High frequency monitoring needed to protect UK rivers!

29/06/2018
Nigel Grimsley from OTT Hydrometry describes relatively new technologies that have overcome traditional barriers to the continuous monitoring of phosphate and nitrate.

The science behind nutrient pollution in rivers is still poorly understood despite the fact that nitrate and phosphate concentrations in Britain’s rivers are mostly unacceptable, although an element of uncertainty exists about what an acceptable level actually is. Key to improving our understanding of the sources and impacts of nutrient pollution is high-resolution monitoring across a broad spectrum of river types.

Background

Green Box Hydro Cycle

Phosphates and nitrates occur naturally in the environment, and are essential nutrients that support the growth of aquatic organisms. However, water resources are under constant pressure from both point and diffuse sources of nutrients. Under certain conditions, such as warm, sunny weather and slow moving water, elevated nutrient concentrations can promote the growth of nuisance phytoplankton causing algal blooms (eurtrophication). These blooms can dramatically affect aquatic ecology in a number of ways. High densities of algal biomass within the water column, or, in extreme cases, blankets of algae on the water surface, prevent light from reaching submerged plants. Also, some algae, and the bacteria that feed on decaying algae, produce toxins. In combination, these two effects can lower dissolved oxygen levels and potentially kill fish and other organisms. In consequence, aquatic ecology is damaged and the water becomes unsuitable for human recreation and more expensive to treat for drinking purposes.

In its State of the Environment report, February 2018, the British Environment Agency said: “Unacceptable levels of phosphorus in over half of English rivers, usually due to sewage effluent and pollution from farm land, chokes wildlife as algal blooms use up their oxygen. Groundwater quality is currently deteriorating. This vital source of drinking water is often heavily polluted with nitrates, mainly from agriculture.”

Good ecological status
The EU Water Framework Directive (WFD) requires Britain to achieve ‘good status’ of all water bodies (including rivers, streams, lakes, estuaries, coastal waters and groundwater) by 2015. However, only 36% of water bodies were classified as ‘good’ or better in 2012. Nutrient water quality standards are set by the Department for Environment, Food & Rural Affairs (DEFRA), so for example, phosphorus water quality standards have been set, and vary according to the alkalinity and height above mean sea level of the river. Interestingly, the standards were initially set in 2009, but in 75% of rivers with clear ecological impacts of nutrient enrichment, the existing standards produced phosphorus classifications of good or even high status, so the phosphorus standards were lowered.

Highlighting the need for better understanding of the relationships between nutrients and ecological status, Dr Mike Bowes from the Centre for Ecology & Hydrology has published research, with others, in which the effects of varying soluble reactive phosphate (SRP) concentrations on periphyton growth rate (mixture of algae and microbes that typically cover submerged surfaces) where determined in 9 different rivers from around Britain. In all of these experiments, significantly increasing SRP concentrations in the river water for sustained periods (usually c. 9 days) did not increase periphyton growth rate or biomass. This indicates that in most rivers, phosphorus concentrations are in excess, and therefore the process of eutrophication (typified by excessive algal blooms and loss of macrophytes – aquatic plants) is not necessarily caused by intermittent increases in SRP.

Clearly, more research is necessary to more fully understand the effects of nutrient enrichment, and the causes of algal blooms.

Upstream challenge
Headwater streams represent more than 70% of the streams and rivers in Britain, however, because of their number, location and the lack of regulatory requirement for continuous monitoring, headwater streams are rarely monitored for nutrient status. Traditional monitoring of upland streams has relied on either manual sampling or the collection of samples from automatic samplers. Nevertheless, research has shown that upland streams are less impaired by nutrient pollution than lowland rivers, but because of their size and limited dilution capacity they are more susceptible to nutrient impairment.

