Greenhouse gases concentrations and fluxes from subtropical small reservoirs in relation with watershed urbanization

Atmospheric Environment 154 (2017) 225e235

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Greenhouse gases concentrations and fluxes from subtropical small reservoirs in relation with watershed urbanization Xiaofeng Wang a, b, Yixin He c, d, Xingzhong Yuan a, b, *, Huai Chen c, d, **, Changhui Peng e, f, Junsheng Yue a, b, Qiaoyong Zhang a, b, Yuanbin Diao a, b, Shuangshuang Liu a, b a

State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400030, China College of Resources and Environmental Science, Chongqing University, Chongqing 400030, China Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization & Ecological Restoration Biodiversity Conservation Key Laboratory of Sichuan Province, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, China d Zoige Peatland and Global Change Research Station, Chinese Academy of Sciences, Hongyuan 624400, China e  Montr Institut des Sciences de l’Environnement, Universit e du Qu ebec a eal (UQAM), 201 Pr esident-Kennedy, Montr eal, H2X 3Y7, Canada f State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, College of Forestry, Northwest A&F University, Yangling, Shaanxi 712100, China b c

h i g h l i g h t s
Higher GHG fluxes from small reservoirs in urban area than the rural ones.
DOC, DTP and chl-a act as potential controls for the reservoirs pCO2 and CO2 flux.
Nutrients of surface water act as good predictors of reservoir CH4 and N2O fluxes.
Primary production and rainfall account for the seasonality of reservoir GHG fluxes.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 9 October 2016 Received in revised form 23 January 2017 Accepted 25 January 2017 Available online 28 January 2017

Greenhouse gas (GHG) emissions from reservoirs and global urbanization have gained widespread attention, yet the response of GHG emissions to the watershed urbanization is poorly understood. Meanwhile, there are millions of small reservoirs worldwide that receive and accumulate high loads of anthropogenic carbon and nitrogen due to watershed urbanization and can therefore be hotspots of GHG emissions. In this study, we assessed the GHG concentrations and fluxes in sixteen small reservoirs draining urban, agricultural and forested watersheds over a period of one year. The concentrations of pCO2, CH4 and N2O in sampled urban reservoirs that received more sewage input were higher than those in agricultural reservoirs, and were 3, 7 and 10 times higher than those in reservoirs draining in forested areas, respectively. Accordingly, urban reservoirs had the highest estimated GHG flux rate. Regression analysis indicated that dissolved total phosphorus, dissolved organic carbon (DOC) and chlorophyll-a (Chl-a) had great effect on CO2 production, while the nitrogen (N) and phosphorus (P) content of surface water were closely related to CH4 and N2O production. Therefore, these parameters can act as good predictors of GHG emissions in urban watersheds. Given the rapid progress of global urbanization, small urban reservoirs play a crucial role in accounting for regional GHG emissions and cannot be ignored. © 2017 Published by Elsevier Ltd.

Keywords: GHG Urbanization Land use Small reservoirs Spatiotemporal variation Potential controls

1. Introduction * Corresponding author. State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400030, China. ** Corresponding author. Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization & Ecological Restoration Biodiversity Conservation Key Laboratory of Sichuan Province, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, China. E-mail address: [email protected] (X. Yuan). http://dx.doi.org/10.1016/j.atmosenv.2017.01.047 1352-2310/© 2017 Published by Elsevier Ltd.

For many years dam projects were considered a beneficial engineering project for their essential roles in water supply, agriculture irrigation, urban landscape, flood control and hydropower. Then people came to realize the adverse environmental impacts brought by dam projects, including habitat fragmentation (Wu et al., 2003) and especially greenhouse gas (GHG) emissions to

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the atmosphere (Hu and Cheng, 2013; de Faria et al., 2015). In recent years, the GHG emissions from artificial reservoirs have created more and more concerns (Guerin et al., 2008; Barros et al., 2011; Bastviken et al., 2011), and have been perceived as a considerable anthropogenic source of GHG. Recent estimates suggest that global hydropower reservoirs would release about 278.7 Tg CO2 yr1 and 9.7 Tg CH4 yr1 (Barros et al., 2011; Hertwich, 2013) whilst N2O emissions cannot be disregarded due to their high Global Warming Potential (GWP) (Hendzel et al., 2005; Liu et al., 2011). However, most of the current research is mainly focused on 1) data accumulation of GHG emissions from large reservoirs in different regions; 2) GHG emissions from fluctuating zone, downstream reaches and spillways of large reservoirs; and 3) the effects of potential environmental factors (e.g., temperature, water depth, dissolved oxygen and others) and contribution of ebullition on GHG emissions (Yang et al., 2014). Only a handful of attentions were given to the GHG emissions from small reservoirs and external driving factors, especially human activities (e.g. agriculture, fertilization, urbanization sewage discharges), that would affect the inputs of carbon (C) and nitrogen (N) from terrestrial systems into the reservoir and then alter the processes of GHG production and consumption (Hosen et al., 2014; Williams et al., 2016). The GHG emission from reservoir is a complex biogeochemical process governed by several biological, chemical and physical factors. Two important factors - 1) the flooded organic soil and vegetation (Giles, 2006), and 2) continuous trapping and accumulation of organic matter and nutrients from the terrestrial systems and upstream (Maeck et al., 2013) - provide material for GHG production. For large dams, higher GHG emissions were originally attributed to the decomposition of flooded organic soil and vegetation in the first few years after damming (Abril et al., 2005; Teodoru et al., 2011). With reservoir aging, the inundated biomass would be reduced due to biological decomposition (Barros et al., 2011), and then the terrestrial and anthropogenic materials that constantly feed into and accumulate in the reservoirs become more important (Vorosmarty et al., 2003; Roland et al., 2010; Maeck et al., 2013) and may act as the sustainable source of biogenic substance for the process of GHG production in process of reservoirs. In small reservoirs, allochthonous material would play a much more important role on the biological process due to their small flooded area, low buffer capacity and high sediment accumulation rates (Barros et al., 2011). Therefore, GHG emissions from small reservoirs may be particularly susceptible to external disturbances in the watershed (e.g. land use types, sewage discharge and fertilization). Over the past few years, a number of studies have demonstrated that human activities in the drainage basin, particularly land use change and agriculture, have severely altered the transport of C and N from the terrestrial system to the inland water (Davidson, 2009; Hosen et al., 2014; Williams et al., 2016), and then impacted the GHG production in rivers and reservoirs (Maeck et al., 2013; Yu et al., 2013; Beaulieu et al., 2014). For example, it has been found that the agricultural activity in the watershed greatly accelerated the CH4 emission from William H. Harsha Lake to the atmosphere due to the high loadings of organic matter (Beaulieu et al., 2014). Yu et al. (2013) indicated that, as the result of more sewage N inputs, rivers draining through urban and suburban areas had higher N2O emissions than those in rural areas. Moreover, data sets from 25 rivers world-wide demonstrated that population density and agricultural activities in the basin could decrease the riverine CO2 flux (Li et al., 2013). GHG emissions from inland waters have definitely been influenced by rapid urbanization and increasingly human activities. Yet despite all that, research about the influence of watershed urbanization and human activities on the GHG emissions from small reservoirs is very scarce, but is extraordinarily exigent.

