Articles | Volume 22, issue 3
https://doi.org/10.5194/acp-22-1861-2022
© Author(s) 2022. 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-22-1861-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Feb 2022
Research article |
| 08 Feb 2022
| 08 Feb 2022
Input-adaptive linear mixed-effects model for estimating alveolar lung-deposited surface area (LDSA) using multipollutant datasets
Institute for Atmospheric and Earth System Research/Physics, Faculty
of Science, University of Helsinki, Helsinki, Finland
Helsinki Institute of Sustainability Science, Faculty of Science,
University of Helsinki, Helsinki, Finland
Martha A. Zaidan
Institute for Atmospheric and Earth System Research/Physics, Faculty
of Science, University of Helsinki, Helsinki, Finland
Helsinki Institute of Sustainability Science, Faculty of Science,
University of Helsinki, Helsinki, Finland
Joint International Research Laboratory of Atmospheric and Earth
System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing
210023, China
Jarkko V. Niemi
Helsinki Region Environmental Services Authority (HSY), P.O. Box 100,
00066 Helsinki, Finland
Erkka Saukko
Pegasor Oy, 33100 Tampere, Finland
Hilkka Timonen
Atmospheric Composition Research, Finnish Meteorological Institute,
00560 Helsinki, Finland
Anu Kousa
Helsinki Region Environmental Services Authority (HSY), P.O. Box 100,
00066 Helsinki, Finland
Joel Kuula
Atmospheric Composition Research, Finnish Meteorological Institute,
00560 Helsinki, Finland
Topi Rönkkö
Aerosol Physics Laboratory, Physics Unit, Faculty of Engineering and Natural Sciences, Tampere University, 33720 Tampere, Finland
Ari Karppinen
Atmospheric Composition Research, Finnish Meteorological Institute,
00560 Helsinki, Finland
Sasu Tarkoma
Department of Computer Science, Faculty of Science, University of
Helsinki, Helsinki, Finland
Markku Kulmala
Institute for Atmospheric and Earth System Research/Physics, Faculty
of Science, University of Helsinki, Helsinki, Finland
Joint International Research Laboratory of Atmospheric and Earth
System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing
210023, China
Tuukka Petäjä
Institute for Atmospheric and Earth System Research/Physics, Faculty
of Science, University of Helsinki, Helsinki, Finland
Joint International Research Laboratory of Atmospheric and Earth
System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing
210023, China
Tareq Hussein
CORRESPONDING AUTHOR
Institute for Atmospheric and Earth System Research/Physics, Faculty
of Science, University of Helsinki, Helsinki, Finland
Department of Physics, the University of Jordan, Amman 11942, Jordan
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Cited
9 citations as recorded by crossref.
- Improving the current air quality index with new particulate indicators using a robust statistical approach P. Fung et al. 10.1016/j.scitotenv.2022.157099
- Ambient air particulate total lung deposited surface area (LDSA) levels in urban Europe X. Liu et al. 10.1016/j.scitotenv.2023.165466
- Observational study of ultrafine particulate matter exposure under different commuting modes in a typical city of the Yangtze River Delta W. Zhang et al. 10.1360/TB-2024-0547
- Mapping CO2 traffic emissions within local climate zones in Helsinki O. Al-Jaghbeer et al. 10.1016/j.uclim.2024.102171
- Research on an adaptive prediction method for restaurant air quality based on occupancy detection Y. Zhao et al. 10.1016/j.buildenv.2024.112145
- Constructing transferable and interpretable machine learning models for black carbon concentrations P. Fung et al. 10.1016/j.envint.2024.108449
- Assessing the inhaled dose of nanomaterials by nanoparticle tracking analysis (NTA) of exhaled breath condensate (EBC) and its relationship with lung inflammatory biomarkers M. Panizzolo et al. 10.1016/j.chemosphere.2024.142139
- Estimating black carbon levels using machine learning models in high-concentration regions P. Gupta et al. 10.1016/j.scitotenv.2024.174804
- City Wide Participatory Sensing of Air Quality A. Rebeiro-Hargrave et al. 10.3389/fenvs.2021.773778
8 citations as recorded by crossref.
- Improving the current air quality index with new particulate indicators using a robust statistical approach P. Fung et al. 10.1016/j.scitotenv.2022.157099
- Ambient air particulate total lung deposited surface area (LDSA) levels in urban Europe X. Liu et al. 10.1016/j.scitotenv.2023.165466
- Observational study of ultrafine particulate matter exposure under different commuting modes in a typical city of the Yangtze River Delta W. Zhang et al. 10.1360/TB-2024-0547
- Mapping CO2 traffic emissions within local climate zones in Helsinki O. Al-Jaghbeer et al. 10.1016/j.uclim.2024.102171
- Research on an adaptive prediction method for restaurant air quality based on occupancy detection Y. Zhao et al. 10.1016/j.buildenv.2024.112145
- Constructing transferable and interpretable machine learning models for black carbon concentrations P. Fung et al. 10.1016/j.envint.2024.108449
- Assessing the inhaled dose of nanomaterials by nanoparticle tracking analysis (NTA) of exhaled breath condensate (EBC) and its relationship with lung inflammatory biomarkers M. Panizzolo et al. 10.1016/j.chemosphere.2024.142139
- Estimating black carbon levels using machine learning models in high-concentration regions P. Gupta et al. 10.1016/j.scitotenv.2024.174804
1 citations as recorded by crossref.
Latest update: 20 Nov 2024
Short summary
We developed an input-adaptive mixed-effects model, which was automatised to select the best combination of input variables, including up to three fixed effect variables and three time indictors as random effect variables. We tested the model to estimate lung-deposited surface area (LDSA), which correlates well with human health. The results show the inclusion of time indicators improved the sensitivity and the accuracy of the model so that it could serve as a network of virtual sensors.
We developed an input-adaptive mixed-effects model, which was automatised to select the best...
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