2021
DOI: 10.1007/s00704-021-03752-4
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Surface-based nocturnal air temperature inversions in southern Poland and their influence on PM10 and PM2.5 concentrations in Upper Silesia

Abstract: The frequency, strength and seasonal variations of surface-based temperature inversions (SBTIs) in Upper Silesia (Southern Poland) were examined using data from January 2001 to September 2020. Based on the air temperatures recorded at the meteorological station of the Institute of Earth Sciences in Sosnowiec (263 m a.s.l.) at heights of 2 m and 88 m above the ground, the vertical temperature gradient of the 100-m layer (γSos100 m) was determined. A lapse rate of γSos100 m > 0.5 K was defined as a temperatur… Show more

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Cited by 11 publications
(14 citation statements)
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“…Based on the average hourly air temperature (AT) data, the vertical gradients determining the state of the atmosphere were calculated as the differences between the AT measured at 2 m (AT2) and 88 m (AT88) above ground level. Next, the differences were converted to the standard meteorological gradient defined as the change per 100 m according to formula 1 [47]. Positive ATGs (γ) indicate surface-based temperature inversion (SBI), while negative ATGs represent strong or conditionally unstable conditions, i.e., a vertical decrease in AT below −0.5 K/100 m.…”
Section: Methodsmentioning
confidence: 99%
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“…Based on the average hourly air temperature (AT) data, the vertical gradients determining the state of the atmosphere were calculated as the differences between the AT measured at 2 m (AT2) and 88 m (AT88) above ground level. Next, the differences were converted to the standard meteorological gradient defined as the change per 100 m according to formula 1 [47]. Positive ATGs (γ) indicate surface-based temperature inversion (SBI), while negative ATGs represent strong or conditionally unstable conditions, i.e., a vertical decrease in AT below −0.5 K/100 m.…”
Section: Methodsmentioning
confidence: 99%
“…We adopted the threshold of γ ± 0.5 K for isothermal conditions to minimize measurement errors that might have not been detected [12,47]. Conditional instability depends on wet adiabatic lapse rate in the moist air.…”
Section: Methodsmentioning
confidence: 99%
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