Assessment of Indirect N2O Emission Factors from Agricultural River

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Environmental Processes

Assessment of Indirect N2O Emission Factors from Agricultural River Networks Based on Long-term Study at High Temporal Resolution XIAOBO QIN, Yong Li, Stefanie Goldberg, Yunfan Wan, Meirong Fan, Yulin Liao, Bing Wang, Qingzhu Gao, and Yu'e Li Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.9b03896 • Publication Date (Web): 23 Aug 2019 Downloaded from pubs.acs.org on August 25, 2019

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Environmental Science & Technology

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Assessment of Indirect N2O Emission Factors from Agricultural River Networks

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Based on Long-term Study at High Temporal Resolution

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Xiaobo Qin* , Yong Li , Stefanie Goldberg§, Yunfan Wan , Meirong Fan , Yulin Liao

4

, Bing Wang , Qingzhu Gao , Yu’e Li

‡

||

Institute of Environment and Sustainable Development in Agriculture, Chinese

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Academy of Agricultural Sciences / Key Laboratory for Agro-Environment, Ministry

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of Agriculture and Rural Affairs. No.12 Zhongguancun South Street, Haidian district,

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Beijing 100081, China

9

‡

Key Laboratory of Agro-ecological Processes in Subtropical Region, Institute of

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Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China

11

§

12

China

13

||

Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 6502021,

Changsha Environmental Protection College, Changsha 410004, China Soils and Fertilizer Institute of Hunan Province, Changsha 410125, China

14 15

*

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Abstract

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Assessment of indirect emission factors (EF5r) of nitrous oxide (N2O) from agricultural

18

river networks remains challenging and results are uncertain due to limited data

19

availability. This study compared two methods of assessing EF5r using data from long-

20

term observations at high temporal resolution in a typical agricultural catchment in

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subtropical central China. The concentration method (Method 1) and the

Corresponding author: [email protected]

ACS Paragon Plus Environment


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Intergovernmental Panel on Climate Change (IPCC) 2006 method (Method 2) were

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employed to evaluate the emission factor. EF5r estimated using Method 1 (i.e., EF5r1)

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was 0.000 77 ± 0.000 25 (0.000 38–0.000 97). EF5r calculated using Method 2 (i.e.,

25

EF5r2) was lower than EF5r1, with a mean value of 0.000 04 (0.000 015–0.000 12). Both

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EF5r1 and EF5r2 were significantly lower than the IPCC 2006 default value of 0.0025,

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suggesting that N2O emissions from China and world river networks may be grossly

28

overestimated. A complex N2O production pathway and diffusion mechanism was

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responsible for transfer of N2O from sediment to river water and then to the atmosphere.

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These findings provide essential data for refining national greenhouse gas inventories

31

and contribute evidence for downward revision of indirect emission factors adopted by

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the IPCC.

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Keywords: Indirect emission factors; nitrous oxide; agricultural river networks;

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EF5r; high temporal resolution

35 36


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INTRODUCTION

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Nitrous oxide (N2O) is a powerful (and the third most important) greenhouse gas. The

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current atmospheric concentration of N2O is 329 ppb1 and this is increasing annually

40

by 0.75 ppb.2 The current N2O concentration is 22% higher than the 270 ppb in the

41

era of industrial revolution.3 N2O not only has a greater global warming potential than

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other greenhouse gases, but also acts as the dominant destroyer of stratospheric

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ozone.4 Global N2O emission is currently 17.9 (8.1–30.7) Tg N a-1, which is divided

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between natural (61.5%, 11.0 (5.4–19.6) Tg N a-1) and anthropogenic sources (38.5%,

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6.9 (2.7–11.1) Tg N a-1) with regards to nitrogen (N) cycling and human

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disturbance,5,6 in which cropland-N2O emissions contributed 1.5–5.0 Tg N a-1.7

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The largest anthropogenic source of N2O is the biological conversion of

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agricultural fertilizer N (4.1 (1.7–4.8) Tg N a-1), of which direct emissions from soil

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(46.3%, 1.8–2.1 Tg N a-1) and animal manure (53.7%, 2.1–2.3 Tg N a-1) contribute the

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most.5–10 However, indirect emissions derived from atmospheric deposition (0.3–0.4

51

Tg N a-1), sewage (0.2–0.3 Tg N a-1) and N leaching and runoff (0.6–1.9 Tg N a-1)11

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cannot be ignored in efforts to refine the national-scale greenhouse gas inventory by

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the Intergovernmental Panel on Climate Change (IPCC). Unfortunately, as an

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important part of indirect N2O emission, little is known about the N2O emissions from

55

streams and rivers of agricultural catchments, and this knowledge gap has caused

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great uncertainty in the global N2O assessment effort.12–19

57 58

Previous studies have reported major pathways responsible for N2O production, which include nitrification, denitrification, coupled nitrification-denitrification,



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nitrifier denitrification and dissimilatory nitrate (NO3--N) reduction to ammonium

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(NH4+-N) (i.e., DNRA, which is performed by fermentative bacteria).20–22 N2O is the

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byproduct of aerobic nitrification, in which the predominant autotrophic nitrifiers

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oxidize the NH4+-N to NO3--N.23–25 Secondly, under low oxygen conditions, N2O is

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the intermediate product of denitrification through reduction dominated by denitrifiers

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of NO3--N to gaseous nitrogen (N2).23,24,26 Furthermore, N2O can be augmented by

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coupled nitrification-denitrification at the aerobic-anaerobic sediment interface, in

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which denitrifiers utilize NO2--N or NO3--N produced by nitrifiers to generate N2O.

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Especially in freshwater, NH4+-N transfers upward in the water column and can be

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nitrified to NO3--N, which can couple with previously existing NO3--N to create a “hot

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spot” of N2O production.15 Additionally, in an oxygen-deficient environment,

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anaerobic mineralized NH4+-N can be oxidized to NO2- by autotrophic nitrifiers and

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continuously converted to N2O, N2 and nitric oxide via the nitrifier denitrification

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process27, as pointed out by Wrage et al.28

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The process of DNRA has also been reported in fresh water conditions.29 Kleso

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et al.30 argued that DNRA is favored when NO3--N is limiting, while denitrification is

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favored when the supply of carbon is limited. Indeed, previous studies have

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demonstrated different effect of dissolved organic carbon (DOC),31 temperature32 and

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dissolved oxygen (DO)33 on N2O production, which is the reflection of their impact

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on these pathways. For example, nitrifier denitrification is favored under high N and

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low organic carbon (OC) concentrations in association with low oxygen. Nitrification

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has been shown to occur in the water column of streams that have high suspended


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solids concentration or experience diffusion from oxic sediment layers.34,35 Moreover,

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coupled nitrification-denitrification at the aerobic-anaerobic sediment interface also

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can augment N2O production.

