Global climate targets demand a rapid transition to carbon neutrality across all industrial sectors, including wastewater management. Wastewater treatment plants are historically energy-intensive and remain significant sources of potent greenhouse gases, primarily nitrous oxide (N₂O) and methane (CH₄). Recent biological interventions have targeted quorum sensing (QS)—a microbial communication mechanism regulating gene expression and community behavior—to optimize biological treatment efficiency. However, the highly context-dependent and sometimes paradoxical effects of QS on simultaneous greenhouse gas mitigation and energy recovery remain poorly resolved. Here we synthesize recent advancements to show that QS operates as a master biological regulator of both direct emissions and energy consumption in wastewater ecosystems. Evidence indicates that QS distinctly modulates N₂O production through concentration- and signal-dependent pathways, while actively suppressing CH₄ escape and enhancing aerobic granulation to cut aeration energy demands. Furthermore, targeted QS deployment in anaerobic digestion accelerates direct interspecies electron transfer, substantially boosting methane recovery to offset operational energy use. These insights reveal that manipulating microbial social networks presents a viable, albeit complex, biological lever for balancing emission reductions with energy optimization. Ultimately, precision control of QS systems offers a transformative technological pathway for achieving carbon-positive wastewater infrastructure.

Data-driven approaches that make timely predictions about pollutant concentrations in the effluent of constructed wetlands are essential for improving the treatment performance of constructed wetlands. However, the effect of the meteorological condition and flow changes in a real scenario are generally neglected in water quality prediction. To address this problem, in this study, we propose an approach based on multi-source data fusion that considers the following indicators: water quality indicators, water quantity indicators, and meteorological indicators. In this study, we establish four representative methods to simultaneously predict the concentrations of three representative pollutants in the effluent of a practical large-scale constructed wetland: (1) multiple linear regression; (2) backpropagation neural network (BPNN); (3) genetic algorithm combined with the BPNN to solve the local minima problem; and (4) long short-term memory (LSTM) neural network to consider the influence of past results on the present. The results suggest that the LSTM-predicting model performed considerably better than the other deep neural network-based model or linear method, with a satisfactory R². Additionally, given the huge fluctuation of different pollutant concentrations in the effluent, we used a moving average method to smooth the original data, which successfully improved the accuracy of traditional neural networks and hybrid neural networks. The results of this study indicate that the hybrid modeling concept that combines intelligent and scientific data preprocessing methods with deep learning algorithms is a feasible approach for forecasting water quality in the effluent of actual engineering.