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Diurnal variations of greenhouse gases emissions from reclamation mariculture ponds
Estuarine, Coastal and Shelf Science 237 (2020) 106677
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Diurnal variations of greenhouse gases emissions from reclamation mariculture ponds Beibei Hu a, b, c, *, Xiaofang Xu a, Junfeng (Jim) Zhang b, c, d, Tianli Wang e, f, Weiqing Meng a, g, Dongqi Wang h, i a
School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, 300387, China Nicholas School of the Environment, Duke University, Durham, NC, 27705, USA Global Health Institute, Duke University, Durham, NC, 27708, USA d Duke Kunshan University, Kunshan, Jiangsu Province, 215316, China e State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an, 710061, China f University of Chinese Academy of Sciences, Beijing, 100049, China g Tianjin Key Laboratory of Water Resources and Environment, Tianjin Normal University, Tianjin, 300387, China h Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, 200241, China i School of Geographical Sciences, East China Normal University, Shanghai, 200241, China b c
A R T I C L E I N F O
A B S T R A C T
Keywords: Greenhouse gases Emission fluxes Diurnal variation Reclamation mariculture
Dissolved concentrations, saturation status, and emission fluxes of greenhouse gases (GHGs) in two reclamation mariculture ponds in the Bohai Gulf, China, were measured using the static headspace gas chromatography method and computed using a two-layer model of diffusive gas exchange. The study was conducted during August 2016 to assess diurnal variations in GHGs emissions. The main influencing factors of GHGs emissions from the ponds under the disturbance of artificial management were identified using Spearman correlation analyses and multiple stepwise regression analyses. Results showed that dissolved GHGs concentrations were stable throughout the day, whereas GHGs emission fluxes showed a clear diurnal variation with larger daytime values. The diurnal variation of N2O emission fluxes was greater than that of CO2 and that of CH4. Results also showed that pH of pond water was negatively correlated with CO2 and N2O emission fluxes and that air tem perature was positively correlated with CH4 and N2O emission fluxes. In addition, ammonia (NHþ 4 -N) was positively correlated with CH4 emission fluxes; water temperature and salinity were positively correlated with N2O emission fluxes. The estimated annual emissions from the reclamation aquaculture in Tianjin and the Bohai Rim region were 2.53 � 105 kg C–CO2, 6.94 � 103 kg C–CH4 and 1.11 � 103 kg N–N2O, and 3.42 � 107 kg C–CO2, 9.36 � 105 kg C–CH4 and 1.50 � 105 kg N–N2O respectively, indicating the important contribution of this fishery sector to GHGs emissions.
1. Introduction With capture fishery production relatively leveling-off since the late 1980s, aquaculture has expanded rapidly to play a significant role in the supply of fish for human consumption (FAO, 2018). However, aqua culture has also caused serious environmental problems, such as impacting the integrity of ecosystem, exhaustion of natural fisheries resources, decrease of biodiversity, eutrophication (Hu et al., 2012), and climate change via GHGs emissions mostly due to nutrients loadings (Hu et al., 2013, 2014, 2016; Robb et al., 2017; Williams and Crutzen, 2010; Wu et al., 2018; Yang et al., 2018a, 2018b).
GHGs from inland freshwater aquaculture ponds have received great attentions worldwide (Chen et al., 2015; Hu et al., 2012; Liu et al., 2016; Williams and Crutzen, 2010; Wu et al., 2018; Yang et al., 2015). How ever, few studies have been conducted on GHGs emissions from mari culture (Chen et al., 2016; Parker et al., 2018; Song, 2015; Song et al., 2017; Yang et al., 2018a, 2018b), despite the fact that the production from mariculture accounts for 36% of the total aquaculture production (FAO, 2016). China alone accounts for more than 60% of total global fish production from aquaculture (FAO, 2016). According to China Fisheries Statistical Yearbook 2017, mariculture areas were estimated to be 21 � 105 hm2 in China, and the total mariculture production were 19 � 106t
* Corresponding author. School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, 300387, China. E-mail address: [email protected] (B. Hu). https://doi.org/10.1016/j.ecss.2020.106677 Received 5 December 2018; Received in revised form 23 February 2020; Accepted 26 February 2020 Available online 29 February 2020 0272-7714/© 2020 Elsevier Ltd. All rights reserved.
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(Fisheries Department of Agriculture Ministry of China, 2017). Recla mation mariculture covered a large and significant area in China’s entire coastal zone (Meng et al., 2017; Yang et al., 2015), which occupied 20.16% of the total mariculture in 2016 (Fisheries Department of Agriculture Ministry of China, 2017). Only a small portion of the nutrient is converted into mariculture production and the feed utiliza tion efficiency is about 4.0–27.4% (Chen et al., 2016). Large-scale arti ficial management such as stocking, feeding, harvesting, water exchange, etc. has also led to a great quantity of carbon and nitrogen from residual feed and feces into mariculture ponds. It is, hence, clear that the development of intensive reclamation mariculture results in a substantially large amount of GHGs emissions (Yuan et al., 2019). However, few studies have been carried out to characterize GHGs emissions at the water-air interface of reclamation mariculture ponds. In previous studies on GHGs emissions from mariculture, field sam plings were collected only in the daytime, although it is impossible for GHGs emissions to remain constant throughout the day (Morin et al., 2014; Xu et al., 2017). Consequently, the results may be biased due to missing data that should capture night-time emissions. Therefore, it is imperative to understand diurnal patterns by measuring both day-time and nigh-time GHG emissions. The objective of the present study is to characterize carbon and ni trogen GHG emissions in two reclamation mariculture ponds of Tianjin around the Bohai Gulf of China. The study findings are expected to: (1) shed light on the diurnal characteristics of the GHGs concentrations and emission fluxes from reclamation mariculture ponds; (2) identify key environmental variables driving GHGs emission fluxes from reclamation
mariculture ponds; (3) estimate GHGs annual emissions from reclama tion mariculture ponds of Tianjin and Bohai Rim region. 2. Materials and methods 2.1. Study area Tianjin, located in the northern part of North China Plain and adja cent to the Bohai Gulf of China, is one of the four municipalities directly under the Central Government and the largest coastal open city in North China (Hu et al., 2018). It has warm temperate continental monsoon climate with marine climate characteristics in the eastern coastal area. Tianjin has about 153 km coastal line and abundant ocean resource (Tianjin Municipal Bureau of Statistics & Survey Office of the National Bureau of Statistics in Tianjin, 2017). In 2016, mariculture areas of Tianjin were about 31.93 km2, and the total mariculture production were 11.33 � 103 t (Fisheries Department of Agriculture Ministry of China, 2017). 2.2. Samples collection and measurement Dagang (DG) and Hangu (HG) were two of the main mariculture areas of Tianjin, principally cultivating Litopenaeus vannamei. Each sampling point was selected at the center of the reclamation mariculture shrimp ponds of DG and HG in Tianjin Binhai New Area, respectively (Fig. 1). To investigate the diurnal variations of GHGs concentrations,
Fig. 1. Locations of the sampling sites. 2
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saturation and air-water interface emission fluxes, we collected water samples and gas samples every 3 h from 11:00 a.m. on August 1 to 11:00 a.m. on August 2, 2016 at the DG pond and the same hours on August 3 to August 4, 2016 at the HG pond. At each sampling point, three parallel surface pond water samples were collected using a gas-tight water sampler and then extruded directly into a 137-ml hermetic sampling tube, which was fully filled and sealed immediately to ensure no bubbles being contained. At the same time, three in situ air samples at each sampling point were collected overhead using aplastic medical syringes (100 ml) and injected into vacuumed gas bags in order to determine ambient GHGs concentrations. Additional 1000 ml of surface pond water at every sampling point was collected to test the water quality parameters. All water samples were kept in ice-filled incubators and transported to the laboratory within 6 h (Hu et al., 2018). During the sampling period, dissolved oxygen (DO) concentrations were measured in situ using a FireSting O2 sensor (PyroScience, Germany). Moreover, water temperature, humidity, pH, Eh, and salinity were detected using an HQ40d monitor (HACH, USA). Air temperature and wind speed were real-time downloaded from the internet (http://www.weather.com.cn/ ). In the laboratory, concentrations of dissolved GHGs in water samples were measured using a static headspace equilibrium technique (Hu et al., 2018). Dissolved nitrogen, in the form of nitrate and nitrite (NO3 þ NO2 -N), and the soluble phosphorus (SP) were determined using a continuous flow analyzer (FUTURA, Alliance, France). The ammonia nitrogen (NHþ 4 -N) was determined with Nessler’s reagent spectropho tometry. Concentrations of GHGs in air samples were analyzed using gas chromatography technique (Agilent 7890A, USA).
previous study (Yu et al., 2013). 2.4. Data analysis Microsoft Excel and SPSS 19.0 software packages (SPSS Inc., Chi cago, USA) were used for statistical analyses. Spearman correlation co efficients and regression equations were applied to identify key environmental variables driving GHGs emissions from the reclamation mariculture ponds. 3. Results Based on a spot investigation, L. vannamei of the two ponds were fed with basically same dosage and caught both twice a day in the summer in order to sell fresh production for the morning and evening markets. However, the nutrient components of the shrimp feed in the two ponds were slightly different (Table 1). 3.1. Physicochemical parameters of reclamation mariculture ponds Due to natural diurnal variation in solar radiation intensity, air temperature, water temperature and humidity showed clear diurnal characteristics (Table 2). The air temperature and water temperature were higher during the daytime and lower during the nighttime, how ever humidity showed opposite. The value of pH varied slightly and the water in both ponds were alkaline throughout the day. The Eh and salinity of the DG pond were fluctuated without diurnal variation fea tures, because it rained three times on the day of sampling. The sampling day at the HG pond was sunny; and salinity of the pond water rose at night and fell during the day following the temperaturevariation. DO varied widely, and had a significant day-night pattern with higher concentrations at daytime because of photosynthesis. The value of DO was much higher for the HG pond than for the DG pond that had weak photosynthesis on the rainy day. The value of SP in the DG pond was more than double than that of the HG pond, likely because the phos phorus content in feed of these two ponds was different (according to Table 1). Furthermore, the low DO in water of the DG pond reduced the mobility of L. vannamei, decreased their appetite, and then increased residue of feed (Liu, 2006). The concentrations of NO3 þ NO2 -N and NHþ 4 -N in both ponds had no significant differences, and both followed irregular diurnal patterns. The average value of NHþ 4 -N in DG pond and HG pond was 2.19 and 2.13 respectively which was both smaller than the safe concentration 2.99 mg/L (Li, 2012).
