Spatial-temporal variation of dissolved N2 and denitrification in an agricultural river network, southeast China

Agriculture, Ecosystems and Environment 189 (2014) 1–10

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Spatial-temporal variation of dissolved N2 and denitrification in an agricultural river network, southeast China Nengwang Chen a,b,c,∗ , Jiezhong Wu a,c , Zhuhong Chen c , Ting Lu c , Longjian Wang c a b c

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

a r t i c l e

i n f o

Article history: Received 12 November 2013 Received in revised form 19 February 2014 Accepted 6 March 2014 Keywords: Denitrification N2 N2 :Ar method Nitrogen removal Jiulong River

a b s t r a c t The spatio-temporal pattern of excess N2 production due to denitrification and gaseous nitrogen (N) removal via N2 emission were investigated for a large agricultural river (the Jiulong River) in southeast China. During 2010–2011, direct measurement of excess dissolved N2 (N2 , denitrification product) using the N2 :Ar ratio method was carried out along the river network (North River and West River). The results showed that N2 ranged from 24 to 71 ␮mol N2 L−1 in the West River, and from 3 to 160 ␮mol N2 L−1 in the North River. Areal mean N2 water–air flux was 8.66 kg N ha−1 yr−1 for the whole river network. “Hot spots” of denitrification were found in the upper North River and lower West River, where animal/human wastes dominated the riverine N source. Dissolved inorganic nitrogen (DIN) and dissolved oxygen are the key factors controlling the spatial variation of N2 . N2 generally peaked in January and during the warmwet season from May through October, indicating an interactive effect of water temperature and DIN concentration on denitrification. Gaseous N removal fraction (Ed = 22% of [DIN]; annual N removal = 24% of riverine N export) through net denitrification is comparable with other river-estuaries around the world. Gaseous N removal effectiveness generally decreased with N enrichment and was constrained in those hyper-N rich river reaches. Management of N loss should consider the terrestrial and aquatic systems, in order to reduce N export from watershed to coast and mitigate eutrophication in this region. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Denitrification is the major biological process through which fixed nitrogen (N) is returned from natural and human-altered systems to the atmosphere, and is thus a balancing mechanism and an important ecosystem service for the removal of anthropogenic N along the terrestrial–freshwater–marine continuum (Galloway et al., 2003). Denitrification represents the main permanent removal of N through the conversion of nitrate to dinitrogen (N2 ) gases (Seitzinger et al., 2006; Curie et al., 2011; Findlay et al., 2011), although other N transformations such as anaerobic ammonium oxidation (Anammox) have also recently been found to remove a small fraction of N in some cases (Dalsgaard et al., 2005; Zhu et al., 2013). Denitrification decreases N transfers originating from land-based and marine sources throughout

∗ Corresponding author at: College of the Environment and Ecology, Xiamen University (Xiang’an Campus), Xiamen 361102, China. Tel.: +86 592 2182201; fax: +86 592 2182201. E-mail addresses: [email protected], [email protected] (N. Chen). http://dx.doi.org/10.1016/j.agee.2014.03.004 0167-8809/© 2014 Elsevier B.V. All rights reserved.

the terrestrial–freshwater–marine continuum (Seitzinger et al., 2006). Denitrification rates in rivers and estuaries vary widely in response to various physical and environmental parameters (PinaOchoa and Alvarez-Cobelas, 2006; Solomon et al., 2009). Therefore, determination of gaseous N removal effectiveness is helpful to examine how N is exported from watershed to coast and to develop appropriate nutrient management strategies. However, direct measurements of denitrification rates are limited and in situ factors controlling their spatial and temporal patterns are not well known (McCutchan and Lewis, 2008; Baxter et al., 2013). In addition, a quantitative understanding of denitrification rates and controlling factors across ecosystems are essential to improve modeling of the global N budget (Davidson and Seitzinger, 2006). Denitrification is also of concern since a considerable amount of N2 O (a greenhouse gas) may be emitted to the atmosphere from denitrification in stream and river networks (Beaulieu et al., 2011). Anthropogenic inputs of nutrients to the Earth’s surface have increased greatly during the past two centuries, leading to highly undesirable changes in ecosystem structure and function (Smith et al., 1999). Nutrient over-enrichment is of increasing concern in subtropical coastal regions, which are often densely populated

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areas experiencing rapid development and widespread changes to the aquatic environment (Chen and Hong, 2012). For example, the Jiulong River suffers greatly from nutrient pollution and eutrophication (algal bloom), especially in reservoirs and the estuary (Li et al., 2011; Chen et al., 2013). Some studies addressing N cycling in this region have been previously reported, and the N budget for the Jiulong River watershed indicates that fertilizer and animal feedstuffs contribute 88% of the total N input (Chen et al., 2008). The Jiulong River is the major N source of estuarine waters (Cao et al., 2005) and currently over 34,000,000 kg N yr−1 is discharged to the estuary (Yan et al., 2012). Various human activities (mainly agriculture development, wastes discharge and dam construction) have substantially altered N biogeochemistry from headwaters to the river and estuary. Thus, the detailed process of N cycling (denitrification) within the Jiulong River and estuary merited further study. A number of methods have been developed to study denitrification in aquatic systems. Traditional methods such as the acetylene block technique always measure denitrification potential rather than the in-situ denitrification rate (Sørensen, 1978). Some other approaches (15 N tracer, mass balance approaches, direct N2 quantification, stoichiometric approaches, etc.) are often time consuming and expensive (Groffman et al., 2006). The recent development of membrane inlet mass spectrometry (MIMS) makes it possible to directly measure dissolved N2 (end-product of denitrification) and study denitrification in aquatic systems. Using MIMS, the production of N2 gas can be quantified by measuring changes in the ratio of N2 to Ar, given that N2 is affected by both biological and physical processes, whereas Ar is affected strictly by physical processes (Kana et al., 1994). This method has several advantages, and has been used to study denitrification in various aquatic environments including rivers (Laursen and Seitzinger, 2002; Pribyl et al., 2005), estuaries (Hartnett and Seitzinger, 2003; Kana et al., 2006) and oceans (Tortell, 2005). We have recently developed an N2 :Ar method for direct measurement of dissolved N2 (Chen et al., 2010; Wu et al., 2013). The N2 supersaturation measured in running water (river runoff) actually corresponds to denitrification occurring upstream and/or in local sediment behind weirs or dams. We here used multi-sampling sites that were evenly distributed along the river channel to understand the spatial variability in denitrification across the whole river network, rather than in specific locations.

