest that the connected estuaries could indeed be making significant direct inputs into the bays. Also, the estuaries and creeks are likely to be depositing fresh sediments and discharging nutrients into the bays that could as well be supporting high carbon and nitrogen cycling rates for both CH4 and N2O production. CH4 and N2O discharge from rivers and estuaries into bays could indeed be significant (Amouroux et al., 2002; Bange et al., 1998; Cynar and Yayanos, 1992; Scranton and McShane, 1991; Zhang et al., 2006). The absence of strong spatial variability is likely to have been partly created by (1) the major inputs (rivers and creeks) being spread along the bays' coastline and/or (2) the strong mixing within the bays that creates homogeneity. On a seasonal basis, the highest and lowest CH4 saturations were measured in summer and winter respectively. Although seasonality for the degree of CH4 saturation was generally not well pronounced, there was a strong seasonality for the CH4 fluxes with highest fluxes in summer and lowest fluxes in winter. This strong seasonality was a result of the combination of inter-seasonal wind speed variability and the surface water CH4 concentration variability. The observed surface water dissolved gas concentration temporal variability should, however, be interpreted with knowledge that in-season variability remains largely unknown. Single season studies with a high measurement frequency would be necessary to assess such variability and evaluate uncertainty associated with limited measurements in estimating seasonal GHG budgets. Seasonal wind variability created strong seasonal k patterns. The contribution of wind, kwind, on k was very strong varying between 72% and 87% of k and over dominated the contribution of currents, kcurrents. It is not surprising therefore that the gas transfer velocity variability followed the patterns of the inter-seasonal wind variability and ended up creating a strong seasonal CH4 flux pattern as the seasonal CH4 concentration pattern was not

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that strong. This finding confirms the importance of wind as a key control of water–air fluxes in the two bays. Though N2O saturation and fluxes did not show a clear seasonal pattern, they had a clear temporal shift. The degree of saturation and fluxes increased from spring 2010 to summer 2011 before decreasing through the seasons to winter 2012. This temporal shift in N2O concentrations was very strong that it masked the strong seasonality of k. Consequently N2O fluxes followed the temporal patterns of N2O saturation. The lack of information on spatial and temporal variability is recognised as a major source of uncertainty in aquatic GHG budgeting (e.g. Bange et al., 1994; Nevison et al., 1995) but studies reporting on both aspects are still scarce (e.g. Bange et al., 1998; Zhang et al., 2006). Many studies may report on spatial variability but they miss capturing seasonal variability (Amouroux et al., 2002; Zhang et al., 2004). Nutrient measurements (e.g. nitrogen (Fig. 3b) and phosphorous (Fig. S1)) showed strong seasonal patterns. Both total nitrogen and phosphorous were highest in the more wet and warmer summers increasing nutrient availability in the bays and lowest in the dry and colder winters. For the period it was measured, water column DOC was also highest in summer. Similarly the peaking of the chlorophyll-a concentrations in summer (especially in 2011) confirms nutrient enrichment in the bays around the time. This high nutrient availability is likely due to increased catchment inflows in the wet summers. The increased nutrient availability in summer coupled with the

warmer temperatures is likely to have enhanced organic carbon contents whose degradation may have lead to a higher CH4 production and thus the observed higher CH4 saturation in summer compared to the other seasons (Ferrón et al., 2007; Inwood et al., 2007; Martin et al., 2001; Xing et al., 2005). The lack of strong correlations between the observed surface water gas concentrations with a number of the measured physicochemical factors, however, suggests that: (1) the measured CH4 and N2O may not have been majorly produced from within the bays, (2) the measured factors were within thresholds as key process controls for nitrogen and carbon cycling in the bays during the different seasons or (3) both CH4 and N2O are majorly produced in sediments and the measured factors do not necessarily reflect the conditions in the sediments (Usui et al., 2001). N2O is majorly produced by nitrification (oxidation of NH+4 to NOx) and denitrification (reduction of NOx to nitrogen gas) while CH4 is produced by methanogenesis. Although there are suggestions for the occurrence of methanogenesis in aerobic water columns (e.g., Damm et al., 2010; Karl et al., 2008), both denitrification and methanogenesis occur principally under anoxic conditions. The water column being well mixed and always oxygenated was conducive for water column nitrification but presumably limited chances for denitrification and methanogenesis. Although we cannot rule out the possibility that high rates of water column nitrification contributed towards keeping the NH+4 concentrations low, the consistent low level

