Seasonal Variation in the Water Quality and Eutrophication of Lake Xingyun in Southwestern China
Abstract
:1. Introduction
- (1)
- Investigate the spatial and temporal distribution characteristics of water quality parameters;
- (2)
- Evaluate Lake XingYun’s water quality and nutrient content;
- (3)
- Investigate the potential source of nutrient dynamics and the relationship between nutrient status and water quality indicators.
2. Materials and Methods
2.1. Study Area
2.2. Study Methods
2.3. Data Analysis
3. Results
3.1. Analysis of Temporal and Spatial Changes
3.1.1. Analysis of Temporal and Spatial Changes in WT
3.1.2. Analysis of Temporal and Spatial Changes in pH
3.1.3. Analysis of Temporal and Spatial Changes of DO
3.1.4. Analysis of Temporal and Spatial Changes of Chlorophyll
3.1.5. Analysis of Temporal and Spatial Changes in Turbidity
3.1.6. Inter-Annual Changes in Water Quality
3.2. Water Quality Evaluation
3.3. Comprehensive Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Beets, D.J.; van der Spek, A.J.F. The Holocene evolution of the barrier and the back-barrier basins of Belgium and the Netherlands as a function of late Weichselian morphology, relative sea-level rise and sediment supply. Geol. En Mijnb. Neth. J. Geosci. 2000, 79, 3–16. [Google Scholar] [CrossRef] [Green Version]
- Duncan, R. Regulating agricultural land use to manage water quality: The challenges for science and policy in enforcing limits on non-point source pollution in New Zealand. Land Use Policy 2014, 41, 378–387. [Google Scholar] [CrossRef] [Green Version]
- Whitehead, P.G.; Wilby, R.L.; Battarbee, R.W.; Kernan, M.; Wade, A.J. A review of the potential impacts of climate change on surface water quality. Hydrol. Sci. J. 2009, 54, 101–123. [Google Scholar] [CrossRef] [Green Version]
- Bilgin, A.; Konanç, M.U. Evaluation of surface water quality and heavy metal pollution of Coruh River Basin (Turkey) by multivariate statistical methods. Environ. Earth Sci. 2016, 75, 1029. [Google Scholar] [CrossRef]
- Abbaspour, K.C.; Yang, J.; Maximov, I.; Siber, R.; Bogner, K.; Mieleitner, J.; Zobrist, J.; Srinivasan, R. Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. J. Hydrol. 2007, 333, 413–430. [Google Scholar] [CrossRef]
- Zhen, S.; Zhu, W. Analysis of isotope tracing of domestic sewage sources in Taihu Lake—A case study of Meiliang Bay and Gonghu Bay. Ecol. Indic. 2016, 66, 113–120. [Google Scholar] [CrossRef]
- Bu, H.; Tan, X.; Li, S.; Zhang, Q. Temporal and spatial variations of water quality in the Jinshui River of the South Qinling Mts., China. Ecotoxicol. Environ. Saf. 2010, 73, 907–913. [Google Scholar] [CrossRef]
- Iscen, C.F.; Emiroglu, Ö.; Ilhan, S.; Arslan, N.; Yilmaz, V.; Akiska, S. Application of multivariate statistical techniques in the assessment of surface water quality in Uluabat Lake, Turkey. Environ. Monit. Assess. 2007, 144, 269–276. [Google Scholar] [CrossRef]
- Van Vliet, M.T.H.; Ludwig, F.; Zwolsman, J.J.G.; Weedon, G.P.; Kabat, P. Global river temperatures and sensitivity to atmospheric warming and changes in river flow. Water Resour. Res. 2011, 47, W02544. [Google Scholar] [CrossRef]
- O’Reilly, C.M.; Alin, S.R.; Plisnier, P.-D.; Cohen, A.S.; McKee, B.A. Climate change decreases aquatic ecosystem productivity of Lake Tanganyika, Africa. Nature 2003, 424, 766–768. [Google Scholar] [CrossRef]