In 2010–2011, direct measurements of excess N2 (N2 ) were carried out to investigate the spatial pattern and temporal dynamics of excess N2 production and emission flux in the Jiulong River-estuary system. This work was accompanied by study of dissolved N2 O (Chen et al., unpublished). Denitrification in the estuary has been discussed earlier, and it was found that denitrification mainly occurred in the upper freshwater sites linked to the riverine N input (Wu et al., 2013). Therefore, this study mainly examined the spatio-temporal variation of denitrification in the Jiulong river network. We hypothesized that differences in N loading sources (human and animal wastes, field runoff), the way that N enters streams (direct discharge or through soil-groundwater) and dominant N form in rivers (ammonium or nitrate) are potential mechanisms influencing the spatial-temporal pattern of net denitrification product (excess N2 , or N2 ) at reach scale. This study provides the first assessment of how gaseous N removal associated with denitrification/nitrification responds to high human perturbation (agriculture development, waste discharge, and dam construction) in a subtropical region in China, and contributes to the growing body of work on N delivery and cycling in aquatic systems in the context of increased human and climate perturbation. 2. Materials and methods 2.1. Description of study site The Jiulong River is a medium-sized subtropical river in southeast China and consists of two major tributaries (North River and West River). The total discharge is 1.24 × 1010 m3 yr−1 , of which the North River accounts for two-thirds. Annual precipitation varies from 1400 to 1800 mm, 75% of which occurs between April and October. The river catchment covers an area of 14,740 km2 , and includes eight cities/counties with a population over 3.5 million. As a result, there are significant human impacts on nutrient biogeochemistry. Land use comprises 70% forest including upland orchard, 18% arable land, 5% urban and residential land, 3% water, and 4% bare and grass land. Over 60% of the watershed remains forested with mixed coniferous trees (Picea spp, Pinus massoniana) and hardwoods (Eucalyptus spp, Lauraceae spp, etc.). The main soil type on hilly land (accounting for 62% of the total catchment) is lateritic red soil (one of the soils forming in subtropical and tropical regions which is rich in iron/aluminum oxide), followed by paddy soil

Table 1 Environmental conditions during denitrification measurements in 2010–2011. Sampling date

2010-1-2 2010-2-2 2010-3-13 2010-4-17 2010-5-15 2010-6-23 2010-7-19 2010-8-20 2010-9-24 2010-10-17 2010-11-17 2010-12-24 2011-2-28 2011-5-10 2011-10-19

Water temperature (◦ C)a

River discharge (m3 s−1 )c

API (mm)b

North River

West River

North River

West River

North River

West River

14.9 17.9 14.6 18.5 22.3 25.2 29.3 30.4 25.3 26.1 20.8 15.3 18.7 24.2 23.3

15.2 18.4 15.5 18.7 21.9 26.9 30.3 30.1 25.0 25.4 21.4 15.9 17.4 25.7 25.3

14.5(I) 9.0(I) 43.2(III) 36.9(III) 45.0(III) 67.4(III) 15.8(II) 12.2(I) 83.4(III) 13.9(I) 1.5(I) 7.3(I) 6.8(I) 28.3(II) 27.2(II)

16.3(II) 3.5(I) 22.8(II) 26.9(II) 34.1(III) 40.8(III) 12.2(I) 15.6(II) 132(III) 15.8(II) 1.9(I) 8.0(I) 3.0(I) 17.8(II) 4.1(I)

77 45 428 459 438 640 415 180 436 160 237 230 234 387 169

34 23 90 258 129 220 78 71 308 130 211 167 170 125 236

Water temperature indicates mean value from measurements at various sites.  all Antecedent precipitation index (API). API = ki × Pi , where Pi is amount of 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. Adapted from Perrone and Madramootoo (1998). c Mean river discharge seven days prior to sampling date. Data are available from China’s national water information database (http://xxfb.hydroinfo.gov.cn/). a

b

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alongside rivers and latosolicred soil on the valley floor. Riverine N is mainly from agricultural sources, but sources differ between the West River and North River. In the upper North River (Longyan city and suburban area) there are large numbers of livestock (e.g., more than 1.4 million pigs), producing a large amount of animal wastes and extreme nutrient pollution. In the upper and mid West River (Pinghe/Nanjing County, a rural area), there are large areas (over 12,000 ha) of cash crops (e.g., Citrus maxima, Dimocarpus longan, Litchi chinensis, various vegetables) where excessive application of chemical fertilizers (over 1000 kg N ha−1 yr−1 ) is common, leading to N loss through field runoff (Chen et al., 2008; Chen and Hong, 2011). Meanwhile, the upper North River and lower West River receive large quantities of human wastes when passing through two large cities (Longyan and Zhangzhou, with a population of 476,000 and 550,000 in 2009, respectively). The quantity of fertilizer consumption and the number of pig farms have increased three to seven fold over the last 30 years (Chen et al., 2013). Although industrial wastewater and animal manure are partly treated (e.g., using oxidation ponds) in Longyan and Zhangzhou, mainly for reduction of chemical oxygen demand (COD), the amount of nutrient discharged to the waterbody has continued to increase. There are over 120 hydropower dams and hundreds of small weirs for irrigation and water extraction within the watershed. In the main branch of the North River there are 12 large hydropower dam reservoirs, but in the West River most dams are located in tributaries rather than in the main branch. The hydraulic gradient of the main branch of the North River (0.1%) is greater than that of the West River (0.03%). 2.2. Sampling and lab analysis Sampling sites were set up across the river network (Fig. 1). Monthly measurements were carried out at 10 sites in the lower watershed in 2010, and in 2011 this was increased to 29 sites covering the entire watershed. Three dam reservoir sites (N8, N11, N15) were chosen to determine outgassing that might occur in the sediment behind the reservoir by comparison with the nearest upstream open channel site. The water depth at the sampling locations was about 0.6–3 m. Field work was carried out under various climate and hydrological conditions (Table 1). Standing in the middle of river bridges, surface water (0.5 m) samples were collected using a long rope and a 5-L Niskin sampler (model QCCC-5, National Ocean Technology Center, China). After the sampler was slowly raised to the bridge, water was carefully introduced into the bottom of a 40 mL sample bottle through a silicone tube. During bottle filling, several volumes were allowed to overflow, avoiding contamination of water samples by atmospheric N2 . 200 ␮L of HgCl2 (to a final concentration of 0.5%, v/v) was added to stop microbial activity and preserve samples. The sample bottle was screw-capped with no head space or bubble, and kept in a cooler box (1–2 ◦ C lower than in-situ water temperature). Duplicate samples were analyzed for dissolved N2 within one week of the sampling. Additional water samples were filtered through acetate cellulose filters (0.45 ␮m) and frozen until analysis of nutrients and DOC (see below). Water temperature, pH, and dissolved O2 were determined in the field using a WTW TetraCon® 325 probe. Dissolved N2 was measured by the N2 :Ar method established by our lab and previously described in Chinese (Chen et al., 2010) and applied to estuary study (Wu et al., 2013). Here we outline the method briefly. The MIMS system (HPR-40) with a probe inlet (Hiden Analytical Co.) was used in this study. The instrument is similar to the one used by Kana et al. (1994) except for a difference in the inlet system. N2 :Ar ratios were calculated from the quadrupole instrument signal (essentially the N2 and Ar pressures at the detector) and calibrated using air-equilibrated water standards. The standards consisted of artificial seawater at a range of