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The indirect N2O emission factor from river networks (EF5r), is one of three

85

factors defined by IPCC (2006)36,37 that must be considered when assessing

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waterborne contributions of N2O from waterbodies to the atmosphere. IPCC defined

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indirect N2O emission factor for N leaching and runoff from arable soils as the ratio

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of N2O-N emitted from leached N and N in runoff divided by the fraction of total N

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input that is lost by leaching and runoff.38 However, to assess EF5r using this method,

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a detailed mass balance is required; mass balance data are difficult to obtain and are

91

often missing from many studies.11,33

92

Alternatively, EF5r is commonly calculated using the ratio of dissolved N2O-N

93

and NO3--N concentrations in the waterbody11 (concentration method). Actually, the

94

concept of EF5r is based on assumptions considering the proportion of N that is

95

nitrified and/or denitrified in the aquatic environment and the N2O that is

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subsequently produced. Originally, EF5r values were generated in 1998 and a value of

97

0.00758 was adopted by the IPCC based on limited available data. Afterwards, the

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value of EF5r was revised downward in 2006 to the currently used value of 0.0025.38

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Despite the revision, this default “Tier 1” emission factor is still poorly constrained

100

both by a paucity of field monitoring data and great uncertainty about water-air N2O

101

exchange relationships, as well as by large variability in environmental conditions.16

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The derivation of EF5r by IPCC has, at least partially, ignored the effect of various



103

potentially decisive conditions of local climate, land use and soil properties.39

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Significant spatiotemporal11,15,38 variation of EF5r has been observed, which may be

105

due to seasonal temperature changes, variable dissolved oxygen and hydrogeological

106

factors.11,32

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Therefore, use of a uniform value for EF5r significantly limits the development of

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national N2O emission inventories. Surveys such as that conducted by Beaulieu et

109

al.13 point out that ‘the IPCC approach of using one emission factor for all streams

110

may be inappropriate because emission factors are highly variable across streams.’

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Moreover, Hiscock et al.40 also indicated that the IPCC methodology may

112

overestimate the large role of anthropogenic sources. For example, Harrison and

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Matson41 concluded that the IPCC default method overestimated N2O emission from

114

drainage canals by 2 to19 times. Significantly, available evidence indicates that the

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magnitude of EF5r should be further revised downward by the IPCC.17,42 Moreover, up

116

to now, little attention has been devoted to assessing EF5r in small-scale river

117

networks using different methodologies; these networks receive large N loads due to

118

intensive farming activities.

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Clearly, the revision of EF5r magnitude requires credible, intensive long-term

120

observations. Unfortunately, previous published studies are of limited usefulness for

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evaluating EF5r because most were based on relatively short-term investigations.

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Furthermore, there remains a lack of sufficient data with high temporal resolution and

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few studies have compared various assessment methodologies. Therefore, much

124

uncertainty still exists about how to accurately determine the N2O contributions of


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rivers, both when refining national greenhouse gas inventories and quantifying the

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global N2O budget. Hence, the objectives of this study were to assess the values of

127

EF5r generated using two methods (the IPCC 2006 method and the concentration

128

method), and to ascertain the environmental factors and the potential mechanism by

129

which N2O is produced and emitted from river networks in a typical agricultural

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catchment. The study was based on the following hypotheses: 1) EF5r assessed using

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two different methods could have different values, 2) significant spatiotemporal

132

variation exists in both dissolved N2O concentration and EF5r, and 3) N2O production

133

and transfer may be controlled by various ecological processes in river networks in

134

typical agricultural catchments.

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MATERIALS AND METHODS

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Experimental Site. The study was conducted in the Tuojia River network within the

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Jinjing catchment of the Xiangjiang River watershed in Changsha, Hunan Province,

139

China (Figure 1). The catchment is located approximately 70 km northeast of

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Changsha City and had a population of 45,000 in 2014. 43 The study area is a typical

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hilly, agricultural catchment in subtropical central China, and has forest, paddy fields

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and tea fields as the three primary land use types, accounting for 33%, 61.1% and

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4.5% of the total catchment area, respectively. The other minor land uses in the

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catchment include reservoir/ponds, residential areas, rivers and roads, collectively

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accounting for 1.4% of the total catchment area.43 The climate in the catchment is



146

subtropical monsoon and humid, with an average annual rainfall of 1300 mm, an

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annual mean air temperature of 17.2 °C and a prevailing wind direction from the north

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and northwest throughout the year. More detailed soil type and geology information

149

can be found in Supporting Information (SI Tables S1 and S2).

150 151

Figure 1. Geographical location of Tuojia River catchment in China and sampling points

152 153

Sample Collection and Analysis. Over a four-year period (March 2013 to December

154

2016), a total of 929 water samples were taken from the Tuojia River and analyzed for

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dissolved N2O concentration and other parameters. Four order reaches44 (W1, W2,

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W3 and W4) were identified from the origin to outlet of Tuojia River and twelve

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locations (Figure 1) were selected for sampling along the four reaches, with every

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three locations representing the upstream, midstream and downstream section in each

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reach, respectively. Thus, the four reaches (with sampling points) were W1 (1/2/3),

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W2 (4/5/6), W3 (7/8/9) and W4 (10/11/12) (numbers are as those displayed in figure

161

1), respectively. Samples were collected at weekly intervals. The high frequency of


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sampling enabled temporal variability in dissolved N2O concentration and EF5r to be

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assessed more precisely.

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Samples for analysis of dissolved N2O concentration were collected from the

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river (at a depth of 0–20 cm) using 60 mL plastic syringes fitted with a three-way

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stopcock. Syringes were flushed three times with water from the sampling point and

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any air bubbles contained in the syringe were carefully expelled before a sample was

168

collected. Triplicate samples were taken at each location and no preservative was

169

added.

