Další formáty:
BibTeX
LaTeX
RIS
@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.
|