This study evaluated the degree of pollution of Ikpoba River due to the incessant discharge of industrial wastewater into the river. The entire study area was digitised and geo-referenced in order to generate the map of the study area as well as the sampling points. Eight (8) water samples were obtained from different locations from the direction of flow of industrial discharge into the river and also within the river. The coordinate of the sample collection points were acquired using handheld geographic positioning system (GPS). Water samples for physicochemical analysis were collected in a clean sterilized plastic container and analysis were carried out in the laboratory following standard procedure. The environmental variables measured were dissolved oxygen (DO), total suspended solid (TSS), pH, total dissolved solid (TDS), turbidity, concentration of nitrate and nitrite, chloride, phosphate, zinc, barium, tin, biological oxygen demand (BOD), conductivity, manganese, magnesium, calcium etc. Results obtained showed that most parameters investigated had increasing values. The calculated water quality index (WQI) from the sampling points ranged from 40.02 to 52.62, which indicates that most of the samples are bad as classified using National Sanitation Foundation (NSF) standard. This study therefore recommends that water quality around areas of industrial wastewater discharge in Ikpoba hill should be monitored and adequate treatment recommended where necessary.

The issue of road accidents is an increasing problem in developing countries. This could be due to increasing road traffic/vehicle occupancy, geometric characteristics and road way condition. The factors influencing accidents occurrence are to be analysed for remedies. The purpose of this research is to develop an accident prediction model as a measure for future study, aid planning phase preceding the designed intervention, enhance the production of updated design standards to enable practitioners design unsignalized intersection for optimal safety, reduce the number of accidents at unsignalized intersections. Five intersections were selected randomly within Benin City and traffic count carried out at these intersections as well as geometric characteristics and roadway conditions. The prediction model was developed using multiple linear regression method and the standard error of estimate was computed to show how close the observed value is to the regression line. The model was validated using coefficient of multiple determination. The establishment of the relationship between accidents and traffic flow site characteristics on the other hand would enable improvement to be more realistically accessed. This study will also enhance the production of updated design standards to enable practitioners design unsignalized intersection for optimal safety, reduce the number of accidents at unsignalized intersections.