- Xu, W.; Duan, L.; Wen, X.; Li, H.; Li, D.; Zhang, Y.; Zhang, H. Effects of Seasonal Variation on Water Quality Parameters and Eutrophication in Lake Yangzong. Water 2022, 14, 2732. [Google Scholar] [CrossRef]
- Huang, C.; Li, Y.; Yang, H.; Sun, D.; Yu, Z.; Zhang, Z.; Chen, X.; Xu, L. Detection of algal bloom and factors influencing its formation in Taihu Lake from 2000 to 2011 by MODIS. Environ. Earth Sci. 2013, 71, 3705–3714. [Google Scholar] [CrossRef]
- Qin, B.; Zhu, G.; Gao, G.; Zhang, Y.; Li, W.; Paerl, H.W.; Carmichael, W.W. A Drinking Water Crisis in Lake Taihu, China: Linkage to Climatic Variability and Lake Management. Environ. Manag. 2009, 45, 105–112. [Google Scholar] [CrossRef] [PubMed]
- Wang, Y.; Ma, H.; Sheng, D.; Wang, D. Assessing the Interactions between Chlorophyll a and Environmental Variables Using Copula Method. J. Hydrol. Eng. 2012, 17, 495–506. [Google Scholar] [CrossRef]
- Modabberi, A.; Noori, R.; Madani, K.; Ehsani, A.H.; Mehr, A.D.; Hooshyaripor, F.; Kløve, B. Caspian Sea is eutrophying: The alarming message of satellite data. Environ. Res. Lett. 2020, 15, 124047. [Google Scholar] [CrossRef]
- Wu, G.; Xu, Z. Prediction of algal blooming using EFDC model: Case study in the Daoxiang Lake. Ecol. Model. 2011, 222, 1245–1252. [Google Scholar] [CrossRef]
- Liu, W.-C.; Yu, H.-L.; Chung, C.-E. Assessment of Water Quality in a Subtropical Alpine Lake Using Multivariate Statistical Techniques and Geostatistical Mapping: A Case Study. Int. J. Environ. Res. Public Health 2011, 8, 1126–1140. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Palma, P.; Alvarenga, P.; Palma, V.L.; Fernandes, R.M.; Soares, A.; Barbosa, I. Assessment of anthropogenic sources of water pollution using multivariate statistical techniques: A case study of the Alqueva’s reservoir, Portugal. Environ. Monit. Assess. 2009, 165, 539–552. [Google Scholar] [CrossRef]
- Joshi, J.; Abhyankar, A.A. Evaluation of Spatial and Temporal Variations in Water Quality of River Ganga. Int. J. Ecol. Dev. 2019, 34, 69–81. [Google Scholar]
- Sun, X.; Zhang, H.; Zhong, M.; Wang, Z.; Liang, X.; Huang, T.; Huang, H. Analyses on the Temporal and Spatial Characteristics of Water Quality in a Seagoing River Using Multivariate Statistical Techniques: A Case Study in the Duliujian River, China. Int. J. Environ. Res. Public Health 2019, 16, 1020. [Google Scholar] [CrossRef] [Green Version]
- Yerel, S. Assessment of Surface Water Quality using Multivariate Statistical Analysis Techniques: A Case Study from Tahtali Dam, Turkey. Asian J. Chem. 2009, 21, 4054–4062. [Google Scholar]
- Najafzadeh, M.; Homaei, F.; Farhadi, H. Reliability assessment of water quality index based on guidelines of national sanitation foundation in natural streams: Integration of remote sensing and data-driven models. Artif. Intell. Rev. 2021, 54, 4619–4651. [Google Scholar] [CrossRef]
- Chen, P.; Li, L.; Zhang, H. Spatio-Temporal Variations and Source Apportionment of Water Pollution in Danjiangkou Reservoir Basin, Central China. Water 2015, 7, 2591–2611. [Google Scholar] [CrossRef] [Green Version]
- Zhang, Y.; Chang, F.; Zhang, X.; Li, D.; Liu, Q.; Liu, F.; Zhang, H. Release of Endogenous Nutrients Drives the Transformation of Nitrogen and Phosphorous in the Shallow Plateau of Lake Jian in Southwestern China. Water 2022, 14, 2624. [Google Scholar] [CrossRef]
- GB3838-2002; Environmental Quality Standard for Surface Water. State Environmental Protection Administration (SEPA): Beijing, China, 2002; pp. 1–8.
