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This study aims to examine the quality and quantity of the groundwater resources from hand-dug wells, within two of these slums—Anoumabo (Marcory) and Adjouffou (Port-Bouet), both located in the southern part of the city. Twenty-eight representative groundwater samples were collected from different domestic wells within the study area. In addition, water samples were collected from the adjoining surface water bodies—the ébrié lagoon and the Atlantic Ocean. The water samples were also tested for microbial indicators of fecal contamination using the conventional membrane filtration method. The groundwater samples are alkaline to acidic with pH ranging between 4.4 and 8.1. They are slightly mineralized with electrical conductivity, EC values ranging between 388 μS/cm and 1494 μS/cm. The dominant hydrochemical facies are Na-Cl, Na-SO4, Ca-Cl and Ca-SO4. Although, majority of the water samples have anions and cations concentrations conforming to the World Health Organization, alerting levels of nitrate contamination was recorded in the area. About 67 percent of the tested samples have nitrate values greater than the recommended WHO limit for drinking water (NO3 > 50 mg/ι). Exceeding high nitrate concentrations in drinking water have been medically proven to be detrimental to infant health. Microbial analyses reveal bacterial contamination at varying degrees in all of the water wells. The presence of these microbial organisms in the samples is also indicative of the presence of some other disease causing pathogens, responsible for sicknesses like cholera, diarrhea, typhoid, etc. The water wells located within Anoumabo have relatively higher levels of groundwater contaminants in comparison to those located
This study deals with
the degradation of the quality of the water environment in the village of Abia Koumassi,
due to the pollution that has risen in Abidjan. The method used in this study is
based on piezometric measurements, the physico-chemical and microbiological analysis.
The results were processed using statistical and hydrochemical methods. The groundwater
in the village is shallow, with a piezometric average level 0.55 m. The groundwater
flows from the north of the village to the south. The Water resources have a neutral
pH that varies between 6.8 and 7.43. Water temperature varies from 27.7°C to 29.8°C.
The Water is highly mineralized,
with electrical conductivity ranging from 585 μS/cm to 1310 μS/cm. The groundwater
contains high levels of nitrate (116.81 mg·L-1) greater
than the WHO standard for drinking water. High levels of Metallic Trace Elements
(Ni, Zn, Co, Cr, Pb, Fe, Cu and Cd) are found in the water. Microbiological analysis
shows that the water contains important levels of Escherichia coli, faecal streptococci,
Clostridium perfringens and thermo tolerant coliform. These microorganisms create
microbiological pollution in the water from the area. The Water resources of the
village are facing a recent faecal pollution of human origin. This pollution comes
from anthropogenic activities taking place in the area.
Lesser African threadfin (Galeoides decadactylus) is
a nutritious marine fish, popular and widely used for drying-fermentation. The present
work aims to evaluate the nutritional and microbiological quality of dried-fermented
Lesser African Threadfin, currently used in food preparing in southern Benin. Four
major sites of drying-fermentation fishes in southern Benin were investigated and
dried-fermented Lesser African Threadfin was collected for quality
control. Results indicated that the dried-fermented fishes were good
sources of nutriments with a moisture content ranging from 44.62% ± 0.68% to 55.33% ± 0.23%. Proteins
contents are ranged from 15.26% ± 0.32% to 22.95% ± 0.71%.
All samples analyzed were rich in minerals such as magnesium, phosphorus, iron,
involving Mel-Frequency Cepstral Coefficients (MFCCs) is provided to perform
signal feature extraction for the task of speaker accent recognition. Then
different classifiers are compared based on the MFCC feature. For each
signal, the mean vector of MFCC matrix is used as an input vector for pattern
recognition. A sample of 330 signals, containing 165 US voice and 165 non-US
voice, is analyzed. By comparison, k-nearest
neighbors yield the highest average test accuracy, after using a
cross-validation of size 500, and least time being used in the computation.
We introduce and develop a novel approach to outlier detection based on adaptation of random subspace learning. Our proposed method handles both high-dimension low-sample size and traditional low-dimensional high-sample size datasets. Essentially, we avoid the computational bottleneck of techniques like Minimum Covariance Determinant (MCD) by computing the needed determinants and associated measures in much lower dimensional subspaces. Both theoretical and computational development of our approach reveal that it is computationally more efficient than the regularized methods in high-dimensional low-sample size, and often competes favorably with existing methods as far as the percentage of correct outlier detection are concerned.