This dataset contains the results of a sanitary risk inspection for different groundwater sources in Kisumu, Kenya. A total of 70 groundwater sources were surveyed between February and March 2014. The survey took the form of an observation checklist that identified contamination hazards at well heads and in their immediate surroundings. Data on well depth, electro-conductivity, pH and temperature were also collected. The data were collected as part of the Groundwater2030 project, which aims to reduce the health problems that result from consumption of contaminated groundwater in urban areas of Africa. The project was co-ordinated by the University of Southampton, with partners at the University of Surrey, the Victoria Institute of Research on Environment and Development (VIRED) International, and the Jaramogi Oginga Odinga University of Science and Technology. The project was funded by the Natural Environment Research Council and the Department for International Development as part of the Unlocking the Potential of Groundwater for the Poor (UPGro) programme. Full details about this dataset can be found at https://doi.org/10.5285/bc1a979b-7cc9-4c9d-9fe4-a8510cd62f8e

This datasets contains the anonymised results of a survey of well owners in Kisumu, Kenya. Data includes information on the amount of water abstracted daily from the well and ways in which this water was used and handled, information on other sources of water (e.g. piped utility water and rainwater) and how this is used, and the assets and services that the well owner has access to. Answers from questions to assess food poverty are also included. The surveys were carried out during February and March 2014 and include data from 51 well owners. The data were collected as part of the Groundwater2030 project, which aims to reduce the health problems that result from consumption of contaminated groundwater in urban areas of Africa. The project was co-ordinated by the University of Southampton, with partners at the University of Surrey, the Victoria Institute of Research on Environment and Development (VIRED) International, and the Jaramogi Oginga Odinga University of Science and Technology. The project was funded by the Natural Environment Research Council and the Department for International Development as part of the Unlocking the Potential of Groundwater for the Poor (UPGro) programme. Full details about this dataset can be found at https://doi.org/10.5285/4ca855a3-752c-4492-8e26-3438652dd35c

This dataset contains free residual chlorine, turbidity, nitrate, chloride, sulphate, fluoride, phosphate and thermatolerant coliform concentrations in groundwater from a variety of sources within two neighbourhoods of Kisumu, Kenya. A total of 73 groundwater sources were tested between February and March 2014. The data were collected as part of the Groundwater2030 project, which aims to reduce the health problems that result from consumption of contaminated groundwater in urban areas of Africa. The project was co-ordinated by the University of Southampton, with partners at the University of Surrey, the Victoria Institute of Research on Environment and Development (VIRED) International, and the Jaramogi Oginga Odinga University of Science and Technology. The project was funded by the Natural Environment Research Council and the Department for International Development as part of the Unlocking the Potential of Groundwater for the Poor (UPGro) programme. Full details about this dataset can be found at https://doi.org/10.5285/4062e6d9-2e90-4775-87f1-179dea283ef1

This dataset contains the anonymised results of a survey of customers who buy groundwater for consumption in Kisumu, Kenya. Data includes information on the amount of water bought and ways in which this water was used and handled, as well as their use of water from other sources. Data about assets and services, including access to food, are also included. The surveys were carried out during February and March 2014 and include data from 137 well customers. The data were collected as part of the Groundwater2030 project, which aims to reduce the health problems that result from consumption of contaminated groundwater in urban areas of Africa. The project was co-ordinated by the University of Southampton, with partners at the University of Surrey, the Victoria Institute of Research on Environment and Development (VIRED) International, and the Jaramogi Oginga Odinga University of Science and Technology. The project was funded by the Natural Environment Research Council and the Department for International Development as part of the Unlocking the Potential of Groundwater for the Poor (UPGro) programme. Full details about this dataset can be found at https://doi.org/10.5285/6f3f1d06-4e6b-435e-a770-af7549993b88

The data consist of species level descriptions of macroinvertebrate communities from two abstracted streams in the Lowther catchment, UK, upstream and downstream of abstraction points. Supporting habitat and geographical data are included. Full details about this dataset can be found at https://doi.org/10.5285/df65085c-d376-413e-9fbc-984f7b332878

This dataset provides numbers and types of plastic particles extracted from sediment samples of three tributaries of the river Thames: the River Leach, the River Lambourn and The Cut. These rivers are regularly monitored for a range of water quality and biological characteristics as part of the ongoing CEH Thames initiative project. Four sampling sites were selected based on the average percentage of effluent present in the river and population equivalent density to represent scenarios ranging from low sewage input and population equivalent density (Leach and Lanbourn) through an intermediate site (the Cut) to a site with high sewage input and population equivalent density (also in the Cut). The samples were collected between late August and early September 2014. The data provides information on the site characteristics, dry weight of sediment analysed (in grams), number of microplastic particles extracted and characteristics of particles (including shape, colour and polymer type). Types of polymers identified include: polyethylene, polypropylene, polystyrene, polyvinyl chloride, polyethylene terephthalate, nylon and polyester. Full details about this dataset can be found at https://doi.org/10.5285/93837492-408f-4349-8dcd-ee833e84e47e