- Zhou, X.F. Characterization and sources of sedimentary organic matter in Xingyun Lake, Jiangchuan, Yunnan, China. Environ. Earth Sci. 2016, 75, 1–11. [Google Scholar] [CrossRef]
- Luo, L.; Zhang, H.; Luo, C.; McBridge, C.; Muraoka, K.; Zhou, H.; Hou, C.; Liu, F.; Li, H. Tributary Loadings and Their Impacts on Water Quality of Lake Xingyun, a Plateau Lake in Southwest China. Water 2022, 14, 1281. [Google Scholar] [CrossRef]
- Chen, X.; Huang, X.; Wu, D.; Chen, J.; Zhang, J.; Zhou, A.; Dodson, J.; Zawadzki, A.; Jacobsen, G.; Yu, J.; et al. Late Holocene land use evolution and vegetation response to climate change in the watershed of Xingyun Lake, SW China. CATENA 2021, 211, 105973. [Google Scholar] [CrossRef]
- Wu, D.; Chen, X.; Lv, F.; Brenner, M.; Curtis, J.; Zhou, A.; Chen, J.; Abbott, M.; Yu, J.; Chen, F. Decoupled early Holocene summer temperature and monsoon precipitation in southwest China. Quat. Sci. Rev. 2018, 193, 54–67. [Google Scholar] [CrossRef]
- Hillman, A.L.; Abbott, M.B.; Finkenbinder, M.S.; Yu, J. An 8600 year lacustrine record of summer monsoon variability from Yunnan, China. Quat. Sci. Rev. 2017, 174, 120–132. [Google Scholar] [CrossRef]
- Zhao, L.; Li, Y.; Zou, R.; He, B.; Zhu, X.; Liu, Y.; Wang, J.; Zhu, Y. A three-dimensional water quality modeling approach for exploring the eutrophication responses to load reduction scenarios in Lake Yilong (China). Environ. Pollut. 2013, 177, 13–21. [Google Scholar] [CrossRef]
- Jin, X. Lacustrine Environment in China; Ocean Press: Beijing, China, 1995. [Google Scholar]
- Xu, M.; Yu, L.; Zhao, Y.; Li, M. The Simulation of Shallow Reservoir Eutrophication Based on MIKE21: A Case Study of Douhe Reservoir in North China. Procedia Environ. Sci. 2012, 13, 1975–1988. [Google Scholar] [CrossRef] [Green Version]
- Doi, H. Spatial patterns of autochthonous and allochthonous resources in aquatic food webs. Popul. Ecol. 2008, 51, 57–64. [Google Scholar] [CrossRef]
- Markovic, S.; Liang, A.; Watson, S.B.; Guo, J.; Mugalingam, S.; Arhonditsis, G.; Morley, A.; Dittrich, M. Biogeochemical mechanisms controlling phosphorus diagenesis and internal loading in a remediated hard water eutrophic embayment. Chem. Geol. 2019, 514, 122–137. [Google Scholar] [CrossRef]
- Yang, C.; Yang, P.; Geng, J.; Yin, H.; Chen, K. Sediment internal nutrient loading in the most polluted area of a shallow eutrophic lake (Lake Chaohu, China) and its contribution to lake eutrophication. Environ. Pollut. 2020, 262, 114292. [Google Scholar] [CrossRef]
- Xie, L.; Xie, P.; Tang, H. Enhancement of dissolved phosphorus release from sediment to lake water by Microcystis blooms—An enclosure experiment in a hyper-eutrophic, subtropical Chinese lake. Environ. Pollut. 2003, 122, 391–399. [Google Scholar] [CrossRef]
