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.

Basic information

Original name

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

Authors

WHITE, Kevin Bradley (124 Canada, belonging to the institution), Ondřej SÁŇKA (203 Czech Republic, belonging to the institution), Lisa Emily MELYMUK (124 Canada, belonging to the institution), Petra PŘIBYLOVÁ (203 Czech Republic, belonging to the institution) and Jana KLÁNOVÁ (203 Czech Republic, guarantor, belonging to the institution)

Edition

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

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10511 Environmental sciences

Country of publisher

Netherlands

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 10.753

RIV identification code

RIV/00216224:14310/21:00122883

Organization unit

Faculty of Science

DOI

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

UT WoS

000691589200004

Keywords in English

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

Abstract

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.

Links

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

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

Investor: Ministry of the Environment of the CR

LM2018121, research and development project

Name: Výzkumná infrastruktura RECETOX (Acronym: RECETOX RI)

Investor: Ministry of Education, Youth and Sports of the CR, RECETOX RI

689443, interní kód MU

Name: ERA-PLANET - The European network for observing our changing planet (Acronym: ERA-PLANET)

Investor: European Union, Climate action, environment, resource efficiency and raw materials (Societal Challenges)

Citovat

WHITE, Kevin Bradley, Ondřej SÁŇKA, Lisa Emily MELYMUK, Petra PŘIBYLOVÁ and 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, vol. 793, November 2021, p. 1-11. ISSN 0048-9697. Available from: 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 and 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, vol.~793, November 2021, p.~1-11. ISSN~0048-9697. Available from: https://dx.doi.org/10.1016/j.scitotenv.2021.148520.

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