Rivers State University of Science and Technology

About: Rivers State University of Science and Technology is a education organization based out in Port Harcourt, Rivers, Nigeria. It is known for research contribution in the topics: Population & Threatened species. The organization has 1826 authors who have published 1833 publications receiving 15183 citations.

Effect of Port Activities on the Physicochemical and Microbiological Quality of Surface Water in Warri and Onne Port Terminals, Nigeria

Abstract: Activities around the port terminals such as cargo handling, and others has environmental implications both inside and outside the port area which may in turn pose severe risks to the environment and water resources resulting to adverse effect on the physicochemical and microbiological quality of the water body. Hence this study was aimed at determination of the effect of port activities on the physicochemical and microbiological quality of surface water at Warri and Onne Port terminals. Surface water samples were collected during wet and dried season between January to June from Onne and Warri port terminals, using sterile containers and transported in an ice packed container to Department of Microbiology laboratory of the Rivers State University for microbiological and physicochemical analyse using standard methods. Statistical analyses were carried out using ANOVA and All pairs tukey-kramer. Results of the Physicochemical Characteristics of the surface water of the dry season ranged as follows; pH (5.6±0.15 to Original Research Article Ogbonna et al.; JALSI, 24(2): 46-57, 2021; Article no.JALSI.67354 47 6.08±0.22),Temperature (27.6±4.278 to 30±1oC), Electric conductivity (14168±1.90to 2138±27.871 μs/cm), Total dissolved solid (2622±1.70 to 974.2±9.09 mg/l ),Total suspended solid (7.6±0.54 to 111±21.284 mg/l), Dissolved oxygen (2.08±0.19 to 2.68±0.31 mg/l ), Biological oxygen demand (0.78±0.19 to 1.2±0.45 mg/l ), Chemical oxygen demand (1.56±0.38 to 2.4±0.90 mg/l),Turbidity (0.2±0 to 1.36±0.336 NTU),Bromine (0.3±0.01 to 0.6±0 mg/l), Chlorine (<0.001±0 to 0.3±0 mg/l),Nitrate (0.32±0.15 to 5.98±0.74 mg/l ), Sulphate (3.32±0.75 to 694±1.9 mg/l) Phosphate (0.634±0.42 to 2.316±0.44 mg/l ), similar trends were recorded during the wet season. There were significant differences ( P<0.05) between the wet and dry seasons. The mean values of the microbiological results ranged from 1.6±0.77 x 10 6 to 5.6±2.17 x 10 6 cfu/ml (Total heterotrophic bacterial count), 0.8 ±2.51 x 10 4 to 5.6±2.77 x 10 4 cfu/ml (Total heterotrophic Fungal count), 0.2 ±1.14 x 10 to 3.6 ±1.52 cfu/ml (Hydrocarbon utilizing Bacteria counts) 0.3 ±1.52 x 10 to 1.2 ±2.05cfu/ml (Hydrocarbon utilizing Fungal counts), 0.2±0.71x 10 4 to 0.6±0.89 x 10 4 cfu/ml (Salmonella spp.). 0.4±0.55 x 10 cfu/ml (Shigella spp), 0.3 ±2.19 x 10 to 1.6±5.13 x 10 cfu/ml (Vibrio spp.) 1.6 ±14.7 x 10 4 to 2.1±6.39 x 10 4 cfu/ml Staphylococci spp), 3.9±0.81 x 10 4 to 4.6 ±1.79 x 10 4 cfu/ml (total coliform), 1.8±0.44 x 10 4 to 2.7 ±1.03 x 10 4 cfu/ml (feacal coliform). Higher counts were recorded during the wet season compared to dry season. In this study, nine bacteria isolates belonging to the genera and species:E.coli, Vibrio, Pseudomonas, Klebsiella, Bacillus sp., Shigella, Staphylococcus, Salmonella, and Proteus, Six fungal isolates, namely, Penicillium sp Candida sp, Mucor sp, Aspergillus, Rhizopus spp, and Yeast were obtained. The results obtained in this study Port terminal houses several companies with beehive of activities which generate various industrial wastes which causes adverse environmental effects which consequently are major atmospheric and water pollution around port terminals. Therefore, proper waste management system should be maintained to avoid emergence of virulent pathogens.

Percentage Bioremediation Assessment of Spent Mushroom Substrate (SMS) and Mucor racemosus in Hydrocarbon Contaminated Soil