According to statistics, there are over 50,000 large dams and millions of smaller impoundments (Barros et al., 2011). In the southwestern mountainous area of China, there are innumerable small dams for a variety of purposes. In this study, we chose 16 smallsized reservoirs, with different land use types in their drainage basin, to perform a seasonal investigation on GHG fluxes and aquatic environment over an entire hydrological year. The objectives of our study were (1) to explore the response of GHG emissions from small reservoirs to the urbanization and human activities in the basin; (2) to analyze the potential factors regulating and indicating GHG emissions in this systems. Our results also provide quantitative data on methane emissions from subtropical small reservoirs. 2. Materials and methods 2.1. Study sites and land use All the 16 small reservoirs are located in the Chongqing (28 100 105110 -110110 E) in southwest China (Fig. 1), where the dam projects appear on most of the rivers as main measures of mountainous rivers development. The study region, with the mountainous and hilly topographic, is a subtropical humid monsoon climate with annual mean temperature of 17.6  C. The average annual precipitation is 1 088.8 mm with clear wet and dry seasons. The wet season is from May to October in which 85% of the total precipitation is occurring. The age of all sampling reservoirs is over 15 years except the Reservoir-15 who was built in 2008 (Table S1). In this study, ten sampling reservoirs are built for the irrigation or water supply purposes, and two reservoirs are built for power generation and four reservoirs are built for recreation and cityscape (Table S1). Except the Reservoir-5, which is a hydroelectric reservoir with the maximum impounded area (4.9 km2), the sampling reservoirs are relatively smaller in size with basin area ranging from 0.38 to 7.5 km2 and with impounded area ranging from 0.02 to 0.25 km2. The dam length of selected sampling reservoirs ranges from 17 to 126 m. In our study, we divided the 16 reservoirs into three groups according to the dominating land use types in each basin (Fig. 1), i.e. forest reservoir groups, agriculture reservoir groups and urban reservoir groups (FRG, ARG and URG for short). Forest group (Reservoir - 1 to 5) is characterized by relatively less human activities and near-natural. Reservoirs in agriculture group (Reservoir - 6 to 12) mainly suffer the effects of agricultural non-point pollution, water and soil loss and cropland management. Urban group (Reservoir - 13 to 16) is located in highly urbanized districts and is worst affected by human activities, e.g. increasing impervious cover (Hosen et al., 2014) and sewage discharge. 32130 N,

2.2. Sample collection and analysis For each sampling reservoir, three sampling sites were selected, four sampling campaigns were conducted quarterly from September 2014 to June 2015, each sampling site included 3 replicates, resulting in a total of 576 samples were collected. Sampling sites in each reservoir were located in the tail, middle and front of the dam, respectively, staying away from any inlets of runoff or sewage. During each sampling campaigns, 100 ml surface water sample for dissolved CH4 and N2O analysis were collected by an airproof water sampler and were injected into the aluminum foil bag (gas sample bag with the largest capacity is 200 ml), which were immediately sealed and ensured bubble-free. Whereafter, we injected 0.2 ml saturated HgCl2 into the sample bag for killing microbe. Meanwhile, we collected three 200 ml atmospheric samples 1 m above the water surface and injected into the gas sample bag for atmospheric CH4 and N2O analysis.

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Fig. 1. Location of sampling reservoirs and corresponding land use in their watersheds.