- Zheng, Y.; Jiang, X.; Hou, L.; Liu, M.; Lin, X.; Gao, J.; Li, X.; Yin, G.; Yu, C.; Wang, R. Shifts in the community structure and activity of anaerobic ammonium oxidation bacteria along an estuarine salinity gradient. J. Geophys. Res. Biogeosci. 2016, 121, 1632–1645. [Google Scholar] [CrossRef] [Green Version]
- Cheng, N.; Liu, L.; Hou, Z.; Wu, J.; Wang, Q. Pollution characteristics and risk assessment of surface sediments in nine plateau lakes of Yunnan Province. In Proceedings of the 4th International Conference on Energy Engineering and Environmental Protection (EEEP), Xiamen, China, 19–21 November 2019. [Google Scholar]
- Liu, C.; Du, Y.; Yin, H.; Fan, C.; Chen, K.; Zhong, J.; Gu, X. Exchanges of nitrogen and phosphorus across the sediment-water interface influenced by the external suspended particulate matter and the residual matter after dredging. Environ. Pollut. 2018, 246, 207–216. [Google Scholar] [CrossRef]
- Pekárová, P.; Halmová, D.; Miklánek, P.; Onderka, M.; Pekár, J.; Škoda, P. Is the Water Temperature of the Danube River at Bratislava, Slovakia, Rising? J. Hydrometeorol. 2008, 9, 1115–1122. [Google Scholar] [CrossRef]
- Borman, M.M.; Larson, L.L. A case study of river temperature response to agricultural land use and environmental thermal patterns. J. Soil Water Conserv. 2003, 58, 8–12. [Google Scholar]
- Huguet, F.; Parey, S.; Dacunha-Castelle, D.; Malek, F. Is there a trend in extremely high river temperature for the next decades? A case study for France. Nat. Hazards Earth Syst. Sci. 2008, 8, 67–79. [Google Scholar] [CrossRef] [Green Version]
- Gradilla-Hernandez, M.S.; de Anda, J.; Garcia-Gonzalez, A.; Meza-Rodríguez, D.; Yebra Montes, C.; Perfecto-Avalos, Y. Multivariate water quality analysis of Lake Cajititlan, Mexico. Environ. Monit. Assess. 2020, 192, 5. [Google Scholar] [CrossRef] [PubMed]
- Flynn, K.J.; Clark, D.R.; Mitra, A.; Fabian, H.; Hansen, P.J.; Glibert, P.M.; Wheeler, G.L.; Stoecker, D.K.; Blackford, J.C.; Brownlee, C. Ocean acidification with (de)eutrophication will alter future phytoplankton growth and succession. Proc. R. Soc. B-Biol. Sci. 2015, 282, 20142604. [Google Scholar] [CrossRef] [PubMed]
- Ramin, N.; Bates, M.H. The effects of pH on the aluminum, iron and calcium phosphate fraction of lake sediments. Water Res. 1979, 13, 813–815. [Google Scholar]
- Brezonik, P.L.; Crisman, T.L.; Schulze, R.L. Planktonic communities in florida softwater lakes of varying ph. Can. J. Fish. Aquat. Sci. 1984, 41, 46–56. [Google Scholar] [CrossRef]