Fig. 1. Map of the Jiulong River-Estuary showing sampling sites. 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). Main stem of the North River and West River were defined as reach N1–N15 and reach W4–W14, respectively; others were defined as tributaries.

salinities (salinity = 0, 10, 20, 30 and 40), and were slowly stirred for a minimum of 52 h to prevent entrainment of gas bubbles associated with turbulent mixing. The coefficients of variation (CV) of the N2 :Ar ratio for replicated real samples was less than 0.35%, corresponding to 2 ␮mol L−1 of N2 concentration. No significant instrument drift was detected during 10 h measurement. Nitrate, nitrite, and ammonium were determined using an AA3 Auto-Analyzer (Bran + Luebbe Co., Germany). The detection limit for NO3 –N, NO2 –N, and NH4 –N was 0.01, 0.03, and 0.04 ␮mol N L−1 , respectively. Dissolved inorganic N (DIN) was summed as NO3 –N + NO2 –N + NH4 –N. The precision for N forms was estimated by repeated determinations of 10% of the samples and was better than 5%. For quality control in the laboratory, we used a Standard Reference Material (SRM) provided by China’s national Environmental Protection Administration to check instrument performance. DOC concentration was measured via high temperature catalytic oxidation after removal of inorganic carbon by oxygen purging, using a 2400 SeriesII CHNS/O Elemental Analyzer (Perkin Elmer, United States). The measurements were performed in triplicate and the analytical precision was within 5%. 2.3. Data analysis To better quantify denitrification and explore the role of this process in N cycling in the Jiulong River-Estuary system, three indexes were calculated as described below. First, excess dissolved N2 (i.e., N2 ) was calculated using Eq. (1), where N2 is actually a measure of net N2 production (gross denitrification minus gross N fixation). Positive values of N2 indicate net denitrification, whereas negative values reflect net N fixation. N2 = [N2 : Ar]measured × [Ar]expected − [N2 ]expected

(1)

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where [N2 :Ar]measured is the measured N2 :Ar ratio in terms of concentration (␮mol L−1 ) that have been calibrated using airequilibrated water standards; [Ar]expected and [N2 ]expected are the concentrations expected if the water was in equilibrium with the atmosphere, and is determined based on measured temperature and salinity using equations derived by Weiss (1970). Second, the water–air fluxes of N2 (F) at each site were calculated using Eq. (2). F = k × Csurface

(2)

where F is the interfacial gas flux (mmol m−2 d−1 ); k is the gas transfer velocity (cm h−1 ); Csurface 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): k = 0.39 u2av

 S −1/2 c 660

(3)

where uav is the long-term averaged wind speed (in this study, uav for the Jiulong River and estuary were taken from 1954 to 2009 meteorological data at Zhangzhou station and Xiamen station, respectively); Sc is the Schmidt number, which was calculated from temperature and salinity according to the polynomial fit given by Wanninkhof (1992). Here, Sc is 660 in seawater and 600 in fresh water. We used long-term averaged wind speeds rather than wind speeds for the sampling periods, because the latter were site and time specific and the main purpose of this study was to compare the spatial variation of water–air fluxes of N2 given similar climate conditions. The third index was the gaseous N removal fraction (Ed , %), which was estimated as N2 divided by [DIN]. Ed is expressed as a fraction of [DIN] rather than the initial total N (=[DIN] + N2 ), which facilitates a extrapolation of N removal capacity in rivers without measured N2 . Ed was calculated site by site for each sampling date to approximately represent the capacity of N removal through net denitrification. However, when N fixation is considerable, using the Ed value will normally result in underestimation of the role of denitrification in N removal. This index was also used to compare our results with published data from other rivers and estuaries. In addition, area-weighted water–air flux of N2 in the main branch of the North River and West River was calculated using the inverse distance weighted (IDW) interpolation technique in ArcGIS (version 9.3). IDW is a weighted distance average that interpolates a raster surface from point values, and the results are acceptable since our sampling sites in the main stem were evenly distributed and sufficiently dense. Areal fluxes of N2 in tributaries were roughly estimated using discrete measurement and their linear relationship to DIN concentration (data not shown here but to be published). A total mass of N removal from each reach and the whole river ecosystem was then summarized and compared to the riverine N export estimated earlier by Yan et al. (2012). This comparison highlighted the significance of denitrification in the N cycle in this area. Statistical analysis (regression, correlation, etc. with significance level at 0.05) was performed using SPSS 17.0 to assess the relationships between parameters. Multifactor linear regression and a stepwise approach was used to explore the main factors controlling the spatial pattern and temporal dynamics of denitrification, and to identify the multicollinearity of various environmental variables including N concentration, temperature, and DO. Mean and standard error (±SE) represented the variation in measurements. To better interpret the results and compare the differences at spatial (river or reach) and temporal scales, measurements in 2010 and 2011 were separated and divided into groups in different ways. For example, 2010 measurements were carried out monthly and covered six sites in the lower West River and four sites in the lower

Fig. 2. Spatial variation of excess N2 production and emission due to denitrification in the Jiulong River-Estuary continuum, mean value of three measurements in 2011. Estuary data adapted from Wu et al. (2013). Bars (height) indicate excess dissolved N2 (N2 ) and estimated water–air N2 flux; circles (size) indicate mean DIN concentration in surface water; TN load (shade polygon) in each county re-plotted from Chen and Hong (2012).

North River. In contrast, 2011 measurements were carried out on three occasions (February, May and October) under various hydrological conditions at 14 sites in the West River and 15 sites in the North River, encompassing the whole river network. Therefore, the 2010 dataset was mainly used to examine seasonal variation and the 2011 dataset was used to explore spatial variation. A subdataset was extracted from four sites (W8, W9, W10 and W11) in the West River and three sites (N12, N13 and N14) in the North River to show the interannual variation indicated by the repeated measurements made in 2010 and 2011. We used one-way ANOVA followed by LSD multiple comparison test to identify significant differences between groups of interest. 3. Results 3.1. Spatial variation of excess dissolved N2 indicating denitrification rate There were strong spatial patterns of denitrification rates along the river to estuary pathway (Fig. 2). The water–air fluxes of N2 ranged from 6.0 to 18.3 mmol N2 m−2 d−1 with an average of 11.3 mmol N2 m−2 d−1 in the West River, and from 0.8 to 42.1 mmol N2 m−2 d−1 with an average of 9.6 mmol N2 m−2 d−1 in the North River. The estimated water–air fluxes of N2 over the estuary area (Fig. 2) were relatively high. This can be ascribed to the different parameters used in Eq. (3), resulting from the higher wind speed than at the watershed sites. Excess dissolved N2 (N2 ) and N concentrations measured in 2011 are summarized by site group in Table 2. As an indication of net denitrification, N2 ranged from 24 to 71 ␮mol N2 L−1 in the West River, and from 3 to 160 ␮mol N2 L−1 in the North River. In general, the upper North River (N1–N4)

