Riverine N2O production, emissions and export from a region dominated by agriculture in Southeast Asia (Jiulong River)

Agriculture, Ecosystems and Environment 208 (2015) 37–47

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Riverine N2O production, emissions and export from a region dominated by agriculture in Southeast Asia (Jiulong River) Nengwang Chen a,b, * , Jiezhong Wu a , Xingpeng Zhou a , Zhuhong Chen a , Ting Lu a a b

Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, College of the Environment and Ecology, Xiamen University, Xiamen, China Key Laboratory of the Coastal and Wetland Ecosystems, Ministry of Education, Xiamen University, Xiamen, China

A R T I C L E I N F O

A B S T R A C T

Article history: Received 2 December 2014 Received in revised form 17 April 2015 Accepted 20 April 2015 Available online 2 May 2015

Intensive use of nitrogen (N) fertilizers in agriculture and the release of reduced N in human and animal wastes have substantially enhanced nitrous oxide (N2O) production from aquatic systems, mainly through nitrification and denitrification. This study investigated the spatial variation of riverine N2O production and emission from a river network in a region dominated by agriculture (Jiulong River) in southeast China, as well as river N2O yield [=4N2O/(4N2O + 4N2)  100]. Three measurements, encompassing 29 sampling sites across two main tributaries (North River and West River), were carried out in February, May and October 2011 under various hydrological conditions. The results showed that dissolved N2O concentrations were always far above saturation (112–4133%), indicating that the river is a major source of N2O, a potent greenhouse gas. The excess dissolved N2O (4N2O) ranged from 4.2 to 363 nmol L1, and the water–air fluxes of N2O varied from 1.1 to 93.8 mmol m2 d1. Dissolved inorganic N (DIN) was the key factor controlling the spatial variation of 4N2O. High N2O production and emission was found in the upper North River where animal and human wastes dominated riverine N sources. River N2O yield tends to increase during low flow periods and in dam-modified river stretches, probably due to enhanced in-stream nitrification. Riverine N2O export to estuary has increased more than 15-fold since the 1980s. Riverine N2O emission from the Jiulong River falls into the lower range of reported values around the world but is anticipated to increase due to changing hydrology and increasing N loading from agriculture. ã 2015 Elsevier B.V. All rights reserved.

Keywords: Nitrous oxide N2O yield Nitrification Denitrification Greenhouse gas Dam construction Jiulong River

1. Introduction Nitrous oxide (N2O) is an important greenhouse gas that contributes to climate change and stratospheric ozone destruction (Hansen and Sato, 2004). N2O emission currently is the single most important ozone-depleting emission and is expected to remain the largest throughout the 21st century (Ravishankara et al., 2009). A recent study suggested that microbial N transformations (e.g., denitrification and nitrification) convert at least 0.68 Tg y1 of anthropogenic N inputs to N2O in river networks, equivalent to 10% of the global anthropogenic N2O emission rate, three times greater than the amount estimated by the Intergovernmental Panel on Climate Change (Beaulieu et al., 2011). Compared with intensive studies on N2O emissions from terrestrial systems (Stehfest and Bouwman, 2006; Snyder et al., 2009), aquatic systems (e.g., rivers, lakes, and estuaries) have received much less attention and remain

* Corresponding author at: College of the Environment and Ecology, Xiamen University, Xiamen 361102, China. Tel.: +86 592 2182201; fax: +86 592 2182201. E-mail address: [email protected] (N. Chen). http://dx.doi.org/10.1016/j.agee.2015.04.024 0167-8809/ ã 2015 Elsevier B.V. All rights reserved.

a major source of uncertainty in the global anthropogenic N2O budget. Although it is widely recognized that human activities on land increase aquatic N2O emissions, quantification is difficult due to lack of experimental data, and it is still difficult to close the global N2O budget (Ivens et al., 2011). In particular, few measurements are available from coastal rivers and estuaries in Asian developing countries, although DIN export from this region is expected to continue to increase until 2030 (Seitzinger et al., 2010). As one of the largest agricultural regions in the world, China has experienced critical levels of nutrient pollution and eutrophication from small streams to large rivers and adjacent estuaries (Zhang et al., 2007; Tao et al., 2010; Yan et al., 2010; Chen et al., 2011, 2013). Global estimates have suggested that China and southeast Asia account for over 50% of dissolved inorganic N (DIN) export by world rivers (Seitzinger and Kroeze, 1998). Therefore, understanding the production, emission of N2O from such systems is of global significance, and is highly relevant to the current effort to further constrain the budgets of greenhouse gases such as N2O. In addition, riverine N2O also influences the biogeochemistry and N budget of the adjacent estuary, particularly for those river-dominated estuaries. A few studies have addressed N2O in China water

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N. Chen et al. / Agriculture, Ecosystems and Environment 208 (2015) 37–47

