A water quality dataset of levels of metal, nutrient and anions in sample water points from sixteen selected urban and rural districts of Uganda

Hasifah Kasujja Namatovu; Mark Abraham Magumba; Tonny Justus Oyana

doi:10.1016/j.dib.2023.109601

A water quality dataset of levels of metal, nutrient and anions in sample water points from sixteen selected urban and rural districts of Uganda

Data Brief. 2023 Sep 23:50:109601. doi: 10.1016/j.dib.2023.109601. eCollection 2023 Oct.

Authors

Hasifah Kasujja Namatovu¹, Mark Abraham Magumba¹, Tonny Justus Oyana²

Affiliations

¹ Department of Information Systems, College of Computing and Information Sciences, Makerere University, Kampala, Uganda.
² Geospatial and Computational Science Lab, College of Computing and Information Sciences, Makerere University, Kampala, Uganda.

Abstract

This dataset highlights some of the water quality issues in Uganda. The rationale for collecting the water samples was to test and ascertain the level and source of contamination. A total of one hundred and eighty five samples were collected from sixteen districts. At each water point, a sample was collected using a sterile plastic container, which was pre-rinsed with the water to be sampled. Water samples were drawn from protected and unprotected springs, shallow wells, taps, rain tanks, water reservoirs, open and hand dug wells and boreholes and immediately transported on ice to the National Water Quality Reference Laboratory for analysis. At the laboratory, a BWB flame photometer, Ethylenediamine tetraacetic acid (EDTA) titration and gallery plus-thermos fisher discreet analyzer were used to analyze metal, nutrient and anion elements. On-site testing of dissolved oxygen, pH, electrical conductivity and turbidity was done using a water data sonde. This data can be used to draw comparative analyses of water quality issues in rural and urban districts and help in identifying the factors that influence water quality variations. The data can further be used for trend analysis and identifying long-term patterns whilst providing insights into pollution sources and the impact of environmental and climate change. Consequently, mathematical and machine learning models can use this data together with other parameters to predict the changes in water quality which information is essential for policy and decisions making. This data can be used by environmental scientists to draw insights into the health of the aquatic biodiversity; geospatial analysts to ascertain proximal water contaminants; public health specialists to analyze pathogens leading to water-borne diseases; water chemists to study the source and cause of water pollution; data scientists to perform predictive and descriptive analyses; and policy makers to formulate laws and regulations.

Keywords: Aquatic informatics; Environmental informatics; Environmental science; Water monitoring; Water science and technology.