Daily Variations in pCO2 and fCO2 in a Subtropical Urbanizing Lake

The transfer of CO₂ from lakes to the atmosphere is a component of the global carbon equilibrium, while the quantification of the CO₂ partial pressure (pCO₂) is critical for exploring the contribution of freshwater CO₂ emissions in the regional/global carbon budget. To investigate the daily variability of pCO₂ and CO₂ fluxes (fCO₂), we conducted in situ biweekly pCO₂ detection at 7:00, 10:00, 14:00, and 17:00 China Standard Time (CST) from Jan. to Sept. 2020 in the subtropical urbanizing Qinglonghu Lake in Chengdu, Sichuan, China. The pCO₂ during the daytime varied greatly from 8.3 to 1,061.3 μatm, with an average of 137.7 μatm, while the average pCO₂ (n = 11) clearly gradually decreased from 7:00 CST (204.9 ± 295.7 μatm) to 17:00 CST (93.5 ± 105.5 μatm). Similarly, the average fCO₂ values were −19.3 (±27.5), −24.8 (±20.7), −29.2 (±9.1) and −30.4 (±10.7) mmol m² h⁻¹ at 7:00–17:00 CST, respectively. Further, we observed a negative correlation between pCO₂ and water temperature and dissolved oxygen, but a positive correlation between pCO₂ and total organic carbon and chlorophyll a. By a systematic overview of previously published data, we also discussed the differences and uncertainties in pCO₂ and fCO₂ estimates at regional and global scales. We therefore speculate that uncertainties may exist in the contributions of CO₂ balance on lake surface in regional/global carbon budgets due to this daily pCO₂ variation.

Introduction

Since the Industrial Revolution, human activities, such as the consumption of fossil fuels and the exacerbation of land development (e.g., uncontrolled deforestation and rapid urbanization), have increased atmospheric carbon (C) by approximately 40% (Xu et al., 2019; Li Q. et al., 2020; Wang et al., 2021). From 2007 to 2016, approximately 10.7 ± 1.2 Gg C yr⁻¹ of anthropogenic carbon was released into the atmosphere globally, of which 4.7 ± 0.1 Gg C yr⁻¹ remains in the atmosphere (Le Quéré et al., 2018; Chen and Hu, 2019; León-Palmero et al., 2020). The absorption of carbon dioxide (CO₂) by the ocean (ca. 2.4 ± 0.5 Gg C yr⁻¹) has caused ocean acidification (an increase of ca. 30% acidity) or surface-water pH decline (ca. 0.1 units), resulting in a decline of marine biodiversity and ecosystem functions (Dickinson et al., 2012; Chen et al., 2017; Chen and Hu, 2019). Furthermore, CO₂ emitted into the atmosphere from aquatic ecosystems, including inland freshwater ecosystems, such as urban lakes, can greatly contribute to climate change, as verified by previous works (IPCC, 2014; Wen et al., 2017). Correspondingly, the global carbon balance and hydrological processes are rapidly becoming urgent issues in studies of anthropogenic impacts.

GRAPHICAL ABSTRACT

GRAPHICAL ABSTRACT

Inland waters are a small but crucial part of the global carbon cycle (Peter et al., 2014; Yang et al., 2019); the net carbon flux of aquatic ecosystems per unit area is greater than that of surrounding terrestrial ecosystems (Karim et al., 2011). Many publications have reported that 90% of inland lakes worldwide are supersaturated with CO₂ compared to the atmosphere and are therefore considered sources of atmospheric CO₂ (Cole et al., 1994; Karim et al., 2011). As a result, CO₂ outgassing from inland lakes represents a significant contribution to the global carbon equilibrium (Marce et al., 2015). Based on previous evaluations, CO₂ emissions from global inland lakes to the atmosphere are more than 1.4 Pg C yr⁻¹ (Li et al., 2012; Keller et al., 2020), of which 60% are from freshwater lakes and 40% are from saline lakes (Cole et al., 1994; Tranvik et al., 2009; Raymond et al., 2013). Furthermore, the level of dissolved CO₂ in inland lakes is higher than the typical level (i.e., 380–420 μatm; Sabine et al., 2004; Abril et al., 2014; Li Q. et al., 2020, Li S. et al., 2020) of atmospheric CO₂, suggesting that freshwater has the potential to degas aqueous CO₂ into the atmosphere (Li et al., 2012). Therefore, CO₂ emissions from inland lakes to the atmosphere are an important part of the global and regional carbon budget.

The surface-water CO₂ partial pressure (hereafter pCO₂) is one of the key parameters quantified in research works to better understand the changes in carbon cycling globally. In general, surface-water pCO₂ is mainly controlled by four interrelated phenomena: thermodynamic effects, physical mixing, biological activities, and water-atmosphere CO₂ inter-exchange (Yang et al., 2019; Wang et al., 2021). Aquatic environmental variables, such as surface acidity (Pardue et al., 1988), trophic states (Tonetta et al., 2014), chlorophyll level (Xu et al., 2019; Yang et al., 2019), and water temperature (Marotta et al., 2009; Kosten et al., 2010), and other parameters, such as dissolved oxygen (DO), wind speed and solar radiation (Marce et al., 2015; Chen et al., 2017), are closely related to these four phenomena and might cause fluctuations in the spatial-temporal variability of pCO₂ and CO₂ evasion. Regarding inland freshwater systems, numerous studies have been conducted to investigate pCO₂ and CO₂ evasion in large rivers (with widths greater than 100 m), such as the Amazon (Richey et al., 2002; Abril et al., 2014), Mississippi (Crawford et al., 2016), Yangtze (Li et al., 2012; Liu et al., 2017) and Yellow Rivers (Ran et al., 2015). The results have revealed that the concentrations of CO₂ in these rivers are higher than those in the overlying atmosphere (i.e., that these rivers exhibit CO₂ supersaturation), suggesting that these rivers are the “source” of CO₂ in the atmosphere (Yoon et al., 2017; Li S. et al., 2020). Unlike the conditional conversion between the “source” and “sink” roles of marine ecosystems (Sabine et al., 2004; Yan et al., 2018), most inland lakes/reservoirs function as net “sources” of CO₂ because they are heterotrophic systems, similar to rivers (Cole et al., 1994; Richey et al., 2002; Gu et al., 2011), while some small productive lakes as carbon “sinks” (Maier et al., 2021 and related refs.). Currently, the temporal variability of CO₂ in lakes/streams is taken into account in many studies, which find similar relationships between photosynthesis and remineralization in response to variations in solar insolation (Alin and Johnson, 2007; Callbeck et al., 2010; Marotta et al., 2010; Tonetta et al., 2014). However, lots of the field data collected for CO₂ evasion estimation have low temporal resolution, such as weekly to quarterly, and therefore ignore the diurnal variation in pCO₂ and thus the CO₂ flux.

