Submitted:
06 September 2024
Posted:
10 September 2024
You are already at the latest version
Abstract
Keywords:
1. Introduction
2. Causes of Eutrophication

3. Effects of Eutrophication on Freshwater Ecosystems
4. Assessment and Monitoring of Eutrophication
4.1. Indicators and Parameters
4.2. Sampling and Analysis Methods
- Sampling Strategies
- 2.
- Laboratory Analysis
- Spectrophotometry is commonly used for nutrient analysis, particularly for total nitrogen (TN) and total phosphorus (TP). This method provides quantitative data essential for assessing eutrophication status.
- Fluorimetry is the preferred method for chlorophyll-a measurement, offering high sensitivity and specificity for this key indicator of algal biomass.
- Titration is typically used for dissolved oxygen determination, providing crucial information about the oxygen status of the water body.
- 3.
- Biological Assessments
- Phytoplankton: Microscopy is used to identify and quantify different algal species, providing insights into the primary producers driving eutrophication.
- Zooplankton: Similar microscopic techniques are employed to assess the abundance and diversity of these important grazers.
- Benthic invertebrates: These organisms are often collected using specialized sampling equipment and then identified and counted under a microscope.
- Molecular techniques: Advanced methods like DNA barcoding or metabarcoding are increasingly used for more precise identification of species, especially for microorganisms.
4.3. Remote Sensing and Modeling Approaches
Remote Sensing
- Chlorophyll-a Detection: Satellites equipped with multispectral sensors can detect and quantify chlorophyll-a concentrations in surface waters. For example, the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) has been instrumental in providing global data on ocean color, which is closely related to phytoplankton abundance (Hussein & Assaf, 2020).
- Algal Bloom Monitoring: Remote sensing allows for the detection and tracking of algal blooms over vast areas. Sensors can detect changes in water color associated with these blooms, providing early warning systems for potentially harmful algal proliferation (Sebastiá-Frasquet et al., 2020)
- Water Clarity Assessment: Satellite data can be used to estimate water transparency, often correlated with the trophic state of water bodies. This is particularly useful for monitoring changes in water clarity over time, which can indicate progressing eutrophication (Hu et al., 2021).
- Temperature Monitoring: Thermal sensors on satellites can measure surface water temperatures, which is crucial for understanding the physical conditions that may promote algal growth and stratification in water bodies (Bouffard et al., 2018).
Hydrodynamic and Ecological Models
- Nutrient Loading Models: Tools like the Soil and Water Assessment Tool (SWAT) simulate the movement of nutrients from terrestrial sources into water bodies. These models help in identifying major nutrient sources and predicting how changes in land use or management practices might affect nutrient inputs (Janjić & Tadić, 2023).
- Water Quality Models: The Water Quality Analysis Simulation Program (WASP) is an example of a model that simulates various water quality parameters, including dissolved oxygen, nutrients, and algal biomass. Such models can predict how changes in nutrient loads might affect water quality over time.
- Ecosystem Models: More complex ecological models integrate physical, chemical, and biological processes to simulate entire ecosystem responses to eutrophication. These models can include factors such as food web dynamics and how changes in nutrient levels might cascade through different trophic levels.
- Climate Change Integration: Advanced models now incorporate climate change scenarios, allowing for predictions of how warming temperatures and changing precipitation patterns might interact with eutrophication processes in the future(Meerhoff et al., 2022).
Integrated Assessment Tools
- Data Fusion: Techniques for combining data from multiple sources (satellite, in-situ sensors, and field sampling) have greatly improved. This allows for more robust and comprehensive assessments of water quality and eutrophication status (Batina & Krtalić, 2024).
- Real-time Monitoring Systems: Integration of remote sensing with in-situ sensors enables real-time or near-real-time monitoring of water bodies. For example, buoy systems equipped with various sensors can provide continuous data on parameters like dissolved oxygen, chlorophyll-a, and nutrient levels, which can be combined with satellite observations for a more complete picture (Gholizadeh et al., 2016).
- Machine Learning and AI: Advanced algorithms are increasingly used to analyze the vast amounts of data generated by these various sources. Machine learning techniques can help in identifying patterns, predicting algal blooms, and even automating the classification of satellite imagery for eutrophication assessment (Tyagi & Chahal, 2020).