2.3. Calculations Concentrations of GHGs (CW) in the mariculture pond water were calculated according to the GHGs concentrations in the headspace of the tube (Hu et al., 2018): CW ¼ ½ðCT
CA Þ � VT þ α � CT � VW � = VW
(1)
Where CT are the GHGs concentrations (μmol⋅L 1) in the headspace of the tube under equilibrium state; CA are the atmospheric concentrations (μmol⋅L 1) of the sampling points; VT is the volume of headspace gas in the tube (L); α is the Bunsen coefficient (Wanninkhof, 1992); VW is the volume of water in the tube (L). Saturation of GHGs in the mariculture pond water was the ratio between the in situ dissolved GHGs concentrations in the mariculture pond water and the calculated saturated GHGs concentrations consistent with ambient air GHGs concentrations: S ¼ CW =CWs ¼ CW =ðα � CA Þ100%
3.2. Concentrations and saturation of dissolved GHGs in reclamation mariculture ponds water
(2)
The range of dissolved CO2 concentrations was 17.5 � 0.2 μmol L 1–18.5 � 0.3 μmol L 1 and 17.1 � 0.1 μmol L 1–17.5 � 0.3 μmol L 1 in the DG and HG ponds, respectively, with average value of 18.0 � 0.2 μmol L 1 and 17.3 � 0.2 μmol L 1 (Fig. 2). Correspondingly, the CO2 saturation varied from 114.9 � 0.4% to 148.8 � 0.4% and from 101.5 � 0.9% to 122.3 � 2.1% in the DG and HG ponds, respectively, with mean values of 132.7 � 1.6% for the DG pond and 108.6 � 1.2% for the HG pond (Fig. 2). Both ponds were oversaturated with CO2. The dissolved CH4 concentrations ranged from 0.05 � 0.00 μmol L 1 to 0.10 � 0.01 μmol L 1 (average: 0.07 � 0.00 μmol L 1) in the DG pond and from 0.06 � 0.00 μmol L 1 to 0.17 � 0.01 μmol L 1 (average: 0.09 � 0.01 μmol L 1) in the HG pond (Fig. 2). Accordingly, the range of CH4 saturation was 2490.5 � 181.2%–5096.5 � 369.2% and 2844.8 � 324.2%–7709.0 � 688.7% in the DG and HG ponds, respectively. CH4 was supersaturated in both ponds, with the average value of 3246.1 � 187.4% in the DG pond and 4388.5 � 438.7% in the HG pond (Fig. 2). The dissolved N2O concentrations varied within small range, which was 11.4 � 0.0–11.5 � 0.0 nmol L 1 with the average of 11.5 � 0.0 nmol L 1 in the DG pond and 11.0 � 0.0 nmol L 1–11.2 � 0.0 nmol L 1
Where CW were calculated from Equation (1); CWs were the saturated GHGs concentrations (μmol⋅L 1); CA were the atmospheric concentra tions (μmol⋅L 1) of the sampling points; α is the Bunsen coefficient (Wanninkhof, 1992). GHGs emission fluxes at air-water interface were determined by the two-layer model of diffusive gas exchange (Liss and Merlivat, 1986): FW
A
(3)
¼ k � ΔC
Where FW-A are the gas exchange fluxes; △C are the difference between GHGs concentrations in the air and water; k is the gas transfer rate and was determined using a wind-dependent model published previously (Yu et al., 2013): k ¼ 1:91 � e0:35μ � ðSc=600Þ
1=2
(4)
Where Sc is the Schmidt number for GHGs calibrated by in situ water temperature; μ is long-term wind speed of sampling sites at 10m height (m⋅s 1). The specific calculation procedure of k was detailed in the 3
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Table 1 Nutrient components of the shrimp feed. DG pond HG Pond
Total phosphorus
Ca
coarse ash
crude fiber
crude fat
crude protein
1.0–2.5% 0.9–1.45%
1.5% 3.0%
�16% �15%
�5.0% �5.0%
�6.0% �4.0%
�42% �40%
Table 2 Ranges and means of physicochemical parameters in the reclamation mariculture ponds of Tianjin. DG pond Air temperature (� C) Water temperature (� C) Humidity (%) pH Eh (mV) Salinity (‰) DO (mg⋅L 1) SP (μg⋅L 1) NO3 þ NO2 -N (mg⋅L 1) 1 NHþ 4 -N (mg⋅L )
HG pond
Minimum
Maximum
Mean
Minimum
Maximum
Mean
24.00 26.90 75.21 7.88 125.90 32.50 2.93 0.61 0.94 1.40
28.00 30.10 88.51 8.05 250.10 33.40 6.35 1.23 0.99 2.92
26.70 28.30 83.55 7.97 169.50 33.00 4.64 0.83 0.96 2.19
25.00 28.50 61.78 8.36 104.60 33.10 8.50 0.29 0.86 1.01
31.00 32.90 83.33 8.56 224.70 37.30 15.00 0.57 1.01 3.52
28.20 30.50 72.41 8.48 168.10 35.60 11.46 0.40 0.92 2.13
Fig. 2. The diurnal variation in CO2, CH4, N2O concentration and saturation in water of the DG and HG ponds (the shaded area of the picture were night hours).
with the average of 11.1 � 0.0 nmol L 1 in the HG pond (Fig. 2). Both ponds were oversaturated with N2O. The corresponding N2O saturation was 178.6 � 0.5%–190.1 � 0.2% and average of 182.1 � 0.5% in the DG pond and 183.5 � 0.2%–203.0 � 0.3% with average of 191.8 � 0.5% in the HG pond (Fig. 2).
nmol m 2⋅h 1 to 315.4 � 0.6 nmol m 2⋅h 1 severally at the DG pond water-air interface, with the average value of 206.7 � 9.4 μmol m 2⋅h 1, 3.1 � 0.2 μmol m 2⋅h 1, and 243.7 � 1.6 nmol m 2⋅h 1; while these three GHGs emission fluxes varied from 15.7 � 10.0 μmol m 2⋅h 1 to 199.8 � 18.6 μmol m 2⋅h 1, 2.2 � 0.1 μmol m 2⋅h 1 to 9.2 � 0.8 μmol m 2⋅h 1, and 162.7 � 0.4 nmol m 2⋅h 1 to 365.6 � 1.1 nmol m 2⋅h 1 respectively at the HG pond water-air interface, with the mean value of 64.1 � 9.7 μmol m 2⋅h 1, 4.3 � 0.5 μmol m 2⋅h 1, and 265.8 � 1.4 nmol m 2⋅h 1 (Fig. 3). Based on the GHGs emission fluxes and surface area of the recla mation mariculture ponds in Tianjin (about 17.8 km2) and Bohai Rim
3.3. Water-air GHGs emissions from reclamation mariculture ponds During the observation, the emission fluxes of CO2, CH4 and N2O ranged from 127.0 � 5.2 μmol m 2⋅h 1 to (332.9 � 2.6 μmol m 2⋅h 1, 1.5 � 0.1 μmol m 2⋅h 1 to 5.5 � 0.4 μmol m 2⋅h 1, and 155.5 � 1.1 4
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Fig. 3. The diurnal variation in CO2, CH4, N2O emission fluxes at water-air interface of the DG and HG ponds (the shaded area of the picture were night hours).
region (about 2.4 � 103 km2), the annual CO2, CH4 and N2O emissions from the reclamation mariculture ponds of Tianjin and Bohai Rim region were estimated to be 2.1 � 107 mol CO2 (i.e., 2.5 � 105 kg C as CO2), 5.8 � 105 mol CH4 (i.e., 6.9 � 103 kg C as CH4) and 4.0 � 104 mol N2O (i.e., 1.1 � 103 kg N as N2O), and 2.9 � 109 mol CO2 (i.e., 3.4 � 107 kg C as CO2), 7.8 � 107 mol CH4 (i.e., 9.4 � 105 kg C- as CH4) and 5.4 � 106 mol N2O (i.e., 1.5 � 105 kg N as N2O) respectively. So the reclamation aquaculture was an important source of anthropogenic GHGs emissions. The large-scale reclamation mariculture ponds of China with similar characteristics could significantly contribute to GHGs emissions, which deserves widespread concern.
Table 4 Regression equations between emission fluxes of GHGs at the water-air interface and environmental variables. Gas diffusive fluxes
Regression equations
F
R2
p
CO2 CH4
Y ¼ 2790.902–323.173XpH Y ¼ 15.895 þ 0.628XATþ1.058XNH4þ-N Y ¼ 356.600 þ 35.210XAT286.882XpHþ22.606 XWTþ18.366XSalinity
21.849 5.433
0.523 0.318
<0.001 <0.02
35.029
0.878
<0.001
N2O
3.4. Correlation between GHGs emissions and environmental variables
AT: air temperature; WT: water temperature.
GHGs emission fluxes were controlled by environmental variables such as air temperature, water temperature, humidity, pH, Eh, salinity, DO, SP, NO3 þ NO2 -N, NHþ 4 -N and so on. Because humidity, pH and salinity were subjected to violate the test of normality, Spearman cor relation analysis was made between emission fluxes of GHGs at water-air interface of ponds and environmental variables by SPSS 22.0 (Table 2). Among the environmental variables, SP (p < 0.05) was positively correlated with CO2 emission fluxes; while pH (p < 0.01), salinity (p < 0.01) and DO (p < 0.01) were negatively correlated with CO2 emission fluxes (Table 3). CH4 emission fluxes had positive correlation only with air temperature (p < 0.05; Table 3). N2O emission fluxes were positively correlated with air temperature (p < 0.01) and water temperature (p < 0.01); while they were negatively correlated to humidity (p < 0.01), Eh (p < 0.01) and NO3 þ NO2–N (p < 0.05; Table 3). Multiple stepwise regression analysis showed that CO2 emission fluxes were remarkably related to pH (p < 0.001) of the water, which accounted for 52.3% of the total variation in CO2 (Table 4). CH4 emis sion fluxes were influenced by air temperature and NHþ 4 -N (p < 0.02), which accounted for 31.8% of the total variation in CH4 (Table 3). N2O emission fluxes were significantly controlled by air temperature, pH, water temperature and salinity (p < 0.001), which accounted for 87.8% of the total variation in N2O (Table 4).