In addition, aquatic pCO₂ is markedly affected by photosynthesis (P) and respiration (R), which are driven by temperature and radiation in different geographic regions (Marotta et al., 2010; Tonetta et al., 2014; Xu et al., 2019). For example, the average CO₂ flux in the headwater catchment area of the Alaska Highlands, in Arctic tundra, is 5.1 μmol C m² s⁻¹ (Crawford et al., 2013), while the average CO₂ emissions in the two reservoirs in Spain (Guadalcacín and Bornos), in the Mediterranean climate, are between 5.6 and 34.7 μmol C m² s⁻¹ (Morales-Pineda et al., 2014). Accordingly, the influences of diel pCO₂ changes on the estimation of CO₂ degassing and uncertainties in these estimates remain unclear, although the estimation of CO₂ emissions on a regional/global scale has been strengthened through many studies. Thus, to reduce the uncertainty of CO₂ evasion estimation, we need to consider the changes in air-water pCO₂ and CO₂ flux (i.e., fCO₂) over the span of a day, thereby limiting these factors that cause data scatters and variances.

There is still a knowledge gap regarding the daily change in pCO₂ and fCO₂, while previous studies mainly focused on weekly to quarterly data. Based on the aforementioned background, an in situ investigation of the daily changes in pCO₂ and fCO₂ in a subtropical urbanized lake (named Qinglonghu Lake) was conducted in Chengdu, Southwest China. Specifically, the objectives of our study are to 1) monitor fortnightly the water-air pCO₂ and related environmental parameters during the daytime from Jan. to Sept. 2020; 2) evaluate fCO₂ by employing the measured pCO₂ and discuss the uncertainties in CO₂ evasion estimation; and 3) explore the roles of environmental factors and the in-lake CO₂ exchange behaviours. The findings will significantly improve our understanding of the mechanisms of daily pCO₂ variations and increase the accuracy of CO₂ evasion estimations in freshwater environments.

Methods

Site Description

This study was conducted at Qinglonghu Lake (longitude 104°11′14″ E, latitude 30°38′26″ N) in Chengdu, Southwest China (Figure 1). The lake is located in the Round-the-city Ecological Zone of Chengdu, which is the largest urban wetland in the city and is used for the construction of a lake-forest system. The lake has an open water surface of approximately 82 ha, a circumference of 12 km, and a depth ranging from 0.5 to 6.0 m (Li and Cai, 2019). The lake was irrigated from the Dongfeng Canal in April 2008 and officially opened in Jan. 2016.

FIGURE 1

FIGURE 1. Geographical location of Qinglonghu Lake in the city of Chengdu, Sichuan, Southwest China. Ws represents the location of water sample collection and on-site dynamic monitoring. The above is a modified graph based on the Google^® Maps (https://www.google.cn/maps).

The study region has a humid subtropical climate with a long frost-free period and abundant rainfall; the average annual air temperature is 16.5°C, with the lowest air temperature in January (4.6°C) and the highest air temperature in July or August (∼37.5°C). During the 9-months investigation period, the average daily air temperature around the lake was −5 to 30°C, with the highest air temperature in August and the lowest air temperature in January (Figure 2A). Moreover, the annual average rainfall is approximately 900 mm. Less precipitation occurs in spring, but more precipitation occurs in summer (e.g., ∼200 mm in Aug. 2020), which can easily cause floods. The annual average number of solar radiation hours is approximately 1,032, and the number of radiation hours from April to August was higher than that from January to March during the study period (Figure 2B).

FIGURE 2

FIGURE 2. Daily air temperature and precipitation ((A); mm d⁻¹) and radiation ((B); h d⁻¹) during the study period in Qinglonghu Lake. The hollow circles represent the dates of sampling and monitoring of water samples. The data in the above figures have been authorized by the National Meterological Data Center of China (https://data,cma,cn/) via the authors’ emails.

Field Measurements

In this study, we conducted biweekly field trips from Jan. to Sept. 2020 to measure pCO₂ and related water quality parameters. Based on previous full-scale investigations, an open site near the lake outflow was selected for water sample collection and parameter monitoring (Figure 1). During each trip, in situ measurements were taken at 7:00, 10:00, 14:00, and 17:00 China Standard Time (CST) at approximately 2 m from the lake shore, which was based on previous studies such as Perkins et al. (2015), Xu et al. (2019), and Yang et al. (2019). All measurements were carried out at the same location. To minimize the influences of rainfall/runoff and avoid the potential bias in measurements, all trips were made on sunny days to avoid weather conditions such as rainfall, winds and runoff by adjusting the timing. All on-site detected parameters (including pH, water temperature (t_water), transparency (TPC), turbidity (FNU), electrical conductivity (EC), total dissolved solids (TDS), DO, bicarbonate (HCO₃⁻; BCB) and carbonate (CO₃²⁻; CB) were measured at a depth of 30–50 cm below the water surface, and recordings were taken after the instrument reading had remained stable for 3–5 min.

Among the detected parameters, TPC was measured using a black-and-white disc with a diameter of 200 mm, while FNU was monitored using a TSS portable turbidity meter (HACH, TSS Portable, Danaher, United States). The other parameters, including pH, EC, TDS, and t_water were measured by a Hanna multiparameter pen-type detector (Hanna-HI9829, Hanna Instruments Co., Ltd., Italy). The DO and DO saturation (DO%) were monitored using a Hanna-HI98186 portable high-precision DO meter.

