This job, on occasion, stinks.But it’s all in a day’s work for <a href="https://aerometrix.ca/" target="_blank">Aerometrix</a>, Canada’s only company specialising in methane detection using drones. It’s not the methane itself that smells – it’s actually an odourless gas – but it’s the locations where methane can be emitted.Imagine flying a massive landfill on a hot day in California.&nbsp;Further imagine that, in order to keep the dust down, the landfill operators have recently sprayed the location with leachate – the slimy runoff juice created by the landfill itself. It’s very biologically active, and it smells really bad.“It’s horrendous – horrible,” chuckles Eric Saczuk, who often carries out the complex flights.“It just ends up just suffusing through you and anything that you’re wearing. It even seems like it goes into your skin.”Thankfully, not all missions are like that. But all of them do achieve results.And now, for multiple reasons, Aerometrix is poised to be taking on many more of them – branching out into detection at oil and gas refineries.Below: Flight Operations Lead Eric Saczuk prepares for an Aerometrix flight<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA2MjA1NTM0ODQwLU1pY3Jvc29mdFRlYW1zLWltYWdlLTUtMi1qcGcud2VicCIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 300px;" class="fr-fic fr-dib" alt="Flight Operations"> <h3 id="isPasted">Why Methane Detection?</h3>When it comes to climate change, methane is an invisible threat. Though we often hear about CO2emissions, methane is a serious problem when it comes to greenhouse gases.“Methane has more than 80 times the warming power of carbon dioxide over the first 20 years after it reaches the atmosphere,” <a href="https://www.edf.org/climate/methane-crucial-opportunity-climate-fight" target="_blank">states the Environmental Defense Fund</a>.“Even though CO2 has a longer-lasting effect, methane sets the pace for warming in the near term.”So there’s increasing urgency to detect and mitigate methane emissions. At landfills, for example, once the emission points are detected, the gas can be trapped – and even used to generate electricity.“The main emphasis recently has been on landfills, both in Canada and the US,” explains Aerometrix Co-Founder Philip Reece. “We flew 16 missions over the last 12 months.”And these missions aren’t simply popping a drone up for a brief flight. Nor are they automated. Every Aerometrix flight has to be carried out manually.“Over large sites like the Vancouver landfill, that took us three days of flying, six hours a day,” says Saczuk. “We fly these missions at five metres above the ground – and there’s lots of things that can get in the way at that level.”Aerometrix flights deploy either the DJI M300 or M350 drone. But the secret sauce is not so much the drone as the sensors. (And it’s most certainly not the leachate.)&nbsp;<h3>Sensors</h3>Aerometrix deploys two different sensors to detect methane. One of them is called an Open Path Laser Spectrometer (OPLS), developed by NASA for use on the Mars Rover. It was designed to detect trace gases. In the case of methane detection, the laser is tuned to a specific frequency that is absorbed when it encounters that particular gas. The greater the absorption, the higher the methane concentration.The sensor requires “clean air” for accurate readings – meaning there can’t be any prop wash or turbulence caused by the drone itself. Aerometrix engineers built a brace that holds the sensor well forward of the drone for this purpose. Having that sensor and rod, of course, upsets the balance of the drone. In fact, Saczuk estimates the weight of the rod and sensor at roughly 800 grams, perched about 1.5 metres forward of the drone.And while the flight controller is capable of compensating for that, Saczuk always performs a calibration once the drone is in the air.“We take off to maybe three or four meters above the ground. Once the drone is airborne, we go into the controller and initiate the calibration. So the drone calculates its revised centre of gravity and knows what its steady state is. The two front propellers then spin a little bit faster to keep the nose from dipping.”Flying manually, Saczuk uses Tripod Mode to limit the drone’s speed. The most accurate readings occur when flying at about five metres per second.On an ideal flight day, there would be a steady wind at around eight metres per second. If the breeze is coming from the east and blowing over the landfill, this provides a couple of advantages. First of all, by positioning operations at the eastern end you can avoid most of the smell. But the real reason is because the drone will begin its flight in clean air not contaminated by methane. That will enable the methane, once detected, to really contrast with the surrounding environment.“What we don’t like is no wind, because then the methane just goes up vertically and it’s variable – it just gets pushed around by a little vortices here and there,” says Saczuk.The drone will make multiple passes (in this example, north and south) over the site. When the laser hits methane, some of those rays will be absorbed and some reflected, depending on the concentration. Flying multiple paths allows enough data to be gathered to create a visualization of methane in a vertical plane.“We’ll do this on the upwind and downwind side of the site as well as a full perimeter to understand where the main emissions source likely are,” he adds.&nbsp;“We shouldn’t be seeing much methane on that eastern side, assuming the wind is coming from the east. And then as we fly the western edge, that would capture all of the methane that’s being pushed by the wind and that would be the downwind curtain.”While Saczuk is piloting, there’s a second controller that displays the data. A Raspberry Pi onboard the drone takes the data from the sensor and merges it with the flight data from the aircraft. So Saczuk can see the invisible gas while piloting.The goal is obtain a really good cross-section, as illustrated below. Feel free to try your hand at the equation.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA2MjA2MjA4MzE2LUZsdXgtQ3VydGFpbi1zY2hlbWF0aWMuanBnLndlYnAiLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="Site Cross Section"><h3 id="isPasted">Sensor Two</h3>The second sensor deployed is called a Laser Falcon. The sensor, mounting hardware and accessories will set you back close to <a href="https://shop.sphengineering.com/products/laser-falcon-methane-detector-bundle" target="_blank">$60k CDN</a>. It is mounted directly on the drone and faces downward.In this case, the laser is factory tuned for methane detection – it is the only gas the Laser Falcon can detect."It's an active sensor that will detect the amount of absorption that's happening. The scattering of the laser in the air tells the sensor how much methane there is not at a point – but through a column of air."In both cases, the data is crunched to make the invisible visible. The result is called a "flux curtain" or "flux plane" – with differing colours representing different concentrations of methane, measured in parts per million. In the graphic below, the greatest concentrations are seen in the middle of the image, just below the centre.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA2MjA2Mzk0NDA4LTIwMjJfMDhfMTNfZjJfZmx1eHBsYW5lLTEucG5nLndlYnAiLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 302px;" class="fr-fic fr-dib" alt="Flux Plane"><h3 id="isPasted">Poised for Growth</h3>In December&nbsp;the Honourable Steven Guilbeault, Minister of Environment and Climate Change, announced draft methane regulations. These regulations aim to reduce methane emissions by 75 per cent by the year 2030, when compared with emission from 2012. The focus is on the oil and gas industry.“Oil and gas facilities are the largest industrial emitters of methane in Canada—they release about half of total methane emissions,” <a href="https://www.canada.ca/en/environment-climate-change/news/2023/12/draft-oil-and-gas-methane-regulations-amendments-published-in-december-2023-to-reduce-emissions-by-75-percent.html" target="_blank">reads the draft</a>.“These releases occur during normal operation of equipment and from leaks. To comply with Canada’s existing methane Regulations, industries had to adopt practices to monitor for leaks and ensure that repairs happen to reduce the amount of gas intentionally vented into the air.“Under the draft methane amendments, the Government of Canada is enhancing the emissions-monitoring requirements through a risk-based approach to structure inspections for fugitive emissions—facilities with equipment that has greater potential for emissions must undertake more frequent inspections. All inspections must be conducted using instruments with a standard minimum detection limit, and repair timelines will depend on emissions rates. Further, the draft regulations introduce an audit system, requiring one annual third-party inspection to validate company program results.”In other words, it won’t be long before oil and gas facilities will need to bring experts like Aerometrix onboard to verify that the reported data is accurate.“Lowering methane emissions from our oil and gas sector is one of the fastest and most cost-effective ways we can cut the pollution that is fueling climate change,” said Minister Guilbeault in <a href="https://www.canada.ca/en/environment-climate-change/news/2023/12/minister-guilbeault-announces-canadas-draft-methane-regulations-to-support-cleaner-energy-and-climate-action.html" target="_blank">this news release</a>.“As the world’s fourth largest oil and gas producer, we have both the responsibility and the know-how to do everything we can. At this time of robust profit margins and high energy prices, there has never been a better time for the oil and gas sector to invest in slashing methane emissions.”&nbsp;<h3>New Investment&nbsp;</h3>In creating Aerometrix, co-Founders <a href="https://aerometrix.ca/company/#founders" target="_blank">Philip Reece and Michael Whiticar</a> developed a solution to a significant and largely invisible problem. Now, with even greater emphasis on reducing methane emissions, Aerometrix has attracted a major new investor.That investor is Omar Asad, the company’s new Director. He sees great potential ahead.“The cutting-edge technology utilised by Aerometrix is unmatched and has already translated into significant savings for clients,” says Asad. “What’s more, we offer both a much-needed and innovative solution – while helping to reduce methane emissions at a critical time.”Asad’s investment, in conjunction with Canada’s impending methane legislation, paves the way for accelerated growth.Below: Eric Saczuk points to the second controller, highlighting real-time methane detection<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1706206889738-MicrosoftTeams-image-1-2-jpg-2.webp" style="width: 300px;" class="fr-fic fr-dib"><h3 id="isPasted">InDro's Take</h3>Philip Reece, of course, is also the Founder and CEO of InDro Robotics. And he’s clearly pleased with both the investment – and the growth trajectory.“Landfill detection alone has kept Aerometrix busy and profitable,” says Reece. “With the pending legislation we are poised for significant growth in the oil and gas sector.“Not only is using these sensors with drones more accurate than traditional hand-held walk-arounds, but Aerometrix has racked up years of experience in turning our findings into clear and actionable data. This company, particularly with Omar onboard, is ready for the next phase of growth.”Interested in learning more about methane detection by Aerometrix? Contact them <a href="mailto:Info@Aerometrix.ca" target="_blank">here</a>.

Empowering Environmental Safety Through Advanced Methane Detection Solutions

Aerometrix Methane Detection

Volatile Organic Compounds (VOC) sensors are key in detecting potentially dangerous air quality. 

