Chen Ding-Jiang, Sun Si-Yang, Jia Ying-Na, Chen Jia-Bo, Lü Jun
College of Environmental Science and Resources, Zhejiang University, Hangzhou 310058, China.
Huan Jing Ke Xue. 2013 Jan;34(1):84-90.
Based on the hydrological difference between the point source (PS) and nonpoint source (NPS) pollution processes and the major influencing mechanism of in-stream retention processes, a bivariate statistical model was developed for relating river phosphorus load to river water flow rate and temperature. Using the calibrated and validated four model coefficients from in-stream monitoring data, monthly phosphorus input loads to the river from PS and NPS can be easily determined by the model. Compared to current hydrologica methods, this model takes the in-stream retention process and the upstream inflow term into consideration; thus it improves the knowledge on phosphorus pollution processes and can meet the requirements of both the district-based and watershed-based wate quality management patterns. Using this model, total phosphorus (TP) input load to the Changle River in Zhejiang Province was calculated. Results indicated that annual total TP input load was (54.6 +/- 11.9) t x a(-1) in 2004-2009, with upstream water inflow, PS and NPS contributing to 5% +/- 1%, 12% +/- 3% and 83% +/- 3%, respectively. The cumulative NPS TP input load during the high flow periods (i. e. , June, July, August and September) in summer accounted for 50% +/- 9% of the annual amount, increasing the alga blooming risk in downstream water bodies. Annual in-stream TP retention load was (4.5 +/- 0.1) t x a(-1) and occupied 9% +/- 2% of the total input load. The cumulative in-stream TP retention load during the summer periods (i. e. , June-September) accounted for 55% +/- 2% of the annual amount, indicating that in-stream retention function plays an important role in seasonal TP transport and transformation processes. This bivariate statistical model only requires commonly available in-stream monitoring data (i. e. , river phosphorus load, water flow rate and temperature) with no requirement of special software knowledge; thus it offers researchers an managers with a cost-effective tool for quantifying TP pollution processes in both district and watershed scales.
基于点源（PS）和非点源（NPS）污染过程的水文差异以及河道内滞留过程的主要影响机制，建立了一个双变量统计模型，用于关联河流磷负荷与河流水流量和温度。利用从河道内监测数据校准和验证的四个模型系数，该模型可以轻松确定每月从点源和非点源输入河流的磷负荷。与当前的水文方法相比，该模型考虑了河道内滞留过程和上游入流项；因此，它提高了对磷污染过程的认识，能够满足基于区域和基于流域的水质管理模式的要求。利用该模型，计算了浙江省长乐河的总磷（TP）输入负荷。结果表明，2004 - 2009年年总TP输入负荷为（54.6±11.9）t·a⁻¹，上游入流、点源和非点源分别贡献5%±1%、12%±3%和83%±3%。夏季高流量期（即6月、7月、8月和9月）非点源TP输入负荷累计量占年总量的50%±9%，增加了下游水体藻类爆发的风险。年河道内TP滞留负荷为（4.5±0.1）t·a⁻¹，占总输入负荷的9%±2%。夏季（即6 - 9月）河道内TP滞留负荷累计量占年总量的55%±2%，表明河道内滞留功能在季节性TP输运和转化过程中起重要作用。这个双变量统计模型只需要常用的河道内监测数据（即河流磷负荷、水流量和温度），不需要特殊的软件知识；因此它为研究人员和管理人员提供了一个经济有效的工具，用于在区域和流域尺度上量化TP污染过程。
