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.
Highlights
Particulate matter is one of the key components determining urban air pollution
The mass concentrations of particulate matter are dominated by large particles, whereas the number concentrations are governed by submicron particles (particle diameter < 1 μm), ultrafine particles (UFPs, dp < 0.1 μm) (e.g. Petäjä et al, 2007; Rönkkö et al, 2017; Zhou et al, 2020)
The surface area of inhaled particulate matter could act as a transport vector for many bacteria and viruses (Liu et al, 2018a), and besides commonly monitored particulate matter number concentration and mass concentration, the surface area of a particle is an important factor when considering the harmfulness of particulate matter (Duffin et al, 2002)
Summary
Particulate matter is one of the key components determining urban air pollution. Particulate matter can be described by a combination of varying concentration (number, surface area and mass) and chemical composition. A clear correlation was found between the emission factors of exhaust plume BC and LDSA in on-road studies for city buses (e.g. Järvinen et al, 2019) These highly correlated relationships provide good grounds for estimating LDSA concentrations and short-term trends by the other pollutants measured at the same site with the use of a data-mining-based approach as statistical models. A data-mining-based approach exploits statistical or machine learning techniques to detect patterns between predictors and dependent variables in the time series data They do not demand in-depth understanding of air pollutant dynamics, but evaluation by experts is still required to determine whether the models work properly. We combine the use of criterion-based feature selection method and the inclusion of random effects, and develop a novel input-adaptive mixed-effects (IAME) model to estimate alveolar LDSA concentrations, which is the first study of this context to our best knowledge. It should be noted that this study discusses LDSA in the alveolar region unless stated otherwise
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