Quantifying and predicting ecological and human health risks for binary heavy metal pollution accidents at the watershed scale using Bayesian Networks.

Sakari Kuikka

Quantifying and predicting ecological and human health risks for binary heavy metal pollution accidents at the watershed scale using Bayesian Networks.

Research output: Contribution to journal › Article › Scientific › peer-review

Original language	Finnish
Journal	Environmental Pollution
ISSN	0269-7491
Publication status	Accepted/In press - 2021
MoE publication type	A1 Journal article-refereed

Access to Document

https://www.sciencedirect.com/science/article/pii/S0269749120368147?via%3Dihub

Cite this

@article{9798b2662bb94cd38e87f740af2315a0,

title = "Quantifying and predicting ecological and human health risks for binary heavy metal pollution accidents at the watershed scale using Bayesian Networks.",

author = "Sakari Kuikka",

year = "2021",

language = "suomi",

journal = "Environmental Pollution",

issn = "0269-7491",

publisher = "ELSEVIER SCI IRELAND LTD",

}

TY - JOUR

T1 - Quantifying and predicting ecological and human health risks for binary heavy metal pollution accidents at the watershed scale using Bayesian Networks.

AU - Kuikka, Sakari

PY - 2021

Y1 - 2021

UR - https://www.sciencedirect.com/science/article/pii/S0269749120368147?via%3Dihub

M3 - Artikkeli

JO - Environmental Pollution

JF - Environmental Pollution

SN - 0269-7491

ER -