ANN-based prediction of ammonia nitrogen for wastewater discharge indicators under carbon neutral trend

Abstract

IntroductionWith the rapid development of society and urbanization, greenhouse gas emissions have increased, leading to environmental problems such as global warming. The rise in urban water consumption has also resulted in increased sewage discharge, exacerbating freshwater scarcity and water pollution. Understanding the current status and spatial distribution of greenhouse gas emissions in China's sewage treatment industry is crucial for emission reduction measures and controlling ammonia nitrogen pollution.MethodsThis study comprehensively investigates greenhouse gas emissions from sewage treatment plants, analyzing influencing factors and predicting future spatial and temporal distributions. The uncertainty of ammonia nitrogen emissions is calculated using the IPCC's error propagation method, considering uncertainty ranges of variables. Additionally, an artificial neural network is employed to predict ammonia nitrogen content in sewage discharge, aiming to prevent excessive levels in wastewater.Results and discussionThe proposed model outperforms others with an R-Squared score of 0.926, demonstrating its superior accuracy in predicting ammonia content in wastewater. These findings contribute to better emission reduction strategies and control of ammonia nitrogen emissions. This model can effectively prevent excessive ammonia nitrogen content in discharged wastewater, contributing to water pollution control. In conclusion, this study highlights the importance of understanding greenhouse gas emissions from sewage treatment plants and their impact on water pollution. The research provides valuable insights into emission reduction measures, emission prediction, and technological innovations suitable for China's specific conditions. By effectively managing ammonia nitrogen emissions and adopting the proposed predictive model, the goals of carbon neutrality and environmental sustainability can be better achieved.