<p>In response to escalating environmental protection standards, enhancing effluent quality (EQ) and process efficiency within wastewater treatment processes (WWTP) has become paramount. Effluent scheduling is a crucial part of WWTPs as it regulates the effluent residence time by adjusting the flow rate, which significantly impacts the biochemical reaction process. However, the discrete regulation and time-varying nature of WWTPs present crucial challenges in achieving effective effluent scheduling. In this study, sampling-based particle swarm optimization is proposed to solve the dynamic effluent scheduling for WWTPs. First, priority-based encoding and decoding methods are proposed to map the relationship between the decision variables and schedules. Second, the Wasserstein distance is introduced to design an initialization strategy to track the new global optimum in the dynamic environment of WWTPs. Third, a velocity update method is designed to improve the search efficiency by sampling the elitist neighbor solution. Fourth, a dynamic constraint handling method is developed to ensure solution feasibility in WWTPs. Finally, the proposed algorithm is tested in Benchmark Simulation Model No.1 to demonstrate its solving ability for the dynamic effluent scheduling problem of WWTPs. Computational experiments with state-of-the-art methods show that the proposed algorithm can achieve superior performance in terms of EQ and process efficiency.</p>