Investigation of Atmospheric Particulates in Two Raingauged Stations, Aba and Umuahia Meteropolis Nigeria Using Artificial Neural Network and Fuzzy Logic

Abstract

This study conducts a thorough and multifaceted analysis of atmospheric particulate matter within the urban conglomerates of Aba and Umuahia, two prominent metropolitan areas in Abia State, Nigeria, both undergoing significant industrial and economic growth. Leveraging on advanced artificial neural network (ANN) and fuzzy logic framework, rainwater samples were meticulously collected from strategically located rain gauge stations, positioned at an optimal elevation of three meters over a carefully designed ten-week sampling period. These rainwater samples were employed to accurately quantify particulate matter concentrations, enabling the assessment of spatial and temporal variations, along with the broader atmospheric deposition dynamics. Results revealed considerable disparities in particulate concentrations, with Aba displaying significantly higher levels than Umuahia, likely attributable to heightened anthropogenic sources such as industrial emissions, vehicular exhaust, and urban activities. The mean particulate concentrations were also computed for both locations, yielding deeper insights into regional atmospheric chemistry. Furthermore, graphical analysis demonstrated an inverse relationship between rainfall frequency and particulate loading, corroborating the hypothesis of precipitation-induced atmospheric cleansing. The effectiveness of ANN based and fuzzy logic environmental models are further validated, underscoring their critical role in forecasting pollutant dispersion and facilitating sustainable urban air quality management policies.