VOC Sensors: A Comprehensive Guide to Their Applications and Maintenance

<h3>A critical public health issue</h3>According to the U.S. Environmental Protection Agency, most Americans spend about 90% of their time indoors, a setting where pollutant levels are often 2 to 5 times higher than outdoors. This heightened concentration of pollutants indoors is a concern, especially since traditional indicators of poor air quality, like smoke or strong smells, are less apparent inside buildings.&nbsp;In this recent episode of the Mouser Electronics podcast "The Tech Between Us," host Raymond Yin soy sits down with Ronan Cooney, Head of Product at Ambisense. The two take a deep dive into the engineering aspect of indoor air quality and environmental sensors.The episode delves into the significance of monitoring key components of indoor air, such as CO2 levels, Volatile Organic Compounds (VOCs), and particulates. This monitoring is essential for maintaining a healthy indoor environment.<h3>Equipping customers with valuable data</h3>A major highlight of the episode is gaining an understanding of the role of Ambisense in this field. Ambisense focuses on equipping customers with valuable data, empowering them to make informed decisions and take appropriate actions based on the insights provided. This approach underscores the importance of data-driven solutions in managing indoor air quality.The podcast also sheds light on the advancements in <a href="https://www.wevolver.com/article/breathing-better-using-environmental-sensors-to-improve-indoor-air-quality" target="_blank" rel="noopener noreferrer">sensor technology</a>. Sensor manufacturers are working towards reducing the costs of these devices while ensuring they maintain high performance. The use of Non-Dispersive Infrared (NDIR) technology for CO2 sensors is now considered a basic standard in the industry, marking a significant step forward in sensor technology.Another key aspect discussed is the integration of the right sensors and IoT sensor nodes. This integration is not just about collecting data but also about interpreting it effectively to improve indoor environments. The use of sensor fusion and algorithms plays a pivotal role here, as it allows for the integration of data from various environmental sensors into a cohesive and comprehensive picture for the end-users.<h3>Sensor fusion meets complex demands</h3>Understanding the correlation between different environmental factors is also crucial. This understanding helps in identifying and addressing various indoor air quality issues more effectively. The fusion of different sensors is vital in gaining a thorough understanding of the dynamics within a space, including factors like occupancy and particulate levels.The use of artificial intelligence (AI) and machine learning in managing sensor data is a prominent theme in this episode. Cooney explains how AI-driven anomaly detection can help identify unusual patterns and issues in air quality, aiding in effective decision-making. The development of AI technology is hailed as a game-changer across industries, with its potential being actively explored to refine data interpretation and enhance user experiences.The episode concludes with a focus on the growing awareness and responsibility towards providing healthy and productive spaces. With the advancements in technology, it is no longer acceptable to be unaware of indoor air quality issues. The technology available today makes it possible to be more proactive in ensuring the health and well-being of individuals in indoor environments.This podcast episode serves as a valuable resource for anyone interested in the field of indoor air quality, offering insights into the latest trends and technologies in sensor and data analysis.<a href="https://youtu.be/Hq8R90FrAiw?list=PLBFFfxVbvpNTzubC_bkcurRZ5bUWCfwSu" target="_blank" rel="noopener noreferrer">Listen to part two of the discussion here.</a>

Discover more about the crucial topic of indoor air quality. 

The Tech Between Us Podcast: Environmental Sensors for Clean Air with Ronan Cooney

Our society relies heavily on plastic products and the amount of plastic waste is expected to increase in the future. If not properly discarded or recycled, much of it accumulates in rivers and lakes. Eventually it will flow into the oceans, where it can form aggregations of marine debris together with natural materials like driftwood and algae. A new study from Wageningen University and EPFL researchers, recently published in Cell iScience, has developed an artificial intelligence-based detector that estimates the probability of marine debris shown in satellite images. This could help to systematically remove plastic litter from the oceans with ships.<h2>Automatic analysis</h2>Accumulations of marine debris are visible in freely available Sentinel-2 satellite images from the European Space Agency that capture coastal areas every 2-5 days worldwide on land masses and coastal areas. Because these amount to terabytes of data, the data needs to be analysed automatically through artificial intelligence models like deep neural networks. Marc Rußwurm, Assistant Professor at Wageningen University and former researcher at EPFL: “These models learn from examples provided by oceanographers and remote sensing specialists, who visually identified several thousand instances of marine debris in satellite images on locations across the globe. In this way they ‘trained’ the model to recognise plastic debris.” Devis Tuia, associate professor at EPFL and Director of the Sion-based Laboratory of Computational Science for the Environment and Earth Observation (ECEO), is corresponding author of the study.<blockquote>The detector remains accurate even in more challenging conditions. For example when cloud cover and atmospheric haze make it difficult for existing models to identify marine debris precisely.<footer>Marc Rußwurm, Assistant Professor at Wageningen University and former researcher at EPFL</footer></blockquote><h2>Improved detection</h2>The researchers developed an AI-based marine debris detector that estimates the probability of marine debris present for every pixel in Sentinel-2 satellite images. The detector is trained following data-centric AI principles that aim to make best use of the limited training data that is available for this problem. One example is the design of a computer vision algorithm that snaps manual annotations from experts precisely to the debris visible in the images. With this tool, oceanographers and remote sensing experts can provide more training data examples by being less precise in the manual clicking of outlines.Overall, this training method combined with the refinement algorithm teaches the deep artificial intelligence detection model to better predict marine debris objects than previous approaches. Rußwurm: “The detector remains accurate even in more challenging conditions. For example when cloud cover and atmospheric haze make it difficult for existing models to identify marine debris precisely.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1701346986180-9a9faa64.jpg" style="width: 766px;" class="fr-fic fr-dib">Heavy precipitation over Durban, South Africa on 22 April 2019, 19:00. Continued precipitation between 18th and 22nd of April lead to the Durban Easter floods 2019 that washed substantial amounts of plastic litter into the Indian ocean, which are visible in Satellite images. Map data © 2023 Google. Precipitation data © JAXA Global Rainfall Watch<h2 id="isPasted">Following plastic debris</h2>Detecting plastics in marine debris under difficult atmospheric conditions with clouds and haze is particularly important, as often plastics are washed into open waters after rain and flood events. This is shown by the Durban Easter floods in South Africa: In 2019 a long period of rain led to overflowing rivers, resulting in much more litter being washed away than normally. It was taken along through the Durban harbour (left picture) into the open Indian Ocean (right picture). In satellite images, such objects floating between clouds are hard to distinguish when using common red-green-blue colour ‘channels’. They can be visualised by switching to other spectral channels, including near-infrared light.<h2>Double view</h2>Apart from more accurate prediction of marine debris aggregations, the detection model will also notice debris in daily-accessible PlanetScope images, coming from cubic nanosatellites. “Combining weekly Sentinel-2 with daily PlanetScope acquisitions can close the gap towards continuous daily monitoring”, explained Rußwurm. “Also, PlanetScope and Sentinel-2 sometimes capture the same patch of marine debris at the same day only a few minutes apart. This double view of the same object at two locations reveals the drift direction due to wind and ocean currents on the water. This information can be used to improve drift estimation models for marine debris.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1701347058805-06fb92a9.jpg" style="width: 766px;" class="fr-fic fr-dib">The scientists tested the AI model predictions on Sentinel-2 satellite images of challenging atmospheric conditions with clouds and haze. In red, the correctly detected debris with plastic waste. © ESA / Cell iScience<h2 id="isPasted">Further explorations</h2>These directions will be explored further in the research of Prof. Marc Rußwurm at Wageningen University together with Prof. Tim van Emmerik, who is an expert in river plastics, and partners in the Netherlands, such as the Ocean Cleanup, who collect plastics on open oceans with dedicated ships. It originates from work in the AI for Detection of Plastics with Tracking (ADOPT) project in collaboration with the Swiss Data Science Center (a joint venture between ETH Zurich and EPFL) and EPFL by Prof. Devis Tuia, Dr. Emanuele Dalsasso, and Prof. Marc Rußwurm that investigates the continuous tracking of marine debris in between satellite imagery further.<h2>Sentinel-2 and PlanetScope</h2><a href="https://sentinel.esa.int/web/sentinel/missions/sentinel-2" target="_blank" rel="noopener">ESA’s Sentinel-2 satellite constellation</a> from the European Space Agency consists of two large satellites that cover the Earth every 2-5 days. They measure the reflected light in multiple spectral channels beyond the visible red-green-blue and up to 10m pixel size. The images are openly available.<a href="https://earth.esa.int/eogateway/missions/planetscope" target="_blank" rel="noopener">PlanetScope Doves and SuperDoves</a> make up a constellation of several hundred small CubeSats (nanosatellites) that cover the Earth every day at 3-7m pixel resolution. The images are not free to acquire, but can be used to a limited extent for research purposes at no cost through a Research and Education programme.<hr>References<a href="https://www.sciencedirect.com/science/article/pii/S2589004223024793" rel="noopener" target="_blank">Marc Rußwurm, Sushen Jilla Venkatesa, Devis Tuia, "Large-scale Detection of Marine Debris in Coastal Areas with Sentinel-2", Cell iScience, 8 November 2023.</a>

Durban's beach after a flood on 13 April 2022, two years after the one of 2019. © iStock/Antonio BlancoDR

A research team from EPFL and Wageningen University has developed a new artificial intelligence model that recognises floating plastics much more accurately in satellite images than before. This could help to systematically remove plastic litter from the oceans with ships.