4. Discussion 4.1. Diurnal variations of dissolved GHGs in the DG and HG ponds Diurnal variations in concentrations of dissolved CO2, CH4 and N2O showed no stable pattern. There were no distinct variations in dissolved CO2 concentrations in the two studied ponds. The dissolved CO2 con centrations irregularly fluctuated during the observation period (Fig. 2), separately reaching its maximum (minimum) at 14:00 (23:00) in the DG pond and at 23:00 (17:00) in the HG pond. The dissolved CO2 concen trations were higher in the DG pond than in the HG pond likely due to higher air temperature of the latter. As the air temperature increases, the stability of carbonic acid in water will decrease as carbonic acid is further decomposed to release CO2, resulting in less and less dissolved CO2 concentrations (Natchimuthu et al., 2014; Lan, 2015). In contrast, to the CH4 concentrations, dissolved N2O concentrations on both ponds had almost no diurnal variation, keeping within a narrow range (Fig. 2), which was in agreement with the finding of a previous study (Yang et al., 2017). 4.2. Diurnal variations of GHGs emission fluxes from the DG and HG ponds Significant differences were observed in diurnal trends of CO2 emission fluxes from these two ponds (Fig. 3). The CO2 emission fluxes
Table 3 Correlations between GHGs emission fluxes at water-air interface of ponds and environmental variables.
CO2 （μmol⋅m
2
⋅h 1）
CH4 （μmol⋅m
2
⋅h 1）
N2O （nmol⋅m a b
2
⋅h 1）
r p N r p N r p N
Air Temperature (� C)
Water Temperature (� C)
Humidity (%)
pH
Eh (mV)
Salinity (‰)
DO (mg⋅L
0.268 0.254 20 0.512a 0.021 20 0.866b 0.000 20
0.311 0.182 20 0.442 0.051 20 0.798b 0.000 20
0.350 0.130 20 0.305 0.191 20 0.740b 0.000 20
0.683b 0.001 20 0.028 0.907 20 0.167 0.481 20
0.003 0.990 20 0.077 0.748 20 0.567b 0.009 20
0.660b 0.002 20 0.095 0.691 20 0.146 0.539 20
0.641b 0.002 20 0.012 0.960 20 0.350 0.130 20
Correlation is significant at the 0.05 level (2-tailed). Correlation is significant at the 0.01 level (2-tailed). 5
1
)
SP (μg⋅L
1
)
0.557a 0.011 20 0.242 0.303 20 0.312 0.180 20
NO3 þ NO2–N (mg⋅L 1)
NH4þ-N (mg⋅L 1)
0.241 0.307 20 0.355 0.125 20 0.459a 0.042 20
0.427 0.060 20 0.205 0.387 20 0.180 0.446 20
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decreased sharply after 17:00 and reached the lowest value at 23:00 for the DG pond, while increased in fluctuation all the time in the HG pond. However, the average CO2 emission fluxes at daytime (DG: 240.4 μmol m 2⋅h 1; HG: 69.7 μmol m 2⋅h 1) were both greater than that at nighttime (DG: 139.3 μmol m 2⋅h 1; HG: 53.0 μmol m 2⋅h 1). The diurnal variation trends of CH4 emission fluxes were similar for the two ponds (Fig. 3), with a bottom period from 17:00 to 5:00 then a rapid increase. Both ponds emitted more CH4 during the day time than during the night hours, with the mean values of 3.8 μmol m 2⋅h 1 for the daytime and 1.6 μmol m 2⋅h 1 for the nighttime from the DG pond, and 5.0 μmol m 2⋅h 1 during the daytime and 3.0 μmol m 2⋅h 1 during the night time from the HG pond. The emission fluxes of N2O demonstrated a similar diurnal variation in both ponds, which decreased around sunset and increased around sunrise (Fig. 3). It was noticeable that more N2O was emitted during the daytime than during the nighttime, with daytime average values of 285.2 nmol m 2⋅h 1 in the DG pond and 316.0 nmol m 2⋅h 1 in the HG pond, and nighttime mean values of 159.6 nmol m 2⋅h 1 in DG pond and 165.4 nmol m 2⋅h 1 in HG pond. The N2O emission fluxes of the daytime from the two ponds were both nearly two times those of the nighttime. The daytime emission fluxes of GHGs from both ponds were larger than the nighttime fluxes, consistent with previous observations (Morin et al., 2014; Xu et al., 2017; Yang et al., 2017). Moreover, the diurnal variation of N2O emission fluxes was greater compared with the diurnal variation of either CO2 or CH4 fluxes. In the daytime, as the solar radi ation enhanced, temperature rose and artificial management (such as feeding, harvesting and water exchange) increased, the microbial ac tivities were enhanced, thus increasing the emission fluxes of GHGs from the water of mariculture pond (Yang et al., 2017). The CH4 and N2O emission fluxes from the HG pond were both greater than that from the DG pond. However, the CO2 emission fluxes were the opposite. The reason was that the value of air temperature, water temperature, pH, DO and salinity were higher in the HG pond than that in the DG pond (Table 2). Beyond that, the photosynthesis in the DG pond was weaker than that in the HG pond owning to the rainy day for the DG pond, while the SP of the former was more than that of the latter because of more phosphorus content in feed of the former (Table 1).
methanogens and favors CH4 production and emissions. NHþ 4 -N is another key factor affecting the CH4 emission fluxes from the aquatic ecosystem, mainly including two aspects: 1) NHþ 4 -N can inhibit CH4 oxidation by restraining growth and activity of methanogenic bacteria, then increases CH4 emissions; 2) NHþ 4 -N provides a nutrient source for methane production (Hu et al., 2018; King and Schnell, 1994; Schrier-Uijl et al., 2011). In the present study, air temperature, pH, water temperature and salinity were key factors influencing N2O emission fluxes (Table 4). N2O emissions can be produced during both microbial nitrification and denitrification; in particular the latter plays an important role because it is one of the intermediate products involved in denitrification (Beaulieu et al., 2010). Previous studies have revealed that N2O emission fluxes were related to thermal regime of aquatic ecosystem (Barnes et al., 1998; Hu et al., 2018; Silvennoinen et al., 2008; Usui et al., 2001; Yang et al., 2017). Under high temperature conditions, life activities (such as foraging, metabolism and so on) of shrimps and other aquatic organisms are vigorous, which may produce strong disturbance to the water body, then increase the content of NO3 and NO2 from the sediments into the water. These life activities could provide material sources for the nitri fication and denitrification process, thereby enhancing the activities of the nitrifying and denitrifying bacteria, accelerating geochemical reac tion and increasing the nitrification or denitrification potential through which N2O emissions are enhanced. Some scholars have found that N2O production in aquatic ecosystem is a by-product of nitrification and denitrification microbial processes, which are controlled by pH (Stow et al., 2005; Clough et al., 2011; Yang et al., 2015). The final reduction of N2O to N2 is mainly determined by the activities of N2O reductase which are activated more with the increase of pH (Stow et al., 2005; Clough et al., 2011; Yang et al., 2015, 2018a). N2O production increased with increasing salinity, resulting from a reduction of the denitrification ef ficiency caused by salinity, through an inhibition of the N2O-reductase, rather than due to increased denitrification rates (Marton et al., 2012; Teixeira et al., 2013; Zhao et al., 2013). In our study, only single-day sample collection was carried out at two mariculture ponds of Tianjin in the summer, which was likely to cause an over or underestimation of the emissions. In the future, samples need to be collected at more mariculture ponds in other areas within consecutive days of every season. Additionally, more environmental variables and artificial cultivation factors on GHGs emissions from reclamation mariculture ponds should be considered and analyzed.
4.3. Key factors for GHGs emissions from reclamation mariculture ponds Among the ten measured environmental variables of this study, only pH affected CO2 emission fluxes at water-air interface. Other studies have shown that CO2 fluxes were negatively correlated with pH (Chen et al., 2016; Schrier-Uijl et al., 2011; Yang et al., 2015, 2018b). The value of water pH reflects the ratio of CO2 accounting for total inorganic carbon (pCO2) in the water column (Chen et al., 2016; Yang et al., 2018b). As pH directly affects the dynamic balance and distribution of carbonate system (CO2, CO23 and HCO3 ) in water, it affects amount of CO2 dissolved in water. When pH is less than a certain critical value (7–8), CO2 of the water is easily saturated, and the higher pCO2 pro motes CO2 emission into the atmosphere. When pH is above the critical value (pH > 8), the dissociative CO2 will be converted into carbonate, resulting in the unsaturation of CO2 in the water. The lower pCO2 of water will facilitate a higher CO2 uptake from the atmosphere via diffusion (Schrier-Uijl et al., 2011; Yang et al., 2015, 2018b). The important factors for CH4 emission fluxes from these ponds were air temperature and NHþ 4 -N (Table 4). Temperature indirectly affects CH4 emission fluxes by influencing microbial activities, redox environ ment, and gas solubility in water (Chen et al., 2016; Schrier-Uijl et al., 2011; Yang et al., 2015, 2017, 2018b). CH4 emission fluxes at the water-air interface of the aquaculture ponds are mainly produced by methanogens decomposition of organic matter at the bottom of the ponds. With the high temperature, shrimps and other aquatic organisms have vigorous life activities and consume large amount of DO, which provides a good growth environment for the metabolism of
5. Conclusion In two large marine reclamation aquaculture ponds, GHGs emission fluxes were greater during daytime than during nighttime. The diurnal variation of N2O emission fluxes was the most significant among the three GHGs. Water pH, air temperature, water temperature, water salinity and ammonia (NHþ 4 -N) were among the key factors affecting GHGs emission fluxes from reclamation mariculture ponds. In addition to natural conditions such as meteorological and hydrological factors, artificial management also significantly affected microbial activities, and then led to GHGs emissions. Efforts should be made to more accu rately characterize GHGs emissions from reclamation mariculture sector in order to develop effective mitigation strategies. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. CRediT authorship contribution statement Beibei Hu: Conceptualization, Writing - original draft, Writing review & editing. Xiaofang Xu: Formal analysis. Junfeng (Jim) Zhang: 6
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Supervision. Tianli Wang: Investigation. Weiqing Meng: Visualization. Dongqi Wang: Methodology.