170

Samples remained in the syringes and were kept in cold storage at 4 °C for no

171

more than 3 h before further treatment. The headspace equilibrium method45 was used

172

to extract N2O and measure dissolved N2O concentration. A 30-mL volume of water

173

sample in each syringe was accurately displaced by 30 mL ultrahigh purity (>

174

99.999%) helium gas in the laboratory. The retrieved sample was subsequently

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shaken for 10 minutes and then allowed to stand and equilibrate for 5 minutes.

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N2O in the headspace was then manually and gently injected into a pre-evacuated

177

vial (12 mL, Labco, UK) and analyzed within 72 h of collection using a gas

178

chromatograph with a micro electron capture detector (μECD). The analytical

179

procedure used 99.999% N2 and 10% CO2 + 90% N2 as the carrier gas and backup

180

gas, respectively. Accuracy of N2O measurements was within ±3% with a detection

181

limit of ~0.0008 μg N L-1. Exchange fluxes of N2O from the river to the atmosphere

182

were estimated using the double-layer diffusion model method as previously reported

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by Liss et al.46 The original concentration of N2O before equilibrium was calculated



184

using the headspace balancing method45 and then the N2O flux was calculated based

185

on an estimate of the gas exchange rate (Kw) using wind speed (U10) with the Schmidt

186

coefficient (Sc).47 The detailed calculation processes48–51 for determining the dissolved

187

N2O concentration and its exchange flux are provided in the Supporting Information

188

(SI Tables S3 and S4).

189

Water and sediment samples for NO3--N, NH4+-N and dissolved organic carbon

190

(DOC) analyses were collected in 250 ml plastic bottles and plastic bags with

191

aluminum foil, respectively, at the same time samples for N2O analysis were

192

collected, and were analyzed within 72 h. The sediment samples were collected by

193

standard manual sampling drill set (Eijkelkamp Soil & Water, Netherland). All these

194

variables are sampled with 3 repetitions.

195

Concentrations of NO3--N and NH4+-N were determined using a flow injection

196

automatic analyzer (AA3, Seal, Germany), which had a coefficient of variation of

197

0.2% and a detection limit of 0.003 mg N L-1. DOC content was determined using a

198

total organic carbon analyzer (TOC-Vwp, Shimadzu, Japan), which had a detection

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range of 0–3000 mg L-1 and a detection limit of 2 μg L-1. Concentration of dissolved

200

oxygen, temperature and conductivity of river water at the time of sampling was

201

measured using a portable multiple meter (AP700, Aquaread Co. LTD., UK).

202

Wind speed, temperature, precipitation and other meteorological data required

203

for calculating diffusive N2O flux were obtained from a weather station installed in

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the catchment with a record frequency of 3-hour. River discharge was monitored daily

205

using flow meters at three locations along Tuojia River (Fuling in W1, Feiyue in W3


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and Tuojia in W4). Additional detailed information can be found in the Supporting

207

Information (SI Figure S1).

208 209

Calculation of EF5r. The EF5r is an index of N2O transfer from river networks to the

210

atmosphere as a fraction of the N loading to the rivers, is one of three factors defined

211

by IPCC (2006)36. According to the provenance of N, the other two factors reflect

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N2O transfers from groundwater or surface drainage (EF5g) and from estuaries

213

(EF5e).37 In this study, two methods were used to calculate the EF5r (Table 1). The

214

first method was the concentration method38 (hereafter, Method 1), in which the value

215

of EF5r1 is calculated using the dissolved N2O concentration (kg N2O-N L-1) divided

216

by the NO3--N concentration (kg NO3--N L-1) in the water column. The second

217

method (IPCC 2006 method)37 (hereafter, Method 2) estimated the value of EF5r2

218

using the N2O emitted from river waterbodies of the whole catchment to the

219

atmosphere (kg N2O-N a-1) divided by the total N (kg N a-1) input to the catchment

220

adjusted by the N leaching coefficient (kg N kg-1 of N addition a-1).

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IPCC defined indirect N2O emission factor (kg N kg-1 of N additions a-1) for N

222

leaching and runoff from arable soils as the ratio of N2O-N emitted from leached N

223

and N in runoff (kg N2O-N a-1) divided by the fraction of total N input that is lost by

224

leaching and runoff (Ninput × FracNLEACH, kg N a-1, where FracNLEACH is the

225

fraction of all N added to, or mineralized within, managed soils that is lost through

226

leaching and runoff).38 In fact, in regions where the water-holding capacity of the soil

227

is exceeded,38,52 30% (ranging from 10% to as much as 80%) of agricultural N is



228

leached due to precipitation or irrigation.36 By this definition, FracNLEACH is

229

determined from the total loading of dissolved organic and inorganic N in river water,

230

divided by the total N input (fertilizer plus livestock manure). However, in this study

231

we used the default value of 0.3 (0.1–0.8) for FracNLEACH.15 Furthermore, Method

232

2 also requires social economic data, which include the land use information, total

233

fertilizer consumption and livestock production information, etc. social statistical

234

information for Jinjing and the Tuojia catchment (such as livestock production) were

235

obtained from the 2016 Statistic Yearbook of Changsha City.53 The total N input to

236

the catchment from animals was then calculated using the emission factors provided

237

by the Provincial Greenhouse Gas Inventory Guide54 of China. The data for use in a

238

geographic information system to describe the Jinjing catchment and river networks

239

were acquired from the National Geomatics Center of China

240

(http://ngcc.sbsm.gov.cn/). More information about the data used can be found in the

241

Supporting Information (SI Tables S5 and S6).

242

Table 1. Calculation of the indirect emission factor of N2O Name

Method

References

Method 1

N O−N EF5r = 2 NO3 − N

Hama-Aziz et al., 201731

Method 2

EF5r =

N 2O − N N input × FracLEACH

IPCC, 200629

243 244

Data Processing and Statistical Analysis. Spatiotemporal variation of results over

245

the 4-year period were analyzed using SAS PROC MIXED55 (V9.3), using the

246

restricted maximum likelihood option and repeated measures with the autoregressive

247

covariance structure.56 Degrees of freedom were calculated using the Satterthwaite


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method.57 Means were separated using Fisher’s protected least significant difference

249

test at the 0.05 significance level,58 and the Tukey-Kramer method was used for p-

250

value adjustment at the significant level of 0.05. The R statistical software59 was used

251

for plotting data (“ggplot2”) and performing correlation analysis (“correplot”).

