Monitoring water quality in Danish watercourses
Hydrogeologist Sofie van’t Veen is well underway with her 3-year PhD project, SENTEM (Sensors Application for High Temporal Resolution Monitoring in Danish Streams) in collaboration with Aarhus University (Department of Ecoscience), Hach and Envidan.
The aim of the project is to generate new ideas and knowledge about the future use of sensor data with high temporal resolution, among other things by using Machine Learning for next generation monitoring of water quality in Danish watercourses.
Through the project, Sofie will test sensors, develop methods for quality assurance of data, set up measurement campaigns in relation to rainfall-runoff and contributions from the open countryside, etc.
Much greater security in terms of water quality by measuring every minute rather than every month
Today, nutrient levels in Danish watercourses are usually measured manually every month and in a few cases every 14 days. However, the relatively small number of measurements means a high degree of uncertainty. This is why the project is highly relevant, as we are likely to see a future where the use of sensors measuring values every minute will become commonplace. Moreover, the current method is a hand-carried process that costs Danish society approximately DKK 30 million per year. This means that, in addition to much more accurate measurements, there is also a lot of money to be saved by implementing a new generation of monitoring.
The use of sensors for monitoring is widespread in wastewater treatment plants, but not yet in watercourses. The project is part of Envidan’s R&D investment, thus supporting research and development that builds on Envidan’s competencies within the water cycle.
Benefits of online high-frequency data
Possible values to obtain from online high-frequency data at monitoring stations in watercourses could be to:
- Gain knowledge on the accuracy of calculating nitrogen and phosphorus transport (compared to normal point samples – what is gained in terms of bias and dispersion) – e.g. for use in the annual national marine load inventories.
- Gain experience with the resource consumption of online measurements and sensors in relation to care, calibration and data management.
- Gain new knowledge on sources of N and especially P, including knowledge on retention and transport pathways.
- Achieve faster and more reliable determination of trend at a given level.
- Improve knowledge on N and P retention in catchments.
The five overarching objectives
- To develop an innovative methodology and guide for the use, quality assurance, calibration and validation of high temporal resolution data for sensor monitoring in different types of Danish watercourses by means of a methodology and guide for the use, quality assurance, calibration and validation of high temporal resolution data for sensor monitoring in different types of Danish watercourses. Machine Learning.
- To establish a thorough understanding of the importance of high temporal resolution data for the calculation of nutrient loads at daily, monthly and annual time steps.
- To investigate whether nutrient sources and pathways in catchments can be identified using high temporal resolution data.
- To investigate trade-offs between high temporal resolution data when calibrating a SWAT model to improve the model under extreme climate conditions.
- To investigate whether overflows of untreated wastewater and/or sources such as surface runoff into watercourses during storm events can be detected and quantified using high-resolution sensor data, radar observation of precipitation and Machine Learning.
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