Articles | Volume 20, issue 21
https://doi.org/10.5194/acp-20-12853-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-20-12853-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Nov 2020
Research article |
| 05 Nov 2020
| 05 Nov 2020
Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements
Atmospheric Sciences Research Center, State University of New York, Albany, New York 12203, USA
Fangqun Yu
Atmospheric Sciences Research Center, State University of New York, Albany, New York 12203, USA
Related authors
Fangqun Yu, Gan Luo, Arshad Arjunan Nair, Sebastian Eastham, Christina J. Williamson, Agnieszka Kupc, and Charles A. Brock
Atmos. Chem. Phys., 23, 1863–1877, https://doi.org/10.5194/acp-23-1863-2023, https://doi.org/10.5194/acp-23-1863-2023, 2023
Short summary
Short summary
Particle number concentrations and size distributions in the stratosphere are studied through model simulations and comparisons with measurements. The nucleation scheme used in most of the solar geoengineering modeling studies overpredicts the nucleation rates and particle number concentrations in the stratosphere. The model based on updated nucleation schemes captures reasonably well some aspects of particle size distributions but misses some features. The possible reasons are discussed.
Huiyun Du, Jie Li, Xueshun Chen, Gabriele Curci, Fangqun Yu, Yele Sun, Xu Dao, Song Guo, Zhe Wang, Wenyi Yang, Lianfang Wei, and Zifa Wang
Atmos. Chem. Phys., 25, 5665–5681, https://doi.org/10.5194/acp-25-5665-2025, https://doi.org/10.5194/acp-25-5665-2025, 2025
Short summary
Short summary
Inadequate consideration of mixing states and coatings on black carbon (BC) hinders aerosol radiation forcing quantification. Core–shell mixing aligns well with observations, but partial internal mixing is a more realistic representation. We used a microphysics module to determine the fraction of embedded BC and coating aerosols, constraining the mixing state. This reduced absorption enhancement by 30 %–43 % in northern China, offering insights into BC's radiative effects.
Hongyu Liu, Bo Zhang, Richard H. Moore, Luke D. Ziemba, Richard A. Ferrare, Hyundeok Choi, Armin Sorooshian, David Painemal, Hailong Wang, Michael A. Shook, Amy Jo Scarino, Johnathan W. Hair, Ewan C. Crosbie, Marta A. Fenn, Taylor J. Shingler, Chris A. Hostetler, Gao Chen, Mary M. Kleb, Gan Luo, Fangqun Yu, Mark A. Vaughan, Yongxiang Hu, Glenn S. Diskin, John B. Nowak, Joshua P. DiGangi, Yonghoon Choi, Christoph A. Keller, and Matthew S. Johnson
Atmos. Chem. Phys., 25, 2087–2121, https://doi.org/10.5194/acp-25-2087-2025, https://doi.org/10.5194/acp-25-2087-2025, 2025
Short summary
Short summary
We use the GEOS-Chem model to simulate aerosol distributions and properties over the western North Atlantic Ocean (WNAO) during the winter and summer deployments in 2020 of the NASA ACTIVATE mission. Model results are evaluated against aircraft, ground-based, and satellite observations. The improved understanding of life cycle, composition, transport pathways, and distribution of aerosols has important implications for characterizing aerosol–cloud–meteorology interactions over WNAO.
Naveed Ahmad, Changqing Lin, Alexis K. H. Lau, Jhoon Kim, Tianshu Zhang, Fangqun Yu, Chengcai Li, Ying Li, Jimmy C. H. Fung, and Xiang Qian Lao
Atmos. Chem. Phys., 24, 9645–9665, https://doi.org/10.5194/acp-24-9645-2024, https://doi.org/10.5194/acp-24-9645-2024, 2024
Short summary
Short summary
This study developed a nested machine learning model to convert the GEMS NO2 column measurements into ground-level concentrations across China. The model directly incorporates the NO2 mixing height (NMH) into the methodological framework. The study underscores the importance of considering NMH when estimating ground-level NO2 from satellite column measurements and highlights the significant advantages of new-generation geostationary satellites in air quality monitoring.
