scholarly journals HABreports: Online Early Warning of Harmful Algal and Biotoxin Risk for the Scottish Shellfish and Finfish Aquaculture Industries

2021 ◽  
Vol 8 ◽  
Author(s):  
Keith Davidson ◽  
Callum Whyte ◽  
Dmitry Aleynik ◽  
Andrew Dale ◽  
Steven Gontarek ◽  
...  
Keyword(s):  
Early Warning ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Warning System ◽  
Environmental Data ◽  
Mitigation Measures ◽  
Traffic Light ◽  
Historical Trends ◽  
Index Approach ◽  
On Line

We present an on-line early warning system that is operational in Scottish coastal waters to minimize the risk to humans and aquaculture businesses in terms of the human health and economic impacts of harmful algal blooms (HABs) and their associated biotoxins. The system includes both map and time-series based visualization tools. A “traffic light” index approach is used to highlight locations at elevated HAB/biotoxin risk. High resolution mathematical modelling of cell advection, in combination with satellite remote sensing, provides early warning of HABs that advect from offshore waters to the coast. Expert interpretation of HAB, biotoxin and environmental data in light of recent and historical trends is used to provide, on a weekly basis, a forecast of the risk from HABs and their biotoxins to allow mitigation measures to be put in place by aquaculture businesses, should a HAB event be imminent.

scholarly journals Landslide Geohazard Monitoring, Early Warning and Stabilization Control Methods

2014 ◽  
Vol 36 (1) ◽  
pp. 3-13 ◽  
Author(s):  
Zbigniew Bednarczyk
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Warning System ◽  
Landslide Monitoring ◽  
High Plasticity ◽  
Monitoring Methods ◽  
Time Data ◽  
Stabilization Control ◽  
Starting Point ◽  
On Line

Abstract This paper is a presentation of landslide monitoring, early warning and remediation methods recommended for the Polish Carpathians. Instrumentation included standard and automatic on-line measurements with the real-time transfer of data to an Internet web server. The research was funded through EU Innovative Economy Programme and also by the SOPO Landslide Counteraction Project. The landslides investigated were characterized by relatively low rates of the displacements. These ranged from a few millimetres to several centimetres per year. Colluviums of clayey flysch deposits were of a soil-rock type with a very high plasticity and moisture content. The instrumentation consisted of 23 standard inclinometers set to depths of 5-21 m. The starting point of monitoring measurements was in January 2006. These were performed every 1-2 months over the period of 8 years. The measurements taken detected displacements from several millimetres to 40 cm set at a depth of 1-17 m. The modern, on-line monitoring and early warning system was installed in May 2010. The system is the first of its kind in Poland and only one of several such real-time systems in the world. The installation was working with the Local Road Authority in Gorlice. It contained three automatic field stations for investigation of landslide parameters to depths of 12-16 m and weather station. In-place tilt transducers and innovative 3D continuous inclinometer systems with sensors located every 0.5 m were used. It has the possibility of measuring a much greater range of movements compared to standard systems. The conventional and real-time data obtained provided a better recognition of the triggering parameters and the control of geohazard stabilizations. The monitoring methods chosen supplemented by numerical modelling could lead to more reliable forecasting of such landslides and could thus provide better control and landslide remediation possibilities also to stabilization works which prevent landslides.

scholarly journals Metagenomic characterization of a harmful algal bloom using nanopore sequencing

2020 ◽  
Author(s):  
Peter W. Schafran ◽  
Victor Cai ◽  
Hsiao-Pei Yang ◽  
Fay-Wei Li
Keyword(s):  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Harmful Algal Bloom ◽  
Environmental Data ◽  
Nanopore Sequencing ◽  
Current Trends ◽  
Oxford Nanopore ◽  
Single Genome ◽  
Long Read

ABSTRACTWater bodies around the world are increasingly threatened by harmful algal blooms (HABs) under current trends of rising water temperature and nutrient load. Metagenomic characterization of HABs can be combined with water quality and environmental data to better understand and predict the occurrence of toxic events. However, standard short-read sequencing typically yields highly fragmented metagenomes, preventing direct connection of genes to a single genome. Using Oxford Nanopore long-read sequencing, we were able to obtain high quality metagenome-assembled genomes, and show that dominant organisms in a HAB are readily identified, though different analyses disagreed on the identity of rare taxa. Genes from diverse functional categories were found not only in the most dominant genera, but also in several less common ones. Using simulated datasets, we show that the Flongle flowcell may provide an option for HAB monitoring with less data, at the expense of failing to detect rarer organisms and increasing fragmentation of the metagenome. Based on these results, we believe that Nanopore sequencing provides a fast, portable, and affordable method for studying HABs.

