Assessment of the activity of the mbalmayo thermal power plant on groundwater quality

Abstract

Abstract Groundwater is essential for daily life in Cameroon, but pollution sources can harm its quality through infiltration and dispersal of contaminants in the ground. This study focuses on estimating groundwater quality near the Mbalmayo Thermal Power Plant using a predictive model combining genetic algorithms and neural networks. The genetic algorithms were used to optimize the objective function, while neural networks learned the data to predict concentration values. From January 2017 to December 2021, several moisture content values were experimentally determined using collected dried and weighed soil samples. The results showed that moisture content varied from 1 to 82%. During this study period, the model takes into account the water content of the soil, porosity and permeability which have the same effect on the concentration level of the fuel oil in the groundwater. The average concentration of fuel oil was below 50 mg/l, which is the World Health Organisation standard However, there is a risk of groundwater pollution by fuel oil in the event of heavy activity at the Mbalmayo thermal power plant in the 0-445 m range. For protection during this period, the results show that the installation of populations on a perimeter located beyond 445 m and the construction of a water purification station are recommended. The results are decision support tools for the authorities.