Open Access
Issue
Knowl. Manag. Aquat. Ecosyst.
Number 422, 2021
Article Number 7
Number of page(s) 6
DOI https://doi.org/10.1051/kmae/2021007
Published online 26 February 2021

© J. Gao et al., Published by EDP Sciences 2021

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Suspended particulate organic matter (SPOM) is an important component of stream systems and is a major pathway of organic matter transport and export in a watershed (Ulseth and Hershey, 2005; Lambert et al., 2017). SPOM is derived from various sources, including autochthonous algae and aquatic plants, allochthonous terrestrial plants, and faecal and animal detritus (Cummins, 1974; Ngugi et al., 2017; Hou et al., 2019).

The stable C isotopic values of SPOM can indicate primary productivity and CO2 concentration (Gu et al., 2006) and track the sources of organic matter (Gu et al., 2011; Hou et al., 2019). The stable N isotopic values of SPOM has been used to help understand various nitrogen cycling processes such as dissolved inorganic nitrogen (DIN) uptake and nitrogen fixation (Gu et al., 2006) and anthropogenic pollution (Ke et al., 2017; Lambert et al., 2017) in aquatic environments. In general, stoichiometric C/N ratios of SPOM are higher for allochthonous terrestrial plants than for algae and aquatic plants (Anderson and Sedell, 1979; Atkinson et al., 2009; Lu et al., 2014). In addition, anthropogenic organic matter tends to increase the δ15N values of aquatic organisms. The nitrate δ15N value of air is close to 0‰, whereas those of septic water, wastewater, and manure are high, varying from 10‰ to 22‰ (McClelland and Valiela, 1998; Anderson and Cabana, 2006). Longitudinal variations in the δ15N characteristics of stream POM can be used to identify N sources in waterways because they provide information on the N source and major biogeochemical processes in aquatic ecosystems (Vander Zanden et al., 2005; Finlay and Kendall, 2007; Ryu et al., 2018; Xuan et al., 2019). So the stable C and N isotopic values and C/N ratios of SPOM have been widely used in aquatic biogeochemistry to identify the sources (terrestrial, freshwater, or marine) and fate of organic matter in aquatic ecosystems based on their unique range of values for different sources (Kendall et al., 2001; Usui et al., 2006; Jha and Masao, 2013).

The Qingjiang River, a subtropical river in Enshi, Hubei Province, Central China, is the first large tributary and water source protection zone in the middle of the Yangtze River after it passes through the Three Gorges Dam (China GEBE, 1993; Cao and Yang, 2015). A distinct longitudinal pattern was observed in the nutrient levels in the Qingjiang River's headwater stream (Liu et al., 2018). Tolerance sensibility of benthic macroinvertebrates along water flow direction was decreased from the mountains (upstream) to urban (downstream) sites in the Qingjiang River's headwater stream, indicating water quality degradation as anthropogenic pollution (Pan et al., 2018). Our study aimed to determine variations in the stable isotopes of C and N from upstream to downstream in the head waters of the Qingjiang River. We hypothesised that the δ15N values would increase, whereas δ13C values would decrease from upstream to downstream as the river is influenced by human activities with associated pollutions.

The total length of this river is 423 km (Cao and Yang, 2015), and the headwater stream of this river selected for this study was approximately 76.8 km. Samples were collected from upstream to downstream at four sites (sites 1–4; Fig. 1). Site 1, the most upstream site, was located in a sparsely populated residential area and is considered the most pristine site because the riparian vegetation consists of intact shrubs and forests (Tab. 1; Liu et al., 2018). Site 2 was located in the middle of a village. The dominant land used here was for fields and residence, and the site was therefore likely contaminated with manure, farmland fertiliser, and other materials. Site 3 was located downstream of Wangying town, which has a population of approximately 25 000 but no wastewater treatment plant. Site 4 was located downstream of Lichuan City, which has a population of approximately 66 000 people and a wastewater treatment plant situated approximately 0.3 km upstream. Tailwater from this treatment plant flows into the river (Tab. 1; Liu et al., 2018). The sewage treatment plant was built in 2010 and uses an oxidation ditch process. The plant treats 20 000 tons of wastewater per day. The chemical oxygen demand (CODCr), total nitrogen (TN), ammonia–nitrogen, and total phosphorus (TP) concentrations of wastewater discharge are 60, 20, 8 (or 15 mg/L when the temperature is <12 °C), and 1.5 mg/L, respectively (China EPA, 2002).

