Industrial and Domestic Waste Management

Assessment of Microbial Contaminants and Antibiotic Susceptibility Patterns of Water Tank Samples from Selected Hostels in Ifite-Awka, Nigeria

2025-08-28T00:54:58+00:00

This research assessed the microbial qualities and antibiotic susceptibility of bacterial isolates from water tank samples collected from ten hostels in Ifite-Awka, Nigeria. The samples were cultured on nutrient agar and Sabouraud dextrose agar, and morphological, biochemical, as well as microscopic analyses were carried out. The bacterial isolates included Staphylococcus aureus, Escherichia coli, Salmonella typhi, Bacillus cereus, and Bacillus subtilis. The fungal isolates included Mucor spp., Aspergillus niger, and Penicillium spp. The colonies ranged from 84 to 234 CFU/ml. Across all species, Levofloxacin and Pefloxacin demonstrated the highest activity, indicating broad-spectrum effectiveness. The poorest results were observed with Rifampicin and Gentamicin, as most isolates exhibited complete resistance. The resistance of coliforms such as E. coli and Salmonella typhi to some β-lactams suggested a potential case of antibiotic misuse within the community. Bacillus cereus exhibited the broadest resistance profile. In the fungal analysis, Mucor spp. was the most common (40%), while Aspergillus niger and Penicillium spp. each accounted for 30%. The presence of coliforms in 45% of the samples indicated poor hygiene and placed people’s health in jeopardy. The lack of routine water tank sanitation and consistent bacterial monitoring in the Awka region was particularly concerning given the presence of pathogenic bacteria and toxin-producing fungi. This research highlighted the importance of strict hygiene practices together with efficient cleaning techniques for water tanks in order to reduce microbial contaminants and coliform bacteria.

Digital Lifestyle and Food Waste Behavior: The Role of Camera Eats First in Cafes and Restaurants in Batam City, Indonesia

2025-10-09T02:04:48+00:00

Camera eat first referred to the habit of photographing food before consumption for sharing on social media. This phenomenon drove shifts in consumer behavior, potentially leading to increased food waste. The objective of this study was to analyze the influence of attitude, emotion, and perceived behavioral control on food waste behavior, with camera eat first included as a mediating variable. A quantitative approach was employed, involving a sample of 340 respondents who were visitors to cafés and restaurants in the Harbour Bay area of Batam. Data were collected using a five-point Likert scale questionnaire and analyzed through Structural Equation Modeling–Partial Least Squares (SEM-PLS) using SmartPLS software, as well as Fuzzy-Set Qualitative Comparative Analysis (fsQCA). The findings revealed that emotions and perceived behavioral control exerted a positive and significant effect on food waste behavior, whereas attitude and camera eat first did not show significant influence. Furthermore, camera eat first was not confirmed as a mediator in the relationships among the studied variables. Complementary results from fsQCA highlighted that the combination of attitude, emotion, and perceived behavioral control constituted the most consistent configuration in explaining food waste behavior. Overall, the study concluded that food waste behavior was more strongly shaped by internal psychological factors than by digital lifestyle trends. Consequently, strategies to reduce food waste should prioritize strengthening self-control, enhancing emotional awareness, and promoting responsible consumption practices, while also accounting for the dynamics of social media engagement in contemporary society.

Optimization of Manufacturing-Remanufacturing Model in Circular Supply Chain Considering Warehouse Capacity Constraints by Using Chinese Pangolin Optimizer Algorithm

2025-08-14T23:43:27+00:00

This research developed an optimization model within a circular supply chain framework incorporating factors such as carbon emissions, social sustainability, and warehouse capacity limitations. The model adopted a modified Economic Order Quantity (EOQ) approach, with a comprehensive cost assessment that included production cost, remanufacturing cost, storage cost, disposal cost, and penalty cost for emissions, all formulated within a Mixed Integer Nonlinear Programming (MINLP) structure. To address the complex nonlinear problem, the metaheuristic Chinese Pangolin Optimizer (CPO) algorithm was applied, as it effectively balanced solution exploration and exploitation. The simulation results indicated the optimal combination of production lot size, remanufacturing, and the share of reusable goods, achieving the minimum total system cost. The sensitivity analysis showed the significant influence of production and remanufacturing costs, emissions, and the rate of product returns on system efficiency. Overall, this research demonstrated more credible, cost-efficient, and sustainable inventory control approaches in a circular supply chain by considering warehouse constraints and applying the CPO.

Analysis of the Impact of Skywalker Drone Battery Waste Management on the Environment Using Linear Programming Method

2025-08-04T07:42:33+00:00

The disposal of lithium-based drone batteries presents a significant environmental challenge due to the presence of heavy metals and hazardous substances. Effective management strategies are essential to reduce pollution and mitigate operational risks associated with improper handling. This study proposes an optimal waste management strategy for Skywalker drone batteries using a Linear Programming (LP) approach. The model incorporates three waste management options: recycling, temporary storage, and final disposal. It also accounts for facility capacity limitations, environmental regulations, and cost constraints. The simulation results demonstrate that the LP model provides an optimal waste allocation scheme. Compared to conventional waste management methods, the LP-based strategy reduces environmental impact and achieves higher cost efficiency. The findings highlight the effectiveness of LP modeling as a decision-support tool for waste management planning. The study recommends the adoption of an LP-based integrated management framework to support future environmental and operational decisions in drone technology.