Application of land use regression modelling to describe
atmospheric levels of semivolatile organic compounds on a
national scale

J 2021

Application of land use regression modelling to describe atmospheric levels of semivolatile organic compounds on a national scale

WHITE, Kevin Bradley, Ondřej SÁŇKA, Lisa Emily MELYMUK, Petra PŘIBYLOVÁ, Jana KLÁNOVÁ et. al.

Základní údaje

Originální název

Application of land use regression modelling to describe atmospheric levels of semivolatile organic compounds on a national scale

Autoři

WHITE, Kevin Bradley (124 Kanada, domácí), Ondřej SÁŇKA (203 Česká republika, domácí), Lisa Emily MELYMUK (124 Kanada, domácí), Petra PŘIBYLOVÁ (203 Česká republika, domácí) a Jana KLÁNOVÁ (203 Česká republika, garant, domácí)

Vydání

Science of the Total Environment, Amsterdam, Elsevier Science, 2021, 0048-9697

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10511 Environmental sciences

Stát vydavatele

Nizozemské království

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 10.753

Kód RIV

RIV/00216224:14310/21:00122883

Organizační jednotka

Přírodovědecká fakulta

DOI

http://dx.doi.org/10.1016/j.scitotenv.2021.148520

UT WoS

000691589200004

Klíčová slova anglicky

Air pollution; Passive air sampling; Polycyclic aromatic hydrocarbons; Polychlorinated biphenyls; Spatial analysis

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 21. 11. 2021 21:44, Mgr. Michaela Hylsová, Ph.D.

Anotace

V originále

Despite the success of passive sampler-based monitoring networks in capturing global atmospheric distributions of semivolatile organic compounds (SVOCs), their limited spatial resolution remains a challenge. Adequate spatial coverage is necessary to better characterize concentration gradients, identify point sources, estimate human exposure, and evaluate the effectiveness of chemical regulations such as the Stockholm Convention on Persistent Organic Pollutants. Land use regression (LUR) modelling can be used to integrate land use characteristics and other predictor variables (industrial emissions, traffic intensity, demographics, etc.) to describe or predict the distribution of air concentrations at unmeasured locations across a region or country. While LUR models are frequently applied to data-rich conventional air pollutants such as particulate matter, ozone, and nitrogen oxides, they are rarely applied to SVOCs. The MONET passive air sampling network (RECETOX, Masaryk University) continuously measures atmospheric SVOC levels across Czechia in monthly intervals. Using monitoring data from 29 MONET sites over a two-year pe-riod (2015-2017) and a variety of predictor variables, we developed LUR models to describe atmospheric levels and identify sources of polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and DDT across the country. Strong and statistically significant (R-2 > 0.6; p < 0.05) models were derived for PAH and PCB levels on a national scale. The PAH model retained three predictor variables - heating emissions represented by domestic fuel consumption, industrial PAH point sources, and the hill:valley index, a measure of site topography. The PCB model retained two predictor variables - site elevation, and secondary sources of PCBs represented by soil concentrations. These models were then applied to Czechia as a whole, highlighting the spatial variability of atmospheric SVOC levels, and providing a tool that can be used for further optimization of sampling network design, as well as evaluating potential human and environmental chemical exposures.

Návaznosti

EHP-CZ02-OV-1-029-2015, interní kód MU

Název: DA VINCI – Zlepšení vizualizace, interpretace a srovnatelnosti dat o organických polutantech v dlouhodobých monitorovacích sítích

Investor: Ministerstvo životního prostředí ČR, DA VINCI – Zlepšení vizualizace, interpretace a srovnatelnosti dat o organických polutantech v dlouhodobých monitorovacích sítích

LM2018121, projekt VaV

Název: Výzkumná infrastruktura RECETOX (Akronym: RECETOX RI)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, RECETOX RI

689443, interní kód MU

Název: ERA-PLANET - The European network for observing our changing planet (Akronym: ERA-PLANET)

Investor: Evropská unie, ERA-PLANET - The European network for observing our changing planet, Climate action, environment, resource efficiency and raw materials (Societal Challenges)

Citovat

WHITE, Kevin Bradley, Ondřej SÁŇKA, Lisa Emily MELYMUK, Petra PŘIBYLOVÁ a Jana KLÁNOVÁ. Application of land use regression modelling to describe atmospheric levels of semivolatile organic compounds on a national scale. Science of the Total Environment. Amsterdam: Elsevier Science, 2021, roč. 793, November 2021, s. 1-11. ISSN 0048-9697. Dostupné z: https://dx.doi.org/10.1016/j.scitotenv.2021.148520.