environments (reservoirs, lakes, rivers, estuaries) which have been subject to intense anthropogenic N inputs over recent decades (Xu et al., 2005; Wang et al., 2007, 2009b, 2012; Chen et al., 2008a; Zhang et al., 2010; Liu et al., 2011; Yang et al., 2011; Yan et al., 2012; Xia et al., 2013). Nevertheless, very few direct observations of the two end-products (N2O and N2) of nitrification and denitrification have been made over river networks to explore N2O production, emission and export to estuary. Furthermore, because N2O is an undesired greenhouse gas it is critical to understand how much N2O relative to N2 emitted from the agriculture impacted aquatic ecosystem. N2O is produced mainly through nitrification and denitrification. Nitrification, an aerobic microbial transformation in which NH4+ is oxidized to NO3 and N2O, has been shown to occur both in sediments (Strauss et al., 2004) and the water column (e.g., pelagic nitrification) (Brion and Billen, 2000). Nitrification can be favored by high NH4+ (primary substrate of nitrification) and large quantities’ of nitrifying bacteria attached to suspended particles (Brion et al., 2000). Increased DIN (NH4+ + NO3) concentrations can also promote denitrification in anoxic environments to enhance N2O production (Cole and Caraco, 2001). In addition, oxygen consumption by respiration and nitrification can lead to hypoxia or anoxia in bottom waters, which further stimulate denitrification and N2O fluxes across the sediment-water interface (Liikanen and Martikainen, 2003). The production of N2O depends on a variety of environmental conditions such as N load, dissolved oxygen (DO), temperature, carbon availability and microbial populations (Mulholland et al., 2008; Rosamond et al., 2011). A large number of dams have been constructed along river channels throughout the world, which changes river flow and substantially influences nutrient cycling (Friedl and Wuest, 2002; Harrison et al., 2009). Therefore, any natural or anthropogenic-induced shifts in the aquatic system are likely to affect the formation of N2O. Several studies have shown that nitrification is a potentially greater source of N2O emissions than denitrification in streams and rivers (Webster et al., 2003; Richardson et al., 2004; Arango and Tank, 2008). Naturally, N2O production and emission fluxes are a function of rates of nitrification and denitrification and are not uniformly distributed geographically. Little is known about the key factors controlling N2O emission from river networks across a variety of land uses in response to increased human impacts. Nutrient over-enrichment is of increasing concern in subtropical coastal regions, which are often densely populated areas experiencing rapid development (Chen and Hong, 2012). As a typical subtropical ecosystem, the Jiulong River and its estuary in southeast China has become N-enriched due to increased N loading from various anthropogenic sources in the last 2–3 decades (Chen et al., 2013). Human activities (e.g., crops planting, animal breeding, dam construction) in the watershed have substantially altered the N budget and riverine export to coast (Cao et al., 2005; Chen et al., 2008b). A previous study showed that tidal mixing leads to a seaward decline of dissolved gaseous N (N2O and N2) concentrations and water–air fluxes along the Jiulong river-estuary gradient (Wu et al., 2013), suggesting that estuarine N2O mainly originated from river input and will ultimately be emitted into the atmosphere as a greenhouse gas. Therefore, it is important to assess the riverine export flux of N2O that influences estuarine N cycling from a long-term perspective. We hypothesized that increased N loading has led to a significant production, emission flux and riverine export of N2O. Extensive surveys were carried out across the two major tributaries (“North River” and “West River”), and two end products (dissolved N2O and N2) of nitrification and denitrification were synchronously measured. Although this study focused on N2O, we stressed the advantage of measuring N2 along with N2O to explore the relative importance of nitrification and denitrification to N2O production. The objectives of the present

study were: (1) to explore spatial variations in riverine N2O concentration and emission fluxes across the river network and key factors controlling the spatial pattern; (2) to assess river N2O yield relative to N2 (data extracted from an accompanying study reported elsewhere (Chen et al., 2014b)) to understand how much of this undesired greenhouse gas has been emitted during N removal from the aquatic system; and (3) to roughly estimate the time series of riverine N2O export to estuary through a model established in relation to DIN concentration, based on the direct measurements from this study and long-term monitoring of DIN. Together with a comparison of worldwide reported data for various aquatic ecosystems (river, reservoir and estuary), this study provides a systematic assessment of how riverine N2O production, emission and export from an agricultural watershed that has responded to increased human perturbation during rapid economic development in China. 2. Materials and methods 2.1. Description of study site The Jiulong River is a medium-sized subtropical river in southeast China, with a drainage area of 14,740 km2. Annual precipitation varies from 1400 to 1800 mm, 75% of which occurs between April and October. The Jiulong River consists of two major tributaries (North River and West River) and the total discharge is 12.4  109 m3 y1, of which the North River accounts for approximately two-thirds. Riverine N is mainly from agricultural sources, but differs between the West River and North River (Chen et al., 2013, 2014b). Livestock are widespread in the upper North River (Longyan city and suburban area), producing a large amount of animal wastes and extreme nutrient pollution. In the upper and mid West River (Pinghe/Nanjing County, a rural area), widespread planting of cash crops (e.g., Citrus maxima,Dimocarpus longan, Litchi chinensis, various vegetables) has resulted in excessive application of chemical fertilizers and leads to significant N loss to streams and river. Case studies have suggested that over application of fertilizer N due to improper methods and timing has resulted in nutrient loss from croplands, particular via surface runoff (Cao et al., 2003; Chen and Hong, 2011). There is a human population of 3.8 million in eight cities/counties within the watershed. The upper North River and lower West River receive a relatively large quantity of human waste when passing through two large cities (Longyan and Zhangzhou) and some densely populated residential towns. The quantity of fertilizer consumption and the number of pig farms have increased significantly over the last 30 years, resulting in nutrient enrichment in the Jiulong River and estuarine water (Chen et al., 2013). Although industrial wastewater and animal manure are partially treated in Longyan and Zhangzhou, mainly for reduction of chemical oxygen demand (COD), the nutrient quantities discharged to the waterbody have continued to increase. There are over 120 hydropower dams and hundreds of small dam reservoirs for irrigation and water extraction within the watershed. Six large hydropower dam reservoirs are distributed in the main branch of the North River, but in the West River most dams are located in tributaries rather than in the main branch. 2.2. Sampling and lab analysis Sampling sites were set up along the river network (see Fig. 2 in Section 3.2). In order to estimate riverine N2O export to estuary, monthly measurements of dissolved N2O and nutrients were conducted in 2010 for the two sites furthest downstream in the main tributaries (N15 at the outlet of the North River, and W14 at the outlet of the West River). Only four measurements were available for site W14. To investigate spatial variation of N2O at the

N. Chen et al. / Agriculture, Ecosystems and Environment 208 (2015) 37–47

1.0

(Hansell’s Laboratory, University of Miami) were used for quality control on a daily basis during sample analysis. Total blanks associated with DOC analyses were generally about 2–3 mmol C L1 and the precision was better than 2% on replicate analyses (He et al., 2010). Nitrogen components were determined using an AA3 Auto-Analyzer (Bran + Luebbe Co., Germany). The detection limit for NO3-N, NO2-N, and NH4-N was 0.1, 0.04, and 0.5 mmol N L1, respectively. Dissolved inorganic N (DIN) was summed as NO3N + NO2-N + NH4-N.