References
• Bowes, M. J., Gozzard, E., Johnson, A. C., Scarlett, P. M., Roberts, C., Read, D. S., et al. (2012a). Spatial and temporal changes in chlorophyll-a concentrations in the River Thames basin, UK: are phosphorus concentrations beginning to limit phytoplankton biomass? Sci. Total Environ. 426, 45–55. doi: 10.1016/j.scitotenv. 2012.02.056
• Bowes, M. J., Ings, N. L., McCall, S. J., Warwick, A., Barrett, C., Wickham, H. D., et al. (2012b). Nutrient and light limitation of periphyton in the River Thames: implications for catchment management. Sci. Total Environ. 434, 201–212. doi: 10.1016/j.scitotenv.2011.09.082
• Dodds, W. K., Smith, V. H., and Lohman, K. (2002). Nitrogen and phosphorus relationships to benthic algal biomass in temperate streams. Can. J. Fish. Aquat Sci. 59, 865–874. doi: 10.1139/f02-063
• McCall, S. J., Bowes, M. J., Warnaars, T. A., Hale, M. S., Smith, J. T., Warwick, A., et al. (2014). Impacts of phosphorus and nitrogen enrichment on periphyton accrual in the River Rede, Northumberland, UK. Inland Waters 4, 121–132. doi: 10.5268/IW-4.2.692
• McCall, S. J., Hale, M. S., Smith, J. T., Read, D. S., and Bowes, M. J. (2017). Impacts of phosphorus concentration and light intensity on river periphyton biomass and community structure. Hydrobiologia 792, 315–330. doi: 10.1007/s10750-016-3067-1

Monitoring technology
Sampling for laboratory analysis can be a costly and time-consuming activity, particularly at upland streams in remote locations with difficult access. In addition, spot sampling reveals nutrient levels at a specific moment in time, and therefore risks missing concentration spikes. Continuous monitoring is therefore generally preferred, but in the past this has been difficult to achieve with the technology available because of its requirement for frequent re-calibration and mains power.

High resolution SRP monitoring has been made possible in almost any location with the launch by OTT Hydromet of the the ‘HydroCycle PO4’ which is a battery-powered wet chemistry analyser for the continuous analysis of SRP. Typically, the HydroCycle PO4 is deployed into the river for monitoring purposes, but recent work by the Environment Agency has deployed it in a flow-through chamber for measuring extracted water.

The HydroCycle PO4 methodology is based on US EPA standard methods, employing pre-mixed, colour coded cartridges for simple reagent replacement in the field. Weighing less than 8kg fully loaded with reagents, it is quick and easy to deploy, even in remote locations. The instrument has an internal data logger with 1 GB capacity, and in combination with telemetry, it provides operators with near real-time access to monitoring data for SRP.

The quality of the instrument’s data is underpinned by QA/QC processing in conjunction with an on-board NIST standard, delivering scientifically defensible results. Engineered to take measurements at high oxygen saturation, and with a large surface area filter for enhanced performance during sediment events, the instrument employs advanced fluidics, that are resistant to the bubbles that can plague wet chemistry sensors.

Environment Agency application
The National Laboratory Service Instrumentation team (NLSI) provides support to all high resolution water quality monitoring activities undertaken across the Agency, underpinning the EA’s statutory responsibilities such as the WFD, the Urban Waste Water Directive and Statutory Surface Water Monitoring Programmes. It also makes a significant contribution to partnership projects such as Demonstration Test Catchments and Catchments Sensitive Farming. Technical Lead Matt Loewenthal says: “We provide the Agency and commercial clients with monitoring systems and associated equipment to meet their precise needs. This includes, of course, nutrient monitoring, which is a major interest for everyone involved with water resources.”

Matt’s team has developed water quality monitoring systems that deliver high resolution remote monitoring with equipment that is quick and easy to deploy. There are two main options. The ‘green box’ is a fully instrumented cabinet that can be installed adjacent to a water resource, drawing water and passing it though a flow-through container with sensors for parameters such as Temperature Dissolved Oxygen, Ammonium, Turbidity, Conductivity pH and Chlorophyll a. Each system is fitted with telemetry so that real-time data is made instantly available to users on the cloud.

Conscious of the need to better understand the role of P in rivers, Matt’s team has integrated a HydroCycle PO4 into its monitoring systems as a development project.
Matt says: “It’s currently the only system that can be integrated with all of our remote monitoring systems. Because it’s portable, and runs on 12 volts, it has been relatively easy to integrate into our modular monitoring and telemetry systems.

