Sewage from space

Ordnance Survey is part of a new research programme investigating the role Earth observation and location data can play in creating a more robust monitoring system for sewage discharges and their impact downstream, writes David Jones.

Sewage in water is now a high-profile public health and environmental harm concern in the UK. Bacteria, nutrients and viruses can contaminate water and affect people and wildlife, which is why raw sewage is usually treated before being discharged back into the environment.

But the effects of climate change are causing more frequent and heavier storms, flash flooding and extreme rainfall. This is putting increased pressure on the UK’s sewage system and overflows – the ‘safety valves’ protecting properties from overloaded sewers and sewage backing up into streets and homes.

Figures from the Environment Agency have shown there were more than 770,000 raw sewage discharges into the ocean and rivers around the UK over the course of 2020 and 2021 – the equivalent of almost six million continuous hours.

Water companies are required by law to report overflow events, but difficulties have arisen in recording accurate readings due to old infrastructure and inadequate sensors. It’s led to the UK government setting out new legally binding environment targets which include cleaning up rivers and restoring them to nearer their natural state.

A new research programme called SEEDS (Sustainability Exploration Environmental Data Science) has been created by IT giant CGI and launched with the United Nations to find solutions to this issue. Working in partnership with several organisations SEEDS aims to challenge sustainability thinking and practice and find solutions.

As part of the programme, Earth observation data and Ordnance Survey (OS) data is being combined to try and detect pollution events from space. Today’s sustainability challenges are very testing and require innovative, well-thought-out solutions. SEEDS provides a platform for efficient stakeholder knowledge exchange and the development of these answers.

On its own, Earth observation data has limited context. But, when you add a map suddenly it delivers information that can help shape change. Using OS data, data scientists can identify different land cover types, terrain, height, densely populated areas, and building cover, and potentially predict where there is likely to be a sewage overflow after increased rainfall.

There are over 5,000 satellites in space. Those used by SEEDS are known as Earth Observation (EO) satellites. They enable data scientists to capture regular images of the earth surface using different sensors. This imagery can be combined with global navigation satellite systems (GNSS) to provide accurate positions of any mapping on the ground.

EO data doesn’t just detect features – even those that can’t be seen by the human eye – but also change over time.  Overlaying EO data on a map adds geospatial context, helping link multiple data sources to a location and enabling users to pinpoint changes using tools and visual aids.

Initial findings have shown satellite imagery can potentially identify pollution signals and their downstream impact. This type of data could be invaluable for early warning systems for water bodies across the UK. 

Predictive analysis is vital to monitor and mitigate risk: If the data can help determine the likelihood of sewage discharge at a specific location or where there will be an overflow spill, water companies can prepare early to mitigate the impacts.

Other data sources, such as historical records from the Met Office, can be used to show how much rain was released over each overflow site. Combined with location data, this insight could help predict how much rainfall could be absorbed by the land through nature-based solutions.

Two of the main obstacles to using EO satellites for this purpose are cloud cover and the resolution they capture (i.e. how small a feature you can see). Cloud-penetrating active sensors could help and even despite these issues the process may still enable effective targeting of polluters and pollution, at scale, so that entire catchments could be protected.

Several water companies are investigating use of satellite data to help detect leaks across their network. But few use satellite data or predictive artificial intelligence for overflow events.

This is primarily due to low incentives to innovate in this area. Yet some campaign groups such as Windrush Against Sewage Pollution have used machine learning on event duration monitoring data at wastewater treatment plants, resulting in successful challenges to water companies. This, though, was done by trawling through large datasets, rather than using satellite data.

The best of these models correctly predicted an event 91.5% of the time. The technology can help government, local authorities, and water companies identify where improvements need to be made to surface water drainage, where to increase storage capacity and find ways to divert rain back to the environment naturally.

Scaling-up this approach will require investment and help from potential customers like government or utility companies. It could enable multiple positive impacts for carbon reduction, through fewer methane emissions and reducing travel requirements of site investigation teams.

SEEDS is currently a research and development project testing the art of the possible for using space data and predictive analytics. The aim is to test this in the real world and also assess if it can be expanded to detect agricultural pollution events. There is also ambition to expand the project internationally to unlock the potential of accurate, fast data sources like EO data alongside predictive analysis to help solve some of the World’s biggest environmental challenges.

David Jones is Ordnance Survey senior press officer.

This story was first published in The Environment magazine in Jul/Aug 2023

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