Sampling was conducted bimonthly in odd-numbered months from May 2016 to March 2017. Water temperature (WT) was measured using a YSI metre (YSI ProPlus, Yellow Springs, OH, USA). Water samples for chemical and chlorophyll a (Chl a) were collected at 0.5-m depth below the water surface (surface water collected if the depth is <0.5 m) from three stations at each site and pooled. Approximately 2.5 L of water was stored in a cooling box and transported to the laboratory where it was analysed according to standard methods (China EPA, 2009), basically corresponding with US standards (APHA, 1998). The Chla concentration was determined spectrophotometrically after sample filtration through cellulose acetate filters and extraction of the filtered materialswith 90% acetone; TP and TN concentrations were determined spectrophotometrically after digestion with persulphate; the potassium permanganate method was used to determine the chemical oxygen demand (CODMn) (China EPA, 2009).

To analyse the stable isotope composition of SPOM in stream water and tailwater from domestic wastewater treatment plants, water was passed through precombusted GF/F filters, which were subsequently freeze dried. All stable isotope samples were stored in a desiccator containing dried allochroic silica gel before analysis. Thereafter, all samples were analysed using a Vario PYRO Cube elemental analyser coupled to anIsoprime-100 isotope ratio mass spectrometer at the Environmental Stable Isotope Laboratory, Chinese Academy of Agriculture Sciences. Stable isotope ratios are expressed in the delta (δ) notation, defined as parts per thousand deviations from a certified standard; δ13C or δ15N = ([R sample/R standard]) − 1) × 1000, where R is the ratio of 13C/12C or 15N/14N. The standards for δ13C and δ15N were a secondary standard of known relation to the international standard of VPDB and atmospheric nitrogen, respectively. The standard errors of the mean for replicates of the same tissue were 0.2‰ for δ13C and 0.2‰ for δ15N.

For statistical analyses, we used SPSS 19.0 for Windows, and all data sets were examined for normality and transformed when needed using ln (x) before analyses (Box and Cox, 1964). Spearman correlation analysis was used to determine significant correlations between physicochemical factors and stable isotope parameters. The differences in physicochemical factors and stable isotope parameters between sites were assessed using repeated-measures analysis of variance (ANOVA) with time as the repeated factor. If a significant difference was noted, Tukey's Multiple Comparison Test post hoc tests were used to detect which treatments differed. Before performing ANOVA, we tested whether the data met the assumptions of homogeneity of variances by using Levene's test. If the assumption was not met, then we used Kruskal–Wallis nonparametric ANOVA.

The WTs were 16.5 °C in May 2016, 20.7 °C in July 2016, 18.4 °C in September 2016, 9.9 °C in November 2016, 6.1 °C in January 2017, and 9.6 °C in March 2017. The mean annual concentrations of TN and TP increased from upstream to downstream (Fig. 2i, ii). TN concentrations did not differ significantly between sites 1–3, whereas it increased markedly to 4.23 mg ∙ L−1at site 4 (one-way ANOVA, Tukey's Multiple Comparison, p < 0.01; Fig. 2i). TP concentrations ranged from 0.02 to 0.16 mg ∙ L−1, and higher at site 4 than at site 1 and 2 (one-way ANOVA, Tukey's Multiple Comparison, p < 0.05, Fig. 2ii). Chla concentrations were low at all sites, ranging from 1.7 to 5.0 µg · L−1 (Fig. 2iii). CODMn concentration was higher at site 4 than at site 1 (one-way ANOVA, Tukey's Multiple Comparison, p < 0.05, Fig. 2ii), but low values were recorded at all sites (Fig. 2iv). The high mean concentrations of TN, TP, and CODMn observed at downstream sites 2 and 3 were probably due to the nutrient contributions from village and town wastewater, agricultural areas, and livestock farming areas (Tab. 1) as by Liu et al. (2018). The high annual mean concentrations of TN observed at site 4 were probably due to the nutrient contributions from the wastewater treatment plants tailwater (Tab. 1, Fig. 1). Similar studies suggested that discharge from the domestic wastewater treatment plant played a crucial role in affecting nutrients dynamics of stream (Tachibana et al., 2001; Jha and Masao, 2013).

