Application of land use regression modelling to describe atmospheric levels of semivolatile organic compounds on a national scale
| Autor: | Jana Klánová, Lisa Emily Melymuk, Ondřej Sáňka, Kevin Bradley White, Petra Přibylová |
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| Rok vydání: | 2021 |
| Předmět: |
Environmental Engineering
Ozone 010504 meteorology & atmospheric sciences Air pollution 010501 environmental sciences Atmospheric sciences medicine.disease_cause 01 natural sciences chemistry.chemical_compound 11. Sustainability medicine Environmental Chemistry Humans Polycyclic Aromatic Hydrocarbons Waste Management and Disposal 0105 earth and related environmental sciences Air Pollutants Land use Elevation Sampling (statistics) 15. Life on land Particulates Pollution Polychlorinated Biphenyls chemistry 13. Climate action Environmental science Spatial variability Particulate Matter Stockholm Convention on Persistent Organic Pollutants Environmental Monitoring |
| Zdroj: | The Science of the total environment. 793 |
| ISSN: | 1879-1026 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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