State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China.
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China.
Sci Total Environ. 2023 Jan 20;857(Pt 3):159714. doi: 10.1016/j.scitotenv.2022.159714. Epub 2022 Oct 24.
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 (COD) 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 COD 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 COD 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.
长期水质预测对水污染控制规划和水资源管理非常重要,但一直以来并未受到太多关注。本研究发现,在环境保护活动的作用下,长江大多数监测站的水质趋势趋于稳定。基于物理机制和随机理论,开发了一种将污染源分解(包括局部点源、局部非点源和上游源)和时间序列分解(包括趋势、季节性和居住性成分)相结合的新型河流水质预测模型。利用长江 76 个监测站的高锰酸盐指数(COD)和总磷(TP)实测水质数据来驱动该模型进行长期水质预测。结果表明,该模型具有可接受的精度。未来,长江大多数监测站的 COD 浓度将达到水质目标,但 28.5%的监测站 TP 浓度将无法达到水质目标。此外,COD 的预测值平均比目标值低 62.2%。然而,TP 的预测值仅比目标值低 24.4%,且在一些监测站 TP 浓度将超过水质目标值的 50%以上。该模型未来有望广泛应用于长期水质预测。