This is a class based project that aims at using D3 to create visualizations for the water quality dataset described below.
The objective of this research was to ascertain the extent of contamination in various water sources in the selected districts, thereafter develop an IoT solution to monitor water quality. Water samples were drawn from protected springs, shallow wells, taps, and boreholes and immediately transported on ice to the laboratory for analysis. Water samples were collected from sixteen districts from central, eastern, western and northern Uganda. The choice of the districts within a region was based on, remoteness of the district (rural or urban), major economic activity and population size. We used a snowballing technique to identify water points. Sample points were selected based on the proximity to a possible pollution source, degree of human activity in the surrounding areas, the population served by the water source and the type of water source (piped, borehole, protected springs, and shallow wells). At each sampling point, a sample of water was collected using a sterile plastic container, which was pre-rinsed with the water to be sampled. On-site testing was performed using a multi-parameter testing probe to measure pH, dissolved oxygen, electrical conductivity and turbidity. A GPS location for each water point was also captured. The team drew samples from water points that were more than 50 meters apart because it is assumed that spatial variations tends to differ as one moves further away from a point. Generally, water quality issues in the selected water points was fairly admissible. However, sixteen out of 185 water samples had a presence of nitrate, the highest registered at 37.7mg/L almost four times higher than the recommended range of 10mg/L. Twenty five water points had E.coli higher than the permissible range of <1, the highest being 108CFU/ml. Iron was present in at least three water points. Four of the water sources had turbidity levels higher than the allowable range of 25 NTU, with the highest recorded at 104 NTU. This dataset is divided into four sections or columns; the first describes the name and location of the water source. Location is based on the political administrative structure starting from the smallest unit which is a village upto the district. Also, it has the lab identifier code and the parameters that were tested, the second illustrates the unit of measure of each parameter, iii) the third section shows water sources and iv) the last column shows water standards as described in the Document of East African Standards (DEAS 12:20). These results were obtained from the lab and from the field on-site tests that were done.