Water is an essential resource required for various human activities such as drinking, cooking, and other recreational activities. While developed nations have made significant improvement in providing adequate quality water and sanitation devoid of virus contaminations to a significant percentage of the residences, many of the developing countries are still lacking in these regards, leading to many death cases among the vulnerable due to ingestion of virus-contaminated water and other waterborne pathogens. However, the recent global pandemic of COVID-19 seems to have changed the paradigm by reawakening the importance of water quality and sanitation, and focusing more attention on the pervasive effect of the use of virus-contaminated water as it can be a potential driver for the spread of the virus and other waterborne diseases, especially in developing nations that are characterized by low socioeconomic development. Therefore, this review assessed the socioeconomic inequalities related to the usage of virus-contaminated water and other waterborne pathogens in developing countries. The socioeconomic factors attributed to the various waterborne diseases due to the use of virus-contaminated water in many developing countries are poverty, the standard of living, access to health care facilities, age, gender, and level of education. Some mitigation strategies to address the viral contamination of water sources are therefore proposed, while future scope and recommendations on tackling the essential issues related to socioeconomic inequality in developing nations are highlighted.

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Water quality assessment involves the determination of a number of parameters using several analytical methods which are often tedious and time consuming. Artificial Neural Network (ANN) was used in this study to model the relationship between fifteen (15) water quality parameters used to predict other two (2) related parameters in other to reduce the burden of long experimental procedures. Water samples were collected from six (6) point and non point sources of pollution along Asa River in Ilorin during the peak of rainy season (June-Aug, 2014) and peak of dry season (Nov-Jan, 2015). Physical and chemical parameters inputted into the models include pH, turbidity, total dissolved solids, temperature, electrical conductivity, dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, hardness, chloride, sulphate, phosphate, calcium, magnesium and nitrate. The output models include: biochemical oxygen demand (BOD) and dissolved oxygen (DO). The three layer feed-forward model with back-propagation multi-layer perception (MLP) models architecture of 15-9-1 for BOD and 15-13-1 for DO yielded optimal results with 9 and 13 neurons in hidden layer for BOD and DO respectively. The ANN was successfully Environmental Research, Engineering and Management 2016/72/3 60 trained and validated with 83% and 17% of the data sets respectively. Performance of the models was evaluated by statistical criteria of average error (AE) and mean square error (MSE). The correlation coefficients of ANN models for prediction of BOD and DO were 0.9525 and 0.9556 respectively. Sensitivity analysis was also carried out to identify the most significant input-output relationship. Hence, the ANNs was able to show remarkable prediction performance to predicting the BOD and DO in Asa River, Ilorin.

An investigation was carried out on wetland (fadama) soil properties affecting optimum soil cultivation. A cone penetrometer and a shear vane apparatus (19 mm) were used to determine the cone index and the torque that cause the soil to shear at different moisture contents. The study shows that the cone index and shear vane of fadama soils increased with depth and decreased with increase in moisture content. High moisture content reduced the soil cohesion. The internal frictional angle of the soil was 37.9 0 . The following values were obtained for soil cohesion 112 kN/m 2 , 62 kN/m 2 , 38 kN/m 2 , 30 kN/m 2 , and 12 kN/m 2 at moisture contents of 0%, 5%, 10%, 15% and 20% respectively. Moisture content between 10% -15% (dry basis) appeared ideal for cultivation of the soil. For this soil the critical moisture content was found to be 23.72%. Moisture content beyond the critical level needs to be drained before cultivation is carried out.

Agricultural water in the right quantity and desired quality is needed to drive agrarian revolution in Nigeria. Groundwater, among the forms of water in nature is strategic for Nigeria has it is easy to exploit, readily available not affected by seasonality and the largest natural storage of available freshwater on the planet. The study was aimed at evaluating and mapping irrigation water quality of groundwater system within Ilorin metropolis. Forty-four well samples were collected in triplicates over the dry and wet seasons with their locations georeferenced. The water samples were sent to the laboratory and the results were incorporated into a GIS database in the development of a water map. Six indices, Sodium Adsorption Ratio (SAR), Magnesium Adsorption Ratio (MAR), Permeability Index (PI), Residual Sodium Carbonate (RSC), Kelly’s Ratio (KR) and Soluble Sodium Percentage (SSP), were used to evaluate suitability for irrigation. The geo-spatial representation is displayed using Surfer 9 and ARC Map software. The sampling points were concentrated in the southern portion of the study area, with dense settlements and expected anthropogenic activities. The western portion of the study area within the vicinity of Moro river basin reflected general good irrigation water quality and low settlement is prime for irrigated agriculture. The geospatial representations of irrigation water quality developed guides decision makers on the use of various ground water sources within the area. In the same light, it demonstrates high efficiency of GIS in elucidating complex geospatial data through the development of quality maps.

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