ORGANIZATIONAL FACTORS AND HUMAN ERRORS IN OIL AND GAS COMPANIES IN THE NIGER DELTA, NIGERIA

2026-06-13T11:10:03+00:00

This study investigated the relationship between organizational factors and human errors in oil and gas companies in the Niger Delta Region of Nigeria. The study was guided by two research objectives which are to determine the prevalence of organizational factors and level of human error in the oil and gas firms and to examine the correlation between the organizational factors and human errors in the oil and gas companies. A descriptive cross-sectional survey design was adopted, and the population consisted of 638 employees across 12 selected oil and gas companies operating in the region. Using the Taro Yamane formula, a sample size of 246 respondents was determined and proportionally allocated across the companies. Data were collected using a structured researcher-developed instrument. Data analysis was carried out using SPSS version 26, applying descriptive statistics (mean and standard deviation) and inferential statistics (Pearson correlation) at a 0.05 level of significance. The results of descriptive statistics revealed high prevalence of human error (mean = 3.39) and high level of organizational factors leading to human error (mean = 3.99). The results of inferential statistics revealed that organizational factors have significantly relationship with occurrence of human errors in oil and gas companies in the Niger Delta, with strong positive correlations observed between each factor and human error occurrence (r = 0.602, respectively; p < 0.05). The study concluded that there is high prevalence of human errors in the Niger Delta oil and gas sectors which is associated with organizational factors. This highlight the need for implementation of tailored training programs, strengthened safety culture, and improved communication and incident reporting systems to reduce error rates

OPTIMIZATION OF ADSORPTION CONDITIONS FOR NUTRIENT REMOVAL FROM AQUEOUS SOLUTIONS: INTEGRATED RSM MODELLING, MECHANISTIC INSIGHTS, AND MULTI-SCALE STATISTICAL VALIDATION

2026-06-26T10:58:03+00:00

Global water systems are increasingly affected by nutrient pollution, particularly nitrate and phosphate from industrial discharge, agricultural intensification, and urban expansion. Ecosystem degradation, harmful algal blooms, loss of oxygen, and the dangers that result for marine life as well as human beings are some of the effects that arise due to such pollutants. The need for sustainable adsorption process can be understood from the limitations of the conventional systems such as biological denitrification, chemical precipitation and membrane filtration, including expensive process, formation of sludge, lack of selectivity and inability to regenerate the system. This study develops an optimized adsorption system for nutrient removal using an integrated framework combining Response Surface Methodology (RSM), mechanistic evaluation, and multi-scale statistical validation. An iron-based metal–organic framework (Fe-MOF), Fe-UPH.COHSE-NH₂, was synthesized via a controlled solvothermal method and functionalized with polyethyleneimine (PEI) to introduce amine-rich active sites. Optimization using a Doehlert design identified an optimal FeCl₃·6H₂O to H₂BDC ratio of 2:1.5, producing a highly crystalline and functionally enhanced adsorbent. A porous and robust matrix with high specific surface area of 533.94 m²/g and good accessibility of active sites (BET R² = 0.9999) was proved from characterisation results obtained from powder x-ray diffraction (PXRD), Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), scanning electron microscope (SEM), and Brunauer-Emmett-Teller (BET) surface area measurement (“BET”). The durability and reusability of the substance were shown from its high crystallinity, rod-shaped porous structure, and high thermal stability at temperatures up to 350°C. This compound had functional groups such as -NH₂ and -COO⁻. Nitrate and phosphate adsorption study with RSM and the assistance of kinetic and isotherm models showed excellent removal efficiency with high predictive accuracy (R² > 0.99). The mechanisms discovered through mechanistic studies were electrostatic attraction, chemisorption of Fe-OH and Fe-NH₂, ligand exchange and intraparticle diffusion. Therefore, in summation, Fe-UPH.COHSE-NH2 provided an environmentally friendly solution for the effective removal of nutrients from wastewaters, due to high efficiency and recyclability.

GPH-International Journal of Applied Science

ORGANIZATIONAL FACTORS AND HUMAN ERRORS IN OIL AND GAS COMPANIES IN THE NIGER DELTA, NIGERIA

OPTIMIZATION OF ADSORPTION CONDITIONS FOR NUTRIENT REMOVAL FROM AQUEOUS SOLUTIONS: INTEGRATED RSM MODELLING, MECHANISTIC INSIGHTS, AND MULTI-SCALE STATISTICAL VALIDATION