Unlike oceanography, there is currently no consistent definition of an appropriate method for measuring or calculating pCO₂ in freshwater. There are direct and indirect methods for measuring pCO₂ using acidimetric titrations. An indirect method that greatly simplifies the alkalinity determination procedure by titrating to pH 4.5 and has been widely used, especially for freshwater with pH > 7 and low organic carbon, i.e., pCO₂ is calculated via pH/alkalinity in particular (Telmer and Veizer, 1999; Butman and Raymond, 2011; Wang et al., 2011). Moreover, currently direct measurements of water pCO₂ remain scarce in freshwater, and most published pCO₂ data are calculated from temperature, pH and total alkalinity using the above indirect method (e.g., Li et al., 2013; Abril et al., 2015; Varol and Li, 2017; Xiao et al., 2020; Ni et al., 2021). As we know, the alkalinity and pH are critical for pCO₂ calculations, in particular, a pH difference of 0.005 unit could affect pCO₂ calculations (Li et al., 2013). Therefore, we carefully scrutinized the values as follows: 1) the pH probes were carefully calibrated, 2) the in situ pH values were recorded when the pH stabilized in 0.01 units for 3–5 min, and 3) three replicates for individual samples were determined for alkalinity and the mean values were taken. Our investigation found that our studied freshwater lake has the pH from circumneutral to basic (∼8.8) and the alkalinity exceeding 1,000 μmol L⁻¹ (Abril et al., 2015), suggesting that the calculation of pCO₂ from pH, alkalinity and temperature is effective reliability. Following the previous classic Eq. 1, therefore, pCO₂ was calculated by detecting the pH, bicarbonate, ion concentrations and Henry’s law constant (K_h) of water (Telmer and Veizer, 1999).

$\mathrm{C}{\mathrm{O}}_2+\mathrm{O}{\mathrm{H}}^{-}+{\mathrm{H}}^{+}\iff {\mathrm{H}}_2\mathrm{C}{\mathrm{O}}_3^{\ast }\iff {\mathrm{H}}^{+}+\mathrm{H}\mathrm{C}{\mathrm{O}}_{3^{-}}\iff 2{\mathrm{H}}^{+}+\mathrm{C}{\mathrm{O}}_3^{2-}(1)$

Thus, we collected 100 ml of water samples into a 250 ml Erlenmeyer flask and then added 4 drops of phenolphthalein indicator. We also employed an HCl standard solution to titrate the solution until it became just faded or colourless and then recorded the amount of standard solution (defined as P) after it turned red. If the solution was colourless after adding the phenolphthalein indicator, it was considered unnecessary to titrate the HCl standard solution again. Subsequently, we continuously added 3 drops of standard methyl orange indicator into the Erlenmeyer flask, standardized the flask with the HCl standard solution until it turned from orange-yellow to orange-red, and then recorded the amount of standard solution (defined as M). Accordingly, the total consumption of HCl standard solution (defined as T) in the water sample was calculated as follows Eq. 2:

$\mathrm{T}=\mathrm{M}+\mathrm{P}(2)$

According to the different values of P (i.e., P = T, p > 1/2T, P = 1/2T, p < 1/2T, P = 0), the following calculation Eqs. 3, 4 of CB and BCB were employed:

$\mathrm{CB}=\left[2\times P\times {\mathrm{C}}_{\mathrm{H}\mathrm{A}}\times 30.005\right]/\mathrm{V}\times 1000(3)$

$\mathrm{BCB}=\left[\left(\mathrm{M}-\mathrm{P}\right)\times {\mathrm{C}}_{\mathrm{H}\mathrm{A}}\times 61.017\right]/\mathrm{V}\times 1000(4)$

where C_HA represents the concentration of HCl standard solution (mol L⁻¹) and V represents the water sample volume (ml). In this study, C_HA was 0.025 mol L⁻¹, while V was 100 ml. In addition, (30.005) indicates the mass of CO₃²⁻ in grams (g) equivalent to 1.00 ml of HCl standard solution (C_HA = 1.00 mol L⁻¹), while (61.017) indicates that of HCO₃⁻ in grams (g).

After completing the on-site data collection, we collected water samples using a homemade polyethylene grab sampler at 7:00, 10:00, 14:00 and 17:00 CST on each trip for laboratory analysis. Briefly, 500 ml water samples were collected, and 0.5 ml MgCO₃ suspension (1%) was added for the determination of water chlorophyll a (Chla). The lake water (20 ml) was filtered with a microporous membrane (50 mm × 0.45 μm; Jiangsu Green-Union Scientific Instrument Co., Ltd., Jiangsu, China) and then stored in polyethylene bottles to determine total carbon (TC) and inorganic carbon (IC). In addition, we collected 50 ml water samples and filtered them with a microporous membrane for anion (i.e., fluoride/F⁻, chloride/Cl⁻, sulfate/SO₄²⁻, and nitrate/NO₃⁻) detection. All samples were stored in acid-washed high-density polyethylene bottles that were capped tightly and placed in cooling vessels with enough ice during transportation.

Laboratory Analyses

The Chla in water samples was detected using the acetone method following a previous protocol (The National Environmental Protection Agency, 2002). Briefly, a quantitative volume of water sample was poured onto the suction filter with a fibre membrane for complete suction filtration. The filter membrane with phytoplankton was then removed, dried at a low temperature in a refrigerator for 8 h, and fully ground with MgCO₃ powder and 2 ml of 90% acetone to extract Chla. The extraction solution was then centrifuged at 3,500 × g for 10 min. The supernatant was ground with 2 ml of acetone, centrifuged again for 10 min, and then transferred/diluted to 10 ml with acetone. Finally, the treated supernatant was detected by using an UV1901PCS ultraviolet spectrophotometer (Youke Instruments, Shanghai, China) at 750, 663, 645, and 630 nm, with 90% acetone as a blank. The Chla concentration (mg m⁻³) was calculated following formula Eq. 5:

$\mathrm{Ch}1a=\left\{\left[11.64\times \left(D_{663}-D_{750}\right)-2.16\times \left(D_{645}-D_{750}\right)+0.10\times \left(D_{630}-D_{750}\right)\right]\times V_1\right\}/V\times \mathrm{CL}(5)$

where V is the volume of the water sample (L), D is the absorbance, V₁ is the constant volume of the extraction solution, and CL is the optical path of the cuvettes (cm).

Four anions were determined by ion chromatography following the National Environmental Protection Standards of China (HJ 84-2016). Briefly, the water samples were filtered through a 0.45 μm microporous membrane to determine the standard curves (F⁻, R² = 0.9996; Cl⁻, R₂ = 0.9991; NO₃⁻, R² = 0.9998; SO₄²⁻, R² = 0.9999). The concentrations (mg L⁻¹) of the four anions were then detected using IC-2800 ion chromatography (Beijing Dongxi Analytical Instrument Co., Ltd., Beijing, China).

To analyze trophic states, the other parameters such as NT (nitrate; Supplementary Methods S1), TP (total phosphorus; Supplementary Methods S2), TDN (total dissolved nitrogen; Supplementary Methods S3) and eutrophication evaluation [TLI (∑); Supplementary Methods S4] also were evaluated.

