Preprints
https://doi.org/10.5194/acp-2021-427
https://doi.org/10.5194/acp-2021-427

  05 Jul 2021

05 Jul 2021

Review status: this preprint is currently under review for the journal ACP.

Input-adaptive linear mixed-effects model for estimating alveolar Lung Deposited Surface Area (LDSA) using multipollutant datasets

Pak Lun Fung1,2, Martha A. Zaidan1,3, Jarkko V. Niemi4, Erkka Saukko5, Hilkka Timonen6, Anu Kousa4, Joel Kuula6, Topi Rönkkö7, Ari Karppinen6, Sasu Tarkoma8, Markku Kulmala1,3, Tuukka Petäjä1,3, and Tareq Hussein1,9 Pak Lun Fung et al.
  • 1Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Finland
  • 2Helsinki Institute of Sustainability Science, Faculty of Science, University of Helsinki, Finland
  • 3Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
  • 4Helsinki Region Environmental Services Authority (HSY), P.O. Box 100, FI-00066 Helsinki, Finland
  • 5Pegasor Ltd., FI-33100 Tampere, Finland
  • 6Atmospheric Composition Research, Finnish Meteorological Institute, FI-00560 Helsinki, Finland
  • 7Aerosol Physics Laboratory, Physics Unit, Tampere University, FI-33720 Tampere, Finland
  • 8Department of Computer Science, Faculty of Science, University of Helsinki, Finland
  • 9Department of Physics, the University of Jordan, Amman 11942, Jordan

Abstract. Lung deposited surface area (LDSA) has been considered to be a better metric to explain nanoparticle toxicity instead of the commonly used particulate mass concentration. LDSA concentrations can be obtained either by direct measurements or by calculation based on the empirical lung deposition model and measurements of particle size distribution. However, the LDSA or size distribution measurements are neither compulsory nor regulated by the government. As a result, LDSA data are often scarce spatially and temporally. In light of this, we develop a novel statistical model, named input-adaptive mixed-effects (IAME) model, to estimate LDSA based on other already existing measurements of air pollutant variables and meteorological conditions. During the measurement period in 2017–2018, we retrieved LDSA data measured by Pegasor AQ Urban and other variables at a street canyon (SC, average LDSA = 19.7 ± 11.3 μm2 cm−3) site and an urban background (UB, average LDSA = 11.2 ± 7.1 μm2 cm−3) site in Helsinki, Finland. For the continuous estimation of LDSA, IAME model is automatised to select the best combination of input variables, including a maximum of three fixed effect variables and three time indictors as random effect variables. Altogether, 696 sub-models were generated and ranked by the coefficient of determination (R2), mean absolute error (MAE) and centred root-mean-square differences (cRMSD) in order. At the SC site, the LDSA concentrations were best estimated by mass concentration of particle of diameters smaller than 2.5 μm (PM2.5), total particle number concentration (PNC) and black carbon (BC), all of which are closely connected with the vehicular emissions. At the UB site the LDSA concentrations were found to be correlated with PM2.5, BC and carbon monoxide (CO). The accuracy of the overall model was better at the SC site (R2 = 0.80, MAE = 3.7 μm2 cm−3) than at the UB site (R2 =  0.77, MAE = 2.3 μm2 cm−3) plausibly because the LDSA source was more tightly controlled by the close-by vehicular emission source. The results also demonstrate that the additional adjustment by taking random effects into account improves the sensitivity and the accuracy of the fixed effect model. Due to its adaptive input selection and inclusion of random effects, IAME could fill up missing data or even serve as a network of virtual sensors to complement the measurements at reference stations.

Pak Lun Fung et al.

Status: open (until 22 Aug 2021)

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  • CC1: 'Comment on acp-2021-427', Santtu Mikkonen, 06 Jul 2021 reply

Pak Lun Fung et al.

Pak Lun Fung et al.

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Short summary
We develop an input-adaptive mixed effects model, which is 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 test the model to estimate lung deposited surface area (LDSA), which correlates well with human’s health. The results show the inclusion of time indicators improves the sensitivity and the accuracy of the model so that it could serve as a network of virtual sensors.
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