Statistical Modeling of Air Pollution: A Data-Driven Approach to Gas Turbine Emissions

Abstract

Air quality is a significant environmental and public health concern, with pollutants such as carbon monoxide (CO) and nitrogen oxides (NOx) contributing to global warming, acid rain, and respiratory diseases. This study applies multivariate statistical methods to analyze long-term trends in ambient air quality and gas turbine emissions using a dataset of 36,733 sensor measurements collected over five years. Using R, statistical techniques including ANOVA, regression modeling, and correlation analysis were employed to assess the relationships between ambient conditions and pollutant levels and to determine the most effective method for long-term air pollution reduction. Findings reveal significant variations in CO and NOx emissions over time, influenced by ambient temperature, pressure, and humidity. The study highlights the importance of data-driven approaches for air quality monitoring and emissions control, with implications for policy recommendations. Because pollutants like carbon monoxide (CO) and nitrogen oxides (NOx) contribute to smog formation, acid rain, global warming, and a number of respiratory illnesses, air quality is a serious environmental and public health concern. Developing successful mitigation solutions requires an understanding of the long-term trends in air pollution and the factors that influence them. This study uses a large dataset of 36,733 sensor measurements gathered over five years to investigate ambient air quality and gas turbine emissions using multivariate statistical approaches. Utilizing R programming, statistical methods like regression modeling, correlation analysis, and analysis of variance (ANOVA) were used to evaluate the dynamic correlations between pollutant levels and environmental factors like temperature, pressure, and humidity.

The results show notable temporal changes in CO and NOx emissions, indicating that pollution levels are mostly determined by environmental conditions. In order to determine the best strategy for reducing air pollution over the long run, the study also assesses several statistical approaches. The findings highlight the importance of data-driven methods in emissions management and air quality monitoring, providing practical information that may guide industrial strategies and regulatory regulations targeted at reducing negative effects on the environment and human health.