Prediction and elucidation of the population dynamics of Microcystis spp. in Lake Dianchi (China) by means of artificial neural networks

Lake Dianchi is a shallow and turbid lake, located in Southwest China. Since 1985, Lake Dianchi has experienced severe cyanabacterial blooms (dominated by Microcystis spp.). In extreme cases, the algal cell densities have exceeded three billion cells per liter. To predict and elucidate the population dynamics ofMicrocystis spp. in Lake Dianchi, a neural network based model was developed. The correlation coefficient (R 2) between the predicted algal concentrations by the model and the observed values was 0.911. Sensitivity analysis was performed to clarify the algal dynamics to the changes of environmental factors. The results of a sensitivity analysis of the neural network model suggested that small increases in pH could cause significantly reduced algal abundance. Further investigations on raw data showed that the response of Microcystis spp. concentration to pH increase was dependent on algal biomass and pH level. When Microcystis spp. population and pH were moderate or low, the response of Microcystis spp. population would be more likely to be positive in Lake Dianchi; contrarily, Microcystis spp. population in Lake Dianchi would be more likely to show negative response to pH increase when Microcystis spp. population and pH were high. The paper concluded that the extremely high concentration of algal population and high pH could explain the distinctive response of Microcystis spp. population to +1 SD (standard deviation) pH increase in Lake Dianchi. And the paper also elucidated the algal dynamics to changes of other environmental factors. One SD increase of water temperature (WT) had strongest positive relationship with Microcystis spp. biomass. Chemical oxygen demand (COD) and total phosphorus (TP) had strong positive effect on Microcystis spp. abundance while total nitrogen (TN), biological oxygen demand in five days (BOD5), and dissolved oxygen had only weak relationship with Microcystis spp. concentration. And transparency (Tr) had moderate positive relationship with Microcystis spp. concentration.Lake Dianchi is a shallow and turbid lake, located in Southwest China. Since 1985, Lake Dianchi has experienced severe cyanabacterial blooms (dominated by Microcystis spp.). In extreme cases, the algal cell densities have exceeded three billion cells per liter. To predict and elucidate the population dynamics ofMicrocystis spp. in Lake Dianchi, a neural network based model was developed. The correlation coefficient (R 2) between the predicted algal concentrations by the model and the observed values was 0.911. Sensitivity analysis was performed to clarify the algal dynamics to the changes of environmental factors. The results of a sensitivity analysis of the neural network model suggested that small increases in pH could cause significantly reduced algal abundance. Further investigations on raw data showed that the response of Microcystis spp. concentration to pH increase was dependent on algal biomass and pH level. When Microcystis spp. population and pH were moderate or low, the response of Microcystis spp. population would be more likely to be positive in Lake Dianchi; contrarily, Microcystis spp. population in Lake Dianchi would be more likely to show negative response to pH increase when Microcystis spp. population and pH were high. The paper concluded that the extremely high concentration of algal population and high pH could explain the distinctive response of Microcystis spp. population to +1 SD (standard deviation) pH increase in Lake Dianchi. And the paper also elucidated the algal dynamics to changes of other environmental factors. One SD increase of water temperature (WT) had strongest positive relationship with Microcystis spp. biomass. Chemical oxygen demand (COD) and total phosphorus (TP) had strong positive effect on Microcystis spp. abundance while total nitrogen (TN), biological oxygen demand in five days (BOD5), and dissolved oxygen had only weak relationship with Microcystis spp. concentration. And transparency (Tr) had moderate positive relationship with Microcystis spp. concentration

Combining hydrogen peroxide addition with sunlight regulation to control algal blooms

The concentration, light conditions during treatment, and the number of hydrogen peroxide (H2O2) additions as well as the H2O2 treatment combined with subsequent shading to control algal blooms were studied in the field (Lake Dianchi, China). The cyanobacterial stress and injury due to H2O2 were dose dependent, and the control effectiveness and degradation of H2O2 were better and faster under full light than under shading. However, H2O2 was only able to control a bloom for a short time, so it may have promoted the recovery of algae and allowed the biomass to rebound due to the growth of eukaryotic algae. A second addition of H2O2 at the same dose had no obvious effect on algal control in the short term, suggesting that a higher concentration or a delayed addition should be considered, but these alternative strategies are not recommended so that the integrity of the aquatic ecosystem is maintained and algal growth is not promoted. Moreover, shading (85%) after H2O2 addition significantly reduced the algal biomass during the enclosure test, no restoration was observed for nearly a month, and the proportion of eukaryotic algae declined. It can be inferred that algal blooms can be controlled by applying a high degree of shading after treatment with H2O2.</p

