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Prediction of size-resolved number concentration of cloud condensation nuclei and long-term measurements of their activation characteristics
Atmospheric aerosol particles acting as cloud condensation nuclei (CCN) are key elements in the hydrological cycle and climate. To improve our understanding of the activation characteristics of CCN and to obtain accurate predictions of their concentrations, a long-term field campaign was carried out...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5517613/ https://www.ncbi.nlm.nih.gov/pubmed/28724981 http://dx.doi.org/10.1038/s41598-017-05998-3 |
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author | Che, H. C. Zhang, X. Y. Zhang, L. Wang, Y. Q. Zhang, Y. M. Shen, X. J. Ma, Q. L. Sun, J. Y. Zhong, J. T. |
author_facet | Che, H. C. Zhang, X. Y. Zhang, L. Wang, Y. Q. Zhang, Y. M. Shen, X. J. Ma, Q. L. Sun, J. Y. Zhong, J. T. |
author_sort | Che, H. C. |
collection | PubMed |
description | Atmospheric aerosol particles acting as cloud condensation nuclei (CCN) are key elements in the hydrological cycle and climate. To improve our understanding of the activation characteristics of CCN and to obtain accurate predictions of their concentrations, a long-term field campaign was carried out in the Yangtze River Delta, China. The results indicated that the CCN were easier to activate in this relatively polluted rural station than in clean (e.g., the Amazon region) or dusty (e.g., Kanpur-spring) locations, but were harder to activate than in more polluted urban areas (e.g., Beijing). An improved method, using two additional parameters—the maximum activation fraction and the degree of heterogeneity, is proposed to predict the accurate, size-resolved concentration of CCN. The value ranges and prediction uncertainties of these parameters were evaluated. The CCN predicted using this improved method with size-resolved chemical compositions under an assumption that all particles were internally mixed showed the best agreement with the long-term field measurements. |
format | Online Article Text |
id | pubmed-5517613 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-55176132017-07-20 Prediction of size-resolved number concentration of cloud condensation nuclei and long-term measurements of their activation characteristics Che, H. C. Zhang, X. Y. Zhang, L. Wang, Y. Q. Zhang, Y. M. Shen, X. J. Ma, Q. L. Sun, J. Y. Zhong, J. T. Sci Rep Article Atmospheric aerosol particles acting as cloud condensation nuclei (CCN) are key elements in the hydrological cycle and climate. To improve our understanding of the activation characteristics of CCN and to obtain accurate predictions of their concentrations, a long-term field campaign was carried out in the Yangtze River Delta, China. The results indicated that the CCN were easier to activate in this relatively polluted rural station than in clean (e.g., the Amazon region) or dusty (e.g., Kanpur-spring) locations, but were harder to activate than in more polluted urban areas (e.g., Beijing). An improved method, using two additional parameters—the maximum activation fraction and the degree of heterogeneity, is proposed to predict the accurate, size-resolved concentration of CCN. The value ranges and prediction uncertainties of these parameters were evaluated. The CCN predicted using this improved method with size-resolved chemical compositions under an assumption that all particles were internally mixed showed the best agreement with the long-term field measurements. Nature Publishing Group UK 2017-07-19 /pmc/articles/PMC5517613/ /pubmed/28724981 http://dx.doi.org/10.1038/s41598-017-05998-3 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Che, H. C. Zhang, X. Y. Zhang, L. Wang, Y. Q. Zhang, Y. M. Shen, X. J. Ma, Q. L. Sun, J. Y. Zhong, J. T. Prediction of size-resolved number concentration of cloud condensation nuclei and long-term measurements of their activation characteristics |
title | Prediction of size-resolved number concentration of cloud condensation nuclei and long-term measurements of their activation characteristics |
title_full | Prediction of size-resolved number concentration of cloud condensation nuclei and long-term measurements of their activation characteristics |
title_fullStr | Prediction of size-resolved number concentration of cloud condensation nuclei and long-term measurements of their activation characteristics |
title_full_unstemmed | Prediction of size-resolved number concentration of cloud condensation nuclei and long-term measurements of their activation characteristics |
title_short | Prediction of size-resolved number concentration of cloud condensation nuclei and long-term measurements of their activation characteristics |
title_sort | prediction of size-resolved number concentration of cloud condensation nuclei and long-term measurements of their activation characteristics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5517613/ https://www.ncbi.nlm.nih.gov/pubmed/28724981 http://dx.doi.org/10.1038/s41598-017-05998-3 |
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