Shixian Zhai, Daniel J. Jacob, Drew C. Pendergrass, Nadia K. Colombi, Viral Shah, Laura Hyesung Yang, Qiang Zhang, Shuxiao Wang, Hwajin Kim, Yele Sun, Jin-Soo Choi, Jin-Soo Park, Gan Luo, Fangqun Yu, Jung-Hun Woo, Younha Kim, Jack E. Dibb, Taehyoung Lee, Jin-Seok Han, Bruce E. Anderson, Ke Li, and Hong Liao
Atmos. Chem. Phys., 23, 4271–4281, https://doi.org/10.5194/acp-23-4271-2023, https://doi.org/10.5194/acp-23-4271-2023, 2023
Short summary
Short summary
Anthropogenic fugitive dust in East Asia not only causes severe coarse particulate matter air pollution problems, but also affects fine particulate nitrate. Due to emission control efforts, coarse PM decreased steadily. We find that the decrease of coarse PM is a major driver for a lack of decrease of fine particulate nitrate, as it allows more nitric acid to form fine particulate nitrate. The continuing decrease of coarse PM requires more stringent ammonia and nitrogen oxides emission controls.
Kun Wang, Xiaoyan Ma, Rong Tian, and Fangqun Yu
Atmos. Chem. Phys., 23, 4091–4104, https://doi.org/10.5194/acp-23-4091-2023, https://doi.org/10.5194/acp-23-4091-2023, 2023
Short summary
Short summary
From 12 March to 6 April 2016 in Beijing, there were 11 typical new particle formation days, 13 non-event days, and 2 undefined days. We first analyzed the favorable background of new particle formation in Beijing and then conducted the simulations using four nucleation schemes based on a global chemistry transport model (GEOS-Chem) to understand the nucleation mechanism.
Fangqun Yu, Gan Luo, Arshad Arjunan Nair, Sebastian Eastham, Christina J. Williamson, Agnieszka Kupc, and Charles A. Brock
Atmos. Chem. Phys., 23, 1863–1877, https://doi.org/10.5194/acp-23-1863-2023, https://doi.org/10.5194/acp-23-1863-2023, 2023
Short summary
Short summary
Particle number concentrations and size distributions in the stratosphere are studied through model simulations and comparisons with measurements. The nucleation scheme used in most of the solar geoengineering modeling studies overpredicts the nucleation rates and particle number concentrations in the stratosphere. The model based on updated nucleation schemes captures reasonably well some aspects of particle size distributions but misses some features. The possible reasons are discussed.
Noah S. Hirshorn, Lauren M. Zuromski, Christopher Rapp, Ian McCubbin, Gerardo Carrillo-Cardenas, Fangqun Yu, and A. Gannet Hallar
Atmos. Chem. Phys., 22, 15909–15924, https://doi.org/10.5194/acp-22-15909-2022, https://doi.org/10.5194/acp-22-15909-2022, 2022
Short summary
Short summary
New particle formation (NPF) is a source of atmospheric aerosol number concentration that can impact climate by growing to larger sizes and under proper conditions form cloud condensation nuclei (CCN). Using novel methods, we find that at Storm Peak Laboratory, a remote, mountaintop site in Colorado, NPF is observed to enhance CCN concentrations in the spring by a factor of 1.54 and in the winter by a factor of 1.36 which can occur on a regional scale having important climate implications.
