Articles | Volume 14, issue 8
https://doi.org/10.5194/amt-14-5535-2021
https://doi.org/10.5194/amt-14-5535-2021
Research article
 | 
13 Aug 2021
Research article |  | 13 Aug 2021

Data imputation in in situ-measured particle size distributions by means of neural networks

Pak Lun Fung, Martha Arbayani Zaidan, Ola Surakhi, Sasu Tarkoma, Tuukka Petäjä, and Tareq Hussein

Related authors

Opinion: Insights into updating Ambient Air Quality Directive 2008/50/EC
Joel Kuula, Hilkka Timonen, Jarkko V. Niemi, Hanna E. Manninen, Topi Rönkkö, Tareq Hussein, Pak Lun Fung, Sasu Tarkoma, Mikko Laakso, Erkka Saukko, Aino Ovaska, Markku Kulmala, Ari Karppinen, Lasse Johansson, and Tuukka Petäjä
Atmos. Chem. Phys., 22, 4801–4808, https://doi.org/10.5194/acp-22-4801-2022,https://doi.org/10.5194/acp-22-4801-2022, 2022
Short summary
Input-adaptive linear mixed-effects model for estimating alveolar lung-deposited surface area (LDSA) using multipollutant datasets
Pak Lun Fung, Martha A. Zaidan, Jarkko V. Niemi, Erkka Saukko, Hilkka Timonen, Anu Kousa, Joel Kuula, Topi Rönkkö, Ari Karppinen, Sasu Tarkoma, Markku Kulmala, Tuukka Petäjä, and Tareq Hussein
Atmos. Chem. Phys., 22, 1861–1882, https://doi.org/10.5194/acp-22-1861-2022,https://doi.org/10.5194/acp-22-1861-2022, 2022
Short summary
Spatiotemporal variation and trends in equivalent black carbon in the Helsinki metropolitan area in Finland
Krista Luoma, Jarkko V. Niemi, Minna Aurela, Pak Lun Fung, Aku Helin, Tareq Hussein, Leena Kangas, Anu Kousa, Topi Rönkkö, Hilkka Timonen, Aki Virkkula, and Tuukka Petäjä
Atmos. Chem. Phys., 21, 1173–1189, https://doi.org/10.5194/acp-21-1173-2021,https://doi.org/10.5194/acp-21-1173-2021, 2021
Short summary
Observations of ozone depletion events in a Finnish boreal forest
Xuemeng Chen, Lauriane L. J. Quéléver, Pak L. Fung, Jutta Kesti, Matti P. Rissanen, Jaana Bäck, Petri Keronen, Heikki Junninen, Tuukka Petäjä, Veli-Matti Kerminen, and Markku Kulmala
Atmos. Chem. Phys., 18, 49–63, https://doi.org/10.5194/acp-18-49-2018,https://doi.org/10.5194/acp-18-49-2018, 2018
Short summary

Related subject area

Subject: Aerosols | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
Spatial analysis of PM2.5 using a concentration similarity index applied to air quality sensor networks
Rósín Byrne, John C. Wenger, and Stig Hellebust
Atmos. Meas. Tech., 17, 5129–5146, https://doi.org/10.5194/amt-17-5129-2024,https://doi.org/10.5194/amt-17-5129-2024, 2024
Short summary
A novel probabilistic source apportionment approach: Bayesian auto-correlated matrix factorization
Anton Rusanen, Anton Björklund, Manousos I. Manousakas, Jianhui Jiang, Markku T. Kulmala, Kai Puolamäki, and Kaspar R. Daellenbach
Atmos. Meas. Tech., 17, 1251–1277, https://doi.org/10.5194/amt-17-1251-2024,https://doi.org/10.5194/amt-17-1251-2024, 2024
Short summary
Towards a hygroscopic growth calibration for low-cost PM2.5 sensors
Milan Y. Patel, Pietro F. Vannucci, Jinsol Kim, William M. Berelson, and Ronald C. Cohen
Atmos. Meas. Tech., 17, 1051–1060, https://doi.org/10.5194/amt-17-1051-2024,https://doi.org/10.5194/amt-17-1051-2024, 2024
Short summary
Enhancing characterization of organic nitrogen components in aerosols and droplets using high-resolution aerosol mass spectrometry
Xinlei Ge, Yele Sun, Justin Trousdell, Mindong Chen, and Qi Zhang
Atmos. Meas. Tech., 17, 423–439, https://doi.org/10.5194/amt-17-423-2024,https://doi.org/10.5194/amt-17-423-2024, 2024
Short summary
Machine learning approaches for automatic classification of single-particle mass spectrometry data
Guanzhong Wang, Heinrich Ruser, Julian Schade, Johannes Passig, Thomas Adam, Günther Dollinger, and Ralf Zimmermann
Atmos. Meas. Tech., 17, 299–313, https://doi.org/10.5194/amt-17-299-2024,https://doi.org/10.5194/amt-17-299-2024, 2024
Short summary

Cited articles

Ahmed, R., Robinson, R., and Mortimer, K.: The epidemiology of noncommunicable respiratory disease in sub-Saharan Africa, the Middle East, and North Africa, Malawi Med. J., 29, 203–211, https://doi.org/10.4314/mmj.v29i2.24, 2017. 
Al-Dabbous, A. N., Kumar, P., and Khan, A. R.: Prediction of airborne nanoparticles at roadside location using a feed-forward artificial neural network, Atmos. Pollut. Res., 8, 446–454, https://doi.org/10.1016/j.apr.2016.11.004, 2017. 
Arhami, M., Shahne, M. Z., Hosseini, V., Haghighat, N. R., Lai, A. M., and Schauer, J. J.: Seasonal trends in the composition and sources of PM2.5 and carbonaceous aerosol in Tehran, Iran, Environ. Pollut., 239, 69–81, https://doi.org/10.1016/j.envpol.2018.03.111, 2018. 
Borgie, M., Ledoux, F., Dagher, Z., Verdin, A., Cazier, F., Courcot, L., Shirali, P., Greige-Gerges, H., and Courcot, D.: Chemical characteristics of PM2.5--0.3 and PM0.3 and consequence of a dust storm episode at an urban site in Lebanon, Atmos. Res., 180, 274–286, https://doi.org/10.1016/j.atmosres.2016.06.001, 2016. 
Cabaneros, S. M., Calautit, J. K., and Hughes, B. R.: A review of artificial neural network models for ambient air pollution prediction, Environ. Modell. Softw., 119, 285–304, https://doi.org/10.1016/j.envsoft.2019.06.014, 2019. 
Download
Short summary
Aerosol size distribution measurements rely on a variety of techniques to classify the aerosol size and measure the size distribution. However, due to the instrumental insufficiency and inversion limitations, the raw dataset contains missing gaps or negative values, which hinder further analysis. With a merged particle size distribution in Jordan, this paper suggests a neural network method to estimate number concentrations at a particular size bin by the number concentration at other size bins.