Lake Water Quality Monitoring Research Articles

Innovative lake pollution profiling: unveiling pollutant sources through advanced multivariate clustering techniques.

In many developed and developing nations, lakes are the primary source of drinking water. In the current scenario, due to rapid mobilization in anthropogenic activities, lakes are becoming increasingly contaminated. Such practices not only destroy lake ecosystems but also jeopardize human health through water-borne diseases. This study employs advanced hierarchical clustering through multivariate analysis to establish a novel method for concurrently identifying significantly polluted lakes and critical pollutants. A systematic approach has been devised to generate rotating component matrices, dendrograms, monoplots, and biplots by combining R-mode and Q-mode analyses. This enables the identification of contaminant sources and their grouping. A case study analyzing five lakes in Bengaluru, India, has been conducted to demonstrate the effectiveness of the proposed methodology. Additionally, one pristine lake from Jammu & Kashmir, India, has been included to validate the findings from the aforementioned five lakes. The study explored correlations among various physical, chemical, and biological characteristics such as temperature, pH, dissolved oxygen, conductivity, nitrates, biological oxygen demand (BOD), fecal coliform (FC), and total coliform (TC). Critical contaminants forming clusters included conductivity, nitrates, BOD, TC, and FC. Factor analysis identified four primary components that collectively accounted for 85% of the overall variance. Following identification of pollution hotspots, the study recommends source-based pollution control and integrated watershed management, which could significantly reduce lake pollution levels. Continuous monitoring of lake water quality is essential for identifying actual contaminant sources. These findings provide practical recommendations for maximizing restoration efforts, enforcing regulations on pollutant sources, and improving water quality conditions to ensure sustainable development of lakes.

Integrating Remote Sensing Methods for Monitoring Lake Water Quality: A Comprehensive Review

Remote sensing methods have the potential to improve lake water quality monitoring and decision-making in water management. This review discusses the use of remote sensing methods for monitoring and assessing water quality in lakes. It explains the principles of remote sensing and the different methods used for retrieving water quality parameters in complex waterbodies. The review highlights the importance of considering the variability of optically active parameters and the need for comprehensive studies that encompass different seasons and time frames. The paper addresses the specific physical and biological parameters that can be effectively estimated using remote sensing, such as chlorophyll-α, turbidity, water transparency (Secchi disk depth), electrical conductivity, surface salinity, and water temperature. It further provides a comprehensive summary of the bands, band combinations, and band equations commonly used for remote sensing of these parameters per satellite sensor. It also discusses the limitations of remote sensing methods and the challenges associated with satellite systems. The review recommends integrating remote sensing methods using in situ measurements and computer modelling to improve the understanding of water quality. It suggests future research directions, including the importance of optimizing grid selection and time frame for in situ measurements by combining hydrodynamic models with remote sensing retrieval methods, considering variability in water quality parameters when analysing satellite imagery, the development of advanced technologies, and the integration of machine learning algorithms for effective water quality problem-solving. The review concludes with a proposed workflow for monitoring and assessing water quality parameters in lakes using remote sensing methods.

Seasonal Monitoring Method for TN and TP Based on Airborne Hyperspectral Remote Sensing Images

Airborne sensing images harness the combined advantages of hyperspectral and high spatial resolution, offering precise monitoring methods for local-scale water quality parameters in small water bodies. This study employs airborne hyperspectral remote sensing image data to explore remote sensing estimation methods for total nitrogen (TN) and total phosphorus (TP) concentrations in Lake Dianshan, Yuandang, as well as its main inflow and outflow rivers. Our findings reveal the following: (1) Spectral bands between 700 and 750 nm show the highest correlation with TN and TP concentrations during the summer and autumn seasons. Spectral reflectance bands exhibit greater sensitivity to TN and TP concentrations compared to the winter and spring seasons. (2) Seasonal models developed using the Catboost method demonstrate significantly higher accuracy than other machine learning (ML) models. On the test set, the root mean square errors (RMSEs) are 0.6 mg/L for TN and 0.05 mg/L for TP concentrations, with average absolute percentage errors (MAPEs) of 23.77% and 25.14%, respectively. (3) Spatial distribution maps of the retrieved TN and TP concentrations indicate their dependence on exogenous inputs and close association with algal blooms. Higher TN and TP concentrations are observed near the inlet (Jishui Port), with reductions near the outlet (Lanlu Port), particularly for the TP concentration. Areas with intense algal blooms near shorelines generally exhibit higher TN and TP concentrations. This study offers valuable insights for processing small water bodies using airborne hyperspectral remote sensing images and provides reliable remote sensing techniques for lake water quality monitoring and management.