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Table 2 Summary of denitrification measurements in the Jiulong River network (2011). Sampling Date

Site groupa

DIN (␮mol N L−1 )

NO3 –N (␮mol N L−1 )

NO2 –N (␮mol N L−1 )

NH4 –N (␮mol N L−1 )

N2 (␮mol N2 L−1 )

Ed (%)b

2011.2

W1–W5 W6–W10 W11–W14 N1–N4 N5–N15

333–516(443) 106–239(175) 268–421(338) 793–973(909) 116–354(236)

323–444(400) 93–158(136) 208–240(229) 156–368(292) 99–280(190)

2–17 (10) 3–20 (9) 7–12 (9) 43–77 (60) 4–15 (9)

8–60 (33) 10–75 (30) 25–202 (101) 421–710 (557) 13–102 (37)

39–54 (46) 48–58 (53) 61–71 (66) 99–160 (120) 25–44 (37)

8–32 (22) 32–52 (40) 48–94 (66) 22–36 (26) 24–52 (34)

2011.5

W1–W5 W6–W10 W11–W14 N1–N4 N5–N15

537–1122(804) 148–270(218) 384–465(418) 381–521(450) 228–385(280)

480–889(697) 133–195(168) 80–348(196) 190–251(213) 167–234(195)

2–27 (17) 6–24 (15) 13–19 (15) 32–40 (36) 15–28 (19)

2–214 (90) 10–61 (35) 104–330 (207) 135–287 (202) 27–127 (65)

27–33 (30) 28–40 (36) 41–47 (44) 36–47 (42) 25–38 (30)

6–10 (8) 28–40 (34) 18–24 (22) 18–20 (18) 16–28 (22)

2011.10

W1–W5 W6–W10 W11–W14 N1–N4 N5–N15

465–614(546) 155–231(197) 274–332(296) 429–574(479) 177–312(246)

460–572(511) 126–180(162) 214–269(228) 201–272(242) 139–228(194)

1–11 (7) 3–12 (8) 6–15 (10) 30–34 (31) 10–23 (16)

4–42 (28) 13–43 (27) 22–102 (58) 140–340 (206) 21–69 (36)

24–37 (33) 25–38 (34) 35–44 (40) 22–43 (29) 3–17 (10)

10–14 (12) 30–42 (34) 24–30 (26) 10–16 (12) 2–16 (8)

a Data in parenthesis indicates average value among sampling sites that have been divided into five groups (upper North River (N1–N4), lower West River (W11–W14), mid West River (W6–W10), upper West River (W1–W5), and lower North River (N5–N15), as shown in Fig. 1). Difference among groups was found to be significant by ANOVA analysis. b Ed = N2 /[DIN] × 100.

700

Upp er River

NH4-N NO2-N NO3-N Δ N2

600 500 400

Lower River

300

100 80 60 40

200

20 N1 N2 N3 N4 N5 N6 N7 N8 N9 N10 N11 N12 N13 N14 N15

100 0

120 Δ N2 (μmol N2 L-1 )

N concentrations (μmol N L-1 )

700

N concentrations (μmol N L-1 )

a. North River

0

b. West River Mid River

Lower River

600

120 100

500

80

400 60

300

40

200

20 W1 W2 W3 W4 W5 W6 W7 W8 W9 W10 W11 W12 W13 W14

100 0

Δ N2 (μmol N2 L-1 )

Upp er River

0

Fig. 3. Spatial pattern of excess dissolved N2 and DIN in the North River (a) and West River (b). Data were averaged for each site from three measurements in 2011 to remove the effect of temporal variance. N2 was expressed as mean ± standard error (SE) whereas mean N concentrations of various forms are stacked.

had the highest value of N2 (average 64 ␮mol N2 L−1 ), followed by the lower West River (W11–W14) (average 50 ␮mol N2 L−1 ), mid West River (W6–W10) (average 41 ␮mol N2 L−1 ), upper West River (W1–W5) (average 36 ␮mol N2 L−1 ), and lower North River (N5–N15) (average 25 ␮mol N2 L−1 ). N2 in the upper North River (N1–N4) and the lower West River (W11–W14) was statistically greater than at other sites. There were contrasting spatial patterns of N2 as well as N concentrations between the North River and West River (Fig. 3). N2 were fairly high in the upper North River channel (N1–N4) and decreased to a relatively stable level in the lower River (N5–N15). Unlike the North River, N2 in the West River increased from upstream to down river. DIN was extremely high in the two upper river channels at stations N1–N4 and W1–W5

(over 700 ␮mol N L−1 ). However, N was dominated by ammonium (49 ± 13% of DIN) in the upper North River channel (N1–N4) but mostly by nitrate (over 70% of DIN) in the West River, except for stations W12–W14 in the lower West River. Starting from site W6, the West River passes through many rural towns (W6–W10) and the densely populated city of Zhangzhou (W11–W14), where ammonium increased significantly. The spatial patterns of NO3 − and NH4 + along the rivers (Fig. 3) were also interesting. The dramatic decrease in N concentrations between stations N4–N5 and W5–W6 is evidently due to dilution by mixing with tributary waters that contain lower N (data not shown). There was no significant correlation between N2 and concentration of N component and other environmental factors (e.g., DO) if all values in the dataset were included. This appears to be due to the wide range of physical–chemical conditions and environmental factors at the watershed scale. Given that the data were normally distributed, multi-linear regression of 2011 measurements was carried out by site group (see Table 2) and showed that DIN and DO together explain 98% variance in N2 between sites N1–N4 (Regression model: N2 = 59.4 + 0.121 × [DIN] − 14.5 × [DO], P < 0.001). A significant correlation between N2 and DIN and DO (P < 0.05) exists in the upper North River and the mid-lower West River (Fig. 4). However, no clear relationship between N2 and DIN or nitrate in the lower North River and the upper West River could be derived. 3.2. Seasonal dynamics and interannual variability of excess dissolved N2 Monthly measurements of N2 in 2010 revealed a dynamic shift between seasons (Fig. 5). N2 ranged from 6.1 to 52.1 ␮mol N2 L−1 in the lower West River, and from 6.9 to 47.1 ␮mol N2 L−1 in the lower North River. Site-based mean N2 in the lower West River (26.9 ± 1.32 ␮mol N2 L−1 ) was a little greater than that in the lower North River (21.8 ± 1.39 ␮mol N2 L−1 ) (P < 0.05). As shown in Fig. 5, high N2 was observed in January and the warm-wet season from May through October. In three measurements during 2011, N2 in February was significantly greater than May and October (data not shown). Both the North and West Rivers had a similar seasonal pattern with the exception of June and July, when the mean N2 of the lower North River sites was much lower than in the lower West River. The monthly mean value of N2 and other factors (i.e., DO, pH, water temperature, DOC, DIN, NH4 –N, NO2 –N, NO3 –N) were used for multi-linear regression. Once two data points, an extremely dry February (lowest API = 9.0,

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N. Chen et al. / Agriculture, Ecosystems and Environment 189 (2014) 1–10

Upper North River

a.