AI helps detecting plastic in oceans

Cities around the world are experiencing a period of rapid expansion. According to the United Nations (UN), 57 percent of people already live in urban areas - and this is set to increase to almost 70 percent by 2050.&nbsp;&nbsp;<h2>The environmental cost of urbanization</h2>This shift is having a significant environmental impact. The amount of solid waste produced by cities is predicted to grow from 1.8 billion tonnes in 2016 to 3.1 billion tonnes in 2050. And, according to the World Health Organization (WHO), 9 out of 10 people in urban areas are already breathing polluted air, while almost 40 percent don’t have access to safe sanitation and clean water - posing serious health concerns. &nbsp; And yet existing waste removal methods are both outdated and inefficient. A study in New Zealand by services company Ventia found that most of the time half the garbage bins being collected were empty, while 10 percent of bins in the most popular locations filled up again in just three or four hours. Such uneven usage not only wastes time and resources, but also creates more exhaust emissions from the trucks that travel around to empty the bins. &nbsp; Traditional methods of air quality monitoring are also flawed. Today’s monitoring equipment is large and expensive, limiting installation to a few permanent locations. That makes meaningful data sparse and limits real-time monitoring - according to a report by the global mobile networks industry association GSMA.&nbsp;<h2>Optimizing waste collection with cellular IoT</h2>The cellular IoT has opened the door to a new approach to maintaining clean cities. Low-cost and widely deployed wireless sensors monitor relevant conditions—like full rubbish bins or changes in air quality—and then, using network connectivity, the sensors can send that data to city administrators. When combined with AI and data analytics, the information can lead to more environmentally friendly decisions.&nbsp;For example, ‘smart bin’ solutions already use sensors to monitor gas and humidity levels, allowing them to detect when bins need to be collected. Christian Wedekind, Senior Product Manager at adhoc networks—a developer of a <a href="https://www.nordicsemi.com/Nordic-news/2022/11/the-adhoc-smart-waste-solution-uses-the-nrf9160-sip" rel="noopener" target="_blank">smart waste solution</a> based on Nordic’s&nbsp;<a href="https://www.nordicsemi.com/products/nrf9160" rel="noopener" target="_blank">nRF9160</a> SiP and cellular IoT connectivity—claims solutions like these have reduced waste disposal emissions by 40 percent.&nbsp; In addition to using cellullar IoT tech to reduce the number of trips, AI solutions are optimizing the garbage collection routes. Ventia discovered that the technology shortened route times from seven-and-a-half hours to four. As a result, the company reduced its truck fleet by eight vehicles, decreased carbon emissions, and generated less noise pollution.&nbsp;<h2>Dynamic air pollution monitoring &nbsp;</h2>A 2017 report found that 95 percent of London’s population has been exposed to air pollution levels exceeding WHO limits by more than 50 percent. To address this issue, a proof-of-concept in Greenwich, in collaboration with GSMA, employed portable air quality sensors on people, bikes, vehicles, and buildings. This allowed the devices to measure air quality at many different locations throughout the day.&nbsp; While dynamic tracking like this doesn’t directly reduce air pollution, it does provide authorities and policymakers with much more specific data so they can understand the root causes and introduce targeted solutions. These include introducing increased toll pricing in high-traffic areas, restricting or closing pollution-heavy facilities, and building support for longer-term plans, like new public transport systems powered by renewable energy. &nbsp;<h2>Combatting invisible wastewater issues&nbsp;&nbsp;</h2>IoT solutions are also helping cities gain visibility into sewerage issues and manage them before they cause problems. Take, for example, the&nbsp;<a href="https://www.nordicsemi.com/Nordic-news/2021/09/metasphere-uses-nrf9160-in-art-sewer" rel="noopener" target="_blank">ART Sewer</a> wastewater and sewerage spill monitoring solution by Metasphere. This compact, battery-powered device fits under a manhole cover where it measures wastewater levels using radar every 15 minutes. Using cellular connectivity, it transmits this information to a data analytics platform, helping prevent wastewater spills caused by heavy rain, blockages, or damaged pipes. &nbsp; Products that can detect leaking pipes—especially in below-ground applications where they may not be visible otherwise—can help by warning companies before there’s significant damage. Predictive maintenance—which uses AI and sensor data to identify signs of future servicing needs—can also benefit sewerage systems by preventing any leaks or broken pipes before they even occur.<h2>The benefits of cellular connectivity&nbsp;&nbsp;</h2>According to the&nbsp;Financial Times, while sensors are getting cheaper, the need for connectivity, computing power, and energy to collect, transmit, and analyze data remains a barrier. &nbsp; Fortunately, the rise of cellular IoT is helping address these issues. Cellular technologies not only offer great coverage in cities, but are also ideal for IoT applications, especially when long battery life is needed. LTE-M supports mobile applications like tracking garbage trucks, while NB-IoT is more suited to stationary devices in areas with poor network coverage, such as sewer monitors.&nbsp; Whether it’s measuring water levels, temperatures, or the health of critical infrastructure, the IoT will soon become vital to cities because the cost of addressing the negatives impacts of these challenges is much higher than solving them with technology.&nbsp;<hr id="isPasted">This article was first published on Nordic's&nbsp;<a href="https://blog.nordicsemi.com/getconnected" target="_blank" rel="nofollow"></a><a href="https://blog.nordicsemi.com/getconnected?utm_campaign=2021%20wevolver" target="_blank" rel="nofollow">Get Connected Blog</a>. &nbsp;

Cities around the world are experiencing a period of rapid expansion. According to the United Nations (UN), 57 percent of people already live in urban areas - and this is set to increase to almost 70 percent by 2050.  

Using the IoT to create a greener future for smart cities

This article originally appeared in&nbsp;<a href="https://www.caltech.edu/about/news/artificial-intelligence-is-key-to-better-climate-models-say-researchers" target="_blank" rel="noopener noreferrer">Caltech</a> and has been republished with permission.<hr>Scientists have become pretty good at predicting the weather, at least up to 10 days in the future. But making accurate, longer-term predictions about how climate is changing remains challenging. The ability to predict climate change is important because it can help guide decisions about insurance coverage in fire- or flood-prone areas; it can also help architects design safer and more comfortable buildings for occupants in a warmer future.Our ability to model climate change has been hampered by the enormous amount of computing power required to simulate all facets of climate, says <a href="https://www.eas.caltech.edu/people/tapio" target="_blank">Tapio Schneider</a>, the Theodore Y. Wu Professor of Environmental Science and Engineering and a senior research scientist at JPL, which Caltech manages for NASA. To make a global climate model accurate, it needs to capture small-scale processes, such as those controlling droplet formation in clouds, over the entire planet. Simulating this huge range of relevant processes is currently impossible.Instead, climate models calculate how temperature, winds, and ocean currents evolve on a mesh with a horizontal resolution around 50–100 kilometers. Smaller-scale processes, such as those in low-lying marine clouds that help shape the climate, are represented by rough approximations called parameterizations. These parameterizations often lack a strong foundation in both scientific principles and data.Recently, a group of climate scientists proposed a massively scaled climate-modeling project as a solution. The project would consist of a series of centers running supercomputers that are staffed by 200–300 scientists each. The goal would be to create climate models with a resolution of 1 kilometer.Schneider says that might prove difficult."Running climate models is enormously computationally expensive," Schneider says. "If you want to make the computational mesh finer by a factor of 10 in the horizontal plane, you need a thousand times the computational power."Schneider says a project of that nature would strain our current computational abilities, and it would not be able to represent the small-scale processes necessary for accurate modeling. It would further concentrate resources among developed nations better able to fund such centers, thereby leaving out developing nations, he says.In <a href="https://www.nature.com/articles/s41558-023-01769-3" target="_blank">a paper</a> recently published in the journal Nature Climate Change, Schneider and 13 other scientists from around the world propose to develop models with a moderately high resolution (tens of kilometers), providing a stronger scientific foundation for parameterizations of smaller-scale processes, using machine learning to fill in the gaps. This, the researchers say, will create robust models without the need for gargantuan computing centers."We cannot possibly resolve the marine clouds over the tropical oceans globally anytime soon because it will require a computer about a hundred billion times faster than the fastest one we have," Schneider says. "What we can do is simulate these clouds in small areas and generate data. Those data can also inform a coarser climate model."Schneider says work on the kinds of AI tools needed to improve the coarser models is happening right now, some of it at Caltech. This technology could do for climate models what better data has done for weather models."Weather forecasts have become quite good over the last few decades. It's a bit of an unsung success story of science," he says. "The primary reason they've become quite good is that they use data much more extensively than they used to. In some ways, we want to achieve the same thing for climate prediction. I think that can be achieved by using AI tools to harness the extensive data about Earth we already have."Andrew Stuart, Bren Professor of Computing and Mathematical Sciences and a co-author of the proposal, has lent his expertise to Schneider's effort, helping build the tools necessary for the project. Stuart says one set of tools aims to make climate models less computationally expensive to run.This can be done by creating parameterizations for coarser models, which Stuart describes as "excellent sketches" of actual physical processes, such as the behavior of individual clouds. In essence, they capture the main features needed to predict the effects that small-scale processes will have on larger-scale processes in a global model simulation. Because they are simplified versions of actual events, they have to be tuned to behave like their real-life counterparts.Even coarser models with parameterizations are expensive to run in terms of computing time and power, and the tuning process means they need to be run repeatedly until the researchers succeed in tuning them to produce reliable and realistic outputs."The primary computational challenge is to perform the tuning with the smallest number of costly model evaluations," Stuart says. "We do that by building on ideas that go back to 1960 and an engineer named Rudolf Kálmán; the ideas have now evolved into what are called ensemble Kalman filters. They allow us to make predictions and calibration with the minimal number of evaluations. They are used in weather forecasting, and we've repurposed them."The second set of tools Stuart has worked on in the climate arena fall under the umbrella of what might be popularly referred to as A.I., and more specifically, machine learning. He says machine learning can help quantify some of the uncertainty inherent in climate modeling and generate new data at scales that are hard to measure."The models have an enormous amount of uncertainty in them," Stuart says. "This is where machine learning enters."The machine learning used by the team has similarities to image generators like OpenAI's DALL-E. In the case of DALL-E, the machine-learning algorithm is trained on a huge set of sample images. So if you ask it, for example, to generate a picture of a man holding an ice cream cone at sunset, it can.The algorithms used in climate science are instead trained to generate pieces—hundreds or thousands of them—that can be plugged into bigger simulations to fill in the missing gaps. Just like DALL-E can conjure up images of apples when asked (because it has learned to identify them), the algorithms used in this research can generate models of processes important to climate modeling, such as droplet formation in clouds.By putting simulated processes of that sort together with real-world data, researchers hope to create realistic predictions about Earth's rapidly changing climate, Schneider says."We can use the physical laws as far as we can, given the limited computational power at our disposal," he says. "What we cannot simulate, we can learn from data."

The butterfly effect—the idea that a butterfly flapping its wings in South America could change the course of a tornado in Texas—has been a popular way to illustrate the difficulty of making predictions about the behavior of chaotic systems like the weather. Because such systems are extremely sensitive to their initial conditions, even the tiniest of changes can send their behavior careening off into unexpected directions.

Artificial Intelligence is Key to Better Climate Models, Say Researchers

A view of a factory cleanroom featuring scientists wearing protective equipment

A Comprehensive Guide to These Essential Environments for High-Precision Manufacturing and Research, Highlighting the Applications, Maintenance and Cleaning Practices

