Students Monitoring Coastal and Inland Waters With the Basic Observation Buoy (BOB)

2012 ◽  
Vol 46 (2) ◽  
pp. 56-64 ◽  
Author(s):  
Lisa G. Adams ◽  
Douglas R. Levin ◽  
Lundie Spence

AbstractThe Basic Observation Buoy (BOB), a student-designed and built monitoring device, provides opportunities for scientific discovery through monitoring and data collection. BOB is a scaled-down buoy fitted with sensors that monitor aquatic and atmospheric parameters in protected coastal and inland waters. Water quality parameters, like temperature, pH, dissolved oxygen, and conductivity, are measured continuously to provide real-time data. Depending on the sensor type, information can be used for education or research purposes. The BOB program was initiated in the Chesapeake Bay region by Levin and expanded to the southeast U.S. coast in 2008, via a series of three workshops funded by the Southeast Coastal Ocean Observing Regional Association (SECOORA) and the Centers for Ocean Science Education Excellence-Southeast (COSEE SE) and led by Spence (Spence et al., 2009) and Adams (Adams et al., 2010). These workshops assisted with the introduction and establishment of new programs in New England and the Great Lakes. BOB is a tool that provides science, technology, engineering, and mathematics (STEM) exploration in real-world situations for middle school to university students. Expected benefits include engagement of students in the scientific process, water quality monitoring experiences, analysis and sharing of real-time data, and the opportunity to contribute their findings to other coastal monitoring databases. Students can upload their data to an online portal supported by SECOORA and housed at the University of North Carolina-Wilmington (<ext-link ext-link-type="uri" href="http://cormp2.das.uncw.edu/dev/">http://cormp2.das.uncw.edu/dev/</ext-link>) or the National Geographic Fieldscope™ program (<ext-link ext-link-type="uri" href="http://chesapeake.fieldscope.org/">http://chesapeake.fieldscope.org/</ext-link>).

Author(s):  
Muhammad Farhan Johan ◽  
◽  
Samihah Abdullah ◽  
Nor Shahanim Mohamad Hadis ◽  
Saodah Omar ◽  
...  

Water quality monitoring (WQM) system is widely being explored as it is needed to prevent the problem of water contamination worldwide. Nowadays, there are various studies on WQM system that are being integrated with Internet of Things (IoT) concept for Wireless Sensor Network (WSN) technology implementation to get real time data measurement. Traditional ways of collecting the data are more time consuming and they lack real time changes in the quality of water. This paper presents the development and implementation of Water Quality Assessment and Monitoring (WQAM) system. The system development used WiFi enabled microcontroller to connect with the IoT environment and store the data in the IoT cloud server. The microcontroller used is Arduino UNO that interacts with three types of sensor probes which are pH, turbidity and temperature probe. All the data measurements is transferred using a WiFi module which is ESP8266. The IoT cloud used to utilize the data frame is ThingSpeak. This system was implemented on Bandar Perda Lake and Derhaka River in Pulau Pinang with two systems implemented at each location. The sensors were placed on the water surface for more accurate measurements. This system continuously measures the readings of pH, turbidity dan temperature on the lake/river for every 1 hour. Twenty readings were taken for every 1 hour within the first 20 minutes with 1 minute interval and the readings were stored in the IoT cloud server. The readings are accessible via ThingSpeak GUI. In conclusion, this system would benefit the authorities to take advantage of using the WQAM system with the aid of the IoT that is less time consuming, less cost and more reliable in real time data reading.


2020 ◽  
Author(s):  
Matthias Maeyens ◽  
Brianna Pagán ◽  
Piet Seuntjens ◽  
Bino Maiheu ◽  
Nele Desmet ◽  
...  