Morin, T.H., Bohrer, G., Naor-Azrieli, L., Mesi, S., Kenny, W.T., Mitsch, W.J., Sch€ afer, K. B.V.R., 2014. The seasonal and diurnal dynamics of methane flux at a created urban wetland. Ecol. Eng. 72, 74–83. Natchimuthu, S., Selvam, B.P., Bastviken, D., 2014. Influence of weather variables on methane and carbon dioxide flux from a shallow pond. Biogeochemistry 119 (1–3), 403–413. Parker, R.W., Blanchard, J.L., Gardner, C., Green, B.S., Hartmann, K., Tyedmers, P.H., Watson, R.A., 2018. Fuel use and greenhouse gas emissions of world fisheries. Nat. Clim. Change 8, 333–339. Robb, D.H.F., MacLeod, M., Hasan, M.R., Soto, D., 2017. Greenhouse gas emissions from aquaculture: a life cycle assessment of three Asian systems. In: FAO Fisheries and Aquaculture Technical Paper No. 609. FAO, Rome. Schrier-Uijl, A.P., Veraart, A.J., Leffeaar, P.A., Berendse, F., Veenendaal, E.M., 2011. Release of CO2 and CH4 from lakes and drainage ditches in temperate wetlands. Biogeochemistry 102 (1–3), 265–279. Silvennoinen, H., Liikanen, A., Torssonen, J., Stange, C.F., Martikainen, P.J., 2008. Denitrification and N2O effluxes in the Bothnian Bay (northern Baltic Sea) river sediments as affected by temperature under different oxygen concentrations. Biogeochernistry 88 (1), 63–72. Song, D., 2015. Production, Distribtution and Flux of Nitrous Oxide in the Sanggou Bay. Master Dissertation. Ocean University of China (In Chinese). Song, H.L., Liu, X.T., Wen, B.L., 2017. Greenhouse gases fluxes at water-air interface of aquaculture ponds in the Yellow River Estuary. Ecol. Environ. Sci. 26 (9), 1554–1561 (In Chinese). Stow, C.A., Walker, J.T., Cardoch, L., Spence, P., Geron, C., 2005. N2O emissions from streams in the neuse river watershed, North Carolina. Environ. Sci. Technol. 39 (18), 6999–7004. Teixeira, C., Magalh, C., Joye, S.B., Bordalo, A.A., 2013. The role of salinity in shaping dissolved inorganic nitrogen and N2O dynamics in estuarine sediment-water interface. Mar. Pollut. Bull. 66, 225–229. Tianjin, 2017. Municipal Bureau of Statistics & Survey Office of the National Bureau of Statistics in Tianjin. Tianjin Statistical Yearbook 2017. China Statistics Press, Beijing (In Chinese). Usui, T., Koike, I., Ogura, N., 2001. N2O production, nitrification and denitrification in an estuarine sediment. Estuar. Coast Shelf Sci. 52 (6), 769–781. Wanninkhof, R., 1992. Relationship between wind speed and gas exchange over the ocean. J. Geophys. Res-Oceans 97 (C5), 7373–7382. Williams, J., Crutzen, P., 2010. Nitrous oxide from aquaculture. Nat. Geosci. 3 (3), 143. Wu, S., Hu, Z.Q., Hu, T., Chen, J., Yu, K., Zou, J.W., Liu, S.W., 2018. Annual methane and nitrous oxide emissions from rice paddies and inland fish aquaculture wetlands in southeast China. Atmos. Environ. 175, 135–144. Xu, X.W.H., Fu, G.H., Zou, X.Q., Ge, C.D., Zhao, Y.F., 2017. Diurnal variations of carbon dioxide, methane, and nitrous oxide fluxes from invasive Spartina alterniflora dominated coastal wetland in northern Jiangsu Province. Acta Oceanol. Sin. 36 (4), 105–113. Yang, P., He, Q.H., Huang, J.F., Tong, C., 2015. Fluxes of greenhouse gases at two different aquaculture ponds in the coastal zone of southeastern China. Atmos. Environ. 115, 269–277. Yang, P., Lai, D.Y.F., Huang, J.F., Tong, C., 2018a. Effect of drainage on CO2, CH4, and N2O fluxes from aquaculture ponds during winter in a subtropical estuary of China. J. Environ. Sci. (China) 65, 72–82. Yang, P., Zhang, Y.F., Lai, D.Y.F., Tan, L.S., Jin, B.S., Tong, C., 2018b. Fluxes of carbon dioxide and methane across the water-atmosphere interface of aquaculture shrimp ponds in two subtropical estuaries: the effect of temperature, substrate, salinity and nitrate. Sci. Total Environ. 635, 1025–1035. Yang, W.B., Yuan, C.S., Tong, C., Yang, P., Yang, L., Huang, B.Q., 2017. Diurnal variation of CO2, CH4, and N2O emission fluxes continuouslymonitored in-situ in three environmental habitats in a subtropical estuarinewetland. Mar. Pollut. Bull. 119, 289–298. Yu, Z.J., Deng, H.G., Wang, D.Q., Ye, M.W., Tan, Y.J., Li, Y.J., Chen, Z.L., Xu, S.Y., 2013. Nitrous oxide emissions in the Shanghai river network: implications for the effects of urban sewage and IPCC methodology. Global Change Biol. 19 (10), 2999–3010. Yuan, J.J., Xiang, J., Liu, D.Y., Kang, H., He, T.H., Kim, S., et al., 2019. Rapid growth in greenhouse gas emissions from the adoption of industrial-scale aquaculture. Nat. Clim. Change 9 (4), 318–322. https://doi.org/10.1038/s41558-019-0425-9. Zhao, W., Wang, Y.Y., LiuS, H., Pan, M.L., Yang, J., ChenS, W., 2013. Denitrification activities and N2O production under salt stress with varying COD/N ratios and terminal electron acceptors. Chem. Eng. J. 215–216, 252-26.
Acknowledgements This work was financially supported by the National Natural Science Foundation of China (NSFC) (Grant No. 41301221, No. 41671467 and No. 41473049). We sincerely thank the editor and the anonymous re viewers for their insightful comments and suggestions. References Barnes, J., Owens, N.J.P., 1998. Denitrification and nitrous oxide concentrations in the Humber estuary, UK, and adjacent coastal zones. Mar. Pollut. Bull. 37, 247–260. Beaulieu, J.J., Shuster, W.D., Rebholz, J.A., 2010. Nitrous oxide emissions from a large, impounded river: the Ohio River. Environ. Sci. Technol. 44 (19), 7527–7533. Chen, Y., Dong, S.L., Wang, F., Gao, Q.F., Tian, X.L., 2016. Carbon dioxide and methane fluxes from feeding and no-feeding mariculture ponds. Environ. Pollut. 212, 489–497. Chen, Y., Dong, S.L., Wang, Z.N., Wang, F., Gao, Q.F., Tian, X.L., Xiong, Y.H., 2015. Variations in CO2 fluxes from gras carp Ctenopharyngodon idella aquaculture polyculture ponds. Aquacult. Env.Interac. 8, 31–40. Clough, T.J., Buckthought, L.E., Casciotti, K.L., Kelliher, F.M., Jones, P.K., 2011. Nitrous oxide dynamics in a braided river system, New Zealand. J. Environ. Qual. 40 (5), 1532–1541. Fisheries Department of Agriculture Ministry of China, 2017. 2017 China Fishery Statistical Yearbook. China Agricultural Press, Beijing (In Chinese). Food and Agriculture Organization of the United Nations (FAO), 2016. The state of world fisheries and aquaculture 2016: coutributing to food security and nutrition for all. http://www.fao.org/3/a-i5555e.pdf. Food and Agriculture Organization of the United Nations (FAO), 2018. The state of world fisheries and aquaculture 2018: meeting the sustainable development goals. http:// www.fao.org/3/i9540en/I9540EN.pdf. Hu, B.B., Wang, D.Q., Zhou, J., Meng, W.Q., Li, C.W., Sun, Z.B., Guo, X., Wang, Z.L., 2018. Greenhouse gases emission from the sewage draining rivers. Sci. Total Environ. 612, 1454–1462. Hu, Z., Lee, J.W., Chandran, K., Kim, S., Khanal, S.K., 2012. Nitrous oxide (N2O) emission from aquaculture: areview. Environ. Sci. Technol. 46 (12), 6470–6480. Hu, Z., Lee, J.W., Chandran, K., Kim, S., Sharma, K., Brotto, A.C., Khanal, S.K., 2013. Nitrogen transformations in intensive aquaculture system and its implication to climate change through nitrous oxide emission. Bioresour. Technol. 130, 314–320. Hu, Z., Lee, J.W., Chandran, K., Kim, S., Sharma, K., Khanal, S.K., 2014. Influence of carbohydrate addition on nitrogen transformations and greenhouse gas emissions of intensive aquaculture system. Sci. Total Environ. 470–471, 193–200. Hu, Z.Q., Wu, S., Ji, C., Zou, J.W., Zhou, Q.S., Liu, S.W., 2016. A comparison of methane emissions following rice paddies conversion to crab-fish farming wetlands in southeast China. Environ. Sci. Pollut. Res. Int. 23 (2), 1505–1515. King, G.M., Schnell, S., 1994. Effect of increasing atmospheric methane concetration on ammonium inhibition of soil methane consumption. Nature 370 (6487), 282–284. Lan, J., 2015. Greenhouse Gases Concentration,Emission and Influencefactors in Farming Waters. Master Dissertation. Huazhong Agricultural University (In Chinese). Liss, P.S., Merlivat, L., 1986. Air-sea Gas Exchange Rates: Introduction and Synthesis, the Role of Air-Sea Exchange in Geochemical Cycling. Springer, pp. 113–127. Li, Y., 2012. Artificial Selection of Penaeus monodon and Impacts of Ammonia-N on Immune Parameters,detoxification. Shanghai Ocean University, Shanghai (In Chinese). Liu, S.W., Hu, Z.Q., Wu, S., Li, S.Q., Li, Z.F., Zou, J.W., 2016. Methane and nitrous oxide emissions reduced following conversion of rice paddies to inland crab-fish aquaculture in Southeast China. Environ. Sci. Technol. 50 (2), 633–642. Marton, J.M., Herbert, E.R., Craft, C.B., 2012. Effects of salinity on denitrification and greenhouse gas production from laboratory-incubated tidal forest soils. Wetlands 32, 347–357. Meng, W.Q., Hu, B.B., He, M.X., Liu, B.Q., Mo, X.Q., Li, H.Y., Wang, Z.L., Zhang, Y., 2017. Temporal-spatial variations and driving factors analysis of coastal reclamation in China. Estuar. Coast Shelf Sci. 191, 39–49.
