Cyanobacterial risk prevention under global warming using an extended Bayesian network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F21%3APU140987" target="_blank" >RIV/00216305:26210/21:PU140987 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/abs/pii/S0959652621019478?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S0959652621019478?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jclepro.2021.127729" target="_blank" >10.1016/j.jclepro.2021.127729</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Cyanobacterial risk prevention under global warming using an extended Bayesian network

Popis výsledku v původním jazyce

Cyanobacterial blooms under global warming are increasing worldwide, producing emerging contaminants, which threaten the health of human beings and aquatic ecosystems. The health burdens warrant the development of a useful risk-assessment tool and a holistic preventive-control scheme to prevent cyanobacterial blooms. This paper aims to integrate cyanobacterial risk assessment and risk preventive control by investigating the relationships amongst cyanobacterial blooms and multi-dimensional influencing variables. Two challenges hinder such a task. First, the time-series variations in cyanobacteria and influencing variables are uncertain and nonlinear. Second, there rarely exists an explicit modelling framework for integrating cyanobacterial risk assessment and risk preventive control. This study builds an extended Bayesian network model and proposes an integrated framework with functions of assessment, inference, preventive control, and visualisation of the risk of cyanobacterial blooms. Field data from a tropical lake are used to evaluate the model and framework. The proposed model achieves better performance than the seven models in comparison. The cyanobacterial risk is anticipated to increase by 38.5% under global warming. On the contrary, guided by the model and framework, the risk could be reduced by about 60% by taking the identified risk preventive control scheme. The cyanobacterial risk prevention would reduce aquatic emerging contaminants in drinking and recreational water sources. © 2021 Elsevier Ltd

Název v anglickém jazyce

Cyanobacterial risk prevention under global warming using an extended Bayesian network

Popis výsledku anglicky

Cyanobacterial blooms under global warming are increasing worldwide, producing emerging contaminants, which threaten the health of human beings and aquatic ecosystems. The health burdens warrant the development of a useful risk-assessment tool and a holistic preventive-control scheme to prevent cyanobacterial blooms. This paper aims to integrate cyanobacterial risk assessment and risk preventive control by investigating the relationships amongst cyanobacterial blooms and multi-dimensional influencing variables. Two challenges hinder such a task. First, the time-series variations in cyanobacteria and influencing variables are uncertain and nonlinear. Second, there rarely exists an explicit modelling framework for integrating cyanobacterial risk assessment and risk preventive control. This study builds an extended Bayesian network model and proposes an integrated framework with functions of assessment, inference, preventive control, and visualisation of the risk of cyanobacterial blooms. Field data from a tropical lake are used to evaluate the model and framework. The proposed model achieves better performance than the seven models in comparison. The cyanobacterial risk is anticipated to increase by 38.5% under global warming. On the contrary, guided by the model and framework, the risk could be reduced by about 60% by taking the identified risk preventive control scheme. The cyanobacterial risk prevention would reduce aquatic emerging contaminants in drinking and recreational water sources. © 2021 Elsevier Ltd

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20704 - Energy and fuels

Projekt

<a href="/cs/project/EF15_003%2F0000456" target="_blank" >EF15_003/0000456: Laboratoř integrace procesů pro trvalou udržitelnost</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Cleaner Production

ISSN

0959-6526

e-ISSN

1879-1786

Svazek periodika

neuveden

Číslo periodika v rámci svazku

312

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

127729-127729

Kód UT WoS článku

000693419300003

EID výsledku v databázi Scopus

2-s2.0-85107282189