- Brock, T.D. Lower pH limit for the existence of blue-green algae: Evolutionary and ecological implications. Science 1973, 179, 480–483. [Google Scholar] [CrossRef] [PubMed]
- Tutmez, B.; Hatipoglu, Z.; Kaymak, U. Modelling electrical conductivity of groundwater using an adaptive neuro-fuzzy inference system. Comput. Geosci. 2006, 32, 421–433. [Google Scholar] [CrossRef] [Green Version]
- Jin, X.L.; Jin, S.; Liu, H.; Li, Y. Research on amount of total phosphorus entering into Fuxian Lake and Xingyun Lake through dry ad wet deposition caused by surrounding phosphorus chemical factories. Environ. Sci. Surv. 2010, 29, 39–42. (In Chinese) [Google Scholar]
- Zhang, X.; Shao, Y. LUCC impact on water quality change in Xingyun Lake basin. E3S Web Conf. 2020, 198, 04025. [Google Scholar] [CrossRef]
- Zhen, L.; Li, F.; Huang, H.; Dilly, O.; Liu, J.; Wei, Y.; Yang, L.; Cao, X. Households’ willingness to reduce pollution threats in the Poyang Lake region, southern China. J. Geochem. Explor. 2011, 110, 15–22. [Google Scholar] [CrossRef]
- Noori, R.; Ansari, E.; Bhattarai, R.; Tang, Q.; Aradpour, S.; Maghrebi, M.; Haghighi, A.T.; Bengtsson, L.; Kløve, B. Complex dynamics of water quality mixing in a warm mono-mictic reservoir. Sci. Total Environ. 2021, 777, 146097. [Google Scholar] [CrossRef]
- Zhou, B.; Fu, X.; Wu, B.; He, J.; Vogt, R.; Yu, D.; Yue, F.; Chai, M. Phosphorus Release from Sediments in a Raw Water Reservoir with Reduced Allochthonous Input. Water 2021, 13, 1983. [Google Scholar] [CrossRef]
- Shou, C.-Y.; Tian, Y.; Zhou, B.; Fu, X.-J.; Zhu, Y.-J.; Yue, F.-J. The Effect of Rainfall on Aquatic Nitrogen and Phosphorus in a Semi-Humid Area Catchment, Northern China. Int. J. Environ. Res. Public Health 2022, 19, 10962. [Google Scholar] [CrossRef] [PubMed]
- Sakamoto, M.S.M.; Maruo, M.; Murase, J.; Song, X.; Zhang, Z. Distribution and dynamics of nitrogen and phosphorus in the Fuxian and Xingyun lake system in the Yunnan Plateau, China. Yunnan Geogr. Environ. Res. 2002, 14, 1–9. [Google Scholar]
- Qin, B.; Paerl, H.W.; Brookes, J.D.; Liu, J.; Jeppesen, E.; Zhu, G.; Zhang, Y.; Xu, H.; Shi, K.; Deng, J. Why Lake Taihu continues to be plagued with cyanobacterial blooms through 10 years (2007–2017) efforts. Sci. Bull. 2019, 64, 354–356. [Google Scholar] [CrossRef] [Green Version]
- Sondergaard, M.; Kristensen, P.; Jeppesen, E. Phosphorus Release from Resuspended Sediment in the Shallow and Wind-Exposed Lake Arreso, Denmark. Hydrobiologia 1992, 228, 91–99. [Google Scholar] [CrossRef]
Parameters | Chl-a | TP | TN | SD |
---|---|---|---|---|
R1j | 1 | 0.84 | 0.82 | −0.83 |
1 | 0.7056 | 0.6724 | 0.6889 | |
