Application of algal bioassays in the determination of eutrophic power of waste water (”Conclusions” only) [Translation from: 1g.Sanita pubbl. 31 257-267, 1975]

The best evidence for establishing the level of eutrophy of a water-body is its algal production which makes it possible to identify the type and the intensity of the eutrophication according to the kind and number of algal species present: when the number of algae exceeds half a million per litre then one speaks o an ”algal bloom”. The scope of the present research aims to verify if the alga Selenastrum capricornutum can be used as a test alga under our culture conditions and to determine the eutrophic level of the secondary effluent of a modern plant for the treatment of domestic discharge and to investigate the eventual ”limiting factors”. Finally this paper aims to study the effect on the secondary effluent of tertiary treatment carried out artificially in the laboratory.

Scientific assessment of marine harmful algal blooms

Algae are the most abundant photosynthetic organisms in marine ecosystems and are essential components of marine food webs. Harmful algal bloom or “HAB” species are a small subset of algal species that negatively impact humans or the environment. HABs can pose health hazards for humans or animals through the production of toxins or bioactive compounds. They also can cause deterioration of water quality through the buildup of high biomass, which degrades aesthetic, ecological, and recreational values. Humans and animals can be exposed to marine algal toxins through their food, the water in which they swim, or sea spray. Symptoms from toxin exposure range from neurological impairment to gastrointestinal upset to respiratory irritation, in some cases resulting in severe illness and even death. HABs can also result in lost revenue for coastal economies dependent on seafood harvest or tourism, disruption of subsistence activities, loss of community identity tied to coastal resource use, and disruption of social and cultural practices. Although economic impact assessments to date have been limited in scope, it has been estimated that the economic effects of marine HABs in U.S. communities amount to at least $82 million per year including lost income for fisheries, lost recreational opportunities, decreased business in tourism industries, public health costs of illness, and expenses for monitoring and management. As reviewed in the report, Harmful Algal Research and Response: A Human Dimensions Strategy1, the sociocultural impacts of HABs may be significant, but remain mostly undocumented.

Scientific assessment of freshwater harmful algal blooms

In 2004, Congress reauthorized the Harmful Algal Bloom and Hypoxia Research and Control Act of 1998 with the Harmful Algal Bloom and Hypoxia Amendments Act (HABHRCA 2004). The 2004 legislation required the generation of five reports, including this "Scientific Assessment of Freshwater Harmful Algal Blooms." HABHRCA 2004 stipulates that this report 1) examine the causes, consequences, and economic costs of freshwater HABs, 2) establish priorities and guidelines for a research program on freshwater HABs, and 3) make recommendations to improve coordination among Federal agencies with respect to research on HABs in freshwater environments. This report is divided into five chapters: Chapter 1 provides the legislative background and process for developing the report, Chapter 2 describes the problem of freshwater and inland HABs in the United States, Chapter 3 outlines the current Federal efforts in freshwater and inland HAB research and response, Chapter 4 discusses the future research priorities, and Chapter 5 delineates opportunities for coordination to advance research efforts. The document is based, in large part, on the proceedings (Hudnell 2008) of the International Symposium on Cyanobacterial Harmful Algal Blooms, a meeting convened by EPA and sponsored by a variety of Federal agencies, to describe current scientific knowledge and identify priorities for future research on CyanoHABs. This report offers a plan for coordinating the important research that is currently ongoing in the United States and for guiding future research directions for Federal programs as well as for state, local, private, and academic institutions in order to maximize advancements. To this end, the Interagency Working Group on Harmful Algal Blooms, Hypoxia, and Human Health (IWG-4H) identifies seven priorities, all of equal weight, for freshwater HAB research and response. These priorities represent research areas where there is the greatest potential for progress in freshwater HAB research. This report does not attempt to assess the relative importance of freshwater HAB research compared to other research areas or other priorities for Federal or state investment.

Temporal Coherence of Chlorophyll a during a Spring Phytoplankton Bloom in Xiangxi Bay of Three-Gorges Reservoir, China

