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

doi:10.1016/j.hal.2020.101807

	Use statistical machine learning to detect nutrient thresholds in Microcystis blooms and microcystin management
	Shan, Kun 1,3; Wang, Xiaoxiao 1,2; Yang, Hong 5; Zhou, Botian 1,3; Song, Lirong 2,4; Shang, Mingsheng 1,3
刊名	HARMFUL ALGAE
	2020-04-01
卷号	94 页码:10
关键词	Bayesian modelling Eutrophication Nutrient thresholds Cyanobacterial blooms Microcystis Microcystin
ISSN号	1568-9883
DOI	10.1016/j.hal.2020.101807
通讯作者	Shan, Kun(shankun@cigit.ac.cn)
英文摘要	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.
资助项目	National Natural Science Foundation of China[51609229] ; National Natural Science Foundation of China[41701247] ; National Natural Science Foundation of China[51979262] ; Chongqing Science and Technology Commission[cstc2017jcyjAX0241] ; Chongqing Science and Technology Commission[cstc2018jscx-msybX0175] ; National Key Scientific and Technological Project of China[2014ZX07104-006] ; National Basic Research Program of China[2008CB418006]
WOS研究方向	Marine & Freshwater Biology
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000532817900005
内容类型	期刊论文
源URL	[http://119.78.100.138/handle/2HOD01W0/10997]
专题	中国科学院重庆绿色智能技术研究院
通讯作者	Shan, Kun
作者单位	1.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, CAS Key Lab Reservoir Environm, Chongqing 400714, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China 4.Chinese Acad Sci, Inst Hydrobiol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Peoples R China 5.Univ Reading, Dept Geog & Environm Sci, Reading RG6 6AB, Berks, England
推荐引用方式 GB/T 7714	Shan, Kun,Wang, Xiaoxiao,Yang, Hong,et al. Use statistical machine learning to detect nutrient thresholds in Microcystis blooms and microcystin management[J]. HARMFUL ALGAE,2020,94:10.
APA	Shan, Kun,Wang, Xiaoxiao,Yang, Hong,Zhou, Botian,Song, Lirong,&Shang, Mingsheng.(2020).Use statistical machine learning to detect nutrient thresholds in Microcystis blooms and microcystin management.HARMFUL ALGAE,94,10.
MLA	Shan, Kun,et al."Use statistical machine learning to detect nutrient thresholds in Microcystis blooms and microcystin management".HARMFUL ALGAE 94(2020):10.