0.5

2.3. Calculation of indicators

2.5

N-N2O (μg N L-1)

39

2.0 y = 0.3074x - 0.0817 R² = 0.6804

1.5

0.0 0.0

2.0

4.0

2.3.1. Excess dissolved N2O (4N2O) To better quantify N2O production, the excess N2O production (i.e., 4N2O, nmol N2O L1) was calculated using Eq. (1).

6.0

DIN (mg N L-1) Fig. 1. Relationship between dissolved N2O and DIN concentration at outlet of the Jiulong River (2010). Site N15 and W14 was the outlet of the North River and West River, respectively. A total of 16 measurements from two sites (one year of monthly measurements in the North River and four measurements in the West River) were included in the regression.

watershed scale, a wide range of measurements were carried out at 29 sites from upstream to downriver. Field work was carried out on three occasions (February, May and October 2011) under various climate and hydrological conditions, encompassing low flow, normal flow and high flow (see Table 1 in Section 3.1). On each sampling occasion, surface (0.5 m) water samples were collected using a glass hydrophore. Water was gravitationally introduced into the bottom of a 40 mL bottle through a silicone tube. During bottle filling, several volumes were allowed to overflow, and 200 mL HgCl2 (to a final concentration of 0.5%) was then added to stop microbial activity. The sample bottle was screw-capped with no head space or bubble, and kept at 4  C until analysis. Duplicate samples were analyzed for dissolved N2O within one week of the sampling. At the same time, a 40 mL water sample was filtered through a Whatman GF/F glass fiber filter (nominal pore size 0.7 mm) and frozen at 20  C until analysis of DOC. Additional 100 mL water samples were filtered through acetate cellulose filters (0.45 mm) and frozen until analysis of nutrients, i.e., nitrate (NO3-N), nitrite (NO2-N), and ammonium (NH4-N). Water temperature, pH, and DO were measured in-situ using a WTW TetraCon1 325 probe. Dissolved N2O was directly measured by Purge and Trap-Gas Chromatography (Chen et al., 2007). The detection limit for N2O was <0.3 nmol L1 and the CV of repeated analysis was less than 5%. Dissolved N2 was measured by the N2:Ar method and data has been published earlier (Chen et al., 2014b). Concentrations of dissolved organic carbon (DOC) were determined by hightemperature catalytic oxidation using a Shimadzu TOC-V CPH TOC analyzer. Deep seawater DOC standard and low carbon water

4N2O = N2O(water) – N2O(eq)

(1)

where N2O(water) is the measured concentration of dissolved N2O (nmol L1) in surface water; N2O(eq) is the concentration expected if the water was in equilibrium with the atmosphere, and is determined based on measured temperature and salinity using equations derived by Weiss and Price (1980). The atmospheric N2O concentration was assumed to correspond to the annual average global concentration at Terceira Inland station (Azores, Portugal), taken from the NOAA/CMDL/CCGG air sampling network (http:// www.cmdl.noaa.gov/). 2.3.2. Water–air emission flux of N2O The water–air flux of N2O at each site was calculated by Eq. (2). F N2 O ¼ k  DN2 O

(2)

where F N2 O is the interfacial gas flux (mmol N2O m d ); k is the gas transfer velocity (cm hr1); 4N2O indicates the gas gradient across the water–air interface, i.e., the difference between measured concentration and expected equilibrium concentration. k was calculated using the quadratic wind speed relationship (Eq. (3)) established by Wanninkhof (1992): 2

1

k = 0.39 uav2 (Sc/600)1/2

(3)

where uav is the long-term averaged wind speed (in this study, uav was taken from 1954 to 2009 meteorological data at Zhangzhou within the watershed); Sc is the Schmidt number, which was calculated from temperature and salinity according to the polynomial fit given by Wanninkhof (1992). Here, Sc is 600 in freshwater. 2.3.3. N2O saturation (%) N2O saturation (%) was calculated as shown in Eq. (4). N2O saturation (%) = N2O(water)/N2O(eq)  100

(4)

Table 1 Environmental conditions in the Jiulong River network during N2O measurements in 2011. Sampling date 2011-2-28 2011-5-10 2011-10-19 a

Water temperature ( C)a

River discharge (m3 s1)c

API (mm)b

North River

West River

North River

West River

North River

West River

18.7 24.2 23.3

17.4 25.7 25.3

6.8(I) 28.3(II) 27.2(II)

3.0(I) 17.8(II) 4.1(I)

234 387 169

170 125 236

Water temperature indicates mean value from all measurement at various sites. P Antecedent precipitation index (API). Adapted from Perrone and Madramootoo (1998). API = ki  Pi, where Pi is precipitation 1, 2, ..., i (i = 14) days prior to the event and k is a constant (k = 0.85). API indicates mean value of four counties (Longyan, Zhangping, Hua’an and Changtai) in the North River and four counties (Pinghe, Nanjing, Zhangzhou and Longhai) in the West River. Three soil antecedent moisture conditions were classified according to API value. Condition I (dry): 0  API  15 mm; condition II (average): 15  API  30 mm; condition III (wet): API > 30 mm. c Mean river discharge seven days prior to sampling date at the most downriver site (Punan gauge in the North River, and Zhengdian gauge in the West River. Data are available from China’s national water information database (http://xxfb.hydroinfo.gov.cn/). b

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where N2O(water) is the measured N2O concentration and N2O(eq) is the expected equilibrium concentration.

measurements for the North River). The analysis therefore focused on assessing the long-term trend of riverine N2O export to the estuary. A larger database for N2O and DIN will be required to update the model and improve its accuracy in future.

2.3.4. River N2O yield (%) The river N2O yield was defined as 4N2O/(4N2 + 4N2O)  100. This indicator could be used to examine the extent of the undesired greenhouse gas N2O emitted relative to N2 during N removal from the running river including surface runoff and groundwater inputs. Based on this indicator, the relative role of nitrification and denitrification as N2O sources in the river network could be discussed. For each specific location, we assumed that the ratio 4N2O/4N2 in the water would be relatively consistent over time if N2O only originated from incomplete denitrification (e.g., due to changing oxygen content). However, this ratio tends to change if nitrification is also occurring, because nitrification produces additional N2O. Therefore, the river N2O yield will increase when nitrification becomes a main source of riverine N2O but will decrease if relatively more N2 is produced via denitrification. Extensive measurements across the river network allow us to compare variations in the ratio (N2O yield) across space or time and help improve understanding of the relative importance of nitrification and denitrification to N2O production.

