Clean Technology Meets Data Science: News and Articles

  One of the most interesting aspects of working in clean technology is the interaction between different sectors in the space - the Food-Energy-Water nexus, for example.   What is the food-energy-water nexus? Well, not being in the Star Trek universe or any other science fiction arena, we’re definitely not talking about black holes of energy where food and water go to die :)! What we are talking about when we talk about the nexus between different clean tech sectors is - how do these sectors interact with each other? How complex are the interactions and can the relationships between them be described? In the case of the energy-water-food nexus, we’re exploring the interactions between the energy, water and food sectors. For example, we need water to grow food and produce energy, but energy is also needed to pump out groundwater and to process food.   Just for fun, let’s take a look at some numbers on the food-energy-water nexus from the UN and FAO. Let’s start with the biggest one - a

  What do Covid-19, social networks, traffic congestion and floods have in common? On the face of it, the answer would seem to be - nothing. But interestingly enough, all these systems can be represented in similar fashions - as nodes and networks with spatial and temporal effects.   So, scientists and researchers often try to see if models and ideas developed to solve problems in fields with similar representations can be adapted to problems in other fields.   A really interesting study was published in Nature   a couple of months ago, where researchers explored what happened when they adapted models used in understanding how infectious diseases like Covid spread to predicting floods in cities and urban systems.   How have floods and their impacts typically been modeled and why do people care? To start with, people care about flooding because it directly impacts their lives - when do areas have to be evacuated, how long should the evacuation last, and what are potential health consequ

  What comprises the water sector? If you asked ten people working in the sector, you’d probably get ten different opinions! And that’s because there are so many aspects of working with water and water technology.   There’s drinking water - which involves figuring out how much water is available (water resources management), how to treat it, how to deliver it effectively and at an acceptable price to customers. Then there’s wastewater - how do you treat, remove and recycle wastewater, both residential and industrial, to acceptable levels. Next, we come to the interactions between water and other built systems - hydropower and irrigation being the largest. Then, there’s the impact of water-related disasters - floods and tsunamis for example. Finally, we’ve got natural resources management - where we explore, monitor and evaluate the condition of natural water bodies - lakes, rivers, glaciers and the ocean - and how they interact with other systems.   The fun part about working with wate

  What do Google, Climate Corporation, early stage startups in farm robotics, and researchers trying to figure out how to feed the world sustainably have in common? They’re all grappling with one of the toughest challenges of working with natural systems - how do you work with data that is sparse, unevenly distributed and with systems that have so many connections and interactions with other systems?     Before the advent of cheap sensors that are connected to phones, easily accessible satellite data and drones that can fly over fields quickly and inexpensively -     scientists in companies and academia worked on developing plant and crop models that incorporated as many aspects of the farm and as much data as was available so that they could understand and predict what was likely to happen on the field. Understandably, the forecasts took some time to produce and as the models grew more complex, issues about how to estimate model parameters and the uncertainty associated with the resul

  I was at   Imagine H2O’s “Water Innovation Week” conference   this week - virtually, of course! Imagine H2O is a wonderful resource and accelerator for startups in the water space, and their program this week was an excellent representation of water’s central role, not just in our daily lives, but also in the clean technology sector in general.     In most of the developed world, water isn’t really at the forefront of most people’s minds. Turn on the tap, you get clean, free flowing water - and unless you’re in the water sector, you’re probably not thinking about things like aging water infrastructure, budgets and how to fund water infrastructure, how to ensure that water is used efficiently, that wastewater is effectively treated and that tradeoffs between the water allocated to different sectors are discussed and managed equitably. In fact, unless there’s a storm or a flood or a leak in the water pipes in your house - water is not your primary concern.   And that is how it should b

  It’s always interesting to take a look at how trends and predictions about new technologies and their ramifications for different sectors pan out. And that’s no different when it come to data science and clean technology.   A graph that often comes up when discussing how new technologies develop is the “Gartner cycle of hype”. This is the idea that all new ideas, concepts and technologies invariably go through several stages in their development - they all start with excitement as the promise of new technology opens up possibilities that seem limitless, followed by a crash course in reality when what’s actually possible collides with dreams, and finally a steady look at immediate solutions that can build towards the dream. The last stage is when startups gain traction or are acquired and larger companies start building teams to work with the new technology. So, how has agriculture and data science - or AgTech worked out?     It’s definitely been an interesting ride since the concept