A preliminary study of direct observation and selected water quality monitoring in Putrajaya Lake: the status between October-December 2022

The objective of this study was to provide the status of water quality based on a direct observation and selected water quality parameters (nitrite, ammonia, and pH) monitoring study between October-December 2022. Between October and November 2022, the levels of the above three surface water quality parameters were measured in 6 different sites throughout Putrajaya Lake. Observational pictures of the sites were also taken, and water samples were collected. In addition to the above observation and water quality monitoring study, the rainfall data was collected from 17th October 2022 to 11th December 2022, cited from the official website of Public Infobanjir (https://publicinfobanjir.water.gov.my/?lang=en) at stations Sg. Anak Gajah and Sg. Limau Manis, was also used to understand the possible correlation. However, further studies are needed. It was found that the water quality status was in good condition, making Putrajaya Lake a safe and suitable lake for aquatic life and recreational activities between October -December 2022. This could be due to the sustainable practices and rainwater harvesting system already implemented at Putrajaya Lake. Putrajaya Lake's water quality monitoring and sustainability practices were well implemented. However, regular maintenance to provide acute monitoring of water quality data at Putrajaya Lakes is necessary in the future.

Water Quality Parameter Retrieval with GF5-AHSI Imagery for Dianchi Lake (China)

In response to the rapid changes in the chlorophyll-a concentration and eutrophication issues in lakes, with Dianchi Lake as an example, a remote sensing estimation model for chlorophyll-a, total phosphorus, and total nitrogen in Dianchi Lake was constructed using the three band method and ratio band method based on the visible-light shortwave infrared (AHSI) hyperspectral satellite data from Gaofen 5 (GF-5) and the water quality data collected at Dianchi Lake. The model results were compared with the multispectral data from the Gaofen 1 (GF-1) wide field-of-view (WFV) camera. The accuracy evaluation results indicate that the overall mean absolute percentage error of the remote sensing estimation models for chlorophyll a, total phosphorus, and total nitrogen are 7.658%, 4.511%, and 4.577%, respectively, which can meet the needs of lake water quality monitoring and evaluation. According to the remote sensing simulation results, chlorophyll a is mainly distributed in the northern part of Dianchi Lake, with phosphorus and nitrogen pollution throughout Dianchi Lake and relatively more abundant in the central and southern regions. The pollution is mainly concentrated in the northern and southern regions of Dianchi Lake, which is consistent with the actual situation. Further confirming the feasibility of using GF-5 satellite AHSI data for water quality parameter retrieval can provide new technical means for relevant departments to quickly and efficiently monitor the inland lake water environment.

Mapping of Chl-a in Indonesia’s Five Priority Lakes uSing Linear Regression (Case Studies: Lake Singkarak, Toba, Maninjau, Matano, and Towuti)

In Indonesia, 15 priority lakes need to be monitored regularly. Model algorithm development is the answer to accelerating lake water quality monitoring through chlorophyll-a concentration monitoring. This research aims to evaluate, develop, and find the most accurate global model algorithm for chlorophyll-a concentration mapping on Lake Singkarak, Toba, Maninjau, Matano, and Towuti. Algorithm development was made based on the existing 22-band ratio algorithm. Leave One Out Cross Validation (LOOCV) was used to evaluate the performance of all tested algorithms through the R2, NMAE, and RMSE accuracy results using 42 in-situ sample points of chlorophyll-a collected on five lakes. Lake Singkarak has an RMSE of 0.1 mg/m3, NMAE of 29.9%, R of 0.7, and R2 of 1.0. Lake Toba in the dry season has RMSE of 0.7 mg/m3, NMAE of 22.5%, R of 0.7 and R2 of 0.3. In the rainy season, Lake Toba has an RMSE of 0.1 mg/m3, NMAE of 7.1%, R of 0.5, and R2 of 0.3. Lake Maninjau has an RMSE of 0.6 mg/m3, NMAE of 32.5%, R of 0.5, and R2 of 0.3. Lake Matano has RMSE of 0.0 mg/m3, NMAE of 4.2%, R of 1.0 and R2 of 1.0. Lake Towuti has an RMSE of 0.0 mg/m3, NMAE of 3.4%, R of 1.0, and R2 of 1.0. The most accurate algorithm for Lake Singkarak, Toba (dry season), Toba (rainy season), Maninjau, Matano, and Towuti are C6, C6, C2, A2, C10, and B3, respectively

