Time Series Analysis and ARIMA Modeling for Forecasting Pollutant Degradation Kinetics in Bioremediation

The efficacy of bioremediation is inherently a temporal process, best characterized by the kinetic degradation curve of the target pollutant. Traditional endpoint analyses fail to capture the dynamics of this process, often ignoring autocorrelation and non-stationarity. This study demonstrates the application of Autoregressive Integrated Moving Average (ARIMA) models, a standard in environmental forecasting, to analyze and forecast time-series data. Following the Box-Jenkins methodology, we constructed an ARIMA (1,1,1) model that successfully captured the underlying structure of the degradation process The model's forecasting performance was validated on a holdout sample, yielding a Mean Absolute Percentage Error (MAPE) of 8.7%. Our analysis provides a robust statistical framework for characterizing degradation trends, quantifying forecast uncertainty with confidence intervals, and moving beyond simplistic hypothesis testing. This work establishes time series analysis as a critical tool for optimizing treatment strategies and providing statistically sound evidence of remediation success.