252

Decision regression tree analysis and factor importance analysis also was conducted

253

using the R software (specifically, “randomForest”, “rpart” and “rpart.plot”).

254

RESULTS AND DISCUSSION

255 256

Assessment of EF5r. Basic information of nutrient content, physicochemical

257

characteristics of water and sediment in Tuojia River are shown in table 2. During the 4-year monitoring period, EF5r1 derived using Method 1 in the four

258 259

reaches of the Tuojia River network varied between 0.0006 and 0.0017, with an

260

overall mean of 0.0012 (for all four reaches). Of all the calculated EF5r1 values using

261

Method 1, more than 90.77% were lower than the IPCC 2006 default value of

262

0.0025;37 furthermore, more than 27% of samples were one order of magnitude lower

263

than the default value. We also modified the EF5r1 estimates using the discharge data

264

of Tuojia River (SI Figure S1). The observed dissolved N2O concentrations were 0.35

265

μg N L-1 (W1), 2.31 μg N L-1 (W2), 1.91 μg N L-1 (W3) and 1.93 μg N L-1 (W4), and

266

the 4-year mean discharges of the Tuojia River at W1, W3 and W4 were 27 678 m3 a-

267

1

268

output from the Tuojia River network (mean of the four reaches) was 17.51 ± 0.78 kg

, 2 497 395 m3 a-1 and 24 703 340 m3 a-1, respectively. As a result, the annual N2O-N



269

N2O-N a-1. Thus, considering the dissolved NO3--N concentration in the different

270

reaches, we assessed that the mean NO3--N loading into the whole catchment was 18

271

021.13 ± 4083.29 kg N a-1. Consequently, the EF5r1 estimated using Method 1 was

272

modified to 0.000 77 ± 0.000 25 (0.000 38–0.000 97).

273

Detailed calculation method of N2O exchange fluxes, nutrient input and spatial

274

information as well as social statistical data that were required to calculate the EF5r2

275

are listed in Tables S5 and S6. Accordingly, the total N input for the Tuojia catchment

276

was calculated for the year 2015 using available data from the statistical yearbooks of

277

China60 and Changsha City53. The total amount of N fertilizer applied across the

278

whole catchment (including paddy rice fields and tea fields) was 1 433 700 kg N a-1,

279

and the N from animal excreta was 26 640.25 kg N a-1, resulting in a total of 1 460

280

340.25 kg N a-1 of N input for the whole Jinjing catchment. Given a FracNLEACH of

281

0.3, the value of “N × FracNLEACH” was 438 102.08 kg N a-1. As a result, the mean

282

EF5r2 value calculated for the Tuojia River network using Method 2 was 0.000 04

283

(0.000 015–0.000 12).

284 285 286


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Table 2. Basic information of nutrient content, physicochemical properties of water and sediment in Tuojia River Variables Water

Unit

Mean ± SD (range)

W1, W2, W3 and W4

1527

1.66 ± 0.29 (0.012–9.59)

0.92, 1.74, 1.98, 1.99

NH4 -N

1.03 ± 0.28 (0.003–8.32)

0.32, 1.35, 1.22, 1.22

DOC

3.63 ± 0.57 (0.33–25.87)

1.67, 3.74, 4.23, 4.88

2.14 ± 0.43 (0.20–121.00)

1.82, 2.15, 2.13, 2.45

20.01 ± 6.91 (-8.33–33.77)

19.15, 20.20, 20.65, 20.04

7.44 ± 2.04 (1.11–15.8)

8.55, 7.38, 6.90, 6.92

134.93 ± 54.46 (9.55–522.04)

77.29, 146.09, 150.59, 165.76

6.39 ± 2.26 (0.03–51.54)

9.31, 6.33, 3.42, 6.50

NH4 -N

8.61 ± 0.93 (0.14–143.69)

9.51, 8.74, 7.28, 8.91

DOC

63.32 ± 14.79 (18.87–350.14)

81.31, 69.62, 52.07, 50.31

10.66 ± 8.52 (0.68–277.07)

8.73, 10.99, 15.19, 7.74

NO3 -N

DOC/NO3

-1

mg L

--

Water temperature Dissolved oxygen content Conductivity Sediment

n

+

-

-

NO3 -N

1560 mg L-1 -1

uS cm

-1

mg kg

398

+

DOC/NO3 Discharge

W1

-3

-1

m d

1461

76.09 (0.43–2494.8)

W3

7259.87 (1.92–60929.34)

W4

67634 (2085–2981264)

288 289 290 291 292



293

Both of the EF5r values estimated using the two methods were substantially less

294

than the current IPCC default EF5r value of 0.0025. This difference indicates that the

295

previous downward revision of the IPCC 2006 default value (from previous value of

296

0.0075 to 0.0025) may still significantly overestimate indirect N2O emissions in

297

agricultural catchments similar to the one examined in this study. The current IPCC

298

default value is three times higher than the EF5r1 (0.000 77) and an astonishingly 60

299

times greater than the EF5r2 (0.000 04) calculated in this study. Actually, most

300

previous studies have estimated EF5r using Method 1 because the detailed mass

301

balance required in Method 2 is cumbersome to develop. For example, Outram and

302

Hiscock39 compared the EF5r values estimated using Methods 1 and 2 and found large

303

differences between the concentration method (0.0011) and the IPCC 2006 method

304

(0.009). Similarly, Hama-Aziz et al.38 showed a four-fold difference in the EF5r values

305

produced using Methods 1 and 2. Therefore, to achieve a more credible assessment

306

and avoid the miscalculations that arise from different approaches, the IPCC may

307

need to propose a single comprehensive and consistent approach20 via a refinement

308

process.