Cleanroom: A Comprehensive Guide to Design, Standards, and Applications

This article is published as part of TechBlick (www.TechBlick.com) series on additive and sustainable electronics. Here, Stephan from the Holst Centre outlines how one can make electronics more sustainable, considering materials, manufacturing, and design aspects. This article appeared originally&nbsp;<a href="https://www.techblick.com/post/roadmap-to-sustainable-printed-electronics-tno-at-holst-centre" target="_blank" rel="noopener noreferrer">here. </a>Author: Stephan Harkema and Corn&eacute; Rentrop | TNO at Holst Centre | <a rel="noopener noreferrer" href="mailto:stephan.harkema@tno.nl" target="_blank">stephan.harkema@tno.nl</a>A growing desire for continuous data collection, real-time information, and connectivity has resulted in increased demand for electronic functionalities that are fully integrated in everyday objects. Consumer electronics, healthcare, wearable electronics, IoT, and smart packaging are examples of market segments that follow this trend. Printed circuit boards (PCBs) are state-of-the-art when it comes to creating electronic functions, but pose challenges with respect to sustainability. These highly integrated electronic products, where plastics, metals and semiconductor components are seamlessly combined, are a challenge to recycle. The only suitable end-of-life scenario is still limited to shredding and incineration. With increasing electrification, digitization, broad wireless integration (IoT) and welfare, the amount of waste from electronics and electronic devices increases drastically. An estimated 1.2 Mtons of Printed Circuit Boards (PCBs) end up in the total amount of annual e-waste[1]. Only a third is recycled in environmentally sound facilities. This means that around 800 million kilograms may be traded, recycled in a non-compliant and polluting manner or may end up on a landfill. There is a more eco-friendly alternative to PCBs: Hybrid and Printed Electronics (HPE). This technology is a big step forward compared to PCBs, but much more is needed to achieve circular production of next-generation electronics.Despite their name, printed circuit boards (PCBs) are not actually printed. They consist of multiple layers of electroplated copper on glass-fiber epoxy substrates onto which electrical components, so-called surface-mounted devices (SMDs) are assembled. The copper layers are etched with hazardous chemicals to create the circuitry. At end-of-life, the encapsulated copper and unrecyclable epoxy carrier make PCBs a recycling challenge. Transitioning from PCBs to HPE (Hybrid and Printed Electronics) provides immediate opportunities to achieve ecological benefits: reduced device thickness, lower weight, printed sensors, and additive instead of deductive manufacturing of the circuitry. Printed electronics is a disruptive technology that is on the verge of breakthrough in the automotive, lighting, and medical domains. Each use case represents a slimmer, lighter, more appealing, more practical, and less polluting alternative to the conventional device based on printed circuit boards (PCBs). However, in both types of electronics, PCBs, and HPE, the circuitry and SMDs contain potentially critical, high-impact metals embedded in plastics to protect them from external influences, such as mechanical damage and moisture. As opposed to PCBs, the environmental impact of HPE can be further reduced by using recyclable, bio-based, and renewable or compostable materials.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk0Nzc0NDU4Nzk1LVNjcmVlbnNob3QgMjAyMy0wOS0xNSAxMDA3MDgucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 754px;" class="fr-fic fr-dib"><figure><figcaption>Figure 1: Roadmap to sustainable electronics</figcaption></figure><h3>Roadmap to sustainable printed electronics</h3>Most, if not all, products are mass produced. A first primary focus lies with a successful market entry which is often achieved by innovative functionalities in an aesthetic product design at competitive price levels. Manufacturing of such products is often not immediately optimized for low material usage, low power usage, and a minimal carbon footprint. Instead, single use and low recyclability are often accepted at this stage to achieve commercial success, which may be essential for the survival probability of the company. Petrochemical materials offer a high degree of reliability, low cost and supply security and are therefore ideally suited for high volume production in this first stage of the roadmap, as shown in Figure 1.A first step towards increasing a product&rsquo;s sustainability is to address, and preferably extend, its end-of-life by enabling repairability, for instance by a modular design with repairable or replaceable components, or by enabling upgrades. Such steps do not immediately improve recyclability or reduce the environmental impact of production and the materials, but they do provide a significant improvement to the environmental impact by the simple fact that the product does not need to be replaced by a new one. Also energy consumption during the use phase may be addressed. For some examples, such as devices containing highpower elements (e.g. lighting), optimization of power usage may be an effective means of lowering the overall environmental impact.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk0Nzc0NDkwMzgyLVROTyBhdCBIb2xzdCBDZW50cmUgKEJFUkxJTi1PY3QtMjAyMykuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 762px;" class="fr-fic fr-dib"><figure><a href="https://www.techblick.com/electronicsreshaped" target="_blank"></a><figcaption>Join us at TechBlick&#39;s Future of Electronics RESHAPED conference &amp; tradeshow in Berlin on 17-18 OCT 2023 - <a target="_blank" rel="noopener noreferrer" data-fr-linked="true" href="//www.techblick.com/electronicsreshaped">www.techblick.com/electronicsreshaped</a>. Contact authors for discounted rates.</figcaption></figure>Following this second stage, a third step may be taken in which a product undergoes a simplification of its design with a reduction of coatings to limit material usage, production steps and overall energy consumption. Printed electronics technology offers an increasingly credible alternative to PCBs and perfectly matches this stage in the roadmap: 1) lower thickness, 2) printed functionalities instead of discrete SMD components that have a higher footprint, 3) recyclable thermoplastic polymers and 4) additive manufacturing as opposed to deductive etching of copper. Nonetheless, challenges still remain in terms of costs and reliability in comparison to the PCB.Circular use of materials, as depicted in the fourth stage in Figure 1, requires more drastic changes, especially to enable other choices at end-of-life. When end-of-life is reached, the product needs to be dismantled into its chemical building blocks. Here, printed electronics also provides opportunities, as many of the starting materials are meltable and therefore relatively easy to dismantle. The favoured cradle-to-cradle recycling approach is a possibility to further reduce the environmental impact. However, seamlessly integrated electronic products manufactured to survive rigorous testing in harsh environments cannot be disassembled so easily, which leaves shredding and incineration of these products as the only option at end-of-life. The plastics serve as energy carriers for metallurgic processes. While metallurgic processing at an industrial scale may be very efficient for metal recovery, the plastics are lost entirely. Recuperation of the plastics faces the challenge of achieving a high enough purity level to reach the material properties of the initial plastic. When combining plastics with coatings or metals or other plastics, purity levels plummet and only secondary use is realistic. Recycling of integrated electronics to yield metals, components and plastics separately and in a pure and circularly usable form therefore requires a method of disassembly prior to recycling. Not only does this allow circular use of all components and materials, it also enables higher recycling rates. It is therefore vital for electronics production in a circular economy to include design-for-recycling principles.In the before-last stage, bio-based materials may be introduced as a replacement for generally used fossil-based polymers. In the final stage, biodegradability is an option for specific use cases where disposal into the environment is likely or a more effective approach to recycling. Devices applied to the human body with a limited lifetime of days to weeks are well-suited to go this far in their sustainable developments, while others intended for long-term use are less likely to benefit from biodegradability. Materials from bio-based origins are considered to be environmentally friendly, but large volume production may put additional strain on food supplies and animal feed (1st generation feedstock) or land use for non-food biomass (2nd generation)[2]. Algae are a promising candidate for third-generation bio-mass due to a very high growth rate (5-10x terrestrial plants)[3]. Moreover, cultivation of algae do not strain food supplies and available cultivated farmland.<h3>Achievable lower environmental impacts</h3>Quantification of the environmental impact typically proceeds through a life-cycle assessment. Guidelines to achieve a lower environmental footprint for printed electronics were provided by Prenzel et al.[4]. Copper has a significantly lower global warming potential than silver, possibly up to a factor of 70[5]. While this choice for a different metal would lead to a more than 95% lower contribution to the GWP, it is limited to the printed circuitry alone, if processing remains otherwise identical. Similar values are possible for the plastic carrier if e.g. paper is a suitable alternative. However, reliability would then be at stake due to a high sensitivity to moisture/water, revealing the potential, but also the intricacies related to developing sustainable electronic products. At a higher integration level, ultra-thin organic photovoltaic solar cells were reported to demonstrate a 10-85% reduction in carbon footprint by a combination of a different substrate, less silver and a modified design[6]. For printed electronics devices closer to industrialisation, a recent life-cycle assessment by Tactotek indicated that, compared to PCBs, in-mould structural electronics (IMSE) enabled a reduction of the carbon footprint by 34-62%, depending on the application and its details[7]. The reductions in carbon footprint for these printed electronics examples are very hopeful and do not stand alone. More examples are available in the literature.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk0Nzc0NTIzNTA3LVROTyBhdCBIb2xzdCBDZW50cmUtYm9vdGguanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 725px;" class="fr-fic fr-dib"><figure><a href="https://www.techblick.com/electronicsreshaped" target="_blank"></a><figcaption>Join us at TechBlick&#39;s Future of Electronics RESHAPED conference &amp; tradeshow in Berlin on 17-18 OCT 2023 - <a target="_blank" rel="noopener noreferrer noopener noreferrer" data-fr-linked="true" href="//www.techblick.com/electronicsreshaped">www.techblick.com/electronicsreshaped</a>. Contact author for discounted rates.&nbsp;</figcaption></figure><h3>Design-for-recycling (DfR)</h3>The benefits of the application of a design-for-recyclability would further enhance these values as a DfR enables the recovery of the materials. When comparing automated shredding and sorting of PCBs containing electronic waste to manual extraction of the PCB for recycling, higher recovery rates were obtained for the latter (95-99% vs 12-60%)[8]. Another study indicated that extensive dismantling and high-quality recycling would lead to the highest reduction of the product&rsquo;s global warming potential[9]. Norgren et al. applied design-for-recycling to several use cases and examined the role of a DfR in a circular economy[10]. As described, design-for-recycling holds the potential to increase the quantity and value of materials recovered and reused from products that have reached their end-of-life. Important to realize is that improved recyclability provides a balance between reliability and recyclability: a part is typically designed to withstand rigorous testing (peel tests, pull-out tests, bending tests, stretching, ..), sometimes in combination with harsh environmental exposure (climate chamber set at elevated temperature and humidity, thermal shock tests, aging test at high T and/or with UV exposure). The specs and standards that must be met are often based on conventional PCB electronics and warrant re-evaluation of their applicability in a circular economy. The additional trade-off of recyclability versus costs may come in next, as additional measures or specialistic materials may be necessary to reach the DfR. The question is then when the gains in environmental impact exceed the potential loss in reliability and or potential increase in costs and which methods for quantification are accepted by the industry and policy makers.TNO at Holst Centre has developed a DfR approach that can be applied to various printed electronics devices, including in-mould structural electronics, flexible lighting, flexible sensors, and (medical) health patches. In this design, the encapsulant of the printed electronics and discrete semiconductor components can be removed at end-of-life. First demonstrations of the principles on in-mould structural electronics (IMSE) in the EU Treasure project led to successful device disassembly[11]. Upon removal of the polycarbonate encapsulant, that had been applied by injection moulding to a polycarbonate substrate with printed silver circuitry, the printed silver circuitry was exposed. Subsequent hydro-metallurgic processes by Treasure partner Univaq (University of L&rsquo;Aquila) could target the printed silver directly and specifically without the need to chemically remove the PC beforehand[12]. A full life-cycle assessment, including the comparison of a recycling end-of-life scenario compared to shredding and incineration, is underway.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk0Nzc0NTU3MjM4LVNjcmVlbnNob3QgMjAyMy0wOS0xNSAxMDA3MjMucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 682px;" class="fr-fic fr-dib"><figure><figcaption>Figure 2: schematic representation of a design-for-recycling with a) an electronic device encapsulated in plastic, b) first stage of dismantling with the removal of the plastic encapsulant, c) &amp; d) second stage of dismantling that targets SMDs and (printed) circuitry and e) plastic substrate with any leftover (graphic) layers. Figure courtesy of Amires.</figcaption><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk0Nzc0NTc4MzkzLVNjcmVlbnNob3QgMjAyMy0wOS0xNSAxMDA3MzQucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 686px;" class="fr-fic fr-dib"></figure><figure><figcaption>Figure 3: a) forced mechanical disassembly of an IMSE device without DfR leading to rupture of the substrate, b) disassembly of an IMSE device with DfR; c,d) Flexible lighting device with DfR in on-state and off-state during peel tests</figcaption></figure><h3>Join us and the global community in Berlin on 17-18 OCT 2023. and let us together RESHAPE the Future of Electronics, making it Additive, Sustainable, Hybrid, Wearable, or 3D.</h3><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk0Nzc0NjMzMzY2LTIuQWdlbmRhLUV4aGliaXRpb24tTE9HT1MtKEJFUkxJTi0yMDIzKS1zbWFsbC5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 738px;" class="fr-fic fr-dib"><h3 data-pm-slice="1 1 []" id="isPasted">Acknowledgements</h3>Holst Centre is a research and innovation partner specialized in health technologies, flexible and wireless electronics, powered by the shared expertise of imec and TNO. This research has been funded by the Dutch Ministry of Economic Affairs, and the European Union&rsquo;s Horizon 2020 research and innovation programme under grant agreement No 101003587 (EU Treasure project), No 101070167 (EU Ecotron project). Original electrical and injection-mould design were realized in the Flexlines projectthat received funding from Interreg Vlaanderen-Nederland. The authors would like to gratefully acknowledge the exceptional contributions from Peter Rensing, Pit Teunissen, Jan van Delft, Jeroen Schram, Gerwin Kircher, Adri van der Waal and dr. Margreet de Kok from TNO at Holst Centre, and Sanne Domensino and Joris Vermeijlen from Fontys University of Applied Sciences. We also gratefully acknowledge the wonderful support provided by Rick Leuven and Yibo Su from BrightlandsMaterial Center for injection moulding with the Flexlines mould.&nbsp;<hr><h3>References:</h3>[1] Bald&eacute;, C.P., D&rsquo;Angelo, E., Luda, V., Deubzer, O., and Keuhr, R. Global Transboundary E-waste Flows Monitor &ndash; 2022, United Nations Institute for Training and Research (2022)[2] Coma M., Martinez-Hernandez E., Abeln, F., Raikova, S., Donnelly, J., Arnot, T.C., Allen, M.J., Hong, D.D., Chuck, C.J.Faraday Discuss., 202, 175 (2017)[3] Maliha A., Abu-Hijleh, B., Energy Systems (2022) doi:10.1007/s12667-022-00514-7[4] Prenzel T.M., Gehring, F., Fuhs, F., Albrecht, S. Mat&eacute;riaux &amp; Techniques 109, 506 (2021) doi: &nbsp;<a rel="noopener noreferrer" href="http://doi.org/10.1051/mattech/2022016" target="_blank">doi.org/10.1051/mattech/2022016</a>[5] Nuss P, Eckelman MJ (2014) PLoS ONE 9(7): e101298. doi:10.1371/journal.pone.0101298 (2014)[6] V&auml;lim&auml;ki, M.K., Sokka, L.I., Peltola, H.B. Int J Adv Manuf Technol 111, 325&ndash;339 (2020). doi:10.1007/s00170-020-06029-8[7] Tactotek webinar &ldquo;Environmental Performance of IMSE&reg;&rdquo;, 2022, <a rel="noopener noreferrer" href="https://www.tactotek.com/resources/webinar-environmentalperformance-of-imse" target="_blank">https://www.tactotek.com/resources/webinar-environmentalperformance-of-imse</a>[8] Ardente F., Mathieux, F., Recchioni, M. Resources, Conservation and Recycling 92, 158-171 (2014) doi:10.1016/j.resconrec.2014.09.005[9] Alston, S., Arnold, J. Environ. Sci. Technol. 45, 21, 9386&ndash;9392 (2011) doi:10.1021/es2016654[10] Norgren A., Carpenter A., Heath, G. Journal of Sustainable Metallurgy 6:761&ndash;774 (2020). doi: 10.1007/s40831-020-00313-3[11] Harkema, S., Rensing, P., Domensino S., Verweijlen J., Godoi-Bizarro, D. submitted for publication[12] Ippolito, N.M.*, Romano, P., Passadoro, M., Pellei, G., Vegli&ograve;, F. Treasure Project H2020 - Recycling of automotive waste for the recovery of precious and critical metals: pilot plant tests. The 16th International Conference on Chemical and Process Engineering, Naples, May 21-24, 2023

