IHB OpenIR  > 藻类生物学及应用研究中心  > 期刊论文
Use statistical machine learning to detect nutrient thresholds in Microcystis blooms and microcystin management
Shan, Kun1,2; Wang, Xiaoxiao2,5; Yang, Hong4; Zhou, Botian1,2; Song, Lirong3,5; Shang, Mingsheng1,2
Corresponding AuthorShan, Kun(shankun@cigit.ac.cn)
2020-04-01
Source PublicationHARMFUL ALGAE
ISSN1568-9883
Volume94Pages:10
AbstractThe 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.
KeywordBayesian modelling Eutrophication Nutrient thresholds Cyanobacterial blooms Microcystis Microcystin
DOI10.1016/j.hal.2020.101807
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Chongqing Science and Technology Commission ; Chongqing Science and Technology Commission ; Chongqing Science and Technology Commission ; Chongqing Science and Technology Commission ; National Key Scientific and Technological Project of China ; National Key Scientific and Technological Project of China ; National Key Scientific and Technological Project of China ; National Key Scientific and Technological Project of China ; National Basic Research Program of China ; National Basic Research Program of China ; National Basic Research Program of China ; National Basic Research Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Chongqing Science and Technology Commission ; Chongqing Science and Technology Commission ; Chongqing Science and Technology Commission ; Chongqing Science and Technology Commission ; National Key Scientific and Technological Project of China ; National Key Scientific and Technological Project of China ; National Key Scientific and Technological Project of China ; National Key Scientific and Technological Project of China ; National Basic Research Program of China ; National Basic Research Program of China ; National Basic Research Program of China ; National Basic Research Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Chongqing Science and Technology Commission ; Chongqing Science and Technology Commission ; Chongqing Science and Technology Commission ; Chongqing Science and Technology Commission ; National Key Scientific and Technological Project of China ; National Key Scientific and Technological Project of China ; National Key Scientific and Technological Project of China ; National Key Scientific and Technological Project of China ; National Basic Research Program of China ; National Basic Research Program of China ; National Basic Research Program of China ; National Basic Research Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Chongqing Science and Technology Commission ; Chongqing Science and Technology Commission ; Chongqing Science and Technology Commission ; Chongqing Science and Technology Commission ; National Key Scientific and Technological Project of China ; National Key Scientific and Technological Project of China ; National Key Scientific and Technological Project of China ; National Key Scientific and Technological Project of China ; National Basic Research Program of China ; National Basic Research Program of China ; National Basic Research Program of China ; National Basic Research Program of China
Indexed BySCI ; SCI
Language英语
Funding ProjectNational 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 Research AreaMarine & Freshwater Biology
WOS SubjectMarine & Freshwater Biology
WOS IDWOS:000532817900005
WOS KeywordCYANOBACTERIAL BLOOMS ; WATER-QUALITY ; LAKE TAIHU ; BAYESIAN NETWORKS ; DRINKING-WATER ; EUTROPHIC LAKE ; CHLOROPHYLL-A ; MODEL ; RESERVOIRS ; NITROGEN
PublisherELSEVIER
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Chongqing Science and Technology Commission ; Chongqing Science and Technology Commission ; Chongqing Science and Technology Commission ; Chongqing Science and Technology Commission ; National Key Scientific and Technological Project of China ; National Key Scientific and Technological Project of China ; National Key Scientific and Technological Project of China ; National Key Scientific and Technological Project of China ; National Basic Research Program of China ; National Basic Research Program of China ; National Basic Research Program of China ; National Basic Research Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Chongqing Science and Technology Commission ; Chongqing Science and Technology Commission ; Chongqing Science and Technology Commission ; Chongqing Science and Technology Commission ; National Key Scientific and Technological Project of China ; National Key Scientific and Technological Project of China ; National Key Scientific and Technological Project of China ; National Key Scientific and Technological Project of China ; National Basic Research Program of China ; National Basic Research Program of China ; National Basic Research Program of China ; National Basic Research Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Chongqing Science and Technology Commission ; Chongqing Science and Technology Commission ; Chongqing Science and Technology Commission ; Chongqing Science and Technology Commission ; National Key Scientific and Technological Project of China ; National Key Scientific and Technological Project of China ; National Key Scientific and Technological Project of China ; National Key Scientific and Technological Project of China ; National Basic Research Program of China ; National Basic Research Program of China ; National Basic Research Program of China ; National Basic Research Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Chongqing Science and Technology Commission ; Chongqing Science and Technology Commission ; Chongqing Science and Technology Commission ; Chongqing Science and Technology Commission ; National Key Scientific and Technological Project of China ; National Key Scientific and Technological Project of China ; National Key Scientific and Technological Project of China ; National Key Scientific and Technological Project of China ; National Basic Research Program of China ; National Basic Research Program of China ; National Basic Research Program of China ; National Basic Research Program of China
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Document Type期刊论文
Identifierhttp://ir.ihb.ac.cn/handle/342005/36605
Collection藻类生物学及应用研究中心_期刊论文
Corresponding AuthorShan, Kun
Affiliation1.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China
2.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, CAS Key Lab Reservoir Environm, Chongqing 400714, Peoples R China
3.Chinese Acad Sci, Inst Hydrobiol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Peoples R China
4.Univ Reading, Dept Geog & Environm Sci, Reading RG6 6AB, Berks, England
5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
Recommended Citation
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.
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