&lt;p&gt;In recent years, extend periods of drought have been affecting the water quality and availability in &amp;#160;the Flanders region in Belgium. Especially the coastal region experienced an increased salinization of ground and surface water. The Flemish government therefore decided to invest in a dense IoT water quality monitoring network aiming to deploy 2500 water quality sensors &amp;#160;primarily in surface water but also in ground water and sewers. The goal of this &quot;Internet of Water&quot; project is to establish an operational state of the art monitoring and prediction system in support of future water policy in Flanders.&amp;#160;&lt;/p&gt;&lt;p&gt;Since Flanders is a relatively small region (13,522&amp;#160;km&amp;#178;), placing this many sensors will result in one of the most dense surface water quality sensor networks in the world. Each sensor will continuously measure several indicators of water quality and transmit the data wirelessly. This allows us to continuously monitor the water quality and build a big enough data set to be able to use a more data driven approach to predicting changes&amp;#160; in water quality. However, as with any sensor system, the quality of the data can vary in time due to problems with the sensors, incorrect calibration or unforeseen issues. Real-time data quality control is crucial to prevent unsound decisions due to faulty data.&lt;/p&gt;&lt;p&gt;This contribution will give a general overview of the network and it&amp;#8217;s specifications, but mainly focus on the implementation of the data stream as well as methods that are implemented to guarantee good data quality. More specifically the architecture and setup of a real-time data quality control system is described. Which will add quality control flags to measurements. &amp;#160;This system is&amp;#160; integrated with the NGSI API introduced by FIWARE, which forces us to make specific design decisions to acommodate to the NGSI API.&lt;/p&gt;


2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
B. Mokhtar ◽  
M. Azab ◽  
N. Shehata ◽  
M. Rizk

This paper presents a comprehensive water quality monitoring system that employs a smart network management, nano-enriched sensing framework, and intelligent and efficient data analysis and forwarding protocols for smart and system-aware decision making. The presented system comprises two main subsystems, a data sensing and forwarding subsystem (DSFS), and Operation Management Subsystem (OMS). The OMS operates based on real-time learned patterns and rules of system operations projected from the DSFS to manage the entire network of sensors. The main tasks of OMS are to enable real-time data visualization, managed system control, and secure system operation. The DSFS employs a Hybrid Intelligence (HI) scheme which is proposed through integrating an association rule learning algorithm withfuzzylogic and weighted decision trees. The DSFS operation is based on profiling and registering raw data readings, generated from a set of optical nanosensors, as profiles of attribute-value pairs. As a case study, we evaluate our implemented test bed via simulation scenarios in a water quality monitoring framework. The monitoring processes are simulated based on measuring the percentage of dissolved oxygen and potential hydrogen (PH) in fresh water. Simulation results show the efficiency of the proposed HI-based methodology at learning different water quality classes.


2003 ◽  
Vol 47 (2) ◽  
pp. 53-61 ◽  
Author(s):  
A. Gunatilaka ◽  
J. Dreher

Water quality monitoring in Europe, especially in transboundary water courses has made steady progress during the last decades through establishment of international commissions. The main activities of these commissions include protection and management of the catchment, sustainable use of the river and establishment of Accident Emergency Warning Systems (AEWS). The latter could be effectively accomplished only through real-time monitoring. Concurrently real-time data have been found important for the monitoring of potable water intake points, wastewater treatment plants, estuaries and in aquaculture. With the recognition of the diversified demand, there are a number of questions to be answered such as: (1) are we satisfied with the existing monitoring systems? (2) is standardisation of the measuring instruments a necessity? (3) do we have foolproof systems for data capture and transmission? (4) are there adequate procedures to analyse vast amount of data generated? We have to answer these questions urgently as the demand for real-time monitoring has been drastically increased.


2020 ◽  
Vol 6 (3) ◽  
pp. 58-71
Author(s):  
Estelle Nicole L. Alave ◽  
◽  
Harvey Andrew G. Lim ◽  
Jericho L. Ronquillo ◽  
Marjun M. Tugade ◽  
...  

Author(s):  
Muhammad Aznil Ab Aziz ◽  
M. F. Abas ◽  
Mohamad Khairul Anwar Abu Bashri ◽  
N. Md. Saad ◽  
M. H. Ariff

Water quality is the main aspect to determine the quality of aquatic systems. Poor water quality will pose a health risk for people and ecosystems. The old methods such as collecting samples of water manually and testing and analysing at lab will cause the time consuming, wastage of man power and not economical. A system is needed to provide a real-time data for environmental protection and tracking pollution sources. This paper aims to describe on how to monitor water quality continuously through IoT platform. Water Quality Catchment Monitoring System was introduced to check and monitor water quality continuously. It’s features five sensors which are temperature sensor, light intensity sensor, pH sensor, GPS tracker and Inertia Movement Unit (IMU). IMU is a new feature in the system where the direction of x and y is determined for planning and find out where a water quality problem exists by determining the flow of water. The system uses an internet wireless connection using the ESP8266 Wi-Fi Shield Module as a connection between Arduino Mega2560 and laptop. ThingSpeak application acts as an IoT platform used for real-time data monitoring.


Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 399-P
Author(s):  
ANN MARIE HASSE ◽  
RIFKA SCHULMAN ◽  
TORI CALDER

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