A growing desire for continuous data collection, real-time information, and connectivity has resulted in increased demand for electronic functionalities that are fully integrated in everyday objects. Consumer electronics, healthcare, wearable electronics, IoT, and smart packaging ...

Roadmap to Sustainable Printed Electronics. TNO at Holst Centre

This article was first published on&nbsp;<a href="https://news.mit.edu/2023/new-machine-learning-model-ocean-currents-0517" id="isPasted" rel="noopener noreferrer" target="_blank">MIT News</a>. &nbsp;<hr>To study ocean currents, scientists release GPS-tagged buoys in the ocean and record their velocities to reconstruct the currents that transport them. These buoy data are also used to identify &ldquo;divergences,&rdquo; which are areas where water rises up from below the surface or sinks beneath it.By accurately predicting currents and pinpointing divergences, scientists can more precisely forecast the weather, approximate how oil will spread after a spill, or measure energy transfer in the ocean. A new model that incorporates machine learning makes more accurate predictions than conventional models do, a <a data-lf-fd-inspected-kn9eq4roawkarlvp="true" href="https://arxiv.org/pdf/2302.10364.pdf" target="_blank">new study</a> reports.A multidisciplinary research team including computer scientists at MIT and oceanographers has found that a standard statistical model typically used on buoy data can struggle to accurately reconstruct currents or identify divergences because it makes unrealistic assumptions about the behavior of water.The researchers developed a new model that incorporates knowledge from fluid dynamics to better reflect the physics at work in ocean currents. They show that their method, which only requires a small amount of additional computational expense, is more accurate at predicting currents and identifying divergences than the traditional model.This new model could help oceanographers make more accurate estimates from buoy data, which would enable them to more effectively monitor the transportation of biomass (such as Sargassum seaweed), carbon, plastics, oil, and nutrients in the ocean. This information is also important for understanding and tracking climate change.&ldquo;Our method captures the physical assumptions more appropriately and more accurately. In this case, we know a lot of the physics already. We are giving the model a little bit of that information so it can focus on learning the things that are important to us, like what are the currents away from the buoys, or what is this divergence and where is it happening?&rdquo; says senior author Tamara Broderick, an associate professor in MIT&rsquo;s Department of Electrical Engineering and Computer Science (EECS) and a member of the Laboratory for Information and Decision Systems and the Institute for Data, Systems, and Society.Broderick&rsquo;s co-authors include lead author Renato Berlinghieri, an electrical engineering and computer science graduate student; Brian L. Trippe, a postdoc at Columbia University; David R. Burt and Ryan Giordano, MIT postdocs; Kaushik Srinivasan, an assistant researcher in atmospheric and ocean sciences at the University of California at Los Angeles; Tamay &Ouml;zg&ouml;kmen, professor in the Department of Ocean Sciences at the University of Miami; and Junfei Xia, a graduate student at the University of Miami. The research will be presented at the International Conference on Machine Learning.<h2>Diving into the data</h2>Oceanographers use data on buoy velocity to predict ocean currents and identify &ldquo;divergences&rdquo; where water rises to the surface or sinks deeper.To estimate currents and find divergences, oceanographers have used a machine-learning technique known as a Gaussian process, which can make predictions even when data are sparse. To work well in this case, the Gaussian process must make assumptions about the data to generate a prediction.A standard way of applying a Gaussian process to oceans data assumes the latitude and longitude components of the current are unrelated. But this assumption isn&rsquo;t physically accurate. For instance, this existing model implies that a current&rsquo;s divergence and its vorticity (a whirling motion of fluid) operate on the same magnitude and length scales. Ocean scientists know this is not true, Broderick says. The previous model also assumes the frame of reference matters, which means fluid would behave differently in the latitude versus the longitude direction.&ldquo;We were thinking we could address these problems with a model that incorporates the physics,&rdquo; she says.They built a new model that uses what is known as a Helmholtz decomposition to accurately represent the principles of fluid dynamics. This method models an ocean current by breaking it down into a vorticity component (which captures the whirling motion) and a divergence component (which captures water rising or sinking).In this way, they give the model some basic physics knowledge that it uses to make more accurate predictions.This new model utilizes the same data as the old model. And while their method can be more computationally intensive, the researchers show that the additional cost is relatively small.<h2>Buoyant performance</h2>They evaluated the new model using synthetic and real ocean buoy data. Because the synthetic data were fabricated by the researchers, they could compare the model&rsquo;s predictions to ground-truth currents and divergences. But simulation involves assumptions that may not reflect real life, so the researchers also tested their model using data captured by real buoys released in the Gulf of Mexico.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1684368694423-ezgif-5-7519252a77.gif" style="width: 766px;" class="fr-fic fr-dib">This shows the trajectories of approximately 300 buoys released during the Grand LAgrangian Deployment (GLAD) in the Gulf of Mexico in the summer of 2013, to learn about ocean surface currents around the Deepwater Horizon oil spill site. The small, regular clockwise rotations are due to Earth&#39;s rotation. Credit: Consortium of Advanced Research for Transport of Hydrocarbons in the EnvironmentIn each case, their method demonstrated superior performance for both tasks, predicting currents and identifying divergences, when compared to the standard Gaussian process and another machine-learning approach that used a neural network. For example, in one simulation that included a vortex adjacent to an ocean current, the new method correctly predicted no divergence while the previous Gaussian process method and the neural network method both predicted a divergence with very high confidence.The technique is also good at identifying vortices from a small set of buoys, Broderick adds.Now that they have demonstrated the effectiveness of using a Helmholtz decomposition, the researchers want to incorporate a time element into their model, since currents can vary over time as well as space. In addition, they want to better capture how noise impacts the data, such as winds that sometimes affect buoy velocity. Separating that noise from the data could make their approach more accurate.&ldquo;Our hope is to take this noisily observed field of velocities from the buoys, and then say what is the actual divergence and actual vorticity, and predict away from those buoys, and we think that our new technique will be helpful for this,&rdquo; she says.&ldquo;The authors cleverly integrate known behaviors from fluid dynamics to model ocean currents in a flexible model,&rdquo; says Massimiliano Russo, an associate biostatistician at Brigham and Women&rsquo;s Hospital and instructor at Harvard Medical School, who was not involved with this work. &ldquo;The resulting approach retains the flexibility to model the nonlinearity in the currents but can also characterize phenomena such as vortices and connected currents that would only be noticed if the fluid dynamic structure is integrated into the model. This is an excellent example of where a flexible model can be substantially improved with a well thought and scientifically sound specification.&rdquo;This research is supported, in part, by the Office of Naval Research, a National Science Foundation (NSF) CAREER Award, and the Rosenstiel School of Marine, Atmospheric, and Earth Science at the University of Miami.<hr>&quot;Reprinted with permission of&nbsp;<a href="https://news.mit.edu/2023/new-machine-learning-model-ocean-currents-0517" id="isPasted" rel="noopener noreferrer" target="_blank">MIT News</a>&quot; &nbsp;

Computer scientists at MIT joined forces with oceanographers to develop a machine-learning model that incorporates knowledge from fluid dynamics to generate more accurate predictions about the velocities of ocean currents. This figure shows drifting buoy trajectories in the Gulf of Mexico superimposed on surface currents. The red dots mark the buoys’ positions on March 9, 2016, and the tails are 14 days long. Image: Edward Ryan and Tamay Özgökmen from the University of Miami.

A new machine-learning model makes more accurate predictions about ocean currents, which could help with tracking plastic pollution and oil spills, and aid in search and rescue.