309

Using the mean of the two EF5r values arising from Methods 1 and 2 (i.e., 0.000

310

41 (0.0002–0.000 55)), we estimated the N2O emissions from China and world river

311

networks. These estimates were based on the research of Beaulieu et al.,264 data from

312

the China Statistical Yearbook 201660 (giving data for the total N fertilizer application

313

in China), and on research by Liang et al.61 (giving the total N from animal excreta in

314

China). Consequently, we estimated that the indirect N2O emissions from river


Page 16 of 36

Page 17 of 36


315

networks of world amounted to 0.07 Tg N a-1 and those from China amounted to

316

0.0041 Tg N a-1 (Table 3), both of which are much lower than the estimates made by

317

Beaulieu et al.26 Our result is just with in the range of indirect N2O emission from N

318

leaching / runoff in China (24 Gg N2O a-1, i.e. 0.015 Tg N a-1) by Zhou et al.62 As a

319

matter of fact, cropland N2O emissions in China is weakening in growth due to

320

nationwide policy interventions.63 Even so, fertilizer-induced N2O emission from

321

China is still 323.8 ± 60.3 Gg N2O-N a-1,64 hence, mitigation technologies should be

322

enhanced to reduce both direct and indirect N loss simultaneously.

323

Table 3. Estimated indirect emission of N2O from river systems of world and China Methodology 24

Beaulieu 2011 IPCC 200629 This study

EF5r (%)

Indirect N2O emissions (River) (Tg N a-1)

0.75 0.25 0.04

World 0.68 0.23 0.07

China 0.08 (0.026–0.202) 0.03 (0.008–0.0067) 0.0041 (0.002–0.019)

324 325

Comparison with Other Studies. A considerable amount of research has been

326

published about EF5r. These studies have used either a single method or at most two

327

methods to evaluate indirect N2O emissions from different river networks around the

328

world. The present study was designed to compare EF5r estimates produced using two

329

widely used methods against the current IPCC EF5r default value. The EF5r values

330

arising from the modified concentration method (Method 1) were similar to those

331

found by Xia et al.,65 but were lower than those of Hinshaw et al.15 and Beaulieu et

332

al.,13 and higher than the findings of Hama-Aziz et al.38 and Clough et al.9,66 The EF5r

333

values arising from the IPCC 2006 method (Method 2) were much lower than the

334

estimates by Hama-Aziz et al.38, Outram and Hiscock39 and Turne et al.16



Page 18 of 36

335

In addition to the current study, a number of previous investigations also

336

estimated EF5r values that were lower than the IPCC (2006) default value (Table 4).

337

Among these, the lowest EF5r (0.000 006) was reported by Clough et al.9 for a spring-

338

fed river in New Zealand. EF5r values of 0.0001, 0.0003 and 0.000 36 were estimated

339

by Hama-Aziz et al.38 and Clough et al.66 for agricultural drains and headwater

340

streams in the United Kingdom (UK) and a spring-fed river in New Zealand. In

341

contrast to these findings, only a few studies have determined EF5r values that were

342

higher than the IPCC default value. Some researchers16 have suggested based on tall-

343

tower measurements that an appropriate EF5r value should be approximately 0.02 for

344

streams of southeastern Minnesota, USA. A comparatively higher EF5r value of 0.009

345

was estimated by Outram and Hiscock39 for surface waterbodies in a lowland arable

346

catchment in the UK.

347

Table 4. EF5r (%) from different studies Methodology

EF5r

IPCC default

0.25

IPCC 2006

0.9

MIN

MAX

Watershed type

References De Klein et al., 200637

Agricultural drain and headwater

Outram and Hiscock, 201239

streams 2

Agriculture

Turne et al., 201516

0.01



streams 0.004 N2O-N/NO3-N

0.0015

0.012

0.11

Agriculture

This study


Outram and Hiscock, 201239

streams 0.036

0.015

0.067

0.0006 1.01

0.003

25

Spring-fed river

Clough et al., 200666

Spring-fed river

Clough et al., 20079


Beaulieu et al., 200813

streams 0.39

0.34

0.44

Agricultural streams

Baulch et al., 201252

0.14

0.12

0.16

Agricultural streams

Baulch et al., 201235

0.09

Sewage-enriched

Xia et al., 201365

0.078

Agriculture

Xia et al., 201365


Page 19 of 36


0.03



streams 0.28

0.12

0.69

Agriculture and waste

Hinshaw et al., 201315

0.077

0.038

0.097

Agriculture

This study

348 349

Spatiotemporal Variation of EF5r1. Spatiotemporal variations of EF5r1 values and of

350

dissolved N2O and NO3--N concentrations are shown in Figure 2 and Table S7,

351

respectively.

352

Temporally, sampling time (annual and seasonal) had significant impacts on

353

dissolved N2O and NO3--N concentrations as well as on EF5r1 estimates. From 2013 to

354

2016, the average dissolved N2O concentration ranged from 0.35 ± 0.05 μg N L-1 at

355

location W1 (minimum) to 2.31 ± 0.15 μg N L-1 at location W2 (maximum), and the

356

mean value for all locations in 2015 was significantly greater than the means in the

357

other 3 years (p < 0.05). As a comparison, the mean value of N2O concentrations in

358

2015 is 3.55 μg N L-1 while the other 3 years are 0.58 μg N L-1, 0.68 μg N L-1 and

359

0.68 μg N L-1, respectively. In addition, the N2O concentrations in summer were

360

significantly higher than those in the other three seasons (based on the mean value of

361

4 years) (p < 0.05). As with N2O concentrations, the diffusive flux of N2O was also

362

lowest (6.58 ± 2.92 μg m2 h-1) at location W1 and highest (49.54 ± 17.74 μg m2 h-1) at

363

location W2. The NO3--N concentration exhibited significant inter-annual variations

364

during 2013 to 2016 (p < 0.05), but the effect of sampling season on NO3--N was not

365

statistically significant (p > 0.05). Spatially, the NO3--N concentration varied from

366

0.92 ± 0.14 mg N L-1 (location W1) to 1.99 ± 0.45 mg N L-1 (location W4). The

367

remarkable effect of sample locations on dissolved N2O and NO3--N concentrations as



Page 20 of 36

368

well as on EF5r values is illustrated in Table S7 and Figure 2. The mean dissolved

369

N2O concentration in reaches W1 to W4 during the 4 years was 0.35 ± 0.05 μg N2O-N

370

L-1, 2.31 ± 0.15 μg N2O-N L-1, 1.91 ± 0.12 μg N2O-N L-1 and 1.93 ± 0.08 μg N2O-N

371

L-1, respectively. The mean dissolved NO3--N concentration in reaches W1–W4 was

372

0.92 ± 0.14 mg N L-1, 1.74 ± 0.12 mg N L-1, 1.98 ± 0.47 mg N L-1 and 1.99 ± 0.45 mg