Algal bloom phenomenon was defined as "the rapid growth of one or more phytoplankton species which leads to a rapid increase in the biomass of phytoplankton", yet most estimates of temporal coherence are based on yearly or monthly sampling frequencies and little is known of how synchrony varies among phytoplankton or of the causes of temporal coherence during spring algal bloom. In this study, data of chlorophyll a and related environmental parameters were weekly gathered at 15 sampling sites in Xiangxi Bay of Three-Gorges Reservoir (TGR, China) to evaluate patterns of temporal coherence for phytoplankton during spring bloom and test if spatial heterogeneity of nutrient and inorganic suspended particles within a single ecosystem influences synchrony of spring phytoplankton dynamics. There is a clear spatial and temporal variation in chlorophyll a across Xiangxi Bay. The degree of temporal coherence for chlorophyll a between pairs of sites located in Xiangxi Bay ranged from -0.367 to 0.952 with mean and median values of 0.349 and 0.321, respectively. Low levels of temporal coherence were often detected among the three stretches of the bay (Down reach, middle reach and upper reach), while high levels of temporal coherence were often found within the same reach of the bay. The relative difference of DIN between pair sites was the strong predictor of temporal coherence for chlorophyll a in down and middle reach of the bay, while the relative difference in Anorganic Suspended Solids was the important factor regulating temporal coherence in middle and upper reach. Contrary to many studies, these results illustrate that, in a small geographic area (a single reservoir bay of approximately 25 km), spatial heterogeneity influence synchrony of phytoplankton dynamics during spring bloom and local processes may override the effects of regional processes or dispersal.

Modelling algal blooms in Lake Dianchi, China using neural networks

Lake Dianchi is one of the most extensively impacted freshwater lakes by algal blooms. To investigate the response of dominant algal genera, neural networks were applied to model the relationship between water quality parameters and the biomass of four dominant genera (Microcystic spp., Anabaena sp., Quadricauda (Turp.) Breb, Pediastrum Mey) in Dianchi. Results showed that the timing and magnitude of algal blooms of Microcystic spp., nabaena sp., Quadricauda (Turp.) Breb, and Pediastrum Mey in Dianchi could be successfully predicted. The evaluation of environmental factors showed that pH had more significant impact on concentrations of all the four dominant algal genera than the nutrient factors, such as total phosphorus and total nitrogen.

Synergistic effect of sophorolipid and loess combination in harmful algal blooms mitigation

The inhibition effect of sophorolipid and removal efficiency of loess on Cochlodinium polykrikoides and Alexandrium tamarense was investigated separately in the laboratory. Based on this, the combination of sophorolipid and loess for harmful algal bloom mitigation was proposed. Algal sedimentation tests in the laboratory and in the field revealed that the combination of sophorolipid and loess showed synergistic effects both on the removal efficiencies and on the mitigation cost. The concentration of 1 g/l loess and 5 mg/l sophorolipid was determined as the optimum ratio for C polykrikoides mitigation. In the field test, the effective concentration of loess and sophorolipid in the combination group was reduced to 10% and 25%, respectively, compared to the non-combination group, and the cost decreased more than 60%. The combination of loess and sophorolipid was considered as a promising novel method in harmful algal bloom mitigation. (C) 2003 Elsevier Ltd. All rights reserved.

Remote, subsurface detection of the algal toxin domoic acid onboard the Environmental Sample Processor: Assay development and field trials

The ability to detect harmful algal bloom (HAB) species and their toxins in real- or near real-time is a critical need for researchers studying HAB/toxin dynamics, as well as for coastal resource managers charged with monitoring bloom populations in order to mitigate their wide ranging impacts. The Environmental Sample Processor (ESP), a robotic electromechanical/fluidic system, was developed for the autonomous, subsurface application of molecular diagnostic tests and has successfully detected several HAB species using DNA probe arrays during field deployments. Since toxin production and thus the potential for public health and ecosystem effects varies considerably in natural phytoplankton populations, the concurrent detection of HAB species and their toxins onboard the ESP is essential. We describe herein the development of methods for extracting the algal toxin domoic acid (DA) from Pseudonitzschia cells (extraction efficiency >90%) and testing of samples using a competitive ELISA onboard the ESP. The assay detection limit is in the low ng/mL range (in extract), which corresponds to low ng/L levels of DA in seawater for a 0.5 L sample volume acquired by the ESP. We also report the first in situ detection of both a HAB organism (i.e., Pseudo-nitzschia) and its toxin, domoic acid, via the sequential (within 2-3 h) conduct of species- and toxin-specific assays during ESP deployments in Monterey Bay, CA, USA. Efforts are now underway to further refine the assay and conduct additional calibration exercises with the aim of obtaining more reliable, accurate estimates of bloom toxicity and thus their potential impacts. Published by Elsevier B.V.

