Muna abdul-ahad
Time series analysis of biochemical oxygen demand (BOD) in the effluents of wastewater treatment plants (WWTPs) is a critical tool for assessing long-term plant performance, detecting operational issues, and identifying seasonal patterns or trends indicating system degradation or overload.
Consistent monitoring of BOD in treated effluents allows for:
- Early detection of deviations from regulatory limits, enabling timely corrective actions;
- Evaluation of treatment efficiency under varying operational and climatic conditions;
- Modeling of seasonal and long-term trends, which supports capacity planning and process optimization;
- Compliance with environmental regulations and contribution to the protection of receiving water bodies (rivers, lakes, etc.).
By applying methods such as ARIMA models, exponential smoothing, Fourier analysis, or machine learning techniques, it becomes possible not only to analyze historical data but also to forecast future BOD levels, contributing to proactive system management.
In the context of climate change and increasing loads on WWTPs, such analysis is essential for strategic planning and sustainable water resource management.
Muna abdul-ahad
Thank you for your question. The above-mentioned steps for time series analysis of BOD data in the effluents of wastewater treatment plants can be fully applied using SPSS. The analysis begins with data entry and preparation (e.g., daily or monthly BOD values), followed by visualization of trends over time using time series plots. SPSS allows decomposition of the time series into trend, seasonal, and random components, which is useful for identifying seasonal patterns and long-term changes. By using the forecasting module (Analyze > Forecasting), ARIMA models can be applied, enabling accurate modeling and prediction of future BOD values, as well as detection of deviations from expected performance. Based on the results, it is possible to assess treatment efficiency, plan plant capacity, and optimize process management. SPSS also automatically generates tables and charts, facilitating interpretation and the creation of technical or scientific reports. This type of analysis is particularly important in the context of climate change and increasing loads on WWTPs, as it supports data-driven decision-making and proactive water resource management.
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