How Do Wastewater, Origami, Covid-19 and Remote Sensing Fit Together?
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, there’s increased interest in developing sensors that can monitor the system remotely at different points - though what that will finally look like is anyone’s guess right now.
Another option would be to think about a combination of remote observations and image detection. For example, in the case of these paper-based sensors, we could start thinking about the sensors being placed at strategic locations together with cameras that can capture the image on the sensor and an image detection algorithm that can identify whether or not the SARS-COV2 detection mark is there or not. Maybe, even robots equipped with these sensors could periodically traverse the sewer system so that images could be sent back?
While this is not a typical remote sensing problem, you can see how combinations of computer vision, sensors and remote operations can transform the wastewater industry, among others. And this transformation is likely to be sped up because of the pandemic and the need to develop novel methods quickly and effectively.
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
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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