Phytoplankton bloom status: Chlorophyll a biomass as an indicator of water quality condition in the southern estuaries of Florida, USA

Altered freshwater inflows have affected circulation, salinity, and water quality patterns of Florida Bay, in turn altering the structure and function of this estuary. Changes in water quality and salinity and associated loss of dense turtle grass and other submerged aquatic vegetation (SAV) in Florida Bay have created a condition in the bay where sediments and nutrients have been regularly disturbed, frequently causing large and dense phytoplankton blooms. These algal and cyanobacterial blooms in turn often cause further loss of more recently established SAV, exacerbating the conditions causing the blooms. Chlorophyll a (CHLA) was selected as an indicator of water quality because it is an indicator of phytoplankton biomass, with concentrations reflecting the integrated effect of many of the water quality factors that may be altered by restoration activities. Overall, we assessed the CHLA indicator as being (1) relevant and reflecting the state of the Florida Bay ecosystem, (2) sensitive to ecosystem drivers (stressors, especially nutrient loading), (3) feasible to monitor, and (4) scientifically defensible. Distinct zones within the bay were defined according to statistical and consensual information. Threshold levels of CHLA for each zone were defined using historical data and scientific consensus. A presentation template of condition of the bay using these thresholds is shown as an example of an outreach product.

Integrated Bayesian network frameworks for modelling complex ecological issues

Ecological problems are typically multi faceted and need to be addressed from a scientific and a management perspective. There is a wealth of modelling and simulation software available, each designed to address a particular aspect of the issue of concern. Choosing the appropriate tool, making sense of the disparate outputs, and taking decisions when little or no empirical data is available, are everyday challenges facing the ecologist and environmental manager. Bayesian Networks provide a statistical modelling framework that enables analysis and integration of information in its own right as well as integration of a variety of models addressing different aspects of a common overall problem. There has been increased interest in the use of BNs to model environmental systems and issues of concern. However, the development of more sophisticated BNs, utilising dynamic and object oriented (OO) features, is still at the frontier of ecological research. Such features are particularly appealing in an ecological context, since the underlying facts are often spatial and temporal in nature. This thesis focuses on an integrated BN approach which facilitates OO modelling. Our research devises a new heuristic method, the Iterative Bayesian Network Development Cycle (IBNDC), for the development of BN models within a multi-field and multi-expert context. Expert elicitation is a popular method used to quantify BNs when data is sparse, but expert knowledge is abundant. The resulting BNs need to be substantiated and validated taking this uncertainty into account. Our research demonstrates the application of the IBNDC approach to support these aspects of BN modelling. The complex nature of environmental issues makes them ideal case studies for the proposed integrated approach to modelling. Moreover, they lend themselves to a series of integrated sub-networks describing different scientific components, combining scientific and management perspectives, or pooling similar contributions developed in different locations by different research groups. In southern Africa the two largest free-ranging cheetah (Acinonyx jubatus) populations are in Namibia and Botswana, where the majority of cheetahs are located outside protected areas. Consequently, cheetah conservation in these two countries is focussed primarily on the free-ranging populations as well as the mitigation of conflict between humans and cheetahs. In contrast, in neighbouring South Africa, the majority of cheetahs are found in fenced reserves. Nonetheless, conflict between humans and cheetahs remains an issue here. Conservation effort in South Africa is also focussed on managing the geographically isolated cheetah populations as one large meta-population. Relocation is one option among a suite of tools used to resolve human-cheetah conflict in southern Africa. Successfully relocating captured problem cheetahs, and maintaining a viable free-ranging cheetah population, are two environmental issues in cheetah conservation forming the first case study in this thesis. The second case study involves the initiation of blooms of Lyngbya majuscula, a blue-green algae, in Deception Bay, Australia. L. majuscula is a toxic algal bloom which has severe health, ecological and economic impacts on the community located in the vicinity of this algal bloom. Deception Bay is an important tourist destination with its proximity to Brisbane, Australia’s third largest city. Lyngbya is one of several algae considered to be a Harmful Algal Bloom (HAB). This group of algae includes other widespread blooms such as red tides. The occurrence of Lyngbya blooms is not a local phenomenon, but blooms of this toxic weed occur in coastal waters worldwide. With the increase in frequency and extent of these HAB blooms, it is important to gain a better understanding of the underlying factors contributing to the initiation and sustenance of these blooms. This knowledge will contribute to better management practices and the identification of those management actions which could prevent or diminish the severity of these blooms.

Planning and implementing trajectories for autonomous underwater vehicles to track evolving ocean processes based on predictions from a regional ocean model

Path planning and trajectory design for autonomous underwater vehicles (AUVs) is of great importance to the oceanographic research community because automated data collection is becoming more prevalent. Intelligent planning is required to maneuver a vehicle to high-valued locations to perform data collection. In this paper, we present algorithms that determine paths for AUVs to track evolving features of interest in the ocean by considering the output of predictive ocean models. While traversing the computed path, the vehicle provides near-real-time, in situ measurements back to the model, with the intent to increase the skill of future predictions in the local region. The results presented here extend prelim- inary developments of the path planning portion of an end-to-end autonomous prediction and tasking system for aquatic, mobile sensor networks. This extension is the incorporation of multiple vehicles to track the centroid and the boundary of the extent of a feature of interest. Similar algorithms to those presented here are under development to consider additional locations for multiple types of features. The primary focus here is on algorithm development utilizing model predictions to assist in solving the motion planning problem of steering an AUV to high-valued locations, with respect to the data desired. We discuss the design technique to generate the paths, present simulation results and provide experimental data from field deployments for tracking dynamic features by use of an AUV in the Southern California coastal ocean.