N2Oexport = f (DIN)  Q

where N2Oexport is dissolved N2O flux to estuary (ton N-N2O y ); f (DIN) is a model describing the relationship between observed DIN concentration (mg N L1) and N2O concentration (mg N-N2O L1) measured monthly in 2010; and Q is river discharge (10 m3 y1). Annual mean DIN concentrations in 1985–2009 were obtained from Xiamen Environmental Monitoring Center and DIN concentrations in 2010–2012 from our seasonal measurement (data not shown). Based on the relationship between dissolved N2O and DIN concentration at the outlet of the Jiulong River (Fig. 1), we developed a linear regression model as shown in Eq. (6). Given that N2O is produced by both denitrification and nitrification, either NO3-N or NH4-N could be a potential predicting variable. NO3-N usually dominates DIN in natural aquatic systems but NH4-N is also considerable in the lower West River, which receives a large amount of waste from the nearby city (Zhangzhou). Indeed, N2O correlated better with DIN than with either NO3-N or NH4-N in this study. We supposed that this model (Eq. (6)) for predicting dissolved N2O based on its correlation with DIN is more acceptable. The standard errors of intercept and slope are 0.1821 and 0.0563, respectively.

2.4. Time series of riverine N2O export to estuary Dissolved N2O data are not available for the past and the time series of riverine N2O export to the Jiulong River estuary is still unknown. Here, we roughly estimated dissolved N2O through a model established in relation to DIN concentrations (Eqs. (5) and (6)) based on the direct measurements made during this study. Due to the large spatial variations, only the measurements in the river-estuary interface (site N15 in the lowest North River and site W14 in the lowest West River) were included to establish a regression model and then calculate the riverine N2O export to estuary. The observed dataset was only 16 measurements (one year of monthly measurements for the North River plus four

ab

4.0

600 450

-1

2.0

a

300

b

150

c

c

c

900 750 600 450 300 150

-1

0

a

b

N1-N4

cd

N5-N15

c W1-W5

bd

W6-W10 W11-W14

350 300 250 200 150 100

-1

ab

Statistical analysis (ANOVA, regression, correlation, etc.) was performed using SPSS 17.0 to assess the relationships between parameters and explore the main factors controlling the spatial variation of N2O. Mean and standard error (SE) represented the variation in the measurements. We used one-way ANOVA to

DOC (μmol L )

a

(6)

2.5. Statistical analysis

NO2-N (μmol L )

6.0

b b

[N-N2O] = 0.0817 + 0. 3074  [DIN]

DIN (μmol L )

-1

NO3-N (μmol L )

-1

NH4-N (μmol L )

-1

DO (mg L )

8.0

(5) 1

a

ab

ab ab

b

75 60

a

45 30

b

15

b

b

b

0

1000

a

800

a

600

b

400

c

c

200 N1-N4

N5-N15

W1-W5

W6-W10 W11-W14

Fig. 2. Spatial pattern of physiochemical variables in the Jiulong River in 2011. Five groups of sampling sites were defined, including upper North River (N1–N4), lower North River (N5–N15), upper West River (W1–W5), mid West River (W6–W10), and lower West River (W11–W14) (make reference to Fig. 4). The horizontal line in the middle of the box represents the median and the square represents the mean; the two horizontal lines of the closed box (side) represent the third quartile and first quartile, respectively. The top and the bottom line indicate the maximum and minimum value. Significant difference between river stretches is marked by a–d (ANOVA, P < 0.05)

N. Chen et al. / Agriculture, Ecosystems and Environment 208 (2015) 37–47

identify significant differences (P = 0.05) among groups of interest. According to the river topography, distribution of external N sources and dam construction, five groups of sampling sites were defined, including upper North River (N1–N4) which receives large quantities of animal/human wastes and urban runoff, lower North River (N5–N15) where cascade dams have been constructed along the main stem, upper West River (W1–W5) which receives large quantities of agricultural runoff, mid West River (W6–W10) including various tributaries, and lower West River (W11–W14) which passes a densely populated urban area (Zhangzhou). 3. Results 3.1. Hydrochemistry Environmental conditions were different during the three measurements in 2011 (Table 1). The region is warm and water temperature was greater than 16  C even in winter (February 2011). Water temperature was almost the same in May and October 2011 (23–24  C). In February, the antecedent precipitation index (API) was at a minimum due to lower rainfall in the dry season. In May (wet season), rainfall and river discharge were markedly increased, but recorded discharge from the gauge in the West River (Zhengdian) was still low, probably due to high water consumption by irrigation in the upstream area. In October, discharge from the North River (Punan) was even lower than in February due to cascade dams’ interception, although API was as high as in May. Spatial variation in hydrochemical parameters existed between river stretches (Fig. 2). Water temperature varied from 16.2  C to 27.2  C, pH from 6.83 to 7.68 and DO from 2.81 to 8.61 mg L1 during the three measurements. DO at sites N1–N4 was relatively low (range 0.91–6.44 mg L1, average 4.8 mg L1) compared with the lower river sites N5–N15 (range 3.99–8.40 mg L1, average 6.05 mg L1). DOC ranged from 86.3 to 377 mmol L1 and higher values were found in the upper North River (215  17.0 mmol L1) and lower West River (207  14.5 mmol L1). The highest DOC (377 mmol L1) was observed at site W3 in the upper West River as it passes through a densely populated county (Pinghe). DIN concentration varied from 106 to 1122 mmol L1 in the West River, and from 116 to 973 mmol L1 in the North River. Mean DIN concentration in the West River (384  33 mmol L1) was comparable with the North River (350  30 mmol L1) (P > 0.05).

