Airborne observations of cloud condensation nuclei spectra and aerosols over East Inner Mongolia View Full Text


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Article Info

DATE

2017-06-29

AUTHORS

Jiefan Yang, Hengchi Lei, Yuhuan Lü

ABSTRACT

A set of vertical profiles of aerosol number concentrations, size distributions and cloud condensation nuclei (CCN) spectra was observed using a passive cloud and aerosol spectrometer (PCASP) and cloud condensation nuclei counter, over the Tongliao area, East Inner Mongolia, China. The results showed that the average aerosol number concentration in this region was much lower than that in heavily polluted areas. Monthly average aerosol number concentrations within the boundary layer reached a maximum in May and a minimum in September, and the variations in CCN number concentrations at different supersaturations showed the same trend. The parameters c and k of the empirical function N = cSk were 539 and 1.477 under clean conditions, and their counterparts under polluted conditions were 1615 and 1.42. Measurements from the airborne probe mounted on a Yun-12 (Y12) aircraft, together with Hybrid Single-Particle Lagrangian Integrated Trajectory model backward trajectories indicated that the air mass from the south of Tongliao contained a high concentration of aerosol particles (1000–2500 cm−3) in the middle and lower parts of the troposphere. Moreover, detailed intercomparison of data obtained on two days in 2010 indicated that the activation efficiency in terms of the ratio of NCCN to Na (aerosols measured from PCASP) was 0.74 (0.4 supersaturations) when the air mass mainly came from south of Tongliao, and this value increased to 0.83 on the relatively cleaner day. Thus, long-range transport of anthropogenic pollutants from heavily polluted mega cities, such as Beijing and Tianjin, may result in slightly decreasing activation efficiencies. More... »

PAGES

1003-1016

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00376-017-6219-y

DOI

http://dx.doi.org/10.1007/s00376-017-6219-y

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https://app.dimensions.ai/details/publication/pub.1090282326


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