Wj | 0.2663 | 0.1879 | 0.1790 | 0.1834 |
Score Value | 0–35 | 36–45 | 46–55 | 56–65 | 66–75 | 76–85 | 86–95 | 96–100 |
---|---|---|---|---|---|---|---|---|
Level | Dystrophic | Lower-Mesotrophic | Mesotrophic | Upper-Mesotrophic | Eutrophic | Hypertrophic | Severe Eutrophic | Abnormal Eutrophic |
Monitoring Site | Depth (m) | 2016.5 | 2016.10 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
TN (mg/L) | TP (mg/L) | Average | Trophic Level | Category | TN (mg/L) | TP (mg/L) | Average | Trophic Level | Category | ||
XYH-1 | 0 | 2.68 | 0.60 | 72.9 | Eutrophic | V | 2.09 | 0.45 | 66.07 | Eutrophic | V |
1 | 2.33 | 0.46 | 70.8 | Eutrophic | V | 2.53 | 0.46 | 67.11 | Eutrophic | V | |
2 | 2.13 | 0.34 | 67.8 | Eutrophic | V | 2.78 | 0.46 | 67.34 | Eutrophic | V | |
3 | 2.34 | 0.30 | 66.9 | Eutrophic | V | 2.82 | 0.46 | 67.19 | Eutrophic | V | |
4 | 2.90 | 0.34 | 67.1 | Eutrophic | V | 2.43 | 0.46 | 66.96 | Eutrophic | V | |
5 | 1.87 | 0.30 | 65.2 | Eutrophic | V | 2.15 | 0.44 | 66.19 | Eutrophic | V | |
6 | 1.93 | 0.29 | 64.5 | Eutrophic | V | 2.60 | 0.46 | 66.30 | Eutrophic | V | |
7 | 1.90 | 0.31 | 64.2 | Eutrophic | V | 2.80 | 0.47 | 66.07 | Eutrophic | V | |
8 | 2.43 | 0.38 | 66.8 | Eutrophic | V | / | / | / | / | / | |
9 | 2.67 | 0.42 | 67.6 | Eutrophic | V | / | / | / | / | / | |
Average | 2.62 | 0.37 | 67.4 | Eutrophic | V | 2.53 | 0.46 | 66.65 | Eutrophic | V | |
XYH-2 | 0 | 1.98 | 0.28 | 65.22 | Eutrophic | V | / | / | / | / | / |
1 | 1.84 | 0.29 | 65.79 | Eutrophic | V | / | / | / | / | / | |
2 | 2.11 | 0.29 | 64.37 | Upper-mesotrophic | V | / | / | / | / | / | |
3 | 1.66 | 0.29 | 65.06 | Eutrophic | V | / | / | / | / | / | |
4 | 1.80 | 0.29 | 64.99 | Upper-mesotrophic | V | / | / | / | / | / | |
5 | 1.96 | 0.28 | 64.36 | Eutrophic | V | / | / | / | / | / | |
6 | 3.44 | 0.29 | 63.83 | Upper-mesotrophic | V | / | / | / | / | / | |
7 | 2.14 | 0.29 | 63.49 | Upper-mesotrophic | V | / | / | / | / | / | |
8 | 2.49 | 0.29 | 63.58 | Upper-mesotrophic | V | / | / | / | / | / | |
9 | 2.65 | 0.38 | 65.19 | Eutrophic | V | / | / | / | / | / | |
Average | 2.2 | 0.30 | 64.59 | Eutrophic | V | / | / | / | / | / | |
XYH-3 | 0 | 2.06 | 0.26 | 64.24 | Upper-mesotrophic | V | 2.24 | 0.42 | 63.98 | Upper-mesotrophic | V |
1 | 1.95 | 0.29 | 65.62 | Eutrophic | V | 1.94 | 0.43 | 64.48 | Upper-mesotrophic | V | |
2 | 1.91 | 0.28 | 63.44 | Upper-mesotrophic | V | 1.91 | 0.44 | 65.46 | Eutrophic | V | |
3 | 1.77 | 0.28 | 63.44 | Upper-mesotrophic | V | 2.32 | 0.42 | 65.46 | Eutrophic | V | |
4 | 1.83 | 0.29 | 63.74 | Upper-mesotrophic | V | 2.61 | 0.43 | 65.42 | Eutrophic | V | |
