Analyzing the Daily Air Quality Index in the U.S. in Time Series

Abstract

This paper presents a comprehensive evaluation of Air Quality Index (AQI) trends in the United States using the ARIMA (0,1,2) model, an established approach in time series analysis. The study focuses on determining daily variations in air quality, given the importance of AQI in public health and environmental policy. The chosen ARIMA model successfully represents AQI variations since it has no autoregressive component, one order of differencing, and two moving average terms. The model anticipates air quality conditions consistently, and its performance is measured using statistical measures such as Mean Error (ME), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). This study is improved by placing the AQI within the context of larger environmental and meteorological variables. This study also investigates the consequences of these estimates for public health and environmental policy, recognizing potential limits and proposing further research approaches. It emphasizes the complexities of air quality forecasting and the possibilities for using advanced modeling approaches and external factors to improve prediction accuracy. The findings are important for environmental analysts, public health professionals, and policymakers because they contribute to a more thorough understanding of air quality dynamics and prediction approaches in the context of environmental health.