Effects of Drinking Water Treatment Processes on Removal of Algal Matter and Subsequent Water Quality

Seasonal algal blooms in drinking water sources have increased significantly over the recent past as a result of increased temperature and nutrient loading in surface water due to agricultural and surface runoff. More than 95% of algal cells can be removed by coagulation and flocculation processes. However, algal organic matter (AOM) is not removed well during coagulation, thus causes several operational challenges in drinking water treatment. This research was conducted to investigate the effectiveness of coagulation, granular activated carbon adsorption, and filtration processes on AOM removal and to evaluate disinfection by-products formation potential with/without UV irradiation. Initially, coagulation performance for the treatment of algae-laden raw water was investigated systematically by central composite design using response surface methodology. The main mechanism of algae and AOM removal was charge neutralization at an optimum pH of around 6.0. Thereafter, the optimum coagulation conditions using alum for AOM of six different algal and cyanobacterial species were determined. The AOM removal by coagulation correlated well with the hydrophobicity of the AOM solution. The disinfection by-product formation potential of the AOM due to chlorination was determined after coagulation. The efficiency and mechanism of AOM removal by granular activated carbon (GAC) adsorption were determined by batch adsorption experiments. The adsorption equilibrium data followed both Langmuir and Freundlich models. The adsorption process followed a pseudo-second-order kinetic model, and the calculated thermodynamic parameters indicated that GAC adsorption for AOM removal was spontaneous and endothermic in nature. The fouling behavior of the microfiltration membranes after GAC adsorption pre-treatment was investigated and the filtration resistance and AOM removal efficiency were determined. The GAC adsorption increased the removal of AOM, decreased membrane fouling, and identified intermediate blocking as the major fouling mechanism of the membrane. The effects of combined low-pressure ultraviolet (LPUV) irradiation and chlorination on the disinfection byproducts (DBPs) formation from AOM was investigated for common algae existed in surface water, AOM degradation was likely promoted by photodegradation of aromatics, and chlorine oxidation/substitution. Insights obtained of this work will help in properly designing and operating the AOM removal and reducing DBPs formation during water treatment of algae-laden source water

Biologically based engineering strategies for remediation of eutrophic shallow lakes: a model study for the Lake Chao in China

