Effect of oil spill on the physicochemical properties of soil: a case study of Etch

Bright Nweke — 2025-05-29

The study area was known as a flashpoint for crude oil pollution. This study consists of three host communities, such as Umuechem and Odagwa/Okoroagu, with Umuanyagu serving as a control. Twelve soil samples were taken from each sampling location, comprising oil-polluted and non-oil-polluted soil, after a reconnaissance study at varying soil depths of 0–15 cm (surface) and 15–30 cm (subsurface). Total Petroleum Hydrocarbon (TPH), pH, electrical conductivity (EC), bulk density, salinity, moisture content, sandy soil, clay soil, and silt soil atomic absorption spectrophotometers, pH meters, and Walkey-black wet oxidation were used to analyze the contaminated and non-contaminated soils. The outcomes of the three sample locations were compared with suggested standards.

Method: Evidence of total soil samples collected was 12. TPH, pH, EC, bulk density, salinity, moisture content, sandy soil, clay soil, and silt soil were analyzed using the descriptive statistics and TPH across the sampled areas. The statistical mean showed a high variability of the physicochemical properties in the oil-polluted and non-oil-polluted soils. These high TPH mean values show that Umuechem, 68,530 and 63,602 mg/kg, and Odagwa/Okoroagu, 38,437 and 24,430 mg/kg, were affected by crude oil spillage compared with Umuanyagu, 315 and 331 mg/kg, without crude oil pollution. Thus, the outcome suggests low soil fertility and decreased plant development due to altered processes in plants growing on soils contaminated by crude oil, which in turn suggests low agricultural production and a diminished standard of living in the impacted areas. In order to satisfy the people and give them the chance to live in a clean environment, it is also recommended that prompt and sustainable intervention be used to fully reclaim the impacted region (soil).

Noise modeling using multiple probability distribution functions

K. G. Gaya — 2025-12-06

Many research has been conducted to develop an appropriate statistical model for assessing noise level potential. The key parameter in estimating this potential is the noise from the generator, which is inherently random, making statistical methods essential for accurate estimation. Consequently, noise level probabilities can be analyzed using various probability distributions. Accurately determining the probability distribution of noise level values is crucial for evaluating the noise level potential of the university. However, this paper applies the use of lognormal, Weibull, Nakagami, and gamma distributions to datasets from a specific location in Adamawa State University (ADSU), Mubi. To identify the most suitable distribution, the study employs the Kolmogorov-Smirnov (K-S) test and Anderson-Darling (A-D) test along with graphical representations of the cumulative distribution function (CDF) and probability distribution function (PDF). The (K-S) test was found to be the best model over the (A-D) test. Based on both graphical analysis and computed goodness-of-fit results, the gamma distribution was found as the bestfitting model with a fitness of 0.13805, followed by Nakagami, Weibull, and lognormal with 0.14130, 0.14579, and 0.15709, respectively. Additionally, 62.54% was found to be the probability of exceeding the critical point (PECP).

BOHR International Journal of Civil Engineering and Environmental Science

Effect of oil spill on the physicochemical properties of soil: a case study of Etch

Noise modeling using multiple probability distribution functions