Paper Title
Development of a Predictive Model for Atmospheric CO2 Concentrations Utilizing Keeling Curve Data

Abstract
The quantity of carbon dioxide (CO2) in the atmosphere is increasing more quickly, which is heightening critical concerns about the mitigation of greenhouse gases, which are the primary contributors of anthropogenic climate change. This intensifying challenge has projected CO2 emissions into a significant global issue, prompting strenuous efforts among atmospheric scientists, climate researchers, and engineers to devise strategies to manage and dwindle the rapid increase in CO2 levels. Creating predictive models that can estimate atmospheric CO2 concentrations and identify the sources of emissions is one well-known strategy that has recorded massive success. In this study, we employed a mathematical modelling approach to analyse the trends shown in Keeling Curve data. Our findings demonstrate that the proposed predictive model performs with high accuracy, effectively capturing the observed trends in the Keeling Curve. The predicted CO2 concentrations exhibit strong alignment with empirical Keeling Curve data, and future projections for the years, 2022-2023 and long-term, 2026 and 2050 are consistent with forecasts reported by other established predictive models. We opined that this novel model represents a valuable tool for researchers and scientists engaged in climate monitoring and forecasting. Keywords - Carbon Dioxide, Atmosphere, Greenhouse Effect, Keeling Curve, Global Warming