The mean δ15N value at upstream locations increased from site 1 to site 3, ranging from 2.4‰ to 5.3‰, but decreased to 2.2‰ at site 4 (Fig. 3i). The SPOM δ15N values increased from site 1 to site 3 from upstream to downstream, reflecting anthropogenic nitrogen pollution. The primary sources of anthropogenic nitrogen were sites 2 and 3 in the form of waste from livestock farming, and sanitary wastewater. The effluents are routinely discharged directly into the stream without any treatment. Hence, increases in SPOM δ15N at downstream locations may be attributable to nitrogen pollution from anthropogenic organic matter. Furthermore, Toda et al. (2002) and Ning et al. (2013) have reported a significant positive relationship between the SPOM and periphyton δ15N values and the percentage contribution to nitrogen loading from sewage and livestock waste. However, in the present study, unexpectedly, the SPOM δ15N drastically decreased at site 4, with high concentrations of TN and TP, which was situated close to the wastewater treatment plant (only 0.3 km at the upstream location of site 4). The δ15N values of stream water at site 4 (2.2‰ ± 1.6‰) (Fig. 3i) and wastewater treatment plant tailwater (2.2% ± 0.6) were similar in our studies. Previous studies reported that isotopic values of sewage solids varied in terms of δ15N (mean, + 3.2‰; range −1.1 to + 7.2‰; n = 7) (Van Dover et al., 1992; Gearing et al., 1991, 1994; Andrews et al., 1998; Ulseth, 2003). Our results suggested that that sewage solids from tailwater contributed to the SPOM composition at site 4.

Additionally, we noted that the SPOM δ13C varied unimodally; it was most positive at site 1 (−21.6‰ ±  1.4‰), with low concentrations of TN and TP, and at site 4 (−22.8‰ ± 1.7‰), with higher concentrations of TN and TP than at the other two sites (Fig. 3ii). Kendall et al. (2001) indicated that the δ13C values of C3 plants, C4 plants, and fresh water phytoplankton are −27‰ (−32‰ to −22‰), −13‰ (−16‰ to −9‰), and −30‰ (−42‰ to −24‰), respectively. The SPOM δ13C in the headwater stream of the Qingjiang River ranged from −29.1‰ to −20.2‰ (Fig. 3ii), suggesting that most of the SPOM in the stream system is governed by Rubisco-mediated photosynthesis (Calvin cycle), but the contributions from C4-based (Hatch–Slack cycle) vascular plant organic matter might also be important. Particularly at site 1, maize (C4 plant) production on both sides of the stream may result in detritus entering the adjacent stream ecosystems, yielding high δ13C values. The δ13C values decreased from site 1 to site 3, suggesting that the contribution from instream algae sources gradually increased, whereas that from terrestrial sources decreased (Fig. 3ii). However, this observed trend in δ13C values changed at site 4 (Fig. 3ii). The mean δ13C values of SPOM were lower (−25.6‰ ± 1.9 ‰) in tailwater from domestic wastewater treatment plants than at site 4 (−22.8‰ ± 1.7‰) in our study. However, other studies reported that isotopic values of sewage solids varied in terms of δ13C (mean, −23.4‰; range −28.5‰ to −16.5‰; n = 13; Gearing et al., 1991, 1994; Van Dover et al., 1992; Andrews et al., 1998). The δ13C value of sewage-effected SPOM collected from site 4 in our study (−22.8‰) was similar to the literature mean value (−23.4‰) (Gearing et al., 1991, 1994; Van Dover et al., 1992; Andrews et al., 1998).