ΔN2 (μmol N2 L-1)

ΔN2 (μmol N2 L-1)

Upp er North River

b. 120

120 y = 0.214x - 67.96 R² = 0.68 5

100

100

80 60

80 60 40

40

y = -16.57x + 143.4 R² = 0.979

20

20 300

400

500

600

700

2.0

800

3.0

DIN (μmol N L-1 ) Mid-Lower West River

c.

5.0

6.0

Mid-Lower West River

d. 60

ΔN2 (μmol N2 L-1)

60

ΔN2 (μmol N2 L-1)

4.0 DO (mg L-1 )

50 40 y = 0.061 x + 28.49 R² = 0.83 7

30

50 40 30

y = -4.896x + 71.49 R² = 0.45 5

20

20 100

200

30 0

400

50 0

DIN (μmol N L-1)

3.0

4.0

5.0

6.0

7.0

8.0

DO (mg L-1 )

Fig. 4. Relationships between excess dissolved N2 and DIN concentration (a and c) and DO (b and d) in the upper North River (a and b) and mid-lower West River (c and d). Three measurements from sites N1–N4 and W6–W14 in February, May and October 2011 are included. Bars indicate standard error of mean over time.

Fig. 5. Seasonal variation of N2 in the Jiulong River, 2010. Bars indicate mean value and one standard error. Difference between rivers or months are statistically significant (P < 0.05). Significant difference by month is marked by a, b, c, d for the lower North River and a , b , c , d for the lower West River.

see Table 1) and an extremely hot August (water temperature over 30 ◦ C) were excluded, a statistical model was developed as: N2 = −43.82 + 0.24 × [DIN] + 1.31 × [Water temperature] (R2 = 0.69; P = 0.017). Likewise, another model was developed for the West River as: N2 = −10.16 + 0.015 × [DIN] + 1.46 × [Water temperature] (R2 = 0.67; P = 0.011). In other words, DIN concentration and temperature explain over 65% of monthly variation in N2 . Interannual variation of N2 in the Jiulong River was also observed (Fig. 6). For the West River, the mean N2 in 2011 (43.1 ± 3.73 ␮mol N2 L−1 ) was generally greater than 2010 (24.9 ± 1.56 ␮mol N2 L−1 ). For the North River, mean N2 in 2011 (24.7 ± 5.25 ␮mol N2 L−1 ) was comparable with that of 2010 (25.5 ± 2.55 ␮mol N2 L−1 ). There were no consistent interannual variations in given months. For example, a high N2 was observed in February 2011 and a low value in October 2011, which was very different to the situation in 2010 where the highest N2 observed

Fig. 6. Interannual variation of N2 in the Jiulong River. Bars indicate mean value and one standard error of mean. North River sites included N12, N13 and N14; West River sites included W8, W9, W10 and W11. Significant difference (P < 0.05) between groups is marked by a, b, c for 2010 and a , b , c for 2011.

in May. Current results reveal that the North River displayed greater temporal dynamics than the West River. 3.3. Nitrogen removal from river network Gaseous N removal fraction (Ed ) averaged 22% of DIN concentration for the whole Jiulong River, and was slightly higher in the West River (average 26%) than in the North River (18%). The estimated gaseous N removal through water–air interface and area-weighted N2 flux in different reaches of the West and North Rivers is summarized in Table 3. In total, the main rivers contribute more N removal than their tributaries due to their large water area and higher N loads. Annual area-weighted N2 flux was 8.66 kg N ha−1 for the whole Jiulong River network, and gaseous N removal (81,166 kg N yr−1 ) was about 24% of the riverine N export at the river mouth (34,800,000 kg N yr−1 ) estimated by Yan et al. (2012).

N. Chen et al. / Agriculture, Ecosystems and Environment 189 (2014) 1–10

7

Table 3 Estimation of gaseous N removal and area-weighted N2 flux in the Jiulong River. Reach

Water area (ha)

North River

Main river (N1–N15) Tributary Subtotal

3177 4256 7433

31,373 25,669 57,042

9.88 6.03 7.67

55 45 100

Main river (W4–W14) Tributary Subtotal Total

1401 540 1941 9374

19,698 4426 24,124 81,166

14.06 8.20 12.43 8.66

82 18 100

West River

a

Gaseous N removal (kg N yr−1 )

Share (%)a

River

Area-weighted N2 flux (kg N ha−1 yr−1 )

Ratio to riverine N export (%)

25.0

21.1 23.7

Indicates contribution of main branch river and tributary to the total gaseous N removal from the Jiulong River network.

4. Discussion 4.1. Factors controlling the spatial variability of denitrification Seitzinger et al. (2006) stated that denitrification occurs when three conditions are satisfied: nitrate is available, oxygen concentrations are reduced, and electron donors are available. For fluvial systems (stream, river), denitrification is typically supported by nitrate that was produced outside of the stream environment, particularly in watersheds with large anthropogenic N inputs. As a facultative anaerobic respiratory pathway, denitrification is broadly dependent upon the absence of oxygen, but appears to occur within anoxic micro-sites in otherwise apparently well oxygenated sediments (Trimmer et al., 2012). Contrasting spatial patterns of N2 exist between the North River and West River (Fig. 3). For the North River, N2 decreased gradually along the N1–N4 channel and then declined to a relatively constant level from site N5 to N15. The value of N2 appeared to follow the pace of DIN shift in terms of concentration and component. Extremely high N2 with large temporal variation (large standard error as shown in Fig. 3a) occurred in the upper North River channel (N1–N4) where it received large nutrient loading from animal farming plots and urban human wastes. Water in this reach was hyper-N rich (DIN > 500 ␮mol N L−1 ) with high ammonium fraction (up to 78% of DIN). DO at sites N1–N4 was relatively low (range 0.91–6.44 mg L−1 , average 4.8 mg L−1 ) compared with sites N5–N15 (range 3.99–8.40 mg L−1 , average 6.05 mg L−1 ). The fairly high nitrite (mid-product of nitrification) at sites N1–N4 suggested a substantial DO consumption by nitrification. Decomposition of organic wastes also leads to DO decline. The low DO would then be expected to stimulate sediment denitrification and produce more excess N2 as observed in this study. This inference was supported by the significant relationships between N2 and DIN and DO (Fig. 4). Multi-linear regression of 2011 measurements showed that DIN and DO together explain 98% variance in N2 between sites N1–N4. This suggests an interactive effect of DIN level and DO on denitrification. N2 was highly correlated with DIN rather than nitrate, implying a coexistence of nitrification and denitrification in the upper North River. Nitrate dominated DIN along the N5–N15 channel and increased gradually due to the addition of nitrate dominated runoff from tributaries (Chen et al., 2012). Denitrification rate in terms of N2 flux (Fig. 2) and N2 (Fig. 3) in the lower North River (N5–N15) varied little and did not correlate with DIN concentration (P > 0.05). In general, the accumulation of nitrate along the main branch of the North River (N5–N15) was not accompanied by incremental N2 . This might be ascribed to unfavorable conditions for denitrification in the river channel, including an observed thin and sandy sediment environment with low N content (0.7–2.5 g N kg−1 ) (unpublished data), a relatively low DOC (mean 153 ␮mol C L−1 ) in surface water compared with the upper river sites (215 ␮mol C L−1 ) indicating decreased organic carbon availability. The lower North