5 | 1.98 | 0.30 | 63.87 | Upper-mesotrophic | V | 2.35 | 0.44 | 65.46 | Eutrophic | V | |
6 | 1.91 | 0.28 | 64.00 | Upper-mesotrophic | V | 1.71 | 0.43 | 65.22 | Eutrophic | V | |
7 | 2.10 | 0.35 | 64.22 | Upper-mesotrophic | V | 1.76 | 0.44 | 65.28 | Eutrophic | V | |
8 | 2.18 | 0.33 | 63.75 | Upper-mesotrophic | V | 1.66 | 0.44 | 65.65 | Eutrophic | V | |
Average | 1.97 | 0.30 | 64.04 | Upper-mesotrophic | V | 2.06 | 0.43 | 65.16 | Eutrophic | V | |
XYH-4 | 0 | 1.41 | 0.24 | 62.30 | Upper-mesotrophic | Ⅳ | / | / | / | / | / |
1 | 1.84 | 0.27 | 63.38 | Upper-mesotrophic | V | / | / | / | / | / | |
2 | 2.00 | 0.26 | 63.22 | Upper-mesotrophic | V | / | / | / | / | / | |
3 | 1.90 | 0.28 | 64.29 | Upper-mesotrophic | V | / | / | / | / | / | |
4 | 2.74 | 0.31 | 65.14 | Eutrophic | V | / | / | / | / | / | |
5 | 2.09 | 0.31 | 65.48 | Eutrophic | V | / | / | / | / | / | |
6 | 2.46 | 0.35 | 65.97 | Eutrophic | V | / | / | / | / | / | |
7 | 2.55 | 0.35 | 65.55 | Eutrophic | V | / | / | / | / | / | |
Average | 2.12 | 0.30 | 64.42 | Upper-mesotrophic | V | / | / | / | / | / | |
XYH-5 | 0 | 1.93 | 0.27 | 65.05 | Eutrophic | V | 1.94 | 0.45 | 65.30 | Eutrophic | V |
1 | 1.86 | 0.27 | 65.51 | Eutrophic | V | 1.84 | 0.47 | 65.58 | Eutrophic | V | |
2 | 1.74 | 0.26 | 64.63 | Upper-mesotrophic | V | 2.90 | 0.46 | 65.74 | Eutrophic | V | |
3 | 1.95 | 0.28 | 64.77 | Upper-mesotrophic | V | 2.82 | 0.46 | 65.54 | Eutrophic | V | |
4 | 2.28 | 0.31 | 66.26 | Eutrophic | V | 2.04 | 0.54 | 66.85 | Eutrophic | V | |
5 | 2.38 | 0.36 | 67.48 | Eutrophic | V | 2.02 | 0.48 | 66.11 | Eutrophic | V | |
Average | 2.02 | 0.29 | 65.62 | Upper-mesotrophic | V | 2.26 | 0.48 | 65.85 | Eutrophic | V |
Monitoring Site | Depth (m) | 2017.5 | 2017.10 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
TN (mg/L) | TP (mg/L) | Average | Trophic Level | Category | TN (mg/L) | TP (mg/L) | Average | Trophic Level | Category | ||
XYH-1 | 0 | 3.2 | 0.31 | 65.07 | Upper-mesotrophic | V | 1.93 | 0.32 | 61.68 | Upper-mesotrophic | V |
1 | 2.61 | 0.29 | 65.50 | Upper-mesotrophic | V | 1.44 | 0.32 | 63.10 | Upper-mesotrophic | V | |
2 | 2.83 | 0.27 | 65.22 | Upper-mesotrophic | V | 1.31 | 0.31 | 62.98 | Upper-mesotrophic | V | |
3 | 2.63 | 0.26 | 64.78 | Upper-mesotrophic | V | 1.31 | 0.31 | 63.06 | Upper-mesotrophic | V | |
4 | 2.19 | 0.27 | 65.34 | Upper-mesotrophic | V | 1.21 | 0.43 | 64.97 | Upper-mesotrophic | V | |
5 | 2.32 | 0.27 | 65.05 | Upper-mesotrophic | V | 1.21 | 0.33 | 63.18 | Upper-mesotrophic | V | |
6 | 2.02 | 0.28 | 65.37 | Upper-mesotrophic | V | 1.35 | 0.33 | 63.06 | Upper-mesotrophic | V | |
7 | 2.39 | 0.34 | 66.42 | Eutrophic | V | 1.24 | 0.33 | 62.89 | Upper-mesotrophic | V | |
