Eutrophication Status Of Lakes Research Articles

An innovative method based on Gaussian cloud distribution and sample information richness for eutrophication assessment of Yangtze's lakes and reservoirs under uncertainty.

The precise assessment of a water body's eutrophication status is essential for making informed decisions in water environment management. However, conventional approaches frequently fail to consider the randomness, fuzziness, and inherent hidden information of water quality indicators. These would result in an unreliable assessment. An enhanced method was proposed for the eutrophication assessment under uncertainty in this study. The multi-dimension gaussian cloud distribution was introduced to capture the randomness and fuzziness. The Shannon entropy based on various sample size and trophic levels was proposed to maximize valuable information hidden in the datasets. Twenty-seven significant lakes and reservoirs located in the Yangtze River Basin were selected to demonstrate the proposed method. The sensitivity and consistency were used to evaluate the accuracy of the proposed method. Results indicate that the proposed method has the capability to effectively assess the eutrophication status of lakes and reservoirs under uncertainty and that it has a better sensitivity since it can identify more than 33-50% trophic levels compared to the traditional methods. Further scenario experiments analysis revealed that the sample information richness, i.e., sample size and the number of trophic levels is of great significance to the accuracy/robustness of the method. Moreover, a sample size of 60 can offer the most favorable balance between accuracy/robustness and the monitoring expenses. These findings are crucial to optimizing the eutrophication assessment.

Driving mechanism of water replenishment on DOM composition and eutrophic status changes of lake in arid and semi-arid regions of loess area

Water replenishment can be a key factor in driving lake eutrophication status. In arid and semi-arid regions of China, water replenishment for a lake has been widely carried out for not only improving water environmental quality, but also maintaining ecological system function. However, it is still unclear in terms of mechanism by which water replenishment drives lake eutrophication status. In this study, fluorescence excitation-emission matrix spectroscopy (EEMs) combined with multiple statistical analysis models (including parallel factor analysis, correlation analysis, redundancy analysis, and partial least squares structural equation modeling) was utilized to reveal potential driving mechanism and causality between water replenishment, dissolved organic matter (DOM) fractions and eutrophic status of Lake Shahu in China. Based on variations of DOM fractions, fulvic-like substances could be accumulated during the replenishment period, while nutrients carried along the replenishment might conduce to increase microbial activities during the non-replenishment period. This should be contributed to an alteration of prominent component from fulvic-like substances to tyrosine-like substances during the replenishment period to non-replenishment period. According to partial least squares structural equation modeling, two potential indirect paths were finally revealed, i.e., water replenishment derived the eutrophic status of Lake Shahu: water replenishment → microbial activity → algae → eutrophication, and water replenishment → microbial activity → eutrophication. This supposed that the water replenishment should indirectly drive the algae and eutrophication of the lake by promoting the transformation of DOM fractions. In addition, natural conditions could indirectly contribute to the eutrophication of the lake through impacting the algae growth. These findings should be conducive to trace the alteration of DOM fractions in lakes by water replenishment and in recognizing potential driving mechanisms of water replenishment on eutrophication of lakes by changing DOM fractions. This could provide basic theoretical support for policymakers to regulate and treat the eutrophication of lakes.

Risk Assessment and Source Identification of Arsenic in Surface Sediments from Caohai Lake, China.

Urban freshwaters containing arsenic are facing an increasing problem of eutrophication. This study evaluated the spatial distribution, ecological risk, and origin of As in surface sediments obtained from Caohai Lake, a typical hypertrophic urban lake in China. It revealed that the total As concentration in surface sediments decreased gradually from north to south, consisted with the lake eutrophication status. The average As level was 161mg/kg, dominated by reducible and oxidisable fractions. The analyses of geoaccumulation index and enrichment factor indicated that As ranged between moderately-to-heavily and heavily contamination, was severely influenced by anthropogenic factors, i.e. industrial discharges and agricultural activities. Risk assessment code and potential ecological risk index results showed moderate to high potential ecological risk could be observed although the current As risk was low, supporting by the experimentally quantified As release data.