Für die Nutzung von eutrophierten Flachseen ist die Prognose der Wasserqualität in diesen Seen von besonderer Bedeutung. In der Arbeit wird der Einfluss der ufernahen Bepflanzung mit Schilf- und Makrophytenbeständen auf die Wasserqualität eines eutrophierten Flachsees modelliert. Die Nährstoffaufnahme der Makrophyten und die Reduzierung der Eutrophierung stehen im komplexen Zusammenhang. Über die Reduzierung des Gesamtphosphors kann in einem phosphorlimitierten Ökosystem die Blaualgenentwicklung beeinflusst werden. Aufbauend auf bestehenden Methoden zur Bestimmung der Biomasseentwicklung von Makrophyten und deren Einfluss auf den Wasserkörper wird ein hydrodynamisches Modell unter Berücksichtigung der Makrophyten auf die Gerinnereibung weiterentwickelt und auf die Modellierung von Schilfbeständen erweitert. Das in dieser Arbeit entwickelte Modell zur Prognose der Wassergüte wurde auf den Chaosee in der Provinz Anhui, China angewendet. Der eutrophe Flachsee wird u. a. für die Trinkwas-serversorgung genutzt, die aufgrund regelmäßiger Blaualgenblüten in den heißen Sommermonaten immer wieder eingestellt werden muss. Messdaten, die für die Modellierung nicht in ausreichender zeitlicher Auflösung vorlagen, wurden durch Literaturangaben und durch Daten, die mit einem dafür erstellten Neuronalen Netz anhand anderer vorhandener Parameter generiert wurden, ergänzt. Aufbauend auf den Modellergebnissen werden neue Strategien zur Sanierung eines eutrophierten Flachsees hinsichtlich ihrer Wirksamkeit untersucht. Es werden Strategien zur Reduzierung des Trophiegrades und zur Verhinderung des Eintrags von Blaualgen in das Rohwasser entwickelt. Für die Verwendung des Rohwassers zur Trinkwasserversorgung wird als Zielgröße der von der WHO empfohlene Grenzwert für die Microcystin-Konzentration im Trinkwasser von 1 µg/l angestrebt. Mit einem für das Schilfwachstum und den unterschiedlichen Ansprüchen der wasserwirtschaftlicher Nutzungen des Chaosees angepassten Wasserstand wird die maximale Assimilationsleistung des Schilfs bezüglich des Gesamtphosphors aufgezeigt. Über die Reduzierung des Gesamtphosphors kann in einem phosphorlimitierten Ökosystem die Blaualgenentwick-lung gesteuert werden, was im Modell über eine Reduzierung des Trophiegrades belegt wird. Zur Reduzierung der durch Blaualgen produzierten Toxinkonzentration werden Maßnahmen entwickelt und deren Wirkungen mit dem Modell belegt. Dazu gehört die Optimierung der Rohwasserentnahme für die Trinkwasserversorgung wie auch die zusätzliche Verminderung anthropogener Phosphoreinträge durch überschlägig dimensionierte Vorsperren. Anhand der Reaktionen des Ökosystems wird ein Alarmplan für Bevölkerung und Behörden mit Nutzungseinschränkungen, technischen Maßnahmen, sowie einem begleitenden, speziell für Blaualgenblüten entwickelten Monitoringprogramm bis zum Rückgang der Blaualgenblü-te erstmals vorgeschlagen.The prediction of water quality is of particular importance for the utilization of eutrophic shallow lakes. The influence of litoral planting with reed and macrophytes on the water quality of an eutrophic shallow lake and its modelling is shown in this paper. The assimilation of nutrients by macrophytes is associated with the reduction of eutrophication in a complex connection. The development of blue-green algae can be influenced by the reduction of total phosphorus in a phosphorus limited ecosystem. A hydrodynamic model is developed by using existing methods for the growth of the macrophytes biomass and their influence on the water body and the ecosystem. This model is extended by modules for the reed population which consider the roughness of the macrophytes to the channel friction. The model for predicting the water quality, which is developed in this study, is applied to Lake Chaohu in the Anhui Province, China. This eutrophic shallow lake is used for drinking water supply. The water intake is suspended during hot summer months due to periodical blooms of blue-green algae. For water quality parameters no records of adequate temporal resolution are available. Thus, the data has been completed with values from the literature and with data generated by a neural network. The coverage of the lake surface by macrophytes and the water level varied in different scenarios simulated by the model. Based on the results of the model new strategies for the remediation of any eutrophic shallow lake in consideration are investigated in respect to their effectivity. Strategies are developed in order to reduce the trophic status and to prevent the blue-green algae from entering the water intake. The WHO threshold for Microcystin concentration in drinking water of 1 µg/l is set as the target value for the use of raw water. Considering a water level adapted for reed growth and the multiple requirements for the water management of Lake Chaohu, the maximum assimilation activity of the reed can be achieved with regard to reduction of total phosphorus. The development of blue-green algae can be controlled in a phosphorus limited ecosystem by reduction of total phosphorus. The model proves the decrease of the trophic status. Measures are developed for the reduction of the concentration of toxin produced by blue-green algae; their effectivity is shown by application of the model. The scenario of different remediation measures results in an optimization of raw water intake for drinking water supply as well as in the additional reduction of phosphorus import by pre-dams. A novel alarm plan for the public, based on the ecosystem’s reactions, is proposed. The alarm plan comprises restrictions of the water usage, technical measures and a special monitoring programme for blue-green algae, which is operated until a decrease of the blue-green algae bloom is observed

Development of An Integrated GIS-Based System for Surface Water Quality Assessment and Management (GIS-SWQAM)