Seasonal Variations in Surface Water Quality in River Njoro and Lake Nakuru, Kenya

Water pollution from rivers and lakes has resulted in serious environmental issues on a global scale. The public's growing concern over the decline in river and lake water quality makes it more important than ever to assess the water quality of these bodies of water over a prolonged period of time. During the rainy and dry seasons of 2022, 120 water samples were collected from 5 monitoring stations located within River Njoro and Lake Nakuru. The samples underwent physicochemical analysis of selected water parameters, which included salinity, temperature, pH, turbidity, total nitrogen (TN), total phosphorus (TP), electric conductivity (EC), and total dissolved solids (TDS). The results were compared using the National Environmental Management Authority (NEMA) of Kenya's guidelines and the results showed that the water quality of the Njoro river and Lake Nakuru fluctuated with the seasons, particularly in the lower water column of river Njoro. The quality parameters had the following values; EC values range between 73.33 ± 0.55 μs/cm - 533.33 ± 0.64 - μs/cm, respectively for both seasons; Salinity values range from 0.085 ± 0.02 ppt - 2.59 ± 0.03 ppt; pH values were between 8.8 – 9.3; Temperature was 23.69 ± 0.07 °C – 14.22± 0.07°C; Turbidity varied significantly – 13.95 ± 0.27 NTU - 36.03 ± 0.35 NTU; TDS between 46.67 ± 0.11 ppm- 243.33 ± 0.04 ppm; TN ranges between 5.97 ± 3.24 mg/L - 8.57 ± 3.18 mg/L and TP 7.49 ± 1.7-9.41 ± 1.84 mg/L in the dry and rainy seasons, for River Njoro and Lake Nakuru respectively. This study showed that the degree to which human activity affects water quality varies with the season. Therefore, most of these factors should be considered while controlling rivers downstream in order to ensure the health of aquatic life, also it is essential to increase lake water quality monitoring and regulate human activity. Additionally, the results revealed that water pollution resulted primarily from domestic wastewater, agricultural runoff and industrial effluents.

A review of the application of the macroinvertebrate-based multimetric indices (MMIs) for water quality monitoring in lakes.

The increasing stress on lake ecosystems is affecting their functioning such as providing goods and services to inhabiting organisms and riparian communities. Monitoring of water quality is important for sustainable management and restoration of lake ecosystems. However, the costs associated with traditional approaches have become prohibitive, while not giving reliable early warning signals on resource conditions. Thus, the current shift in the use of bioindicators and multimetric indices (MMIs) in the monitoring of water quality is currently gaining global recognition with more emphasis on its application in lotic ecosystems. Therefore, this paper provides an elaborated insight into the application of macroinvertebrate-based MMIs in lentic ecosystems and the successes achieved so far. The various metrics and indices, the development strategies, application challenges, the use of macroinvertebrates as bioindicators, and the future projection of enhancing MMI usage in lentic environment monitoring, particularly in developing countries, are extensively covered. The use of MMI as a rapid lake biomonitoring tool needs to be adopted for sustainable applications in lake ecosystem management and as an integrated approach to monitoring human-induced stress especially in developing countries where there is a paucity of information.