8 | 2.61 | 0.36 | 66.43 | Eutrophic | V | 1.31 | 0.34 | 63.15 | Upper-mesotrophic | V | |
9 | 2.59 | 0.35 | 66.52 | Eutrophic | V | 1.28 | 0.42 | 64.39 | Upper-mesotrophic | V | |
Average | 2.54 | 0.30 | 65.57 | Upper-mesotrophic | V | 1.36 | 0.34 | 63.25 | Upper-mesotrophic | V | |
XYH-3 | 0 | 2.75 | 0.31 | 64.77 | Upper-mesotrophic | V | 1.76 | 0.33 | 63.00 | Upper-mesotrophic | V |
1 | 2.44 | 0.32 | 65.73 | Upper-mesotrophic | V | 1.77 | 0.33 | 63.72 | Upper-mesotrophic | V | |
2 | 2.05 | 0.30 | 65.38 | Upper-mesotrophic | V | 1.66 | 0.33 | 64.33 | Upper-mesotrophic | V | |
3 | 2.43 | 0.30 | 65.46 | Upper-mesotrophic | V | 1.71 | 0.29 | 63.75 | Upper-mesotrophic | V | |
4 | 2.06 | 0.27 | 65.23 | Upper-mesotrophic | V | 1.67 | 0.28 | 63.20 | Upper-mesotrophic | V | |
5 | 2.41 | 0.29 | 65.94 | Upper-mesotrophic | V | 1.45 | 0.31 | 63.21 | Upper-mesotrophic | V | |
6 | 2.08 | 0.35 | 67.35 | Eutrophic | V | 1.44 | 0.31 | 63.08 | Upper-mesotrophic | V | |
7 | 2.03 | 0.45 | 68.68 | Eutrophic | V | 1.06 | 0.33 | 63.43 | Upper-mesotrophic | V | |
8 | / | / | / | / | / | 1.16 | 0.36 | 63.70 | Upper-mesotrophic | V | |
Average | 2.28 | 0.33 | 66.07 | Eutrophic | V | 1.52 | 0.32 | 63.49 | Upper-mesotrophic | V | |
XYH-5 | 0 | 1.87 | 0.34 | 65.69 | Upper-mesotrophic | V | 1.64 | 0.34 | 68.62 | Eutrophic | V |
1 | 1.45 | 0.30 | 65.11 | Upper-mesotrophic | V | 1.73 | 0.38 | 69.77 | Eutrophic | V | |
2 | 1.78 | 0.31 | 65.17 | Upper-mesotrophic | V | 1.62 | 0.33 | 68.62 | Eutrophic | V | |
3 | 1.94 | 0.31 | 65.02 | Upper-mesotrophic | V | 1.61 | 0.35 | 68.43 | Eutrophic | V | |
4 | 2.27 | 0.32 | 65.74 | Upper-mesotrophic | V | 1.25 | 0.31 | 66.63 | Eutrophic | V | |
5 | 2.24 | 0.33 | 65.61 | Upper-mesotrophic | V | 1.31 | 0.31 | 66.17 | Eutrophic | V | |
Average | 1.93 | 0.32 | 65.39 | Upper-mesotrophic | V | 1.53 | 0.34 | 68.04 | Eutrophic | V |
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Zeng, Y.; Chang, F.; Wen, X.; Duan, L.; Zhang, Y.; Liu, Q.; Zhang, H. Seasonal Variation in the Water Quality and Eutrophication of Lake Xingyun in Southwestern China. Water 2022, 14, 3677. https://doi.org/10.3390/w14223677
Zeng Y, Chang F, Wen X, Duan L, Zhang Y, Liu Q, Zhang H. Seasonal Variation in the Water Quality and Eutrophication of Lake Xingyun in Southwestern China. Water. 2022; 14(22):3677. https://doi.org/10.3390/w14223677
Chicago/Turabian StyleZeng, Yanbo, Fengqin Chang, Xinyu Wen, Lizeng Duan, Yang Zhang, Qi Liu, and Hucai Zhang. 2022. "Seasonal Variation in the Water Quality and Eutrophication of Lake Xingyun in Southwestern China" Water 14, no. 22: 3677. https://doi.org/10.3390/w14223677