Abstract
The rapid growth of industries and urbanization has significantly contributed to air pollution, leading to detrimental effects on both human health and the environment. Airborne pollutants, such as fine particulate matter (PM2.5, PM10), and gaseous pollutants, including NOx, SO2 , and CO, are closely associated with serious health conditions like lung cancer, cardiovascular diseases, and respiratory disorders. While pollution levels are influenced by these pollutants, meteorological factors such as temperature, humidity, and wind speed also play a critical role in shaping air quality. Accurate prediction of the Air Quality Index (AQI), which reflects the combined impact of pollutants and meteorological conditions, remains a challenging task.This paper presents a comprehensive study on predicting air quality levels in New York City using 24 years of meteorological data and pollutant concentrations, including PM2.5, CO, SO2 , and Ozone. Leveraging advanced time series forecasting models, we aim to capture the temporal dynamics of air quality in response to climate variables such as temperature, precipitation, wind speed, and humidity. Our models accurately forecast pollutant levels and the Air Quality Index (AQI), providing insights into the influence of meteorological factors on urban air quality. The results demonstrate the effectiveness of these models in short-term air quality prediction, which can contribute to the development of early warning systems and inform public health interventions. This research highlights the critical role that climate patterns play in pollution levels, offering a data-driven foundation for urban environmental management.