River (N5–N15) passes through various cascade dams which were expected to influence nitrate biogeochemistry. A lack of correlation between N2 and nitrate is logical due to the great difference in nitrate delivery and cycling processes between reservoir and river channel. In addition to denitrification, nitrate can be removed by assimilatory nitrate uptake, dissimilatory reduction of nitrate to ammonium (DNRA), chemoautotrophic denitrification via sulfur or iron oxidation, and anammox, as well as abiotic nitrate removal processes (Burgin and Hamilton, 2007). Current results cannot further examine the dominant processes of nitrate in the whole river-reservoir system. Nevertheless, there was a relative accumulation of N2 at three dam reservoir sites (N8, N11, N15) compared with the nearest upstream open channel site (Fig. 3a). In other words, construction of dams in the lower North River increases sedimentation, thereby facilitating the denitrification process and accumulation of N2 in the reservoir. Harrison et al. (2009) found reservoirs to be hotspots of N removal since lentic water bodies (lakes and reservoirs) offer ideal conditions for N burial in sediments or permanent loss via denitrification. In the West River, N2 increased gradually from upstream tributaries to lower river (Fig. 3b). Unlike the upper North River, N2 in the upper West River (W1–W5) was relatively low despite the high concentration of DIN in the water. The upper West River was as hyper-N rich as the upper North River (N1–N4), but DIN was dominated by nitrate rather than ammonium (Fig. 3b). In the catchment of the upper West River (Pinghe County), C. maxima is widely cultivated and intensive ammonium-based fertilization contributes to the high N loading. Most of the ammonium runoff from cropland should be first nitrified to nitrate during subsurface runoff before being discharged to streams. After crossing the water table, nitrate in ground water is subject to varying degrees of denitrification depending on the geochemical conditions in the aquifer (Hiscock et al., 1991). In reviewing denitrification across landscapes and waterscapes, Seitzinger et al. (2006) found that terrestrial soils and groundwater are responsible for much denitrification at the watershed scale, although their per-area denitrification rates are approximately one-tenth the per-area rates of denitrification in open waters (lakes, rivers, estuaries, continental shelves, and oceanic oxygen minimum zones). Given the relatively low N2 in the upper West River (W1–W5), we suspect that the majority of denitrification in the upper West River occurs in groundwater aquifers rather than the river channel, although no direct evidence for this was found in this study. In contrast, in the upper North River (N1–N4) animal and human wastes directly enter the waterbody via surface runoff, so denitrification should occur within the river channel, following oxidization of ammonium from animal and human wastewater flows to nitrite and nitrate. This contrasting spatial pattern of N2 and dominant N forms along the West and North Rivers reflects the distinct N biogeochemical behaviors associated with N sources and the way that N export occurs from land to river. In the upper North River and mid-lower West River, the significant relationships between N2 and DIN and DO (P < 0.05)

N. Chen et al. / Agriculture, Ecosystems and Environment 189 (2014) 1–10

80 N1-N4 N5-N15 W1-W5 W6-W10 W11-W14 Estuary

70 60 Ed (%)

(Fig. 4) suggest that denitrification was controlled by an interactive effect of N loading and DO. The accumulation of N2 along the lower West River (W11–W14) was likely due to the flat channel and longer residence time. As demonstrated above, the denitrification “hot spots” were in the upper North River and lower West River, where N level and DO are the main factors influencing N2 . However, the spatial patterns of N removal are quite different at the reach scale. In general, N2 is high in N-rich rivers, with the exception of the orchard-dominated upper West River where denitrification might be occurring in the groundwater (in-depth investigation of this possibility is beyond the scope of this study). Our results suggest that the spatial variation of N2 largely depends on the riverine N concentration and dominant N forms (ammonium or nitrate) associated with land-based anthropogenic sources (animal and human wastes or fertilizer loss) and the way that N enters rivers (direct discharge or through soil-groundwater). In addition, hydrology (change of residence time by dam construction) likely has an influence on the accumulation of denitrification products (dissolved N2 ), although no quantitative relationship could be found. Based on current results, we suppose that benthic denitrification is the most important pathway of N removal for the whole river system, despite the fact that in some cases denitrification occurs in the low oxygen water column (Seitzinger et al., 2006), and even on suspended sediment in oxic waters of turbid rivers (Liu et al., 2013). In situ measurements at the sediment–water interface was beyond the scope of this study but deserves further examination, especially the coupled influence of sediment characteristics and river hydrology on the denitrification process and release of excess N2 .

50 40 30

a.

20 10 0 0

20

40

60 80 100 120 140 160 ΔN2 (μmol N2 L-1)

80 b.

70 Regression of West River

60 Ed (%)

8

y = 6014x -0.96 R² = 0.742 (P<0.05)

50 40 30 20 10 0 0

20 0

40 0 60 0 80 0 DIN (μmol N L-1 )

1,000 1,200

Fig. 7. Relationships between N removal fraction (Ed ) and N2 (a) and DIN level (b) in the Jiulong River-Estuary system. Data were grouped by reach from 2011 measurements; estuary data indicates mean value of four cruises in 2010–2011. The x and y axis in the figure are not independent variables but used to illustrate the Ed pattern in relation to riverine N.

4.2. Factors controlling the temporal dynamics of denitrification Here we discuss the key factors controlling the monthly dynamics of N2 in 2010. To avoid the overlay effect of spatial variation, a sub-dataset of two sites (N12 and N15) in the lower North River was selected, since their N2 was at the same level. The significant correlation between water temperature and DIN concentration (P < 0.05) in the West River and North River suggests an interactive effect of N level and temperature on the temporal dynamics of denitrification, given that the two variables together explain more than 65% of monthly variation in N2 . From the correlation coefficients of independents, water temperature seems more important than N concentration in controlling seasonal dynamics of denitrification. Previous studies have shown that high temperature always stimulates the activities of denitrifying bacteria (Seitzinger, 1988; Bouletreau et al., 2012; Wertz et al., 2013). In addition, high water temperature leading to lower expected equilibrium concentration does not lead to an increase in N2 . While it is true that the solubility of N2 is lower in warmer temperatures, the only reason there would be excess N2 is through net denitrification. N inputs influence denitrification rates in aquatic ecosystems, but hydrology influences the proportion of N inputs that are denitrified. For example, high water salinity and drought negatively affected the stream-denitrifying capacity in two agriculturally influenced temporary Mediterranean saline streams (Arce et al., 2013). For the Jiulong River, N enrichment of water during the dry season (winter) together with a longer residence time may enhance the denitrification rate. In the rainy season, runoff provides more available nitrate and carbon, but high flow (short residence time) restrains the accumulation of denitrification products. Due to the wide ranges of physical–chemical conditions and environmental factors at the watershed scale, it is extremely difficult to identify the drivers of temporal patterns in denitrification. The interannual variation of N2 between 2010 and 2011 (Fig. 6) reflects the highly dynamic nature of denitrification in the

