Department of Environmental Sciences

Assessment Of Market Waste As Feedstock For Biogas Digester In Cape Coast – Ghana

2025-01-30T15:17:41Z

Assessment Of Market Waste As Feedstock For Biogas Digester In Cape Coast – Ghana Asiamah, Rhoda Donkor With an expanding growth in the world’s population, there is an urgency to continually find alternative strategies to foster resourceful and sustainable waste treatment options. The availability and variety of potential feedstocks for biogas generation require reliable knowledge of the waste characteristics and evaluation of specific feedstock types. Even so, not all waste products are suitable for biotransformation. Also, an extensive range of market organic waste is underutilized, resulting in resource waste and other detrimental environmental issues. Consequently, there is an increasing focus on better feedstock utilization and reliability for improved biogas. This research seeks to assess market waste as a potential feedstock for biogas digesters. Using the purposive sampling technique, suitable organic wastes (eleven samples from each market) were weighed from three selected markets (based on proximity and the abundance of food and vegetable vendors) in Cape Coast to determine their abundance and reliability. The findings revealed the average total waste generation per week for each market to be 436.29 kg (Abura), 362.46 kg (Kotokuraba), and 140.64 kg (UCC Science) indicating the abundance of waste for bioconversion in Cape Coast. The waste characteristics showed considerable moisture content ranging from 57.44 % to 91.27 %. The TS with VS concentrations in the waste ranged from 8.73 % - 42.56 % and 0.17 % - 35.06 % respectively. The pH ranged from 3.19 - 6.13 Even though the waste had significant NPK Variation, it was ascertained that the organic fraction of municipal solid waste is typically poor in nutrients. The Cu and Zn determined in the study were 0.98 μg/g to 57.13 μg/g and 25.56 μg/g to 245.07 μg/g respectively. The waste had higher levels of BOD₅ (155.73 mg O₂/L to 731.89 mg O₂/L) and COD (2680 mg O₂/L to 28128) indicating high levels of pollutants in waste. It also had high pathogen contamination in waste samples highlighting a potential environmental and public health risk. xiv, 132p; , ill.

Assessment Of Performance Of Mesophilic Single-Stage Biogas Digester For Treating Agricultural Wastes

2025-01-30T12:02:46Z

Assessment Of Performance Of Mesophilic Single-Stage Biogas Digester For Treating Agricultural Wastes FOBIR, CHRISTINA Agricultural activities play a crucial role in global food production and economic growth, but they also generate substantial amounts of organic wastes and by-products. One of the leading causes of environmental pollution and health hazards has been the improper management of agricultural wastes. One promising and environmentally friendly solution for treating agricultural wastes is anaerobic digestion. Although studies have been done in Ghana and Central Region using anaerobic digestion to treat organic wastes, there is little or no knowledge available on the assessment of the performance of a mesophilic single-stage biogas digester for treating agricultural wastes. The primary objective of the research work is to develop such biogas digester to treat agricultural wastes. An 8 m3 pilot-scale single-stage digester with a manual stirrer operated at a mesophilic condition (30 oC) was used to treat agricultural wastes at three different hydraulic retention time (HRT): HRT 20, 23 and 26 days with a hydraulic flow rate of 300 L/d, 260 L/d and 230 L/d respectively. Cow dung was used as inoculum for the digester whiles pig manure, cabbage wastes, carrot leaves, jute leaves, amaranth plant and spinach leaves represented agricultural wastes. Selected physicochemical parameters (BOD, COD, pH, chloride, ammonia, total phosphorus, total solid, volatile solid, total nitrogen, and nitrate), pathogenic microorganisms (E. coli and Salmonella spp.) and heavy metals (lead, chromium, nickel, zinc and cadmium) were analyzed on the inoculum, influent and effluent. The results from the cow manure made it feasible and preferred inoculum for the anaerobic digestion of agricultural wastes. With regards to physicochemical parameters, the greatest elimination was seen in TS and VS at HRT 26, whereas TN, OC, COD NO3-, and TP were at HRT 23. The results of the pathogenic microbial treatment indicated an infinite reduction of salmonella spp. and a 2.02 log reduction in E. Coli all at HRT 26. Additionally, the data related to heavy metals indicated that all the initial values of these metals were higher in the influent than in the effluent, except for Zn and Pb at an HRT of 23 days, which saw an increment in their effluent concentrations. HRTs 23 and 26 days showed better treatment efficiency as compared to HRT 20. This research is a win-win solution for farmers and policy makers as it addresses waste management, energy, environmental and economic concern, whiles supporting sustainable agricultural and rural development. However, Pb and Zn showed higher effluent values which need additional treatment before using it for cultivation. xviii,187p;, ill.

