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人工堰坝河流中藻类的生长:流量对温度的主导影响。

Algae development in rivers with artificially constructed weirs: Dominant influence of discharge over temperature.

机构信息

Faculty of Liberal Education, Seoul National University, Seoul, 08826, Republic of Korea.

School of Computer Engineering and Applied Mathematics, Hankyong National University, Anseong, 17579, Republic of Korea.

出版信息

J Environ Manage. 2024 Mar;355:120551. doi: 10.1016/j.jenvman.2024.120551. Epub 2024 Mar 9.

Abstract

Algal blooms contribute to water quality degradation, unpleasant odors, taste issues, and the presence of harmful substances in artificially constructed weirs. Mitigating these adverse effects through effective algal bloom management requires identifying the contributing factors and predicting algal concentrations. This study focused on the upstream region of the Seungchon Weir in Korea, which is characterized by elevated levels of total nitrogen and phosphorus due to a significant influx of water from a sewage treatment plant. We employed four distinct machine learning models to predict chlorophyll-a (Chl-a) concentrations and identified the influential variables linked to local algal bloom events. The gradient boosting model enabled an in-depth exploration of the intricate relationships between algal occurrence and water quality parameters, enabling accurate identification of the causal factors. The models identified the discharge flow rate (D-Flow) and water temperature as the primary determinants of Chl-a levels, with feature importance values of 0.236 and 0.212, respectively. Enhanced model precision was achieved by utilizing daily average D-Flow values, with model accuracy and significance of the D-Flow amplifying as the temporal span of daily averaging increased. Elevated Chl-a concentrations correlated with diminished D-Flow and temperature, highlighting the pivotal role of D-Flow in regulating Chl-a concentration. This trend can be attributed to the constrained discharge of the Seungchon Weir during winter. Calculating the requisite D-Flow to maintain a desirable Chl-a concentration of up to 20 mg/m across varying temperatures revealed an escalating demand for D-Flow with rising temperatures. Specific D-Flow ranges, corresponding to each season and temperature condition, were identified as particularly influential on Chl-a concentration. Thus, optimizing Chl-a reduction can be achieved by strategically increasing D-Flow within these specified ranges for each season and temperature variation. This study highlights the importance of maintaining sufficient D-Flow levels to mitigate algal proliferation within river systems featuring weirs.

摘要

藻类水华会导致水质恶化、异味、口感问题以及人工建造的堰中有害物质的存在。通过有效的藻类水华管理来减轻这些不利影响,需要确定促成因素并预测藻类浓度。本研究集中于韩国顺川堰的上游区域,由于污水处理厂大量进水,该区域总氮和总磷水平升高。我们采用了四个不同的机器学习模型来预测叶绿素-a(Chl-a)浓度,并确定了与当地藻类水华事件相关的有影响的变量。梯度提升模型深入探索了藻类发生与水质参数之间的复杂关系,从而能够准确识别因果因素。模型确定了排放流量(D-Flow)和水温是 Chl-a 水平的主要决定因素,特征重要性值分别为 0.236 和 0.212。通过利用每日平均 D-Flow 值,可以提高模型精度,随着每日平均时间跨度的增加,模型精度和 D-Flow 的显著性都得到增强。Chl-a 浓度升高与 D-Flow 和温度降低相关,这突出了 D-Flow 在调节 Chl-a 浓度方面的关键作用。这种趋势可归因于顺川堰在冬季的限制排放。计算维持不同温度下理想的 20 mg/m³ Chl-a 浓度所需的 D-Flow 表明,随着温度升高,对 D-Flow 的需求呈上升趋势。确定了与每个季节和温度条件对应的特定 D-Flow 范围,这些范围对 Chl-a 浓度有特别大的影响。因此,通过在每个季节和温度变化的指定范围内有策略地增加 D-Flow,可以实现 Chl-a 减少的优化。本研究强调了在具有堰的河流系统中保持足够的 D-Flow 水平以减轻藻类增殖的重要性。

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