Aerosol iron solubility in global marine atmosphere projected by deep learning Extended Data Table 3

Published: 27 June 2022| Version 1 | DOI: 10.17632/xxww7g3pxm.1
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Description

Dateset of aerosol iron (Fe) solubility projected by the Deep Learning Neutral Network (DLNN) model based on the contents of total Fe, sulfate, nitrate and oxalate in aerosol particles, the particle size and air relative humidity in the ranges of actual atmosphere worldwide reported in the literature. The concentration range of total Fe in global aerosols was between 0.1 ng/m3 and 86000 ng/m3 (Mahowald et al., 2018; Shalley et al., 2018). In the preparation of the dataset, the range was divided into six intervals with different increments according to the occurrence frequency of total Fe concentration in aerosols, namely, 0.1-1 ng/m3 with an increment of 0.1; 1-10 ng/m3 with an increment of 1; 10-200 ng/m3 with an increment of 10; 200-1000 ng/m3 with an increment of 100; 1000-2000 ng/m3 with an increment of 500, and 2000-86000 ng/m3 with an increment of 2000. The concentration range of sulfate and nitrate in aerosol particles was reported from 0 to 110 μg/m3 and 0 to 70 μg/m3, respectively, and their maximum concentrations were the average levels observed in haze aerosols at the city of Xi'an in China (Shen et al., 2009). Here, we set the sulfate and nitrate concentrations as 0-110 μg/m3 with an increment of 5 and 0-70 μg/m3 with an increment of 5, respectively. The concentration range of oxalate in aerosols varied from 0 to 2 μg/m3, and its maximum value was from the coastal city of Qingdao, China (Zhang et al., 2018). The size ranges of particles were divided 9-stage of ≤0.43, 0.43–0.65, 0.65–1.1, 1.1–2.1, 2.1–3.3, 3.3–4.7, 4.7–7.0, 7.0–11, and >11 μm, and the corresponding particle size was set 0.1, 0.54, 0.875, 1.6, 2.7, 4.0, 5.85, 9.0, 55.5 μm, respectively. The air relative humidity was set from 10% to 100% with an increment of 5%. We considered all combinations of the values of the six factors and projected the Fe solubility in aerosols in each case by our constructed and trained DNLL model. In total, we obtained 59,053,994 sets of data including the six controlling factors and the projected Fe solubility (data size about 2.5G). A higher resolution prediction of Fe solubility in global aerosols can be obtained by further reducing the increment of each factor. References 1.Mahowald, N. M. et al. Aerosol trace metal leaching and impacts on marine microorganisms. Nat. Commun. 9, 2614 (2018). 2.Shelley, R. U., Landing, W. M., Ussher, S. J., Planquette, H. & Sarthou, G. Regional trends in the fractional solubility of Fe and other metals from North Atlantic aerosols (GEOTRACES cruises GA01 and GA03) following a two-stage leach. Biogeosciences 15, 2271-2288 (2018). 3.Shen, Z. et al. Ionic composition of TSP and PM2.5 during dust storms and air pollution episodes at Xi'an, China. Atmos. Environ. 43, 2911-2918 (2009). 4.Zhang, S., Shi, J., Yao, X. & Gao H. Distribution of oxalate in atmospheric aerosols and the related influencing factors in qingdao, during winter and spring. Huan jing ke xue 39, 1512-1519 (2018).

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Institutions

Ocean University of China

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Atmospheric Aerosols, Iron, Solubility, Deep Learning

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