- Decision Support Systems: Integrated tools that combine all these data sources and analytical techniques are being developed to support decision-making. These systems can provide water managers with up-to-date information on water quality, predictions of future conditions, and assessments of different management options (Liu et al., 2010).
5. Management Strategies
5.1. Preventive Measures
Nutrient Reduction at Source
Land Use Management
Best Agricultural Practices
5.2. Remediation Techniques
Bio-Manipulation
Chemical Treatments
Aeration and Circulation
5.3. Policy and Regulatory Approaches
Water Quality Standards
Nutrient Trading Programs
International Cooperation
6. Case Studies
6.1. Successful Management Examples
6.2. Challenges and Lessons Learned
7. Future Perspectives
7.1. Emerging Technologies for Eutrophication Management
7.2. Research Needs and Knowledge Gaps
7.3. Potential Impacts of Climate Change on Management Strategies
8. Conclusion
Funding
Conflict of Interest
List of Abbreviations
| AI | Artificial Intelligence |
| BOD | Biological Oxygen Demand |
| DO | Dissolved Oxygen |
| HABs | Harmful Algal Blooms |
| LVEMP | Lake Victoria Environmental Management Project |
| MTR | Mean Tropic Rank |
| N | Nitrogen |
| P | Phosphorus |
| SeaWiFS | Sea-Viewing Wide Field-of-View Sensor |
| TN | Total Nitrogen |
| TP | Total Phosphorus |
| WASP | Water Quality Analysis Simulation Program |
| WFD | Water Framework Directive |
| SWAT | Soil and Water Assessment Tool |
References
- Batina, A.; Krtalić, A. Integrating Remote Sensing Methods for Monitoring Lake Water Quality: A Comprehensive Review. Hydrology 2024, 11, 92. [Google Scholar] [CrossRef]
- Beaulieu, J.J.; DelSontro, T.; Downing, J.A. Eutrophication will increase methane emissions from lakes and impoundments during the 21st century. Nat. Commun. 2019, 10, 1375. [Google Scholar] [CrossRef]
- Bergström, U.; Sundblad, G.; Downie, A.; Snickars, M.; Boström, C.; Lindegarth, M. Evaluating eutrophication management scenarios in the Baltic Sea using species distribution modelling. J. Appl. Ecol. 2013, 50, 680–690. [Google Scholar] [CrossRef]
- Bezerra, L.A.V.; Angelini, R.; Vitule, J.R.S.; Coll, M.; Sánchez-Botero, J.I. Food web changes associated with drought and invasive species in a tropical semiarid reservoir. Hydrobiologia 2018, 817, 475–489. [Google Scholar] [CrossRef]
- Birgand, F.; Aveni-Deforge, K.; Smith, B.; Maxwell, B.; Horstman, M.; Gerling, A.B.; Carey, C.C. First report of a novel multiplexer pumping system coupled to a water quality probe to collect high temporal frequency in situ water chemistry measurements at multiple sites. Limnol. Oceanogr. Methods 2016, 14, 767–783. [Google Scholar] [CrossRef]
- Blaen, P.J.; Khamis, K.; Lloyd, C.E.M.; Bradley, C.; Hannah, D.; Krause, S. Real-time monitoring of nutrients and dissolved organic matter in rivers: Capturing event dynamics, technological opportunities and future directions. Sci. Total Environ. 2016, 569, 647–660. [Google Scholar] [CrossRef]
- Bouffard, D.; Kiefer, I.; Wüest, A.; Wunderle, S.; Odermatt, D. Are surface temperature and chlorophyll in a large deep lake related? An analysis based on satellite observations in synergy with hydrodynamic modelling and in-situ data. Remote Sens. Environ. 2018, 209, 510–523. [Google Scholar] [CrossRef]
- Cantoral Uriza, E.A. Cyanotoxins bioaccumulation in freshwater ecosystems in Latin America: a review. Hidrobiológica 2023, 33, 353–365. [Google Scholar] [CrossRef]
