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An improved source apportionment mixing model combined with a Bayesian approach for nonpoint source pollution load estimation

机译：改进的源分配混合模型与贝叶斯方法相结合的非点源污染负荷估算

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摘要

A nonpoint source (NPS) loads evaluation method based on the Bayesian source apportionment mixing model was established in this research. The model assumed that (1) the pollutant concentration from each source mixed with the others in the monitoring section during transport, (2) transport only considered first-order attenuation, (3) point source pollution had relatively stable emissions, and (4) the measurement error was random, unrelated, and consistent with a normal distribution (mean of 0). All unknown parameters in the model were taken as random variables, and their posterior distributions were derived by Markov chain Monte Carlo procedures based on historical data, literature, and empirical information. The outflow system of the Huaihe River was adopted as a case study to verify the feasibility of the model. Gelman-Rubin, automatic frequency control statistics, and the determination coefficient (R-2) verified the reliability. The results showed that the total loads of ammonia nitrogen (NH4+), chemical oxygen demand, total nitrogen, and total phosphorus from NPSs accounted for 16.35-27.58%, 18.78-25.69%, 21.68-29.71%, and 42.11-52.09%, respectively. The parameter sensitivity analysis showed that prior distribution of NPS concentration was the most sensitive one, which should be determined reasonably based on the empirical or historical information.
机译：本研究建立了一种基于贝叶斯源分配混合模型的非点源负荷评估方法。该模型假设（1）在运输过程中，在监测期间，每种污染源的污染物浓度在监测区域中相互混合；（2）仅考虑一阶衰减进行运输；（3）点源污染具有相对稳定的排放；以及（4）测量误差是随机的，不相关的并且与正态分布一致（平均值为0）。该模型中所有未知参数均作为随机变量，并基于历史数据，文献和经验信息，通过马尔可夫链蒙特卡罗方法得出其后验分布。以淮河出水系统为例，验证了该模型的可行性。 Gelman-Rubin，自动频率控制统计数据和确定系数（R-2）验证了可靠性。结果表明，NPS的氨氮（NH4 +），化学需氧量，总氮和总磷的总负荷分别占16.35-27.58％，18.78-25.69％，21.68-29.71％和42.11-52.09％。 。参数敏感性分析表明，NPS浓度的先验分布是最敏感的，应根据经验或历史信息合理确定。

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