Input-adaptive linear mixed-effects model for estimating alveolar lung-deposited surface area (LDSA) using multipollutant datasets
<p>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 depositi...
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Copernicus Publications
2022-02-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://acp.copernicus.org/articles/22/1861/2022/acp-22-1861-2022.pdf |
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| author | P. L. Fung P. L. Fung M. A. Zaidan M. A. Zaidan M. A. Zaidan J. V. Niemi E. Saukko H. Timonen A. Kousa J. Kuula T. Rönkkö A. Karppinen S. Tarkoma M. Kulmala M. Kulmala T. Petäjä T. Petäjä T. Hussein T. Hussein |
| author_facet | P. L. Fung P. L. Fung M. A. Zaidan M. A. Zaidan M. A. Zaidan J. V. Niemi E. Saukko H. Timonen A. Kousa J. Kuula T. Rönkkö A. Karppinen S. Tarkoma M. Kulmala M. Kulmala T. Petäjä T. Petäjä T. Hussein T. Hussein |
| author_sort | P. L. Fung |
| collection | DOAJ |
| description | <p>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 <span class="inline-formula">=</span> 19.7 <span class="inline-formula">±</span> 11.3 <span class="inline-formula">µ</span>m<span class="inline-formula"><sup>2</sup></span> cm<span class="inline-formula"><sup>−3</sup></span>)
site and an urban background (UB, average LDSA <span class="inline-formula">=</span> 11.2 <span class="inline-formula">±</span> 7.1 <span class="inline-formula">µ</span>m<span class="inline-formula"><sup>2</sup></span> cm<span class="inline-formula"><sup>−3</sup></span>)
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
(<span class="inline-formula"><i>R</i><sup>2</sup></span>), 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 <span class="inline-formula">µ</span>m (PM<span class="inline-formula"><sub>2.5</sub></span>), 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 PM<span class="inline-formula"><sub>2.5</sub></span>, BC and carbon monoxide (CO). The accuracy of the
overall model was better at the SC site (<span class="inline-formula"><i>R</i><sup>2</sup>=0.80</span>, MAE <span class="inline-formula">=</span> 3.7 <span class="inline-formula">µ</span>m<span class="inline-formula"><sup>2</sup></span> cm<span class="inline-formula"><sup>−3</sup></span>)
than at the UB site (<span class="inline-formula"><i>R</i><sup>2</sup>=0.77</span>, MAE <span class="inline-formula">=</span> 2.3 <span class="inline-formula">µ</span>m<span class="inline-formula"><sup>2</sup></span> cm<span class="inline-formula"><sup>−3</sup></span>), 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<span id="page1862"/> 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.</p> |
| first_indexed | 2024-12-13T00:51:39Z |
| format | Article |
| id | doaj.art-b31924245e2041ad84026bc847184bb5 |
| institution | Directory Open Access Journal |
| issn | 1680-7316 1680-7324 |
| language | English |
| last_indexed | 2024-12-13T00:51:39Z |
| publishDate | 2022-02-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Chemistry and Physics |
| spelling | doaj.art-b31924245e2041ad84026bc847184bb52022-12-22T00:04:54ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242022-02-01221861188210.5194/acp-22-1861-2022Input-adaptive linear mixed-effects model for estimating alveolar lung-deposited surface area (LDSA) using multipollutant datasetsP. L. Fung0P. L. Fung1M. A. Zaidan2M. A. Zaidan3M. A. Zaidan4J. V. Niemi5E. Saukko6H. Timonen7A. Kousa8J. Kuula9T. Rönkkö10A. Karppinen11S. Tarkoma12M. Kulmala13M. Kulmala14T. Petäjä15T. Petäjä16T. Hussein17T. Hussein18Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, FinlandHelsinki Institute of Sustainability Science, Faculty of Science, University of Helsinki, Helsinki, FinlandInstitute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, FinlandHelsinki Institute of Sustainability Science, Faculty of Science, University of Helsinki, Helsinki, FinlandJoint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, ChinaHelsinki Region Environmental Services Authority (HSY), P.O. Box 100, 00066 Helsinki, FinlandPegasor Oy, 33100 Tampere, FinlandAtmospheric Composition Research, Finnish Meteorological