- Chang, N.-B.; Imen, S.; Vannah, B. Remote sensing for monitoring surface water quality status and ecosystem state in relation to the nutrient cycle: a 40-year perspective. Crit. Rev. Environ. Sci. Technol. 2015, 45, 101–166. [Google Scholar] [CrossRef]
- Cook, S.C.; Housley, L.; Back, J.A.; King, R.S. Freshwater eutrophication drives sharp reductions in temporal beta diversity. Ecology 2018, 99, 47–56. [Google Scholar] [CrossRef]
- Cuni-Sanchez, A.; Sullivan, M.J.P.; Platts, P.J.; Lewis, S.L.; Marchant, R.; Imani, G.; Hubau, W.; Abiem, I.; Adhikari, H.; Albrecht, T.; et al. High aboveground carbon stock of African tropical montane forests. Nature 2021, 596, 536–542. [Google Scholar] [CrossRef]
- Ding, S.; Chen, M.; Gong, M.; Fan, X.; Qin, B.; Xu, H.; Gao, S.; Jin, Z.; Tsang, D.C.W.; Zhang, C. Internal phosphorus loading from sediments causes seasonal nitrogen limitation for harmful algal blooms. Sci. Total Environ. 2018, 625, 872–884. [Google Scholar] [CrossRef]
- Dodds, W.K.; Bouska, W.W.; Eitzmann, J.L.; Pilger, T.J.; Pitts, K.L.; Riley, A.J.; Schloesser, J.T.; Thornbrugh, D.J. Eutrophication of U.S. Freshwaters: Analysis of Potential Economic Damages. Environ. Sci. Technol. 2009, 43, 12–19. [Google Scholar] [CrossRef]
- El-Sheekh, M.; Abdel-Daim, M.M.; Okba, M.; Gharib, S.; Soliman, A.; El-Kassas, H. Green technology for bioremediation of the eutrophication phenomenon in aquatic ecosystems: a review. African J. Aquat. Sci. 2021, 46, 274–292. [Google Scholar] [CrossRef]
- Ephraim Motaroki Menge Evaluating the impact of land-use change on Arbuscular Mycorrhizal Fungi (AMF) diversity and function. Int. J. Sci. Res. Arch. 2023, 10, 546–556. [CrossRef]
- Firehun, Y.; Struik, P.C.; Lantinga, E.A.; Taye, T. Joint use of insects and fungal pathogens in the management of water hyacinth (Eichhornia crassipes): perspectives for Ethiopia. J. Aquat. Plant Manag. 2013, 51, 109–121. [Google Scholar]
- Gallardo, B.; Clavero, M.; Sánchez, M.I.; Vilà, M. Global ecological impacts of invasive species in aquatic ecosystems. Glob. Chang. Biol. 2016, 22, 151–163. [Google Scholar] [CrossRef]
- García–Nieto, P.J.; García–Gonzalo, E.; Fernández, J.R.A.; Muñiz, C.D. Forecast of chlorophyll-a concentration as an indicator of phytoplankton biomass in El Val reservoir by utilizing various machine learning techniques: A case study in Ebro river basin, Spain. J. Hydrol. 2024, 639, 131639. [Google Scholar] [CrossRef]
- Gholizadeh, M.H.; Melesse, A.M.; Reddi, L. A comprehensive review on water quality parameters estimation using remote sensing techniques. Sensors 2016, 16, 1298. [Google Scholar] [CrossRef]
- Glibert, P.M. Eutrophication, harmful algae and biodiversity — Challenging paradigms in a world of complex nutrient changes. Mar. Pollut. Bull. 2017, 124, 591–606. [Google Scholar] [CrossRef]
- Golubkov, M.S.; Golubkov, S.M. Secchi Disk Depth or Turbidity, Which Is Better for Assessing Environmental Quality in Eutrophic Waters? A Case Study in a Shallow Hypereutrophic Reservoir. Water 2023, 16, 18. [Google Scholar] [CrossRef]
- Griffith, A.W.; Gobler, C.J. Harmful algal blooms: A climate change co-stressor in marine and freshwater ecosystems. Harmful Algae 2020, 91, 101590. [Google Scholar] [CrossRef]
- Grizzetti, B.; Vigiak, O.; Udias, A.; Aloe, A.; Zanni, M.; Bouraoui, F.; Pistocchi, A.; Dorati, C.; Friedland, R.; De Roo, A.; et al. How EU policies could reduce nutrient pollution in European inland and coastal waters. Glob. Environ. Chang. 2021, 69, 102281. [Google Scholar] [CrossRef]