Research on application of a new bottom trap technology to catch particles rich in nutrients in a large shallow lake

The resuspension and sedimentation of particulate matter and the release of nutrients from sediment are important factors affecting the eutrophication of shallow lakes. The capture and removal of particles rich in nitrogen, phosphorus, and other nutrients at the bottom of lakes is of great significance for improving the management and eutrophication status of lakes. This study investigated the feasibility of applying lake bottom trap technology in seven different locations in Lake Chaohu, which is the fifth largest freshwater lake in China. The results showed that the trap in the western part of Lake Chaohu had the highest sedimentation rate and could capture most of the nutrients. The sedimentation rates were higher in spring and summer than in autumn and winter. The bottom trap effectively collected and preserved chlorophyll a, organic matter, total nitrogen, and total phosphorus. The trap per meter length (15–20 m wide) could catch 20.7–27.6 m3 of particles rich in nutrients with a water content of 50–70%, organic matter content of 281.9–375.8 kg, total nitrogen content of 24.5–32.6 kg, and total phosphorus content of 10.5–14 kg. The proposed bottom trap had little impact on the benthic organism system of the lake. The bottom trap technology used in this study could solve the problem of nitrogen, phosphorus, and algae accumulation in lakes and reservoirs, broaden the utility of lake hydrodynamics in environmental pollution control, and provide new ideas and strategies for the control and management of cumulative pollution in shallow lakes and reservoirs.

Eutrophication Status of Lakes in Inner Hanoi and a Case Study of Cu Chinh Lake

Eutrophication Status of Lakes in Inner Hanoi and a Case Study of Cu Chinh Lake

Recognition criteria and application of lake eutrophication evaluation based on set pair analysis

The set pair analysis evaluation method has been applied to evaluate water quality. However, in real-life applications, because traditional assessing methods usually yield extreme results and are of poor resolution, a hybrid recognition criterion, entropy weight and set pair analysis model is established. The concepts and calculated methods of assessment grade fuzzy characteristic and confidence criterion were proposed in order to solve the problem of determination of the magnitude of lake eutrophication. And the weight value was calculated by entropy weight method and finally got the evaluation grade as a confidence interval based on confidence level. Compared with the set pair analysis based on triangular fuzzy number method (SPA(TFN)) and the improved set pair analysis method (ISPA), the evaluating results of this model are more reasonable and its resolving power is higher. This study offer new insights and possibility to carry out an efficient way for lake eutrophication status evaluation. The study also provides scientific reference in lake risk management for local and national governmental agencies.

STATUS PREDICTION BY 3D FRACTAL NET CNN BASED ON REMOTE SENSING IMAGES

The contradiction between the supply and demand of water resources is becoming increasingly prominent, whose main reason is the eutrophication of rivers and lakes. However, limited and inaccurate data makes it impossible to establish a precise model to successfully predict eutrophication levels. Moreover, it is incompetent to distinguish the degree of eutrophication status of lakes by manual calculation and processing. Focusing on these inconveniences, this study proposes 3D fractal net CNN to extract features in remote sensing images automatically, aiming at achieving scientific forecasting on eutrophication status of lakes. In order to certificate the effectiveness of the proposed method, we predict the state of the water body based on remote sensing images of natural lake. The images in natural lake were accessed by MODIS satellite, cloud-free chlorophyll inversion picture of 2009 was resized into [Formula: see text] patches, which were collected as training and testing samples. In the total of 162 pictures, our study makes three consecutive pictures as a set of data so as to attain 120 group of training and 40 testing data. Taking one set of data as input of the neural network and the next day’s eutrophication level as labels, CNNs act considerable efficiency. Through the experimental results of 2D CNN, 3D CNN and 3D fractal net CNN, 3D fractal net CNN has more outstanding performance than the other two, with the prediction accuracy of 67.5% better than 47.5% and 62.5%, respectively.