Research Progress on Remote Sensing Monitoring of Lake Water Quality Parameters

Lakes are an important component of the hydrosphere and are important freshwater resources. The water environment of lakes has become increasingly polluted, and monitoring the dynamic changes in lake water quality is of great significance for ecological environment protection. Remote sensing can provide technical support for lake water quality monitoring, overcoming the low-cost and poor timeliness characteristics of manual sampling; thus, it has been widely used in lake water quality monitoring. Remote sensing can be used to monitor many indicators, including suspended solids, chlorophyll a, soluble organic matter, dissolved oxygen, transparency, etc. Although remote sensing provides a new method for lake water quality monitoring, there are still some problems in practical application. ① It is difficult to accurately retrieve the component changes in different substances in lake water from the signals received by remote sensing, resulting in the limitation of remote sensing accuracy. Atmospheric correction can eliminate the images reflected by factors such as atmosphere and light. In order to improve the accuracy of water quality monitoring, higher-accuracy atmospheric correction algorithms are needed. However, at present the atmospheric correction algorithm is not mature enough and lacks the portability between sensors. Therefore, atmospheric correction is still a difficult problem of remote sensing retrieval in lake water quality. ② Due to seasonal and spatial constraints, there are differences in surface optical properties of different lakes and biological optical properties, resulting in changes in remote sensing reflectance. There is also a lack of portability of the model established by using limited measured data. ③ The interference of aquatic plants and external forces (wind, fish, etc.) causes the error between the measured data and the model estimation, and the synchronization of the data is difficult to guarantee, which will introduce large errors to the lake water quality model, resulting in a decrease in the reliability of the model. ④ The spatio-temporal scale of measured data and remote sensing data do not match, and it is difficult to capture dynamic and fine changes in water quality. More precise observation of water quality is needed that can capture rapid changes in lakes. However, it is still challenging to obtain real-time measured data that meet the requirements. Therefore, it is necessary to further understand the spectral characteristics of water quality parameters, combining multi-source data and other hydrological models. In the future, a retrieval algorithm with low dependence will be developed, and migrated models will be constructed to break the regional limitations of the model. Additionally, remote sensing promotes the operational development and early warning for the water quality of lakes.

Prediction of lake chlorophyll concentration using the BP neural network and Sentinel-2 images based on time features.

One of the most important indicators of lake eutrophication is chlorophyll-a (Chl-a) concentration, which is also an essential component of lake water quality monitoring. It is an efficient, economical and convenient method to monitor the Chl-a concentration through remote sensing images. Taking the Wuliangsuhai Lake as an example, the relevant bands of Sentinel-2 images were used as the input and the Chl-a concentration as the output to build neural network models. In the process of building the model, we mainly studied and tested the impact of adding time features to the model input on the model accuracy. Through the experiment, it was found that the month and day difference features of remote sensing images and Chl-a measurement could significantly improve the prediction accuracy of Chl-a concentration in varying degrees. Finally, it was determined that the neural network prediction model with 12 bands of Sentinel-2 images combined month features as inputs and one hidden layer, eight neurons and Chl-a concentration as outputs was the best. Then, the accuracy of the model was validated when the test set accounts for 20 and 30%, and good results were obtained.

Spatio-Temporal Water Quality Assessment and Pollution Source Apportionment of Lake Chamo using Water Quality Index and Multivariate Statistical Techniques

Despite the current management effort to reduce pollution of water bodies, lakes are far more vulnerable to pollution than the ocean and rivers. Point and non-point pollution are ending on the lake bodies and concerned bodies should trigger interventions. This study aims to assess the effect of pollution on the water quality of Chamo lake using the weighted arithmetic water quality index (WQI) and multivariate statistical methods. Fifteen monitoring points are used to collect water samples from the lake for dry and rainy periods. Twenty-three physical-chemical parameters were analyzed in the water quality laboratory of Arba Minch University, out of which five parameters were analyzed using a multi-meter at the field. The physicochemical water quality analysis result of Lake Chamo revealed that the constituent’s concentration did not show significant variation spatially. The factor analysis result in the Lake Chamo revealed that it had extracted seven principal components that can be explained with a total variance of 86.3%. More than 65% of the monitoring stations in Lake Chamo have an acceptable water quality for irrigation. Moreover, some of the monitoring stations, closer to high human activities, are relatively more polluted than others. Besides, some sensitive parameters such as turbidity have clearly shown that the WQI value can be changed with the parameters changing. Based on clustering analysis, most (47%) of the monitoring are characterized by the recipients of uncontrolled public and state farm and agricultural wastes. The study recommends integrated watershed management and source-based pollution control, which could significantly decrease the pollution level in Lake Chamo after identifying the pollution hotspot areas. Continuous Lake water quality monitoring is necessary for deciding lake pollution.