- Hu, M.; Ma, R.; Cao, Z.; Xiong, J.; Xue, K. Remote estimation of trophic state index for inland waters using Landsat-8 OLI imagery. Remote Sens. 2021, 13, 1988. [Google Scholar] [CrossRef]
- Hussein, N.M.; Assaf, M.N. Multispectral Remote Sensing Utilization for Monitoring Chlorophyll-a Levels in Inland Water Bodies in Jordan. Sci. World J. 2020, 2020, 5060969. [Google Scholar] [CrossRef]
- Hwang, S.-J. Eutrophication and the Ecological Health Risk. Int. J. Environ. Res. Public Health 2020, 17, 6332. [Google Scholar] [CrossRef]
- Intasan, W.; Chaichana, R.; Anurakpongsatorn, P. Efficiency of Glutinous Rice Straw Extracts (RD-Six) and Water Hyacinth in Inhibiting Algal Growth and Reducing Nutrients from a Hyper-eutrophic Pond. Environ. Nat. Resour. J. 2021, 19, 24–33. [Google Scholar] [CrossRef]
- Jacobson, P.C.; Hansen, G.J.A.; Bethke, B.J.; Cross, T.K. Disentangling the effects of a century of eutrophication and climate warming on freshwater lake fish assemblages. PLoS One 2017, 12, e0182667. [Google Scholar] [CrossRef]
- Janjić, J.; Tadić, L. Fields of application of SWAT hydrological model—a review. Earth 2023, 4, 331–344. [Google Scholar] [CrossRef]
- Jaroszewicz, B.; Cholewińska, O.; Chećko, E.; Wrzosek, M. Predictors of diversity of deadwood-dwelling macrofungi in a European natural forest. For. Ecol. Manage. 2021, 490. [Google Scholar] [CrossRef]
- Jarvie, H.P.; Sharpley, A.N.; Withers, P.J.A.; Scott, J.T.; Haggard, B.E.; Neal, C. Phosphorus Mitigation to Control River Eutrophication: Murky Waters, Inconvenient Truths, and “Postnormal” Sciencea. J. Environ. Qual. 2013, 42, 295–304. [Google Scholar] [CrossRef]
- Jilbert, T.; Couture, R.-M.; Huser, B.J.; Salonen, K. Preface: Restoration of eutrophic lakes: current practices and future challenges. Hydrobiologia 2020, 847, 4343–4357. [Google Scholar] [CrossRef]
- Kakade, A.; Salama, E.-S.; Han, H.; Zheng, Y.; Kulshrestha, S.; Jalalah, M.; Harraz, F.A.; Alsareii, S.A.; Li, X. World eutrophic pollution of lake and river: Biotreatment potential and future perspectives. Environ. Technol. Innov. 2021, 23, 101604. [Google Scholar] [CrossRef]
- Keerthi Reddy, B.; Meena, S.; Brath Gautam, P.; Kumar Meena, K.; Chandra Rai, D. Optical, thermal, FTIR, SEM-EDX and 1H NMR analysis of Chenopodium album (Bathua) powder prepared using different drying techniques. Microchem. J. 2024, 201, 110537. [Google Scholar] [CrossRef]
- Kratina, P.; Greig, H.S.; Thompson, P.L.; Carvalho-Pereira, T.S.A.; Shurin, J.B. Warming modifies trophic cascades and eutrophication in experimental freshwater communities. Ecology 2012, 93, 1421–1430. [Google Scholar] [CrossRef]
- Landry, J.S.; Ramankutty, N. Carbon cycling, climate regulation, and disturbances in Canadian Forests: Scientific principles for management. Land 2015, 4, 83–118. [Google Scholar] [CrossRef]
- Le Moal, M.; Gascuel-Odoux, C.; Ménesguen, A.; Souchon, Y.; Étrillard, C.; Levain, A.; Moatar, F.; Pannard, A.; Souchu, P.; Lefebvre, A.; et al. Eutrophication: A new wine in an old bottle? Sci. Total Environ. 2019, 651, 1–11. [Google Scholar] [CrossRef]
- Liu, S.; Duffy, A.H.B.; Whitfield, R.I.; Boyle, I.M. Integration of decision support systems to improve decision support performance. Knowl. Inf. Syst. 2010, 22, 261–286. [Google Scholar] [CrossRef]