A better way to study ocean currents

It&rsquo;s easy to feel overwhelmed when you&rsquo;re building low power electronics. There are so many different components and technologies to learn that it can be hard to know where to begin.Recently, one of our clients wanted to redesign their field-deployed IoT environmental monitor to a smaller, lighter, and more power-efficient package with longer battery life and could be placed more easily into remote locations. In order to achieve this, they faced a choice of going with a Linux-based single-board-computer with a microprocessor, or a microcontroller-based compute platform.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjc5OTQ4MjY5NDYzLTE2Nzk5NDgyNjk0NjMucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" alt="" width="100%" height="auto" srcset="https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-environmental-monitoring-2.jpg 1000w, https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-environmental-monitoring-2-300x200.jpg 300w, https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-environmental-monitoring-2-768x512.jpg 768w" sizes="(max-width: 1000px) 100vw, 1000px" class="fr-fic fr-dii">Environmental sensors may require either a microcontroller or microprocessor to compute&nbsp;In this post, we look at the differences between single board computers and microcontroller kits when it comes to <a href="https://www.mistywest.com/posts/5-reasons-to-use-an-iot-framework/" target="_blank">designing a successful remote sensor</a>, so that you have the information you need to choose the right solution for future applications.&nbsp;<h2>A summary of differences between microcontroller kits and single-board computers (SBCs)</h2>A microcontroller is a small all-in-one computing platform with features like onboard memory, built-in timers, IO handling, and other key features for interacting with electrical hardware.&nbsp;Microcontrollers&rsquo; common uses are devices like remote controls, toys, industrial equipment, cars, and implantable medical devices. You can find them in microcontroller kits like <a href="https://www.arduino.cc/" target="_blank">Arduino</a>.&nbsp;<a data-rel="lightbox-image-0" data-rl_caption="An Arduino Uno, one of the microcontroller boards in the Arduino family" data-rl_title="" href="https://www.mistywest.com/wp-content/uploads/2022/06/Arduino_Uno_-_R3.jpg" target="_blank" title=""><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjc5OTQ4MjY5MjAzLTE2Nzk5NDgyNjkyMDMucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" alt="" width="100%" height="auto" srcset="https://www.mistywest.com/wp-content/uploads/2022/06/Arduino_Uno_-_R3.jpg 600w, https://www.mistywest.com/wp-content/uploads/2022/06/Arduino_Uno_-_R3-300x300.jpg 300w, https://www.mistywest.com/wp-content/uploads/2022/06/Arduino_Uno_-_R3-150x150.jpg 150w" sizes="(max-width: 600px) 100vw, 600px" class="fr-fic fr-dii">An Arduino Uno, one of the microcontroller boards in the Arduino family</a>&nbsp;A microprocessor is similar to a conventional computer CPU, which is only the processor, and needs to be connected to external memory, timers, storage, and IO peripherals in order to function. Microprocessors are generally more powerful, and allow for a more granular hardware design by selecting the exact memory, timers and other features that one needs. You&rsquo;ll find microprocessors in consumer computing equipment, and the increase of small and cheap single-board computers (SBCs) like <a href="https://www.raspberrypi.org/" target="_blank">Raspberry Pi</a> has enabled them to be used in new applications like smart devices.&nbsp;<a href="https://opensource.com/resources/raspberry-pi" target="_blank"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjc5OTQ4MjY5NDk4LTE2Nzk5NDgyNjk0OTcucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" alt="" width="100%" height="auto" srcset="https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-raspberry-pi.jpg 675w, https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-raspberry-pi-300x205.jpg 300w" sizes="(max-width: 675px) 100vw, 675px" class="fr-fic fr-dii">Raspberry Pi single-board computer</a>&nbsp;<h2>Strengths and Weaknesses</h2>When it comes to computing power, how resource-intensive is your problem? Will it be simple sensor reading (low intensity), image processing (high intensity), or machine vision/AI (very intense)? For development, can you use an off-the-shelf operating system or reuse other people&rsquo;s code? And how much power consumption or battery life will you require?To help you evaluate the strengths and weaknesses between your options, we&rsquo;ve added them to Table 1 below, followed by a detailed breakdown of each feature.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjc5OTQ4MjY5MDgyLTE2Nzk5NDgyNjkwODIucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" alt="" width="100%" height="auto" srcset="https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-microcontroller-microprocessor-compare-v3-01.jpg 717w, https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-microcontroller-microprocessor-compare-v3-01-300x193.jpg 300w" sizes="(max-width: 717px) 100vw, 717px" class="fr-fic fr-dii">Table 1: strengths and weaknesses between the computing devices&nbsp;<h3>Computing power</h3>Some microcontrollers are designed with low power consumption in mind and are specified to be run at reduced clock frequencies, meaning the chip computes more slowly but with less power consumption. This makes microcontrollers good for less compute-intensive applications like sensor reading, serial communication, or mechanical control systems, but they don&rsquo;t tend to have the processing power for computationally intensive tasks, like image processing.In comparison, there&rsquo;s often much more computing power available on commercial microprocessor boards like Raspberry Pi, which can support more complicated tasks like streaming video or running a local website. There are also microprocessors with additional modules, such as the <a href="https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-nano/" target="_blank">Nvidia Jetson Nano</a>, which has a graphics processing unit for tasks like machine vision and AI.&nbsp;<a href="https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-nano/education-projects/" target="_blank"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjc5OTQ4MjY5MDA3LTE2Nzk5NDgyNjkwMDcucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" alt="" width="100%" height="auto" srcset="https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-jetson-nano-nvdia.jpg 800w, https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-jetson-nano-nvdia-300x169.jpg 300w, https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-jetson-nano-nvdia-768x432.jpg 768w" sizes="(max-width: 800px) 100vw, 800px" class="fr-fic fr-dii">Nvidia Jetson Nano</a>&nbsp;<h3>Available packages, development resources, and support</h3>If you pick an established platform like Arduino or Raspberry Pi, there are many existing open-source libraries and a community that can help debug issues. However, If you pick a more specialized and less common platform, you may be on your own when sorting through documentation and trying to fix the issue. It is important for you to decide if you need the specialized features of a particular platform and trade that against the supportWith embedded Linux solutions, most of the basic drivers for common connections like ethernet, audio, and video are already available, tested and stable. In contrast, you may need to write the drivers for your particular microcontroller to connect with peripherals, which can be time-consuming and challenging.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjc5OTQ4MjY4NzkwLTE2Nzk5NDgyNjg3OTAucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" alt="" width="100%" height="auto" srcset="https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-power-consumption-graphic-01.png 612w, https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-power-consumption-graphic-01-300x74.png 300w" sizes="(max-width: 612px) 100vw, 612px" class="fr-fic fr-dii">&nbsp;<h3>Power consumption</h3>Microcontroller-based boards usually use less energy than microprocessor-based SBCs. One of the reasons is that microcontrollers generally run at lower clock frequencies than microprocessors which in turn also means reduced computing power.&nbsp;<h3>Coding language support</h3>If you&rsquo;re running your code on a microcontroller, you&rsquo;re likely going to need to build your software with a lower level language like C or C++. On a microprocessor with a Linux distribution, you will have the choice between many more languages. This can help to create POCs (Proof Of Concepts) at a much faster pace.&nbsp;There are many libraries for peripherals like cameras, sensors, etc. written in higher-level languages like Python, for instance. By picking a platform that can run those languages you will be able to leverage those open source libraries. However, If your solution requires a lot of low-level hardware manipulation, this may not be helpful.&nbsp;<h3>Code portability</h3>How tightly integrated is the code with the hardware? If you write a bare-metal application for a microcontroller, it will be challenging to switch over to another platform &ndash; but projects that are running FreeRTOS, for example, make switching easier. If you&rsquo;re using a Linux-based SBC and peripherals over protocol like USB, it will be even easier to change the compute platform because there&rsquo;s often libraries and drivers available. In summary, depending on the tools you are planning to use some platforms might be more flexible to be interchangeable than others.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjc5OTQ4MjY4ODY2LTE2Nzk5NDgyNjg4NjYucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" alt="" width="100%" height="auto" srcset="https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-boot-time-graphic-01.png 892w, https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-boot-time-graphic-01-300x100.png 300w, https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-boot-time-graphic-01-768x257.png 768w" sizes="(max-width: 892px) 100vw, 892px" class="fr-fic fr-dii">&nbsp;<h3>Boot time</h3>The boot time of a SBC with embedded Linux on it is significantly greater than that of a microcontroller, be it bare-metal or running an RTOS. When utilizing an SBC, an application must not depend on fixed time slots during start up and a few seconds for it to come up must be acceptable.&nbsp;<h3>Cost</h3>In general, microcontrollers cost less than microprocessors. As an example, the <a href="https://www.pjrc.com/teensy/" target="_blank">top of the line Teensy</a> costs $26 and the flagship <a href="https://store-usa.arduino.cc/collections/boards/products/arduino-uno-rev3" target="_blank">Arduino board (Uno)</a> costs $25, whereas the flagship <a href="https://www.raspberrypi.com/products/raspberry-pi-4-model-b/" target="_blank">Raspberry Pi board (Pi 4)</a> starts at $35 and more expensive boards like the <a href="https://store.nvidia.com/en-us/jetson/store/?page=1&limit=9&locale=en-us&product=117&product_filter=117~3,118~3,119~4,120~3,173~1" target="_blank">Jetson Nano</a> may range up to $130. There is a wide range of costs in both domains and plenty of choices available on the market.&nbsp;<h2>Conclusion</h2>Ultimately, SBCs and microcontroller kits are both solutions for embedded applications that require computing; it&rsquo;s the use case that will inform which selection is the right one to make.SBCs with an embedded Linux OS, even though more costly, offer greater computing power, and allow a quick start to your project, with a wide range of options for your application&rsquo;s programming language, very active online communities and advantageous portability. Microcontrollers, on the other hand, are much more energy efficient and allow full control and fine tuning of their internal configuration &ndash; but additional time for development of features might need to be considered.In the case for our client, there were tradeoffs for both; a microcontroller-based platform/MCU could achieve lower power consumption, but would require more custom firmware development to integrate. The SBC accelerated development by providing a lot of open-source software packages and drivers that could be rapidly configured, but at the expense of higher power consumption.When it comes to making the right selection, if a quick prototype for a proof of concept is needed or if the device is going to be used for inference, object localization or other demanding computing tasks, an SBC is likely the better solution. The portability of the code and the flexibility of the system will simplify development and maintenance significantly. This article was originally written by Malcolm Dimeglio, Software Developer at MistyWest, <a href="https://www.mistywest.com/posts/choosing-the-right-computing-device-for-a-low-power-sensor-application/" rel="noopener noreferrer" target="_blank">for the MistyWest blog</a>.

Here's what you should consider when designing a successful remote sensor.

