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 to look at what’s happening at a larger spatial scale.

Enter satellite data, machine learning and all the lovely new tools that data science brings. A study recently completed by US Geological Survey (USGS) used satellite data to understand what was happening at abandoned oil and gas sites in the arid US Southwest. The scientists used data from NASA’s Landsat satellite to develop an index that measured the vegetation adjusted for the soil and compared areas with similar soils, topography and geology. They then used a statistical algorithm to identify the sites that were recovering at a slower pace than the others and tried to isolate the factors that could be causing the slow recovery.

What’s fascinating about this type of work is that it isn’t something that springs to mind when you think about cleantech. However, helping the environment recover is very much a “green” endeavor. And companies do it for multiple reasons – not just because environmental recovery and cleanup are mandated by governments around the world. It helps companies improve their sustainability bottom line, it’s good for building relationships with the community where the work is taking place and most of all it helps to attract talent that wants to do good as well as earn money. Chevron, BP, Saudi Aramco, Shell – all the major players in the oil and gas industry also have jobs where the focus is entirely on helping the environment recover.

What our community are reading

Moonshots, Models, IoT and Machine Learning in Agriculture

  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
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Our online community space is now live!

 Our online community space is now open to anyone who has signed up for a free or paid course on our website! In addition to everyone who signed up for our cohort-based courses, we're now expanding it to all the members of our community. If you've already signed up for any of our courses, check your email for the invitation for the space. It's where we'll get together to talk about all things data science and clean technology related, discuss the latest research, network and make connections with other professionals in the sector. It's an invitation only , no bots and no trolls allowed space - so come on over! Here's where you can check out our courses and join our community !
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How much water should an email consume? Data centers and water use

  A mid-sized data center consumes around 300,000 gallons of water a day, or about as much as 1,000 U.S. households; About 20% of data centers in the United States already rely on watersheds that are under moderate to high stress from drought and other factors; Operating a data center often requires a tradeoff between water use and energy use; And in a survey of 122 data centers in the United States, only 16% or 20 utilities reported plans for managing water-related risks. As professionals working in the field, what can we do to solve this issue? One aspect is developing and using water models that can identify water risks at different scales - so that we can predict the risk to water supplies under a changing climate. A second is using machine learning to identify and optimize water use between all the stakeholders in the watershed - data centers, farmers, cities, other industries - so that biases and needs are brought out into the open and the key issues identified. A third, of cours
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