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Diurnal variations of greenhouse gases emissions from reclamation mariculture ponds Beibei Hu a, b, c, *, Xiaofang Xu a, Junfeng (Jim) Zhang b, c, d, Tianli Wang e, f, Weiqing Meng a, g, Dongqi Wang h, i a
School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, 300387, China Nicholas School of the Environment, Duke University, Durham, NC, 27705, USA Global Health Institute, Duke University, Durham, NC, 27708, USA d Duke Kunshan University, Kunshan, Jiangsu Province, 215316, China e State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an, 710061, China f University of Chinese Academy of Sciences, Beijing, 100049, China g Tianjin Key Laboratory of Water Resources and Environment, Tianjin Normal University, Tianjin, 300387, China h Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, 200241, China i School of Geographical Sciences, East China Normal University, Shanghai, 200241, China b c
A R T I C L E I N F O
A B S T R A C T
Keywords: Greenhouse gases Emission fluxes Diurnal variation Reclamation mariculture
Dissolved concentrations, saturation status, and emission fluxes of greenhouse gases (GHGs) in two reclamation mariculture ponds in the Bohai Gulf, China, were measured using the static headspace gas chromatography method and computed using a two-layer model of diffusive gas exchange. The study was conducted during August 2016 to assess diurnal variations in GHGs emissions. The main influencing factors of GHGs emissions from the ponds under the disturbance of artificial management were identified using Spearman correlation analyses and multiple stepwise regression analyses. Results showed that dissolved GHGs concentrations were stable throughout the day, whereas GHGs emission fluxes showed a clear diurnal variation with larger daytime values. The diurnal variation of N2O emission fluxes was greater than that of CO2 and that of CH4. Results also showed that pH of pond water was negatively correlated with CO2 and N2O emission fluxes and that air tem perature was positively correlated with CH4 and N2O emission fluxes. In addition, ammonia (NHþ 4 -N) was positively correlated with CH4 emission fluxes; water temperature and salinity were positively correlated with N2O emission fluxes. The estimated annual emissions from the reclamation aquaculture in Tianjin and the Bohai Rim region were 2.53 � 105 kg C–CO2, 6.94 � 103 kg C–CH4 and 1.11 � 103 kg N–N2O, and 3.42 � 107 kg C–CO2, 9.36 � 105 kg C–CH4 and 1.50 � 105 kg N–N2O respectively, indicating the important contribution of this fishery sector to GHGs emissions.
1. Introduction With capture fishery production relatively leveling-off since the late 1980s, aquaculture has expanded rapidly to play a significant role in the supply of fish for human consumption (FAO, 2018). However, aqua culture has also caused serious environmental problems, such as impacting the integrity of ecosystem, exhaustion of natural fisheries resources, decrease of biodiversity, eutrophication (Hu et al., 2012), and climate change via GHGs emissions mostly due to nutrients loadings (Hu et al., 2013, 2014, 2016; Robb et al., 2017; Williams and Crutzen, 2010; Wu et al., 2018; Yang et al., 2018a, 2018b).
GHGs from inland freshwater aquaculture ponds have received great attentions worldwide (Chen et al., 2015; Hu et al., 2012; Liu et al., 2016; Williams and Crutzen, 2010; Wu et al., 2018; Yang et al., 2015). How ever, few studies have been conducted on GHGs emissions from mari culture (Chen et al., 2016; Parker et al., 2018; Song, 2015; Song et al., 2017; Yang et al., 2018a, 2018b), despite the fact that the production from mariculture accounts for 36% of the total aquaculture production (FAO, 2016). China alone accounts for more than 60% of total global fish production from aquaculture (FAO, 2016). According to China Fisheries Statistical Yearbook 2017, mariculture areas were estimated to be 21 � 105 hm2 in China, and the total mariculture production were 19 � 106t
* Corresponding author. School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, 300387, China. E-mail address: [email protected] (B. Hu). https://doi.org/10.1016/j.ecss.2020.106677 Received 5 December 2018; Received in revised form 23 February 2020; Accepted 26 February 2020 Available online 29 February 2020 0272-7714/© 2020 Elsevier Ltd. All rights reserved.
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(Fisheries Department of Agriculture Ministry of China, 2017). Recla mation mariculture covered a large and significant area in China’s entire coastal zone (Meng et al., 2017; Yang et al., 2015), which occupied 20.16% of the total mariculture in 2016 (Fisheries Department of Agriculture Ministry of China, 2017). Only a small portion of the nutrient is converted into mariculture production and the feed utiliza tion efficiency is about 4.0–27.4% (Chen et al., 2016). Large-scale arti ficial management such as stocking, feeding, harvesting, water exchange, etc. has also led to a great quantity of carbon and nitrogen from residual feed and feces into mariculture ponds. It is, hence, clear that the development of intensive reclamation mariculture results in a substantially large amount of GHGs emissions (Yuan et al., 2019). However, few studies have been carried out to characterize GHGs emissions at the water-air interface of reclamation mariculture ponds. In previous studies on GHGs emissions from mariculture, field sam plings were collected only in the daytime, although it is impossible for GHGs emissions to remain constant throughout the day (Morin et al., 2014; Xu et al., 2017). Consequently, the results may be biased due to missing data that should capture night-time emissions. Therefore, it is imperative to understand diurnal patterns by measuring both day-time and nigh-time GHG emissions. The objective of the present study is to characterize carbon and ni trogen GHG emissions in two reclamation mariculture ponds of Tianjin around the Bohai Gulf of China. The study findings are expected to: (1) shed light on the diurnal characteristics of the GHGs concentrations and emission fluxes from reclamation mariculture ponds; (2) identify key environmental variables driving GHGs emission fluxes from reclamation
mariculture ponds; (3) estimate GHGs annual emissions from reclama tion mariculture ponds of Tianjin and Bohai Rim region. 2. Materials and methods 2.1. Study area Tianjin, located in the northern part of North China Plain and adja cent to the Bohai Gulf of China, is one of the four municipalities directly under the Central Government and the largest coastal open city in North China (Hu et al., 2018). It has warm temperate continental monsoon climate with marine climate characteristics in the eastern coastal area. Tianjin has about 153 km coastal line and abundant ocean resource (Tianjin Municipal Bureau of Statistics & Survey Office of the National Bureau of Statistics in Tianjin, 2017). In 2016, mariculture areas of Tianjin were about 31.93 km2, and the total mariculture production were 11.33 � 103 t (Fisheries Department of Agriculture Ministry of China, 2017). 2.2. Samples collection and measurement Dagang (DG) and Hangu (HG) were two of the main mariculture areas of Tianjin, principally cultivating Litopenaeus vannamei. Each sampling point was selected at the center of the reclamation mariculture shrimp ponds of DG and HG in Tianjin Binhai New Area, respectively (Fig. 1). To investigate the diurnal variations of GHGs concentrations,
Fig. 1. Locations of the sampling sites. 2
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saturation and air-water interface emission fluxes, we collected water samples and gas samples every 3 h from 11:00 a.m. on August 1 to 11:00 a.m. on August 2, 2016 at the DG pond and the same hours on August 3 to August 4, 2016 at the HG pond. At each sampling point, three parallel surface pond water samples were collected using a gas-tight water sampler and then extruded directly into a 137-ml hermetic sampling tube, which was fully filled and sealed immediately to ensure no bubbles being contained. At the same time, three in situ air samples at each sampling point were collected overhead using aplastic medical syringes (100 ml) and injected into vacuumed gas bags in order to determine ambient GHGs concentrations. Additional 1000 ml of surface pond water at every sampling point was collected to test the water quality parameters. All water samples were kept in ice-filled incubators and transported to the laboratory within 6 h (Hu et al., 2018). During the sampling period, dissolved oxygen (DO) concentrations were measured in situ using a FireSting O2 sensor (PyroScience, Germany). Moreover, water temperature, humidity, pH, Eh, and salinity were detected using an HQ40d monitor (HACH, USA). Air temperature and wind speed were real-time downloaded from the internet (http://www.weather.com.cn/ ). In the laboratory, concentrations of dissolved GHGs in water samples were measured using a static headspace equilibrium technique (Hu et al., 2018). Dissolved nitrogen, in the form of nitrate and nitrite (NO3 þ NO2 -N), and the soluble phosphorus (SP) were determined using a continuous flow analyzer (FUTURA, Alliance, France). The ammonia nitrogen (NHþ 4 -N) was determined with Nessler’s reagent spectropho tometry. Concentrations of GHGs in air samples were analyzed using gas chromatography technique (Agilent 7890A, USA).
previous study (Yu et al., 2013). 2.4. Data analysis Microsoft Excel and SPSS 19.0 software packages (SPSS Inc., Chi cago, USA) were used for statistical analyses. Spearman correlation co efficients and regression equations were applied to identify key environmental variables driving GHGs emissions from the reclamation mariculture ponds. 3. Results Based on a spot investigation, L. vannamei of the two ponds were fed with basically same dosage and caught both twice a day in the summer in order to sell fresh production for the morning and evening markets. However, the nutrient components of the shrimp feed in the two ponds were slightly different (Table 1). 3.1. Physicochemical parameters of reclamation mariculture ponds Due to natural diurnal variation in solar radiation intensity, air temperature, water temperature and humidity showed clear diurnal characteristics (Table 2). The air temperature and water temperature were higher during the daytime and lower during the nighttime, how ever humidity showed opposite. The value of pH varied slightly and the water in both ponds were alkaline throughout the day. The Eh and salinity of the DG pond were fluctuated without diurnal variation fea tures, because it rained three times on the day of sampling. The sampling day at the HG pond was sunny; and salinity of the pond water rose at night and fell during the day following the temperaturevariation. DO varied widely, and had a significant day-night pattern with higher concentrations at daytime because of photosynthesis. The value of DO was much higher for the HG pond than for the DG pond that had weak photosynthesis on the rainy day. The value of SP in the DG pond was more than double than that of the HG pond, likely because the phos phorus content in feed of these two ponds was different (according to Table 1). Furthermore, the low DO in water of the DG pond reduced the mobility of L. vannamei, decreased their appetite, and then increased residue of feed (Liu, 2006). The concentrations of NO3 þ NO2 -N and NHþ 4 -N in both ponds had no significant differences, and both followed irregular diurnal patterns. The average value of NHþ 4 -N in DG pond and HG pond was 2.19 and 2.13 respectively which was both smaller than the safe concentration 2.99 mg/L (Li, 2012).