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of NH+4 concentrations implies that water column nitrification was likely limited. Moreover, all known N2O production mechanisms are also enhanced at low DO levels (Naqvi et al., 2009). The high water column DO level is, thus, also likely to have further limited water column N2O production. The observed CH4 and N2O produced from within the bays, therefore, is likely to have been produced in sediments where the anoxic conditions would have been ideal for both denitrification and methanogenesis (Amouroux et al., 2002; Bange et al., 1998; Seitzinger, 1988; Usui et al., 2001). Consequently, nutrient concentrations in sediments rather than in the overlying water may have determined the nitrogen and carbon cycling rates producing the measured surface water CH4 and N2O (LaMontagne et al., 2003; Seitzinger, 1988). Coupling of surface water, water column and sediment studies in different seasons may be necessary to quantify the relative contribution of the different production processes to the observed surface water CH4 and N2O concentrations and also explain temporal variability. Flooding in Summer 2011 is likely to have distorted the temporal patterns of both CH4 and N2O in the bays. During and following the flooding period, the bay received large catchment runoff inflows. The floods distorted the spatial– temporal patterns of CH4 and N2O in the estuaries discharging into the bay (Musenze et al., 2014) and are likely to have had a similar impact on the bays. A consistently low uniform CH4 saturation profile was measured in the Brisbane River estuary in summer 2011. It is likely that the large volume of catchment inflows heavily diluted the CH4 concentrations as observed from the lower salinity in summer 2011 compared to the other measurement seasons. Dilution of CH4 concentrations is a likely common occurrence in tropical systems following (extreme) rain events (Koné et al., 2010). This could conceivably explain why the summer 2011 percent CH4 saturation was lower than that of summer 2012. Mineralisation of the organic matter deposited during these floods may, however, have supported high carbon cycling rates (Borges and Abril, 2011; Inwood et al., 2007) throughout 2011 masking seasonal effects that would have resulted say due to temperature variability and generally resulted in 2011 having significantly higher N2O (Hdf,N = 67.41,95, p < 0.001) saturation compared to 2012. The decrease of DO towards the bottom in the water column in summer 2011 (Fig. 5b) could be a confirmation of a big organic matter load deposited by the floods leading to higher DO consumption by microbial processes. Gradual depletion of the nitrogen and organic matter pool deposited in the bays during the floods could be a reason for the clear gradual decrease of N2O saturation and fluxes up to winter 2012. Given that our study did not target specific N2O and CH4 production processes, further studies including mass balancing that would quantify the inputs from external sources such as estuaries and creeks (Zhang et al., 2006) coupled with water column and sediment isotope studies to elucidate the production mechanisms (Khalil and Baggs, 2005; Toyoda et al., 2009; Whiticar and Faber, 1986) may facilitate a detailed understanding of the CH4 and N2O spatial–temporal patterns within the bays.

3.3. Uncertainty in emission estimates We explored the use of 5 wind-based and 4 current-based models resulting into 20 model combinations to evaluate the

uncertainties associated with the choice of the models (Eqs. S(4)–(12)). A similar approach was also used by Musenze et al. (2014) to evaluate uncertainty in k estimation during flux estimations for the Brisbane River estuary. Consistent with the findings of (Musenze et al., 2014), LM86 and RH06 gave the lowest kwind estimate while RC01 gave the highest kwind estimate (Fig. S4a). Similarly, LD67 and W84 gave the lowest and highest kcurrents estimates (Fig. S4b). Estimates of kwind by different models differed by a factor of 3.7 while kcurrents estimates differed by a factor of 4.7. The combination of LM86 and LD67, and RC01 and W84 would then be used to estimate the respective lowest and highest gas transfer velocities within the two bays. Regardless of the model combinations used to determine the resultant k, the influence of wind always dominated the influence of currents (on average by 80%). Consequently, k estimation would be more sensitive to wind measurement (Hahm et al., 2006) and choice of the wind model than the currents measurement and choice of currents model. This was in contrast to the dominance by currents in the Brisbane River estuary (Musenze et al., 2014) and highlights the relative importance of different factors as regards parameterisation of the gas transfer velocity and generally water–air gas exchange in different systems (e.g. Raymond and Cole, 2001). We extrapolated the total CH4 and N2O emissions from the two bays to be the range 12,400–42,200 t CO2-e/year. The broadness of this range highlights: (1) the uncertainty currently surrounding emissions estimates due to uncertainty inherent in the current estimation models, (2) the current knowledge limitation of the water–air gas exchange and (3) the need to build consensus amongst the existing water–air gas transfer parameterisations (e.g., Nevison et al., 1995). Our study indicates that inter-seasonal CH4 and N2O emissions variability may be significant. The results revealed likelihood of extreme under- (60%) or over- (70%) estimation of annual emissions as a result of attempting to extrapolate single-seasons' estimates to annual emissions. In-season variability, however, remains to be elucidated. There is a need for measurements to be done in all seasons with multiple measurement campaigns in each season whenever possible as to resolve uncertainty associated with temporal variability during estimation of aquatic GHG emissions.

Acknowledgments This work was funded by the Australian Research Council (ARC), Healthy Waterways Ltd and Seqwater through an industry linkage grant (ARC Linkage project # LP100100325). We thank Dr. Alberto Vieira Borges, Dr. Philip M. Nyenje, Dr. R. Kulabako, Dr. U. Bagampadde and Irene Nansubuga for the discussions during data analysis. The authors greatly appreciate the support of EHMP–DERM management, tech staff and crews of Seratta and Seriops cruises (2010–2012) including but not limited to: M. Holmes, R. Williams, J. Fels, P. Hough, M. Davidson, J. Gruythuysen, D. Renouf, J. Ferris, M. Waller, J. Hodge and K. Reeves. Mr. Frank Akampa assisted with ArcGIS mapping work. Additional water quality data was obtained from DERM under the DSITIA single supply license.

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Appendix A. Supplementary data Supplementary data and material associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.jes. 2014.06.049.

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