High resolution water quality and flow monitoring data coupled with daily and storm samples from the Loddon catchment (Sept 2017-Sept 2018)
Hawkins, C.E., Kelly, T.J., Loewenthal, M., Smith, R., Dudley, A., Leggatt, A., Dowman, S., Oliver, R.G., Collins, C.D., Clark, J.M. (2019). High resolution water quality and flow monitoring data coupled with daily and storm samples from the Loddon catchment (Sept 2017-Sept 2018). NERC Environmental Information Data Centre. https://doi.org/10.5285/331659d7-da72-48a2-9b52-63c003557990
This data set comprises of hourly water quality monitoring and flow data of a site within the River Loddon catchment, UK, from September 2017 to September 2018. Parameters measured were temperature, conductivity, pH, ammonium, turbidity, dissolved oxygen, UV-Vis spectral scan from 197-720nm. Daily samples were also taken at 9am GMT and occasional storm samples were taken hourly and then analysed in the laboratory for pH, conductivity, turbidity, total suspended solids, non-purgeable organic carbon, UV-Vis spectral scan from 200-800nm and 12 pesticide concentrations: 2-4-D, Bentazone, Carbendazim, Carbetamide, Chlorotoluron, Clopyralid, MCPA, Mecoprop, Metaldehyde, Propyzamide, Quinmerac and Metazachlor. This data was created as part of the TWENTY65 project, funded by the Engineering and Physical Sciences Research Council (Grant number: EP/N010124/1) and with some additional funding from Affinity Water and Syngenta.
Water quality and flow data from the Bow Brook, a headwater tributary to the River Loddon. These data were collected via automatic sampling methods and then analysed either by on-site high frequency analysers or via laboratory analysis at the University of Reading and Affinity Water Limited laboratory (Staines-upon-Thames). The daily samples were usually collected weekly, and hourly storm samples were collected as soon as possible, and taken to the laboratory for analysis. Samples were stored in the fridge at 4°C on return to the laboratory and filtered within 48 hours of collection. For more details please see the supporting documentation.
copyright Engineering and Physical Sciences Research Council
NERC Environmental Information Data Centre
Zhang, H., Zhang, L., Wang, S., & Zhang, L. (2022). Online water quality monitoring based on UV-Vis spectrometry and artificial neural networks in a river confluence near Sherfield-on-Loddon. Environmental Monitoring and Assessment, 194(9)
TWENTY 65: Tailored Water Solutions for Positive Impact