In situ water total alkalinity (TA) was measured for partial pressure of CO2 (pCO2) calculation using a fixed endpoint titration method with 0.02 mol/L muriatic acid (HCl) (Telmer and Veizer, 1999; Yao et al., 2007; Wang et al., 2011). In situ water pH was measured using the calibrated acidometer with a precision of ±0.01. Then, physicochemical parameters of the surface water, including water temperature (WT), dissolved oxygen (DO), conductivity (Con), and chlorophyll-a (Chl-a), were measured by the calibrated Manta™ 2 Multiparameter System (Eureka Company, USA). In situ wind speed at 1 m above the water surface (U1) was measured by Kestrel2500 anemometer (NK, USA). For CH4 and N2O concentration determination, we injected 80 ml ultra-pure N2 into the aluminum foil bag that with 100 ml water sample to create a headspace. Then, we shake the aluminum foil bag vigorously for 5 min to let dissolved CH4 and N2O diffuse out and gases to equilibrium between the liquid phase and headspace. 5 ml of the headspace gas, and 10 ml from gas sample bag that with atmospheric samples, were drawn out separately and analyzed by gas chromatograph (PE Clarus 500, PerkinElmer, Inc., USA). The detailed operation procedure of gas chromatograph for CH4 and N2O concentration has been described in Supporting Information and elsewhere (Chen et al., 2009; Zhu et al., 2013). Moreover, triplicate 500 - ml water samples below the surface were collected brought back to the laboratory for the measurement of total organic carbon (TOC) and nutrients, including total nitrogen þ (TN), nitrate-nitrogen (NO 3 ), ammonium-nitrogen (NH4 ), total phosphorus (TP), dissolved total phosphorus (DTP), orthophos2 phate (PO34 ) and sulfate (SO4 ). TOC analyzer (MultiN/C2100 TOC/ TN, Jena) was used to measure the TOC. Ultraviolet spectrophotometry (Unico UV-2000) was used for nutrients determinations

and more details following the China National standard method which can be downloaded from http://www.sac.gov.cn/. 2.3. pCO2 and CO2 flux calculation Generally, there are two methods for pCO2 measurements. One is headspace extraction technique, a direct method, and has been described in detail by Hope et al. (1995). The other is indirect calculation based on water temperature, pH and total alkalinity. On account of much simplified procedure, pH/alkalinity based method has been widely employed for pCO2 calculation in most inland water, especially in water with pH > 6 (Telmer and Veizer, 1999; Yao et al., 2007; Butman and Raymond, 2011; Wang et al., 2011; Ran et al., 2015). In this study, given that the range of the pH values observed in all samples was 7.10e9.2, more than 99% of the total alkalinity is considered as bicarbonate ion (Yao et al., 2007). So, we employed the pH/alkalinity based method for pCO2 calculation and detailed algorithm has been presented in the Supporting Information and reference (Yao et al., 2007; Wang et al., 2011; Li et al., 2012). The CO2 flux (FCO2) of the watereair interface was estimated according to the theoretical diffusion model:

FCO2 ¼ k0  kh  pCO2;w  kh  pCO2;a




where the k0 in cm$h1 is the piston velocity of CO2 (also known as the gas exchange coefficient); kh represents Henry's constant corrected using the given temperature; pCO2,w and pCO2,a in matm represent the partial pressure of CO2 in surface water and in equilibrium with atmosphere (380 matm), respectively.

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This paper, based on the previous studies, calculated the k0 level of water surface in each sampling reservoir using an empirical function of wind speed and temperature that has been widely used in inland water systems (Telmer and Veizer, 1999; Raymond and Cole, 2001; Yao et al., 2007; Li et al., 2012; Ran et al., 2015):

functions between GHG emissions and other environmental parameters. The results of differential and correlation analysis were considered statistically significant when p < 0.05.

k0 ¼ 2:06e0:37U  ðSc=600Þ0:5

3.1. Ancillary water quality parameters

Sc ¼ 1911:1  118:11  t þ 3:4527  t2  0:04132t3

Spatial and temporal variations of water quality parameters were showed in Fig. S1 and Fig. S2. The water temperature (T) and pH values in the surface water of the sampling reservoirs ranged from 12.6 to 32.1  C and 7.1 to 9.2, respectively, and had no significant difference among different reservoirs in the same season (p > 0.05). DO of sampled urban reservoirs showed slightly lower than forested and agricultural ones with range of 2.8e12.2 mg L1. Chlorophyll a (Chl-a), which is usually used to indicate the eutrophic state of water, was much higher in spring in this study. It was found that the conductivity (Con) of surface water in urban and agricultural reservoirs (averaged 715 ± 189 mS cm1 and 596 ± 175 mS cm1, respectively) were significantly higher than that in forested reservoirs (averaged 339 ± 64 mS cm1). Chemical parameters, including N (TN and NO 3 ) and P (TP, DTP and PO34 ), had obviously rising gradient from forested reservoirs to agricultural ones, and to urban ones (Fig. S2). Reservoir-15 and Reservoir-16 were characterized by the exceptionally high nutrient content due to the input of a great deal of sewage.