“The HydroCycle PO4 measures SRP so if we need to monitor other forms of P, we will use an auto sampler or deploy a mains-powered monitor. However, monitoring SRP is important because this is the form of P that is most readily available to algae and plants.”

Explaining the advantages of high resolution P monitoring, Matt refers to a deployment on the River Dore. “The data shows background levels of 300 µg P/l, rising to 600 µg P/l following heavy rain, indicating high levels of P in run-off.”

Nitrate
Similar to phosphates, excessive nitrate levels can have a significant impact on water quality. In addition, nitrates are highly mobile and can contaminate groundwater, with serious consequences for wells and drinking water treatment. Nitrate concentrations are therefore of major interest to the EA, but traditional monitoring technology has proved inadequate for long-term monitoring because of a frequent recalibration requirement. To address this need, which exists globally, OTT Hydromet developed the SUNA V2, which is an optical nitrate sensor, providing high levels of accuracy and precision in both freshwater and seawater.

The NLSI has evaluated the SUNA V2 in well water and Matt says: “It performed well – we took grab samples for laboratory analysis and the SUNA data matched the lab data perfectly. We are therefore excited about the opportunity this presents to measure nitrate continuously, because this will inform our understanding of nitrate pollution and its sources, as well as the relationship between groundwater and surface water.”

Summary
The new capability for high-resolution monitoring of nutrients such as phosphorus will enable improved understanding of its effects on ecological status, and in turn will inform decisions on what acceptable P concentrations will be for individual rivers. This is vitally important because the cost of removing P from wastewater can be high, so the requirements and discharge limits that are placed on industrial and wastewater companies need to be science based and supported by reliable data. Similarly, nitrate pollution from fertilizer runoff, industrial activities and wastewater discharge, has been difficult to monitor effectively in the past because of the technology limitations. So, as improved monitoring equipment is developed, it will be possible to better understand the sources and effects, and thereby implement effective prevention and mitigation strategies.

@OTTHydrometry @EnvAgency @CEHScienceNews #Water #Environment

Simulating the Effect of Climate Change on Agriculture.

01/12/2017
Increased atmospheric CO2 levels and climate change are believed to contribute to extreme weather conditions, which is a major concern for many. And beyond extreme events, global warming is also predicted to affect agriculture.1,2

While climate change is expected to affect agriculture and reduce crop yields, the complete effects of climate change on agriculture and the resultant human food supplies are yet to be fully understood.2,3,4

Simulating a Changing Climate
In order to understand how changes in CO2, temperature and water availability caused by climate change have an impact on crop growth and food availability, Researchers often use controlled growth chambers to grow plants in conditions that mimic the predicted atmospheric conditions at the end of the century. These controlled growth chambers enable precise control of temperature, CO2 levels, humidity, water availability, light quality and soil quality, allowing Scientists to study how plant growth changes in response to elevated temperatures, elevated CO2 levels and altered water availability.

However, plant growth / behaviour in the field considerably varies from in growth chambers. Owing to differences in light intensity, light quality, evaporative demand, temperature fluctuations and other abiotic and biotic stress factors, the growth of plants in tiny, controlled growth chambers does not always sufficiently reflect plant growth in the field. Moreover, the less realistic the experimental conditions used during simulation experiments of climate change, the less likely the resultant predictions will reflect reality.4

Several attempts have been made over the past 30 years to more closely stimulate climate change growing scenarios including free air CO2 enrichment, open top chambers, free air temperature increases and temperature gradient tunnels, although all these methods are known to have major disadvantages. For instance, chamber-less CO2 exposure systems do not enable stringent control of gas concentrations, while other systems suffer from “chamber effects” such as changes in humidity, wind velocity, temperature, soil quality and light quality.4,5

Spanish Researchers have recently reported temperature gradient greenhouses and growth chamber greenhouses, which are specifically designed to remove some of the disadvantages of simulating the effects of climate change on crop growth in growth chambers. An article reporting their methodology was featured in Plant Science in 2014, describing how the Researchers used temperature gradient greenhouses and growth chamber greenhouses to simulate climate change conditions and study plant responses.4

Choosing the Right Growth Chamber
Compared to traditional growth chambers, temperature gradient greenhouses and controlled growth chambers offer increased working area, allowing them to work as greenhouses without the necessity for isolation panels while still allowing precise control of various environmental factors such as temperature, CO2 concentration and water availability.

