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1.
Geophys Res Lett ; 48(23)2021 Dec 16.
Artigo em Inglês | MEDLINE | ID: mdl-35136274

RESUMO

Leveraging aerosol data from multiple airborne and surface-based field campaigns encompassing diverse environmental conditions, we calculate statistics of the oxalate-sulfate mass ratio (median: 0.0217; 95% confidence interval: 0.0154-0.0296; R = 0.76; N = 2,948). Ground-based measurements of the oxalate-sulfate ratio fall within our 95% confidence interval, suggesting the range is robust within the mixed layer for the submicrometer particle size range. We demonstrate that dust and biomass burning emissions can separately bias this ratio toward higher values by at least one order of magnitude. In the absence of these confounding factors, the 95% confidence interval of the ratio may be used to estimate the relative extent of aqueous processing by comparing inferred oxalate concentrations between air masses, with the assumption that sulfate primarily originates from aqueous processing.

2.
Sci Data ; 7(1): 128, 2020 04 29.
Artigo em Inglês | MEDLINE | ID: mdl-32350280

RESUMO

Size-resolved aerosol samples were collected in Metro Manila between July 2018 and October 2019. Two Micro-Orifice Uniform Deposit Impactors (MOUDI) were deployed at Manila Observatory in Quezon City, Metro Manila with samples collected on a weekly basis for water-soluble speciation and mass quantification. Additional sets were collected for gravimetric and black carbon analysis, including during special events such as holidays. The unique aspect of the presented data is a year-long record with weekly frequency of size-resolved aerosol composition in a highly populated megacity where there is a lack of measurements. The data are suitable for research to understand the sources, evolution, and fate of atmospheric aerosols, as well as studies focusing on phenomena such as aerosol-cloud-precipitation-meteorology interactions, regional climate, boundary layer processes, and health effects. The dataset can be used to initialize, validate, and/or improve models and remote sensing algorithms.

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