TC and IC were analyzed using a Total Organic Carbon Analyser (TOC-L CPH Basic System; Shimadzu Co., Kyoto, Japan). Briefly, 0–50 ml of organic carbon (i.e., potassium hydrogen phthalate), inorganic carbon (i.e., sodium carbonate/bicarbonate) and KNO₃ standard solution were added to 50 ml colorimetric tubes and then diluted. The standard curves (IC: R² = 1.0000; TC: R² = 1.0000; see Supplementary Figure S1) were measured by using the TOC-L CPH analyser. Before detection, these samples were bubbled with nitrogen to remove inorganic carbon (CO₂) (Kortelainen, 1993; Bisutti et al., 2004). Moreover, a filter was used when the tested water samples contained insoluble particles, and the pore size of the filter membrane was ≤60 µm. During the detection, to ensure that no-air (CO₂) entered, the sampling tubes were submerged below the liquid surface, and the nozzle of the tube was placed at about 1/3 of the height of the solution near the bottom of the container. The calculations of TC and IC (mg L⁻¹) were based on the peak height of the absorption peak, which subtracts the correction value of the peak height from the blank test absorption peak. Accordingly, the total organic carbon (TOC) concentration (mg L⁻¹) was estimated using Eq. 6

$\mathrm{TOC=TC-IC}(6)$

pCO₂ and CO₂ Flux Calculation

Detection of pCO₂

Previous studies have confirmed that dissolved inorganic carbon in water is composed of HCO₃⁻, CO₃²⁻, H₂CO₃, and dissolved CO₂. When the aqueous solution is in equilibrium, the concentration of each component is related to the pH, temperature and ionic strength of the water (ref. Cole and Caraco, 1998; Telmer and Veizer, 1999; Yao et al., 2007; Abril et al., 2015). Accordingly, in our work, based on the pH, HCO₃⁻, CO₃²⁻, K_h and ions in water, the water-air pCO₂ was calculated employing the CB equilibrium model (see Supplementary Methods S5 in detail). Based on the Henry’s Law, pCO₂ (μatm) is calculated by the following Eq. 7:

$p\mathrm{C}{\mathrm{O}}_2=\left[{\mathrm{H}}_2\mathrm{C}{\mathrm{O}}_3^{\ast }\right]/K_{CO2}=\alpha ({\mathrm{H}}^{+})·\alpha \left(\mathrm{H}\mathrm{C}{\mathrm{O}}_3^{\ast }\right)/\left(K_{CO2}·K_1\right)(7)$

where α (H⁺) and α (HCO₃⁻) represent the ion activities of [H⁺] and [HCO₃⁻], respectively (Eqs. 8, 9), while I represents the ionic strength Eq. 10.

$\alpha \left({\mathrm{H}}^{+}\right)={10}^{-\left[\mathrm{pH}\right]}(8)$

$\alpha \left(\mathrm{H}\mathrm{C}{\mathrm{O}}_3^{-}\right)=\left[\mathrm{H}\mathrm{C}{\mathrm{O}}_3^{-}\right]\times {10}^{-0.5\sqrt{I}}(9)$

$I=0.5\left(\left[{\mathrm{K}}^{+}\right]+4\left[\mathrm{C}{\mathrm{a}}^{2+}\right]+\left[\mathrm{N}{\mathrm{a}}^{+}\right]+4\left[\mathrm{M}{\mathrm{g}}^{2+}\right]+\left[\mathrm{C}{1}^{-}\right]+4\left[\mathrm{S}{\mathrm{O}}_4^{2-}\right]+\left[\mathrm{N}{\mathrm{O}}^{3-}\right]+\left[\mathrm{H}\mathrm{C}{\mathrm{O}}_3^{-}\right]\right)(10)$

Calculation of fCO₂

CO₂ diffusion at the water-air interface is affected by factors such as the difference in pCO₂ between the atmosphere and water, temperature, salinity, and wind speed. Therefore, the calculation of water-air fCO₂ in our study was based on the stagnant-layer model as follows Eq. 11 (ref. Cai and Wang, 1998; Bade and Cole, 2006; see Supplementary Methods S6 in detail):

$f\mathrm{C}{\mathrm{O}}_2=K_T{K}_H\left[p\mathrm{C}{\mathrm{O}}_{2\left(\mathrm{water}\right)}-p\mathrm{C}{\mathrm{O}}_{2\left(\mathrm{air}\right)}\right](11)$

where fCO₂ indicates the flux of CO₂ at the water-air interface (mmol m² h⁻¹), K_H indicates the solubility of CO₂ at a certain temperature (mol L⁻¹ atm⁻¹; ref. Weiss, 1970), K_T represents the gas exchange rate of CO₂ (cm h⁻¹; ref. Katul and Liu, 2017). Moreover, K_T was converted from the standardized Schmidt number of 600 (K₆₀₀) according to the following Eq. 12 (Jahne et al., 1987):

$K_T=K_{600}\times {\left(\frac{600}{S{c}_{CO2}}\right)}^n(12)$

where n is the Schmidt number exponent that depends on the surface state of the water. n is 0.50 when the wind speed exceeds 3.7 m s⁻¹ and 0.75 when the wind speed is lower than 3.7 m s⁻¹ (Guérin et al., 2007). Based on the findings of Cole and Caraco (1998), the Schmidt number is taken as 0.67 under normal circumstances. [Sc_CO2] represents the Schmidt number of CO₂ at a given temperature (t) (Wanninkhof, 1992). The following Eq. 13 was used to obtain the value of K₆₀₀ (Vachon and Prairie, 2013):

$K_{600}=2.07+0.215U_{{10}^{1.7}}(13)$

where U₁₀ is the wind speed normalized to a height of 10 m above the water surface at the sampling time (m s⁻¹; ref. Cole and Caraco, 1998). The data were compiled from the database of the National Meteorological Science Data Center of China (http://data.cma.cn/, Chengdu, Sichuan). This observation station is located in Longquanyi, Chengdu, which is close to the studied lake (less than 5 km away). The data were authorized by the application and were selected based on the average maximum value of 10 min of measurement on the sampling day.

Data Analysis

In this study, all statistical analyses were performed by IBM-SPSS Statistics software (IBM Corp., Armonk, New York, United States) from the contribution software platform provided (http://ms.sicau.edu.cn/soft/detail/52) for Tukey’s tests at the significance level of 0.05. The figures constructed in the present study, including Figures 4, 6, were generated using SigmaPlot 14.0 (Systat Software Inc., San Jose, California, United States).