Researchers have used these greenhouses to investigate the potential effects of climate change on the growth of grapevine, alfalfa and lettuce.

CO2 Sensors for Climate Change Research
Researchers investigating the effects of climate change on plant growth using greenhouses or growth chambers will require highly accurate CO2 measurements.

The Spanish Researchers used Edinburgh Sensors Guardian sensor in their greenhouses to provide accurate and reliable CO2measurements. As a customer-focused provider of high-quality gas sensing solutions, Edinburgh Sensors has been delivering gas sensors to the research community since the 1980s.4,6

The Guardian NG from Edinburgh Sensors
The Edinburgh Sensors Guardian NG provides precise CO2 measurements in research greenhouses simulating climate change scenarios. The sensor provides near-analyser quality continuous measurement of CO2 concentrations, operates in temperatures of 0-45 °C and relative humidity of 0-95%, and has a CO2 detection range of 0 to 3000 ppm. These features make Guardian NG suitable for use in greenhouses with conditions meant to simulate climate change scenarios.

In addition, the Guardian NG can be easily installed as a stand-alone product in greenhouses to measure CO2, or in tandem with CO2 controllers as done by the Spanish Researchers in their temperature gradient and growth control greenhouses.4,6

Conclusions
In order to understand the potential effects of climate change on plant growth and crop yields, it is important to simulate climate change scenarios in elevated CO2 concentrations. For such studies, accurate CO2 concentration measurements are very important.

References

@Edinst #agriculture

No escape even for agrochemicals!

28/09/2017
In this article key points that are covered in depth in the IDTtechEX published report “Agricultural Robots and Drones 2017-2027: Technologies, Markets, Players” by Dr Khasha Ghaffarzadeh and Dr Harry Zervos are discussed. 

New robotics is already quietly transforming many aspects of agriculture, and the agrochemicals business is no exception. Here, intelligent and autonomous robots can enable ultraprecision agriculture, potentially changing the nature of the agrochemicals business. In this process, bulk commodity chemical suppliers will be transformed into speciality chemical companies, whilst many will have to reinvent themselves, learning to view data and artificial intelligence (AI) as a strategic part of their overall crop protection offerings.

Computer vision
Computer vision is already commercially used in agriculture. In one use case, simple row-following algorithms are employed, enabling a tractor-pulled implement to automatically adjust its position. This relieves the pressure on the driver to maintain an ultra-accurate driving path when weeding to avoid inadvertent damage to the crops.

The computer vision technology is however already evolving past this primitive stage. Now, implements are being equipped with full computer systems, enabling them to image small areas, to detect the presence of plants, and to distinguish between crop and weed. The system can then instruct the implement to take a site-specific precision action to, for example, eliminate the weed. In the future, the system has the potential to recognize different crop and weed types, enabling it to take further targeted precision action.

This technology is already commercial, although at a small scale and only for specific crops. The implements are still very much custom built, assembled and ruggedized for agriculture by the start-ups themselves. This situation will continue until the market is proven, forcing the developers to be both hardware and software specialists. Furthermore, the implements are not yet fully reliable and easy to operate, and the upfront machine costs are high, leading the developers to favour a robotic-as-a-service business model.

Nonetheless, the direction of travel is clear: data will increasingly take on a more prominent (strategic) role in agriculture. This is because the latest image processing techniques, based on deep learning, feed on large datasets to train themselves. Indeed, a time-consuming challenge in applying deep learning techniques to agriculture is in assembling large-scale sets of tagged data as training fodder for the algorithms. The industry needs its equivalents of image databases used for facial recognition and developed with the help of internet images and crowd-sourced manual labelling.

