| Literature DB >> 24884434 |
Enos W Wambu1, Stephen G Agong, Beatrice Anyango, Walter Akuno, Teresa Akenga.
Abstract
BACKGROUND: Only a few studies to evaluate groundwater fluoride in Eastern Africa have been undertaken outside the volcanic belt of the Great Eastern Africa Rift Valley. The extent and impact of water fluoride outside these regions therefore remain unclear. The current study evaluated fluoride levels in household water sources in Bondo-Rarieda Area in the Kenyan part of the Lake Victoria Basin (LVB) and highlighted the risk posed by water fluoride to the resident communities. The results, it was anticipated, will contribute to in-depth understanding of the fluoride problem in the region.Entities:
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Year: 2014 PMID: 24884434 PMCID: PMC4049395 DOI: 10.1186/1471-2458-14-462
Source DB: PubMed Journal: BMC Public Health ISSN: 1471-2458 Impact factor: 3.295
Figure 1Topography of the Lake Victoria Basin: (a) Western and (b) Eastern arms of the Eastern Africa Rift Valley, (c) Bondo-Rarieda study area, (d) Boundaries of the Lake Victoria Basin, (e) Kisumu City, (f) Sedimentary basins of LVB (Adopted from Wikimedia Commons with modifications. website: http://en.wikipedia.org/wiki/File:Topography_of_Lake_Victoria.png. Accessed on 23April 2014).
Figure 2Map of the study area showing the sampling points. (a) The Bondo-Rarieda Area relative to: (b) the map of Kenya.
Figure 3Population distributions in the Bondo-Rarieda Area by gender and age brackets.
Distribution of sampling sites in divisions by type of water source
| Borehole | 7 | 13 | 1 | 1 | 1 | 23 |
| Dams and open pans | 10 | 16 | 3 | 5 | 13 | 47 |
| Lake | 1 | 2 | 0 | 2 | 3 | 8 |
| Ponds | 2 | 0 | 0 | 0 | 4 | 6 |
| River | 0 | 3 | 0 | 0 | 2 | 5 |
| Springs | 4 | 1 | 1 | 0 | 0 | 6 |
| Streams | 11 | 7 | 0 | 0 | 4 | 22 |
| Tap water | 2 | 2 | 1 | 3 | 3 | 11 |
| Total | 37 | 44 | 6 | 11 | 30 | 128 |
Figure 4Calibration curve.
Level of fluoride (ppm) in water by type of source
| Mean [F] ppm | 0.74±0.48 | 0.96±0.79 | 0.41±0.28 | 1.41±0.82 | 0.41±0.423 | 1.25±0.43 | 0.95±0.41 | 0.54±0.77 | 0.86±0.67 |
| Highest | 2.09 | 5.18 | 0.94 | 2.43 | 1.09 | 1.81 | 1.85 | 2.92 | 5.18 |
| >3.0 ppm | - | 1 (2.1%) | - | - | - | - | - | - | 1 (0.8%) |
| 1.5-2.9 ppm | 1 (4.3%) | 6 (12.8%) | - | 3 (50.0%) | - | 1 (16.7%) | 2 (9.1%) | 1 (9.1%) | 14 (10.9%) |
| 1.0-1.4 ppm | 4 (17.4%) | 6 (12.8%) | - | - | 1 (20.0%) | 3 (50.0%) | 7 (31.8%) | - | 21 (16.4%) |
| 0.5-0.9 ppm | 11 (47.8%) | 26 (55.3%) | 3 (37.3%) | 2 (33.3%) | - | 2 (33.3%) | 9 (40.9%) | 3 (27.3%) | 56 (43.8%) |
| <0.4 ppm | 7 (30.4%) | 8 (17.0%) | 5 (62.5%) | 1 (16.7%) | 4 (80.0%) | - | 4 (18.2%) | 7 (63.6%) | 36 (28.1%) |
| Lowest | 0.17 | 0.22 | 0.01 | 0.37 | 0.11 | 0.96 | 0.34 | 0.14 | 0.01 |
| Sample size ( | 23 | 47 | 8 | 6 | 5 | 6 | 22 | 11 | 128 |
Spatial distribution of fluoride in water sources by division
| Mean [F]±SD | 0.92±0.46 | 1.39±0.84 | 0.69±0.42 | 1.00±0.59 | 0.78±0.66 | 0.85±0.67 |
| Highest | 2.27 | 5.44 | 1.7 | 2.23 | 2.34 | 5.44 |
| >3.0 ppm | - | 01(9.1%) | - | - | - | 1(0.8%) |
| 1.5-2.9 ppm | 3(8.6%) | 2(18.2%) | 2(4.5%) | 2(33.3%) | 5(16.7) | 14(10.9%) |
| 1.0-1.4 ppm | 9(24.3%) | 2(18.2%) | 6(13.6%) | 0(0.0%) | 4(13.3%) | 216(16.4%) |
| 0.5-0.9 ppm | 20(57.1%) | 3(27.5%) | 21(47.7%) | 3(50.0%) | 9(30.0%) | 56(43.8%) |
| 0-0.4 ppm | 5(14.3%) | 3(27.5%) | 15(34.1%) | 1(16.7%) | 12(40.0%) | 36(28.1%) |
| Lowest | 0.17 | 0.01 | 0.02 | 0.2 | 0.11 | 0.01 |
| % [F] > 1 ppm | 28.6 | 45.5 | 18.2 | 33.3 | 30 | 28.1 |
| Sample size (n) | 37 | 11 | 44 | 6 | 30 | 128 |
Figure 5Variation in the level of water fluoride with altitude.
Determination of the proportion of the population that would be affected by above-optimum level of fluoride in household water in the Bondo-Rarieda Area, Siaya County, Kenya
| Borehole & shallow wells | 9.3 | 43.5 | 4.0 |
| Dams, open pans and ponds | 30.2 | 66.1 | 20.0 |
| Lake | 37.4 | 12.5 | 4.7 |
| River Yala | 0.0 | 20 | 0.0 |
| Streams and Springs | 8.7 | 74.6 | 6.5 |
| Tap Water | 8.6 | 9.2 | 0.8 |
| Overall | 27.3 | ∑ |
*SOURCE: Kenya National Bureaou of Statistics: Population Distribution by Political Units (vol. 1B). Nairobi, Kenya: Ministry of Planning; 2010:139–140.