1200

The highest DIN was found in the upper North River (N1–N4) (613  66 mmol L1) and upper West River (598  53 mmol L1), followed by lower West River (351 19 mmol L1), while relatively low values were found in the lower North River (254  10 mmol L1) and mid West River (197  12 mmol L1). In general, nitrate dominated DIN in water (74%  2%). Typically, ammonium dominated DIN in the upper North River (32–78% of DIN) and lower West River (up to 77%). Nitrite (mid-product of nitrification or denitrification) was averaged as 16.8  1.4 mmol L1, but was as high as 29.8–76.7 mmol L1 in the upper North River. For the North River, there was a stronger correlation between DIN and NH4-N (R2 = 0.94) and NO2-N (R2 = 0.85), compared with NO3-N (R2 = 0.47). However, DIN correlated well with NO3-N (R2 = 0.88) rather than NH4-N in the West River (Fig. 3). 3.2. Excess dissolved N2O, water–air fluxes, and N2O yield N2O concentrations in the Jiulong River network were above atmospheric equilibrium in all sampling periods, with a wide range of saturation from 145% to 4133% (average 708%). A strong spatial variation of 4N2O was observed throughout the river network and estuary (Fig. 4). As an indication of N2O production, 4N2O ranged from 10.4 to 114.7 nmol L1 in the West River, and from 4.2 to 363.4 nmol L1 in the North River. For the North River, 4N2O increased from N1 and stayed at a high level at stations N2–N4 and then declined to a relatively constant level in the lower North River (N5–N15). The water–air fluxes of N2O (Fig. 4) displayed a similar spatial pattern to 4N2O, ranging from 2.5 to 34.5 mmol m2 d1 in the West Jiulong River, and from 1.1 to 93.8 mmol m2 d1 in the North Jiulong River. 4N2O as well as water–air fluxes in the upper North River (N1– N4) were significantly greater than in other river stretches (Fig. 5). The upper North River had the highest value of 4N2O (average 145.9 nmol L1), followed by the upper West River (W1–W5) (average 47.5 nmol L1), lower West River (W11–W14) (average 41.3 nmol L1), lower North River (N5–N15) (average 30.5 nmol L1), and mid West River (W6–W10) (average 30.3 nmol L1). Site-based mean water–air flux of N2O in the West River (10.9  1.08 mmol m2 d1) was lower than that in the North River (16.0  3.25 mmol m2 d1) (P < 0.05). The correlations between 4N2O and environmental variables were identified (Table 2). 4N2O significantly correlated with

A. North River NO3-N y = 2.745x - 221.17 2 R = 0.4606

1000 800

NO2-N

600

y = 12.184x + 80.445 2 R = 0.8510

400 -1

DIN (μmol L )

41

200

NH4-N

0 0 100 200 B. West River

300

400

500

600

700

800

900

y = 1.243x + 200.924 2 R = 0.9403

1200

NO3-N

1000

y = 1.018x + 69.356 2 R = 0.8808

800

NO2-N

600

y = 11.988x + 250.42 2 R = 0.097

400 200

NH4-N

0 0

100

200

300 400 500 600 -1 N forms (μmol L )

700

800

900

y = 1.140x + 311.385 2 R = 0.117

Fig. 3. Relationships between DIN and N components in the North River (A) and West River (B).

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N. Chen et al. / Agriculture, Ecosystems and Environment 208 (2015) 37–47

Fig. 4. Spatial variation of 4N2O and water–air fluxes in the Jiulong River-Estuary continuum as mean value of three measurements in 2011. N2O in estuary adapted from Wu et al. (2013). Bars (height) indicate 4N2O and estimated water–air N2O flux; circles (size) indicate mean DIN concentration and pie indicates associated N composition in surface water. Triangles indicate six large hydropower dams constructed along the main branch of the North River.

concentrations of all N forms (P < 0.05), but was found to correlate better with NH4-N than with NO3-N according to nonparametric correlations (Spearman’s rho) (Table 2). A multi-linear regression of three measurements suggested that DIN and ammonium together explain 95% variance in 4N2O between sites N1–N4 (regression model: 4N2O = –285.8 + 1.035  DIN – 0.63  NH4-N, R2 = 0.95, P < 0.001). 4N2O also varied between February, May and October 2011. For the West Jiulong River, 4N2O in May (54.4  7.34 nmol L1) was significantly higher than in February (34.0  5.04 nmol L1) and October (30.5  3.73 nmol L1). There was no significant difference between the three measurements in the mid West River. For the North Jiulong River, 4N2O in February (102.8  35.3 nmol L1) was far greater than in May and October (average 38.5  5.52 nmol L1 and 42.5  6.01 nmol L1), but there was no significant difference between three measurements in the lower North River. At the upper North River (N1–N4) only, 4N2O in February was extremely high (312.2  37.8 nmol L1), and was four times greater than in May (61.9  9.11 nmol L1) and October (63.5  6.28 nmol L1).

The highest N2O yield of the North River was found in October 2011 (0.37%), followed by February (0.12%) and May (0.11%). In contrast, the highest N2O yield in the West River was observed in May 2011 (0.15%), followed by October (0.10%) and February (0.072%). In total, averaged N2O yield in the North River was 0.20%, twice as high as the West River (0.10%). The mean N2O yield of each river stretch was upper North River (0.22%), lower North River (0.20%), upper West River (0.14%), lower West River (0.089%), and mid West River (0.074%). 3.3. Riverine N2O export to estuary Using the regression model (Eq. (6)) and annual mean DIN concentration and discharge in the Jiulong River (West River + North River) in 1985–2012, riverine N2O export to estuary was estimated (Fig. 6). During the 1985–2012 period, DIN concentrations in the two tributaries (North River and West River) increased from less than 50 mmol L1 to 300–400 mmol L1. Discharge ranged from 5.68 to 18.7  109 m3 between dry years

N. Chen et al. / Agriculture, Ecosystems and Environment 208 (2015) 37–47

dry year

15

250

dry year

200

North River DIN West River DIN

150

10

-1

River DIN (μmol L )

300 dry year

20

15

350

mean

25

3

20

400

-9

wet year

Discharge (10 m )

wet year

30 Riverine N2O export (ton N)

43

10 5 0

100 5

50

0 1985

0 1990

1995

2000 Year

2005

2010

Fig. 6. Change in water discharge, DIN concentration in the Jiulong River and N2O export to estuary in 1985–2012. DIN data adapted from Chen et al. (2013).

