用混合统计模型在农业影响地区区分废水的点源和非点源营养负载
Jordan T Jatko1, Ryan A McManamay1, Megan T Woodyard1
1Department of Environmental Science, Baylor University, Waco, TX 76706, USA; Center for Reservoir and Aquatic Sciences Research (CRASR), Baylor University Waco, TX 76706, USA.
The Science of the total environment
|December 24, 2023
概括
一个新的三相统计模型准确地估计了农村河流中的营养负载,即使数据有限. 该方法预测由于废水处理厂的强化,营养度增加和吸收效率下降.
科学领域:
- 环境科学 环境科学
- 水的质量 水的质量
- 统计建模 统计建模
背景情况:
- 从点和非点来源的营养污染导致有害的藻类繁殖.
- 目前的营养负载模型需要大量的数据,这对于小农村溪流通常是不可用的.
- 污水处理厂 (WWTP) 的强化可以加剧营养污染.
研究的目的:
- 开发和验证一种新的三相混合统计建模方法,用于估计有限数据的溪流中的营养负载.
- 评估WWTP强化对中德克萨斯州河流中营养度和吸收效率的影响.
- 为数据稀缺环境中水资源管理提供预测工具.
主要方法:
- 通过使用通用线性混合模型 (GLMM) 开发了一种三相混合阶段统计建模方法.
- 两个德克萨斯州中部的河流,其中一个受WWTP影响,被监测了13个月的关键营养参数.
- 非点源变量 (土地利用,地形学) 和点源估计被整合到模型中.
主要成果:
- 第I阶段模型使用非点和时间数据成功预测了参考流中的营养度.
- 第二阶段模型显示了点源污染的意义,但缺乏对未来水文系统的预测能力.
- 第三阶段模型,包括吸收变量和WWTP强化,预测了显著的营养增加和吸收效率下降.
结论:
- 拟议的三相混合模型有效地估计了数据有限的农村河流中的营养负载.
- 预计WWTP强化将显著增加营养度,并降低流吸收效率.
- 这种建模方法为在不断变化的环境条件下管理水质提供了有价值的工具.
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