Physical properties of the sub-micrometer aerosol over the Amazon rain forest during the wet-to-dry season transition - comparison of modeled and measured CCN concentrations

[摘要] Sub-micrometer atmospheric aerosol particles were studied in the Amazonregion, 125 km northeast of Manaus, Brazil (-1°55.2'S, 59°28.1'W).The measurements were performed during the wet-to-dry transition period,4-28 July 2001 as part of the LBA (Large-Scale Biosphere AtmosphereExperiment in Amazonia) CLAIRE-2001 (Cooperative LBA Airborne RegionalExperiment) experiment. The number size distribution was measured with twoparallel differential mobility analyzers, the hygroscopic growth at 90%RH with a Hygroscopic Tandem Mobility Analyzer (H-TDMA) and theconcentrations of cloud condensation nuclei (CCN) with a cloud condensationnuclei counter. A model was developed that uses the H-TDMA data to predictthe number of soluble molecules or ions in the individual particles and thecorresponding minimum particle diameter for activation into a cloud dropletat a certain supersaturation. Integrating the number size distribution abovethis diameter, CCN concentrations were predicted with a time resolution of10 min and compared to the measured concentrations. During the study period,three different air masses were identified and compared: clean background,air influenced by aged biomass burning, and moderately polluted air fromrecent local biomass burning. For the clean period 2001, similar number sizedistributions and hygroscopic behavior were observed as during the wetseason at the same site in 1998, with mostly internally mixed particles oflow diameter growth factor (~1.3 taken from dry to 90% RH). Duringthe periods influenced by biomass burning the hygroscopic growth changedslightly, but the largest difference was seen in the number sizedistribution. The CCN model was found to be successful in predicting themeasured CCN concentrations, typically within 25%. A sensitivity studyshowed relatively small dependence on the assumption of which model saltthat was used to predict CCN concentrations from H-TDMA data. One strengthof using H-TDMA data to predict CCN concentrations is that the model canalso take into account soluble organic compounds, insofar as they go intosolution at 90% RH. Another advantage is the higher time resolutioncompared to using size-resolved chemical composition data.

[学科分类] 大气科学