Fig. 5. Spatial pattern of 4N2O and F N2 O in the Jiulong River in 2011. Five groups of sampling sites were defined, including upper North River (N1–N4), lower North River (N5–N15), upper West River (W1–W5), mid West River (W6–W10), and lower West River (W11–W14) (make reference to Fig. 4). The horizontal line in the middle of the box represents the median and the square represents the mean; the two horizontal lines of the closed box (side) represent the third quartile and first quartile, respectively. The top and the bottom line indicate the maximum and minimum value. Significant difference between river stretches is marked by a and b (ANOVA, P < 0.05)

and wet years. Accordingly, N2O export to the estuary increased from 1.5 to 21 ton N-N2O y1. From the 1980s to present, there has been a 15-fold increase in riverine N2O export to estuary, which can be ascribed to increased watershed N loads (Chen and Hong, 2012). 4. Discussion 4.1. Factors controlling N2O production across river network Extremely high 4N2O (as well as water–air flux) was found in the upper North River (N1–N4), which received large N loading from animal farming plots and dense residence around Longyan City. This phenomenon was consistent with the finding that the highest emission rates of N2O were observed in streams draining urban basins across the United States (Beaulieu et al., 2011). The upper North River was characterized as hyper-N rich (DIN = 612.9  66.21 mmol L1) with relatively more ammonium N (32–78% of DIN), reflecting the high N loading from anthropogenic sources (human and animal wastes). In addition, the lower DO (average 4.8 mg L1) and higher nitrite (mid-product of nitrification) implied substantial nitrification accompanied by DO consumption. In addition, respiration, particularly in locations which received large amount of organic wastes, may lower DO and facilitate denitrification in sediment, which can also contribute to the accumulation of excess N2O in the water column. Previous analysis suggested that DIN and DO together explain 98% variance in 4N2 between sites N1–N4 (Chen et al., 2014b). Incubation of a sediment core from a eutrophic lake suggested that the high NH4+ addition increased nitrification and associated oxygen consumption, Table 2 Nonparametric correlations (Spearman’s rho) between

causing a decrease in sediment O2 content and in accumulation of NO3 and N2O, which were effectively reduced to N2 by denitrification (Liikanen and Martikainen, 2003). Therefore, the highest 4N2O observed in the upper North River was probably due to important in-stream activities related to ammonium input (denitrifying activity at the sediment-water interface and nitrifying activity in the water column. This inference is partly supported by the multi-linear regression which showed that DIN and NH4-N together explained 95% variance in 4N2O between sites N1–N4. In contrast to the high 4N2 observed (Chen et al., 2014b), 4N2O (as well as N2O yield) at site N1 (urban channel) was significantly lower compared to sites N2–N4, reflecting a downriver delivery of products of nitrification after large amounts of wastes from urban areas are discharged into the rivers. The finding of high 4N2 but low 4N2O at site N1 also implied that nitrification mainly occurred in the water column. A gradual decrease in ammonium and increase in nitrate along sites N1–N4 (Fig. 4) further suggested an obvious N transformation from ammonium to nitrate along the river channel. Therefore, more N2O production was expected to accumulate in the water column at sites N2–N4. In addition, the relatively high N2O accumulation at sites N2–N4 might be partly due to the fact that the high NO3 availability suppresses nitrous oxide reductase (nos), the enzyme that reduces N2O to N2 (Beaulieu et al., 2011). The large decline in both 4N2O and DIN concentration between sites N4–N5 was ascribed to a dilution process by addition of tributary water with low N concentration (DIN = 160 mmol L1, from another study). 4N2O (as well as water– air flux) in the lower North River (N5–N15) varied little (Fig. 4). However, a relatively high 4N2O was observed in those sites in front of dams (e.g., N7, N11) compared with the nearest upstream sites (Fig. 4). There are six large hydropower dams distributed in the middle North River, and the longer water residence time in reservoirs has been found to enhance N2O production and emission (Chen et al., 2014a). However, we supposed that the strong mixing with spillouts from the bottom of the dam in those cascade reservoirs resulted in a rapid equilibrium and gas escape to atmosphere through the water–air interface. As a result, an accumulation of N2O could not be observed along the lower North River where many dams are constructed.

4N2O and environmental variables.

River

Water temperature

pH

DO

NO3-N

NO2-N

NH4-N

DIN

DRP

DOC

North River West River

0.017 0.411b

0.216 0.046

0.416b 0.589b

0.457b 0.378a

0.786b 0.778b

0.824b 0.739b

0.851b 0.590b

0.636b 0.488b

0.514b 0.588b

a b

Correlation is significant at the 0.05 level. Correlation is significant at the 0.01 level.

44

N. Chen et al. / Agriculture, Ecosystems and Environment 208 (2015) 37–47

In the West River, 4N2O increased gradually along the upper river, but declined sharply to a low level in the middle tributary and then increased again in the lower river (Fig. 4). This spatial pattern of 4N2O was correlated with nutrient concentrations given the correlations between 4N2O and N forms (Table 2). 4N2O at sites W6–W7 were fairly low since these two streams have low N concentrations that might weaken nitrification and/or denitrification. High 4N2O was found as expected at stream sites W8 and W10 with high N concentration and at those sites where the lower river (W12–W14) passes through a densely populated area (Zhangzhou City), and NH4-N concentration increased apparently from site W11 to W14. Correlations between 4N2O and environmental factors are presented in Table 2. In general, 4N2O was positively correlated with concentrations of N forms and DOC but negatively correlated with DO. No correlation could be found between 4N2O and water temperature as only three measurements were carried out in this study. According to R2 values in regressions, the variation of 4N2O in the Jiulong River was largely explained by N concentration. The North River seems to have a stronger correlation between 4N2O and DIN than the West River. As a whole, anthropogenic N loading and associated DIN concentration became key factors controlling N2O production across the river network. 4.2. Factors regulating river N2O yield N2O may be produced as a result of nitrification and/or denitrification. By comparing three measurements under different hydrological conditions (Table 3), the highest N2O yield of the North River occurred in October 2011 (0.37%) with the lowest water discharge (169 m3 s1), while lower values were found in February (0.12%) and May (0.11%) under higher flow conditions (234– 387 m3 s1). Similarly, the highest N2O yield in the West River was observed in May 2011 (0.15%) under the lowest discharge condition (125 m3 s1), compared with February (0.072%) and October (0.10%) under higher flow conditions (170–236 m3 s1). This suggests that the longer water residence time enhanced nitrification and produced

Table 3 Regression analysis between dependent (N2O yield) and independent.

