RESUMO
The long-term prediction of water quality is important for water pollution control planning and water resource management, but it has received little attention. In this study, the water quality trend in the Yangtze River is found to stabilize at most monitoring stations under environmental protection activities. Based on the physical mechanism and stochastic theory, a novel river water quality prediction model combining pollution source decomposition (including local point, local nonpoint and upstream sources) and time series decomposition (including trend, seasonal and residential components) is developed. The observed water quality data from 76 monitoring stations in the Yangtze River, including permanganate index (CODMn) and total phosphorus (TP), are used to drive this model to make long-term water quality predictions. The results show that this model has an acceptable accuracy. In the future, the concentration of CODMn will meet the water quality targets at most stations in the Yangtze River, but the concentration of TP will not be able to meet the water quality target at 28.5 % of the stations. Furthermore, the prediction value of CODMn is 62.2 % lower than the target on average. However, the prediction value of TP is only 24.4 % lower than the target on average, and it will exceed the water target by >50 % at some stations. This model has the potential to be widely used for long-term water quality prediction in the future.