Water Quality Grade Identification for Lakes in Middle Reaches of Yangtze River Using Landsat-8 Data with Deep Neural Networks (DNN) Model

Water quality grade is an intuitive element for people to understand the condition of water quality. However, in situ water quality grade measurements are often labor intensive, which makes measurement over large areas very costly and laborious. In recent years, numerous studies have demonstrated the effectiveness of remote sensing techniques in monitoring water quality. In order to automatically extract the water quality information, machine learning technologies have been widely applied in remote sensing data interoperation. In this study, Landsat-8 data and deep neural networks (DNN) were employed to identify the water quality grades of lakes in two cities, Wuhan and Huangshi, in the middle reach of the Yangtze River, central China. Additionally, linear support vector machine (L-SVM), random forest (RF), decision tree (DT), and multi-layer perceptron (MLP) were selected as comparative methods. The experimental results showed that DNN achieved the most promising performance compared to the other approaches. For the lakes in Wuhan, DNN gave water quality results with overall accuracy (OA) of 93.37% and Kappa of 0.9028. For the lakes in Huangshi, OA and kappa given by DNN were 96.39% and 0.951, respectively. The results show that the use of remote sensing images for water quality grade monitoring is effective. In the future, our method can be used for water quality monitoring of lakes in large areas at a low cost.

Open and Cost-Effective Digital Ecosystem for Lake Water Quality Monitoring.

In some sectors of the water resources management, the digital revolution process is slowed by some blocking factors such as costs, lack of digital expertise, resistance to change, etc. In addition, in the era of Big Data, many are the sources of information available in this field, but they are often not fully integrated. The adoption of different proprietary solutions to sense, collect and manage data is one of the main problems that hampers the availability of a fully integrated system. In this context, the aim of the project is to verify if a fully open, cost-effective and replicable digital ecosystem for lake monitoring can fill this gap and help the digitalization process using cloud based technology and an Automatic High-Frequency Monitoring System (AHFM) built using open hardware and software components. Once developed, the system is tested and validated in a real case scenario by integrating the historical databases and by checking the performance of the AHFM system. The solution applied the edge computing paradigm in order to move some computational work from server to the edge and fully exploiting the potential offered by low power consuming devices.

Citizen Science Impact on Environmental Monitoring towards SDGs Indicators: The CASE of SIMILE Project

This study presents the ongoing activities of citizen science (CS) monitoring of lake water quality that was developed in the SIMILE project. After presenting the tools and the initiatives that were implemented to gather volunteer contributions, the CS data are analyzed: to understand the main factors driving the contribution provided by citizens, through a proximity analysis using hierarchical clustering; to evaluate their added value and complementary nature in view of the proposed integrated lake monitoring with respect to remote sensing (RS) water quality parameters maps and in situ high-frequency monitoring (HFM) data that were acquired in the framework of SIMILE project, through an intercomparison of temperature data; and to highlight water quality patterns in the lakes under study. It is then discussed how the project outcomes could directly or indirectly contribute to the measurement of specific Sustainable Development Goals (SDGs) indicators highlighting the potential impact of CS activities. The conclusions highlight the complementary nature of CS and the need of supporting activities for a continuous lake water quality monitoring. The possibility for the partner local authorities to maintain the CS monitoring establishing a Citizen Observatory (CO) and the opportunity to extend the SIMILE approach to neighboring lakes and beyond is also discussed.

LAKE WATER QUALITY MONITORING TOOLS

Abstract. Lakes as ecosystems provide many goods and services. To benefit from them in long term we must assure sustainable management. SIMILE (informative System for the Integrated Monitoring of Insubric Lakes and their Ecosystems) project is focused on developing efficient monitoring of lake water quality since it gives the critical input for adequate management. The lakes of interest for SIMILE are the Insubric lakes Como, Lugano, and Maggiore. The paper is focused on describing which tools are used in the SIMILE project to exploit different sources of lake water quality data: in-situ high-frequency monitoring (HFM) through sensors, satellite observations, and data collected by citizens. Even though the paper is focused on the SIMILE project, and thus on tools and procedures for the Insubric lakes, it can serve as an example for other lakes too, especially because the tools developed in the project, such as a collaborative platform for sharing satellite-derived water quality parameters, and mobile application and web administrator interface for citizen science, are free and open-source, they can be easily adapted if needed. Moreover, the procedures for the processing of data coming from different sources are based on free (and often also open source) software and are well documented. The tools and procedures described in this paper might be a foundation for similar practice for lakes worldwide, and thus a step forward the 6th Sustainable Development Goal (SDG) of the United Nations (“Ensure availability and sustainable management of water and sanitation for all”).

