Abstract
Accurate assessment of greenhouse gas emissions from wastewater treatment plants is crucial for mitigating climate change. N2O is a potent greenhouse gas that is emitted from wastewater treatment plants during the biological denitrification process. In this study, we developed and evaluated deep learning models for predicting N2O emissions from a WWTP in Switzerland. Six key parameters were selected to obtain the optimal LSTM model by adjusting experimental parameter conditions. The optimal parameter condition was achieved with 150 neurons, the tanh activation function, the RMSprop optimization algorithm, a learning rate of 0.001, no dropout regularization, and a batch size of 128. Under the same conditions, we compared the performance of recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) networks. We found that LSTM models outperformed RNN models in predicting N2O emissions. The optimal LSTM model achieved a 36% improvement in mean absolute error (MAE), a 19% improvement in root mean squared error (RMSE), and a 6.92% improvement in R2 score compared to the RNN model. Additionally, LSTM models demonstrated better resilience to sudden changes in the target sequence, exhibiting a 9.54% higher percentage of explained variance compared to RNNs. These results highlight the potential of LSTM models for accurate and robust prediction of N2O emissions from wastewater treatment plants, contributing to effective greenhouse gas mitigation strategies.
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Xiaozhen Xu: Investigation, Data curation, Programming, Writing-original draft. Anlei Wei: Conceptualization, Methodology, Supervision, Writing-review and editing. Songjun Tang: Investigation. Qi Liu: Investigation, Conceptualization. Hanxiao Shi: Investigation, Visualization. Wei Sun: Writing-review & editing. All authors read and approved the final manuscript.
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Xu, X., Wei, A., Tang, S. et al. Prediction of nitrous oxide emission of a municipal wastewater treatment plant using LSTM-based deep learning models. Environ Sci Pollut Res 31, 2167–2186 (2024). https://doi.org/10.1007/s11356-023-31250-9
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DOI: https://doi.org/10.1007/s11356-023-31250-9