Resources Environment and Information Engineering

Air pollution is a major life-threatening problem in industrialized and commercially vibrant cities like Aba metropolis and its suburbs in Abia State Nigeria. The study of selected air pollutants in these areas were performed using Matrix Laboratory (MATLAB) and Artificial Neural Networks (ANN) pollution models. Primary data was collected by conducting sampling analysis on air samples during dry and rainy seasons from 2024 and 2025. MATLAB and ANN pollution models were generated by integrating measurements and spatial databases using polynomial expressions. The MATLAB 7^th degree linear regression polynomial described the relationship between dependent and independent variables for the pollutants. The correlation methods verified that most MATLAB models could accurately predict or forecast concentration levels. Also the Artificial Neural Network demonstrated tracking of the actual plots on MATLAB. The analysis of variance (ANOVA) was also deployed which showed p < 0.05 for carbon monoxide (CO), nitrogen dioxide (NO₂), sulphur dioxide (SO₂), and total particulate matter (TPM), indicating that, there was a significant impact by the seasons on the concentrations of  all gaseous pollutants under study(i.e. seasonal variations of concentration was highly affected by the two seasons). ANN was able to track all gaseous pollutants represented by MATLAB successfully above 50%.

Prognostic landscape-ecological scenarios for the near future of the biosphere have been considered for the first time by taking a large region--the Volga River basin--as an example. The analysis was based on a method of regional landscape-ecological prognosis--developed by the author using discrete mathematics methods. Analytical and cartographic models of future landscape-ecological conditions (for 2050, 2075, and 2100) were obtained. The mechanisms of shifts in the mosaic structure of vegetation, soils, and landscapes have been revealed in the study area under different disturbance scenarios of the climatic system--scenarios anticipated in the foreseeable future (by the end of the 21^st century). Forthcoming anthropogenic warming, accompanied by an excessive increase in surface runoff, will occur at the expense of a relative decrease in evapotranspiration, and particularly in groundwater flow. A progressively intensifying thermo-arid bioclimatic trend is predicted, with a general northward shift of zonal boundaries and corresponding changes in the soil water regime and vegetation cover structure. The prognostic models show the convergence of phytocoenoses into new zonal vegetation types.

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.