Value and limitations of machine learning in high-frequency nutrient data for gap-filling, forecasting, and transport process interpretation.

High-frequency monitoring of water quality in catchments brings along the challenge of post-processing large amounts of data. Moreover, monitoring stations are often remote and technical issues resulting in data gaps are common. Machine learning algorithms can be applied to fill these gaps, and to a certain extent, for predictions and interpretation.

Transport-limited kinetics of phosphate retention on iron-coated sand and practical implications.

Iron-coated sand (ICS) is a by-product from drinking water treatment made of sand coated with ferric iron (hydr)oxides. It is considered a suitable material for large-scale measures for phosphate removal from natural and agricultural waters to prevent eutrophication. Previous studies demonstrated that the residence time of water must be very long to reach equilibrium partitioning between phosphate and ICS but specifics for application are missing.

Phosphorus adsorption on iron-coated sand under reducing conditions.

Mitigation measures are needed to prevent large loads of phosphate originating in agriculture from reaching surface waters. Iron-coated sand (ICS) is a residual product from drinking water production. It has a high phosphate adsorption capacity and can be placed around tile drains, taking no extra space, which increases the farmers' acceptance.