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

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.

Fine and Ultrafine Particle Pollution Before and After a Smoking ban in the Catering Industry in Vienna

In the catering industrytobacco smoke was the primary source of fine and ultrafine particles, which are well known for their health-damaging effects. As shown in studies, attempts to reduce passive smoking in the catering industry of Vienna, like separated smoking rooms, failed to reduce fine and ultrafine particle concentrations effectively. On November 1st 2019, an enlarged non-smoker’s protection law was introduced, including a total smoking-ban in the catering industry. 40 hospitality venues with areas for smokers and non-smokers before the ban had been selected as typical Viennese cafes, pubs, bars and discotheques to be sampled unannounced. Concentrations of fine particle mass (PM10, PM2.5, PM1) and ultrafine particle number (PNC) and lung deposited surface area (LDSA) could be measured before and after the introduction of the smoking-ban in 39 venues at nearly identical locations and under comparable circumstances. Results showed a statistically significant decline in both fine and ultrafine particle concentrations in the former smoking areas for all parameters as well as in the former non-smoking areas for PM2.5, PM1 and LDSA. After the ban concentrations in former smoking areas and non-smoking areas showed no significant differences any more. From these results the smoking-ban successfully removed particles from breathing air of guests and staff, however, some outliers in the study after the ban point to the necessity of repeated controls in Vienna. Also, outside Vienna the compliance with the law should be controlled in the Austrian hospitality industry.

Workers’ Exposure Assessment during the Production of Graphene Nanoplatelets in R&D Laboratory

Widespread production and use of engineered nanomaterials in industrial and research settings raise concerns about their health impact in the workplace. In the last years, graphene-based nanomaterials have gained particular interest in many application fields. Among them, graphene nanoplatelets (GNPs) showed superior electrical, optical and thermal properties, low-cost and availability. Few and conflicting results have been reported about toxicity and potential effects on workers’ health, during the production and handling of these nanostructures. Due to this lack of knowledge, systematic approaches are needed to assess risks and quantify workers’ exposure to GNPs. This work applies a multi-metric approach to assess workers’ exposure during the production of GNPs, based on the Organization for Economic Cooperation and Development (OECD) methodology by integrating real-time measurements and personal sampling. In particular, we analyzed the particle number concentration, the average diameter and the lung deposited surface area of airborne nanoparticles during the production process conducted by thermal exfoliation in two different ways, compared to the background. These results have been integrated by electron microscopic and spectroscopic analysis on the filters sampled by personal impactors. The study identifies the process phases potentially at risk for workers and reports quantitative information about the parameters that may influence the exposure in order to propose recommendations for a safer design of GNPs production process.