Browsing by Author "Donald, Pita"

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Design of an integral sliding mode controller for reducing CO2 emissions in the transport sector to control global warming
(Scientific Reports, 2025) Mehmood, Abid; Mohsan, Hassan; Donald, Pita; Almazah, Mohammed
Carbon dioxide (CO2) is the significant contributor to greenhouse gases and plays a crucial role in the greenhouse effect and climate change. The primary source of CO2 emissions is fossil fuel combustion, basically due to human activities and transportation activities. The objective of this research is to develop a dynamic model aimed at mitigating global warming by reducing atmospheric CO2 emissions resulting from the transportation sector. The model includes equations for atmospheric CO2 emissions, human population, vehicle population, and global warming. Initially, the stability of the model at each equilibrium point is determined by analyzing the eigenvalues of the Jacobian matrix. Subsequently, sensitivity analysis is performed to predict the impact of any parameter of a vehicle population and CO2 emissions causing global warming. The vehicle parameters are then optimized by applying an integral sliding mode controller (ISMC) to decrease CO2 emissions and minimize global warming. The ISMC method effectively reduces CO2 emissions and offers stability for human and vehicle populations, ultimately leading to a reduction in global warming. It is has been found that reducing the vehicle population by 20% can lead to about 4% reduction in CO2 emissions. This study integrates optimization control techniques to develop a comprehensive model to address CO2 emissions and global warming, providing a robust framework for sustainable environmental management.
Mathematical modeling of vehicle carbon dioxide emissions
(Cell Press, 2024-01-02) Mayengo, Maranya; Donald, Pita; Lambura, Aristide
The demand for transportation, driven by an increasing global population, is continuously rising. This has led to a higher number of vehicles on the road and an increased reliance on fossil fuels. Consequently, the rise in atmospheric carbon dioxide (𝐶𝑂2) levels has contributed to global warming. Therefore, it is important to consider sustainable transportation practices to meet climate change mitigation targets. In this research paper, a non-linear mathematical model is developed to study the dynamics of atmospheric 𝐶𝑂2 concentration in relation to human population, economic activities, forest biomass, and vehicle population. The developed model is analyzed qualitatively to understand the long-term behavior of the system’s dynamics. Model parameters are fitted to actual data of world population, human economic activities, atmospheric 𝐶𝑂2, forest biomass, and vehicle population. It is shown that increased vehicular 𝐶𝑂2 emissions have a potential contribution to the increase in atmospheric 𝐶𝑂2 and the decline of human population. Numerical simulations are carried out to verify the analytical findings and we performed global sensitivity analysis to explore the impacts of different sensitive parameters on the 𝐶𝑂2 dynamics.
Mathematical models for vehicular carbon dioxide emission
(NM-AIST, 2024-08) Donald, Pita
The increasing demand for transportation due to a growing global population has led to more vehicles on the road and increased use of fossil fuels, resulting in higher atmospheric carbon dioxide (CO2) levels and contributing to global warming. Thus, adopting sustainable trans portation practices is crucial for achieving climate change goals, specifically the reduction of greenhouse gas emissions to mitigate global warming. This study presents a nonlinear mathe matical model to analyze the dynamics and control of atmospheric CO2 concentration in rela tion to vehicle emissions. The model is qualitatively analyzed to understand long-term system behavior. Model parameters are calibrated using real-world data on world population, eco nomic activities, atmospheric CO2, forest biomass, and vehicle numbers. Results describes the dependence between vehicle CO2 emissions and atmospheric CO2 levels and impact human population decline. Numerical simulations validate analytical findings, and global sensitivity analysis explores the influence of various parameters on CO2 dynamics. An optimal control problem is formulated and solved by using Pontryagin’s principle, establishing optimality con ditions. Solving the problem reveals that reducing vehicle emissions, implementing reforesta tion efforts, adopting green economy practices, and curbing fossil-fueled vehicle production can cut atmosphericCO2 levels by 2.866%. Consequently, addressing climate change linked to increased atmospheric CO2 concentration is achievable through these measures.
Modeling and control of global warming driven by transportation-induced carbon dioxide emissions through green economy investments
(lsevier, 2025-04-22) Donald, Pita; Salamida, Daudi; Panga, Paul; Mayengo, Maranya
Global warming poses a significant threat to the environment and human well-being, necessitating urgent mitigation measures. This study develops a mathematical model to analyze the impact of fossil-fueled vehicle emissions on atmospheric carbon dioxide (𝐶𝑂2) concentrations and global warming. The model assumes that global warming is driven by rising atmospheric 𝐶𝑂2 levels, which are influenced by vehicle population growth. System parameters are calibrated using global datasets on atmospheric 𝐶𝑂2 concentration, human population, vehicle production, global temperature, and Gross Domestic Product (GDP). Model validation against global data demonstrates excellent predictive performance, as confirmed by statistical metrics. Sensitivity analysis reveals that vehicle growth rate, 𝐶𝑂2-induced global warming, and population-driven temperature increases are key contributors to rising global temperatures. To stabilize the system, investment in the green economy, transitioning from fossil-fueled to clean energy vehicles, and implementing economic policies to curb temperature rise are essential, as confirmed by the numerical simulation of an optimal control problem. Numerical simulations further validate the analytical findings and explore the impact of parameter variations on system behavior. This article integrates an optimal control framework into a dynamic system to formulate data-driven strategies for minimizing global warming while ensuring sustainable economic growth.