U ¼ 1:22  U1 where U is the wind speed at 10 m above the water for each reservoir; U1 is wind speed at 1 m above the water surface; Sc is temperature adjusted Schmidt numbers of CO2 (Raymond and Cole, 2001) and t is the in situ water temperature (unit in  C). 2.4. Calculation of CH4 and N2O concentrations and fluxes Concentrations of dissolved CH4 and N2O (Cw) in the water samples were calculated with the appropriate Bunsen coefficient corrected for temperature according to Yamamoto's and Wanninkhof's models (Yamamoto et al., 1976; Wanninkhof, 1992). Detailed algorithm has been presented in the Supporting Information and reference (Wang et al., 2009a). In this study, we estimated CH4 and N2O emission rates via the general two-layer model of diffusive gas exchange (Liss and Slater, 1974) which has been widely used inland water systems (Wang et al., 2009a; Kone et al., 2010; Yu et al., 2013). In this model, it is assumed that the gas exchange across the air-water interface only uses molecular processes and obeys Henry's law. Calculation of the diffusive airewater fluxes of CH4 and N2O (F, i.e., FCH4 or FN2O) were all in accordance with the equation:

F ¼ k0  ðCw  Ca Þ Cw is the gas concentration in surface water, Ca means the measured CH4 and N2O concentrations of the ambient atmospheric gas sample in each sampling site. k0 represents the gas transfer velocity and was computed from the wind-temperature-dependent models built by Raymond and Cole (2001):

k0 ¼ 1:91  e0:35U  ðSc=600Þ0:5 where the U is the wind speed at 10 m above the water for each reservoir. Sc is the Schmidt number of CH4 and N2O and was calculated using the in situ water temperature (t) (Wanninkhof, 1992):

ScðCH4 Þ ¼ 1897:8  114:28 t þ 3:2902  t2  0:039061  t3 ScðN2 OÞ ¼ 2301:1  151:1 t þ 4:7364  t2  0:059431  t3

2.5. Statistical analysis SPSS (Version17.0, SPSS Inc., Chicago, IL, USA) has been used in this study to perform the requisite statistical tests. The significant difference of mean pCO2, gas concentration and fluxes among the reservoir groups were tested using the one-way ANOVA followed by the Duncan test. Correlation analysis was conducted and Pearson correlation was used to determine the relationship of GHG concentration and fluxes to environmental factors. Moreover, we performed simple linear regression to test for the possible

3. Results

3.2. pCO2 and CO2 emissions In this study, the mean annual pCO2 in 16 sampling reservoirs were 1.6e21 times supersaturated with respect to the atmospheric equilibrium value (380 matm), indicated that those reservoirs acted as a constant source of CO2 to the atmosphere (Fig. 2a and Table S2). The difference of pCO2 among the sampling reservoirs was significant. The highest mean pCO2 was found in the Reservoir-15 (8 204 ± 7 471 matm) which is located in urban area (URG), while the lowest mean pCO2 was found in Reservoir 2 (613 ± 341 matm). Averaged pCO2 in URG (4 267 ± 2 831 matm) was significantly higher than those in FRG (1 374 ± 1 080 matm) and ARG (1841 ± 659 matm) according to one-way ANOVA (p < 0.01). A clear seasonal pattern was found; pCO2 in autumn (4 305 ± 2 390 matm) and winter (2 785 ± 4 067 matm) were markedly higher than those in spring (549 ± 543 matm) and summer (1 318 ± 648 matm). Also, we found half of the samples taken in the spring were unsaturated in CO2 with pCO2 < 380 matm. Based on the pCO2 and k0 (2.07e4.21 cm h1), the calculated CO2 flux from sampling reservoirs ranged from 7.9 ± 11.4 mmol m2$d1 in Reservoir 2 to 232 ± 215 mmol m2$d1 in Reservoir 15 with an overall mean of 65.0 ± 62.0 mmol m2$d1 (Fig. 3a and Table S3). Meanwhile, CO2 flux in URG, ranging from 10.0e510 mmol m2$d1 with average of 137.5 ± 81.0 mmol m2$d1, were significantly higher than those in FRG (11.5e172.7 mmol m2$d1, average of 30.4 ± 32.2 mmol m2$d1) and ARG (10.1e271.6 mmol m2$d1, mean of 48.2 ± 17.0 mmol m2$d1). In spring, there were 8 reservoirs with a CO2 flux rate <0 mmol CO2$m1$d1 and absorbing CO2 from atmosphere. 3.3. CH4 concentration and flux The dissolved CH4 concentration in all samples were supersaturated with respect to the atmosphere for the entire study period, and ranged from 0.06 to 1.88 mmol L1 (average 0.34 ± 0.32 mmol L1, Fig. 2b and Table S2), equivalent to a saturation ratio of 2 684%e83,668%. Significant differences of CH4

X. Wang et al. / Atmospheric Environment 154 (2017) 225e235

Fig. 2. Spatial and temporal distribution of pCO2 (a), CH4 concentration (b) and N2O concentration (c) in the surface water of the 16 reservoirs sampled during the sampling period.

concentration were found among FRG, ARG and URG (Fig. 2b). The URG had the highest CH4 concentration (averaging 0.76 ± 0.43 mmol L1), which was approximately 3 and 6 times higher than CH4 concentration in ARG (mean was 0.25 ± 0.09 mmol L1) and FRG (mean was 0.13 ± 0.03 mmol L1), respectively. The seasonal variations of CH4 concentration were not significant, however, the highest CH4 concentration was found in spring (0.55 ± 0.51 mmol L1), followed by winter (0.37 ± 0.39 mmol L1) and autumn (0.27 ± 0.29 mmol L1), with the lowest mean values in summer (0.18 ± 0.19 mmol L1). For reservoirs in this study, site-mean k0 for flux rate calculation varied little with a range of 1.58e3.43 cm h1 due to the steady winds. By using k0 values and the two-layer model, the CH4 flux from all sampling reservoirs ranged from 0.18 ± 0.07 (Reservoir 3) to 4.89 ± 3.32 mmol m2$d1 (Reservoir 16) with an overall mean of 0.94 ± 1.34 mmol m2$d1. CH4 flux rates were significantly different among the FRG, ARG and URG (p < 0.01), and averaged 0.25 ± 0.07, 0.52 ± 0.25 and 2.74 ± 1.94 mmol m2$d1 from FRG, ARG and URG, respectively (Fig. 3b and Table S3). In most sampling reservoirs, CH4 flux rates were highest in spring and lowest in summer, while those in winter and autumn were intermediate (Fig. 3b). 3.4. N2O concentration and flux The annual averaged concentrations of dissolved N2O in