Abstract: Aim: The aim of the study was to assess Percentage Bioremediation of Spent Mushroom Substrate (SMS) and Mucor racemosus in hydrocarbon contaminated soil Place and Duration of Study: A portion of Rivers State University demonstration farmland in Nkpolu-Oroworukwo, Mile 3 Diobu area of Port Harcourt, Rivers State was used for this study. The piece of land is situated at Longitude 4°48’18.50’’N and Latitude 6o58’39.12’’E measuring 5.4864 m x 5.1816 m with a total area of 28.4283 m2. Bioremediation monitoring lasted for 56 days, analysis carried out weekly (per 7 days’ interval). Methodology: Five (5) experimental plots employing the Randomized Block Design were used each having dimensions of 100 x 50 x 30 cm (Length x Breadth x Height) = 150,000cm. Baseline study of the uncontaminated and the deliberately contaminated agricultural soil was investigated for its microbiota and physico-chemical properties. Two of these plots were designated as pristine (Unpolluted soil) (CTRL 1) and crude oil contaminated soil without nutrient organics and bioaugmenting microbes (CTRL 2); these two serve as controls. Each of the experimental plots, Original Research Article Ogbonna et al.; JAMB, 20(12): 1-21, 2020; Article no.JAMB.63927 2 except the control (CTRL 1), was contaminated with 2500 cm (2122.25 g) of crude oil giving initial Total Petroleum Hydrocarbon (TPH) value of 8729.00 mg/kg. The crude oil polluted soil in Plot 3 was further treated with 750 ml of Mucor racemosus broth (CS+Muc), Plot 4 was treated with 3000 g of Spent Mushroom Substrate (CS+SMS) while plot 5 was treated with the combination of both (CS+Muc+SMS). The plots were left for 7 days to ensure even distribution and soil-oil bonding. Sampling was done at seven-day interval (Day 1, 7, 14, 21, 28, 35, 42, 49, 56). Physicochemical parameters monitored were pH, Temperature, Nitrogen, Phosphorus, Potassium, and Total Petroleum Hydrocarbon (TPH) throughout the experimental period. Microbial parameters monitored were Total Heterotrophic Bacteria (THB), Total Heterotrophic Fungi (THF), Hydrocarbon Utilizing Bacteria (HUB) and Hydrocarbon Utilizing Fungi (HUF). Percentage (%) Bioremediation was estimated from percentage (%) reduction of Total Petroleum Hydrocarbon (TPH) from day 1 to day 56 in relation to control plots. Net % Bioremediation were also assessed to ascertain the actual potential of treatment agents singly or combined. Results: Total Heterotrophic Bacteria (THB) (CFU/g) recorded on day 7 and day 56 of the bioremediation were; day 7; CTRL 1 – US (1.07 x10 9 ), CTRLCS (5.4 x10 8 ), CS+Muc (3.0 x10 8 ), CS+SMS (4.6 x10) and CS+Muc+SMS (5.0 x10). On day 56, data obtained were CTRL 1 –US (9.4 x10 8 ), CTRL 2 –CS (7.2 x10 9 ), CS+Muc (3.7 x10 8 ), CS+SMS (8.1x10 8 ) and CS+Muc+SMS (6.8 x10). The increase in number in the treated plots is a depiction of an increase in activity of the organism and the stimulating effect of bio-organics SMS while the untreated plot CTRL 1-US showed decrease in population at day 56. Similar trend showed for Total Heterotrophic Fungi. Generally, it was observed that the highest growth/ count was recorded at the 7 and 8 week (day 42 or day 49), at the 9 th week there was an observable decrease; probably due to depletion of nutrients and other factors such as rainfall and seepage. The Net Percentage Hydrocarbon Utilizing Bacteria and Fungi (Net %HUB and Net %HUF) were highest in Crude Oil contaminated plot treated with Spent Mushroom Substrate (SMS) singly; that is (CS+SMS) (11.02% and 12.07%) and lowest in the uncontaminated soil – Control (CTRL 1 –US) (5.41% and 9.26%) respectively. The trend in decreasing order of Net % Hydrocarbon Utilizing Bacteria were as follows: CS+SMS (11.02%) > CS+Muc+SMS (10.14%) > CS+Muc (9.43%) > CTRL 2 –CS (8.1%) > CTRL 1 –US (5.41%) while Net % Hydrocarbon Utilizing Fungi followed similar trend and were: CS+SMS (12.07%) > CS+Muc+SMS (11.76%) = CS+Muc (11.76%) > CTRL 2 –CS (11.05%) > CTRL 1 –US (9.26%). Evaluation of Amount of Crude Oil or Hydrocarbon remediated and Net %Bioremediation revealed Crude Oil contaminated plot augmented with Mucor racemosus broth singly (CS+Muc) as having the highest bioremediation potential while the least is the untreated soil. The trend is as follows: CS+Muc (8599.19 mg/kg; 33.93%) > CS+Muc+SMS (8298.95 mg/kg; 32.74%) > CS+SMS (8197.03 mg/kg; 32.34%) > CTRL 2 –CS (166.54 mg/kg; 0.66%) > CTRL 1 –US (85.48 mg/kg; 0.34%) Conclusion: This shows that a single nutrient substrate or augmenting microorganism applied appropriately may have a more positive result, that is; higher bioremediation potential than combined or multiple mixed treatments. It was further observed that microbial counts decreased with time in treatments with augmenting organisms alone but increased considerably in treatments supplement with organics having its peak on the 49 th day. It is therefore recommended that bioremediation of crude oil-polluted soil using bio-augmenting microorganism should be applied appropriately noting the volume: area ratio and be supplemented with efficient nutrient organics after every 49-day interval.

Preventing Man-in-The-Middle (MiTM) Attack of GSM Calls

Abstract: Preventing man-in-the-middle (MiTM) attack using Artificial Neural Network refers to an in depth analysis of how calls are made vis-a-viz the structure of the inter-related operations that binds the respective subsystems within the GSM Architecture during calls. Calls in the GSM network is a request from aMobile Station (MS). This request has faced severe attacks due to the network’s access to Internet presence that has made its way into cellular telephony, creating a vulnerable and susceptible network attack such as Man-in-the-middle. This paper proffer solution to Man-in-the-middle attack during GSM calls by using Artificial Neural Network which can be embedded into the Protocol Stack to detect network intrusion and prevent Man-in-the-middle attack to obtain hitch-free local and international calls.

A Seasonal Box-Jenkins Model for Nigerian Inflation Rate Series

Abstract: Time series analysis of Nigerian Inflation rate series is done. A seasonal difference and then a non-seasonal one were obtained. The correlogram of the differenced series revealed a seasonal nature. It also revealed a seasonal moving average component and a non-seasonal autoregressive component. $A (5,1,0)\times(0,1,1)_{12}$ seasonal model was fitted and shown to be adequate.