Katherine R. Travis, James H. Crawford, Gao Chen, Carolyn E. Jordan, Benjamin A. Nault, Hwajin Kim, Jose L. Jimenez, Pedro Campuzano-Jost, Jack E. Dibb, Jung-Hun Woo, Younha Kim, Shixian Zhai, Xuan Wang, Erin E. McDuffie, Gan Luo, Fangqun Yu, Saewung Kim, Isobel J. Simpson, Donald R. Blake, Limseok Chang, and Michelle J. Kim
Atmos. Chem. Phys., 22, 7933–7958, https://doi.org/10.5194/acp-22-7933-2022, https://doi.org/10.5194/acp-22-7933-2022, 2022
Short summary
Short summary
The 2016 Korea–United States Air Quality (KORUS-AQ) field campaign provided a unique set of observations to improve our understanding of PM2.5 pollution in South Korea. Models typically have errors in simulating PM2.5 in this region, which is of concern for the development of control measures. We use KORUS-AQ observations to improve our understanding of the mechanisms driving PM2.5 and the implications of model errors for determining PM2.5 that is attributable to local or foreign sources.
Yanda Zhang, Fangqun Yu, Gan Luo, Jiwen Fan, and Shuai Liu
Atmos. Chem. Phys., 21, 17433–17451, https://doi.org/10.5194/acp-21-17433-2021, https://doi.org/10.5194/acp-21-17433-2021, 2021
Short summary
Short summary
This paper explores the impacts of dust on summertime convective cloud and precipitation through a numerical experiment. The result indicates that the long-range-transported dust can notably affect the properties of convective cloud and precipitation by enhancing immersion freezing and invigorating convection. We also analyze the different dust effects predicted by the Morrison and SBM schemes, which are partially attributed to the saturation adjustment approach utilized in the bulk schemes.
Shixian Zhai, Daniel J. Jacob, Jared F. Brewer, Ke Li, Jonathan M. Moch, Jhoon Kim, Seoyoung Lee, Hyunkwang Lim, Hyun Chul Lee, Su Keun Kuk, Rokjin J. Park, Jaein I. Jeong, Xuan Wang, Pengfei Liu, Gan Luo, Fangqun Yu, Jun Meng, Randall V. Martin, Katherine R. Travis, Johnathan W. Hair, Bruce E. Anderson, Jack E. Dibb, Jose L. Jimenez, Pedro Campuzano-Jost, Benjamin A. Nault, Jung-Hun Woo, Younha Kim, Qiang Zhang, and Hong Liao
Atmos. Chem. Phys., 21, 16775–16791, https://doi.org/10.5194/acp-21-16775-2021, https://doi.org/10.5194/acp-21-16775-2021, 2021
Short summary
Short summary
Geostationary satellite aerosol optical depth (AOD) has tremendous potential for monitoring surface fine particulate matter (PM2.5). Our study explored the physical relationship between AOD and PM2.5 by integrating data from surface networks, aircraft, and satellites with the GEOS-Chem chemical transport model. We quantitatively showed that accurate simulation of aerosol size distributions, boundary layer depths, relative humidity, coarse particles, and diurnal variations in PM2.5 are essential.
Gongda Lu, Eloise A. Marais, Tuan V. Vu, Jingsha Xu, Zongbo Shi, James D. Lee, Qiang Zhang, Lu Shen, Gan Luo, and Fangqun Yu
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-428, https://doi.org/10.5194/acp-2021-428, 2021
Revised manuscript not accepted
Short summary
Short summary
Emission controls were imposed in Beijing-Tianjin-Hebei in northern China in autumn-winter 2017. We find that regional PM2.5 targets (15 % decrease relative to previous year) were exceeded. Our analysis shows that decline in precursor emissions only leads to less than half (43 %) the improved air quality. Most of the change (57 %) is due to interannual variability in meteorology. Stricter emission controls may be necessary in years with unfavourable meteorology.