- Loiselle, S.A.; Gasparini Fernandes Cunha, D.; Shupe, S.; Valiente, E.; Rocha, L.; Heasley, E.; Belmont, P.P.; Baruch, A. Micro and Macroscale Drivers of Nutrient Concentrations in Urban Streams in South, Central and North America. PLoS One 2016, 11, e0162684. [Google Scholar] [CrossRef]
- Malone, T.C.; Newton, A. The Globalization of Cultural Eutrophication in the Coastal Ocean: Causes and Consequences. Front. Mar. Sci. 2020, 7. [Google Scholar] [CrossRef]
- Meerhoff, M.; Audet, J.; Davidson, T.A.; De Meester, L.; Hilt, S.; Kosten, S.; Liu, Z.; Mazzeo, N.; Paerl, H.; Scheffer, M.; et al. Feedback between climate change and eutrophication: revisiting the allied attack concept and how to strike back. Inl. Waters 2022, 12, 187–204. [Google Scholar] [CrossRef]
- Mishra, R.K. The Effect of Eutrophication on Drinking Water. Br. J. Multidiscip. Adv. Stud. 2023, 4, 7–20. [Google Scholar] [CrossRef]
- Nielsen, R.; Hoff, A.; Waldo, S.; Hammarlund, C.; Virtanen, J. Fishing for nutrients – economic effects of fisheries management targeting eutrophication in the Baltic Sea. Ecol. Econ. 2019, 160, 156–167. [Google Scholar] [CrossRef]
- Paul, B.; Purkayastha, K. Das; Bhattacharya, S.; Gogoi, N. Eco-bioengineering tools in ecohydrological assessment of eutrophic water bodies. Ecotoxicology 2022, 31, 581–601. [Google Scholar] [CrossRef]
- Pesce, M.; Critto, A.; Torresan, S.; Giubilato, E.; Santini, M.; Zirino, A.; Ouyang, W.; Marcomini, A. Modelling climate change impacts on nutrients and primary production in coastal waters. Sci. Total Environ. 2018, 628–629, 919–937. [Google Scholar] [CrossRef]
- Qin, B.; Zhou, J.; Elser, J.J.; Gardner, W.S.; Deng, J.; Brookes, J.D. Water Depth Underpins the Relative Roles and Fates of Nitrogen and Phosphorus in Lakes. Environ. Sci. Technol. 2020, 54, 3191–3198. [Google Scholar] [CrossRef]
- Randrianasolo, Z.H.; Razafimahatratra, A.R.; Razafinarivo, R.N.G.; Randrianary, T.; Rakotovololonalimanana, H.; Rajemison, A.H.; Mamitiana, A.; Andriamanalina, R.L.; Rakotosoa, A.; Ramananantoandro, T. Which allometric models are the most appropriate for estimating aboveground biomass in secondary forests of Madagascar with Ravenala madagascariensis? Sci. African 2019, 6, e00147. [Google Scholar] [CrossRef]
- Raza, A.; Razzaq, A.; Mehmood, S.S.; Zou, X.; Zhang, X.; Lv, Y. Impact of Climate Change on Crops Adaptation and Strategies to Tackle Its Outcome : A Review. Plants 2019, 8, 34. [Google Scholar] [CrossRef]
- Robson, B.J. When do aquatic systems models provide useful predictions, what is changing, and what is next? Environ. Model. Softw. 2014, 61, 287–296. [Google Scholar] [CrossRef]
- Rodgers, E.M. Adding climate change to the mix: responses of aquatic ectotherms to the combined effects of eutrophication and warming. Biol. Lett. 2021, 17. [Google Scholar] [CrossRef]
- Rosset, V.; Angélibert, S.; Arthaud, F.; Bornette, G.; Robin, J.; Wezel, A.; Vallod, D.; Oertli, B. Is eutrophication really a major impairment for small waterbody biodiversity? J. Appl. Ecol. 2014, 51, 415–425. [Google Scholar] [CrossRef]
- Sagova-Mareckova, M.; Boenigk, J.; Bouchez, A.; Cermakova, K.; Chonova, T.; Cordier, T.; Eisendle, U.; Elersek, T.; Fazi, S.; Fleituch, T. Expanding ecological assessment by integrating microorganisms into routine freshwater biomonitoring. Water Res. 2021, 191, 116767. [Google Scholar] [CrossRef]
