Cyanobacterial risk prevention under global warming using an extended Bayesian network

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Cyanobacterial risk prevention under global warming using an extended Bayesian network

Original language description

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

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20704 - Energy and fuels

Project

<a href="/en/project/EF15_003%2F0000456" target="_blank" >EF15_003/0000456: Sustainable Process Integration Laboratory (SPIL)</a><br>

Continuities

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

Publication year

2021

Confidentiality

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

Name of the periodical

Journal of Cleaner Production

ISSN

0959-6526

e-ISSN

1879-1786

Volume of the periodical

neuveden

Issue of the periodical within the volume

312

Country of publishing house

US - UNITED STATES

Number of pages

13

Pages from-to

127729-127729

UT code for WoS article

000693419300003

EID of the result in the Scopus database

2-s2.0-85107282189