Cyanobacteria, also known as blue-green algae for a long time, are the most ancient and problematic bloom-forming phylum on earth. An alert levels framework has been established by World Health Organization(WHO) to prevent the potential harmful cyanobacterial blooms. Normally, low cyanobacteria levels are found in surface water. 2000 cyanobacterial cells/mL and 100,000 cyanobacterial cells/mL are established for WHO Alert Level 1 and 2, respectively. However, eutrophication, climate change and other factors may promote the spread of cyanobacteria and increase the occurrence of harmful cyanobacterial blooms in water on a global scale. Hence, a rapid real time cyanobacteiral monitoring system is required to protect public health from the cyanotoxins produced by toxic cyanobacterial species. Current methods to control or prevent the development of harmful cyanobacterial blooms are either expensive, time consuming or not effective in the long term. The best method to control the blooms is to prevent the formation of the blooms at the very beginning. Although emerging advanced autofluorescence-based sensors, imaging flow cytometry applications, and remote sensing have been utilized for rapid real-time enumeration and classification of cyanobacteria, the need to accurately monitor low-level cyanobacterial species in water remains unsolved. Microflow cytometry has been employed as a functional cell analysis technique in past decades, and it can provide real-time, accurate results. The autofluorescence of cyanobacterial pigments can be used for determination and counting of cyanobacterial density in water. A pre-concentration system of an automated cyanobacterial concentration and recovery system (ACCRS) based on tangential flow filtration and back-flushing technique was applied to reduce the sample assay volume and increase the concentration of target cells for further cell capture and detection. In this project, a microflow cytometry platform with a microfluidic device and an automated pre-concentration system was established to monitor cyanobacteria and provide early warning alerts for potential harmful blooms. In this work, quantification of low-level cyanobacterial samples (∼ 5 cyanobacterial cells/mL) in water has been achieved by using a microflow cytometer together with a pre-concentration system (ACCRS). Meanwhile, this platform can also provide early warning alerts for potential harmful cyanobacterial blooms at least 15 days earlier before reaching WHO Alert Level 1. Results have shown that this platform can be applied for rapid determination of cyanobacteria and early warning alerts can be triggered for authorities to protect the public and the environment. / Thesis / Doctor of Engineering (DEng) / Harmful cyanobacterial blooms have been a rising risk to the public heath across the world in recent decades. Alert levels of cyanobacteria in water has also been established. In this case, a rapid on-side monitoring system for cyanobacteria is required. In this thesis, a microflow cytometer platform combined with a bacterial concentration and recovery system was built to quickly monitor the relatively low level of cyanobacteria for early warning alerts. A pre-enrichment system based on tangential flow filtration and back-flushing technique was applied to increase the concentration levels of microbial samples and a microfluidic device capable of collecting phycocyanin fluorescence was designed to count cyanobacterial cells. The limit of quantification for cyanobacterial concentration based on the microflow cytometry platform was as low as ∼ 5 cells/mL. We can claim that the microflow cytometry platform can provide useful early warning alerts for the decision-makers to control the potential harmful cyanobacterial blooms at the very early stage and protect the aquatic animals and public health.