Carbon to nitrogen ratios can be also used to identify organic matter sources because the molar C/N ratios of plankton and bacteria (6–7 and 4–5, respectively) are much lower than those of organic matter derived from higher plants (>20; Hedges et al., 1997; Sarma et al., 2012). The molar C/N ratio of SPOM was between 6.7 and 17.6 (Fig. 3iii), with a higher ratio observed in the upper stream (13.1 ± 2.3) than in the lower stream (8.5 ± 1.5). Molar C/N ratio were also significantly negatively correlated with Chla (p < 0.05, n = 24). However, the SPOM C/N ratios were similar between stream water at site 4 (8.5 ± 1.5) and tailwater (6.2 ± 0.9) in our study, suggesting that sewage solids from tailwater contributed to the composition of SPOM at site 4. Thus, these results suggested the organic matter derived from terrestrial organic matter decreased and that from plankton or bacteria increased from site 1 to site 4.

Overall, our results revealed that nutrient input increased from upstream to downstream in the headwater stream of the Qingjiang River; however, variations in SPOM δ15N and δ13C between sites exhibited a unimodal pattern unlike variation in nutrient concentrations. The variations in the SPOM δ15N exhibited no correlation with TN and TP concentrations (p > 0.05, n = 24), suggesting that different processes affect the δ15N isotopic values of SPOM samples. The unimodal variations in the SPOM δ13C and the negative correlations between molar C/N ratio and Chl a concentration suggested that organic matter derived from terrestrial organic matter decreased and that from plankton or bacteria increased from upstream to downstream. These results suggested the tailwater from the wastewater treatment plant affected the positive relationship between the SPOM δ15N value and the percentage contribution to nitrogen loading from sewage and livestock waste.

Acknowledgments

The authors thank Linfeng Liu, Chao Pan, Cheng Liu for field and laboratory support. This manuscript was edited by Wallace Academic Editing. This study was supported by the National Natural Science Foundation of China (Grant No. 31500378, 91647207, 31670367), by Nature Science Foundation of Hubei Province (2020CFB537) and by major project ofScience and Technology Department of Hubei Province (2018ZYYD037).

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Cite this article as: Gao J, Zhang Z, Zhong P, Yang C, Liao M, Jiao Y. 2021. Longitudinal variation characteristics of stable isotope ratios of suspended particulate organic matter in the headwaters of the Qingjiang River, China. Knowl. Manag. Aquat. Ecosyst., 422, 7.

All Tables

Table 1

Features of sampling sites in the headwater stream of the Qingjiang River.

All Figures

thumbnail Fig. 1

Location of sampling sites in the headwater stream of the Qingjiang River. S1 to S4 denote sampling sites 1 to 4. Wastewater treatment plant located approximately 0.3 km upstream of the sampling site 4. Tailwater flowed into the stream.

In the text
thumbnail Fig. 2

Chemical and biological characteristics of the headwater stream of the Qingjiang River (mean ± standard deviation). Vertical lines indicate standard deviation. Figure i, ii, iii and iv denote TN, TP, Chla and CODMn concentrations, respectively. Letters a and b indicate significant (p < 0.05) differences for physicochemical factors. Sampling sites that do not have a significantly different concentrations share a common letter.

In the text
thumbnail Fig. 3

Mean annual SPOM δ13C, δ15N, and molar C/N ratio in the headwater stream of the Qingjiang River. Vertical bars indicate standard deviations. Figure i, ii and iii denote SPOM δ13C values, δ15N values, and molar C/N ratio, respectively. Letters a, b, and c indicate significant (p < 0.05) differences for δ13C, δ15N, and C/N ratio. Sampling sites that do not have significantly different values share a common letter.

In the text

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