Xueshun Chen, Fangqun Yu, Wenyi Yang, Yele Sun, Huansheng Chen, Wei Du, Jian Zhao, Ying Wei, Lianfang Wei, Huiyun Du, Zhe Wang, Qizhong Wu, Jie Li, Junling An, and Zifa Wang
Atmos. Chem. Phys., 21, 9343–9366, https://doi.org/10.5194/acp-21-9343-2021, https://doi.org/10.5194/acp-21-9343-2021, 2021
Short summary
Short summary
Atmospheric aerosol particles have significant climate and health effects that depend on aerosol size, composition, and mixing state. A new global-regional nested aerosol model with an advanced particle microphysics module and a volatility basis set organic aerosol module was developed to simulate aerosol microphysical processes. Simulations strongly suggest the important role of anthropogenic organic species in particle formation over the areas influenced by anthropogenic sources.
Xiaojing Shen, Junying Sun, Fangqun Yu, Ying Wang, Junting Zhong, Yangmei Zhang, Xinyao Hu, Can Xia, Sinan Zhang, and Xiaoye Zhang
Atmos. Chem. Phys., 21, 7039–7052, https://doi.org/10.5194/acp-21-7039-2021, https://doi.org/10.5194/acp-21-7039-2021, 2021
Short summary
Short summary
In this work, we revealed the changes of PNSD and NPF events during the COVID-19 lockdown period in Beijing, China, to illustrate the impact of reduced primary emission and elavated atmospheric oxidized capicity on the nucleation and growth processes. The subsequent growth of nucleated particles and their contribution to the aerosol pollution formation were also explored, to highlight the necessity of controlling the nanoparticles in the future air quality management.
Ling Liu, Fangqun Yu, Kaipeng Tu, Zhi Yang, and Xiuhui Zhang
Atmos. Chem. Phys., 21, 6221–6230, https://doi.org/10.5194/acp-21-6221-2021, https://doi.org/10.5194/acp-21-6221-2021, 2021
Short summary
Short summary
Trifluoroacetic acid (TFA) was previously proved to participate in sulfuric acid (SA)–dimethylamine (DMA) nucleation in Shanghai, China. However, complex atmospheric environments can influence the nucleation of aerosol significantly. We show the influence of different atmospheric conditions on the SA-DMA-TFA nucleation and find the enhancement by TFA can be significant in cold and polluted areas, which provides the perspective of the realistic role of TFA in different atmospheric environments.
Cited articles
Albrecht, B. A.: Aerosols, Cloud Microphysics, and Fractional Cloudiness,
Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989. a
Behrens, B., Salwen, C., Springston, S., and Watson, T.: ARM: AOS: aerosol
chemical speciation monitor, https://doi.org/10.5439/1046180, 1990. a
Bey, I., Jacob, D. J., Yantosca, R. M., Logan, J. A., Field, B. D., Fiore,
A. M., Li, Q., Liu, H. Y., Mickley, L. J., and Schultz, M. G.: Global
modeling of tropospheric chemistry with assimilated meteorology: Model
description and evaluation, J. Geophys. Res.-Atmos.,
106, 23073–23095, https://doi.org/10.1029/2001JD000807, 2001. a, b
Breiman, L.: Bagging predictors, Mach. Learn., 24, 123–140,
https://doi.org/10.1007/bf00058655, 1996. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001. a, b, c
Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J.: Classification