2.3. Calculations Concentrations of GHGs (CW) in the mariculture pond water were calculated according to the GHGs concentrations in the headspace of the tube (Hu et al., 2018): CW ¼ ½ðCT
CA Þ � VT þ α � CT � VW � = VW
(1)
Where CT are the GHGs concentrations (μmol⋅L 1) in the headspace of the tube under equilibrium state; CA are the atmospheric concentrations (μmol⋅L 1) of the sampling points; VT is the volume of headspace gas in the tube (L); α is the Bunsen coefficient (Wanninkhof, 1992); VW is the volume of water in the tube (L). Saturation of GHGs in the mariculture pond water was the ratio between the in situ dissolved GHGs concentrations in the mariculture pond water and the calculated saturated GHGs concentrations consistent with ambient air GHGs concentrations: S ¼ CW =CWs ¼ CW =ðα � CA Þ100%
3.2. Concentrations and saturation of dissolved GHGs in reclamation mariculture ponds water
(2)
The range of dissolved CO2 concentrations was 17.5 � 0.2 μmol L 1–18.5 � 0.3 μmol L 1 and 17.1 � 0.1 μmol L 1–17.5 � 0.3 μmol L 1 in the DG and HG ponds, respectively, with average value of 18.0 � 0.2 μmol L 1 and 17.3 � 0.2 μmol L 1 (Fig. 2). Correspondingly, the CO2 saturation varied from 114.9 � 0.4% to 148.8 � 0.4% and from 101.5 � 0.9% to 122.3 � 2.1% in the DG and HG ponds, respectively, with mean values of 132.7 � 1.6% for the DG pond and 108.6 � 1.2% for the HG pond (Fig. 2). Both ponds were oversaturated with CO2. The dissolved CH4 concentrations ranged from 0.05 � 0.00 μmol L 1 to 0.10 � 0.01 μmol L 1 (average: 0.07 � 0.00 μmol L 1) in the DG pond and from 0.06 � 0.00 μmol L 1 to 0.17 � 0.01 μmol L 1 (average: 0.09 � 0.01 μmol L 1) in the HG pond (Fig. 2). Accordingly, the range of CH4 saturation was 2490.5 � 181.2%–5096.5 � 369.2% and 2844.8 � 324.2%–7709.0 � 688.7% in the DG and HG ponds, respectively. CH4 was supersaturated in both ponds, with the average value of 3246.1 � 187.4% in the DG pond and 4388.5 � 438.7% in the HG pond (Fig. 2). The dissolved N2O concentrations varied within small range, which was 11.4 � 0.0–11.5 � 0.0 nmol L 1 with the average of 11.5 � 0.0 nmol L 1 in the DG pond and 11.0 � 0.0 nmol L 1–11.2 � 0.0 nmol L 1
Where CW were calculated from Equation (1); CWs were the saturated GHGs concentrations (μmol⋅L 1); CA were the atmospheric concentra tions (μmol⋅L 1) of the sampling points; α is the Bunsen coefficient (Wanninkhof, 1992). GHGs emission fluxes at air-water interface were determined by the two-layer model of diffusive gas exchange (Liss and Merlivat, 1986): FW
A
(3)
¼ k � ΔC
Where FW-A are the gas exchange fluxes; △C are the difference between GHGs concentrations in the air and water; k is the gas transfer rate and was determined using a wind-dependent model published previously (Yu et al., 2013): k ¼ 1:91 � e0:35μ � ðSc=600Þ
1=2
(4)
Where Sc is the Schmidt number for GHGs calibrated by in situ water temperature; μ is long-term wind speed of sampling sites at 10m height (m⋅s 1). The specific calculation procedure of k was detailed in the 3
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Table 1 Nutrient components of the shrimp feed. DG pond HG Pond
Total phosphorus
Ca
coarse ash
crude fiber
crude fat
crude protein
1.0–2.5% 0.9–1.45%
1.5% 3.0%
�16% �15%
�5.0% �5.0%
�6.0% �4.0%
�42% �40%
Table 2 Ranges and means of physicochemical parameters in the reclamation mariculture ponds of Tianjin. DG pond Air temperature (� C) Water temperature (� C) Humidity (%) pH Eh (mV) Salinity (‰) DO (mg⋅L 1) SP (μg⋅L 1) NO3 þ NO2 -N (mg⋅L 1) 1 NHþ 4 -N (mg⋅L )
HG pond
Minimum
Maximum
Mean
Minimum
Maximum
Mean
24.00 26.90 75.21 7.88 125.90 32.50 2.93 0.61 0.94 1.40
28.00 30.10 88.51 8.05 250.10 33.40 6.35 1.23 0.99 2.92
26.70 28.30 83.55 7.97 169.50 33.00 4.64 0.83 0.96 2.19
25.00 28.50 61.78 8.36 104.60 33.10 8.50 0.29 0.86 1.01
31.00 32.90 83.33 8.56 224.70 37.30 15.00 0.57 1.01 3.52
28.20 30.50 72.41 8.48 168.10 35.60 11.46 0.40 0.92 2.13
Fig. 2. The diurnal variation in CO2, CH4, N2O concentration and saturation in water of the DG and HG ponds (the shaded area of the picture were night hours).
with the average of 11.1 � 0.0 nmol L 1 in the HG pond (Fig. 2). Both ponds were oversaturated with N2O. The corresponding N2O saturation was 178.6 � 0.5%–190.1 � 0.2% and average of 182.1 � 0.5% in the DG pond and 183.5 � 0.2%–203.0 � 0.3% with average of 191.8 � 0.5% in the HG pond (Fig. 2).
nmol m 2⋅h 1 to 315.4 � 0.6 nmol m 2⋅h 1 severally at the DG pond water-air interface, with the average value of 206.7 � 9.4 μmol m 2⋅h 1, 3.1 � 0.2 μmol m 2⋅h 1, and 243.7 � 1.6 nmol m 2⋅h 1; while these three GHGs emission fluxes varied from 15.7 � 10.0 μmol m 2⋅h 1 to 199.8 � 18.6 μmol m 2⋅h 1, 2.2 � 0.1 μmol m 2⋅h 1 to 9.2 � 0.8 μmol m 2⋅h 1, and 162.7 � 0.4 nmol m 2⋅h 1 to 365.6 � 1.1 nmol m 2⋅h 1 respectively at the HG pond water-air interface, with the mean value of 64.1 � 9.7 μmol m 2⋅h 1, 4.3 � 0.5 μmol m 2⋅h 1, and 265.8 � 1.4 nmol m 2⋅h 1 (Fig. 3). Based on the GHGs emission fluxes and surface area of the recla mation mariculture ponds in Tianjin (about 17.8 km2) and Bohai Rim
3.3. Water-air GHGs emissions from reclamation mariculture ponds During the observation, the emission fluxes of CO2, CH4 and N2O ranged from 127.0 � 5.2 μmol m 2⋅h 1 to (332.9 � 2.6 μmol m 2⋅h 1, 1.5 � 0.1 μmol m 2⋅h 1 to 5.5 � 0.4 μmol m 2⋅h 1, and 155.5 � 1.1 4
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Fig. 3. The diurnal variation in CO2, CH4, N2O emission fluxes at water-air interface of the DG and HG ponds (the shaded area of the picture were night hours).
region (about 2.4 � 103 km2), the annual CO2, CH4 and N2O emissions from the reclamation mariculture ponds of Tianjin and Bohai Rim region were estimated to be 2.1 � 107 mol CO2 (i.e., 2.5 � 105 kg C as CO2), 5.8 � 105 mol CH4 (i.e., 6.9 � 103 kg C as CH4) and 4.0 � 104 mol N2O (i.e., 1.1 � 103 kg N as N2O), and 2.9 � 109 mol CO2 (i.e., 3.4 � 107 kg C as CO2), 7.8 � 107 mol CH4 (i.e., 9.4 � 105 kg C- as CH4) and 5.4 � 106 mol N2O (i.e., 1.5 � 105 kg N as N2O) respectively. So the reclamation aquaculture was an important source of anthropogenic GHGs emissions. The large-scale reclamation mariculture ponds of China with similar characteristics could significantly contribute to GHGs emissions, which deserves widespread concern.
Table 4 Regression equations between emission fluxes of GHGs at the water-air interface and environmental variables. Gas diffusive fluxes
Regression equations
F
R2
p
CO2 CH4
Y ¼ 2790.902–323.173XpH Y ¼ 15.895 þ 0.628XATþ1.058XNH4þ-N Y ¼ 356.600 þ 35.210XAT286.882XpHþ22.606 XWTþ18.366XSalinity
21.849 5.433
0.523 0.318
<0.001 <0.02
35.029
0.878
<0.001
N2O
3.4. Correlation between GHGs emissions and environmental variables
AT: air temperature; WT: water temperature.
GHGs emission fluxes were controlled by environmental variables such as air temperature, water temperature, humidity, pH, Eh, salinity, DO, SP, NO3 þ NO2 -N, NHþ 4 -N and so on. Because humidity, pH and salinity were subjected to violate the test of normality, Spearman cor relation analysis was made between emission fluxes of GHGs at water-air interface of ponds and environmental variables by SPSS 22.0 (Table 2). Among the environmental variables, SP (p < 0.05) was positively correlated with CO2 emission fluxes; while pH (p < 0.01), salinity (p < 0.01) and DO (p < 0.01) were negatively correlated with CO2 emission fluxes (Table 3). CH4 emission fluxes had positive correlation only with air temperature (p < 0.05; Table 3). N2O emission fluxes were positively correlated with air temperature (p < 0.01) and water temperature (p < 0.01); while they were negatively correlated to humidity (p < 0.01), Eh (p < 0.01) and NO3 þ NO2–N (p < 0.05; Table 3). Multiple stepwise regression analysis showed that CO2 emission fluxes were remarkably related to pH (p < 0.001) of the water, which accounted for 52.3% of the total variation in CO2 (Table 4). CH4 emis sion fluxes were influenced by air temperature and NHþ 4 -N (p < 0.02), which accounted for 31.8% of the total variation in CH4 (Table 3). N2O emission fluxes were significantly controlled by air temperature, pH, water temperature and salinity (p < 0.001), which accounted for 87.8% of the total variation in N2O (Table 4).
