Feature Data acquisition monitoring water quality Sensor solutions for
akes around the world not only supply water, food and energy, but they also provide flood control, recreation and support the tourist industry. During the early part of the 20th century, however, many lakes in Western Europe were affected by raw sewage and fertilizer run-off, which degraded the economic benefits and caused severe ecological damage and loss of species.
L
High frequency data is needed to understand how different environmental pressures affect lake condition in order to protect the future of fresh water. To help, a system featuring sensors, dataloggers, computing and telemetry has been developed to collect and monitor ecological data from lakes across the UK Water quality monitoring stations
While improvements in waste water management and farming practices have started to reverse these effects, rates of recovery are low and other environmental pressures – climate change, invasion of non-native species, water abstraction and atmospheric deposition, etc. – have added pressure. As an example, cyanobacterial blooms, which are potentially toxic to livestock, pets and humans, are currently widespread in many lakes around the world.
Lake condition can change rapidly for a number of reasons. For a start the generation time of the largely microbial populations that control ecological structure and function is very short, sometimes just days; but also short-term weather events such as storms, flood or a period of hot weather, can be a strong influence. High frequency data is therefore needed to understand how the different environmental pressures affect lakes and to forecast future responses. To help, the UKLEON (United Kingdom Lake Ecological Observatory
Network) project has developed a sensing system which uses new technology in sensors, dataloggers, computing and telemetry to collect high frequency ecological data automatically from remote sites. While providing huge advantages for scientific research, this could also help provide up-to-date information to environmental managers or water companies.
UKLEON is funded as part of the Natural Environment Research Council’s Sensor Network Programme and is led from the NERC Centre for Ecology & Hydrology (CEH) in collaboration with the Universities of Glasgow, Lancaster, Loughborough and University College London, and also with Natural Resources Wales. UKLEON is contributing to the NERC Consortium Grant GloboLakes (
www.globolakes.ac.uk), which is investigating the state of lakes and their response to climatic and other environmental drivers of change at a global scale using satellite data; the COST action NETLAKE that is building a network of AWQMS around Europe (
https://www.dkit.ie/netlake); and a project led from the USA, GLEON (Global Lake Ecological Observatory Network), that is building a global network of lake AWQMS (
www.gleon.org).
Example of real-time data
A network of automatic water quality monitoring stations (AWQMS) has been deployed across lakes in the UK – five sites in England, three in Wales and one in Northern Ireland. These cover a variety of lake types and sizes, ranging from unproductive to productive, small to large, shallow to deep, and lowland to upland. Each AWQMS is equipped with a meteorological station to record local weather. Underwater, there is a chain of platinum resistance thermometers measuring temperature at a range of depths. Sub-surface, several sondes measure temperature, conductivity, pH, carbon dioxide, underwater light and dissolved oxygen. The latter variable is measured using fluorescence which is said to be much more reliable and stable than the electro-chemical technology used previously.
‘Each AWQMS records around 30 different variables every four minutes, producing around 43.4 million data points annually from across the entire network’
The concentrations of biological pigments chorophyll a (the green pigment in all photosynthetic algae and plants) and phycocyanin (a blue pigment that is specific to cyanobacteria or blue-green algae) are also measured using fluorescence. In addition, a sensor designed to measure carbon dioxide in air has been modified with a waterproof membrane which enables it to measures carbon dioxide in water. Finally, an underwater light sensor is used to measure the light in water in comparison to a similar sensor just above the water surface. Bio-fouling of the sensors, especially in productive sites, has been largely overcome with automatic wipers to remove biofilm. Each AWQMS is powered by lead-acid batteries with most of the power in the summer provided by two solar panels.
A website has been set up that shows the data in real-time or for past periods; and a particular site or variable of interest can be selected. It is possible to compare variables such as water temperature or solar radiation to be compared across the network of sites.
Recording variables Each AWQMS records around 30 different variables every four minutes, producing around 43.4 million data points annually from across the entire network. Due to this mass of data, a bespoke piece of data loading software (UKLOADER) has been created that automati- cally collects the information sent from each buoy by telemetry, and loads it into an Oracle database held at CEH Lancaster. The database implements a simple form of quality control by applying ‘flags’ to the data when, for example, data exceed a reasonable range, where there is a known problem such as low battery voltage, or where sondes have been serviced and maintained at that particular time.
As many users will not need to deal with the very high resolution data, the database also automatically produces hourly and daily averages for all variables.
NERC Centre for Ecology & Hydrology
www.nerc.ac.uk
22 Enter 662 NOVEMBER 2013 Instrumentation
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44