Jiulong River. In addition, the lower North River (N12–N14) displayed greater seasonal differences compared to the lower West River (W8–W11). The difference in temporal pattern between the two rivers is an interesting finding but it was not possible to demonstrate a physical mechanism in the present study. The available data suggests that this difference cannot be well explained by the temporal patterns in N concentration, organic carbon (DOC) or any other variables that differ between the two rivers over an annual period, and specific causes of the difference will need to be further explored. 4.3. The role of denitrification in the river network and estuarine N cycle The average Ed was 22% of DIN for the whole Jiulong River (Table 2). Average Ed was higher in the West River (average 26%) than in the North River (average 18%), which is consistent with the greater area-weighted N2 flux in the West Jiulong River (12.43 kg N ha−1 yr−1 ) compared with the North River (7.67 kg N ha−1 yr−1 ) (Table 3). High Ed was logically a result of more excess N2 from net denitrification (Fig. 7a). However, this value was generally decreased due to hyper-N enrichment (Fig. 7b). For the West River, Ed was negatively correlated to DIN (P < 0.05), and was largest in the mid river (W6–W10), followed by lower river (W11–W14) and upper river (W1–W5). This suggests that N removal capacity will be constrained in hyper-N rich rivers. Ed was mostly greater than 20% where riverine DIN was less than 400 ␮mol N L−1 , but this was not true for the North River where Ed was still low even when DIN was about 200–400 ␮mol N L−1 . This might be associated with the steeper hydraulic gradient of the North River channel and bottom outflow from cascade reservoirs that help to rapidly achieve a water–air equilibrium of excess dissolved N2 . Cascade hydropower dam reservoirs along the lower North River have greatly altered river hydrology and as a result

N. Chen et al. / Agriculture, Ecosystems and Environment 189 (2014) 1–10

9

Table 4 Denitrification rate (flux) and N removal fraction in selected rivers around the world. Study site

Land use type

Jiulong River (China) Changjiang River (China) Iroquois River (USA) Sugar Creek (USA)

Agricultural land Mixed land Agricultural land Agricultural land Natural land and agricultural land Urban and agricultural land

Swale-Ouse River (UK)

South Platte River (USA) a b c

Method of analysis

Denitrification rate (␮mol N m−2 h−1 )

N removal fraction (%)

Reference

2010.1–2011.10

N2 :Ar/MIMS

33 to 1754

24a ; 22 b

This study

2002.8–2003.3

N2 :Ar/MIMS

4280

1–2

Yan et al. (2004)

1999.9–2001.9

N2 :Ar/MIMS

−8 to 1288

2–17c

1999.9–2001.9

N2 :Ar/MIMS

−99 to 4371

11–19

Smith et al. (2006)

1995.8–1996.12

Acetylene inhibition

0 to 883

∼5

Pattinson et al. (1998)

2000.5–2001.8 2000.5–2001.8

Mass balance N2 :Ar/MIMS

863 to 15,625 0 to 9167

34–45 34c

Pribyl et al. (2005) Pribyl et al. (2005)

Study period

Smith et al. (2006) c

Ratio of gaseous N removal to riverine N export. Estimated as mol ratio: Ed = N2 /[DIN] × 100. Proportion of nitrate load denitrified per meter of stream length; remainder calculated as gaseous N removal/DIN load.

there is no regular relationship between Ed and DIN or clear spatial pattern of N2 , as already discussed. Gaseous N removal via water–air emission from the Jiulong River network was about 24% of the riverine N export at the river mouth. In the Jiulong River Estuary, annual gaseous N removal has been estimated as 24% of the DIN load (same as riverine export) (Wu et al., 2013). Given the size of the water area in the river network (93.7 km2 ) and estuary (100 km2 ), their removal capacities were similar. Overall, the role of denitrification in the N cycle in the Jiulong River-Estuary system is comparable to other systems around the world (Table 4). The gaseous N removal fraction (Ed ) of the Jiulong River was slightly greater than the two NO3 -rich streams draining agricultural land in the upper Mississippi River Basin (Smith et al., 2006). In general, the denitrification rate is relatively high in human perturbed watersheds. Actually, land use in the watershed is the dominant determinant for N export and denitrification rate. Driven by growing human needs, deforestation to arable land and urban land has resulted in nutrient enrichment in soil and water. A comparative study on different types of streams (forest, degraded urban, and restored streams) found that urbanization influences organic carbon sources and quality in streams which have impact on downstream denitrification (Newcomer et al., 2012). The relationship between land use change and denitrification rate remain unknown but deserve further study. Although all the present estimates have some uncertainty due to the limitation of each method of analysis, our results do indicate considerable N removal in the whole Jiulong River-Estuary system. Denitrification presents opportunities for managing ecosystem properties to control where, when and how much N is denitrified. The Jiulong River watershed has experienced widespread human activities and large land use/cover change in recent decades (Huang et al., 2012). The current application rate of fertilizer (>1000 kg N ha−1 ) in the Jiulong River watershed was fairly high compared with FAO recommended rates (300–500 kg N ha−1 for most crops). Nutrient delivery to river is anticipated to increase since the intensively fertilized croplands will continue to release surplus N over a long time period, even if future N inputs are reduced. In the Jiulong River Watershed, N loading is likely to further increase, particularly in urbanized areas where human and animal wastes are poorly managed, and in the lower North River area where industrial activities continue to increase. Our results suggest that the capacity of permanent N removal through outgassing of N2 can be constrained in hyper N-rich rivers. Therefore, the most appropriate way to manage N loss is through reducing anthropogenic inputs. The efforts to reduce N inputs from adjacent