Results and Discussion

The Sink-Source Behaviours of CO₂ in the Mesoeutrophic Urbanizing Lake

As urbanization has accelerated in recent years, anthropogenic waste emissions have increased sharply, which has inevitably caused the deterioration of water quality in urbanizing lakes, the changes in hydrological processes, and the destruction of the water-nutrient-carbon cycle (Li S. et al., 2020). Therefore, studying the carbon dynamics of lakes and their CO₂ exchange behaviour is of great significance for understanding the carbon emissions of urbanizing lakes in developing countries.

In the present study, our field investigations showed that on all most of the sampling days from Jan. to Sept. 2020 except April 5 (with the highest pCO₂ at 14:00 CST; Figure 3B), May 6 (with the lowest pCO₂ at 10:00 CST; Figure 3C) and Sept. 5 (with the highest pCO₂ at 14:00 CST; Figure 3K), the concentrations of pCO₂ significantly decreased from early morning (at 7:00 CST) to late afternoon (at 17:00 CST). Among the daily pCO₂, the lowest values occurred between 14:00 and 17:00 CST, and the highest occurred between 7:00 and 10:00 CST (Figures 3A–L, 4). Furthermore, the average pCO₂ (n = 11) showed a clear and gradually decreasing trend from 7:00 CST to 17:00 CST, with average decreases rate of 39.0% from 7:00 (240.9 ± 295.7 μatm) to 10:00 CST (146.9 ± 220.5 μatm), 28.3% from 10:00 to 14:00 CST (105.4 ± 85.3 μatm), and 11.3% from 14:00 to 17:00 CST (93.5 ± 105.5 μatm) (Figure 4A). These findings revealed that this studied lake could be a sink of CO₂ throughout the season from January to September (except for August 19), while a sink-source phenomenon was discovered in Capitol/University Lake in central Louisiana, United States (Xu et al., 2019; Yang et al., 2019).

FIGURE 3

FIGURE 3. Hourly measurements of water pCO₂ and estimation of fCO₂ in Qinglonghu Lake. (A–K): The dots represents the pCO₂ (solid line) and fCO₂ (dotted lines) at each sampling time (n = 1). (L): The dots represent the average pCO₂ (solid line) and fCO₂ (dotted line) of all measurements (n = 11) at each sampling time during the study.

FIGURE 4

FIGURE 4. Hourly and monthly changes in water pCO₂ (A,C) and fCO₂ (B,D) at different sampling times Jan. to Sept. 2020 in Qinglonghu Lake. The black line, red line, lower edge, upper edge, bars, dots in or outside the boxes refer to median and mean values, 25^th and 75^th, 5^th and 95^th, and <5^th and >95^th percentiles of all data, respectively. Figures A and B were drawn based on the data of all measures values (n = 11) at each sampling time during the study period, while Figures C and D were drawn based on the data of all measurements (n = 4, 8) in each sapmling month.

Interestingly, the pCO₂ on the sampling dates other than August 19 was below 400 μatm (Figure 3), although the pCO₂ values in the mornings were higher than those in the afternoons (Figure 5), which may be due mainly to the large amount of atmospheric precipitation (i.e., 80–200; Table 1) compared to that in our previous study (0–85 mm of rainfall; Yang et al., 2019). Studies showed that precipitation affects aquatic pCO₂, mainly due to its impact on soil respiration and carbon transport (Hope et al., 2004; Li et al., 2017). After short-term atmospheric precipitation, the biodegradation and decomposition of the labile fractions toward the top soil layers via runoff (Ran et al., 2015) are also important sources of dissolved CO₂. However, for the lake area during the dry season, long-term and high-intensity floods offset the contribution of soil CO₂ erosion input (Luo et al., 2019), thereby significantly diluting the control of lake pCO₂ and CO₂ exchange. Further, many previous studies have confirmed that precipitation stimulates complex chemical reactions at the water-air interface. The principle of these processes can be explained by the classic carbonate equilibrium equation as follows Eq. 14:

$\mathrm{C}{\mathrm{O}}_2+{\mathrm{H}}_2\mathrm{O}\leftrightarrow {\mathrm{H}}_2\mathrm{C}{\mathrm{O}}_3\leftrightarrow {\mathrm{H}}^{+}+\mathrm{H}\mathrm{C}{\mathrm{O}}_3^{-}\leftrightarrow 2{\mathrm{H}}^{+}+\mathrm{C}{\mathrm{O}}_3^{2-}(14)$

FIGURE 5

FIGURE 5. Differences in average pCO₂ (A), fCO₂ (B), t_water (C), and DO (D) between the morning hours (7:00–10:00 CST) and the afternoon hours (4:00–17:00 CST). The rectangular bars represent the average values of all measurements at two tome points, while the error bars represents the standard deviations (SDs) among a total of 24 testing results. Different lowercase letters indicate that the mean values are significantly different between morning and afternoon hours at p < 0.005 according to Turkey’ tests.

TABLE 1

TABLE 1. Hourly changes in water quality parameters in Qinglonghu Lake.

When rainwater with a pH value lower than the lake water falls on the lake surface, the amount of free ion H⁺ greatly increases, causing the balance of carbonates existing in the water to be disturbed and moving to the left. Therefore, in the case of excessive H⁺, especially acid rain in urban areas, an extra amount of CO₂ will be produced in the surface water. Furthermore, atmospheric precipitation represents a certain chemical pressure, which depends on the quasi-stable daily rhythm of CO₂ consumption and emission by aquatic organisms. Once the CO₂ content in the surface water is excessive for a short period of time, the biota cannot respond quickly to this change, causing most of the gas to be discharged into the atmosphere. These findings have been confirmed by previous studies on the influence of atmospheric precipitation on the changes in CO₂ content at the water-air interface of the Lake Baikal in southern Eastern Siberia, Russia (Domysheva et al., 2007), University Lake in Baton Rouge, Louisiana, United States (Xu et al., 2019) and other lakes (Hastie et al., 2018 and related refs.). Accordingly, in our study, precipitation on the lake surface facilitated CO₂ exchange and can be regarded as an additional source. Besides, because the sampling site is relatively open, the overall wind speed is relatively high; this wind accelerates the exchange of water-soluble gases with the atmosphere, decreasing the pCO₂ at the water-air interface. In agreement with this study, previous publications have reported that the regression between pCO₂ and wind speed was the largest with a no-time-lag negative correlation (Morales-Pineda et al., 2014), suggesting that the acceleration of CO₂ gas-water exchange, as the main process, promoted the decrease in pCO₂ (Podgrajsek et al., 2015; Shao et al., 2015). Therefore, combining the daily CO₂ dynamic information from atmospheric precipitation and wind-speed improves the accuracy of CO₂ estimation.

