Daily Time Series Analysis of Ambient Ozone and Fine Particulate Matter Levels in Corpus Christi, Texas

Abstract Air pollution represents an environmental health exposure associated with cardiovascular, respiratory, and neurological diseases. The temporal dynamics of key air pollutants, such as ozone (O 3 ) and fine particulate matter (PM 2.5 ), are poorly understood, especially along the Texas industrial coastal region. This study utilizes daily time series analysis to investigate temporal trends in ambient O 3 and PM 2.5 levels across four monitoring sites in the petrochemical-intensive city of Corpus Christi. Five years (2019–2023) of data from the U.S. Environmental Protection Agency (USEPA) on daily maximum 8-h concentrations of O 3 (ppm) and daily mean concentration of PM 2.5 (µg/m 3 ) were used. Using classical seasonal decomposition and Autoregressive Moving Average (ARMA) modeling, we identified distinct seasonal patterns and temporal variability. Overall, O 3 and PM 2.5 concentrations showed an inverse relationship ( ρ = -0.197; p < 0.001). Distinct seasonal patterns emerged: O 3 peaked in spring with summer minima, while PM 2.5 demonstrated summer maxima. Both pollutants demonstrated statistically significant associations with average ambient temperature, average wind speed, and precipitation. Most notably, temperature was inversely correlated with O 3 ( ρ = -0.373; p < 0.001) but positively with PM 2.5 concentrations ( ρ = 0.438; p < 0.001), indicating differential temperature influence. PM 2.5 also exhibited an extreme spike (70.5 μg/m 3 ) associated with a Saharan dust event in June 2020. ARMA models elucidated that elevated O₃ levels persisted daily (ARMA(1,1), p < 0.0001), while PM 2.5 oscillated (ARMA(3,4), p < 0.0001). The successful 30-day forecasting capability in this study provides essential tools for early warning systems and evidence-based air quality interventions in coastal petrochemical communities.