fields should be made to reduce downstream N loading (Roley et al., 2012). From a watershed-scale management perspective, mitigation strategies for N pollution should target not only the aquatic system but also the terrestrial system. In addition to N2 , N2 O (an important greenhouse gas) and other N gases (NO) can be produced and emitted to the atmosphere during microbial denitrification despite the fact that less than 1% of denitrified N is converted to N2 O (Beaulieu et al., 2011). N2 O and NO should be taken into consideration since in intensive agriculture and livestock husbandry, river channels may act as strong sources of N2 O and NO when removing the redundant N from the watershed system. A companion study on N2 O production suggests that dissolved N2 O concentrations in the Jiulong River are always far above saturation (112–4133%) (Chen et al., unpublished). 5. Conclusions Excess dissolved N2 (N2 ) and water–air fluxes of N2 displayed a strong spatial variation from the Jiulong River network to the estuary. “Hot spots” of denitrification were found in the upper North River and lower West River where animal and human wastes dominated riverine N sources. DIN and DO were the key factors controlling the spatial variation of N2 . Net denitrification (N2 ) and N removal fraction (Ed ) in the orchard-dominated upper West River was relatively low, possibly because denitrification may be occurring in groundwater aquifers. The lack of a regular spatial pattern in the lower North River may be ascribed to (1) the steep hydraulic gradient of the open channel helping to achieve a rapid water–air N2 equilibrium, and (2) altered hydrology resulting from cascade dams. Denitrification products were observed to accumulate in flat channels (lower West River) and in dam reservoirs (North River), indicating that geomorphology (hydraulic gradient) and hydrology (residence time) played an important role in the accumulation of denitrification products. In summary, various human perturbations (discharge of animal and human wastes, over-fertilization, dam construction) have substantially altered nutrient biogeochemical behaviors and shaped the spatial pattern of gaseous N removal in the Jiulong River network. In general, a high denitrification rate was observed in January and the warm-wet season from May through October. The seasonal dynamics of denitrification were linked to an interactive effect of water temperature and N concentration. Inter-annual variability exists, but the North River displayed more seasonal difference than the West River. Gaseous N removal (Ed = 22% of DIN concentration; annual N removal = 24% of riverine N export) through N2 emission due to denitrification is

10

N. Chen et al. / Agriculture, Ecosystems and Environment 189 (2014) 1–10

comparable with other rivers/estuaries around the world, but gaseous N removal effectiveness was constrained in hyper-N rich waters. Further examination of N cycling processes and detailed mechanisms will be necessary to assess the effectiveness of management efforts to reduce riverine N export and mitigate the problem of coastal eutrophication. Acknowledgments This research was supported by the National Natural Science Foundation of China (No. 41076042; 41376082), and the Fundamental Research Funds for the Central Universities (2012121053). We thank Jonathan Vause for his assistance with English editing and the anonymous reviewers for helpful comments and suggestions which improved the manuscript. References Arce, M.I., Gomez, R., Suarez, M.L., Vidal-Abarca, M.R., 2013. Denitrification rates and controlling factors in two agriculturally influenced temporary Mediterranean saline streams. Hydrobiologia 700, 169–185. Baxter, A.M., Johnson, L., Royer, T., Leff, L.G., 2013. Spatial differences in denitrification and bacterial community structure of streams: relationships with environmental conditions. Aquat. Sci. 75, 275–284. Beaulieu, J.J., Tank, J.L., Hamilton, S.K., Wollheim, W.M., Hall, R.O., Mulholland, P.J., Peterson, B.J., Ashkenas, L.R., Cooper, L.W., Dahm, C.N., Dodds, W.K., Grimm, N.B., Johnson, S.L., McDowell, W.H., Poole, G.C., Valett, H.M., Arango, C.P., Bernot, M.J., Burgin, A.J., Crenshaw, C.L., Helton, A.M., Johnson, L.T., O’Brien, J.M., Potter, J.D., Sheibley, R.W., Sobota, D.J., Thomas, S.M., 2011. Nitrous oxide emission from denitrification in stream and river networks. Proc. Nat. Acad. Sci. U.S.A. 108, 214–219. Bouletreau, S., Salvo, E., Lyautey, E., Mastrorillo, S., Garabetian, F., 2012. Temperature dependence of denitrification in phototrophic river biofilms. Sci. Total Environ. 416, 323–328. Burgin, A.J., Hamilton, S.K., 2007. Have we overemphasized the role of denitrification in aquatic ecosystems? A review of nitrate removal pathways. Front. Ecol. Environ. 5, 89–96. Cao, W.Z., Hong, H.S., Yue, S.P., 2005. Modelling agricultural nitrogen contributions to the Jiulong River estuary and coastal water. Global Planet. Change 47, 111–121. Chen, N.W., Hong, H.S., 2011. Nitrogen export by surface runoff from a small agricultural watershed in southeast China: seasonal pattern and primary mechanism. Biogeochemistry 106, 311–321. Chen, N.W., Hong, H.S., 2012. Integrated management of nutrients from the watershed to coast in the subtropical region. Curr. Opin. Environ. Sustainability 4, 233–242. Chen, N.W., Hong, H.S., Zhang, L.P., Cao, W.Z., 2008. Nitrogen sources and exports in an agricultural watershed in Southeast China. Biogeochemistry 87, 169–179. Chen, N.W., Peng, B.R., Hong, H.S., Turyaheebwa, N., Cui, S.H., Mo, X.J., 2013. Nutrient enrichment and N:P ratio decline in a coastal bay-river system in southeast China: the need for a dual nutrient (N and P) management strategy. Ocean Coast. Manage. 81, 7–13. Chen, N.W., Wu, J.Z., Duan, H.Y., Wei, T.Z., 2010. N2 :Ar method for direct measurement of denitrificaion product (dissolved N2) using membrane inlet mass spectrometry (MIMS) (in Chinese). Acta Sci. Circum. 30, 2479–2483. Chen, N.W., Wu, J.Z., Hong, H.S., 2012. Effect of storm events on riverine nitrogen dynamics in a subtropical watershed, southeastern China. Sci. Total Environ. 431, 357–365. Curie, F., Ducharne, A., Bendjoudi, H., Billen, G., 2011. Spatialization of denitrification by river corridors in regional-scale watersheds: case study of the Seine river basin. Phys. Chem. Earth 36, 530–538. Dalsgaard, T., Thamdrup, B., Canfield, D.E., 2005. Anaerobic ammonium oxidation (anammox) in the marine environment. Res. Microbiol. 156, 457–464. Davidson, E.A., Seitzinger, S., 2006. The enigma of progress in denitrification research. Ecol. Appl. 16, 2057–2063. Findlay, S.E.G., Mulholland, P.J., Hamilton, S.K., Tank, J.L., Bernot, M.J., Burgin, A.J., Crenshaw, C.L., Dodds, W.K., Grimm, N.B., McDowell, W.H., Potter, J.D., Sobota, D.J., 2011. Cross-stream comparison of substrate-specific denitrification potential. Biogeochemistry 104, 381–392. Galloway, J.N., Aber, J.D., Erisman, J.W., Seitzinger, S.P., Howarth, R.W., Cowling, E.B., Cosby, B.J., 2003. The nitrogen cascade. Bioscience 53, 341–356. Groffman, P.M., Altabet, M.A., Bohlke, J.K., Butterbach-Bahl, K., David, M.B., Firestone, M.K., Giblin, A.E., Kana, T.M., Nielsen, L.P., Voytek, M.A., 2006. Methods for measuring denitrification: diverse approaches to a difficult problem. Ecol. Appl. 16, 2091–2122. Harrison, J.A., Maranger, R.J., Alexander, R.B., Giblin, A.E., Jacinthe, P.A., Mayorga, E., Seitzinger, S.P., Sobota, D.J., Wollheim, W.M., 2009. The regional and global significance of nitrogen removal in lakes and reservoirs. Biogeochemistry 93, 143–157.

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