Seasonal Variation and Spatial Heterogeneity of Water Quality Parameters in Lake Chenghai in Southwestern China

Seasonal dynamics and the vertical stratification of multiple parameters, including water temperature (WT), dissolved oxygen (DO), pH, and chlorophyll-a (Chl-a), were analyzed in Lake Chenghai, Northern Yunnan, based on monitoring data collected in 2015 (October), 2016 (March, May, July), 2017 (March, June, October), 2018 (August), and 2020 (June, November). The results indicate that the lake water was well mixed in winter and spring when the water quality was stable. However, when WT becomes stratified in summer and autumn, the Chl-a content and pH value changed substantially, along with the vertical movement of the thermocline. With rising temperature, the position of the stratified DO layer became higher than the thermocline, leading to a thickening of the water body with a low DO content. This process induced the release of nutrients from lake sediments and promoted eutrophication and cyanobacteria bloom. The thermal stratification structure had some influence on changes in DO, pH, and Chl-a, resulting in the obvious stratification of DO and pH. In summer, with an increase in temperature, thermal stratification was significant. DO and pH achieved peak values in the thermocline, and exhibited a decreasing trend from this peak, both upward and downward. The thermocline was anoxic and the pH value was low. Although Chl-a maintained a low level below the thermocline and was not high, there was a sudden increase in the surface layer, which should be urgently monitored to prevent large-scale algae reproduction and even local outbreaks in Lake Chenghai. Moreover, Lake Chenghai is deeper in the north and shallower in the south: this fact, together with the stronger wind–wave disturbance in the south, results in surface WT in the south being lower than that in the north year-round. This situation results in a gradual diminution of aquatic plants from north to south. Water quality in the lake’s southern extent is better than that in the north, exhibiting obvious spatial heterogeneity. It is recommended that lake water quality monitoring should be strengthened to more fully understand lake water quality and take steps to prevent further deterioration.

The human factor: Weather bias in manual lake water quality monitoring

AbstractSampling bias due to weather conditions has been anecdotally reported; however, in this analysis we demonstrate that manual lake sampling is significantly more likely to take place in “fair weather” conditions. We show and quantify how a manual lake monitoring program in Maine, USA, is biased due to wind speed, rainfall intensity, and air temperature. Emulating a manually sampled water quality (WQ) data set, we show that, on average, manual sampling recorded, depending upon depth, higher water temperature (between 0.4°C and 1.2°C), lower dissolved oxygen (DO) (between −0.8 and −0.4 mgL−1), and higher chlorophyll values (2.0 μgL−1) than average automated monitoring. By analyzing the actual manual monitoring data, we show that manually collected lake water temperatures are on average 1.0°C higher in the epilimnion and 0.5°C (corrected for sensor lag) higher in the hypolimnion compared to those collected using automated methods. We attribute these differences in WQ measurement values to the weather‐induced manual sampling bias. We believe that the nature of weather bias on manual monitoring will always record higher water temperatures, higher chlorophyll, and lower DO than automatic monitoring. The methodologies presented in this study will apply to similar manually sampled lake monitoring programs and the manual sampling bias will likely affect other WQ parameters. The weather‐induced water temperature bias reported is of the same order of magnitude as the root mean square errors reported in many lake models and is therefore considered substantial. If generally applicable and not corrected for, these results will have important implications for climate models, and similar applications, where manually collected WQ data are employed.

Dissolved Oxygen Concentration Prediction Model Based on WT-MIC-GRU-A Case Study in Dish-Shaped Lakes of Poyang Lake.

Dissolved oxygen concentration has the characteristics of nonlinearity, time series and instability, which increase the difficulty of accurate prediction. In order to accurately predict the dissolved oxygen concentration in the dish-shaped lakes in Poyang Lake of Jiangxi Province, China, a dissolved oxygen concentration prediction model, based on wavelet transform (WT)-based denoising, maximal information coefficient (MIC)-based feature selection, and the gated recurrent unit (GRU), was proposed for this study. In experiments, the proposed model showed good prediction performance, achieving a root-mean-square error (RMSE) of 0.087 mg/L, a mean absolute percentage error (MAPE) of 0.723%, and a coefficient of determination (R2) as high as 0.998. It shows that the prediction model based on the combination of the wavelet transform and the GRU has a relatively high prediction accuracy and a better fitting effect. The model proposed in this study can provide a reference for protecting this type of lake-water body and the restoration of missing values in lake water quality monitoring data.