In not too distant a future, a series of image processing algorithms will emerge, each focused on some set of crop or weed type. In time, these capabilities will inevitably expand, allowing the algorithms to become applicable to a wider set of circumstances. In parallel, and in tandem with more accumulated data (not just images but other indicators such NDVA too), algorithms will offer more insight into the status of different plants, laying the foundation of ultra-precision farming on an individual plant basis.

Agriculture is a challenging environment for image processing. Seasons, light, and soil conditions change, whilst the plant themselves transform shape as they progress through their different stages of growth. Nonetheless, the accuracy threshold that the algorithms in agriculture must meet are lower than those found in many other applications such as autonomous general driving. This is because an erroneous recognition will, at worse, result in elimination of a few healthy crops, and not in fatalities. This, of course, matters economically but is a not safety critical issue and is thus not a showstopper.

This lower threshold is important because achieving higher levels of accuracy becomes increasingly challenging. This is because after an initial substantial gain in accuracy improvement the algorithms enter the diminishing returns phase where lots more data will be needed for small accuracy gains. Consequently, algorithms can be commercially rolled out in agriculture far sooner, and based on orders of magnitude lower data sizes and with less accuracy, than in many other applications.

Navigational autonomy
Agriculture is already a leading adapter of autonomous mobility technology. Here, the autosteer and autoguide technology, based on outdoor RTK GPS localization, are already well-established. The technology is however already moving towards full level-5 autonomy. The initial versions are likely to retain the cab, enabling the farmer/driver to stay in charge, ready to intervene, during critical tasks such as harvesting. Unmanned cable versions will also emerge when technology reliability is proven and when users begin to define staying in charge as remote fleet supervision.

The evolution towards full unmanned autonomy has major implications. As we have discussed in previous articles, it may give rise to fleets of small, slow, lightweight agricultural robots (agrobots). These fleets today have limited autonomous navigational capability and suffer from limited productivity, both in individual and fleet forms. This will however ultimately change as designs/components become standardized and as the cost of autonomous mobility hardware inevitably goes down a steep learning curve.

Agrobots of the future
Now the silhouette of the agrobots of the future may be seen: small intelligent autonomous mobile robots taking precise action on an individual plant basis. These robots can be connected to the cloud to share learning and data, and to receive updates en mass. These robots can be modular, enabling the introduction of different sensor/actuator units as required. These robots will never be individually as productive as today’s powerful farm vehicles, but can be in fleet form if hardware costs are lowered and the fleet size-to-supervisor ratio is increased.

What this may mean for the agrochemicals business is also emerging. First, data and AI will become an indispensable part of the general field of crop protection, of which agrochemical supply will become only a subset, albeit still a major one. This will mandate a major rethinking of the chemical companies’ business model and skillsets. Second, non-selective blockbuster agrochemicals (together with engineered herbicide resistant seeds) may lose their total dominance. This is because the robots will apply a custom action for each plant, potentially requiring many specialized selective chemicals.

These will not happen overnight. The current approach is highly productive, particularly over large areas, and off-patent generic chemicals will further drive costs down. The robots are low-lying today, constricting them to short crops. Achieving precision spraying using high boys will be a mechanical and control engineering challenge. But these changes will come, diffusing into general use step by step and plant by plant. True, this is a long term game, but playing it cannot be kicked into the long grass for long.

@IDTechEx #Robotics #Agriculture #PAuto

Simulating agricultural climate change scenarios.

19/09/2017
Extreme weather, believed to result from climate change and increased atmospheric CO2 levels, is a concern for many. And beyond extreme events, global warming is also expected to impact agriculture.(Charlotte Observer, 7 Sept 2017)

Although it is expected that climate change will significantly affect agriculture and cause decreases in crop yields, the full effects of climate change on agriculture and human food supplies are not yet understood. (1, 2 & 3 below)

Simulating a Changing Climate
To fully understand the effects that changes in temperature, CO2, and water availability caused by climate change may have on crop growth and food availability, scientists often employ controlled growth chambers to grow plants in conditions that simulate the expected atmospheric conditions at the end of the century. Growth chambers enable precise control of CO2 levels, temperature, water availability, humidity, soil quality and light quality, enabling researchers to study how plant growth changes in elevated CO2 levels, elevated temperatures, and altered water availability.

