Port Harcourt and Omoku, two densely populated towns in Rivers State, Nigeria, are hubs of industrial, commercial, and residential activities, characterized by the presence of oil and gas industries, educational institutions, large markets, and, in the case of Omoku, access to free electric power supply. These activities contribute to environmental degradation through industrial effluents, domestic waste, automobile emissions, gas flaring, and biomass burning, which release aerosols that form harmful particulates in the atmosphere. This study assesses the concentration of atmospheric particulates in rainwater samples from both towns collected between April and October, employing various analytical methods. pH and temperature were measured. The HANNA DR 2010 spectrophotometer was used to assess parameters like color, turbidity, total dissolved solids (TDS), total suspended solids (TSS), and electrical conductivity. Titrametric methods were used to assess alkalinity, total hardness, and calcium hardness; a different method was used to estimate magnesium hardness. Spectrophotometry was used to measure the levels of nitrate, sulfate, and phosphate, while atomic absorption spectrophotometry (AAS) was used to measure the levels of trace metals, such as iron, lead, manganese, chromium, copper, and zinc. The outcomes were compared to the allowable limits set by the World Health Organization (WHO). Correlation analysis yielded a significant r-value of 0.990 for particulate levels between Port Harcourt and Omoku, while the calculated pollution indices for rainwater were 0.532 for Port Harcourt and 0.522 for Omoku; both were below the critical value of 1. The study also revealed an inverse relationship between rainfall frequency and pollutant concentration, demonstrating that increased rainfall improves air quality by reducing particulate levels. These findings underscore the environmental implications of industrial and domestic activities in these regions. The results were analyzed using fuzzy logic and artificial neural network (ANN).

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Port Harcourt and Omoku, two densely populated towns in Rivers State, Nigeria, are hubs of industrial, commercial, and residential activities, characterized by the presence of oil and gas industries, educational institutions, large markets, and, in the case of Omoku, access to free electric power supply. These activities contribute to environmental degradation through industrial effluents, domestic waste, automobile emissions, gas flaring, and biomass burning, which release aerosols that form harmful particulates in the atmosphere. This study assesses the concentration of atmospheric particulates in rainwater samples from both towns collected between April and October, employing various analytical methods. pH and temperature were measured. The HANNA DR 2010 spectrophotometer was used to assess parameters like color, turbidity, total dissolved solids (TDS), total suspended solids (TSS), and electrical conductivity. Titrametric methods were used to assess alkalinity, total hardness, and calcium hardness; a different method was used to estimate magnesium hardness. Spectrophotometry was used to measure the levels of nitrate, sulfate, and phosphate, while atomic absorption spectrophotometry (AAS) was used to measure the levels of trace metals, such as iron, lead, manganese, chromium, copper, and zinc. The outcomes were compared to the allowable limits set by the World Health Organization (WHO). Correlation analysis yielded a significant r-value of 0.990 for particulate levels between Port Harcourt and Omoku, while the calculated pollution indices for rainwater were 0.532 for Port Harcourt and 0.522 for Omoku; both were below the critical value of 1. The study also revealed an inverse relationship between rainfall frequency and pollutant concentration, demonstrating that increased rainfall improves air quality by reducing particulate levels. These findings underscore the environmental implications of industrial and domestic activities in these regions. The results were analyzed using fuzzy logic and artificial neural network (ANN).

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.