373

N L-1, respectively. Correspondingly, the mean EF5r1 values were 0.0006 ± 0.00006,

374

0.0017 ± 0.0006, 0.0013 ± 0.0006 and 0.0012 ± 0.0003 for reaches W1 to W4,

375

respectively. W2

Dissovled N2O concentration (μg N/L)

W1 2013 3

W4

●●

2015 ●

●

3

2016

●

● ●

● ●●

30

3

●

2 ●●

● ●● ●● ●

● ● ●● ●

● ● ● ● ● ●

●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ●

●

● ● ● ● ●● ● ●

●

●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

1

● ●●

● ● ● ● ● ● ● ● ● ● ● ● ●

0

spring summer autumn winter

● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ●

● ● ●

● ● ● ● ● ● ● ●● ● ● ● ●

10

●● ●

●

●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

0


0.8 ●

●

● ●

● ● ● ● ● ● ●

● ● ●

1

● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ●

●

●● ●

● ● ● ● ●● ● ● ● ● ●

●

● ●● ● ● ●

EF5r (%)

0.6

0.2

Nitrate concentration (mg N/L)

377 378

1

0.2

● ●● ● ●

376

● ●

● ● ● ● ● ●

spring summer autumn winter ●

●

0.15

●

●

●● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ●● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ●

0.0


8

●

●

● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●

●

●

● ● ●

● ● ● ● ●●

●

●

● ● ● ● ● ●

●

4 ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ●

●

2

● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●


●

●

● ● ● ●

● ● ● ● ● ● ●

0

1

● ● ● ● ● ● ● ● ● ● ● ● ● ●

●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ●

●

● ● ● ● ● ● ● ● ● ● ● ● ●

●

● ● ● ● ● ● ● ● ● ● ●

●

● ● ● ● ● ● ● ● ● ● ●

●

● ● ● ●● ● ●

● ●

●

● ● ● ● ●

●

● ● ● ● ● ● ●

0.00

● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ●

spring summer autumn winter ●

● ● ●

1

● ● ●

●● ●

2

● ● ● ●

0.05

●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ●


●

● ● ●


●

● ●

● ● ● ● ●

● ● ● ●

● ●

3

● ●

●

0.10

●●

● ● ● ● ● ●


● ●

0

● ●

●

●●

●●

2

●

● ● ● ● ● ● ●

●

2

0.4

0.4

6

● ● ● ● ● ● ●

● ● ●

0.20

●

●●

0.0

● ● ● ● ● ● ● ●

0.25

●●

0.6 ●

0


3

●●

●

2

●

● ●

● ● ●

● ●

● ● ● ● ● ● ●

20

●●

● ● ● ● ●

●

● ●

●● ● ●

●

● ● ● ● ● ●

●●

● ●

2

●●

1

W3

2014

●

●

●

● ● ● ● ● ● ● ● ● ● ● ●

●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

3 ● ●

●

2

● ● ● ● ● ● ●

● ●

● ● ● ● ● ●


1

● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ●

● ● ● ●

● ● ● ●

●

● ● ● ● ● ● ●

● ● ●


Figure 2. Temporal and spatial variations of EF5r (method 1), dissolved N2O and nitrate concentrations from 2013 to 2016

379 380 381

All three variables followed the same spatial distribution pattern in which the lowest values occurred in reach W1 (furthest upstream), the highest values occurred


Page 21 of 36


in reach W2, and the values decreased in a downstream direction in reaches W3 and

383

W4. However, according to the Tukey's studentized range (HSD) test in the SAS

384

GLM procedure55, only the values from reach W1 were significantly different from

385

those in other reaches (p < 0.05). This may be mainly due to the remarkable

386

difference of ambient variants and N input50,67 as well as oxygen availability between

387

reach W1 and the other three reaches. In fact, both dissolved N2O concentration

388

(power function) and EF5r1 values (logarithmic function) decreased nonlinearly with

389

the increase of Strahler stream order for reaches W2 to W4 (Figure 3). The nonlinear

390

relationships are in accord with the findings of Turner et al.,16 who showed that N2O

391

emissions from rivers were negatively and exponentially correlated with the increase

392

of Strahler stream order. EF5r

0.17

Dissolved N2O Stream order and EF5r

2.25

EF5r (%)

Stream order and N2O

0.15

0.13

0.11 1.5

393 394

2.10

y = 2.359x-0.18 R2 = 0.9348 y = -0.065ln(x) + 0.2078 R2 = 0.873

1.95

Dissolved N2O concentration (µg N L-1)

382

1.80 2.0

2.5

3.0

3.5

4.0

4.5

Strahler stream order

Figure 3. Relationship between EF5r and Strahler stream order

395 396

Effect of Environmental Factors. Many previous studies (both long-term38 and

397

short-term9) have explored the N2O emissions from river networks and shown that

398

spatiotemporal variation of dissolved N2O concentration and EF5r values exists in

399

various watershed types. Based on a 2-year monitoring study of a headwater ditch in



400

Sichuan Province, China, Tian et al.68 showed that N2O emissions during the summer

401

and autumn were higher than those in spring and winter; the differences were

402

attributed to the higher NO3--N concentration and sediment-water interface

403

temperature in summer and autumn. Cooper et al.11 demonstrated that dissolved N2O

404

concentrations were highly dependent on hydrogeological conditions in the UK, being

405

greatest during summer and autumn in regions overlying unconfined and semi-

406

confined bedrock and during winter in areas underlain by confined chalk. Cooper et

407

al.11 also found that EF5r values were highest during summer/autumn and lowest

408

during spring across all the topography types they studied.