And Regression Trees, Routledge, https://doi.org/10.1201/9781315139470, 1984. a
Chen, X. and Xie, S.: ARM: ARMBE: Atmospheric measurements,
https://doi.org/10.5439/1095313, 1994. a, b, c
Christopoulos, C. D., Garimella, S., Zawadowicz, M. A., Möhler, O., and
Cziczo, D. J.: A machine learning approach to aerosol classification for
single-particle mass spectrometry, Atmos. Meas. Tech., 11,
5687–5699, https://doi.org/10.5194/amt-11-5687-2018, 2018. a
Dou, X. and Yang, Y.: Comprehensive evaluation of machine learning techniques
for estimating the responses of carbon fluxes to climatic forces in different
terrestrial ecosystems, Atmosphere, 9, 83, https://doi.org/10.3390/atmos9030083, 2018. a
Evans, M. and Jacob, D. J.: Impact of new laboratory studies of N2O5 hydrolysis
on global model budgets of tropospheric nitrogen oxides, ozone, and OH,
Geophys. Res. Lett., 32, L09813, https://doi.org/10.1029/2005GL022469, 2005. a
Filzmoser, P., Fritz, H., and Kalcher, K.: pcaPP: Robust PCA by Projection
Pursuit, available at: https://CRAN.R-project.org/package=pcaPP (last access: 20 August 2020), r package
version 1.9-73, 2018. a
Fountoukis, C. and Nenes, A.: ISORROPIA II: a computationally efficient thermodynamic equilibrium model for K+–Ca
Fuchs, J., Cermak, J., and Andersen, H.: Building a cloud in the southeast
Atlantic: understanding low-cloud controls based on satellite observations
with machine learning, Atmos. Chem. Phys., 18,
16537–16552, https://doi.org/10.5194/acp-18-16537-2018, 2018. a
Giglio, L., Randerson, J. T., and van der Werf, G. R.: Analysis of daily,
monthly, and annual burned area using the fourth-generation global fire
emissions database (GFED4), J. Geophys. Res.-Biogeo., 118, 317–328, https://doi.org/10.1002/jgrg.20042, 2013. a
Grange, S. K., Carslaw, D. C., Lewis, A. C., Boleti, E., and Hueglin, C.:
Random forest meteorological normalisation models for Swiss PM10
trend analysis, Atmos. Chem. Phys., 18, 6223–6239,
https://doi.org/10.5194/acp-18-6223-2018, 2018. a
Guenther, A. B., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T.,
Emmons, L. K., and Wang, X.: The Model of Emissions of Gases and Aerosols
from Nature version 2.1 (MEGAN2.1): an extended and updated framework for
modeling biogenic emissions, Geosci. Model Dev., 5, 1471–1492,
https://doi.org/10.5194/gmd-5-1471-2012, 2012. a
Hageman, D., Behrens, B., Smith, S., Uin, J., Salwen, C., Koontz, A.,
Jefferson, A., Watson, T., Sedlacek, A., Kuang, C., Dubey, M., Springston,
S., and Senum, G.: ARM: Aerosol Observing System (AOS): aerosol data, 1-min,
Mentor-QC Applied, https://doi.org/10.5439/1025259, 1996. a, b, c
Hageman, D., Behrens, B., Smith, S., Uin, J., Salwen, C., Koontz, A.,
Jefferson, A., Watson, T., Sedlacek, A., Kuang, C., Dubey, M., Springston,
S., and Senum, G.: ARM: Aerosol Observing System (AOS): cloud condensation
nuclei data, https://doi.org/10.5439/1150249, 2017. a, b, c
Holdridge, D. and Kyrouac, J.: ARM: ARM-standard Meteorological Instrumentation
at Surface, https://doi.org/10.5439/1025220, 1993. a, b, c, d
Hoppel, W. A., Frick, G. M., Fitzgerald, J. W., and Wattle, B. J.: A Cloud