Evaluation of trophic state for inland waters through combining Forel-Ule Index and inherent optical properties

Eutrophication is a severe environmental pollution problem for inland waters and poses significant threats to the water safety. Monitoring trophic state of inland waters using optical remote sensing generally requires the inversion of water quality parameters, such as chlorophyll-a, secchi depth, etc. However, the accurate inversion of these individual indicators remains challenging, while the associated retrieval errors can propagate and degrade the evaluation of trophic state. Hence, we proposed a novel monitoring method by developing a Trophic State Index (TSI) based on optical remote-sensing parameters, i.e., Forel-Ule index (FUI) and non-water absorption coefficient at 674 nm (referred to as at−w(674)) retrieved from Sentinel-3 Ocean and Land Color Instrument (OLCI) imagery. The estimated TSI showed favorable correspondence with observed water quality data, including coefficient of determination (r2 = 0.91), root mean squared error (RMSE = 5.54), and mean absolute percentage error (MAPE = 10.69%). Using the Sentinel-3 OLCI data, the proposed method also had very good performance in the field spectrum (MAPE = 5.25 % , RMSE = 3.36). The monthly trophic state evaluation also showed congruence (MAPE = 12.51 % , RMSE = 6.41) with surface water quality monthly report (SWQMR) from the Ministry of Environment and Ecology of the People's Republic of China. The monthly TSI showed favorable agreement for 23 ungauged lakes (RMSE = 7.26, MAPE = 12.78%), indicating potential utility for regional lake water quality monitoring. The proposed method was then applied to 47 other large (>50 km2) water bodies in the Middle-and-Lower watershed of Yangtze River and the Huaihe watershed to evaluate the spatial and temporal variation of trophic state from 2016 to 2020. The TSI results revealed several lakes, such as Lake Honghu and Lake Luoma, with rapidly deteriorating water quality during the study period, while other lakes show relative improvement (e.g., Xiashan Reservoir), indicating unbalanced environmental pressure over the region. Overall, this study showed promising performance and potential for satellite-based monitoring of regional aquatic environments.

Review on the control and mitigation strategies of lake cyanobacterial blooms

由于人类活动和全球气候变化的叠加影响，湖泊富营养化和蓝藻水华仍是未来相当长一段时间内的水生态环境问题.蓝藻水华暴发会引发湖泊生态系统的灾害和饮用水安全风险，因此湖内蓝藻水华防控必不可少.现有蓝藻水华防控长效方法主要基于营养盐控制理论、浅水湖泊稳态转换理论和生物操纵理论，技术措施包括内源营养盐控制、生态修复、生物操纵.应急处置方法可以削减局部水域蓝藻水华强度，主要包括物理、化学方法.本文基于国内外研究治理案例，梳理了单项蓝藻防控技术运用的边界条件、蓝藻削减效果及防控成本.湖泊富营养化控制和蓝藻水华防控是一个长期而艰巨的系统工程，必须采取流域污染削减和湖内防控相结合的治理策略.湖泊水质与水生态的持续跟踪监测以及水质和水生态预测模型的构建，是动态调整治理方案、保障治理效果长效稳定的基础.;Lake eutrophication and cyanobacterial blooms are water ecological and environmental problems for a long time to come, due to the synergistic effects of human activities and global climate change. The outbreak of lake cyanobacterial blooms would entail water ecological risk and drinking water crisis, thus curative methods to suppress the proliferation of cyanobacteria bloom are imperative. Nutrient control, shallow lake regime shifts and biological manipulation are theoretical basis for the long-term strategies for cyanobacteria blooms prevention and control. Accordingly, external and internal nutrient loading reduction, ecological restoration and biomanipulation are sustainable control and management approaches. In addition, methods used for emergency situations can mitigate the intensity of cyanobacterial bloom, mainly including physical and chemical approaches. Based on the review of the domestic and international cases on cyanobacterial bloom control, we sort out the applicable conditions, cyanobacterial mitigation efficiency and performance cost of each approach. In fact, lake eutrophication management and cyanobacterial bloom mitigation are a long-term and systematic process. These in-lake strategies should invariably be accompanied by nutrient input reductions. The continuous monitoring of lake water quality and water ecology as well as the building of prediction model support the further adjustment of strategies and measures to ensure long-term success and sustainability.