409

Our findings are in line with those of Cooper et al.,11 and the patterns we

410

identified are broadly consistent with the temporal variability in dissolved N2O

411

concentrations reported previously15,50,69,70. In our study, the temporal variation of

412

dissolved N2O concentration may probably due to the seasonal change of water

413

temperature and dissolved oxygen content (DO), which direct adjust the process of

414

denitrification under higher loading of NO3--N in summer time as described by Tian

415

et al.69 It is worth noting that, the relationship between dissolved N2O concentration

416

and river discharge is non-significant (p > 0.05), for W1, W3 and W4, the r value is -

417

0.06, -0.1 and 0.08, respectively. So we did not find the dominant effect of discharge

418

on dissolved N2O concentration, which has been discovered by Cooper et al.11 In

419

contrast, however, Hama-Aziz et al.38 reported that dissolved N2O concentrations

420

were lower in summer than the other seasons and ascribed this to the substantial

421

decrease in NO3--N concentrations during the summer as a result of a decline in river


Page 22 of 36

Page 23 of 36


422

water flow, as well as to a decrease in potentially leachable NO3--N due to nutrient

423

uptake by crops or other plants during the summer. The conclusions reached by

424

Hama-Aziz et al.38 concurred with those of Zhang et al.67 and Cooper et al.,11 who

425

suggested that riverine N2O and NO3--N concentrations were lowest during spring and

426

summer and highest in winter due to higher N leaching rates during the wetter

427

antecedent conditions of winter.

428

Our results also revealed that emission factors were not uniform across all

429

locations (W1 to W4), with W1 exhibiting the smallest EF5r and W2 the largest. This

430

pattern was in accordance with the findings of Starry et al.,71 who demonstrated that

431

the dissolved N concentration in headwater ditches receiving farmland drainage

432

varied spatially. Thus, dissolved N2O concentration and N2O emissions are also likely

433

to exhibit corresponding variations. The results from both studies indicate that the

434

influence of watershed nutrient loading on N cycling may be somewhat greater than

435

the effect of geographical locations. On the contrary, Cooper et al.11 claimed that no

436

evidence exists of a dilution effect (spatially from the origin to the outlet of a

437

catchment) in N2O concentrations at sites in large primary rivers, and that there is no

438

evidence of a strong N2O degassing signal as water moves downstream through a

439

catchment. Therefore, the hydrogeological conditions at sampling sites may remain

440

the dominant determinant of N2O concentration and EF5r, regardless of discharge or

441

stream order.14

442

Variation of N2O concentration and EF5r values also may be attributed to the

443

variation of dissolved organic carbon (DOC),31 temperature32 and DO33 of river water,



444

both spatially and temporally. The correlation matrix of various variables showed that

445

there was strong correlation between N2O concentration and DO (p < 0.0001)

446

(negative) as well as with temperature (p < 0.0001) (positive) (SI Figure S2).

447

Simultaneously, there were strong relationships among EF5r and the ratio of

448

DOC/NO3 (r = 0.12, p < 0.01), DO (r = -0.24, p < 0.0001) and temperature (r = 0.22,

449

p < 0.0001). Furthermore, decision regression tree and factor importance analysis

450

illustrated the dominance of DO and temperature among the various environmental

451

factors (Figure 4).

452

Carbon is the energy source for denitrification, and higher amounts of available

453

carbon can support higher denitrification rates31,72. As described by Cooper et al., 11

454

denitrification also may be inhibited in sites overlying unconfined bedrock by the

455

relatively low availability of labile carbon, with a mean DOC/NO3 ratio < 1 at

456

unconfined chalk sites and >1 at confined sites.

457

Previous studies demonstrated that increasing temperature can accelerate

458

denitrification73,74 such that N2O production also is expected to be stimulated with

459

increasing temperature.31,74,75 Tian et al.68 found that N2O emissions during summer

460

and autumn were greater than those in spring and winter and ascribed the difference to

461

higher water NO3--N concentration and temperature in summer/autumn.

462

Venkiteswaran et al.32 considered that the increase in N2O fluxes with increasing

463

temperature indicated that microbial N2O production may be temperature sensitive,

464

and/or that high temperature decreases the saturation of dissolved N2O, resulting in a

465

higher flux with more stable N2O production. Moreover, N2O production is strongly


Page 24 of 36

Page 25 of 36


466

limited by DO, which indicates that most N2O is produced by denitrification in

467

hypoxic areas; this observation suggests the importance of temporal and spatial

468

“hotspots” in the annual N2O flux of a whole river.64 Our results show that most N2O

469

was produced during periods of low oxygen condition (DO < 6.3 mg L-1, Figure 4).

470

Therefore, quantification of the hypoxia extent may be a necessary step to quantifying

471

N2O fluxes in lotic systems.76 (b) Importance of parameter to EF5r

(a) EF5r

0.12 100%

DO >= 6.3 < 6.3 0.066 73%

DO

0.26 27%

DOC/NO3 < 2.2

T < 29 >= 29

●

DOC

>= 2.2 0.33 18%

DOC/NO3

DOC/NO3 >= 3.2 < 3.2 0.51 8%

T < 26

●

DOC

●

CON

●

●

DOC/NO3

●

NO3

T DO

●

●

CON

●

●

>= 26 0.78 4%

T

NH4

●

●

DOC/NO3 < 2.5 >= 2.5

0.062 72%

0.3 1%

0.099 8%

0.18 10%

0.2 4%

0.36 2%

NH4

NO3

●

1.2 2%

8

10

12

14

●

0

1

%IncMSE

3

4

5

6

(d) Importance of parameter to N2O

(c) N2O

1.6 100%

2

IncNodePurity

DO >= 6.3 < 6.3 DO

3.4 27%

DOC/NO3 >= 4.7

●

DOC/NO3

< 4.7 4.4 19%

NO3

T

●

DO

●

●

DOC/NO3

●

●

DOC/NO3 < 2.2 >= 2.2

CON

DOC

●

●

6.8 11%

DOC

T < 26

NO3

●

●

>= 26 3.5 5%

NH4 < 1.2

CON >= 163 < 163

>= 1.2

472

0.87 73%

0.9 7%

1.4 8%

2 3%

NH4

10 5%

5.4 2%

4 2%

15 3%

T

NH4

●

CON

●

9

●

10

11

12

13

14

%IncMSE

15

16

●

0

200

400

600

800 1000

1200

IncNodePurity

473

Figure 4. Decision tree and importance analysis illustrating the relationships among environmental

474

parameters and EF5r (a) (b) or dissolved N2O concentration (c) (d).