Choosing The Right Computing Device For A Low Power Sensor Application

In places across the U.S., tree cover is shrinking &ndash; forests are burned by wildfires on the West Coast and drowned by rising sea levels along the East. From the ground, it&rsquo;s hard to assess the scale of the losses and the effects disappearing trees have on atmospheric carbon dioxide levels and climate change.NASA research scientist Jon Ranson is working to improve new technologies for studying trees from above, so future Earth-observing missions can more accurately assess forest health.&ldquo;Trees do a huge service for the planet, in terms of sequestering carbon dioxide, taking it out of the atmosphere and putting it into wood,&rdquo; Ranson said, &ldquo;but trees are very sensitive to our changing climate. We&rsquo;re trying to see the changes going on in forest ecosystems. If you detect things early enough, you might be able to do something about it.&rdquo;When measured from aircraft and satellites, the wavelengths of light reflected by plants tell scientists about the amount of photosynthesis occurring, and therefore how much atmospheric carbon dioxide trees take up and store. The current standard for studying vegetation is called NDVI, or Normalized Differential Vegetation Index, which is the average of two broad portions of the infrared spectrum. NASA&rsquo;s NDVI record goes back 40 years, providing a low-resolution but accurate picture of forest health.While NDVI is good at assessing vegetation quantity and vigor broadly, Ranson said, breaking down infrared and visible light into many more wavelengths, a technology called hyperspectral imaging, can provide insight on plants&rsquo; water content, chlorophyll and even changes in health. &nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1679491783339-mlbs_landscape2+%281%29.jpg" style="width: 766px;" class="fr-fic fr-dib">In the Mountain Lake region near Blacksburg, VA, a National Ecological Observatory Network tower contains CO2 sensors that can help validate measurements taken by aircraft or satellites. Credits: Courtesy Jon Ranson&ldquo;Vegetation has these broad spectral properties,&rdquo; said Ian Adams, Earth Sciences Division Technologist at NASA&rsquo;s Goddard Space Flight Center. &ldquo;With hyperspectral imaging, you get lots of different measurements at smaller, closer together frequencies. There is a lot more information we can pull out if we can get better spectral resolution.&rdquo;By dramatically increasing the number of frequencies available for researchers to study, Ranson&rsquo;s work serves the priorities of the National Academies of Science&rsquo;s most recent Earth Science decadal survey, which determines the field&rsquo;s long-term priorities. The survey lists &ldquo;surface topography and vegetation,&rdquo; including forests, as a key area of study in need of more advanced technology.&nbsp;&ldquo;Strategically for NASA, and more broadly for the remote sensing community, hyperspectral is one of the areas we see as the future,&rdquo; Adams said.To make use of hyperspectral imaging by future orbital missions, data analysis techniques are first proven closer to the ground.Ranson&rsquo;s team fitted a Skyfish drone (UAV) from partner institution Virginia Tech with a visible and infrared (VIS/IR) hyperspectral camera and lidar equipment. They flew the imaging equipment over forests near Blacksburg, VA, in a region called Mountain Lake. Comparing their UAV observations with actual CO2 levels recorded by sensors on a nearby National Ecological Observatory Network tower, Ranson&rsquo;s team was able to refine calculations about how much carbon the forest removed from the atmosphere.These comparisons allowed them to further refine techniques for interpreting the hyperspectral data. For instance, Plants stressed by too much sunlight may release pigments to shield their chloroplasts, a condition his sensors can detect. If plants get too much shade, they may grow leaves with larger surface-areas, which can cause the sensor to overestimate plant productivity. Ranson wants to add short-wave infrared sensors to better distinguish reflections from leaves and other parts of plants, eliminating another possible source of error.<h2>The view from space: Seeing the forest for the trees</h2>Ranson&rsquo;s goal is to study forests on a global scale from space.&ldquo;We&rsquo;re trying to get the complete picture,&rdquo; Ranson said. &ldquo;So that when we go to space, we know how to find the answer to the question, &lsquo;Are our forests healthy? And if not, why not?&rsquo;&rdquo;Scientists and engineers at Goddard have been developing a mission concept called Concurrent Artificially intelligent Spectrometry and Adaptive Lidar System, or CASALS. If selected, CASALS would send a satellite with lidar and hyperspectral cameras into space. A constellation of such satellites could take the frequent measurements necessary to assess changes in forest productivity over time, using models perfected by Ranson&rsquo;s UAV flights.&ldquo;Forests take up as much carbon as the ocean does, yet forests cover only 9 percent of Earth&rsquo;s surface,&rdquo; said Ranson. &ldquo;So, if something goes wrong with our forests, it&rsquo;s a dramatic issue. We&rsquo;ve got this great, great resource in forests, and we need to care for them so they&rsquo;ll care for us.&rdquo;

In places across the U.S., tree cover is shrinking – forests are burned by wildfires on the West Coast and drowned by rising sea levels along the East. From the ground, it’s hard to assess the scale of the losses and the effects disappearing trees have on atmospheric carbon dioxide levels and climate change.

NASA To Measure Forest Health from Above

The global agricultural and farming industry is facing a perfect storm. Earlier this month, the world population hit eight billion for the first time, in the two weeks since it has grown another 1.5 million. That is a lot of extra mouths to feed when there are already 690 million people going hungry, according to the Food and Agriculture Organization of the United Nations (FAO).At the same time, agricultural production is literally and figuratively getting battered due to climate change. Increasing temperatures, weather variability, reductions in water availability and extreme weather events are causing mayhem for a sector trying to keep up with demand.A recent study published in Nature Climate Change claims climate change is costing the global farming sector 21 percent of its productivity each year.For the UN&rsquo;s Sustainability Development Goal (SDG) of &lsquo;Zero Hunger&rsquo; by 2030 to have any hope of success, the agriculture sector is being asked to ramp up farm production in the face of climate change.At the same time the industry is being warned that a &ldquo;farming-as-usual&rdquo; approach won&rsquo;t work because of the environmental toll agriculture itself puts on our natural resources. For example, the World Bank claims the sector is already responsible for up to a third of total greenhouse gas emissions.Evidently, a new approach is required, and the industry is betting big on smart farming practices and connected technologies offering a solution.<h2>Today&#39;s cellular IoT smart farm</h2>Smart farming&mdash;or precision agriculture, as it is also known&mdash;is focused on providing the sector with the infrastructure to leverage advanced wireless cloud-connected IoT technologies for tracking, monitoring, automating and analyzing operations.More succinctly, a smart farm is one that can generate maximum yield with minimum resources, with little or no human interaction. Or at least that is the goal.How achievable that goal is relies heavily if not entirely on the technology supporting it. Using a combination of connectivity solutions, sensors, autonomous machines, drones and data analytics, farmers can receive a real-time picture of their crops and livestock and can respond instantly to that data to maximize yield.For example, <a href="https://www.nordicsemi.com/News/2022/11/The-leova-SMART-employs-the-nRF9160-SiP?utm_campaign=2021%20wevolver" rel="noopener noreferrer" target="_blank">field-mounted devices</a> packed with microclimate measurement sensors can use cellular IoT connectivity to instantly report data to a cloud platform to alert of an impending frost or a potential fungal disease. This enables farmers to take remedial action immediately or harvest early.On a beef or dairy farm, animal ear tags can also host a range of sensors to record an animal&#39;s behavior and location, wirelessly relaying that information over long distances to a rancher.That data can be used in a host of different ways, for example, to trigger health alerts if an animal isn&rsquo;t moving or even for estrus detection, identifying when a female animal is ready to breed.<h2>Farms the size of countries rely on cellular IoT or Bluetooth LE</h2>The ability to relay this data over long distances using low-power cellular IoT or Bluetooth LE in combination with a cellular gateway is fundamental. Australia, for example, has no fewer than eight beef farms over 12,000 km2, each the size of a small country where physically inspecting cattle daily is impractical.Arable holdings, while not entirely on the same scale, still defy in-person inspection, with the largest single-fenced field sown with wheat measuring 141 km2.IoT infrastructure is still lacking in some regions, but according to McKinsey, by 2030, advanced IoT connectivity of some type will cover 80 percent of the world&rsquo;s rural areas. This should enable the smart farm to really hit its stride. It&rsquo;s already a sleeping IoT giant.In 2000, there were exactly zero connected devices on any of the world&rsquo;s farms; by 2020 that number was two billion. According to analyst reports, by 2035 the world&rsquo;s 525 million farms will employ more than 20 billion wirelessly connected smart devices to help them maximize yields and productivity.<h2>Everything automated with cellular IoT and Bluetooth LE agriculture</h2>Tomorrow&rsquo;s farm will also increasingly rely on autonomous vehicles. Machine learning algorithms and GNSS positioning are being used to develop self-driving harvesting machinery.The combination of precise positioning and onboard machine learning would allow farmers to focus on the technical side of processing and harvesting. At the same time, it could significantly boost yields, reduce harvesting losses and avoid unnecessary passes and fuel consumption by plotting the most effective way to reap a field.Unmanned Aerial Vehicles (UAVs)&mdash;otherwise known as drones&mdash;are becoming increasingly common for real time data gathering for surveying and mapping, keeping pests away, monitoring water levels, and checking the health of the crops using optical crop sensors. Meanwhile, on the ground, autonomous robots resembling miniature monster trucks can maneuver around fields, scanning crops for signs of pests and disease, stripping away weeds, or even harvesting crops. These solutions may not eliminate human interaction in farming, but they enable farmers to allocate their time to more profitable, less manual tasks.<h2>Cellular IoT and Bluetooth LE agriculture has a sensor for everything</h2>This is only the beginning. Bluetooth LE and cellular IoT wireless devices can monitor crops and automatically switch irrigation on and off based on soil moisture. <a href="https://www.nordicsemi.com/News/2020/06/CoreKinect-farm-storage-tank-level-monitor-employs-nRF9160?utm_campaign=2021%20wevolver" rel="noopener noreferrer" target="_blank">Connected level sensors</a> can ensure farms never run out of fuel or fertilizer. Wireless vibration and temperature sensors can successfully predict the need for maintenance or the impending breakdown of any one of a number of machines, from bailers and threshers to sorters and planters. And water quality sensors can be used to protect freshwater ecosystems from spills or agrochemical discharge, enabling proactive risk management and pollution control before the water even leaves the farm.The introduction of this technology cannot come too soon. Agriculture is no stranger to innovation, and connected technologies based on the IoT will help farmers feed an expanding global population while increasing environmental sustainability, aims that frequently come into conflict with each other. It will also ensure the livelihood of the 40 percent of the world&rsquo;s population who work in agriculture, and one hopes, lift millions out of poverty.<hr>This article was first published on Nordic&#39;s&nbsp;<a href="https://blog.nordicsemi.com/getconnected" target="_blank" rel="nofollow" id="isPasted"></a><a href="https://blog.nordicsemi.com/getconnected?utm_campaign=2021%20wevolver" rel="nofollow" target="_blank">Get Connected Blog</a>. &nbsp;

The global agricultural and farming industry is facing a perfect storm. Earlier this month, the world population hit eight billion for the first time, in the two weeks since it has grown another 1.5 million. That is a lot of extra mouths to feed when there are already 690 million people going hungry, according to the Food and Agriculture Organization of the United Nations (FAO).

