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dc.contributor.authorJury, Mark R.
dc.date.accessioned2017-06-09T16:51:44Z
dc.date.available2017-06-09T16:51:44Z
dc.date.issued2017
dc.identifier.issn1558-8424
dc.identifier.otherams-75456.pdf
dc.identifier.urihttp://onlinelibrary.yabesh.ir/handle/yetl/4217794
dc.description.abstracthe meteorology of air pollution episodes on South Africa?s Highveld was studied using OMI and AIRS satellite estimates, MERRA2 reanalysis model products and in-situ weather data. Surface-layer SO2 and NO2 display high concentrations during winter (May-July) and provide a focus for statistical analysis of monthly and daily time series. Highveld area averaged monthly model SO2 was temporally correlated with boundary layer height (-0.76) and temperature lapse rate (+0.65) in the period 1980-2015, but relationships with winds were weak. Daily Highveld area satellite NO2 was related to dewpoint temperature (-0.59) and exhibited pulsing in the range 7-24 days in the period 2005-2015. High concentrations of these short-lived locally-generated air pollutants were found over and southeast of Johannesburg due to urban and industrial emissions.The spatial regression of daily NO2 onto regional sea level air pressure fields in May-July 2005-2015 revealed the slow eastward movement of an anticyclone. At the climate timescale, Pacific La Nina conditions favored an increase of May-July SO2 concentrations when SST in the equatorial Atlantic were warmer than normal. The meteorological pattern underlying the highest ranked air pollution event of 18-25 July 2008 was characterized by sharp anticyclonic curvature of low level winds that induce subsidence, and consequently a stable lapse rate and low dewpoint temperature (-5C). The wind vorticity exerted a stronger influence on dispersion than the surface divergence. This new understanding will underpin better air quality forecasts over the South African Highveld.
dc.publisherAmerican Meteorological Society
dc.titleStatistics and meteorology of air pollution episodes over the South African Highveld based on satellite-model datasets
dc.typeJournal Paper
dc.journal.volume056
dc.journal.issue006
dc.journal.titleJournal of Applied Meteorology and Climatology
dc.identifier.doi10.1175/JAMC-D-16-0354.1
dc.journal.fristpage1583
dc.journal.lastpage1594
dc.treeJournal of Applied Meteorology and Climatology:;2017:;volume( 056 ):;issue: 006
dc.contenttypeFulltext


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