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....

  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 inlet...

  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 result...

  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 ...