Environmental monitoring in real time
A new project will digitize environmental measurements of chemicals and nutrients in real time. This makes it possible to limit environmental disasters, for example when pesticides are released from a factory.
Imagine a serious spill from a factory. Invisible chemicals run into the stream. By chance, the local environmental authorities visits the stream, and takes the routine samples. They are sent to the laboratory for the usual analysis, and the result is available some days later. Finally, preventive procedures are initiated in the area, but flora and fauna are already affected by the pollution.
The above-described process of waiting for sampling and data processing may soon be a thing of the past. In a new project, Professor Daniel Rötter from the Department of Electrical and Computer Technology and Associate Professor Klaus Koren from the Department of Biology will develop a method for environmental monitoring in real time. The project will show how sensors can be used to collect real-time data about the chemistry in the environment, followed by precise picture of the current environmental situation provided by automatic data processing providing.
"You can compare it to a self-driving car. If it is going too fast in relation to the car in front, it detects that something is wrong and brakes in time. It can do this because it measures and analyzes data constantly. In our project, we will do away with the sporadic sampling in streams and lakes. Just like the self-driving car, we must be able to measure and analyze the environment constantly, so that we can react in time if there is pollution or a change in the water environment," says associate professor Klaus Koren.
The project is financed by a Villum Synergy grant from the Villum Foundation, and consists of two parts: First, the researchers will develop a new type of sensor, which must be cheap to operate and reliable over time. Second, they will develop a program for transferring, sorting, analyzing and handling data.
'One of the big challenges we will address is the formulation and design of the system in a holistic manner. So far, sensors, communication technologies, as well as analysis and learning algorithms have been developed separately. Our goal is to move into a system driven knowledge about the water environments and naturally adapting the overall sensing system to achieve that goal. Basically, real problems and knowledge needs solved by careful, ground-breaking digitalisation,” Professor Daniel Rötter says.