utilizing autoregressive integrated moving average model to analyze temporal dynamics of methane pollutants in markazi province

Methane, the second most important greenhouse gas with significant global warming potential, plays a key role in worsening climate change. accurate monitoring of its temporal variations is crucial for understanding climate trends and developing emission reduction strategies. this study analyzed temporal changes and forecasted methane concentrations in markazi province (iran) using autoregressive integrated moving average (arima) algorithm. monthly airs sensor data at pressure levels from 10 to 850 hectopascals (hpa) between 2002 and 2024 were examined. the accuracy of satellite data was validated by comparison with ground-based measurements, showing strong correlations between 0.88 and 0.91. the highest average monthly methane concentration and standard deviation were recorded at 700 hpa (±1890.09 ppb, 40.75), while the lowest were at 10 hectopascals (±80.958 ppb, 8.40). the mann-kendall test revealed a significant upward trend in methane levels over the past two decades across all pressure levels, especially between 200 and 500 hectopascals. arima model coefficients were estimated using data from 2002 to 2023. then, methane concentrations for 2023 to 2024 were forecasted and compared with satellite observations. the model achieved an average correlation coefficient of 0.86 and a root mean square error of about 16 ppb, demonstrating effective prediction of methane concentrations.