Application of land use regression modelling to describe atmospheric levels of semivolatile organic compounds on a national scale
Language English Country Netherlands Media print-electronic
Document type Journal Article
PubMed
34328963
DOI
10.1016/j.scitotenv.2021.148520
PII: S0048-9697(21)03592-0
Knihovny.cz E-resources
- Keywords
- Air pollution, Passive air sampling, Polychlorinated biphenyls, Polycyclic aromatic hydrocarbons, Spatial analysis,
- MeSH
- Air Pollutants * analysis MeSH
- Humans MeSH
- Environmental Monitoring MeSH
- Particulate Matter analysis MeSH
- Polychlorinated Biphenyls * analysis MeSH
- Polycyclic Aromatic Hydrocarbons * analysis MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- Air Pollutants * MeSH
- Particulate Matter MeSH
- Polychlorinated Biphenyls * MeSH
- Polycyclic Aromatic Hydrocarbons * MeSH
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 period (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 (R2 > 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.
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