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Highly Acidic Ambient Particles, Soluble Metals, and Oxidative Potential: A Link between Sulfate and Aerosol Toxicity
Open accessAbstract
Soluble transition metals in particulate matter (PM) can generate reactive oxygen species in vivo by redox cycling, leading to oxidative stress and adverse health effects. Most metals, such as those from roadway traffic, are emitted in an insoluble form, but must be soluble for redox cycling. Here we present the mechanism of metals dissolution by highly acidic sulfate aerosol and the effect on particle oxidative potential (OP) through analysis of size distributions. Size-segregated ambient PM were collected from a road-side and representative urban site in Atlanta, GA. Elemental and organic carbon, ions, total and water-soluble metals, and water-soluble OP were measured. Particle pH was determined with a thermodynamic model using measured ionic species. Sulfate was spatially uniform and found mainly in the fine mode, whereas total metals and mineral dust cations were highest at the road-side site and in the coarse mode, resulting in a fine mode pH < 2 and near neutral coarse mode. Soluble metals and OP peaked at the intersection of these modes demonstrating that sulfate plays a key role in producing highly acidic fine aerosols capable of dissolving primary transition metals that contribute to aerosol OP. Sulfate-driven metals dissolution may account for sulfate-health associations reported in past studies.
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Cited by
The acidity of atmospheric particles and clouds
Pye, H. O. T., Nenes, A., Alexander, B., Ault, A. P., Barth, M. C., Clegg, S. L., Collett Jr., J. L., Fahey, K. M., Hennigan, C. J., Herrmann, H., et al. (2020) · Atmospheric Chemistry and Physics · doi:10.5194/acp-20-4809-2020 · Cited by 785
Self-citationGuo, H., Weber, R. J., & Nenes, A. (2017) · Scientific Reports · doi:10.1038/s41598-017-11704-0 · Cited by 268
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Self-citationAerosol pH and its driving factors in Beijing
Ding, J., Zhao, P., Su, J., Dong, Q., Du, X., & Zhang, Y. (2019) · Atmospheric Chemistry and Physics · doi:10.5194/acp-19-7939-2019 · Cited by 256
Shi, L., Zhu, Q., Wang, Y., Hao, H., Zhang, H., Schwartz, J., Amini, H., van Donkelaar, A., Martin, R. V., Steenland, K., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2211282119 · Cited by 145
Self-citation
Metadata
- Authors
- Fang, T., Guo, H., Zeng, L., Verma, V., Nenes, A., & Weber, R. J.
- Venue
- Environmental Science & Technology
- Publisher
- American Chemical Society (ACS)
- Published
- 2017-02-13
- Volume
- 51
- Issue
- 5
- Pages
- 2611-2620
- ISSN
- 0013-936X (ISSN-L)
- 1520-5851
- OpenAlex
- W2583909753
- Cited by
- 584
- References
- 106
- FWCI
- 23.17
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