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Algae and cyanobacteria occur naturally in source waters and are known to cause extensive problems in the drinking water treatment industry. Cyanobacteria (especially Anabaena sp. and Microcystis sp.) are responsible for many water treatment problems in drinking water treatment works (DWTW) all over the world because of their ability to produce organic compounds like cyanotoxins (e.g. microcystin) and taste and odour compounds (e.g. geosmin) that can have an adverse effect on consumer health and consumer confidence in tap water. Therefore, the monitoring of cyanobacteria in source waters entering DWTW has become an essential part of drinking water treatment management. Managers of DWTW, rely heavily on results of physical, chemical and biological water quality analyses, for their management decisions. But results of water quality analyses can be delayed from 3 hours to a few days depending on a magnitude of factors such as: sampling, distance and accessibility to laboratory, laboratory sample turn-around times, specific methods used in analyses etc. Therefore the use of on-line (in situ) instruments that can supply real-time results by the click of a button has become very popular in the past few years. On-line instruments were developed for analyses like pH, conductivity, nitrate, chlorophyll-a and cyanobacteria concentrations. Although, this real-time (on-line) data has given drinking water treatment managers a better opportunity to make sound management decisions around drinking water treatment options based on the latest possible results, it may still be “too little, too late” once a sudden cyanobacterial bloom of especially Anabaena sp. or Microcystis sp. enters the plant. Therefore the benefit for drinking water treatment management, of changing the focus from real-time results to future predictions of water quality has become apparent. The aims of this study were 1) to review the environmental variables associated with cyanobacterial blooms in the Vaal Dam, as to get background on the input variables that can be used in cyanobacterial-related forecasting models; 2) to apply rule-based Hybrid Evolutionary Algorithms (HEAs) to develop models using a) all applicable laboratory-generated data and b) on-line measureable data only, as input variables in prediction models for harmful algal blooms in the Vaal Dam; 3) to test these models with data that was not used to develop the models (so-called “unseen data”), including on-line (in situ) generated data; and 4) to incorporate selected models into two cyanobacterial incident management protocols which link to the Water Safety Plan (WSP) of a large DWTW (case study : Rand Water). During the current study physical, chemical and biological water quality data from 2000 to 2009, measured in the Vaal Dam and the 20km long canal supplying the Zuikerbosch DWTW of Rand Water, has been used to develop models for the prediction of Anabaena sp., Microcystis sp., the cyanotoxin microcystin and the taste and odour compound geosmin for different prediction or forecasting times in the source water. For the development and first stage of testing the models, 75% of the dataset was used to train the models and the remaining 25% of the dataset was used to test the models. Boot-strapping was used to determine which 75% of the dataset was to be used as the training dataset and which 25% as the testing dataset. Models were also tested with 2 to 3 years of so called “unseen data” (Vaal Dam 2010 – 2012) i.e. data not used at any stage during the model development. Fifty different models were developed for each set of “x input variables = 1 output variable” chosen beforehand. From the 50 models, the best model between the measured data and the predicted data was chosen. Sensitivity analyses were also performed on all input variables to determine the variables that have the largest impact on the result of the output. This study have shown that hybrid evolutionary algorithms can successfully be used to develop relatively accurate forecasting models, which can predict cyanobacterial cell concentrations (particularly Anabaena sp. and Microcystis sp.), as well as the cyanotoxin microcystin concentration in the Vaal Dam, for up to 21 days in advance (depending on the output variable and the model applied). The forecasting models that performed the best were those forecasting 7 days in advance (R2 = 0.86, 0.91 and 0.75 for Anabaena[7], Microcystis[7] and microcystin[7] respectively). Although no optimisation strategies were performed, the models developed during this study were generally more accurate than most models developed by other authors utilising the same concepts and even models optimised by hill climbing and/or differential evolution. It is speculated that including “initial cyanobacteria inoculum” as input variable (which is unique to this study), is most probably the reason for the better performing models. The results show that models developed from on-line (in situ) measureable data only, are almost as good as the models developed by using all possible input variables. The reason is most probably because “initial cyanobacteria inoculum” – the variable towards which the output result showed the greatest sensitivity – is included in these models. Generally models predicting Microcystis sp. in the Vaal Dam were more accurate than models predicting Anabaena