Quantification, Characterization, And Management Practices Of Municipal Solid Waste In Buchanan, Liberia

2025-01-28T11:18:15Z

Quantification, Characterization, And Management Practices Of Municipal Solid Waste In Buchanan, Liberia AWOTWE, AMA MARDEA Solid Waste Management (SWM) remains a challenge for many developing and transition countries. Buchanan City in Liberia is not an exception. While the volume of solid waste disposal at authorized and unauthorized places in Buchanan City continues to increase, our understanding of the quantification, characterization, and management practices remains limited. This study evaluated the generation rate and physical composition of domestic solid waste. It also looked at the management practices, institutional framework, and community‟s willingness to pay for improved waste management service in Buchanan City. The ASTM D521-92 standard was followed to quantify and characterize the municipal solid waste generated in Buchanan City. A descriptive cross-sectional survey was also employed to assess the institutional framework and management practices of municipal solid waste in Buchanan. The mean waste generated was in the order 2342.4 kg >1957.3 kg >1865.5 kg for the middle-income, high-income, and low-income areas respectively. The high-income area recorded the highest per capita waste generation rate (PCWGR) of 0.84 kg/capita/day whilst the lowest of 0.34 kg/capita/day was recorded in the low-income area. On average, the organic waste fraction recorded the highest of 19.7% across the three income levels. The study shows that awareness among residents regarding waste management strategies was low. Furthermore, most respondents expressed their willingness to contribute financially to improved waste collection services in Buchanan City. Residents also identified the inadequacy of waste bins as the greatest challenge to the current waste management system of Buchanan. There is a need for some form of institutional outreach or public education efforts to improve the residents‟ awareness of the city‟s waste management strategy. Also, adequate waste bins should be made available to enhance the efficient management of waste in the city. xiv,154p:, ill.

Predicting Groundwater Quality Parameters Using Supervised Machine Learning

2025-01-23T12:02:57Z

Predicting Groundwater Quality Parameters Using Supervised Machine Learning ABOAH, MICHAEL Studies relating to groundwater have asserted that groundwater quality assessment is difficult, time-consuming and costly. An easy, vigorous, cost and time-effective tool is needed to predict water quality. The study employed supervised learning algorithms (decision tree regression and polynomial regression techniques) to build a model for assessing and predicting groundwater quality using easily measured parameters. The study employed experimental design (factorial design) and random sampling technique (multistage sampling technique) for the data collection. Model performance determinants such as R2, RMSE and d-statistics were used to compare the performance of the model with aqueous geochemical models such as Visual Minteq, Phreeq C and Wateq4F. ANOVA was used to determine the significance mean differences in the predicted groundwater chemical parameters of the study regions. The estimated R-square, RMSE and d-statistics for Visual Minteq (0.997, 16.97 and .987), Phreeq C (0.999, 33.16 and 0.960), Wateq4F (0.972, 15.33 and 0.988) and machine learning model (0.999, 1.690 and 1.00) indicated that the model developed has high predicting ability over the aqueous geochemical models. Model validating tools like accuracy (0.96), RMSE (1.690) and R2 (> 95%) demonstrated that the model could be used to forecast groundwater quality with high accuracy using easily measured parameters. ANOVA test demonstrated significant mean differences in the predicted groundwater chemical parameters of the study regions (P-value = 0.00 < 0.05). It is recommended that artificial intelligence tools, such as supervised learning as an easy, time and cost-effective way of predicting water quality should be encouraged in groundwater assessment. XVII,210p:, ill.