It is an fact that surface water receives a large volume of pollutants from industrial, agricultural, and municipal sources. The adverse health and environmental effects of surface water pollution have been a major concern in environmental management. Water quality models are useful tools to simulate the complex transport and fate of pollutants in a water body and predict the short-term and long-term effects on water quality variation. The emergence of spatial information technologies, such as Geographic Information System (GIS) make it possible to assess and predict surface water quality with more details with respect to spatial information. The focuses of this thesis is to develop a comprehensive system named as GIS-SWQAM, which includes: (1) the development of a GIS-based water quality assessment system to assess the water quality and provide spatial distribution of water quality variables; (2) the development of an artificial neural network model to predict the change of water quality variables; (3) the development of a user interface that integrates the above models and functions; furthermore, a comparative analysis of the modeling approach developed in the GIS-SWQAM and the commercial model MIKE 21 was performed through field case studies. The GIS-based water quality and ecological risk assessment models (MWQ module for marine water quality assessment and LWQ module for lake water quality assessment) are developed by integrating a fuzzy risk assessment model, a eutrophication risk assessment model, a heavy metal risk assessment model, a dynamic database, the ArcGIS Engine, and a graphical user interface (GUI). The assessment results are both spatially and visually presented in the form of contour maps and color-coded maps that indicate risk levels. A large amount of data with both spatial and temporal distributions is managed by the developed system and analyzed by the assessment modules. The developed MWQ and LWQ modules are respectively applied in the Liaodong Bay of China and Lake Champlain. The MWQ and LWQ produce risk maps that depict the spatial distribution of integrated water quality index values, eutrophication risk levels and heavy metal risk levels in the study area. The maps generated can provide a better understanding of the distribution of the water quality and ecological risk levels. The primary factors that affect the water quality are subsequently examined using the visualized results. An artificial neural network model with the back-propagation algorithm (BPANN) is first developed using Matlab to predict the chlorophyll-a concentration in Lake Champlain. Then, the algorithm of the BPANN model is built using the C# programing language and integrated with GIS and the database to build the ANN module, which is applied to predict the total phosphorus concentration in Lake Champlain. The best performing model is determined among the results of models built with different combination of input variables, which are preliminarily selected by linear correlation analysis and domain knowledge. Subsequently, the performances of the BPANN models are validated by a new set of field data. Similar to the MWQ and LWQ modules, the ANN module also produces the spatial distribution maps of the predicted concentrations; errors made during the prediction are presented in the user interface. The results indicate that the developed BPANN models can provide acceptable prediction results and can be used to provide a quick modeling assessment of water quality variation for managers. In this thesis, the MIKE 21 FM software is also used to establish a hydrodynamic model coupled with a transport model to simulate the total phosphorus concentration in Lake Champlain. A comparative analysis is performed between the results of the MIKE 21 model and the BPANN model. The results of the MIKE 21 model are acceptable, but not as good as that of the BPANN model. This further verifies that the developed BPANN model is a reliable tool to assess the lake eutrophication and to help managing lake water quality. The developed system can be also applied to surface water management in other area

Determining the drivers of harmful algal blooms and their impact on public water supply resilience during droughts

Climate change poses a significant risk to water supply resilience. Reduced summer rainfall combined with elevated temperatures are likely to threaten both water quantity and water quality. With respect to water quality, shallow warmer reservoirs can increase risk of biological causes of poor quality, for example due to harmful algal blooms, cyanobacteria toxins and taste and odour metabolites. This was investigated at Llandegfedd, the largest potable water reservoir and a critical water supply system in Wales. The overarching aim addressed in this thesis was to determine if the reservoir would be vulnerable to poor water quality events in the future. To this end, the project comprised four main areas of research: 1. Historical data; 2. Water nutrient dynamics; 3. Sediment nutrient dynamics; 4. Effects of extreme weather events upon water quality. Analysis of water samples indicated phosphorus limitation and the reservoir overall was classified as oligotrophic; unusual for a lowland reservoir. Mass balance analysis demonstrated low internal loading potential even in summer months, which was supported by sediment phosphorus measurements. Allochthonous sources of phosphorus were responsible for the low levels of bioavailable P in the water column. In contrast, the shallowest site at the northern end of the reservoir, adjacent to the main pumped inflow, was eutrophic and at highest risk of water quality deterioration, cyanobacterial growth and geosmin production. Analysis of extreme weather events indicated this risk to be compounded by pulse rainfall events. The reservoir relied upon an allochthonous source of phosphorous, with the refilling process also changing nutrient dynamics through increasing phosphorous concentrations. An additional finding of this study was that analysis of nutrients pre and post water abstraction tower failure around 1999/2000 indicated that the current water abstraction method is inappropriate for monitoring water quality risk at this site, demonstrating the need for enhanced monitoring. This study has shown that Llandegfedd Reservoir remains oligotrophic despite potential for internal P loading, suggesting possible future issues with water quality at the site. This is compounded by extreme weather events and patterns of drawdown and refilling. This is an important warning to the wider Water Industry that oligotrophic water supply reservoirs still require significant monitoring to assess water supply resilience in the face of climate change

Development of Deep Learning Hybrid Models for Hydrological Predictions

The Abstract is currently unavailable, due to the thesis being under Embargo