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@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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