Machine-learning-assisted inference of the particle charge fraction and the ion-induced nucleation rates during new particle formation events
<p>The charge state of atmospheric new particles is controlled by both their initial charge state upon formation and subsequent interaction with atmospheric ions. By measuring the charge state of growing particles, the fraction of ion-induced nucleation (<span class="inline-formula&quo...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-07-01
|
| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://acp.copernicus.org/articles/25/7431/2025/acp-25-7431-2025.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1839629524778614784 |
|---|---|
| author | P. Wang Y. Zhao J. Wang J. Wang V.-M. Kerminen J. Jiang C. Li |
| author_facet | P. Wang Y. Zhao J. Wang J. Wang V.-M. Kerminen J. Jiang C. Li |
| author_sort | P. Wang |
| collection | DOAJ |
| description | <p>The charge state of atmospheric new particles is controlled by both their initial charge state upon formation and subsequent interaction with atmospheric ions. By measuring the charge state of growing particles, the fraction of ion-induced nucleation (<span class="inline-formula"><i>F</i><sub>IIN</sub></span>) within total new particle formation (NPF) can be inferred, which is critical for understanding NPF mechanisms. However, existing theoretical approaches for predicting particle charge states suffer from inaccuracies due to simplifying assumptions; hence their ability to infer <span class="inline-formula"><i>F</i><sub>IIN</sub></span> is sometimes limited. Here we develop a numerical model to explicitly simulate the charging dynamics of new particles. Our simulations demonstrate that both particle growth rate and ion concentration substantially influence the particle charge state, while ion–ion recombination becomes important when the charged particle concentrations are high. Leveraging a large set of simulations, we constructed two regression models using residual neural networks. The first model (ResFWD) predicts the charge state of growing particles with known <span class="inline-formula"><i>F</i><sub>IIN</sub></span> values, while the second model (ResBWD) operates in reverse to estimate <span class="inline-formula"><i>F</i><sub>IIN</sub></span> based on the charge fraction of particles at prescribed sizes. Good agreement between the regression models and benchmark simulations demonstrates the potential of our approach for analyzing ion-induced nucleation events. Sensitivity analysis further reveals that ResFWD and the benchmark simulations exhibit similar sensitivity to noises in the input parameters, but the robustness of the ResBWD simulations depends on retention of initial particle charge state at the prescribed sizes. Our study provides insights into charging dynamics of atmospheric new particles and introduces a new method for assessing ion-induced nucleation rates.</p> |
| format | Article |
| id | doaj-art-79e65c46d4a844dbbc28c16f24f108aa |
| institution | Matheson Library |
| issn | 1680-7316 1680-7324 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Chemistry and Physics |
| spelling | doaj-art-79e65c46d4a844dbbc28c16f24f108aa2025-07-15T04:42:27ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242025-07-01257431744610.5194/acp-25-7431-2025Machine-learning-assisted inference of the particle charge fraction and the ion-induced nucleation rates during new particle formation eventsP. Wang0Y. Zhao1J. Wang2J. Wang3V.-M. Kerminen4J. Jiang5C. Li6School of Environmental Science and Engineering, Shanghai Jiao Tong University, 200240 Shanghai, ChinaSchool of Environmental Science and Engineering, Shanghai Jiao Tong University, 200240 Shanghai, ChinaState Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science and Technology, 210000 Nanjing, ChinaCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,School of Atmospheric Physics, Nanjing University of Information Science and Technology, 210000 Nanjing, ChinaInstitute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, 00014 Helsinki, FinlandState Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084 Beijing, ChinaSchool of Environmental Science and Engineering, Shanghai Jiao Tong University, 200240 Shanghai, China<p>The charge state of atmospheric new particles is controlled by both their initial charge state upon formation and subsequent interaction with atmospheric ions. By measuring the charge state of growing particles, the fraction of ion-induced nucleation (<span class="inline-formula"><i>F</i><sub>IIN</sub></span>) within total new particle formation (NPF) can be inferred, which is critical for understanding NPF mechanisms. However, existing theoretical approaches for predicting particle charge states suffer from inaccuracies due to simplifying assumptions; hence their ability to infer <span class="inline-formula"><i>F</i><sub>IIN</sub></span> is sometimes limited. Here we develop a numerical model to explicitly simulate the charging dynamics of new particles. Our simulations demonstrate that both particle growth rate and ion concentration substantially influence the particle charge state, while ion–ion recombination becomes important when the charged particle concentrations are high. Leveraging a large set of simulations, we constructed two regression models using residual neural networks. The first model (ResFWD) predicts the charge state of growing particles with known <span class="inline-formula"><i>F</i><sub>IIN</sub></span> values, while the second model (ResBWD) operates in reverse to estimate <span class="inline-formula"><i>F</i><sub>IIN</sub></span> based on the charge fraction of particles at prescribed sizes. Good agreement between the regression models and benchmark simulations demonstrates the potential of our approach for analyzing ion-induced nucleation events. Sensitivity analysis further reveals that ResFWD and the benchmark simulations exhibit similar sensitivity to noises in the input parameters, but the robustness of the ResBWD simulations depends on retention of initial particle charge state at the prescribed sizes. Our study provides insights into charging dynamics of atmospheric new particles and introduces a new method for assessing ion-induced nucleation rates.</p>https://acp.copernicus.org/articles/25/7431/2025/acp-25-7431-2025.pdf |
| spellingShingle | P. Wang Y. Zhao J. Wang J. Wang V.-M. Kerminen J. Jiang C. Li Machine-learning-assisted inference of the particle charge fraction and the ion-induced nucleation rates during new particle formation events Atmospheric Chemistry and Physics |
| title | Machine-learning-assisted inference of the particle charge fraction and the ion-induced nucleation rates during new particle formation events |
| title_full | Machine-learning-assisted inference of the particle charge fraction and the ion-induced nucleation rates during new particle formation events |
| title_fullStr | Machine-learning-assisted inference of the particle charge fraction and the ion-induced nucleation rates during new particle formation events |
| title_full_unstemmed | Machine-learning-assisted inference of the particle charge fraction and the ion-induced nucleation rates during new particle formation events |
| title_short | Machine-learning-assisted inference of the particle charge fraction and the ion-induced nucleation rates during new particle formation events |
| title_sort | machine learning assisted inference of the particle charge fraction and the ion induced nucleation rates during new particle formation events |
| url | https://acp.copernicus.org/articles/25/7431/2025/acp-25-7431-2025.pdf |
| work_keys_str_mv | AT pwang machinelearningassistedinferenceoftheparticlechargefractionandtheioninducednucleationratesduringnewparticleformationevents AT yzhao machinelearningassistedinferenceoftheparticlechargefractionandtheioninducednucleationratesduringnewparticleformationevents AT jwang machinelearningassistedinferenceoftheparticlechargefractionandtheioninducednucleationratesduringnewparticleformationevents AT jwang machinelearningassistedinferenceoftheparticlechargefractionandtheioninducednucleationratesduringnewparticleformationevents AT vmkerminen machinelearningassistedinferenceoftheparticlechargefractionandtheioninducednucleationratesduringnewparticleformationevents AT jjiang machinelearningassistedinferenceoftheparticlechargefractionandtheioninducednucleationratesduringnewparticleformationevents AT cli machinelearningassistedinferenceoftheparticlechargefractionandtheioninducednucleationratesduringnewparticleformationevents |