Impact of water environment factors on eutrophication status of Lake Ulansuhai based on monitoring data in 2013-2018

为探明寒旱区浅水型湖泊乌梁素海水环境因子对水体营养状态的影响程度，以2013—2018年1月和7月湖泊水环境监测数据为基础，对具有典型代表性监测点的水体营养状态与水环境因子（盐度、pH值和水深）之间建立向量自回归模型（VAR）模型，通过方差分解方法分析水环境因子对水体营养状态的贡献水平，同时界定出水体营养状态处于最佳水平条件下的水环境因子的适宜范围.结果表明：水环境因子对水体营养状态的方差贡献初期表现显著而后趋于稳定，且盐度、pH值和水深指标的综合方差贡献率最高可达66.62%；并以全湖94.4%的水体面积不呈现富营养化状态作为标准界定出乌梁素海水体处于最佳营养状态水平条件下盐度、pH值和水深指标的适宜范围，分别为0.06~2.68 g/L、7.50~8.63和1.76~3.50 m，且湖泊在此营养水平条件下全湖盐度、pH值和水深指标的均值分别为1.55 g/L、8.15和2.33 m.因此，可以通过人类活动调控湖泊水环境因子值，并实施以水养湖的策略来推动湖泊水体营养状态向良性发展.;To get a better understanding of the impact of the major water environment factors on the eutrophication status of Lake Ulansuhai located in a cold and arid region, VAR model was introduced to identify the impact based on the water environment factors (such as salinity, pH and water depth) monitored in January and July between 2013 and 2018 at the typical representative sites in the lake. The contribution of the water environment factors to the eutrophication status was analyzed by the variance decomposition analysis method. The ranges of the water environment factors were suggested that would construct an appropriate eutrophication status in the lake. The results showed that the variance contribution of the water environmental factors to the eutrophication status of the lake was significant at the initial stage and then tended to be stable. The contribution rate by means of the comprehensive variance of salinity, pH, and water depth was up to 66.62%. This suggested that if the indexes of salinity, pH, and water depth in Lake Ulansuhai were controlled among the range of 0.06-2.68 g/L, 7.50-8.63, and 1.76-3.50 m respectively, the 94.4% of the lake open water area would not presented as eutrophication. In this case, the average of the salinity, pH, and water depth would be 1.55 g/L, 8.15, and 2.33 m, respectively. This indicated that the lake eutrophication status could be controlled to some extend through artificially regulating the lake water environment factors.

Evaluation of lake eutrophication status based on set pair analysis and confidence intervals

Because traditional assessing methods usually yield extreme results and are of poor resolution, a set pair analysis based on triangular fuzzy number is established. Set pair analysis method is an uncertainty analysis method of the system whose formula structure analysis of connection degree is established by the identity degree, discrepancy degree and contrary degree to deal with all kinds of uncertainty and fuzzy problems. The measured value and the lake quality criteria are two interrelated sets put together to compose a set pair with respect to the problem of determination of the magnitude of lake eutrophication. The basic expression of the degree of couplet is extended at different levels and at the same level to form a multi-element connection number, in order to set up a five-element connection number in relation to I-V grades evaluation standard commonly used in lake assessment. The discrepancy degree is expanded into partial identity in discrepancy, entirety discrepancy and partial contrary in discrepancy, this forms a multivariate connection number of identity, partial identity in discrepancy, entirety discrepancy, partial contrary in discrepancy and contrary. By introducing triangular fuzzy number the number of links to get the evaluation grade for a confidence interval was obtained, weight value calculated by weighted average method and finally got the evaluation grade as a confidence interval based on confidence level. The case study shows that the method presents the evaluating grade as a confidence interval which has advantages of high resolution and information utilization.

