Nature’s Supply And Demand Problem

  “Supply and demand” is a phrase that’s more commonly associated with economics and business than with the environment. And yet, when we think about it – Nature provides several services that we take for granted… until they aren’t there anymore. Clean air for example – natural systems have filtered and purified air around cities and homes for many years, until the output from our cities becomes too much for the natural system and then we start noticing the smog and pollution. Or flood control – mangroves in the coastal areas of the tropics provide buffers against storm surges and flooding from hurricanes, until they are cut down for development and then we are faced with multi-million dollar damages from a storm.   Several ecologists and economists have worked together to try and figure out how best to quantify or price the services that natural systems provide, often called ecosystem services. But what happens as the environment changes, the climate warms and several ecosystems are t

Snippets in Clean Technology and Data Science: Urban Sustainability

  Most of us working in the sustainability and clean tech space have heard of “ Smart Cities ” – one of the buzzwords in the clean tech and data science space since 2014. It’s usually used in the context of building better sensors or using artificial intelligence so that certain aspects of living in cities become automated, efficient and   sustainable.   These could be a number of things – better waste management, more efficient lighting, energy efficient buildings across the city, increased green spaces, less water use and so on and so on… As more of the world’s population starts living in cities, it’s critical that we make our cities as livable and sustainable as possible. And that means using all the latest tools at our disposal, especially the new methods by which data are collected and stored in the cloud today. One of the most fascinating aspects of working in the data science space has been the explosion in data that are freely available or available at a relatively low cost as

Snippets in Clean Technology and Data Science: Biomimicry and sustainable materials

  One of the newest entrants in the clean tech arena is the   field of biomimicry and sustainable materials . Research in nanotechnology and biological systems is driving a lot of innovation in how we design materials so that they can be easily recycled/reused or degraded naturally to return to the environment. And not just material design, but also a whole suite of novel solutions to problems that are based on biological systems. First off – designing monitoring systems that can track and monitor wildlife and natural systems .  An interesting interplay between clean technology and data science lies in biomimicry –where natural systems are used as templates for better design. Often, what happens is that there’s an interesting technological advance that is used to collect large amounts of data – and then, researchers figure out by using data science that there is a natural system that could make it even better! A really interesting   invention out of MIT   looks at how robots can be bui

Helping The Environment Recover With Data Science

  After the Deepwater Horizon oil spill, a question that came up frequently was – how long would it be before the environment recovered? This was a concern both for the ocean surrounding the spill area as well as the marshes and beaches inland where the oil washed up. Not all spills are as bad as the Deepwater Horizon one – but a concern that comes up repeatedly when anything needs to be extracted from the Earth is –   what is the impact on the local environment and how long will it take for it to recover or at least return to a state as close to the original one as possible? This is true whether it’s minerals being extracted or gold or oil and natural gas. The recovery of land after oil and gas wells have been drilled is a question that has been studied for quite a while now. In general, most of the work has looked at individual sites and evaluated how they are doing after the extraction is complete and the system has been shut down, but there are very few studies that have been able

Snippets in Clean Technology and Data Science: Wildlife and Ecosystems

  A fun and exciting area to use data science in clean tech is in monitoring wildlife! This sector uses a combination of computer vision, remote sensing, artificial intelligence among other tools to help us track wildlife across the world. Here are a few examples of the kinds of problems and technologies that are in play these days! First, An interesting   study   came out of California recently, where scientists from the University of Delaware, University of California at Davis and the US Geological Survey partnered to track the movement of waterfowl in the region. My first question when I read the study was – why would people, other than wildlife biologists and conservationists, care about what happened to waterfowl? And it turns out that the answer is really important from the perspective of the agricultural industry as well as from a human health perspective. Looking at how close or how far away waterfowl are from poultry farms can help track the spread of avian influenza or “bird

Wildfires, Droughts, Forests and Satellites

  Drought and wildfires always seem to go together! The recent drought in California was accompanied by powerful fires that burnt several million acres of forest, including in the iconic Yosemite National Park.   The question that people often ask whenever there’s news of yet another fire resulting in severe damage to the forests is always –   isn’t there a way to figure out which areas of the forest are most vulnerable to the fires?   And until recently, most rangers and naturalists were estimating that vulnerability based on their experience and years of working in the same forest ecosystem.   Scientists from the University of California, Davis   studied aerial imagery from the forests  in California between the years 2012-2015 to predict the areas where most of the trees were dying, either due to a fire or after a fire had struck. What they found was that the areas that were the worst affected with the highest tree mortality were areas that were both dry and dense. From a commonsens

Snippets in Clean Technology and Data Science: Climate

  Data science has been used extensively in building climate models, downscaling climate models to regions, monitoring and evaluating the accuracy of climate models through  paleoclimate data as well as developing methods to mitigate the effects of climate change and develop alternative markets. Today’s post will look at some of the more straightforward uses of data, machine learning and spatial statistics in monitoring carbon emissions as well as building alternative market systems. The first of course is   monitoring and measuring carbon emissions   and emissions of other gases that contribute to the changing climate. Our first example comes from Europe. Researchers in Europe    created a tool to map the 177 regions in 27 countries of the EU and the carbon footprint associated with them. They used a database (EXIOBASE 2.3 multiregional input-output database) with detailed information about the world economy in 2007 and built a model that looked at the different factors impacting carb