@article{1804214,
   author = {White, Kevin Bradley and Sáňka, Ondřej and Melymuk, Lisa Emily and Přibylová, Petra and Klánová, Jana},
   article_location = {Amsterdam},
   article_number = {November 2021},
   doi = {http://dx.doi.org/10.1016/j.scitotenv.2021.148520},
   keywords = {Air pollution; Passive air sampling; Polycyclic aromatic hydrocarbons; Polychlorinated biphenyls; Spatial analysis},
   language = {eng},
   issn = {0048-9697},
   journal = {Science of the Total Environment},
   title = {Application of land use regression modelling to describe atmospheric levels of semivolatile organic compounds on a national scale},
   url = {https://www.sciencedirect.com/science/article/pii/S0048969721035920?via%3Dihub},
   volume = {793},
   year = {2021}
}

TY  - JOUR
ID  - 1804214
AU  - White, Kevin Bradley - Sáňka, Ondřej - Melymuk, Lisa Emily - Přibylová, Petra - Klánová, Jana
PY  - 2021
TI  - Application of land use regression modelling to describe atmospheric levels of semivolatile organic compounds on a national scale
JF  - Science of the Total Environment
VL  - 793
IS  - November 2021
SP  - 1-11
EP  - 1-11
PB  - Elsevier Science
SN  - 00489697
KW  - Air pollution
KW  - Passive air sampling
KW  - Polycyclic aromatic hydrocarbons
KW  - Polychlorinated biphenyls
KW  - Spatial analysis
UR  - https://www.sciencedirect.com/science/article/pii/S0048969721035920?via%3Dihub
N2  - Despite the success of passive sampler-based monitoring networks in capturing global atmospheric distributions of semivolatile organic compounds (SVOCs), their limited spatial resolution remains a challenge. Adequate spatial coverage is necessary to better characterize concentration gradients, identify point sources, estimate human exposure, and evaluate the effectiveness of chemical regulations such as the Stockholm Convention on Persistent Organic Pollutants. Land use regression (LUR) modelling can be used to integrate land use characteristics and other predictor variables (industrial emissions, traffic intensity, demographics, etc.) to describe or predict the distribution of air concentrations at unmeasured locations across a region or country. While LUR models are frequently applied to data-rich conventional air pollutants such as particulate matter, ozone, and nitrogen oxides, they are rarely applied to SVOCs. The MONET passive air sampling network (RECETOX, Masaryk University) continuously measures atmospheric SVOC levels across Czechia in monthly intervals. Using monitoring data from 29 MONET sites over a two-year pe-riod (2015-2017) and a variety of predictor variables, we developed LUR models to describe atmospheric levels and identify sources of polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and DDT across the country. Strong and statistically significant (R-2 > 0.6; p < 0.05) models were derived for PAH and PCB levels on a national scale. The PAH model retained three predictor variables - heating emissions represented by domestic fuel consumption, industrial PAH point sources, and the hill:valley index, a measure of site topography. The PCB model retained two predictor variables - site elevation, and secondary sources of PCBs represented by soil concentrations. These models were then applied to Czechia as a whole, highlighting the spatial variability of atmospheric SVOC levels, and providing a tool that can be used for further optimization of sampling network design, as well as evaluating potential human and environmental chemical exposures.
ER  -

WHITE, Kevin Bradley, Ondřej SÁŇKA, Lisa Emily MELYMUK, Petra PŘIBYLOVÁ a Jana KLÁNOVÁ. Application of land use regression modelling to describe atmospheric levels of semivolatile organic compounds on a national scale. \textit{Science of the Total Environment}. Amsterdam: Elsevier Science, 2021, roč.~793, November 2021, s.~1-11. ISSN~0048-9697. Dostupné z: https://dx.doi.org/10.1016/j.scitotenv.2021.148520.

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