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Journal article

Investigation of the diurnal pattern of the vertical distribution of pollen in the lower troposphere using LIDAR

Noh Y, Lee H, Mueller D, Lee K, Shin D, Shin S, Choi T, Choi Y, Kim K ...see all

Atmospheric Chemistry and Physics, vol. 13, issue 15 (2013) pp. 7619-7629

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Abstract

The diurnal pattern of the vertical distribution of biogenic pollen in the lower troposphere was investigated by LIDAR. Meteorological data were taken at the ground. Pollen concentrations were measured at the surface using a Burkard 7-day-recording volumetric spore sampler. Aerosol extinction coefficients and depolarization ratios at 532 nm were obtained from LIDAR measurements in spring (4 May–2 June) 2009 in Gwangju, South Korea. Linear volume depolarization ratios varied between 0.08 and 0.14 and were observed only during daytime (09:00–17:00 local time (LT)) during days of high pollen concentration (4 to 9 May). Daily average pollen concentrations ranged 1000–2500 cm−3 in the same period. The temporal evolution of the vertical distribution of the linear volume depolarization ratio showed a specific diurnal pattern. Linear volume depolarization ratios of more than 0.06, were measured near the surface in the morning. High depolarization ratios were detected up to 2 km aboveground between 12:00 and 14:00 LT, whereas high depolarization ratios were observed only close to the surface after 17:00 LT. Low values of depolarization ratios (≤0.05) were detected after 18:00 LT until the next morning. During the measurement period, the daily variations of the high depolarization ratios close to the surface showed correlation to number concentration measurements of pollen. This finding suggests that high depolarization ratios could be attributed to enhanced pollen concentrations. The diurnal characteristics of the high values of depolarization ratios are thought to be closely associated with turbulent transport. Diurnal and vertical characteristics of pollen, if measured continuously, could be used to improve the accuracy of pollen-forecasting models via data assimilation studies.

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