In our study, the daily average pCO₂ for the whole period (i.e., Jan. to Sept. 2020) was 108.2 ± 100.8 μatm; at the monthly level, the highest daily average pCO₂ was August (308.5 ± 407.4 µatm), while the lowest was July (23.3 ± 16.4 µatm) (Figure 4C). Correspondingly, the levels of daily average fCO₂ decreased from 7:00 to 17:00 CST (from −19.3 ± 27.5 to −30.4 ± 10.7 mmol m² h⁻¹) (Figures 3, 4), while the daily average fCO₂ ranged from −10.4 (±38.5) mmol m² h⁻¹ in Aug. 2020 to −27.3 (±7.2) mmol m² h⁻¹ in April 2020, with an average fCO₂ of −27.3 (±10.7) mmol m² h⁻¹ (Figures 4B,D). These lower values indicate that the biological productivity at 1.0 m depth far surpassed CO₂ dissolution into the water. Similarly, a previous work regarding lake CO₂ evasion in central Louisiana, United States, showed that lake water can serve alternately as a seasonal source-sink of CO₂ (Xu and Xu, 2015). However, the global size distribution of lakes and ponds is a key source of uncertainty in calculating gas exchange and its contribution. Holgerson and Raymond (2016) analyzed the CO₂ concentration of 427 lakes/ponds with a surface area ranging from 2.5 m² to 674 km². The results showed that very small ponds accounted for 8.6% of the global lakes/pond area, but accounted for 15.1% of CO² emissions, and decreased with the increasing in the area of these lakes, which may be due to shallow water, high sediment and edge-to-water volume ratios, and frequent mixing (Kankaala et al., 2013; Holgerson, 2015). Therefore, whether the seasonal source-sink of lakes contribute to the global carbon budget depends on the flux (grams of CO₂ per unit area) and the relative contribution of such lake systems to the global lake area, which needs further investigation against our studied lake.

Many previous studies have confirmed that the two main biogeochemical processes influencing pCO₂ changes in freshwater systems are photosynthesis (P) and respiration (R) (Alin and Johnson, 2007). In this study, according to the variability of daily pCO₂ and fCO₂ (Figures 3, 4), the CO₂ dynamics in the mesoeutrophic urbanizing lake were mainly driven by P during the daytime and impacted by the balance of P and R (Tranvik et al., 2009; Karim et al., 2011). Similar findings have been reported in our previous works (Xu et al., 2019; Yang et al., 2019) and in other studies of aquatic systems (Peng et al., 2012; Tonetta et al., 2014; Wang et al., 2021). In general, the P and R processes follow a circadian rhythm; that is, C fixation is limited to the day, but R occurs through the whole 24 h cycle (Schelske, 2006). Furthermore, strong negative correlations between DO and both pCO₂ and fCO₂ occurred during the daytime (i.e., −0.482^** for pCO₂/DO and −0.502^** for fCO₂/DO) in this study (Table 2, Supplementary Table S1), suggesting the pCO₂ on the water surface mainly caused mainly by R is higher than the atmospheric CO₂ when P is less than R (i.e., P:R < 1.0). In other words, this process is heterotrophic. In contrast, pCO₂ in autotrophic ecosystems is lower than CO₂ in the atmosphere (Gu et al., 2011). When P: R > 1.0, the lake may be supersaturated with CO₂. Previous studies showed that the inorganic carbon loading is the main factor influencing dissolved CO₂ concentration in lakes/reservoirs in the United States (Mcdonald et al., 2013). Further, water temperature could cause the concentrations of dissolved gases to rise, and the daily trends of pCO₂ and DO can become similar because both gases become more difficult to dissolve at higher temperatures. However, in the present work, pCO₂ and DO exhibited opposite responses to water temperature changes (Table 2, Supplementary Table S1; Figures 5A,D), indicating that the main force influencing dissolved gas concentration is the balance between P and R (Xu et al., 2019).

TABLE 2

TABLE 2. Comprehensive nutritional status index TLI (∑) of Qinglonghu Lake.

Studies have shown that lakes in temperate regions could be considered heterotrophic when the concentration of dissolved organic carbon is higher than 6.0 mg C L⁻¹ (Carignan et al., 2000). It is generally believed that due to heterogeneous loading of organic matter, pCO₂ increases as the organic carbon in lake water increases (Sobek et al., 2005; Xu et al., 2019). In this study, a similar positive correlation between TOC and pCO₂ was detected at all water sampling points (Supplementary Table S1), indicating that 7:00 CST may be the transition point between P and R. Moreover, a significantly positive relationship between TOC and Chla (p <0.05) was observed, indicating the TOC in this productive lake mainly sources from autochthonous production (Figure 6A–C, Supplementary Figure S5). In accordance with the results of Gu et al. (2011), the aforementioned result indicated that this phenomenon may be related to the consumption and decomposition of chlorophyll-containing materials overnight and the daytime production of aquatic organisms, which further supports the conclusion that major biological processes are controlling factors.

FIGURE 6

FIGURE 6. Monthly changes in water TC, IC, and TOC in Qinglonghu Lake. The symbol of the box diagrams is similar to Figure 4. The above figires were made based on the data of all measurements (n = 4, 8) in each sampling month.

Main Environmental Variables Contributing to the Dynamics of CO₂

A previous investigation by Sobek et al. (2003) confirmed that TOC and pCO₂ are co-variable in boreal lakes, which is interpreted as in-lake R of mainly terrestrial derived from TOC or DOC. However, in addition to the formation of aquatic R of pCO₂ in the lake sediments, pCO₂ supersaturation on the water surface may be the result of terrestrial R, by inputting groundwater with supersaturated pCO₂ from soil R (Worrall et al., 2005; Stets et al., 2009). In this study, the CO₂ dynamics during the daytime caused by pCO₂ changes were mainly P and were driven by the availability of sunlight and nutrients, while R was regulated by water temperature and organic loading (Yvon-Durocher et al., 2010; Yang et al., 2019; Wang et al., 2021). However, current discussions on whether lakes are oversaturated have put forward to various hypotheses. For example, Weyhenmeyer (2008) et al. proposed that if the aquatic R of TOC is the main factor for predicting pCO₂ supersaturation along aquatic conduits, the observed increase in dissolved organic carbon (DOC) in many old/new rivers of northern latitudes may lead to an increase in CO₂ outflow. The other scholars speculated that if a large part of aquatic pCO₂ can be related to terrestrial R transferring to aquatic conduits through groundwater input, the conclusion that TOC is the main causal predictor of CO₂ supersaturation in boreal lakes will need to be reconsidered (Algesten et al., 2004; Sobek et al., 2005). Comprehensively, Humborg et al. (2010) has verified, in the Swedish watershed, CO₂ supersaturation along aquatic conduits is limited by terrestrial/aquatic R and weathering. Therefore, the mechanisms causing over- or under-saturation in freshwater lakes are complicated, which needs to be studied from multiple carriers and perspectives.