IDENTIFYING THE EUTROPHICATION STATUS OF SHALLOW WATERS BASED ON THE ESTIMATED TROPHIC STATE INDEX FROM SATELLITE DATA

Remote sensing techniques have been widely used to measure the qualitative parameters of waterbodies. This work identifies the eutrophication status of 6 small and shallow lakes, including Hoan Kiem, Ba Mau, Bay Mau, Dam Tri, Truc Bach and Hai Ba Trung, in the Hanoiurban area using an empirical model developed from the in situ trophic state index (TSI) and Landsat 8 satellite data acquired on 21 September 2016 and 10 December 2016. The strong correlation between TSI values and the reflectance ratio of Landsat 8 band 3 versus band 2 (R2 = 0.69) allowed the quantification of the TSI by an exponential function (MSE = 0.04). Validation of the model for other water samples collected on the dates shows a high accuracy (R2 = 0.85; MSE = 0.4). Maps of TSI over the 6 lakes on the two dates surveyed clarified the eutrophication status of these lakes in autumn 2016 and demonstrate the trend in identifying the distribution of TSI from space. Study methods and data stated in this study confirm the high applicability of Landsat 8 satellite data for monitoring shallow lake eutrophication status.

Eutrophication Evaluation of Lake Based on Cloud Model

Considering the fuzzy and stochastic feature in assessment, a new assessment model based on cloud theory is proposed. The new model can realize greatly the conversion from qualitative conception to quantitative model. Taking the new model into evaluating water quality of lake eutrophication, the evaluation model based on cloud theory can not only show the fuzzy feature of grade, but also indicate random attribute of membership. The method is developed to evaluate lake eutrophication status, and the results show that the new evaluation model is feasible and can effectively evaluate eutrophication level.

Projecting the impact of regional land-use change and water management policies on lake water quality: an application to periurban lakes and reservoirs.

As the human population grows, the demand for living space and supplies of resources also increases, which may induce rapid change in land-use/land-cover (LULC) and associated pressures exerted on aquatic habitats. We propose a new approach to forecast the impact of regional land cover change and water management policies (i.e., targets in nutrient loads reduction) on lake and reservoir water eutrophication status using a model that requires minimal parameterisation compared with alternative methods. This approach was applied to a set of 48 periurban lakes located in the Ile de France region (IDF, France) to simulate catchment-scale management scenarios. Model outputs were subsequently compared to governmental agencies’ 2030 forecasts. Our model indicated that the efforts made to reduce pressure in the catchment of seepage lakes might be expected to be proportional to the gain that might be obtained, whereas drainage lakes will display little improvement until a critical level of pressure reduction is reached. The model also indicated that remediation measures, as currently planned by governmental agencies, might only have a marginal impact on improving the eutrophication status of lakes and reservoirs within the IDF region. Despite the commitment to appropriately managing the water resources in many countries, prospective tools to evaluate the potential impacts of global change on freshwater ecosystems integrity at medium to large spatial scales are lacking. This study proposes a new approach to investigate the impact of region-scale human-driven changes on lake and reservoir ecological status and could be implemented elsewhere with limited parameterisation. Issues are discussed that relate to model uncertainty and to its relevance as a tool applied to decision-making.

Cost effective prediction of the eutrophication status of lakes and reservoirs

Summary1. Eutrophication is a serious threat in many parts of the world, and identifying the environmental factors that determine the spatial distribution of eutrophicated waterbodies as well as the development of management tools is a challenge.2. In this study, data from the Ile‐de‐France region were analysed to determine if catchment scale environmental variables could predict concentrations of chlorophyll a (used as a proxy for eutrophication status) of artificial lakes and reservoirs.3. General additive models (GAM) and random forest models (RF) displayed greater predictive power than generalised linear models, indicating the importance of non‐monotonic relationships. Using RF modelling, very high predictive accuracy was achieved for both continuous and binomial (eutrophic or not) response variables (continuous: R2 = 0.715; binomial: kappa = 0.764, 89% of waterbodies were accurately predicted). The better predictive power and robustness of RF versus GAM was attributed to the formers ability to better handle complex interactions between predictors and to account for threshold effects.4. Our results confirmed the close link between the water quality of lakes and reservoirs and the characteristics of their catchments. Moreover, we also showed that (i) simple (e.g. linear and/or monotonic) relationships between catchment land use and water quality were only found for sub‐regional datasets, and (ii) land use needs to be considered in association with complementary environmental variables (hydromorphological variables) to best assess its impact on water quality.