- Savage, C.; Leavitt, P.R.; Elmgren, R. Effects of land use, urbanization, and climate variability on coastal eutrophication in the Baltic Sea. Limnol. Oceanogr. 2010, 55, 1033–1046. [Google Scholar] [CrossRef]
- Schneider, S.C.; Biberdžić, V.; Braho, V.; Gjoreska, B.B.; Cara, M.; Dana, Z.; Đurašković, P.; Eriksen, T.E.; Hjermann, D.; Imeri, A. Littoral eutrophication indicators are more closely related to nearshore land use than to water nutrient concentrations: A critical evaluation of stressor-response relationships. Sci. Total Environ. 2020, 748, 141193. [Google Scholar] [CrossRef]
- Sebastiá-Frasquet, M.-T.; Aguilar-Maldonado, J.-A.; Herrero-Durá, I.; Santamaría-del-Ángel, E.; Morell-Monzó, S.; Estornell, J. Advances in the monitoring of algal blooms by remote sensing: A bibliometric analysis. Appl. Sci. 2020, 10, 7877. [Google Scholar] [CrossRef]
- Seitzinger, S.P.; Mayorga, E.; Bouwman, A.F.; Kroeze, C.; Beusen, A.H.W.; Billen, G.; Van Drecht, G.; Dumont, E.; Fekete, B.M.; Garnier, J.; et al. Global river nutrient export: A scenario analysis of past and future trends. Global Biogeochem. Cycles 2010, 24. [Google Scholar] [CrossRef]
- Sharpley, A. Managing agricultural phosphorus to minimize water quality impacts. Sci. Agric. 2016, 73, 1–8. [Google Scholar] [CrossRef]
- Silvenius, F.; Grönman, K.; Katajajuuri, J.-M.; Soukka, R.; Koivupuro, H.-K.; Virtanen, Y. The Role of Household Food Waste in Comparing Environmental Impacts of Packaging Alternatives. Packag. Technol. Sci. 2014, 27, 277–292. [Google Scholar] [CrossRef]
- Sinha, E.; Michalak, A.M.; Balaji, V. Eutrophication will increase during the 21st century as a result of precipitation changes. Science (80-. ). 2017, 357, 405–408. [Google Scholar] [CrossRef]
- Stuart, M.B.; McGonigle, A.J.S.; Willmott, J.R. Hyperspectral Imaging in Environmental Monitoring: A Review of Recent Developments and Technological Advances in Compact Field Deployable Systems. Sensors 2019, Vol. 19, Page 3071 2019, 19, 3071. [Google Scholar] [CrossRef]
- Sun, H.; Lu, X.; Yu, R.; Yang, J.; Liu, X.; Cao, Z.; Zhang, Z.; Li, M.; Geng, Y. Eutrophication decreased CO2 but increased CH4 emissions from lake: A case study of a shallow Lake Ulansuhai. Water Res. 2021, 201, 117363. [Google Scholar] [CrossRef]
- Sun, Y.-F.; Guo, Y.; Xu, C.; Liu, Y.; Zhao, X.; Liu, Q.; Jeppesen, E.; Wang, H.; Xie, P. Will “Air Eutrophication” Increase the Risk of Ecological Threat to Public Health? Environ. Sci. Technol. 2023, 57, 10512–10520. [Google Scholar] [CrossRef]
- Sundblad, G.; Bergström, L.; Söderqvist, T.; Bergström, U. Predicting the effects of eutrophication mitigation on predatory fish biomass and the value of recreational fisheries. Ambio 2020, 49, 1090–1099. [Google Scholar] [CrossRef]
- Suresh, K.; Tang, T.; van Vliet, M.T.H.; Bierkens, M.F.P.; Strokal, M.; Sorger-Domenigg, F.; Wada, Y. Recent advancement in water quality indicators for eutrophication in global freshwater lakes. Environ. Res. Lett. 2023, 18, 063004. [Google Scholar] [CrossRef]
- Tamm, M.; Freiberg, R.; Tõnno, I.; Nõges, P.; Nõges, T. Pigment-Based Chemotaxonomy - A Quick Alternative to Determine Algal Assemblages in Large Shallow Eutrophic Lake? PLoS One 2015, 10, e0122526. [Google Scholar] [CrossRef]
- Tekile, A.; Kim, I.; Lee, J.-Y. 200 kHz Sonication of Mixed-Algae Suspension from a Eutrophic Lake: The Effect on the Caution vs. Outbreak Bloom Alert Levels. Water 2017, 9, 915. [Google Scholar] [CrossRef]