229

Fig. 3. Spatial and temporal distribution of estimated GHG fluxes from the sampling reservoirs, and CO2 flux shown in (a), CH4 flux in (b) and N2O flux in (c).

sampling sites ranged from 0.012 to 0.379 mmol L1 (corresponding to saturations of 191%e6 434%) with an average of 0.087 ± 0.110 mmol L1, and showed significant differences (p < 0.05) among the sampling reservoirs (Fig. 2c and Table S2). The averaged N2O concentrations in URG (0.200 ± 0.179 mmol L1) were statistically greater than those in ARG (0.069 ± 0.38 mmol L1) and FRG (0.020 ± 0.008 mmol L1). However, some reservoirs in ARG (i.e., Reservoir 7, 8, 9, 10) had higher N2O concentrations than Reservoir 13 and 14 which were in URG. No consistent seasonal pattern of N2O concentrations was present in our study, despite strong seasonal variability of N2O concentration was shown in most reservoirs (Fig. 2c). In this study, calculated k0 for N2O flux calculations ranged from 1.46 to 4.36 cm h1, showed seasonal variation due to the temperature changes, but no significant differences among different reservoirs and reservoir groups (Fig. 3c). N2O flux from sampling reservoirs showed extreme variations from 0.002 ± 0.003 mmol m2$d1 (Reservoir 1) to 0.663 ± 0.985 mmol m2$d1 (Reservoir 16), with an average value of 0.131 ± 0.179 mmol m2$d1. The N2O flux from 4 reservoirs in URG (0.059e0.663 mmol m2$d1) was significantly higher than from reservoirs in FRG (0.002e0.031 mmol m2$d1) (Fig. 3c and Table S3). Most reservoirs in ARG also showed higher N2O flux rates (Fig. 3c).

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Table 1 Relationships between GHG (pCO2, CH4 and N2O) concentration, estimated GHG flux rate and environmental variables in the sampling sites (tested by non-parametric correlations using Pearson's rho). Letter “r” is the Pearson coefficient, “p” value is the significance. There is prominent correlativity between the two sides when the “p” value is less than 0.05. The gray zones show where the correlativity was highly significant (p < 0.01). The WT is water temperature, Cla-a is chlorophyll-a, Con is conductivity, DO is dissolved oxygen. WT pCO2 CO2 flux CH4 conc CH4 flux N2O conc N2O flux n

r p r p r p r p r p r p

0.168 0.185 0.236 0.061 0.073 0.565 0.049 0.701 0.143 0.259 0.223 0.076 64

Cla-a

pH **

0.483 0.000 0.479** 0.000 0.194 0.125 0.242 0.054 0.242 0.054 0.239 0.057 64

Con **

0.666 0.000 0.617** 0.000 0.135 0.289 0.206 0.102 0.313* 0.012 0.272* 0.030 64

DOC *

0.311 0.012 0.250* 0.046 0.445** 0.000 0.465** 0.000 0.612** 0.000 0.520** 0.000 64

0.743** 0.000 0.692** 0.000 0.215 0.087 0.337* 0.006 0.482** 0.000 0.394** 0.001 64

3.5. Relations with environmental variables Dissolved GHG concentrations and emissions of the 16 small sampling reservoirs were plotted against environmental factors. Significant relationships existed between pCO2 & CO2 flux and factors including DOC, DTP and eutrophication condition (Cla-a) (Table 1, Fig. 4) (p < 0.01), with Pearson correlation coefficient > 0.3. When using all of the pCO2 and CO2 flux data points, the simple linear regressions demonstrated that DOC, DTP and Cla-a could act as good predictors for pCO2 (Fig. 4). Correlation analysis showed that CH4 concentration and flux had close relationships to the phosphorus (TP, DTP and PO34 ), nitrogen (TN and NO 3 ) and conductivity of surface water (p < 0.01) (Table 1, Fig. 5). For N2O concentration and flux, all nutrients content (N and P), DOC and conductivity were the potential factors influencing N2O production and emission in small reservoirs (Table 1, Fig. 6). Linear best-fit equations between GHG concentrations and fluxes and potential factors were constructed and shown in Figs. 4e6 and Table S4. We used principal component analysis to analyze the relationships among all environmental variables and outputted four components that accounted for 80.5% of the total variance (Table 2). The first component, contributing to 44.3% of the variance, had several  eigenvectors (TP, DTP, PO34 , TN, NO3 and Con) and consequently been seen as predictor of water quality. The second component accounted for 20.2% of observed variance. The pH, DO, Cla-a and DOC, reflecting the water's biochemical processes and carbon content, were significantly correlated with the second component. The third and fourth components explained 8.2% and 7.2% of total

DO 0.227 0.072 0.218 0.083 0.014 0.912 0.028 0.828 0.121 0.341 0.161 0.205 64

TN 0.146 0.249 0.126 0.321 0.667** 0.000 0.678** 0.000 0.679** 0.000 0.582** 0.000 64

NO 3

NHþ 4 *

0.281 0.025 0.243 0.053 0.321* 0.010 0.481** 0.000 0.564** 0.000 0.603** 0.000 64