West River NO3-N (mmol L1) NO2-N (mmol L1) NH4-N (mmol L1) DIN (mmol L1) NH4-N/DIN (%) NO3-N/DIN (%) North Rivera NO3-N (mmol L1) NO2-N (mmol L1) NH4-N (mmol L1) DIN (mmol L1) NH4-N/DIN (%) NO3-N/DIN (%)

Best regression formula

R2

P

n

y = 0.0340 + 2.18  104x y = 0.0239 + 6.98  103x y = 0.0734 + 4.44  104x y = 0.00951 + 2.40  104x y = 0.0897 + 7.04  104x y = 0.154 – 6.54  104x

0.427 0.421 0.219 0.601 0.0275 0.0259

<0.0001 <0.0001 0.002 <0.0001 0.294 0.31

42 42 42 42 42 42

0.8 3

y = 0.0866 + 1.02  10 x y = 0.0317 + 4.21 103x y = 0.0875 + 4.92  104x y = 0.00395 + 3.48  104x y = 0.0140 + 4.27  103x y = 0.0397 + 4.00  103x

0.480 0.711 0.704 0.722 0.638 0.639

<0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

34 34 34 34 34 34

Overall (Jiulong River)b NO3-N (mmol L1) y = 0.0552 + 2.19  104x NO2-N (mmol L1) y = 0.0452 + 2.18  103x NH4-N (mmol L1) y = 0.0703 + 4.78  104x DIN (mmol L1) y = 0.00917 + 2.80  104x NH4-N/DIN (%) y = 0.0633 + 2.35  103x NO3-N/DIN (%) y = 0.276 + 2.20  103x

0.212 0.546 0.508 0.606 0.247 0.259

<0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

76 76 76 76 76 76

a Data from sampling sites in river stretch impacted by dam construction and site N1 (urban channel) were excluded from the regression analysis. b Dataset includes the two tributary (West River and North River).

N 2O yield (%)

Independent

relatively more N2O. Averaged N2O yield from the North River (0.20%) was double that of the West River (0.10%), implying a relatively important role of in-stream nitrification as a source of riverine N2O in the North River (further discussed below). A strong positive relationship existed between DIN and NH4-N in the North River (Fig. 3), indicating that those anthropogenic N sources (human/animal wastes) directly discharged reduced N (ammonium) into the river and were converted into N2O through in-stream nitrification. This statement can be confirmed by the fact that river DIN was positively correlated with NO2-N (R2 = 0.85) (Fig. 3A), given that NO2-N is a mid-product of nitrification. However, in the West River, which drains large areas of cropland, DIN correlated well with NO3-N rather than NH4-N (Fig. 3), suggesting that most reduced N (e.g., ammonium fertilizer lost from soil) had been transformed to nitrate beyond the river channel, in field runoff and/or ground water. In this scenario, some excess N2O dissolved in catchment waterways will leak before entering the river channel, so little in-stream nitrification occurs. This will lead to a lower N2O yield in the West River (0.10%), although DIN concentration in the West River (384  33 mmol L1) was greater than the North River (350  30 mmol L1). In summary, distinct N sources (human/animal waste versus lost fertilizer) and transport pathway (directly discharged versus draining through field runoff or ground runoff) determined the different magnitude of N2O yield between the North River and the West River. Dam construction has been shown to change river hydrology and biogeochemistry (Friedl and Wuest, 2002). A good correlation between N2O yield and NO2-N and ratio NH4-N/DIN was found in the North River during the higher flow period (February and May 2011) (Fig. 7). However, no correlation could be derived in the low flow period (October 2011). Particularly, a fairly high N2O yield (0.20–0.82%) appeared at most sampling sites located in river stretches that had been modified by cascade dam reservoirs (Fig. 7). To take one example, the N2O yield at site N7 (average 0.20%) in front of the dam was far in excess of the nearest upstream site N6 (average 0.11%). This could be ascribed to the fact that dam reservoirs intercept water and increase water residence time, especially in the low flow period, which should facilitate nitrification in the water column and produce more N2O (Chen et al., 2014a). Construction of dam reservoirs has also been found to enhance sediment denitrification and produce more N2O (Chen et al., 2014a). Therefore, the river N2O yield significantly increased because both nitrification and sediment denitrification produce N2O at same time, so relatively more N2O accumulated in the water column. Unlike the North River, there were few dam reservoirs in the main stretch of the West River (most dams are located in the

February May October

Sampling sites passing dams

0.6 y = 0.00425x + 0.0144 2 (R = 0.62)

0.4 0.2

Site N1

0.0 0

10

20

30 40 50 NH4-N/DIN (%)

60

70

80

Fig. 7. Relationships between river N2O yield and NO2-N and ratio NH4-N/DIN in the North River. Data points in inset box indicates sampling sites in river stretches impacted by dam construction and site N1 (urban channel), which were excluded from the regression.

