Use statistical machine learning to detect nutrient thresholds in Microcystis blooms and microcystin management

Shan Kun; Wang Xiaoxiao; Yang Hong; Zhou Botian; Song Lirong; Shang Mingsheng

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Use statistical machine learning to detect nutrient thresholds in Microcystis blooms and microcystin management

机译：使用统计机器学习来检测微囊杆菌和微囊藻氏植物管理中的营养阈值

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The frequency of toxin-producing cyanobacterial blooms has increased in recent decades due to nutrient enrichment and climate change. Because Microcystis blooms are related to different environmental conditions, identifying potential nutrient control targets can facilitate water quality managers to reduce the likelihood of microcystins (MCs) risk. However, complex biotic interactions and field data limitations have constrained our understanding of the nutrient-microcystin relationship. This study develops a Bayesian modelling framework with intracellular and extracellular MCs that characterize the relationships between different environmental and biological factors. This model was fit to the across-lake dataset including three bloom-plagued lakes in China and estimated the putative thresholds of total nitrogen (TN) and total phosphorus (TP). The lake-specific nutrient thresholds were estimated using Bayesian updating process. Our results suggested dual N and P reduction in controlling cyanotoxin risks. The total Microcystis biomass can be substantially suppressed by achieving the putative thresholds of TP (0.10 mg/L) in Lakes Taihu and Chaohu, but a stricter TP target (0.05 mg/L) in Dianchi Lake. To maintain MCs concentrations below 1.0 mu g/L, the estimated TN threshold in three lakes was 1.8 mg/L, but the effect can be counteracted by the increase of temperature. Overall, the present approach provides an efficient way to integrate empirical knowledge into the data-driven model and is helpful for the management of water resources.

机译：由于养分富集和气候变化，近几十年来，毒素生产的毒素绽放的频率增加。由于微囊瓣绽放与不同的环境条件有关，因此确定潜在的营养控制目标可以促进水质管理人员降低微囊藻（MCS）风险的可能性。然而，复杂的生物相互作用和现场数据限制限制了我们对营养微胱氨酸关系的理解。该研究开发了一种贝叶斯建模框架，具有细胞内和细胞外的MCS，其特征在于不同的环境和生物因素之间的关系。该型号适合跨湖数据集，包括三个盛开的湖泊湖泊，估计了总氮（TN）和总磷（TP）的推定阈值。使用贝叶斯更新过程估计了湖泊特异性营养阈值。我们的结果表明了控制氰毒素风险的双重N和P.通过在湖太湖和巢湖中实现TP（0.10mg / L）的推定阈值，可以基本上抑制总微囊气体生物质，而是在滇池的转化TP靶（0.05mg / L）。为了维持低于1.0μg/ l以下的MCS浓度，三湖中的估计的TN阈值为1.8mg / L，但效果可以通过温度的增加来抵消。总的来说，本方法提供了将经验知识集成到数据驱动模型中的有效方法，并有助于管理水资源。

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来源
《Harmful Algae》 |2020年第4期|101807.1-101807.10|共10页
作者
Shan Kun; Wang Xiaoxiao; Yang Hong; Zhou Botian; Song Lirong; Shang Mingsheng;
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作者单位

Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Chongqing Key Lab Big Data & Intelligent Comp Chongqing 400714 Peoples R China|Chinese Acad Sci Chongqing Inst Green & Intelligent Technol CAS Key Lab Reservoir Environm Chongqing 400714 Peoples R China;

Chinese Acad Sci Chongqing Inst Green & Intelligent Technol CAS Key Lab Reservoir Environm Chongqing 400714 Peoples R China|Univ Chinese Acad Sci Beijing 100049 Peoples R China;

Univ Reading Dept Geog & Environm Sci Reading RG6 6AB Berks England;

Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Chongqing Key Lab Big Data & Intelligent Comp Chongqing 400714 Peoples R China|Chinese Acad Sci Chongqing Inst Green & Intelligent Technol CAS Key Lab Reservoir Environm Chongqing 400714 Peoples R China;

Chinese Acad Sci Inst Hydrobiol State Key Lab Freshwater Ecol & Biotechnol Wuhan 430072 Peoples R China|Univ Chinese Acad Sci Beijing 100049 Peoples R China;

Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Chongqing Key Lab Big Data & Intelligent Comp Chongqing 400714 Peoples R China|Chinese Acad Sci Chongqing Inst Green & Intelligent Technol CAS Key Lab Reservoir Environm Chongqing 400714 Peoples R China;

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正文语种 eng
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关键词
Bayesian modelling; Eutrophication; Nutrient thresholds; Cyanobacterial blooms; Microcystis; Microcystin;

机译：贝叶斯造型;富营养化;营养阈值;蓝藻绽放;微囊杆菌;微囊藻;

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1. Establishment of season-specific nutrient thresholds and analyses of the effects of nutrient management in eutrophic lakes through statistical machine learning [J] . Tong Yindong, Xu Xiwen, Zhang Shuliang, Journal of Hydrology . 2019,第期

机译：通过统计机器学习建立季节特异性营养阈值和营养管理营养管理影响的分析
2. Statistical study on the effects of environmental factors on the growth and microcystins production of bloom-forming cyanobacterium—Microcystis aeruginosa [J] . Y. Jiang, B. Ji, R.N.S. Wong, Harmful Algae . 2008,第2期

机译：环境因素对开花的蓝藻铜绿微囊藻生长和微囊藻毒素产生影响的统计研究
3. Microcystis on development of early life stage Japanese medaka ( Oryzias latipes): Comparative toxicity of natural blooms, cultured Microcystis and microcystin-LR]]> [J] . Spencer R. Saraf, Amy Frenkel, Matthew J. Harke, Aquatic Toxicology . 2018,第期

机译：<！[CDATA [CDATA [CDATA] 微囊杆菌和微囊杆菌]]]>
4. The Role of Microcystins in Maintaining Colonies of Bloom-Forming Microcystis spp [C] . Nanqin Gan, Yan Xiao, Zhongxing Wu, International Symposium on Microcystis Studies from Asia. . 2011

机译：微囊藻毒素在维持形成花粉的微囊藻属菌落中的作用
5. Application of Statistical and Machine Learning Techniques to Detect Rare Events in High Frequency Financial Data And Assess Corporate Credit Rating [D] . Golbayani, Parisa . 2019

机译：统计和机器学习技术在高频财务数据中检测稀有事件及评估企业信用评级
6. Variability in microcystin quotas during a Microcystis bloom in a eutrophic lake [O] . Susanna A. Wood, Jonathan Puddick, Ian Hawes, 2021

机译：微囊杆菌在富营养湖中的微囊杆菌配额的可变性
7. Application of Bayesian network including Microcystis morphospecies for microcystin risk assessment in three cyanobacterial bloom-plagued lakes, China [O] . Kun Shan, Mingsheng Shang, Botian Zhou, 2019

机译：贝叶斯网络在三种蓝藻灌注湖中微囊虫风险评估中的微囊杆菌形态学

Use statistical machine learning to detect nutrient thresholds in Microcystis blooms and microcystin management

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