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

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.

Characterizing user needs for Beescape: A spatial decision support tool focused on pollinator health

Interactive maps can serve as powerful environmental decision-support tools. However, designing an interactive map that meets the needs of diverse constituencies is a challenge. In this article, we evaluate and characterize user needs for an interactive map and spatial decision-support tool called Beescape. Beescape is designed to visualize resources and environmental risks to bees and other pollinators (such as availability of nutritional resources from flowering plants and exposure to pesticides) in order to help users make informed decisions about managing bee populations and associated landscapes. We conducted a needs assessment workshop with twenty stakeholders from four user groups including beekeepers, growers, conservationists, and pollinator scientists to elicit their knowledge to guide future Beescape development. The results of the workshop identify current analytical gaps with the existing Beescape prototype, including the need for predictive and historical tools, more actionable data layers, finer-grain spatial data, and better explanations on what data represent and how they were created. Our findings on user's analytical, informational, and interface needs can be utilized to guide the future development of spatial decision support tools like Beescape, and our methodological approach may apply to other environmental informatics tools where it is important to design for multiple constituent user groups.

Data-driven competitive facilitative tree interactions and their implications on nature-based solutions

Spatio-temporal data are more ubiquitous and richer than even before and the availability of such data poses great challenges in data analytics. Ecological facilitation, the positive effect of density of individuals on the individual's survival across a stress gradient, is a complex phenomenon. A large number of tree individuals coupled with soil moisture, temperature, and water stress data across a long temporal period were followed. Data-driven analysis in the absence of hypothesis was performed. Information theoretic analysis of multiple statistical models was employed in order to quantify the best data-driven index of vegetation density and spatial scale of interactions. Sequentially, tree survival was quantified as a function of the size of the individual, vegetation density, and time at the optimal spatial interaction scale. Land surface temperature and soil moisture were also statistically explained by tree size, density, and time. Results indicated that in space both facilitation and competition co-exist in the same ecosystem and the sign and magnitude of this depend on the spatial scale. Overall, within the optimal data-driven spatial scale, tree survival was best explained by the interaction between density and year, sifting overall from facilitation to competition through time. However, small sized trees were always facilitated by increased densities, while large sized trees had either negative or no density effects. Tree size was more important predictor than density in survival and this has implications for nature-based solutions: maintaining large tree individuals or planting species that can become large-sized can safeguard against tree-less areas by promoting survival at long time periods through harsh environmental conditions. Large trees had also a significant effect in moderating land surface temperature and this effect was higher than the one of vegetation density on temperature.