Institute, 00560 Helsinki, FinlandHelsinki Region Environmental Services Authority (HSY), P.O. Box 100, 00066 Helsinki, FinlandAtmospheric Composition Research, Finnish Meteorological Institute, 00560 Helsinki, FinlandAerosol Physics Laboratory, Physics Unit, Faculty of Engineering and Natural Sciences, Tampere University, 33720 Tampere, FinlandAtmospheric Composition Research, Finnish Meteorological Institute, 00560 Helsinki, FinlandDepartment of Computer Science, Faculty of Science, University of Helsinki, Helsinki, FinlandInstitute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, FinlandJoint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, ChinaInstitute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, FinlandJoint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, ChinaInstitute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, FinlandDepartment of Physics, the University of Jordan, Amman 11942, Jordan<p>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 <span class="inline-formula">=</span> 19.7 <span class="inline-formula">±</span> 11.3 <span class="inline-formula">µ</span>m<span class="inline-formula"><sup>2</sup></span> cm<span class="inline-formula"><sup>−3</sup></span>) site and an urban background (UB, average LDSA <span class="inline-formula">=</span> 11.2 <span class="inline-formula">±</span> 7.1 <span class="inline-formula">µ</span>m<span class="inline-formula"><sup>2</sup></span> cm<span class="inline-formula"><sup>−3</sup></span>) 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 (<span class="inline-formula"><i>R</i><sup>2</sup></span>), 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 <span class="inline-formula">µ</span>m (PM<span class="inline-formula"><sub>2.5</sub></span>), 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 PM<span class="inline-formula"><sub>2.5</sub></span>, BC and carbon monoxide (CO). The accuracy of the overall model was better at the SC site (<span class="inline-formula"><i>R</i><sup>2</sup>=0.80</span>, MAE <span class="inline-formula">=</span> 3.7 <span class="inline-formula">µ</span>m<span class="inline-formula"><sup>2</sup></span> cm<span class="inline-formula"><sup>−3</sup></span>) than at the UB site (<span class="inline-formula"><i>R</i><sup>2</sup>=0.77</span>, MAE <span class="inline-formula">=</span> 2.3 <span class="inline-formula">µ</span>m<span class="inline-formula"><sup>2</sup></span> cm<span class="inline-formula"><sup>−3</sup></span>), 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<span id="page1862"/> 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.</p>https://acp.copernicus.org/articles/22/1861/2022/acp-22-1861-2022.pdf |
| spellingShingle | P. L. Fung P. L. Fung M. A. Zaidan M. A. Zaidan M. A. Zaidan J. V. Niemi E. Saukko H. Timonen A. Kousa J. Kuula T. Rönkkö A. Karppinen S. Tarkoma M. Kulmala M. Kulmala T. Petäjä T. Petäjä T. Hussein T. Hussein Input-adaptive linear mixed-effects model for estimating alveolar lung-deposited surface area (LDSA) using multipollutant datasets Atmospheric Chemistry and Physics |
| title | Input-adaptive linear mixed-effects model for estimating alveolar lung-deposited surface area (LDSA) using multipollutant datasets |
| title_full | Input-adaptive linear mixed-effects model for estimating alveolar lung-deposited surface area (LDSA) using multipollutant datasets |
| title_fullStr | Input-adaptive linear mixed-effects model for estimating alveolar lung-deposited surface area (LDSA) using multipollutant datasets |
| title_full_unstemmed | Input-adaptive linear mixed-effects model for estimating alveolar lung-deposited surface area (LDSA) using multipollutant datasets |
| title_short | Input-adaptive linear mixed-effects model for estimating alveolar lung-deposited surface area (LDSA) using multipollutant datasets |
| title_sort | input adaptive linear mixed effects model for estimating alveolar lung deposited surface area ldsa using multipollutant datasets |
| url | https://acp.copernicus.org/articles/22/1861/2022/acp-22-1861-2022.pdf |
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