sp. concentrations and models with a shorter prediction time (e.g. 7 days in advance) were statistically more accurate than models with longer prediction times (e.g. 14 or 21 days in advance). The multi-barrier approach in risk reduction, as promoted by the concept of water safety plans under the banner of the Blue Drop Certification Program, lends itself to the application of future predictions of water quality variables. In this study, prediction models of Anabaena sp., Microcystis sp. and microcystin concentrations 7 days in advance from the Vaal Dam, as well as geosmin concentration 7 days in advance from the canal were incorporated into the proposed incident management protocols. This was managed by adding an additional “Prediction Monitoring Level” to Rand Waters’ microcystin and taste and odour incident management protocols, to also include future predictions of cyanobacteria (Anabaena sp. and Microcystis sp.), microcystin and geosmin. The novelty of this study was the incorporation of future predictions into the water safety plan of a DWTW which has never been done before. This adds another barrier in the potential exposure of drinking water consumers to harmful and aesthetically unacceptable organic compounds produced by cyanobacteria. / PhD (Botany), North-West University, Potchefstroom Campus, 2015

Algae and cyanobacteria occur naturally in source waters and are known to cause extensive problems in the drinking water treatment industry. Cyanobacteria (especially Anabaena sp. and Microcystis sp.) are responsible for many water treatment problems in drinking water treatment works (DWTW) all over the world because of their ability to produce organic compounds like cyanotoxins (e.g. microcystin) and taste and odour compounds (e.g. geosmin) that can have an adverse effect on consumer health and consumer confidence in tap water. Therefore, the monitoring of cyanobacteria in source waters entering DWTW has become an essential part of drinking water treatment management. Managers of DWTW, rely heavily on results of physical, chemical and biological water quality analyses, for their management decisions. But results of water quality analyses can be delayed from 3 hours to a few days depending on a magnitude of factors such as: sampling, distance and accessibility to laboratory, laboratory sample turn-around times, specific methods used in analyses etc. Therefore the use of on-line (in situ) instruments that can supply real-time results by the click of a button has become very popular in the past few years. On-line instruments were developed for analyses like pH, conductivity, nitrate, chlorophyll-a and cyanobacteria concentrations. Although, this real-time (on-line) data has given drinking water treatment managers a better opportunity to make sound management decisions around drinking water treatment options based on the latest possible results, it may still be “too little, too late” once a sudden cyanobacterial bloom of especially Anabaena sp. or Microcystis sp. enters the plant. Therefore the benefit for drinking water treatment management, of changing the focus from real-time results to future predictions of water quality has become apparent. The aims of this study were 1) to review the environmental variables associated with cyanobacterial blooms in the Vaal Dam, as to get background on the input variables that can be used in cyanobacterial-related forecasting models; 2) to apply rule-based Hybrid Evolutionary Algorithms (HEAs) to develop models using a) all applicable laboratory-generated data and b) on-line measureable data only, as input variables in prediction models for harmful algal blooms in the Vaal Dam; 3) to test these models with data that was not used to develop the models (so-called “unseen data”), including on-line (in situ) generated data; and 4) to incorporate selected models into two cyanobacterial incident management protocols which link to the Water Safety Plan (WSP) of a large DWTW (case study : Rand Water). During the current study physical, chemical and biological water quality data from 2000 to 2009, measured in the Vaal Dam and the 20km long canal supplying the Zuikerbosch DWTW of Rand Water, has been used to develop models for the prediction of Anabaena sp., Microcystis sp., the cyanotoxin microcystin and the taste and odour compound geosmin for different prediction or forecasting times in the source water. For the development and first stage of testing the models, 75% of the dataset was used to train the models and the remaining 25% of the dataset was used to test the models. Boot-strapping was used to determine which 75% of the dataset was to be used as the training dataset and which 25% as the testing dataset. Models were also tested with 2 to 3 years of so called “unseen data” (Vaal Dam 2010 – 2012) i.e. data not used at any stage during the model development. Fifty different models were developed for each set of “x input variables = 1 output variable” chosen beforehand. From the 50 models, the best model between the measured data and the predicted data was chosen. Sensitivity analyses were also performed on all input variables to determine the variables that have the largest impact on the result of the output. This study have shown that hybrid evolutionary algorithms can successfully be used to develop relatively accurate forecasting models, which can predict cyanobacterial cell concentrations (particularly Anabaena sp. and Microcystis sp.), as well as the cyanotoxin microcystin concentration in the Vaal Dam, for up to 21 days in advance (depending on the output variable and the model applied). The forecasting models that performed the best were those forecasting 7 days in advance (R2 = 0.86, 0.91 and 0.75 for Anabaena[7], Microcystis[7] and microcystin[7] respectively). Although no optimisation strategies were performed, the models developed during this study were generally more accurate than most models developed by other authors utilising the same concepts and even models optimised by hill climbing and/or differential evolution. It is speculated that including “initial cyanobacteria inoculum” as input variable (which is unique to this study), is most probably the reason for the better performing models. The results show that models developed from on-line (in situ) measureable data only, are almost as good as the models developed by using all possible input variables. The reason is most probably because “initial cyanobacteria inoculum” – the variable towards which the output result showed the greatest sensitivity – is included in these models. Generally models predicting Microcystis sp. in the Vaal Dam were more accurate than models predicting Anabaena sp. concentrations and models with a shorter prediction time (e.g. 7 days in advance) were statistically more accurate than models with longer prediction times (e.g. 14 or 21 days in advance). The multi-barrier approach in risk reduction, as promoted by the concept of water safety plans under the banner of the Blue Drop Certification Program, lends itself to the application of future predictions of water quality variables. In this study, prediction models of Anabaena sp., Microcystis sp. and microcystin concentrations 7 days in advance from the Vaal Dam, as well as geosmin concentration 7 days in advance from the canal were incorporated into the proposed incident management protocols. This was managed by adding an additional “Prediction Monitoring Level” to Rand Waters’ microcystin and taste and odour incident management protocols, to also include future predictions of cyanobacteria (Anabaena sp. and Microcystis sp.), microcystin and geosmin. The novelty of this study was the incorporation of future predictions into the water safety plan of a DWTW which has never been done before. This adds another barrier in the potential exposure of drinking water consumers to harmful and aesthetically unacceptable organic compounds produced by cyanobacteria. / PhD (Botany), North-West University, Potchefstroom Campus, 2015

Thesis (S.M. in Biology)--Joint Program in Biological Oceanography (Massachusetts Institute of Technology, Dept. of Biology; and the Woods Hole Oceanographic Institution), 1999. / Includes bibliographical references (leaves 23-30). / Phytoplankton mortality (herbivory) and bacterivory were examined experimentally in West Neck Bay and Coecles Harbor, Long Island, NY from April through September, 1998. Small algae (<5 [tm diameter) dominated phytoplankton communities in both ecosystems throughout the summer, and zooplankton were also small (mostly <40 tm). Generally, plankton abundances were indicative of eutrophic ecosystems. Oscillations in standing stocks and mortality of prey indicated tight coupling of growth and grazing mortality in both bays. Phytoplankton mortality rates accounted for the removal of 14% to 65% of total phytoplankton standing stocks daily, while bacterivory accounted for the removal of 14% to 88% of total bacterial standing stocks daily. Estimates of carbon consumption revealed high energy flux through the nano- and microzooplankton assemblages of these estuarine environments. / by Katie Rose Boissonneault Cellineri. / S.M.in Biology

Cyanobacterial blooms in lakes impact human health, the economy, and ecosystem health. It is predicted that climate change will promote and increase the frequency and intensity of cyanobacterial blooms due to unique physiological adaptions that allow cyanobacteria to exploit warm stable water bodies. Key cyanobacteria physiological adaptions include nitrogen fixation, buoyancy regulation and higher optimum growth temperatures. The largest uncertainty of predicting the effect of climate change is in understanding how the interactions among species will change. Adding to the ambiguity, cyanobacteria physiological adaptions can vary based on lakespecific ecotypes and can have different sensitivities to temperature. It is critical to understand how cyanobacterial physiological adaptions impact species interactions in order to improve and devise adaptable, short‐term management methods for bloom control. This study investigated how weather patterns and algal buoyancy regulation influence the competition and accumulation of two bloom‐forming buoyant cyanobacteria species (Aphanizomenon flos‐aquae (APFA) and toxin‐forming Microcystis aeruginosa (MSAE)) in Upper Klamath Lake (UKL), Oregon. The focus was confirming the buoyancy rate of the APFA in Upper Klamath Lake and exploring whether short‐term weather conditions could lead to dangerous accumulations of APFA or MSAE. A sensitivity analysis was conducted on the model's buoyancy terms and growth curves to see if the outcome of competition was influenced by these parameters. UKL specific buoyancy rates were measured on APFA from samples taken directly from the lake in the