0.045 0.725 0.013 0.922 0.196 0.121 0.146 0.248 0.431** 0.000 0.297* 0.017 64

TP *

0.271 0.030 0.259* 0.039 0.828** 0.000 0.747** 0.000 0.695** 0.000 0.567** 0.000 64

DTP

PO34

0.333** 0.007 0.329* 0.008 0.826** 0.000 0.764** 0.000 0.763** 0.000 0.640** 0.000 64

0.319* 0.010 0.266* 0.034 0.842** 0.000 0.713** 0.000 0.703** 0.000 0.520** 0.000 64

variance respectively. Only water temperature was found to be correlated with the fourth components. According to a stepwise multiple linear regression analysis, the first component had significant influence on CH4 and N2O concentration and flux rates (p < 0.001) and could be depicted by the equations that follow. The second component was found to be the dominant influence for CO2 emission (p < 0.001). pCO2 ¼ 2 239e1 658  Component 2 (r ¼ 0.56) CO2 Flux ¼ 64.9e60.2  Component 2 þ 40.1  Component 1 (r ¼ 0.55) CH4 Concentration ¼ 340 þ 305  Component 1 (r ¼ 0.80) CH4 Flux ¼ 940 þ 1 303  Component 1 (r ¼ 0.75) N2O Concentration ¼ 86.5 þ 96.2  Component 1 (r ¼ 0.81) N2O Flux ¼ 204 þ 240  Component 1 þ 90  Component 3 (r ¼ 0.73)

4. Discussions 4.1. Urban small reservoirs acted as GHG emission hot spots As expected, the survey in this study showed that the dissolved GHG concentrations and emissions from small reservoirs draining

Fig. 4. Linear relation of estimated pCO2 with DOC (a), DTP (b) and Cla-a (c). Each pCO2 data point is calculated from three independent sampling sites.

X. Wang et al. / Atmospheric Environment 154 (2017) 225e235

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 þ 3Fig. 5. Simple linear regression analysis of estimated CH4 concentration with TN (a), NO 3 -N (b), DIN (DIN ¼ NO3 -N þ NH4 ) (c), TP (d), DTP (e), PO4 (f) for Forested, Agricultural and Urban Reservoir groups.

urban and agricultural watersheds were higher than those from reservoirs draining from predominantly forested watersheds, and that sampled urban reservoirs had consistently higher pCO2 and CH4 concentrations than agricultural reservoirs (Figs. 2 and 3).

Comparably high GHG concentrations or emissions have been reported from other eutrophic urban water bodies and water bodies influenced by sewage discharge (Silvennoinen et al., 2008; Wang et al., 2009b; Li et al., 2012; Noriega et al., 2013; Yu et al., 2013;

 þ 3Fig. 6. Simple linear regression analysis of estimated N2O concentration with TN (a), NO 3 -N (b), DIN (DIN ¼ NO3 -N þ NH4 ) (c), TP (d), DTP (e), PO4 (f) for Forested, Agricultural and Urban Reservoir groups.

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Table 2 Results of principal component analysis (PCA) for environmental variables in the surface water of the sampling reservoirs. Correlation significant at p < 0.001. The gray zones show where the correlativity was significant. Environmental variables

Component 1

Component 2

Component 3

Component 4

WT pH Con Cla-a DO TN NO 3 NHþ 4 TP DTP PO34 DOC Variance explained (%)

0.006 0.048 0.140 0.048 0.001 0.159 0.127 0.086 0.154 0.161 0.148 0.069 44.3

0.129 0.314 0.067 0.292 0.259 0.074 0.056 0.182 0.010 0.013 0.039 0.243 20.2

0.588 0.090 0.118 0.011 0.225 0.036 0.477 0.232 0.169 0.203 0.309 0.167 8.2

0.590 0.289 0.149 0.247 0.223 0.016 0.203 0.235 0.274 0.238 0.331 0.264 7.8

Burgos et al., 2015). Relatively higher GHG concentrations and fluxes in the small reservoirs in urban area were attributable to the higher pollution load, as reflected by the water quality parameters (i.e., conductivity, N and P. Fig. S1 and Fig. S2). Watershed urbanization actually has an impact on GHG concentrations in small reservoirs. In our study, the mean of pCO2 (4 267 matm) and CH4 concentrations (0.764 mmol L1) in sampled urban reservoirs were 3 and 7 times higher than those in forested reservoirs, and were higher than those in Balbina reservoir (pCO2 and CH4 concentrations were 990 matm and 0.17 mmol L1, respectively) (Kemenes et al., 2007, 2011) and Petit-Saut Reservoir (pCO2 and CH4 concentrations were 3 340 ± 802 matm and 0.081 ± 0.076 mmol L1, respectively) (Guerin et al., 2007), two tropical reservoirs. We noticed that pCO2 from urban and agricultural reservoirs were markedly higher than most of China's reservoirs, including Xinanjiang (Wang et al., 2015), Danjiangkou reservoir (Li and Zhang, 2014), Cascade reservoirs on the Maotiao River (Wang et al., 2011) and Three Gorges reservoir (Li et al., 2014) (Table S5). Moreover, the CH4 concentrations of sampled urban reservoirs in our study were in agreement with what was reported in Lake Wohlen (0.850 mmol L1), which is a small reservoir (2.5 km2) located downstream of Bern, Switzerland and three municipal sewage plants, and has been proved to be a producer of extreme CH4 emissions (Delsontro et al., 2010) (Table S6). The averaged N2O concentration in sampled urban reservoirs was 10 times as higher as that in forested reservoirs, and was ~ 4e5 times higher than those in Hongjiadu reservoir and Wujiangdu reservoir which are two subtropical reservoirs also located in southwest China (Liu et al., 2011). Compared to other reservoirs worldwide (no data from tropical reservoirs), N2O concentration in small reservoirs with urban and agricultural watersheds was much higher (Table S7). Urban small reservoirs played a significant role of GHG emission hotspot resulting from the higher GHG concentration gradients between surface water and atmosphere in this study. The estimated CO2 flux rate from urban small reservoirs (149 ± 68 mmol m2$d1) was equivalent to 3 and 5 times those from agricultural and forested reservoirs, respectively, even exceeded the range reported for other reservoirs worldwide (ranging from 7 to 103 mmol m2$d1, compilations of literature before 2010 by Barros et al., 2011, and others shown in Table S5). The calculated CH4 flux from urban small reservoirs was lower than those from two tropical reservoirs (4.0 mmol m2$d1 for Petit-Saut and 3.9 mmol m2$d1 for Balbina) (Guerin et al., 2007; Kemenes et al., 2007) mainly because our results did not consider the contribution of ebullition that has always been considered as a vitally important pathway for CH4 emissions from reservoirs with