Chamber Study of the Effect That Nonprecipitating Water Clouds Have on the
Aerosol Size Distribution, Aerosol Sci. Technol., 20, 1–30,
https://doi.org/10.1080/02786829408959660, 1994. a
Hughes, M., Kodros, J., Pierce, J., West, M., and Riemer, N.: Machine Learning
to Predict the Global Distribution of Aerosol Mixing State Metrics,
Atmosphere, 9, 15, https://doi.org/10.3390/atmos9010015, 2018. a
Huttunen, J., Kokkola, H., Mielonen, T., Mononen, M. E. J., Lipponen, A.,
Reunanen, J., Lindfors, A. V., Mikkonen, S., Lehtinen, K. E. J., Kouremeti,
N., Bais, A., Niska, H., and Arola, A.: Retrieval of aerosol optical depth
from surface solar radiation measurements using machine learning algorithms,
non-linear regression and a radiative transfer-based look-up table,
Atmos. Chem. Phys., 16, 8181–8191,
https://doi.org/10.5194/acp-16-8181-2016, 2016. a
Jefferson, A.: Aerosol Observing System (AOS) Handbook, Tech. rep., DOE
Office of Science Atmospheric Radiation Measurement (ARM) Program,
https://doi.org/10.2172/1020729, 2011. a
Jin, J., Lin, H. X., Segers, A., Xie, Y., and Heemink, A.: Machine learning for
observation bias correction with application to dust storm data assimilation,
Atmos. Chem. Phys., 19, 10009–10026,
https://doi.org/10.5194/acp-19-10009-2019, 2019. a
Joutsensaari, J., Ozon, M., Nieminen, T., Mikkonen, S., Lähivaara, T.,
Decesari, S., Facchini, M. C., Laaksonen, A., and Lehtinen, K. E. J.:
Identification of new particle formation events with deep learning,
Atmos. Chem. Phys., 18, 9597–9615,
https://doi.org/10.5194/acp-18-9597-2018, 2018. a
Keller, C. A., Long, M. S., Yantosca, R. M., Da Silva, A. M., Pawson, S., and
Jacob, D. J.: HEMCO v1.0: a versatile, ESMF-compliant component for
calculating emissions in atmospheric models, Geosci. Model Dev.,
7, 1409–1417, https://doi.org/10.5194/gmd-7-1409-2014, 2014. a
Kendall, M.: Rank Correlation Methods, Theory and applications of rank
order-statistics, Griffin, London, 202 pp., 1970. a
Kulkarni, G.: aosacsm.b1, https://doi.org/10.5439/1558768, 2019. a
Martin, R. V., Jacob, D. J., Yantosca, R. M., Chin, M., and Ginoux, P.: Global
and regional decreases in tropospheric oxidants from photochemical effects of
aerosols, J. Geophys. Res.-Atmos., 108, 4097,
https://doi.org/10.1029/2002jd002622, 2003. a
Mauceri, S., Kindel, B., Massie, S., and Pilewskie, P.: Neural network for
aerosol retrieval from hyperspectral imagery, Atmos. Meas.
Tech., 12, 6017–6036, https://doi.org/10.5194/amt-12-6017-2019, 2019. a
McLeod, A.: Kendall: Kendall rank correlation and Mann-Kendall trend test,
available at: https://CRAN.R-project.org/package=Kendall (last access: 20 August 2020), r package version
2.2, 2011. a
Merikanto, J., Spracklen, D. V., Mann, G. W., Pickering, S. J., and Carslaw,
K. S.: Impact of nucleation on global CCN, Atmos. Chem.
Phys., 9, 8601–8616, https://doi.org/10.5194/acp-9-8601-2009, 2009. a
Murray, L. T., Jacob, D. J., Logan, J. A., Hudman, R. C., and Koshak, W. J.:
Optimized regional and interannual variability of lightning in a global
chemical transport model constrained by LIS/OTD satellite data, J.