summer of 2015. Tracking software was used to measure APFA movement through water, and individual colony movement was averaged to obtain a single buoyancy rate. There was a high degree of agreement between the calculated APFA buoyancy rate in UKL (0.89 ± 0.34 m hr-1) with the rate published by Walsby (1995; 0.9 ± 0.5 m hr-1). This study investigated how weather patterns and buoyancy regulation influenced the outcome of competition between APFA and MSAE. Weather and water column temperature data were collected from UKL in the summer of 2016. A onedimensional hydrodynamic model was used to calculate the lake's thermal and turbulence structure on days with contrasting weather patterns (hot/cool and windy/calm). A competition model was used to calculate the accumulation of APFA and MSAE cells in regular intervals through the water column under the various weather scenarios. MSAE accumulation was significantly influenced by the thermal and turbulence regimes, but APFA maintained high accumulations under every regime and was the better competitor under every thermal and turbulence regime. MSAE was more negatively impacted by high turbulence than low temperatures. APFA's optimum temperature growth curve was found to be important in determining the outcome of competition between APFA and MSAE. Surprisingly, competition was not sensitive to changes in buoyancy rates. Buoyancy was not found to be a function of algal accumulation under any thermal and turbulence regime. The impacts of climate change and human‐induced enrichment has the potential to change existing patterns of species interactions in lentic systems. Restoration and management efforts should consider the significance of cascading ecological responses to climate change. Understanding how key physiological adaptions operate is the first step to assessing the scope of this impact. While buoyancy might not play a large role in competition in UKL, it might be possible to use mixing to suppress MSAE because it is negatively impacted by high turbulence. If MSAE hot spots become a reoccurring problem in UKL, lakes managers might be able to use localized mixing to suppress MSAE blooms in these problem areas.

Thesis (M.A.)--University of Wisconsin, 1949. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 157-159).

This thesis has two parts. In the first part there is described an optimal methodological process for screening tests used in subsequently. Fertilized fish eggs of Oryzias latipes were incubated in 6 tests with different numbers (from 1 to 6) with standard conditions in ISO water. In the tests, hatching performance and duration of embryonic development were investigated and the results were applied on screening tests. The other part of the thesis is aimed on the proof of potential toxic effects of water with cyanobacterial water bloom. The fertilized eggs of Oryzias latipes were embedded into the test in stage 6 to 8. Three samples of cyanobacterial biomass from free waterbodies with known species composition and microcystin {--} LR, YR and RR contents were tested. The hatching performance, duration of embryonic development, lethal and sublethal effects were monitored during the tests. The tests were performed according to the OECD 212. There were detected significant differences in hatching performance, duration of embryonic development and in some cases also in induction of deformities between the control group and the tested groups.

Animals living in marine sediments (the second largest habitat on earth) play a major role in global biogeochemical cycling. By feeding on organic matter from settled phytoplankton blooms they produce food for higher trophic levels and nutrients that can fuel primary production. In the Baltic Sea, anthropogenic stresses, such as eutrophication and introductions of invasive species, have altered phytoplankton dynamics and benthic communities. This thesis discusses the effects of different types of phytoplankton on the deposit-feeding community and the importance of benthic biodiversity for fate of the phytoplankton bloom-derived organic matter. Deposit-feeders survived and fed on settled cyanobacterial bloom material and in doing so accumulated the cyanobacterial toxin nodularin. Their growth after feeding on cyanobacteria was much slower than on a diet of spring bloom diatoms. The results show that settling blooms of cyanobacteria are used as food without obvious toxic effects, although they do not sustain rapid growth of the fauna. Since all tested species accumulated the cyanotoxin, negative effects higher up in the food web can not be ruled out. Both species composition and richness of deposit-feeding macrofauna influenced how much of the phytoplankton bloom material that was incorporated in fauna or retained in the sediment. The mechanism behind the positive effect of species richness was mainly niche differentiation among functionally different species, resulting in a more efficient utilization of resources at greater biodiversity. This was observed even after addition of an invasive polychaete species. Hence, species loss can be expected to affect benthic productivity negatively. In conclusion, efficiency in organic matter processing depends both on pelagic phytoplankton quality and benthic community composition and species richness. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Manuscript. Paper 4: In press. Paper 5: Manuscript.</p>