rich organic matter (Delsontro et al., 2010). In fact, we did observe extensive ebullition in sampled urban reservoirs during the previous campaign, especially in spring (Fig. S4). Even so, the mean CH4 flux in sampled urban reservoirs were still higher than estimates from most other subtropical reservoirs (Table S6), while the averaged CH4 flux of agricultural and forested reservoirs (0.41 ± 0.23 mmol m2$d1) is in accord with that from China's hydropower reservoirs (0.33 ± 0.27 mmol m2$d1) estimated by (Li et al., 2015) and from Three Gorges reservoir (0.39 ± 0.57 mmol m2$d1) by (Chen et al., 2011). More striking, as a long-lived potent GHG with high radiative forcing, N2O emissions from the sampled urban reservoirs were much higher than most midlatitude and boreal reservoirs, and were even 3 times higher than some tropical reservoirs (Table S7). However, when excluding the Reservoirs 15 and 16 (tow reservoirs receiving waste water from the nearby settlements) from the data set, the mean N2O flux from sampled urban reservoirs (0.064 ± 0.050 mmol m2$d1) was lower than that from agricultural reservoirs, but was still higher than most of China's reservoirs (Table S7). Urbanization, especially sewage discharge and high nutrients loading, have stimulated biogeochemical processes in the small urban reservoirs, and then promoted them to critical “hotspots” of GHG emissions and urban climate change. 4.2. Potential controls on GHG emission from small reservoirs suffering anthropogenic disturbance High spatial variability of GHG emission from aquatic ecosystems is commonly related to multiple environmental factors (Wang et al., 2009b; Barros et al., 2011; Liu et al., 2011; Zhu et al., 2013; Li and Zhang, 2014; Burgos et al., 2015). The differences in the production processes of CO2, CH4 and N2O lead to their different variation pattern and potential controls. Generally, primary production (photosynthesis) and in situ respiration of organic carbon within the aquatic system have been regarded as the two main regulators for CO2 concentration in inland water (Telmer and Veizer, 1999; Cole et al., 2001; Yao et al., 2007; Wang et al., 2011; Raymond et al., 2013; Li and Zhang, 2014). Similar to another study (Li and Zhang, 2014), we found that pCO2 had strong positive correlations with DOC, but negative correlations with Chl-a (Pearson correlation coefficient were 0.743 and 0.483, respectively) (Table 1 and Fig. 4). Damming, on the one hand, retains and accumulates organic matter from upstream and land runoff, and then provides material for in situ respiration. But on the other hand, damming also creates the conditions for plankton and algae growth due to the thermal stratification, high water residence time and low turbidity (Saito et al., 2001; Wang et al., 2011), and then enhance photosynthesis and draw down