4. Discussion 4.1. Diurnal variations of dissolved GHGs in the DG and HG ponds Diurnal variations in concentrations of dissolved CO2, CH4 and N2O showed no stable pattern. There were no distinct variations in dissolved CO2 concentrations in the two studied ponds. The dissolved CO2 con centrations irregularly fluctuated during the observation period (Fig. 2), separately reaching its maximum (minimum) at 14:00 (23:00) in the DG pond and at 23:00 (17:00) in the HG pond. The dissolved CO2 concen trations were higher in the DG pond than in the HG pond likely due to higher air temperature of the latter. As the air temperature increases, the stability of carbonic acid in water will decrease as carbonic acid is further decomposed to release CO2, resulting in less and less dissolved CO2 concentrations (Natchimuthu et al., 2014; Lan, 2015). In contrast, to the CH4 concentrations, dissolved N2O concentrations on both ponds had almost no diurnal variation, keeping within a narrow range (Fig. 2), which was in agreement with the finding of a previous study (Yang et al., 2017). 4.2. Diurnal variations of GHGs emission fluxes from the DG and HG ponds Significant differences were observed in diurnal trends of CO2 emission fluxes from these two ponds (Fig. 3). The CO2 emission fluxes
Table 3 Correlations between GHGs emission fluxes at water-air interface of ponds and environmental variables.
CO2 （μmol⋅m
2
⋅h 1）
CH4 （μmol⋅m
2
⋅h 1）
N2O （nmol⋅m a b
2
⋅h 1）
r p N r p N r p N
Air Temperature (� C)
Water Temperature (� C)
Humidity (%)
pH
Eh (mV)
Salinity (‰)
DO (mg⋅L
0.268 0.254 20 0.512a 0.021 20 0.866b 0.000 20
0.311 0.182 20 0.442 0.051 20 0.798b 0.000 20
0.350 0.130 20 0.305 0.191 20 0.740b 0.000 20
0.683b 0.001 20 0.028 0.907 20 0.167 0.481 20
0.003 0.990 20 0.077 0.748 20 0.567b 0.009 20
0.660b 0.002 20 0.095 0.691 20 0.146 0.539 20
0.641b 0.002 20 0.012 0.960 20 0.350 0.130 20
Correlation is significant at the 0.05 level (2-tailed). Correlation is significant at the 0.01 level (2-tailed). 5
1
)
SP (μg⋅L
1
)
0.557a 0.011 20 0.242 0.303 20 0.312 0.180 20
NO3 þ NO2–N (mg⋅L 1)
NH4þ-N (mg⋅L 1)
0.241 0.307 20 0.355 0.125 20 0.459a 0.042 20
0.427 0.060 20 0.205 0.387 20 0.180 0.446 20
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Estuarine, Coastal and Shelf Science 237 (2020) 106677
decreased sharply after 17:00 and reached the lowest value at 23:00 for the DG pond, while increased in fluctuation all the time in the HG pond. However, the average CO2 emission fluxes at daytime (DG: 240.4 μmol m 2⋅h 1; HG: 69.7 μmol m 2⋅h 1) were both greater than that at nighttime (DG: 139.3 μmol m 2⋅h 1; HG: 53.0 μmol m 2⋅h 1). The diurnal variation trends of CH4 emission fluxes were similar for the two ponds (Fig. 3), with a bottom period from 17:00 to 5:00 then a rapid increase. Both ponds emitted more CH4 during the day time than during the night hours, with the mean values of 3.8 μmol m 2⋅h 1 for the daytime and 1.6 μmol m 2⋅h 1 for the nighttime from the DG pond, and 5.0 μmol m 2⋅h 1 during the daytime and 3.0 μmol m 2⋅h 1 during the night time from the HG pond. The emission fluxes of N2O demonstrated a similar diurnal variation in both ponds, which decreased around sunset and increased around sunrise (Fig. 3). It was noticeable that more N2O was emitted during the daytime than during the nighttime, with daytime average values of 285.2 nmol m 2⋅h 1 in the DG pond and 316.0 nmol m 2⋅h 1 in the HG pond, and nighttime mean values of 159.6 nmol m 2⋅h 1 in DG pond and 165.4 nmol m 2⋅h 1 in HG pond. The N2O emission fluxes of the daytime from the two ponds were both nearly two times those of the nighttime. The daytime emission fluxes of GHGs from both ponds were larger than the nighttime fluxes, consistent with previous observations (Morin et al., 2014; Xu et al., 2017; Yang et al., 2017). Moreover, the diurnal variation of N2O emission fluxes was greater compared with the diurnal variation of either CO2 or CH4 fluxes. In the daytime, as the solar radi ation enhanced, temperature rose and artificial management (such as feeding, harvesting and water exchange) increased, the microbial ac tivities were enhanced, thus increasing the emission fluxes of GHGs from the water of mariculture pond (Yang et al., 2017). The CH4 and N2O emission fluxes from the HG pond were both greater than that from the DG pond. However, the CO2 emission fluxes were the opposite. The reason was that the value of air temperature, water temperature, pH, DO and salinity were higher in the HG pond than that in the DG pond (Table 2). Beyond that, the photosynthesis in the DG pond was weaker than that in the HG pond owning to the rainy day for the DG pond, while the SP of the former was more than that of the latter because of more phosphorus content in feed of the former (Table 1).
methanogens and favors CH4 production and emissions. NHþ 4 -N is another key factor affecting the CH4 emission fluxes from the aquatic ecosystem, mainly including two aspects: 1) NHþ 4 -N can inhibit CH4 oxidation by restraining growth and activity of methanogenic bacteria, then increases CH4 emissions; 2) NHþ 4 -N provides a nutrient source for methane production (Hu et al., 2018; King and Schnell, 1994; Schrier-Uijl et al., 2011). In the present study, air temperature, pH, water temperature and salinity were key factors influencing N2O emission fluxes (Table 4). N2O emissions can be produced during both microbial nitrification and denitrification; in particular the latter plays an important role because it is one of the intermediate products involved in denitrification (Beaulieu et al., 2010). Previous studies have revealed that N2O emission fluxes were related to thermal regime of aquatic ecosystem (Barnes et al., 1998; Hu et al., 2018; Silvennoinen et al., 2008; Usui et al., 2001; Yang et al., 2017). Under high temperature conditions, life activities (such as foraging, metabolism and so on) of shrimps and other aquatic organisms are vigorous, which may produce strong disturbance to the water body, then increase the content of NO3 and NO2 from the sediments into the water. These life activities could provide material sources for the nitri fication and denitrification process, thereby enhancing the activities of the nitrifying and denitrifying bacteria, accelerating geochemical reac tion and increasing the nitrification or denitrification potential through which N2O emissions are enhanced. Some scholars have found that N2O production in aquatic ecosystem is a by-product of nitrification and denitrification microbial processes, which are controlled by pH (Stow et al., 2005; Clough et al., 2011; Yang et al., 2015). The final reduction of N2O to N2 is mainly determined by the activities of N2O reductase which are activated more with the increase of pH (Stow et al., 2005; Clough et al., 2011; Yang et al., 2015, 2018a). N2O production increased with increasing salinity, resulting from a reduction of the denitrification ef ficiency caused by salinity, through an inhibition of the N2O-reductase, rather than due to increased denitrification rates (Marton et al., 2012; Teixeira et al., 2013; Zhao et al., 2013). In our study, only single-day sample collection was carried out at two mariculture ponds of Tianjin in the summer, which was likely to cause an over or underestimation of the emissions. In the future, samples need to be collected at more mariculture ponds in other areas within consecutive days of every season. Additionally, more environmental variables and artificial cultivation factors on GHGs emissions from reclamation mariculture ponds should be considered and analyzed.
4.3. Key factors for GHGs emissions from reclamation mariculture ponds Among the ten measured environmental variables of this study, only pH affected CO2 emission fluxes at water-air interface. Other studies have shown that CO2 fluxes were negatively correlated with pH (Chen et al., 2016; Schrier-Uijl et al., 2011; Yang et al., 2015, 2018b). The value of water pH reflects the ratio of CO2 accounting for total inorganic carbon (pCO2) in the water column (Chen et al., 2016; Yang et al., 2018b). As pH directly affects the dynamic balance and distribution of carbonate system (CO2, CO23 and HCO3 ) in water, it affects amount of CO2 dissolved in water. When pH is less than a certain critical value (7–8), CO2 of the water is easily saturated, and the higher pCO2 pro motes CO2 emission into the atmosphere. When pH is above the critical value (pH > 8), the dissociative CO2 will be converted into carbonate, resulting in the unsaturation of CO2 in the water. The lower pCO2 of water will facilitate a higher CO2 uptake from the atmosphere via diffusion (Schrier-Uijl et al., 2011; Yang et al., 2015, 2018b). The important factors for CH4 emission fluxes from these ponds were air temperature and NHþ 4 -N (Table 4). Temperature indirectly affects CH4 emission fluxes by influencing microbial activities, redox environ ment, and gas solubility in water (Chen et al., 2016; Schrier-Uijl et al., 2011; Yang et al., 2015, 2017, 2018b). CH4 emission fluxes at the water-air interface of the aquaculture ponds are mainly produced by methanogens decomposition of organic matter at the bottom of the ponds. With the high temperature, shrimps and other aquatic organisms have vigorous life activities and consume large amount of DO, which provides a good growth environment for the metabolism of
5. Conclusion In two large marine reclamation aquaculture ponds, GHGs emission fluxes were greater during daytime than during nighttime. The diurnal variation of N2O emission fluxes was the most significant among the three GHGs. Water pH, air temperature, water temperature, water salinity and ammonia (NHþ 4 -N) were among the key factors affecting GHGs emission fluxes from reclamation mariculture ponds. In addition to natural conditions such as meteorological and hydrological factors, artificial management also significantly affected microbial activities, and then led to GHGs emissions. Efforts should be made to more accu rately characterize GHGs emissions from reclamation mariculture sector in order to develop effective mitigation strategies. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. CRediT authorship contribution statement Beibei Hu: Conceptualization, Writing - original draft, Writing review & editing. Xiaofang Xu: Formal analysis. Junfeng (Jim) Zhang: 6
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Supervision. Tianli Wang: Investigation. Weiqing Meng: Visualization. Dongqi Wang: Methodology.
