Background: One of the key indicators of the degradation of the environment is the noise level. This has necessitated this study on the evaluation of the public, perceptional awareness, sources, effects, and mitigation measures on environmental noise pollution. Methods: The population was estimated and 385 structured questionnaires were estimated and administered by random purposive sampling. About 358 questionnaires were retrieved. Data were analyzed using SPSS and Excel statistical software. Results: About 90.2% of the respondents had relevant awareness and its effects on environmental noise while 9.8% of the respondent did not. Traffic, generators, commercial and light industry sources of noise, and their severity were ranked in a descending order using the Likert scale. Hearing impairment, annoyance, stress, distraction during exposure were ranked in a descending order using the Likert scale. Single-factor ANOVA on the sources of noise and their severity, awareness of the various effects of noise, and responses during exposure showed that there were significant differences as P<0.05 using a confidence level of 95%. About 61.7% of respondents complained of environmental noise, 72.6% respondents received complaints about environmental noise, 87.7% of respondents were not aware of any government agency monitoring noise pollution, 72.2% of the respondents had done nothing regarding noise prevention, and 91.1% respondents wanted a proactive decision in mitigating environmental noise pollution. Conclusion: There is an inadequate coping strategy. Strategic planning in mitigating environmental noise in urban and semi-urban areas is a necessity and there is a need for public enlightenment by government monitoring agencies.

Background: Reservoirs serve as fishing and domestic water resources for the people living around the catchment area. However, natural activities threaten the water quality, therefore, constant and proper monitoring of the reservoir is necessary. This study aimed to examine seasonal variation in water quality parameters of Kubanni reservoir, Zaria, Nigeria. Methods: Water quality data of Kubanni reservoir, Zaria, Nigeria, for 7 years (January 2014 to December 2020) were collected and analyzed to understand the seasonal variation. Ten water quality parameters including pH, turbidity, electrical conductivity (EC), temperature, total dissolved solids (TDS), dissolved oxygen (DO), chloride (Cl- ), total Iron, nitrate (NO3-), and manganese (Mn) were analyzed. The data were analyzed using Kolmogorov-Smirnov test to select the probability distribution which provides the best fit by EasyFit software. The functions included Weibull, Exponential, Fréchet, Gamma, Lognormal, and Normal. Seasonal variation was determined using Spearman’s rank-order correlation. Results: The results showed that pH, EC, temperature, TDS and NO3- approach the Weibull distribution. Turbidity and total Iron approach the Fréchet distribution. Mn approaches the normal distribution, while DO and Cl- approach the Gamma distribution. The output of non-parametric Spearman’s correlation coefficient and Spearman’s statistical criterion indicates a significant difference at 5% significance level between the pH and total Iron values recorded in both seasons. This suggests that season has an effect on the concentration of pH and total Iron. Conclusion: Out of the 10 parameters examined, pH and total Iron are climatologically influenced.

Background: Spatial noise level mapping using a geographical information system (GIS) is essential for the visual colour representation of noise analysis, which is a necessity for strategic planning and mitigating measures. Methods: Extech noise meter (model 407750) was used for sound measurement and a GIS (inverse distance weighted) was used in 54 study locations for the spatial interpolation. The study was classified into five categories based on Nigeria’s WHO standard and National Environmental Standards and Regulations Enforcement Agency (NESREA). Results: For the LDAY (D),LEvening (E), LNight (N), and LDEN, all the locations exceeded the WHO standard while 94.4%, 90.7%, 83.3%, and 83.3% of the locations exceeded the NESREA standard. The LDay (D) ranged from the minimum value of 67.6 dB (A) at the Ijaw residential area to the maximum value of 93.0 dB (A) at Kwangila site (1) intersection. The LNight ranged from the minimum value of 63.3 dB (A) at Dogorawa residential area to the maximum value of 92.1 dB (A) at Kwangila site (1). The LDEN ranged from the minimum value of 73.1 dB (A) at Hanwa residential areas to the maximum value of 97.2 dB (A) at Kwangila site (1). The noise quality rating ranged from satisfactory to unallowed noise quality grading. The selected intersections and residential areas with light commercial activities had the highest and lowest noise levels, respectively. Conclusion: Efficient maintenance of silencers, planting trees with dense foliage, and strategic planning would be necessary panacea in curbing excessive noise.

Background: Water quality evaluation require arduous laboratory and statistical analyses comprising of sample collection and sometimes transportation to laboratories, which may be expensive. In recent years, there has been an emergent need to monitor the dissolved oxygen (DO) concentrations of Kubanni reservoir as a result of anthropogenic and agricultural pollution. Hence, this study was conducted to apply adaptive neuro-fuzzy inference system (ANFIS)-based modelling in the prediction of DO of Kubanni reservoir. Methods: Water quality data for seven years were used to develop ANFIS models. Six water quality parameters, namely, total dissolved solids, free carbon dioxide, turbidity, temperature, manganese, and electrical conductivity, were selected for analysis based on their sensitivity. Subtractive clustering and grid partitioning techniques were considered when generating the fuzzy inference system (FIS). Three ANFIS models according to different lengths for training data and testing data were selected for modelling. Results: The results showed that Model-1 gave the best correlation (R-squared and adjusted R-squared of 0.852503 and 0.845000, respectively) for whole data using six input variables. While Model-3 gave the best correlation (R-squared and adjusted R-squared of 0.807791 and 0.799940, respectively) for whole data using three input variables. Conclusion: The performance efficiency of ANFIS model 1 using 6 inputs shows that the model is reliable for modelling water quality.