Please use this identifier to cite or link to this item: https://apo.ansto.gov.au/dspace/handle/10238/9417
Title: Characterising diurnal & synoptic timescale changes in urban air quality using Radon-222
Authors: Chambers, SD
Kikaj, D
Podstawczyńska, A
Williams, AG
Crawford, J
Griffiths, AD
Keywords: Daily variations
Radon 222
Urnan areas
Air quality
Atmospheres
Emission
Pollution
Temperature inversions
Issue Date: 1-May-2020
Publisher: Europenan Geosciences Union
Citation: Chambers, S., Kikaj, D., Podstawczyńska, A., Willaiams, A., Crawford, J., & Griffiths, A. (2020) Characterising diurnal & synoptic timescale changes in urban air quality using Radon-222. Paper presented at the EGU General Assembly 2020 Online, 4-8 May 2020.
Series/Report no.: EGU2020-1506
Abstract: Urban air quality is strongly influenced by the atmosphere’s ability to disperse primary emissions and opportunities for secondary pollution formation. In mid- to high-latitude regions that experience enduring winter snow cover or soil freezing, regional subsidence and stagnation associated with persistent anti-cyclonic conditions such as the “Siberian High” can lead to “cold pool” or “persistent inversion” events. These events can result in life-threatening pollution episodes that last for weeks. While often associated with complex topography [1,2], persistent inversion events can also influence the air quality of urban centres in flat, inland regions [3]. This presentation will describe a recently-developed radon-based technique for identifying and characterising synoptic-timescale persistent inversion events, which is proving to be a simple and economical alternative to contemporary meteorological approaches that require regular sonde profiles [1]. Furthermore, key assumptions of the radon-based technique to characterise diurnaltimescale changes in the atmospheric mixing state described by Chambers et al. [4] are violated during persistent inversion conditions. Here we demonstrate how atmospheric class-typing, through successive application of radon-based techniques for identifying synoptic- and diurnaltimescale changes in the atmospheric mixing state, improves understanding of atmospheric controls on urban air quality in non-summer months across the full diurnal cycle. This knowledge translates directly to statistically-robust techniques for assessing public exposure to pollution, and for evaluating the efficacy of pollution mitigation measures. Lastly, we show how atmospheric class-typing can be used to enhance the evaluation of chemical transport models. © Author(s) 2020
Gov't Doc #: 9481
URI: https://presentations.copernicus.org/EGU2020/EGU2020-1506_presentation.pdf
http://apo.ansto.gov.au/dspace/handle/10238/9417
https://doi.org/10.5194/egusphere-egu2020-1506
Appears in Collections:Conference Publications

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