Associations between air pollution in the industrial and suburban parts of Ostrava city and their use
Jazyk angličtina Země Nizozemsko Médium print-electronic
Typ dokumentu časopisecké články
PubMed
28685369
DOI
10.1007/s10661-017-6094-0
PII: 10.1007/s10661-017-6094-0
Knihovny.cz E-zdroje
- Klíčová slova
- Air monitoring, Industrial part, Pollutant association, Suburban part,
- MeSH
- látky znečišťující vzduch analýza MeSH
- lidé MeSH
- monitorování životního prostředí * MeSH
- ozon analýza MeSH
- pevné částice analýza MeSH
- velkoměsta MeSH
- znečištění ovzduší analýza statistika a číselné údaje MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Evropa MeSH
- velkoměsta MeSH
- Názvy látek
- látky znečišťující vzduch MeSH
- ozon MeSH
- pevné částice MeSH
Selecting the locations and numbers of air quality monitoring stations is challenging as these are expensive to operate. Representative concentrations of pollutants in certain areas are usually determined by measuring. If there are significant correlations with concentrations of other pollutants or with other monitoring sites, however, concentrations could also be computed, partly reducing the costs. The aim of this study is to provide an overview of such possible relationships using data on concentrations of ambient air pollutants obtained in different areas of a larger city. Presented are associations between industrial (IP) and suburban parts (SP) as well as correlations between concentrations of various pollutants at the same site. Results of air pollutant monitoring come from Ostrava, an industrial city in Central Europe with a population of over 300,000. The study showed that certain pollutants were strongly correlated, especially particulate matter (r = 0.940) and ozone (r = 0.923) between the IP and SP. Statistically significant correlations were also found between different pollutants at the same site. The highest correlations were between PM10 and NO2 (r IP = 0.728; r SP = 0.734), NO2 and benzo(a)pyrene (r IP = 0.787; r SP = 0.697), and NO2 and ozone (r IP = -0.706; r SP = -0.686). This could contribute to more cost-effective solutions for air pollution monitoring in cities and their surroundings by using computational models based on the correlations, optimization of the network of monitoring stations, and the best selection of measuring devices.
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