Previous works showed that productive systems are more likely to be CO₂ sinks (Marotta et al., 2010; Yang et al., 2019), while a higher nutrient could promote the biological activity of aquatic P and then leading to more CO₂ absorption (Cole and Caraco, 2001). Based on the detection of Chla, TP, TN and TPC, the nutritional status index [i.e., TLI (∑)] of this urbanizing lake was 63.15 (Table 2, Supplementary Figure S2), suggesting that a variable relationships between pCO₂ and N/P was found (Supplementary Table S2, Supplementary Methods S4; Li et al., 2018), which thus was defined as a middle-eutropher lake. The average DO and TP of this lake (including four anions; Supplementary Figure S6, Supplementary Table S3) were recorded as 8.43 (Supplementary Figure S3) and <0.01 mg L⁻¹ (Supplementary Figure S2), respectively, and therefore being classified as class I abiding by the national standard (GB 3838-2002). For lakes with different nutrients, the diel CO₂ changes are different. For example, the dynamic changes in CO₂ in a typical tropical productive lake were highly dependent on biological metabolism during the diurnal cycle (Reis and Barbosa, 2014), while no significant diel changes in fCO₂ were found in oligotrophic lakes (Morin et al., 2018). In addition, diurnal changes in CO₂ and related parameters in lakes with different nutrients change seasonally (e.g., Supplementary Figure S2–S4). In this study, the daily variations in pCO₂ on April 5 and Sept. 5 2020, were opposite each other (Figure 3). Similarly, during the peak period of Chla, the diurnal changes in August and September were obvious, whereas no obvious temporal changes occurred in the other months (Shao et al., 2015).

Besides, changes in lake CO₂ are also closely related to temperature and pH. In this study, a weak negative correlation between water temperature and daily pCO₂ was observed (Table 2), indicating an increase in gas solubility and a decrease in CO₂ absorption at low temperatures. The diurnal changes in pCO₂ affected by water temperature were therefore usually smaller (R² = −0.030 for pCO₂; R² = −0.043 for fCO₂; Table 3, Supplementary Table S3) than the biologically induced changes (Nimick et al., 2011; Morales-Pineda et al., 2014). Moreover, the lake water temperature during the daytime was almost constant in this study (Figure 5C); further, the average daily t_water was negatively correlated with daily pCO₂ (Table 3), indicating that daily t_water influenced on the CO₂ level in the late afternoon. Therefore, we speculate that there may be a temperature threshold in subtrophic water. When the t_water exceeds this threshold, pCO₂ might be extremely low in the late afternoon due to active P. Accordingly, there is a relationship between CO₂ and pH in lakes, as shown in this study (R² = −0.823^** for pCO₂, R² = −0.825^** for fCO₂; Supplementary Table S1), suggesting that the critical threshold for the pH value shifts from the CO₂ absorption capacity to the emission source (Wilson-McNeal et al., 2020)). In river systems, the river represents a CO₂ sink when the pH value exceeds 8.59 but acts as a CO₂ source when the pH value is less than 8.59 (Li et al., 2020a, Li et al., 2020b). Therefore, we speculate that the increases in EC and nutrient levels in the lake system are closely related to urban development, and that anthropogenic emissions may lower the pH value (see Supplementary Table S1), leading to an increase in aqueous pCO₂ (Richey et al., 2002; Ran et al., 2015; Liu et al., 2017).

TABLE 3

TABLE 3. Daily correlation between pCO₂ and fCO₂ and time in Qinglonghu Lake (n = 4).

Uncertainties in CO₂ Evasion Evaluation Owing to Daily pCO₂ Fluctuations

Owing to the variations of pCO₂, the interaction of CO₂ source-sink within a day is driven by many factors, especially daylight and thus the biological processes in the lake (Cole and Caraco, 1998; Maberly et al., 2013; León-Palmero et al., 2020). Findings of our study indicated, due to daily fluctuations, considerable uncertainties in the current estimations of CO₂ evasion from regional and global lake systems. On the one hand, most current estimates of annual CO₂ evasion from freshwater lakes are mainly calculated using data collected from measurements with poor temporal coverage (i.e., weekly or monthly) (Cole et al., 2007) or low-frequency time series (Peter et al., 2014). For daily change, as in our study, the time frame (from 7:00 to 17:00 CST) for observing a decrease in pCO₂ is actually short, the variable level therefore may cause an underestimation or overestimation for the daily CO₂ based on one-time measurement. On the other hand, the observations were made at 3 h intervals from morning to afternoon (Figures 3, 4), with no measurements conducted during the nighttime. The CO₂ evasion during the nighttime could be stronger than that during the daytime, which may be due to darkness favors R over P (Xu et al., 2019), or to physical changes in the water column (Wang et al., 2021). For instance, Reis and Barbosa (2014) observed a tropical productive lake in Brazil for two consecutive days showing, the average pCO₂ (565 matm from 21:00 to 5:00 BRT) during the nighttime was higher than that during the daytime (436.1 matm from 9:00 to 17:00 BRT). In agreement, based on direct measurements in the Ross Barnett reservoir, fCO₂ during the nighttime (0.39 μmol m⁻² s⁻¹) was ca. 70% greater than that during the daytime (0.23 μmol m⁻² s⁻¹ from 08:00 to 20:00 CST) over the 1-year study period (Liu et al., 2016). In addition, Gu et al. (2011) presented an investigation of limnological data collected from 1987 to 2006, suggesting the average pCO₂ (i.e., 224 µatm) at nighttime was slightly higher than that during the daytime but undersaturation with reference to atmospheric pCO₂. A study conducted in Lake Lochaber in eastern Nova Scotia, Canada, showed that 65–95% of total CO₂ emissions in a day actually occur at night, which is 21:00 to 7:00 Atlantic Daylight Time (Spafford and Risk, 2018).