Application of artificial neural networks for classifying lake eutrophication status

AbstractSeveral artificial neural network architectures were ‘trained’ on data from the Eastern Lake Survey – Phase I of the Environmental Protection Agency's National Surface Water Survey in order to investigate which physical–chemical parameters are possibly of greatest importance in determining the eutrophication status of lakes. From the 110 available lake parameters in the Survey, 60 were chosen as input to the neural networks. The traditional eutrophication classification scheme of Vollenweider was used for comparative purposes. The various artificial neural network simulations showed that, in addition to total phosphorus and inorganic nitrogen, turbidity, specific conductance, lake elevation and hydrogen ion concentration were identified as the most significant parameters affecting the classification of lakes in regard to their eutrophication status. These results suggest a conceivable association between these parameters and lake eutrophication, thereby indicating a need for further study on these relationships. A model simulation utilizing an unsupervised neural network did not provide much insight into the lake eutrophication status, but did show that the available physical–chemical lake data could be categorized according to physical region, thereby providing an indication that the lake data used in this study were region‐dependent.

Spatial and seasonal variation in greenhouse gas and nutrient dynamics and their interactions in the sediments of a boreal eutrophic lake

Dynamics of greenhouse gases, CH4, CO2 and N2O, and nutrients, NO 2 − + NO 3 − , NH 4 + and P, were studied in the sediments of the eutrophic, boreal Lake Kevaton in Finland. Undisturbed sediment cores taken in the summer, autumn and winter from the deep and shallow profundal and from the littoral were incubated in laboratory microcosms under aerobic and anaerobic water flow conditions. An increase in the availability of oxygen in water overlying the sediments reduced the release of CH4, NH 4 + and P, increased the flux of N2O and NO 2 − + NO 3 − , but did not affect CO2 production. The littoral sediments produced CO2 and CH4 at high rates, but released only negligible amounts of nutrients. The deep profundal sediments, with highest carbon content, possessed the greatest release rates of CO2, CH4, NH 4 + and P. The higher fluxes of these gases in summer and autumn than in winter were probably due to the supply of fresh organic matter from primary production. From the shallow profundal sediments fluxes of CH4, NH4 + and P were low, but, in contrast, production of N2O was the highest among the different sampling sites. Due to the large areal extension, the littoral and shallow profundal zones had the greatest importance in the overall gas and nutrient budgets in the lake. Methane emissions, especially the ebullition of CH4 (up to 84% of the total flux), were closely related to the sediment P and NH 4 + release. The high production and ebullition of CH4, enhances the internal loading of nutrients, lake eutrophication status and the impact of boreal lakes to trophospheric gas budgets.

On the comprehensive evaluation methods for lake eutrophication

从对湖泊富营养化评价的一般方法入手,综述了国内外有关湖泊富营养化综合评价的一些方法.提出:营养状态指数法(TSI)由于可对湖泊营养状态进行连续的数值化的分级,从而为湖泊富营养化机理的定量研究提供了坚实的基础,应是今后湖泊富营养化评价中的主要方法.文中对综合评价指标的定权方法亦作了分析,认为层次分析法较理想.并给出了TSI及其修正方法中的权重分配.最后.文中讨论了综合评价与其他分析方法如聚类分析等的关系问题.;On the basis of general methods for evaluating lake cutrophicalion, the anthor summarized some comprehensive assessment methods for lake eutrophication reported at home and abroad. It is pointed out that the trophic state index should be the major method for evaluating lake eutrophication status, since it could provide a continuous numerical class of lake trophic state and a rigorous foundation of quantitative studies of eutrophication mechanism. After analyzing some determination methods of weight distribution in lhe comprehensive indices, ihe anthor insists that the hierarchical analysis bo quite perfect to determine weights. This paper shows the weight distribution of TSI and its modifiers. Additionally, il is discussed in brief the relations between comprehensive evaluation and other statistical methcxls such as cluster analysis in the last part of this paper.