However, plant behavior in the field often differs significantly from in growth chambers. Due to differences in light quality, light intensity, temperature fluctuations, evaporative demand, and other biotic and abiotic stress factors, the growth of plants in small, controlled growth chambers doesn’t always adequately reflect plant growth in the field and the less realistic the experimental conditions used during climate change simulation experiments, the less likely the resultant predictions will reflect reality.3

Over the past 30 years, there have been several attempts to more closely simulate climate change growing scenarios including open top chambers, free air CO2 enrichment, temperature gradient tunnels and free air temperature increases, though each of these methods has significant drawbacks.

For example, chamber-less CO2 exposure systems do not allow rigorous control of gas concentrations, while other systems suffer from “chamber effects” included changes in wind velocity, humidity, temperature, light quality and soil quality.3,4

Recently, researchers in Spain have reported growth chamber greenhouses and temperature gradient greenhouses, designed to remove some of the disadvantages of simulating the effects of climate change on crop growth in growth chambers. A paper reporting their methodology was published in Plant Science in 2014 and describes how they used growth chamber greenhouses and temperature gradient greenhouses to simulate climate change scenarios and investigate plant responses.3

Choosing the Right Growth Chamber
Growth chamber and temperature gradient greenhouses offer increased working area compared with traditional growth chambers, enabling them to work as greenhouses without the need for isolation panels, while still enabling precise control of CO2 concentration, temperature, water availability, and other environmental factors.

Such greenhouses have been used to study the potential effects of climate change on the growth of lettuce, alfalfa, and grapevine.

CO2 Sensors for Climate Change Research
For researchers to study the effects of climate change on plant growth using growth chambers or greenhouses, highly accurate CO2 measurements are required.

The Spanish team used the Edinburgh Sensors Guardian sensor in their greenhouses to provide precise, reliable CO2 measurements. Edinburg Sensors is a customer-focused provider of high-quality gas sensing solutions that have been providing gas sensors to the research community since the 1980s.3,5

The Guardian NG from Edinburgh Sensors provides accurate CO2 measurements in research greenhouses mimicking climate change scenarios. The Edinburgh Sensors Guardian NG provides near-analyzer quality continuous measurement of CO2 concentrations. The CO2 detection range is 0-3000 ppm, and the sensor can operate in 0-95% relative humidity and temperatures of 0-45 °C, making it ideal for use in greenhouses with conditions intended to mimic climate change scenarios.

Furthermore, the Guardian NG is easy to install as a stand-alone product in greenhouses to measure CO2, or in combination with CO2 controllers as done by the Spanish team in their growth control and temperature gradient greenhouses.4,6 Conclusions Simulating climate change scenarios in with elevated CO2 concentrations is essential for understanding the potential effects of climate change on plant growth and crop yields. Accurate CO2 concentration measurements are essential for such studies, and the Edinburgh Sensors Guardian NG is an excellent option for researchers building research greenhouses for climate change simulation.

References

  1. Walthall CL, Hatfield J, Backlund P, et al. ‘Climate Change and Agriculture in the United States: Effects and Adaptation.’ USDA Technical Bulletin 1935, 2012. Available from: http://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1000&context=ge_at_reports
  2. https://www.co2.earth/2100-projections Accessed September 7th, 2017.
  3. Morales F, Pascual I, Sánchez-Díaz M, Aguirreolea J, Irigoyen JJ, Goicoechea N Antolín MC, Oyarzun M, Urdiain A, ‘Methodological advances: Using greenhouses to simulate climate change scenarios’ Plant Science 226:30-40, 2014.
  4. Aguirreolea J, Irigoyen JJ, Perez P, Martinez-Carrasco R, Sánchez-Díaz M, ‘The use of temperature gradient tunnels for studying the combined effect of CO2, temperature and water availability in N2 fixing alfalfa plants’ Annals of Applied Biology, 146:51-60, 2005.
  5. https://edinburghsensors.com/products/gas-monitors/guardian-ng/ Accessed September 7th, 2017.
@Edinst #PAuto #Food