The uncertainties among studies in CO₂ estimates may also be partly due to differences in geographic region (Schelske, 2006). In a study of carbon dynamics in two Mediterranean reservoirs in southern Spain, by relying on different models, Morales-Pineda et al. (2014) found that the average CO₂ emissions ranged from 8.0 to 12.5 mmol C m⁻² d⁻¹ in Guadalcacín but ranged from 33.0 to 50.0 mmol C m⁻² d⁻¹ in Bornos. Further, on a daily scale, variability in pCO₂ was closely related to the diurnal cycle of metabolic activity, with a minimum pCO₂ value between 15:00 and 18:00 Central European Time and a maximum value between 22:00 and 06:00 CET. Reis and Barbosa (2014) also observed a significant difference in CO₂ flux between daytime and nighttime in Lake Carioca, Brazil. Interestingly, CO₂ outgassing was lower at 1:00 Brasilia Time than at other times of day, with a maximum of 2.4 mmol C m⁻² d⁻¹ and an average of 0.9 mmol C m⁻² d⁻¹. Watras et al. (2015) focused on two small seepage lakes in northern Wisconsin, suggesting that the diel cycle is controlled by biological activities, which mediate the production and destruction of organic matter based on daily CO₂ dynamics (0.32 mg C m⁻² d⁻¹). From October 2011 to September 2013, a strong diurnal change in fCO₂ from −0.45 to 0.98 g C m⁻² d⁻¹ in western Lake Erie in North America was found by Shao et al. (2015). However, no clear diurnal variation was observed in a small boreal lake (i.e., Lake Kuivajärvi) in Finland, although the average diel fCO₂ was 0.7 μmol C m⁻² s⁻¹ (Mammarella et al., 2015), while the clear diurnal cycles of fCO₂ in three lakes with different characteristics in southwestern Sweden (Natchimuthu et al., 2017).

Furthermore, previous studies have also proved that C derived from terrestrial systems could cause CO₂ oversaturation in lakes, where DOC serve as a direct substrate for CO₂ production or as a proxy for inorganic C loading (Roehm et al., 2009; Humborg et al., 2010). In this regard, the relationship between pCO₂ and DOC could be a key feature for explaining the regional differences in lake pCO₂ regulation (Lapierre and del Giorgio, 2012). A strong positive correlation in pCO₂-DOC indicates that in-situ oxidation of terrestrial organic C plays a leading role in the excessive CO₂ production in lakes, or the significant contribution of DOC related driving factors to pCO₂ effects (Larsen et al., 2011). On the contrary, a weak/negative correlation would indicate either a strong contribution from other drivers that are not related to DOC, or drivers that are related to DOC but have an opposite effect on pCO₂, respectively. Lapierre and del Giorgio (2012) found that the proxy for lake metabolic balance and terrestrial C exports accounted for a large part of these patterns in the lake pCO₂-DOC relationship, indicating that the key driver of the difference in pCO₂-DOC is the integration of the average regional TP: DOC ratio and altitude. Based on the findings from these and our studies, we argue that great uncertainties may exist in current regional and global estimations of CO₂ evasion from lake systems.

Conclusion

In the current work, we systematically investigated the daily fluctuations in pCO₂ and related parameters at 3 h intervals from morning to afternoon from Jan. to Sept., 2020 in a subtropical urbanizing lake. Several interesting findings have resulted from the work. From early mornings to late afternoons, strongly decreasing pCO₂ and fCO₂ trends were observed except on 2 days on which unusual rainfall/wind occurred. The daily pCO₂ was highest at 7:00 CST (1,061.3 µatm on August 19) and lowest at 17:00 CST (12.6 µatm on August 6), while the average pCO₂ was highest at 7:00 CST (204.9 ± 295.7 µatm) and lowest at 17:00 CST (93.5 ± 105.5 µatm). As a result, the highest daily fCO₂ occurred in the morning hours (−19.3 ± 27.5 and −24.8 ± 20.7 mmol m⁻² h⁻¹ at 7:00 and 10:00 CST, respectively) and in the afternoon hours (−29.2 ± 9.1 and −30.4 ± 10.7 mmol m⁻² h⁻¹ at 14:00 and 17:00 CST, respectively). These findings revealed that the daily CO₂ exchange in the studied subtropical urbanizing lake reflects the dynamics of ecosystem metabolism and remineralization reactions. Accordingly, we speculate that nighttime pCO₂/fCO₂ may be much higher than daytime pCO₂/fCO₂, which would indicate that the entire lake is undersaturated and a carbon sink during daylight hours. In addition, there was a strong negative correlation between pCO₂ and each of pH, t_water, and DO but a positive correlation between pCO₂ and each of TOC and Chla, suggesting that uncertainties in the estimation of fCO₂ at regional/global scales owing to some factors such as geographical region and temporal coverage. Future work needs to constrain these critical factors and overall uncertainty and determine the levels of accuracy needed to enable robust CO₂ estimations at different scales.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

Author Contributions

RY: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data Curation, Writing-Original Draft, Visualization. YC: Software, Formal analysis, Investigation. JD: Project administration, Funding acquisition. XP: Conceptualization, Writing-Review and Editing, Funding acquisition. JL: Investigation. ZZ: Investigation. HS: Conceptualization, Methodology, Resources, Data Curation, Writing-Review and Editing, Supervision, Project administration, Funding acquisition.

Funding

This work was partially supported by the Specialized Fund for the Post-Disaster Reconstruction and Heritage Protection in Sichuan (grant No., 5132202019000128), the Technology Innovation R & D Project in Chengdu (grant No., 2019-YFYF-00040-SN), the Landscape and Recreation Research Center Project in Sichuan (grant No., JGYQ2019025), the Science and Technology Innovation Seedling Project in Sichuan (grant No., 2020118) and the Innovation and Entrepreneurship Training Program for College Students at Sichuan Agricultural University and in Sichuan Province (grant Nos., 202010626104, S202010626104).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors acknowledge the Sichuan Keshengxin Environmental Technology Co., Ltd., College of Environmental Sciences and Institute of Landscape Architecture at Sichuan Agricultural University in Chengdu, China, for assisting in the detection of partial water samples for this study. Sincere thanks also go to Drs. Li Li and Shiliang Liu; Mses. Jia Wei, Yuling Qiu, Kezhu Lu, Qianrui Liu, and Xiaoyang Ke; Messrs. Di Li, Ting Lei, Xingyu Zhu, Jianglin Yu, Lijuan Yang, and Hongyu Wu; and other research assistants for their outstanding laboratory and field assistance. The authors also thank Drs. Y. Jun Xu and Zhen Xu from Louisiana State University in Baton Rouge, United States, for their previous trial-design guidance; the Qinglonghu Lake Management Center for providing permission and transportation for the field trials; American Journal Experts (https://www.aje.com/#) for their language editing services for our draft manuscript; and the editors and two reviewers for their helpful comments and suggestions.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feart.2021.805276/full#supplementary-material

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