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the pCO2 in water. Consequently, the balance between photosynthesis and respiration within the reservoir may dominate the reservoir properties of ‘heterotrophy’ or ‘autotrophy’. Moreover, the input of urban sewage runoff, rich in labile organic matter and dissolved inorganic carbon (DIC) (Hosen et al., 2014; Williams et al., 2016) may be responsible for the high pCO2 in sampled urban reservoirs. In addition, pCO2 was strongly related to TP and DTP in our study (Table 1) and also in other research (Finlay et al., 2009; Li et al., 2012, 2013; Li and Zhang, 2014). The degradation of small molecule organic matter under anaerobic conditions in the sediment by methanogens (i.e., methanogenesis) and methane oxidation in an aerobic water column are two important processes to dominate the aquatic CH4 concentration and emission (Kemenes et al., 2007; Bastviken et al., 2011; Brooker et al., 2014). Therefore, scientists often deemed DOC and DO could be keys factors influencing the aquatic CH4 emission (Chen et al., 2009; Wang et al., 2009a). However, CH4 concentration and flux in this study showed weaker correlation with the DOC concentration of surface water and no correlation with DO (Table 1). In this study, P concentration in surface water was positively correlated with CH4 concentration and acted as a good predictor for CH4 emissions from small reservoirs (Table 1 and Fig. 5), in agreement with the study in Nordic boreal lakes (Yang et al., 2015), but it contradicts previous report on a freshwater marsh in the Sanjiang Plain (Song et al., 2012). One possible reason could be that high P input promoted phytoplankton production, then phytoplankton death supplied small molecule carbon to the sediment, which subsequently promoted the methanogenesis (Yang et al., 2015). Meanwhile, we also found that CH4 concentration was significantly positively related to TN, which could explain the 46% variability in CH4 concentration (Table 1 and Fig. 5). On the one hand, high N loading, especially in urban reservoirs with C enrichment, can mitigate N limitation to microbes and then improve the activity of methanogens, while on the other hand, nitrogenous salts in water column have been shown to inhibit methanotrophic bacteria via increasing osmotic pressure (Liu and Greaver, 2009). Urban sewage discharges and agricultural nonpoint pollution may change the CH4 production and emission via increasing the input of anthropogenic N and P. As supported by numerous studies, TN and NO 3 in surface water draining from agricultural watersheds serves as effective predictor of N2O production and emission (Guerin et al., 2008; Yu et al., 2013). TN and NO 3 can explain the 46% and 32% variability in N2O concentration, respectively, in this study (Table 1 and Fig. 6). While scientists have gradually realized the promotion of N enrichment on N2O emission, the role of P in influencing N2O dynamics in aquatic ecosystems has been less studied (Wang et al., 2009b; Liu et al., 2011; Chen et al., 2015). The present study found TP, DTP and PO34 were most significantly correlated to N2O emission, and explained the 49%, 59% and 48% variability in N2O concentration (Table 1 and Fig. 6). However, more detailed mechanisms of anthropogenic P loading affecting the N2O emission from reservoirs may be in existence and should be studied urgently.

Moreover, rainfall and its dilutive effect have been often considered as the important control on the pCO2 in aquatic system (Li et al., 2012, 2013; Li and Zhang, 2014). A constant rainfall lasting 22 days in June 2015 greatly contributed to the lower pCO2 (Fig. S3). Temperature was not the dominating factor for seasonal changes of pCO2 in this study, although many researchers found it positively correlated to pCO2 and CO2 emission by influencing the in situ respiration (Yao et al., 2007; Kemenes et al., 2011; Li et al., 2012; Li and Zhang, 2014). Furthermore, we found that algal bloom and constant rainfall also account for the seasonal variations of CH4 concentration. A study in wetlands showed that CH4 emissions are controlled by primary production (King et al., 2002). Evidences has shown that the residues of phytoplankton and autotrophic algae at peak growing season may supply the labile C source for methanogenesis in sediment (Silvennoinen et al., 2008; West et al., 2012; Beaulieu et al., 2014; Yang et al., 2015). Meanwhile, scientists suggested that the low CH4 emission in wet season could at least be partly attributed to the dilution by rainfall, despite a high temperature (Kone et al., 2010; Sawakuchi et al., 2014). The lower CH4 concentration and emission in September 2014 and June 2015 may be explained by the greater dilution and greater oxidation for CH4 from sediments.

4.3. Seasonal variation and influencing factors

This study investigated the GHG concentrations in 16 small reservoirs with different land use in their basins and estimated the GHG flux rate at the airewater interface using the gas diffusion model. GHG fluxes in small reservoirs in urban and agricultural areas were much higher than that from forested reservoirs due to greater input of anthropogenic C and N via sewage discharge and fertilizer loss. DOC, DTP and primary production (Chl-a) were found to have significant correlation with pCO2 in such reservoirs, while the N and P content of surface water were closely related to CH4 and N2O concentrations and could act as good predictors for CH4 and N2O concentrations and fluxes from such reservoirs. Moreover,

In this study, pCO2 in surface water showed prominent seasonal fluctuations, the lower values were found in spring (Mar-2015) and summer (Jun-2015), and the higher value was found in autumn (Sep-2014). Photosynthesis has been found to play important role in the seasonal patterns (Raymond et al., 2013; Li and Zhang, 2014; Huang et al., 2015), and may account for the lowest pCO2 value in the spring, the season which had the highest mean Chl-a (Fig. S1 and Fig. 4c) and has been considered to be the peak algal bloom season in the Yangtze River basin (Cao et al., 2011; Liu et al., 2012).

4.4. Implications In the past twelve years, scholars have gradually realized the fact that large dams constantly emit a quantity of GHG to the atmosphere (Hendzel et al., 2005; Giles, 2006; Bastviken et al., 2011; Raymond et al., 2013; Beaulieu et al., 2014). Disappointingly, the importance of GHG emissions from small dams has largely been overlooked, although there are numerous small reservoirs worldwide that receive and trap a high input of anthropogenic organic carbon and nitrogen and then play a potentially significant role in emitting GHG (Maeck et al., 2013). Moreover, human activities, particularly land use change and agricultural fertilization, have severely increased the input of anthropogenic C and nutrients for all inland water ecosystems (Yu et al., 2013; Hosen et al., 2014; Williams et al., 2016), but have been overlooked as an accelerator influencing the GHG emissions from surface water. In the present study, we did preliminary research on the GHG concentration and emissions in small reservoirs draining urban, agricultural and forested watersheds, and found significant GHG emissions from urban reservoirs, and somewhat higher GHG emissions from agricultural watersheds. Therefore such small reservoirs in urban areas should never be considered a negligible source of GHG, and human activities in the watersheds even play an even more important role on promoting GHG emissions from aquatic systems than damming itself. More studies on GHG emissions from such small reservoirs are urgently needed for assessing the GHG budget of global reservoirs. 5. Conclusion

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