Determinants of spatial variability of air pollutant concentrations in a street canyon network measured using a mobile laboratory and a drone

Urban air pollutant concentrations are highly variable both in space and time. In order to understand these variabilities high-resolution measurements of air pollutants are needed. Here we present results of a mobile laboratory and a drone measurements made within a street-canyon network in Helsinki, Finland, in summer and winter 2017. The mobile lab-oratory measured the total number concentration (N) and lung-deposited surface area (LDSA) of aerosol particles, and the concentrations of black carbon, nitric oxide (NOx) and ozone (O3). The drone measured the vertical profile of LDSA. The main aims were to examine the spatial variability of air pollutants in a wide street canyon and its immediate surroundings, and find the controlling environmental variables for the observed variability's.The highest concentrations with the most temporal variability were measured at the main street canyon when the mo-bile laboratory was moving with the traffic fleet for all air pollutants except O3. The street canyon concentration levels were more affected by traffic rates whereas on surrounding areas, meteorological conditions dominated. Both the mean flow and turbulence were important, the latter particularly for smaller aerosol particles through LDSA and N. The formation of concentration hotspots in the street network were mostly controlled by mechanical processes but in winter thermal processes became also important for aerosol particles. LDSA showed large variability in the profile shape, and surface and background concentrations. The expected exponential decay functions worked better in well -mixed conditions in summer compared to winter. We derived equation for the vertical decay which was mostly con-trolled by the air temperature. Mean wind dominated the profile shape over both thermal and mechanical turbulence. This study is among the first experimental studies to demonstrate the importance of high-resolution measurements in understanding urban pollutant variability in detail.Peer reviewe

Nonvolatile ultrafine particles observed to form trimodal size distributions in non-road diesel engine exhaust

Some recent findings regarding the negative health effects of particulate matter increase the relevance of the detailed characteristics of particulate emissions from different sources and especially the nonvolatile fraction of particles. In this study, the nonvolatile fraction of ultrafine particulate emissions from a non-road diesel engine was studied. The measurements were carried out in an engine laboratory and the exhaust sample was taken from the engine-out location with various steady state driving modes. Four different fuels, including fossil fuel, soybean methyl ester (SME), rapeseed methyl ester (RME), and renewable paraffinic diesel (RPD), were used. In the sampling system, the sample was diluted and led through a thermodenuder removing the volatile fraction of particles. The measured particle size distributions of nonvolatile particles were found to be trimodal. Based on the size distribution data as well as the morphology and elemental composition of particles in transmission electron microscopy (TEM) samples, we were able to draw conclusions from the most probable origin of the different particle modes, and the modes were named accordingly. From larger to smaller in particle size, the modes were a soot mode, lubricating oil originated core (LC) mode, and a fuel originated core (FC) mode. All of these three modes were detected with every driving mode, but differences were seen, for example, between different fuels. In addition, a trade-off was observed in the concentrations of the LC mode and the soot mode as a function of the engine torque.© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.fi=vertaisarvioitu|en=peerReviewed

Spatiotemporal variation and trends in equivalent black carbon in the Helsinki metropolitan area in Finland

In this study, we present results from 12 years of black carbon (BC) measurements at 14 sites around the Helsinki metropolitan area (HMA) and at one background site outside the HMA. The main local sources of BC in the HMA are traffic and residential wood combustion in fire-places and sauna stoves. All BC measurements were conducted optically, and therefore we refer to the measured BC as equivalent BC (eBC). Measurement stations were located in different environments that represented traffic environment, detached housing area, urban background, and regional background. The measurements of eBC were conducted from 2007 through 2018; however, the times and the lengths of the time series varied at each site. The largest annual mean eBC concentrations were measured at the traffic sites (from 0.67 to 2.64 mu g m(-3)) and the lowest at the regional background sites (from 0.16 to 0.48 mu g m(-3)). The annual mean eBC concentrations at the detached housing and urban background sites varied from 0.64 to 0.80 mu g m(-3) and from 0.42 to 0.68 mu g m(-3), respectively. The clearest seasonal variation was observed at the detached housing sites where residential wood combustion increased the eBC concentrations during the cold season. Diurnal variation in eBC concentration in different urban environments depended clearly on the local sources that were traffic and residential wood combustion. The dependency was not as clear for the typically measured air quality parameters, which were here NOx concentration and mass concentration of particles smaller that 2.5 mu m in diameter (PM2.5). At four sites which had at least a 4-year-long time series available, the eBC concentrations had statistically significant decreasing trends that varied from -10.4 % yr(-1) to -5.9 % yr(-1). Compared to trends determined at urban and regional background sites, the absolute trends decreased fastest at traffic sites, especially during the morning rush hour. Relative long-term trends in eBC and NOx were similar, and their concentrations decreased more rapidly than that of PM2.5. The results indicated that especially emissions from traffic have decreased in the HMA during the last decade. This shows that air pollution control, new emission standards, and a newer fleet of vehicles had an effect on air quality.Peer reviewe

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 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.Peer reviewe

Chemical and physical characterization of oil shale combustion emissions in Estonia

In this study, oil shale combustion emission measurements were conducted in a 60 kW(th) Circulating Fluidized Bed combustion test facility located in a laboratory-type environment. A comprehensive set of instruments including a nitrate-ion-based Chemical Ionization Atmospheric Pressure interface Time-of-Flight Mass Spectrometer, a Soot-Particle Aerosol Mass Spectrometer, and a Potential Aerosol Mass (PAM) chamber was utilized to investigate the chemical composition and concentrations of primary and secondary emissions in oil shale combustion. In addition, the size distribution of particles (2.5-414 nm) as well as concentration and composition of gaseous precursors were characterized. Altogether 12 different experiments were conducted. Primary emissions were studied in seven experiments and aged emissions using PAM chamber in five experiments. Combustion temperatures and solid fuel circulation rates varied between different experiments, and it was found that the burning conditions had a large impact on gaseous and particulate emissions. The majority of the combustion particles were below 10 nm in size during good burning whereas in poor burning conditions the emitted particles were larger and size distributions with 2-3 particle modes were detected. The main submicron particle chemical component was particulate organic matter (POM), followed by sulfate, chloride, nitrate, and ammonium. The secondary particulate matter formed in the PAM chamber was mostly POM and the concentration of POM was many orders of magnitude higher in aged aerosol compared to primary emissions. A significant amount of aromatic volatile organic compounds (VOCs) was measured as well. VOCs have the potential to go through gas-to-particle conversion during the oxidation process, explaining the observed high concentrations of aged POM. During good combustion, when VOC emissions were lower, over 80% of SO2 was oxidized either to gaseous H2SO4 (37%) or particulate sulfate (46%) in the PAM chamber, which mimic the atmospheric processes taken place in the ambient air after few days of emission.Peer reviewe