Morin, T.H., Bohrer, G., Naor-Azrieli, L., Mesi, S., Kenny, W.T., Mitsch, W.J., Sch€ afer, K. B.V.R., 2014. The seasonal and diurnal dynamics of methane flux at a created urban wetland. Ecol. Eng. 72, 74–83. Natchimuthu, S., Selvam, B.P., Bastviken, D., 2014. Influence of weather variables on methane and carbon dioxide flux from a shallow pond. Biogeochemistry 119 (1–3), 403–413. Parker, R.W., Blanchard, J.L., Gardner, C., Green, B.S., Hartmann, K., Tyedmers, P.H., Watson, R.A., 2018. Fuel use and greenhouse gas emissions of world fisheries. Nat. Clim. Change 8, 333–339. Robb, D.H.F., MacLeod, M., Hasan, M.R., Soto, D., 2017. Greenhouse gas emissions from aquaculture: a life cycle assessment of three Asian systems. In: FAO Fisheries and Aquaculture Technical Paper No. 609. FAO, Rome. Schrier-Uijl, A.P., Veraart, A.J., Leffeaar, P.A., Berendse, F., Veenendaal, E.M., 2011. Release of CO2 and CH4 from lakes and drainage ditches in temperate wetlands. Biogeochemistry 102 (1–3), 265–279. Silvennoinen, H., Liikanen, A., Torssonen, J., Stange, C.F., Martikainen, P.J., 2008. Denitrification and N2O effluxes in the Bothnian Bay (northern Baltic Sea) river sediments as affected by temperature under different oxygen concentrations. Biogeochernistry 88 (1), 63–72. Song, D., 2015. Production, Distribtution and Flux of Nitrous Oxide in the Sanggou Bay. Master Dissertation. Ocean University of China (In Chinese). Song, H.L., Liu, X.T., Wen, B.L., 2017. Greenhouse gases fluxes at water-air interface of aquaculture ponds in the Yellow River Estuary. Ecol. Environ. Sci. 26 (9), 1554–1561 (In Chinese). Stow, C.A., Walker, J.T., Cardoch, L., Spence, P., Geron, C., 2005. N2O emissions from streams in the neuse river watershed, North Carolina. Environ. Sci. Technol. 39 (18), 6999–7004. Teixeira, C., Magalh, C., Joye, S.B., Bordalo, A.A., 2013. The role of salinity in shaping dissolved inorganic nitrogen and N2O dynamics in estuarine sediment-water interface. Mar. Pollut. Bull. 66, 225–229. Tianjin, 2017. Municipal Bureau of Statistics & Survey Office of the National Bureau of Statistics in Tianjin. Tianjin Statistical Yearbook 2017. China Statistics Press, Beijing (In Chinese). Usui, T., Koike, I., Ogura, N., 2001. N2O production, nitrification and denitrification in an estuarine sediment. Estuar. Coast Shelf Sci. 52 (6), 769–781. Wanninkhof, R., 1992. Relationship between wind speed and gas exchange over the ocean. J. Geophys. Res-Oceans 97 (C5), 7373–7382. Williams, J., Crutzen, P., 2010. Nitrous oxide from aquaculture. Nat. Geosci. 3 (3), 143. Wu, S., Hu, Z.Q., Hu, T., Chen, J., Yu, K., Zou, J.W., Liu, S.W., 2018. Annual methane and nitrous oxide emissions from rice paddies and inland fish aquaculture wetlands in southeast China. Atmos. Environ. 175, 135–144. Xu, X.W.H., Fu, G.H., Zou, X.Q., Ge, C.D., Zhao, Y.F., 2017. Diurnal variations of carbon dioxide, methane, and nitrous oxide fluxes from invasive Spartina alterniflora dominated coastal wetland in northern Jiangsu Province. Acta Oceanol. Sin. 36 (4), 105–113. Yang, P., He, Q.H., Huang, J.F., Tong, C., 2015. Fluxes of greenhouse gases at two different aquaculture ponds in the coastal zone of southeastern China. Atmos. Environ. 115, 269–277. Yang, P., Lai, D.Y.F., Huang, J.F., Tong, C., 2018a. Effect of drainage on CO2, CH4, and N2O fluxes from aquaculture ponds during winter in a subtropical estuary of China. J. Environ. Sci. (China) 65, 72–82. Yang, P., Zhang, Y.F., Lai, D.Y.F., Tan, L.S., Jin, B.S., Tong, C., 2018b. Fluxes of carbon dioxide and methane across the water-atmosphere interface of aquaculture shrimp ponds in two subtropical estuaries: the effect of temperature, substrate, salinity and nitrate. Sci. Total Environ. 635, 1025–1035. Yang, W.B., Yuan, C.S., Tong, C., Yang, P., Yang, L., Huang, B.Q., 2017. Diurnal variation of CO2, CH4, and N2O emission fluxes continuouslymonitored in-situ in three environmental habitats in a subtropical estuarinewetland. Mar. Pollut. Bull. 119, 289–298. Yu, Z.J., Deng, H.G., Wang, D.Q., Ye, M.W., Tan, Y.J., Li, Y.J., Chen, Z.L., Xu, S.Y., 2013. Nitrous oxide emissions in the Shanghai river network: implications for the effects of urban sewage and IPCC methodology. Global Change Biol. 19 (10), 2999–3010. Yuan, J.J., Xiang, J., Liu, D.Y., Kang, H., He, T.H., Kim, S., et al., 2019. Rapid growth in greenhouse gas emissions from the adoption of industrial-scale aquaculture. Nat. Clim. Change 9 (4), 318–322. https://doi.org/10.1038/s41558-019-0425-9. Zhao, W., Wang, Y.Y., LiuS, H., Pan, M.L., Yang, J., ChenS, W., 2013. Denitrification activities and N2O production under salt stress with varying COD/N ratios and terminal electron acceptors. Chem. Eng. J. 215–216, 252-26.
Acknowledgements This work was financially supported by the National Natural Science Foundation of China (NSFC) (Grant No. 41301221, No. 41671467 and No. 41473049). We sincerely thank the editor and the anonymous re viewers for their insightful comments and suggestions. References Barnes, J., Owens, N.J.P., 1998. Denitrification and nitrous oxide concentrations in the Humber estuary, UK, and adjacent coastal zones. Mar. Pollut. Bull. 37, 247–260. Beaulieu, J.J., Shuster, W.D., Rebholz, J.A., 2010. Nitrous oxide emissions from a large, impounded river: the Ohio River. Environ. Sci. Technol. 44 (19), 7527–7533. Chen, Y., Dong, S.L., Wang, F., Gao, Q.F., Tian, X.L., 2016. Carbon dioxide and methane fluxes from feeding and no-feeding mariculture ponds. Environ. Pollut. 212, 489–497. Chen, Y., Dong, S.L., Wang, Z.N., Wang, F., Gao, Q.F., Tian, X.L., Xiong, Y.H., 2015. Variations in CO2 fluxes from gras carp Ctenopharyngodon idella aquaculture polyculture ponds. Aquacult. Env.Interac. 8, 31–40. Clough, T.J., Buckthought, L.E., Casciotti, K.L., Kelliher, F.M., Jones, P.K., 2011. Nitrous oxide dynamics in a braided river system, New Zealand. J. Environ. Qual. 40 (5), 1532–1541. Fisheries Department of Agriculture Ministry of China, 2017. 2017 China Fishery Statistical Yearbook. China Agricultural Press, Beijing (In Chinese). Food and Agriculture Organization of the United Nations (FAO), 2016. The state of world fisheries and aquaculture 2016: coutributing to food security and nutrition for all. http://www.fao.org/3/a-i5555e.pdf. Food and Agriculture Organization of the United Nations (FAO), 2018. The state of world fisheries and aquaculture 2018: meeting the sustainable development goals. http:// www.fao.org/3/i9540en/I9540EN.pdf. Hu, B.B., Wang, D.Q., Zhou, J., Meng, W.Q., Li, C.W., Sun, Z.B., Guo, X., Wang, Z.L., 2018. Greenhouse gases emission from the sewage draining rivers. Sci. Total Environ. 612, 1454–1462. Hu, Z., Lee, J.W., Chandran, K., Kim, S., Khanal, S.K., 2012. Nitrous oxide (N2O) emission from aquaculture: areview. Environ. Sci. Technol. 46 (12), 6470–6480. Hu, Z., Lee, J.W., Chandran, K., Kim, S., Sharma, K., Brotto, A.C., Khanal, S.K., 2013. Nitrogen transformations in intensive aquaculture system and its implication to climate change through nitrous oxide emission. Bioresour. Technol. 130, 314–320. Hu, Z., Lee, J.W., Chandran, K., Kim, S., Sharma, K., Khanal, S.K., 2014. Influence of carbohydrate addition on nitrogen transformations and greenhouse gas emissions of intensive aquaculture system. Sci. Total Environ. 470–471, 193–200. Hu, Z.Q., Wu, S., Ji, C., Zou, J.W., Zhou, Q.S., Liu, S.W., 2016. A comparison of methane emissions following rice paddies conversion to crab-fish farming wetlands in southeast China. Environ. Sci. Pollut. Res. Int. 23 (2), 1505–1515. King, G.M., Schnell, S., 1994. Effect of increasing atmospheric methane concetration on ammonium inhibition of soil methane consumption. Nature 370 (6487), 282–284. Lan, J., 2015. Greenhouse Gases Concentration,Emission and Influencefactors in Farming Waters. Master Dissertation. Huazhong Agricultural University (In Chinese). Liss, P.S., Merlivat, L., 1986. Air-sea Gas Exchange Rates: Introduction and Synthesis, the Role of Air-Sea Exchange in Geochemical Cycling. Springer, pp. 113–127. Li, Y., 2012. Artificial Selection of Penaeus monodon and Impacts of Ammonia-N on Immune Parameters,detoxification. Shanghai Ocean University, Shanghai (In Chinese). Liu, S.W., Hu, Z.Q., Wu, S., Li, S.Q., Li, Z.F., Zou, J.W., 2016. Methane and nitrous oxide emissions reduced following conversion of rice paddies to inland crab-fish aquaculture in Southeast China. Environ. Sci. Technol. 50 (2), 633–642. Marton, J.M., Herbert, E.R., Craft, C.B., 2012. Effects of salinity on denitrification and greenhouse gas production from laboratory-incubated tidal forest soils. Wetlands 32, 347–357. Meng, W.Q., Hu, B.B., He, M.X., Liu, B.Q., Mo, X.Q., Li, H.Y., Wang, Z.L., Zhang, Y., 2017. Temporal-spatial variations and driving factors analysis of coastal reclamation in China. Estuar. Coast Shelf Sci. 191, 39–49.
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