Geophys. Res.-Atmos., 117, D20307, https://doi.org/10.1029/2012jd017934, 2012. a
Nair, A. A., Yu, F., and Luo, G.: Spatioseasonal Variations of Atmospheric
Ammonia Concentrations Over the United States: Comprehensive
Model-Observation Comparison, J. Geophys. Res.-Atmos.,
124, 6571–6582, https://doi.org/10.1029/2018JD030057, 2019. a, b
Ng, N. L., Herndon, S. C., Trimborn, A., Canagaratna, M. R., Croteau, P. L.,
Onasch, T. B., Sueper, D., Worsnop, D. R., Zhang, Q., Sun, Y. L., and Jayne,
J. T.: An Aerosol Chemical Speciation Monitor (ACSM) for Routine Monitoring
of the Composition and Mass Concentrations of Ambient Aerosol, Aerosol
Sci. Technol., 45, 780–794, https://doi.org/10.1080/02786826.2011.560211,
2011. a, b, c
Noether, G. E.: Why Kendall Tau?, Teaching Statistics, 3, 41–43,
https://doi.org/10.1111/j.1467-9639.1981.tb00422.x, 1981. a
Okamura, R., Iwabuchi, H., and Schmidt, K. S.: Feasibility study of multi-pixel
retrieval of optical thickness and droplet effective radius of inhomogeneous
clouds using deep learning, Atmos. Meas. Tech., 10,
4747–4759, https://doi.org/10.5194/amt-10-4747-2017, 2017. a
Park, R. J.: Natural and transboundary pollution influences on
sulfate-nitrate-ammonium aerosols in the United States: Implications for
policy, J. Geophys. Res., 109, D15204, https://doi.org/10.1029/2003jd004473,
2004. a
Probst, P., Wright, M. N., and Boulesteix, A.-L.: Hyperparameters and tuning
strategies for random forest, Wiley Interdisciplinary Reviews: Data Mining
and Knowledge Discovery, 9, e1301, https://doi.org/10.1002/widm.1301, 2019. a
Pye, H. O. T. and Seinfeld, J. H.: A global perspective on aerosol from
low-volatility organic compounds, Atmos. Chem. Phys., 10,
4377–4401, https://doi.org/10.5194/acp-10-4377-2010, 2010. a, b
Ritsche, M.: ARM Surface Meteorology Systems Instrument Handbook, Tech. rep.,
Office of Scientific and Technical Information (OSTI),
https://doi.org/10.2172/1007926, 2011. a
Roberts, G. C. and Nenes, A.: A Continuous-Flow Streamwise Thermal-Gradient CCN
Chamber for Atmospheric Measurements, Aerosol Sci. Technol., 39,
206–221, https://doi.org/10.1080/027868290913988, 2005. a
Seinfeld, J. H., Bretherton, C., Carslaw, K. S., Coe, H., DeMott, P. J.,
Dunlea, E. J., Feingold, G., Ghan, S., Guenther, A. B., Kahn, R., Kraucunas,
I., Kreidenweis, S. M., Molina, M. J., Nenes, A., Penner, J. E., Prather,
K. A., Ramanathan, V., Ramaswamy, V., Rasch, P. J., Ravishankara, A. R.,
Rosenfeld, D., Stephens, G., and Wood, R.: Improving our fundamental
understanding of the role of aerosol-cloud interactions in the climate
system, P. Natl. Acad. Sci. USA, 113, 5781–5790,
https://doi.org/10.1073/pnas.1514043113, 2016. a
Shi, Y. and Flynn, C.: ARM: Aerosol Observing System (AOS): cloud condensation
nuclei data, averaged, https://doi.org/10.5439/1095312, 2007. a, b, c
Smith, S., Salwen, C., Uin, J., Senum, G., Springston, S., and Jefferson, A.:
ARM: AOS: Cloud Condensation Nuclei Counter, https://doi.org/10.5439/1256093,
2011a. a, b, c
Smith, S., Salwen, C., Uin, J., Senum, G., Springston, S., and Jefferson, A.:
ARM: AOS: Cloud Condensation Nuclei Counter (Single Column), averaged,
https://doi.org/10.5439/1342133, 2011b. a, b, c
Springston, S.: ARM: AOS: Sulfur Dioxide Analyzer, https://doi.org/10.5439/1095586, 2012. a
Twomey, S. A.: The Influence of Pollution on the Shortwave Albedo of Clouds,
J. Atmos. Sci., 34, 1149–1152,
https://doi.org/10.1175/1520-0469(1977)034<1149:TIOPOT>2.0.CO;2, 1977.
