Input-adaptive linear mixed-effects model for estimating alveolar lung-deposited surface area (LDSA) using multipollutant datasets
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Published:2022-02-08
Issue:3
Volume:22
Page:1861-1882
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ISSN:1680-7324
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Container-title:Atmospheric Chemistry and Physics
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language:en
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Short-container-title:Atmos. Chem. Phys.
Author:
Fung Pak LunORCID, Zaidan Martha A.ORCID, Niemi Jarkko V., Saukko Erkka, Timonen HilkkaORCID, Kousa Anu, Kuula JoelORCID, Rönkkö Topi, Karppinen AriORCID, Tarkoma Sasu, Kulmala MarkkuORCID, Petäjä TuukkaORCID, Hussein Tareq
Abstract
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 developed a novel statistical
model, named the 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, the IAME model was 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 submodels were generated and ranked by the coefficient of determination
(R2), mean absolute error (MAE) and centred root-mean-square
difference (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 demonstrated that the additional adjustment by taking random
effects into account improved 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.
Funder
Academy of Finland H2020 European Research Council Urban Innovative Actions
Publisher
Copernicus GmbH
Subject
Atmospheric Science
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