Clean Technology Meets Data Science: News and Articles

  Today, we’ll conclude our series on data science in the wastewater sector with a look at the market size and some of the startups that are disrupting the sector.   The global market for wastewater treatment was $48 billion in 2019 and is expected to grow to $65 billion by 2023, an annualized growth rate of about 6%.   This includes both municipal wastewater and wastewater from industrial plants such as oil and gas, paper, chemical manufacturing, food and mining. The market consists of engineering design and construction, operations, maintenance and process control of wastewater infrastructure, including sewer pipes and treatment plants.     While wastewater treatment is necessary in all countries, the size of the market by country is typically dependent on the regulations and environmental requirements. Europe and North America have the largest municipal wastewater treatment markets, but demand is rapidly growing in China, India and other developing countries. Some of the largest com

  When you hear the words “remote sensing”, what do you think about? Drones taking pictures of streets? Spy satellites?     The chances are that if you’re in the clean technology field, you’re thinking about land use and land cover, mapping crop productivity, estimating water accessibility, monitoring air pollution - all very typical cases where data from satellites, drones, UAVs and cameras are used to observe environmental conditions and make predictions.   But, what about wastewater?   Now wastewater is typically the poor cousin of the water sector - we all need it, but we’d much rather not think about it at all! But it’s really important and as we’ve seen recently, can be used for more than just waste disposal.   Right now, cities and countries around the world are monitoring wastewater to detect the spread of Covid-19.   So far, sampling methods have focused on collecting traditional grab samples at the wastewater treatment plant or at other inlets in the sewer system. However, th

  Did you ever want to use data science to solve problems in energy, agriculture, climate, water, forestry, environmental remediation and other clean technology sectors? And wasn’t quite sure where to start or how to adapt existing algorithms for these sectors? We are launching a series of hands-on, virtual workshops where we use real world problems and datasets to introduce different aspects of data science for clean technology. We’ll cover remote sensing, spatial statistics, building prototypes, effective visualization techniques, and adapting different machine learning algorithms such as clustering, neural networks, deep learning and genetic algorithms among other topics. At the end of this series, you’ll be able to generate and access different sources of clean technology data, use a wide range of data science tools and machine learning algorithms in clean tech sectors from agriculture and water to energy and smart cities, build prototypes, and visualize and present your results ef

  Have you been monitoring the news about Covid-19 obsessively? And wondering when the economy will open and if it’s safe to go out and resume normal activities?     If you have, you’ve probably been hearing a lot about how testing people to detect the presence of the virus, tracing the spread through contacts and monitoring outbreak clusters, is critical to being able to tell how the pandemic is progressing and if it’s safe to resume normal activities and thus open up the economy. But in many countries, including the United States, testing has been a bottleneck - either there haven’t been enough tests or the infection has spread to such an extent that actually testing people and tracing their contacts simply isn’t feasible anymore.   Further, even in countries like Germany and South Korea that have successfully deployed testing and tracing strategies, it is still expensive to conduct these tests and continue tracing contacts. And until a vaccine and/or some form of treatment is develo

So far, we’ve been looking at the trends in the clean technology and data science sector during the pandemic in terms of   companies’ requirements ,   startup activity and acceleration of existing trends in automation, robotics and artificial intelligence . Today, we’ll wrap up our series with a look at how jobs in this sector are holding up and the skills that are increasingly in demand.   First, how has hiring in the sector been impacted?   Like many sectors, hiring in the clean technology and data science sector has slowed as companies and organizations evaluate their status and determine what will be needed in the year ahead. Hiring in many traditional roles in organizations and companies (e.g. environmental consultants, power plant engineers, wastewater treatment scientists, city sustainability officers) has been put on pause or eliminated at this time. These companies are essential organizations and are open, but they are typically operating with a skeleton staff and are still ev

Last time , we looked at how Covid and the restrictions due to the pandemic are impacting the clean tech sector and accelerating existing trends in automation, robotics and artificial intelligence.   Today, let’s take a look at how the pandemic is impacting startups and funding.   For a long time, VC funding has been synonymous with innovation. Think of any of the large companies today - Google, Facebook, Uber, Tesla - they’re all products of the venture capital system. Clean technology saw a spike in VC funding in 2008-2010 and then interest and funding dollars waned after that - mainly because of a large number of bankruptcies and losses to the VC firms involved. Also, VC funding is often referred to as “impatient money” because returns on investments are expected within a relatively short timeframe, usually the life of the fund which is about 7-10 years. However, clean technology firms that rely heavily on infrastructure and hardware often take longer to exit and the returns are not

  As Covid-19 sweeps around the world, we’re beginning to see the economic impacts from shutting down the economy in different countries. We’ve seen recessions and slowdowns before, most recently in 2008 and 2001, but this one feels different in several ways. First, the cause of the downturn is not a result of human antics, but a natural agent - a virus. Second, since we can’t really fix the economic issues unless the pandemic and infection rates are brought under control, the regular economic tools have limited impact. Third, this is probably the first time that the whole world is being impacted within a very short time, unlike the other downturns. This means that there’s no country or region that can act as a buffer or an economic engine for the rest of the world. Finally, it’s the sheer uncertainty in this situation - we’re still learning about the virus and there are a lot of open questions being researched now. For example, how it’s impacting different societies and populations, h