475

Note: Parameters entering the model were dissolved oxygen (DO), temperature (T), ratio of dissolved

476

organic carbon to nitrate (DOC/NO3), DOC, conductivity (CON), ammonium nitrogen (NH4) and

477

nitrate nitrogen (NO3). In figure (a) and (c), values at the ends of each terminal node indicate the EF5r

478

or N2O concentration (μg N L-1) and their percentage of the total observations (%). In figure (b) and (d)

479

(parameter scores based on random forest with 1000 trees), random forest computes two qualitative

480

measures that describe the predictive power of the original measures: the Increased Mean Square Error

481

(%IncMSE) and Increased Impurity Index (IncNodePurity). %IncMSE measures the effect on the



482

predictive power when the value of a specific original parameter is randomly permuted. If the random

483

permutation drastically changes the predicted value (as measured by the mean squared error), then the

484

original parameter is considered critical. IncNodePurity measures the total increase in the homogeneity

485

of the data samples from splitting them on a given parameter.

486 487

N2O Production Pathway. Denitrification has been identified as the main process

488

responsible for the production of N2O in soil,68 while fewer studies have measured

489

N2O yield from denitrification in streams and rivers. In the present study, we did not

490

find a significant positive linear relationship between dissolved N2O concentration

491

and NO3--N (SI Figure S2). Rather, the variation of EF5r1 values showed that a non-

492

linear correlation between dissolved N2O and NO3--N existed (Figure 2 and SI Figure

493

S2), which indicated that denitrification may not dominate N2O production, at least

494

not in agricultural river networks such as the one studied in this research. Our results

495

showed that there was an inverse trend for the distribution of NH4+-N, NO3--N, DOC

496

and DOC/NO3 in river water and sediment at all four reaches (W1–W4) of the Tuojia

497

River network (Figure 5). In water column samples, all of the parameters (NO3--N,

498

DOC and DOC/NO3) increased in the downstream direction at river reaches from W1

499

to W4, whilst in sediment NO3--N and DOC decreased in the downstream direction at

500

the different reaches except W4. As a result, the ratio DOC/NO3 in both water and

501

sediment exhibited an increasing trend in the downstream direction. Additionally, the

502

non-negligible NH4+-N concentrations in both sediment and river water (Figure 5)

503

suggested that DNRA may occur and contribute to N2O loading as well as NO3--N


Page 26 of 36

Page 27 of 36


504

reduction. Consequently, we speculated that DNRA contributed a large part of N2O

505

production in the reaches of W2 to W4 (DOC/NO3 = 2.26 ± 0.48 (0.36–121.00))

506

while denitrification dominated the N2O production in reach W1 (DOC/NO3 = 1.82 ±

507

0.30 (0.19–37.41)). Therefore, there may be a spatial variation of oxygen throughout

508

the river network as well as in the N2O production pathway within the catchment (SI

509

Figure S3). 6

Water

mg L-1

5 4 3 2 1 0 NH4

NO3

DOC

DOC/NO3

0

mg kg-1

20 40 60 80

W1

W2

W3

W4

Sediment

510

100

511

Figure 5. Distribution of NH4-N, NO3-N, DOC, DOC/NO3 in water and sediment from four reaches

512

(W1, W2, W3 and W4) of Tuojia river systems during 2013 to 2016

513 514

Implications for Future Studies. Two methods for assessing EF5r based on long-

515

term (4 years) river monitoring conducted at high temporal resolution were evaluated

516

to address the existing uncertainty in estimating indirect N2O emission factors for



517

river networks in agricultural catchments. The key findings from this work and

518

implications for future studies can be summarized as follows.

519

1) Comparisons of assessment methodologies and long-term observations with

520

high temporal resolution are not only necessary for reducing uncertainty in evaluating

521

the global N2O budget, but also for refining the values of EF5r used by the IPCC.

522

Moreover, remarkable spatiotemporal variation of EF5r indicates that various

523

regionally specific EF5r values, instead of a single fixed value, are essential for

524

developing accurate national greenhouse gas inventories.

525

2) Local climatic variables and geographical factors cause the variation in EF5r

526

estimates. Dissolved N2O concentration in river waterbodies is traditionally

527

considered derived from anoxic environments, via various sources, such as vertical

528

and lateral transport from profundal and littoral sediments. Among various

529

environmental elements in this study, dissolved oxygen and temperature controlled

530

the diffusion of N2O from rivers to the atmosphere as well as the variation of EF5r.

531

Although the relationship between dissolved N2O concentration and NO3--N as well

532

as NH4+-N was statistically non-significant, it delineated a complex and variable

533

production pathway of N2O (nitrification, denitrification, DNRA and/or their

534

coupling) in both sediment and the river water column. Thus, a more detailed

535

elucidation of the mechanism by which N2O is produced and transferred in river

536

networks, consider sediment and water column simultaneously, is necessary in future

537

research.

538


Page 28 of 36

Page 29 of 36


539

ASSOCIATED CONTENT

540

Supporting Information

541

1) Geological and soil and information of Tuojia River catchment

542

2) Seven additional tables and 3 figures supporting the main text. Geographical information of sampling points; Soil chemical and physical

543 544

properties of the different reaches in Tuojia River; Calculation of the exchange flux of

545

N2O; Estimation of gas exchange rate (Kw) of N2O; Detailed nutrient input and spatial

546

information of Tuojia Catchment; Animal excretion nitrogen outlet in Changsha,

547

Jinjing and Tuojia; Statistic analysis by PROC MIXED of SAS; Windspeed (a), air

548

temperature and precipitation (b), discharge of Fuling (W1) (c), Feiyue (W3) (d) and

549

Tuojia (W4) (e) in Jinjing catchment; Correlation matrix of various environmental

550

factors with dissolved N2O and NO3--N concentration and EF5r; Spatial vriation of

551

oxygen condition and N2O production pathway in the catchment scale.

552

553

AUTHOR INFORMATION

554

Corresponding Author

555

*

556

Notes

557

The authors declare no competing financial interests.

E-mail: [email protected]

558



559

ACKNOWLEDGEMENTS

560

The authors thank the journal editor and reviewers for their hard work and

561

constructive suggestions. The financial support from National Natural Science

562

Foundation of China (41775157, 41475129) are gratefully acknowledged.

563 564


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Low N2O & EF5r

Page 36 of 36

Low N2O & EF5r

Low N2O & EF5r

N gh

Hi

High N2O & EF5r

& EF 5 r

Medium N2O & EF5r

Oxygen rich

Low N2O & EF5r

O

2

Low N2O & EF5r

Anthropogenic intervention

Denitrification

mixed process


Hypoxia

DNRA

Assessment of Indirect N2O Emission Factors from Agricultural River

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