Assessing vertical and horizontal movements of algal blooms in a coastal water using satellite remote sensing for optimal operation of desalination plants

2019 ◽  
Vol 19 (7) ◽  
pp. 2123-2130
Author(s):  
H. Chen ◽  
M.-H. Park
Keyword(s):  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Arabian Gulf ◽  
Warning System ◽  
Optimal Operation ◽  
Freshwater Resource ◽  
Emerging Issues ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Operating Strategies ◽  
Desalination Plants

Abstract Harmful algal blooms (HABs) are global concerns in coastal waters due to diffuse pollution and climate change. Emerging issues of HABs include their impact on desalination operations for water supply. This study utilizes composite satellite images to detect movement and propagation of algal blooms. Time series images from the Moderate-Resolution Imaging Spectroradiometer (MODIS) were used for monitoring chlorophyll-a in the Persian (Arabian) Gulf, which neighboring countries depend upon for desalination as their freshwater resource. Bi-daily MODIS data from the Terra and Aqua satellites were used to detect both vertical migration and horizontal movement of algal blooms. The results will be useful for creating an early warning system for desalination plants to anticipate operating strategies and intake locations to minimize impacts.

scholarly journals Toward Global Drought Early Warning Capability: Expanding International Cooperation for the Development of a Framework for Monitoring and Forecasting

2013 ◽  
Vol 94 (6) ◽  
pp. 776-785 ◽  
Author(s):  
Will Pozzi ◽  
Justin Sheffield ◽  
Robert Stefanski ◽  
Douglas Cripe ◽  
Roger Pulwarty ◽  
...  
Keyword(s):  
Early Warning ◽  
Warning System ◽  
Early Warning Systems ◽  
Mitigation Measures ◽  
Drought Monitoring ◽  
Warning Systems ◽  
Management Plans ◽  
Monitoring And Forecasting ◽  
Global Coverage ◽  
Drought Early Warning

Drought is a global problem that has far-reaching impacts, especially on vulnerable populations in developing regions. This paper highlights the need for a Global Drought Early Warning System (GDEWS), the elements that constitute its underlying framework (GDEWF), and the recent progress made toward its development. Many countries lack drought monitoring systems, as well as the capacity to respond via appropriate political, institutional, and technological frameworks, and these have inhibited the development of integrated drought management plans or early warning systems. The GDEWS will provide a source of drought tools and products via the GDEWF for countries and regions to develop tailored drought early warning systems for their own users. A key goal of a GDEWS is to maximize the lead time for early warning, allowing drought managers and disaster coordinators more time to put mitigation measures in place to reduce the vulnerability to drought. To address this, the GDEWF will take both a top-down approach to provide global realtime drought monitoring and seasonal forecasting, and a bottom-up approach that builds upon existing national and regional systems to provide continental-to-global coverage. A number of challenges must be overcome, however, before a GDEWS can become a reality, including the lack of in situ measurement networks and modest seasonal forecast skill in many regions, and the lack of infrastructure to translate data into useable information. A set of international partners, through a series of recent workshops and evolving collaborations, has made progress toward meeting these challenges and developing a global system.

scholarly journals An Affordable Fast Early Warning System for Edge Computing in Assembly Line

2018 ◽  
Vol 9 (1) ◽  
pp. 84 ◽  
Author(s):  
Muhammad Syafrudin ◽  
Norma Fitriyani ◽  
Ganjar Alfian ◽  
Jongtae Rhee
Keyword(s):  
Decision Making ◽  
Early Warning ◽  
Early Warning System ◽  
Assembly Line ◽  
Warning System ◽  
Edge Computing ◽  
Environmental Data ◽  
Proposed Model ◽  
Smart Factories ◽  
Abnormal Events

Maintaining product quality is essential for smart factories, hence detecting abnormal events in assembly line is important for timely decision-making. This study proposes an affordable fast early warning system based on edge computing to detect abnormal events during assembly line. The proposed model obtains environmental data from various sensors including gyroscopes, accelerometers, temperature, humidity, ambient light, and air quality. The fault model is installed close to the facilities, so abnormal events can be timely detected. Several performance evaluations are conducted to obtain the optimal scenario for utilizing edge devices to improve data processing and analysis speed, and the final proposed model provides the highest accuracy in terms of detecting abnormal events compared to other classification models. The proposed model was tested over four months of operation in a Korean automobile parts factory, and provided significant benefits from monitoring assembly line, as well as classifying abnormal events. The model helped improve decision-making by reducing or preventing unexpected losses due to abnormal events.

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