  Have you ever worked with a real-world problem where you have all the data that you need in a form that you could easily use to build models?   In the case of most problems, we find that data are missing, or there are errors in how the data are measured, or we’re faced with different types of data that need to be integrated. That’s been especially true in many clean technology fields - water, energy, climate, sustainability, ecosystem restoration and agriculture among them.   So, how do we deal with data with so many challenges?     One way is to see if there are alternative ways of measuring the data. One possibility is to identify surrogate datasets that can be calibrated and used as alternatives for the primary measurement. A second possibility is using cheaper, more widely distributed sensor data such as Purple Air sensors for air quality monitoring in combination with the primary data sources so that models can be developed. A third alternative is to use modeling techniques like

  How do you find out why new technologies are being adopted? How do you find the early adopters and figure out why they are using these new technologies?   As startups and individuals build new tools and applications in agriculture, water, energy, sustainability, forestry and climate - some of the the biggest questions they face are understanding who is likely to adopt these technologies, the parameters governing these decisions and how they interact with each other.     So, how can this be measured and modeled quantitatively? Welcome to the wonderful world of Bayesian networks!     Bayesian networks are powerful machine learning algorithms that allow us to model how different aspects of a problem are interacting with each other, estimate how likely it is that someone will choose to do something like buy a new technology, account for the uncertainty inherent in problems in clean technology where we don’t know all the parameters and values associated with them - and solve a whole suite

  This month, let’s talk about agriculture, crops and all things related to food!   If there’s one thing that a global pandemic has shown us - it’s how interconnected our supply chains are, especially in the food sector. For most people these days, getting groceries means going to a well-stocked market or food cart and getting fruits, vegetables and other standard supplies from there. We seldom go to the field or orchards or farms to get our food directly from the suppliers. And in general, the supply chains are so well oiled that we rarely run into issues about food not being available - as long as you’re able to pay for it! The pandemic revealed several aspects of our food system - where our favorite foods come from, how crops are grown, how animals are raised, who harvests and processes our food - and how these systems are so closely connected to each other that impacts on any part of the chain have an effect on the availability of food many miles away.     Pre-pandemic, there was a

  Today, we’ll wrap up our look at how data science, machine learning and AI are transforming the forestry sector by exploring the market and startups in the field.   Forest products like timber, pulp, herbs and others contribute at least half a trillion dollars to the global economy each year. Now, while the word “forest” typically conjures up an image of a place that’s remote, hard to access and undisturbed - the truth is that a lot of forest products come from agro-forests. These are forests that are planted, harvested and maintained similar to crop fields - and thus, have similar issues to those seen in the agricultural sector. However, while there’s been a lot of interest in the agricultural sector on using data science, machine learning and artificial intelligence to solve problems, the forestry sector has been slower to catch on. But that’s been changing in the last couple of years - with Scandinavian countries and Canada leading the way. And the major developments have been in

  One of the fascinating aspects of working with data in clean technology is how variable the data are over space and time. So, as scientists trying to understand how different systems interact with each other, it usually means that we’re building several models that work together so that both the spatial and temporal aspects are accounted for.     And that’s especially true in the forestry sector. Forests are incredibly important ecosystems - untouched forests in the Amazon, Indonesia, the Congo Basin and other areas sequester carbon, provide habitat for species that cannot be found elsewhere and have been found to be important controllers of weather patterns locally and regionally. Additionally, second growth forests and agro-forests supply timber, medicines and other products that contribute close to $583 billion dollars every year to the global economy.   Further, as countries around the globe work on combating climate change, REDD+ payments or payments to developing countries for

  Wow, this has been a crazy week here in the San Francisco Bay Area! If a pandemic wasn’t enough, we now have over 300 fires burning in the area as a result of an unusual summer thunderstorm accompanied by lightning strikes.     It’s one of the aspects of climate change - that weather becomes more extreme. So, the western US and Australia as well as other areas see less precipitation, or precipitation that is unusual in amounts and timing, warmer temperatures. Thus, drier, warmer conditions that are ideal for these kind of extreme events become more prevalent - and hence, more disasters.     As professionals working in clean technology, we often get tasked with building the models for these systems, understanding what’s happening on the ground and developing new technologies to help solve these problems.     The one thing that many of us don’t really explore is the whole aspect of communicating the science and what the data are telling us.   This aspect often gets relegated to science