- Tewabe, D.; Asmare, E.; Zelalem, W.; Mohamed, B. Identification of impacts, some biology of water hyacinth (Eichhornia crassipes) and its management options in Lake Tana, Ethiopia. Net J. Agric. Sci. 2017, 5, 8–15. [Google Scholar] [CrossRef]
- Tyagi, A.K.; Chahal, P. Artificial intelligence and machine learning algorithms. In Challenges and applications for implementing machine learning in computer vision; IGI Global, 2020; pp. 188–219. [Google Scholar]
- Van Caneghem, J.; De Greef, J.; Block, C.; Vandecasteele, C. NOx reduction in waste incinerators by selective catalytic reduction (SCR) instead of selective non catalytic reduction (SNCR) compared from a life cycle perspective: a case study. J. Clean. Prod. 2016, 112, 4452–4460. [Google Scholar] [CrossRef]
- Van Ginkel, C. Eutrophication: Present reality and future challenges for South Africa. Water SA 2011, 37. [Google Scholar] [CrossRef]
- VanBavel, E.; Tuna, B.G. Integrative Modeling of Small Artery Structure and Function Uncovers Critical Parameters for Diameter Regulation. PLoS One 2014, 9, e86901. [Google Scholar] [CrossRef]
- Vantarakis, A. Eutrophication and Public Health. Chem. Lake Restor. Technol. Innov. Econ. Perspect. 2021, 23–47. [Google Scholar]
- Wan, Q.; Song, H.; Ju, K.; Zhang, X.; Wang, C.; Li, L.; Xue, F. Purification of Eutrophic River Water Using a Biological Aerated Filter with Functional Filler. Polish J. Environ. Stud. 2021, 30, 1841–1852. [Google Scholar] [CrossRef]
- Wang, H.; García Molinos, J.; Heino, J.; Zhang, H.; Zhang, P.; Xu, J. Eutrophication causes invertebrate biodiversity loss and decreases cross-taxon congruence across anthropogenically-disturbed lakes. Environ. Int. 2021, 153, 106494. [Google Scholar] [CrossRef]
- Wurtsbaugh, W.A.; Paerl, H.W.; Dodds, W.K. Nutrients, eutrophication and harmful algal blooms along the freshwater to marine continuum. WIREs Water 2019, 6. [Google Scholar] [CrossRef]
- Xu, D.; Cai, Y.; Jiang, H.; Wu, X.; Leng, X.; An, S. Variations of Food Web Structure and Energy Availability of Shallow Lake with Long-Term Eutrophication: A Case Study from Lake Taihu, China. CLEAN – Soil, Air, Water 2016, 44, 1306–1314. [Google Scholar] [CrossRef]
- Yan, Z.; Han, W.; Peñuelas, J.; Sardans, J.; Elser, J.J.; Du, E.; Reich, P.B.; Fang, J. Phosphorus accumulates faster than nitrogen globally in freshwater ecosystems under anthropogenic impacts. Ecol. Lett. 2016, 19, 1237–1246. [Google Scholar] [CrossRef]
- Yu, W. Regional Algae Bloom: Natural Disaster Causes Economic Setback in Private Fishing Charter in Southwest Florida. IOP Conf. Ser. Earth Environ. Sci. 2021, 657, 012046. [Google Scholar] [CrossRef]
- Zhan, Y.; Yao, Z.; Groffman, P.M.; Xie, J.; Wang, Y.; Li, G.; Zheng, X.; Butterbach-Bahl, K. Urbanization can accelerate climate change by increasing soil <scp> N 2 O </scp> emission while reducing <scp> CH 4 </scp> uptake. Glob. Chang. Biol. 2023, 29, 3489–3502. [Google Scholar] [CrossRef]
- Zhang, H.; Zhang, P.; Wang, H.; García Molinos, J.; Hansson, L.; He, L.; Zhang, M.; Xu, J. Synergistic effects of warming and eutrophication alert zooplankton predator–prey interactions along the benthic–pelagic interface. Glob. Chang. Biol. 2021, 27, 5907–5919. [Google Scholar] [CrossRef]
- Zhang, Y.; Li, M.; Dong, J.; Yang, H.; Van Zwieten, L.; Lu, H.; Alshameri, A.; Zhan, Z.; Chen, X.; Jiang, X.; Xu, W.; et al. A Critical Review of Methods for Analyzing Freshwater Eutrophication. Water 2021, 13, 225. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).