Cooperative multi-AUV tracking of phytoplankton blooms based on ocean model predictions

In recent years, ocean scientists have started to employ many new forms of technology as integral pieces in oceanographic data collection for the study and prediction of complex and dynamic ocean phenomena. One area of technological advancement in ocean sampling if the use of Autonomous Underwater Vehicles (AUVs) as mobile sensor plat- forms. Currently, most AUV deployments execute a lawnmower- type pattern or repeated transects for surveys and sampling missions. An advantage of these missions is that the regularity of the trajectory design generally makes it easier to extract the exact path of the vehicle via post-processing. However, if the deployment region for the pattern is poorly selected, the AUV can entirely miss collecting data during an event of specific interest. Here, we consider an innovative technology toolchain to assist in determining the deployment location and executed paths for AUVs to maximize scientific information gain about dynamically evolving ocean phenomena. In particular, we provide an assessment of computed paths based on ocean model predictions designed to put AUVs in the right place at the right time to gather data related to the understanding of algal and phytoplankton blooms.

Autonomous underwater vehicle trajectory design coupled with predictive ocean models : a case study

Data collection using Autonomous Underwater Vehicles (AUVs) is increasing in importance within the oceano- graphic research community. Contrary to traditional moored or static platforms, mobile sensors require intelligent planning strategies to manoeuvre through the ocean. However, the ability to navigate to high-value locations and collect data with specific scientific merit is worth the planning efforts. In this study, we examine the use of ocean model predictions to determine the locations to be visited by an AUV, and aid in planning the trajectory that the vehicle executes during the sampling mission. The objectives are: a) to provide near-real time, in situ measurements to a large-scale ocean model to increase the skill of future predictions, and b) to utilize ocean model predictions as a component in an end-to-end autonomous prediction and tasking system for aquatic, mobile sensor networks. We present an algorithm designed to generate paths for AUVs to track a dynamically evolving ocean feature utilizing ocean model predictions. This builds on previous work in this area by incorporating the predicted current velocities into the path planning to assist in solving the 3-D motion planning problem of steering an AUV between two selected locations. We present simulation results for tracking a fresh water plume by use of our algorithm. Additionally, we present experimental results from field trials that test the skill of the model used as well as the incorporation of the model predictions into an AUV trajectory planner. These results indicate a modest, but measurable, improvement in surfacing error when the model predictions are incorporated into the planner.

Networked aquatic microbial observing systems: An overview

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Trajectory design for autonomous underwater vehicles based on ocean model predictions for feature tracking

Trajectory design for Autonomous Underwater Vehicles (AUVs) is of great importance to the oceanographic research community. Intelligent planning is required to maneuver a vehicle to high-valued locations for data collection. We consider the use of ocean model predictions to determine the locations to be visited by an AUV, which then provides near-real time, in situ measurements back to the model to increase the skill of future predictions. The motion planning problem of steering the vehicle between the computed waypoints is not considered here. Our focus is on the algorithm to determine relevant points of interest for a chosen oceanographic feature. This represents a first approach to an end to end autonomous prediction and tasking system for aquatic, mobile sensor networks. We design a sampling plan and present experimental results with AUV retasking in the Southern California Bight (SCB) off the coast of Los Angeles.

Implementation of an embedded sensor network for the coordination of Slocum gliders for coastal monitoring and observation

Autonomous Underwater Vehicles (AUVs) are revolutionizing oceanography through their versatility, autonomy and endurance. However, they are still an underutilized technology. For coastal operations, the ability to track a certain feature is of interest to ocean scientists. Adaptive and predictive path planning requires frequent communication with significant data transfer. Currently, most AUVs rely on satellite phones as their primary communication. This communication protocol is expensive and slow. To reduce communication costs and provide adequate data transfer rates, we present a hardware modification along with a software system that provides an alternative robust disruption- tolerant communications framework enabling cost-effective glider operation in coastal regions. The framework is specifically designed to address multi-sensor deployments. We provide a system overview and present testing and coverage data for the network. Additionally, we include an application of ocean-model driven trajectory design, which can benefit from the use of this network and communication system. Simulation and implementation results are presented for single and multiple vehicle deployments. The presented combination of infrastructure, software development and deployment experience brings us closer to the goal of providing a reliable and cost-effective data transfer framework to enable real-time, optimal trajectory design, based on ocean model predictions, to gather in situ measurements of interesting and evolving ocean features and phenomena.