N. Chen et al. / Agriculture, Ecosystems and Environment 208 (2015) 37–47

remote upstream), and no obvious effect of dam construction on N2O yield could be detected. Current results demonstrate that N2O yield varied with changing hydrological conditions and differed between the North River and the West River due to dam construction. In other words, the river N2O yield (relative magnitude of N2O production over N2) tends to increase in the low flow period and at those river stretches regulated by dam reservoirs. A high N2O yield is not desirable and this indicator should be considered when evaluating river restoration projects addressing mitigation of N pollution and reducing greenhouse gas emissions. Nevertheless, some other environmental factors may influence N2O yield in different ways. A recent study by Laverman et al. (2010) showed that N2O production rates through denitrification are low under optimal or no stress conditions (complete anoxia, high non-limiting nitrate and carbon concentrations). An incubation experiment suggested that higher denitrification at the oxicanoxic interface may have led to relatively higher N2O production, as suboptimal oxygen concentrations have been shown to increase N2O production (Tallec et al., 2008). In the dry season (February 2011), DO at site N1 (urban channel) was as low as 0.91 mg L1, corresponding to low N2O yield (0.13%) given a large 4N2 (Fig. 7). A recent study on a large impounded river (the Ohio River) suggests that microbial activity in the water column and sediments was a source of N2O, and water column production rates were nearly double those of the sediments (Beaulieu et al., 2010). We did not expect a linear relationship everywhere between N2O yield against water oxygen levels since many disturbance variables (damming, water level) exist in this human perturbed river. Nevertheless, our results preliminarily demonstrate that nitrification was evidently occurring in the Jiulong River, especially in the North River where a large amount of human and animal wastes (containing high ammonium-N) are received.

45

4.3. Global significance of riverine N2O The N2O water–air fluxes of the Jiulong River falls into the lower range of reported values (Fig. 8). In the Jiulong River network, dissolved N2O concentrations were above atmospheric equilibrium in all sampling periods (saturation 145–4133%), indicating that the river acts as an important source of N2O to the atmosphere. Although the N2O yield shared only 0.01–0.82% of the total gaseous N removal (4N2O + 4N2) in the Jiulong River, N2O emission should not be overlooked as it is a potent greenhouse gas. The method developed by the IPCC to tabulate emissions from rivers is based on a limited dataset and the assumption that simple relationships between nitrate and rates of N2O emission exist. However, the assumption may not hold across all rivers due to variable N2O yields and variable contributions of nitrification and denitrification to emissions (Reay et al., 2003; Clough et al., 2006). As discussed above, there are high spatial variations in N2O production and emission from the Jiulong River network. The river N2O yield varied widely across different river stretches and tends to increase in low flow periods and at river stretches modified by dam reservoirs. Fortunately, with the exception of the river stretch modified by dam reservoirs during low flow conditions, it seems that river N2O yield can be predicted using the ratio of NH4-N to DIN, although this finding requires further validation in other river systems. Riverine N2O export to estuary increased gradually in the past decades in response to nutrient enrichment and discharge fluctuations. Given that watershed N export from this region is predicted to continue to increase until 2030 (Seitzinger et al., 2010), riverine N2O production, emission and export to estuary are anticipated to increase in coming decades. In the context of increasing climate change and human perturbation, much more observation across landscapes and waterscapes is necessary to close the N2O budget at regional and global scales. 5. Conclusions

1200

-2

-1

FN2O (μmol m d )

600 250 200 150 100 50 0 n = 87

Jiulong River

59

Rivers

18

Reservoirs

31

Estuaries

Fig. 8. N2O water–air fluxes from the Jiulong River and reported value in various aquatic ecosystems (not including lakes and sea) around the world. The horizontal line in the middle of the box represents the median and the square represents the mean; the two horizontal lines of the closed box (side) represent the third quartile and first quartile, respectively. The top and the bottom line indicate the maximum and minimum value. Reported data has been compiled from publications (Barnes and Owens, 1998; Garcia-Ruiz et al., 1999; McMahon and Dennehy, 1999; De Wilde and de Bie, 2000; Cole and Caraco, 2001; Huttunen et al., 2002; Lima et al., 2002; Harrison and Matson, 2003; LaMontagne et al., 2003; Laursen and Seitzinger, 2004; Harrison et al., 2005; Hendzel et al., 2005; Sikar et al., 2005; Stow et al., 2005; Hlavacova et al., 2006; Tallec et al., 2006; Clough et al., 2007; Ferron et al., 2007; Beaulieu et al., 2008; Guerin et al., 2008; Rajkumar et al., 2008; Silvennoinen et al., 2008; Wilcock and Sorrell, 2008; Garnier et al., 2009; Toyoda et al., 2009; Wang et al., 2009a; Beaulieu et al., 2010; Goncalves et al., 2010; Zhang et al., 2010; Barnes and Upstill-Goddard, 2011; Baulch et al., 2011a,b, 2012; Deemer et al., 2011; Liu et al., 2011; Yang et al., 2011; Bouillon et al., 2012; Diem et al., 2012; Outram and Hiscock, 2012; Rosamond et al., 2012; Yan et al., 2012; Daniel et al., 2013; Hinshaw and Dahlgren, 2013; Yu et al., 2013; Zhao et al., 2013; Zhu et al., 2013; Marwick et al., 2014; Musenze et al., 2014; Venkiteswaran et al., 2014; Xia et al., 2014).

Riverine N2O production and emission displayed a strong spatial variation across the Jiulong River network. High 4N2O as well as water–air fluxes were found in river stretches (upper North River) where animal and human wastes dominated riverine N sources. DIN was the key factor controlling the spatial variation of 4N2O as a whole. The river network acts as an important source of N2O to atmosphere since N2O saturation was always above 100% (145–4133%). River N2O yield correlates well with the NH4-N/DIN ratio. Higher river N2O yield was observed under low flow conditions and in stretches of the North Jiulong River modified by cascade dam reservoirs. A high N2O yield is not desirable and this indicator should be considered in evaluating river restoration projects addressing mitigation of N pollution and reducing greenhouse gas emissions. Riverine N2O export to estuary increased over 15-fold in response to increased N loads during the 1985–2012 period. water–air emission flux of N2O in the Jiulong River fell into the lower range of reported values around the world but is anticipated to increase due to changing hydrology and increasing human activities. Acknowledgements This research was supported by the National Natural Science Foundation of China (No. 41076042; 41376082), the Fundamental Research Funds for the Central Universities (2012121053), and the Program for New Century Excellent Talents in University (NCET-130496). We thank Longjian Wang and Hua Lin for assistance in sampling and lab analysis, and Jonathan Vause for assistance with English editing.

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