Agriculture gets smart with cellular IoT and Bluetooth LE

In this episode, we talk about how a method leveraging ground data and satellite imagery can help prevent wetland losses. &nbsp;<iframe height="200px" width="100%" frameborder="no" scrolling="no" seamless="" src="https://player.simplecast.com/a470d96e-2d6f-42ab-9aff-e52672c3e85a?dark=false" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe><hr>This podcast is sponsored by <a href="https://www2.mouser.com/" id="isPasted" rel="nofollow" target="_blank">Mouser Electronics</a>. &nbsp;&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1669714706413-Mouser-sponsor-1-a.jpg" style="width: 766px;" class="fr-fic fr-dib"><hr><h3 id="isPasted">EPISODE NOTES</h3>(2:42) -&nbsp;<a href="https://www.wevolver.com/article/satellites-help-scientists-track-dramatic-wetlands-loss-in-louisiana" target="_blank">Satellites Help Scientists Track Dramatic Wetlands Loss in Louisiana</a>This episode was brought to you by Mouser, our favorite place to get electronic components for any project. Click <a href="https://resources.mouser.com/space/communications-in-space-a-deep-subject" target="_blank">HERE</a> to learn more about the intricacies of deep space communication!<hr>As always, you can find these and other interesting &amp; impactful engineering articles on Wevolver.com.To learn more about this show, please visit our<a href="https://www.wevolver.com/profile/the.next.byte" target="_blank">&nbsp;shows page</a>. By following the page, you will get automatic updates by email when a new show is published. Be sure to give us a follow and review on<a href="https://podcasts.apple.com/nl/podcast/the-next-byte/id1548255861?l=en" rel="nofollow" target="_blank">&nbsp;Apple&nbsp;</a>podcasts,<a href="https://open.spotify.com/show/6lPPsRtegnivsRUEsQ09R7?si=IPMiah7xT_apJtPjqvXMlA" rel="nofollow" target="_blank">&nbsp;Spotify</a>, and most of your favorite podcast platforms!

The researchers mapped land change in coastal Louisiana from 1984 to 2020. Basins that failed to build new soil, such as Terrebonne and Barataria, experienced the most land loss – more than 180 square miles (466 square kilometers). Credit: Jensen et al. Journal of Geophysical Research: Biogeosciences

In this episode, we talk about how a method leveraging ground data and satellite imagery can help prevent wetland losses. 

Podcast: Fighting Wetland Loss From Outer Space

This article was discussed in our <a data-saferedirecturl="https://www.google.com/url?q=https://www.wevolver.com/profile/the.next.byte&source=gmail&ust=1669765825866000&usg=AOvVaw0adN1ijmCNIvr_GfjyI5jV" href="https://www.wevolver.com/profile/the.next.byte" id="isPasted" rel="noopener noreferrer" target="_blank">Next Byte podcast</a>.&nbsp;<iframe height="200px" width="100%" frameborder="no" scrolling="no" seamless="" src="https://player.simplecast.com/a470d96e-2d6f-42ab-9aff-e52672c3e85a?dark=false" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>The full article will continue below.<hr>From Lake Pontchartrain to the Texas border, Louisiana has lost enough wetlands since the mid-1950s to cover the entire state of Rhode Island. Using a first-of-its kind model, NASA-funded researchers quantified those wetlands losses at nearly 21 square miles (54 square kilometers) per year since the early 1980s.In the <a href="https://doi.org/10.1029/2022JG006794" target="_blank">new study</a>, scientists used the NASA/USGS <a href="https://landsat.gsfc.nasa.gov/" target="_blank">Landsat</a> satellite record to track shoreline changes across Louisiana from 1984 to 2020. Some of those wetlands were submerged by rising seas; others were disrupted by oil and gas infrastructure and hurricanes. But the primary driver of losses was coastal and river engineering, which can have positive or negative effects depending on how it is implemented.Centimeter by centimeter, wetlands are built by the slow accumulation &ndash; accretion &ndash; of mineral sediment and organic material carried by rivers and streams. Accretion makes new soil and counters erosion, the sinking of land, and the rise of sea level.Human intervention and engineering often hold back or divert the flow of sediments that naturally accrete to build and replenish wetlands. For instance, reinforced levees and thousands of miles of canals and excavated banks have isolated many wetlands from the Mississippi River and the network of streams that course through its delta like veins and capillaries. In a few cases, engineering projects have added sediment to delta areas and built new land.<a data-caption="The researchers mapped land change in coastal Louisiana from 1984 to 2020. Basins that failed to build new soil, such as Terrebonne and Barataria, experienced the most land loss – more than 180 square miles (466 square kilometers). Credit: Jensen et al. Journal of Geophysical Research: Biogeosciences" data-fancybox="" data-src="https://d2pn8kiwq2w21t.cloudfront.net/original_images/e1-wetlands_land_loss_map.jpeg" href="https://d2pn8kiwq2w21t.cloudfront.net/original_images/e1-wetlands_land_loss_map.jpeg" target="_blank"></a><div data-v-47d42ddc=""><a data-caption='The researchers mapped land change in coastal Louisiana from 1984 to 2020. Basins that failed to build new soil, such as Terrebonne and Barataria, experienced the most land loss – more than 180 square miles (466 square kilometers). Credit: Jensen et al. Journal of Geophysical Research: Biogeosciences' data-fancybox="" data-src="https://d2pn8kiwq2w21t.cloudfront.net/original_images/e1-wetlands_land_loss_map.jpeg" href="https://d2pn8kiwq2w21t.cloudfront.net/original_images/e1-wetlands_land_loss_map.jpeg" target="_blank"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjY4MTM4NTc4OTIwLWUxLXdldGxhbmRzX2xhbmRfbG9zc19tYXAud2lkdGgtMTMyMC5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 766px;" class="fr-fic fr-dib">The researchers mapped land change in coastal Louisiana from 1984 to 2020. Basins that failed to build new soil, such as Terrebonne and Barataria, experienced the most land loss &ndash; more than 180 square miles (466 square kilometers). Credit: Jensen et al. Journal of Geophysical Research: Biogeosciences</a></div>By analyzing Landsat imagery with tools from cloud computing, the researchers developed a remote sensing model that focused on accretion or the lack of it. Basins that failed to build new soil, such as Terrebonne and Barataria, experienced the most land loss over the study period &ndash; more than 180 square miles (466 square kilometers). Other areas gained ground, including 33.6 square miles (87 square kilometers) of new land in the <a href="https://earthobservatory.nasa.gov/world-of-change/WaxLake" target="_blank">Atchafalaya Basin</a> and 43 square miles (112 square kilometers) in the area known as the &ldquo;Bird&rsquo;s Foot Delta&rdquo; at the mouth of the Mississippi River.&ldquo;The Louisiana coastal system is highly engineered,&rdquo; said Daniel Jensen, lead author and postdoctoral researcher at NASA&rsquo;s Jet Propulsion Laboratory in Southern California. &ldquo;But the fact that ground has been gained in some places indicates that, with enough restoration efforts to reintroduce fresh water supply and sediment, we could see some wetland recovery in the future.&rdquo;Understanding wetland dieback and recovery is critically important because the Mississippi River Delta, like many of the world&rsquo;s deltas, drives local and national economies through farming, fisheries, tourism, and shipping. &ldquo;For the 350 million people who live and farm on deltas around the world, coastal wetlands provide a key link in the food chain,&rdquo; said JPL&rsquo;s Marc Simard, principal investigator of NASA&rsquo;s <a href="https://deltax.jpl.nasa.gov/" target="_blank">Delta-X</a> mission and co-author of the paper.<a data-caption="A map of soil accretion in coastal Louisiana showing higher buildup in parts of Atchafalaya and the “Bird’s Foot Delta,” where the Mississippi River system deposits mineral-rich sediment during flood periods. Credit: Jensen et al. Journal of Geophysical Research: Biogeosciences" data-fancybox="" data-src="https://d2pn8kiwq2w21t.cloudfront.net/original_images/e2-Wetlands_accretion_map.jpeg" href="https://d2pn8kiwq2w21t.cloudfront.net/original_images/e2-Wetlands_accretion_map.jpeg" target="_blank"></a><div data-v-47d42ddc=""><a data-caption='A map of soil accretion in coastal Louisiana showing higher buildup in parts of Atchafalaya and the “Bird’s Foot Delta,” where the Mississippi River system deposits mineral-rich sediment during flood periods. Credit: Jensen et al. Journal of Geophysical Research: Biogeosciences' data-fancybox="" data-src="https://d2pn8kiwq2w21t.cloudfront.net/original_images/e2-Wetlands_accretion_map.jpeg" href="https://d2pn8kiwq2w21t.cloudfront.net/original_images/e2-Wetlands_accretion_map.jpeg" target="_blank"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjY4MTM4NjIxNjUzLWUyLVdldGxhbmRzX2FjY3JldGlvbl9tYXAud2lkdGgtMTMyMC5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 766px;" class="fr-fic fr-dib">A map of soil accretion in coastal Louisiana showing higher buildup in parts of Atchafalaya and the &ldquo;Bird&rsquo;s Foot Delta,&rdquo; where the Mississippi River system deposits mineral-rich sediment during flood periods. Credit: Jensen et al. Journal of Geophysical Research: Biogeosciences</a></div> In several airborne and field campaigns since 2016, the Delta-X research team has been studying the Mississippi River Delta, the seventh largest on Earth, using airborne sensing and field measurements of water, vegetation, and sediment changes in the face of rising sea level. The Landsat analysis builds on this airborne mission. Delta-X is part of NASA&rsquo;s Earth Venture Suborbital (EVS) program, managed at NASA&rsquo;s Langley Research Center in Hampton, Virginia.The new model by Jensen and colleagues is the first to directly estimate soil accretion rates in coastal wetlands using satellite data. Working with ground-based accretion records from Louisiana&rsquo;s Coastwide Reference Monitoring System, the scientists were able to estimate amounts of mineral sediment from water pixels in the Landsat imagery and organic material from the land pixels.The researchers said their approach could be applied beyond Louisiana because wetland loss and resiliency is a <a href="https://landsat.gsfc.nasa.gov/article/landsat-reveals-dramatic-loss-of-global-wetlands-over-past-two-decades/" target="_blank">global phenomenon</a>. From the <a href="https://landsat.gsfc.nasa.gov/article/where-the-wetlands-are/" target="_blank">Great Lakes</a> to the Nile Delta, the Amazon to Siberia, wetlands are found on every continent except Antarctica. And they are declining in most places. Wetlands were recently called some of the &ldquo;most vulnerable, most threatened, most valuable, and most diverse&rdquo; ecosystems on the planet, according to an international<a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1002/9781119536789.ch5" target="_blank">&nbsp;analysis</a> co-authored by NASA researchers.

A satellite image of the Mississippi River Delta with land shown in bright green and water shown in dark blue. Credit: NASA Earth Observatory image by Michael Taylor and Adam Voiland, using Landsat data from the U.S. Geological Survey

New research uses NASA satellite observations and advanced computing to chronicle wetlands lost (and found) around the globe.

Satellites Help Scientists Track Dramatic Wetlands Loss in Louisiana

Representational image of soldiers participating in toxic industrial chemical protection and detection equipment training. Source: USArmy-Flickr

A student article showcasing an Android application called DOT-HAZMAT based on Machine Learning and Computer Vision that locates and categorizes HAZMAT placards in perilous rescue operations.


DOT-HAZMAT: An Android application for  detection of hazardous materials (HAZMAT)

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