a
Uin, J.: Cloud Condensation Nuclei Particle Counter (CCN) Instrument
Handbook, Tech. rep., DOE Office of Science Atmospheric Radiation Measurement
(ARM) Program, https://doi.org/10.2172/1251411, 2016. a
van Donkelaar, A., Martin, R. V., Leaitch, W. R., Macdonald, A. M., Walker,
T. W., Streets, D. G., Zhang, Q., Dunlea, E. J., Jimenez, J. L., Dibb, J. E.,
Huey, L. G., Weber, R., and Andreae, M. O.: Analysis of aircraft and
satellite measurements from the Intercontinental Chemical Transport
Experiment (INTEX-B) to quantify long-range transport of East Asian sulfur
to Canada, Atmos. Chem. Phys., 8, 2999–3014,
https://doi.org/10.5194/acp-8-2999-2008, 2008. a
Watson, T., Aiken, A., Zhang, Q., Croteau, P., Onasch, T., Williams, L., and
Flynn, C. F.: First ARM Aerosol Chemical Speciation Monitor Users' Meeting
Report, Tech. rep., DOE Office of Science Atmospheric Radiation Measurement
(ARM) Program, https://doi.org/10.2172/1455055, 2018. a
Watson, T. B.: Aerosol Chemical Speciation Monitor (ACSM) Instrument
Handbook, Tech. rep., DOE Office of Science Atmospheric Radiation Measurement
(ARM) Program, https://doi.org/10.2172/1375336, 2017. a
Yu, F.: A secondary organic aerosol formation model considering successive
oxidation aging and kinetic condensation of organic compounds: global scale
implications, Atmos. Chem. Phys., 11, 1083–1099,
https://doi.org/10.5194/acp-11-1083-2011, 2011. a, b, c, d
Yu, F. and Luo, G.: Simulation of particle size distribution with a global
aerosol model: contribution of nucleation to aerosol and CCN number
concentrations, Atmos. Chem. Phys., 9, 7691–7710,
https://doi.org/10.5194/acp-9-7691-2009, 2009. a, b, c, d
Yu, F., Ma, X., and Luo, G.: Anthropogenic contribution to cloud condensation
nuclei and the first aerosol indirect climate effect, Environ. Res.
Lett., 8, 024029, https://doi.org/10.1088/1748-9326/8/2/024029, 2013. a
Yu, F., Luo, G., Nadykto, A. B., and Herb, J.: Impact of temperature dependence
on the possible contribution of organics to new particle formation in the
atmosphere, Atmos. Chem. Phys., 17, 4997–5005,
https://doi.org/10.5194/acp-17-4997-2017, 2017. a
Yu, F., Nadykto, A. B., Herb, J., Luo, G., Nazarenko, K. M., and Uvarova,
L. A.: H2SO4-H2O-NH3 ternary
ion-mediated nucleation (TIMN): Kinetic-based model and comparison with CLOUD
measurements, Atmos. Chem. Phys., 18, 17451–17474,
https://doi.org/10.5194/acp-2018-396, 2018. a
Zaidan, M. A., Haapasilta, V., Relan, R., Junninen, H., Aalto, P. P., Kulmala,
M., Laurson, L., and Foster, A. S.: Predicting atmospheric particle formation
days by Bayesian classification of the time series features, Tellus B, 70, 1–10,
https://doi.org/10.1080/16000889.2018.1530031, 2018. a
Download
The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.
- Article
(7023 KB) - Full-text XML
Short summary
Small particles in the atmosphere can affect cloud formation and properties and thus Earth's energy budget. These cloud condensation nuclei (CCN) contribute the largest uncertainties in climate change modeling. To reduce these uncertainties, it is important to quantify CCN numbers accurately, measurements of which are sparse. We propose and evaluate a machine learning method to estimate CCN, in the absence of their direct measurements, using more common measurements of weather and air quality.
Small particles in the atmosphere can affect cloud formation and properties and thus Earth's...
Altmetrics
Final-revised paper
Preprint