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Atmospheric aerosols play an important role in air pollution. Aerosol particle chemical composition is highly variable depending on the season, hour of the day, day of the week, meteorology, and location of the measurement site. Long measurement periods and highly time-resolved data are required in order to achieve a statistically relevant amount of data for assessing those variations and evaluating pollution episodes. In this study, we present continuous atmospheric PM1 (particulate matter < 1 µm) concentration and composition measurements at an urban street canyon site located in Helsinki, Finland. The study was performed for 4.5 years (2015–2019) and involved highly time-resolved measurements by taking advantage of a suite of online state-of-the-art instruments such as an aerosol chemical speciation monitor (ACSM), a multi-angle absorption photometer (MAAP), a differential mobility particle sizer (DMPS), and an Aethalometer (AE). PM1 consisted mostly of organics, with mean mass concentrations of 2.89 µg m−3 (53 % of PM1) followed by inorganic species (1.56 µg m−3, 29 %) and equivalent black carbon (eBC, 0.97 µg m−3, 18 %). A trend analysis revealed a decrease in BC from fossil fuel (BCFF), organics, and nitrate over the studied years. Clear seasonal and/or diurnal variations were found for the measured atmospheric PM1 constituents. Particle number and mass size distributions over different seasons revealed the possible influence of secondary organic aerosols (SOAs) during summer and the dominance of ultrafine traffic aerosols during winter. The seasonality of measured constituents also impacted the particle's coating and absorptive properties. The investigation of pollution episodes observed at the site showed that a large fraction of aerosol particle mass was comprised of inorganic species during long-range transport, while during local episodes eBC and organics prevailed together with elevated particle number concentration. Overall, the results increased knowledge of the variability of PM1 concentration and composition in a Nordic traffic site and its implications on urban air quality. Considering the effects of PM mitigation policies in northern Europe in the last decades, the results obtained in this study may be considered illustrative of probable future air quality challenges in countries currently adopting similar environmental regulations.

Residential wood combustion (RWC) is an important contributor to air quality in numerous regions worldwide. This study is the first extensive evaluation of the influence of RWC on ambient air quality in several Nordic cities. We have analyzed the emissions and concentrations of PM2.5 in cities within four Nordic countries: the metropolitan areas of Copenhagen, Oslo and Helsinki, and Umeå. We have evaluated the emissions for the relevant urban source categories and modelled atmospheric dispersion on regional and urban scales. The emission inventories for RWC were based on local surveys, the amount of wood combusted, combustion technologies and other relevant factors. The accuracy of the predicted concentrations was evaluated based on urban concentration measurements. The predicted annual average concentrations ranged spatially from 4 to 7 μg/m3 (2011), from 6 to 10 μg/m3 (2013), from 4 to more than 13 μg/m3 (2013) and from 9 to more than 13 μg/m3 (2014), in Umeå, Helsinki, Oslo and Copenhagen, respectively. The higher concentrations in Copenhagen were mainly caused by the higher long-range transported background. The annual average fractions of PM2.5 concentrations attributed to RWC within the considered urban regions ranged spatially from 0 to 15 %, from 0 to 20 %, from 8 to 30 % and from 0 to 60 % in Helsinki, Copenhagen, Umeå and Oslo, respectively. In particular, the contributions of RWC in central Oslo were larger than 40 % as annual averages. In Oslo, wood combustion was used mainly for the heating of larger blocks of flats. On the contrary, in Helsinki, RWC was solely used in smaller detached houses. In Copenhagen and Helsinki, the highest fractions occurred outside the city center in the suburban areas. In Umeå, the highest fractions occurred both in the city centre and its surroundings. Stricter and more efficient emission regulations should be set in the Nordic countries with respect to RWC, especially in urban areas, for the protection of human health.

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.

This study investigated the sources of black carbon (BC) at two contrasting urban environments in Helsinki, Finland; residential area and street canyon. The sources of BC were explored by using positive matrix factorization (PMF) for the organic and refractory black carbon (rBC) mass spectra collected with a soot particle aerosol mass spectrometer (SP-AMS). Two sites had different local BC sources; the largest fraction of BC originated from biomass burning at the residential site (38 %) and from the vehicular emissions at the street canyon (57 %). Also, the mass size distribution of BC diverged at the sites as BC from traffic was found at the particle size of ~100–150 nm whereas BC from biomass combustion was detected at ~300 nm. At both sites, a large fraction of BC was associated with urban background or long-range transported BC indicated by the high oxidation state of organics related to those PMF factors. The results from the PMF analysis were compared with the source apportionment from the aethalometer model calculated with two pair of absorption Ångström values. It was found that several PMF factors can be attributed to wood combustion and fossil fuel fraction of BC provided by the aethalometer model. In general, the aethalometer model showed less variation between the sources within a day than PMF being less responsive to the fast changes in the BC sources at the site. The results of this study increase understanding of the limitations and validity of the BC source apportionment methods in different environments. Moreover, this study advances the current knowledge of BC sources and especially the contribution of residential combustion in urban areas.

Air quality prediction with black-box (BB) modelling is gaining widespread interest in research and industry. This type of data-driven models work generally better in terms of accuracy but are limited to capture physical, chemical and meteorological processes and therefore accountability for interpretation. In this paper, we evaluated different white-box (WB) and BB methods that estimate atmospheric black carbon (BC) concentration by a suite of observations from the same measurement site. This study involves data in the period of 1st January 2017–31st December 2018 from two measurement sites, from a street canyon site in Makelankatu and from an urban background site in Kumpula, in Helsinki, Finland. At the street canyon site, WB models performed ( R 2 = 0.81–0.87) in a similar way as the BB models did ( R 2 = 0.86–0.87). The overall performance of the BC concentration estimation methods at the urban background site was much worse probably because of a combination of smaller dynamic variability in the BC values and longer data gaps. However, the difference in WB ( R 2 = 0.44–0.60) and BB models ( R 2 = 0.41–0.64) was not significant. Furthermore, the WB models are closer to physics-based models, and it is easier to spot the relative importance of the predictor variable and determine if the model output makes sense. This feature outweighs slightly higher performance of some individual BB models, and inherently the WB models are a better choice due to their transparency in the model architecture. Among all the WB models, IAP and LASSO are recommended due to its flexibility and its efficiency, respectively. Our findings also ascertain the importance of temporal properties in statistical modelling. In the future, the developed BC estimation model could serve as a virtual sensor and complement the current air quality monitoring. Main findings White-box models are preferred over black-box models in estimating black carbon because they are closer to physics-based models, and it is easier to spot the relative importance of the predictor variable. The black carbon model could serve as a virtual sensor integrating into air quality network in support with real measurements, so as to complement the current air quality index.

In this study, the NOx emissions of four Euro 6 diesel passenger cars ranging from Euro 6 b to Euro 6 d-TEMP in different ambient conditions and driving routes were investigated with a Portable Emissions Measurement System (PEMS) and continuous NOx concentration monitoring device. A model was also generated for translating NOx concentration values into a gram basis. The results suggest that there is a marked difference in NOx emissions based on the Euro 6 step for the car is type approved. The study showed that the conformity factor for NOx emissions on a route in a city environment (“City route”) changed from 0.65 to 5.2 depending on the Euro 6 step and car. Surprisingly, a Euro 6 b car equipped with Selective Catalytic Reduction SCR system and updated engine control unit (ECU) software for lower tailpipe NOx emissions provided lower average NOx emissions than a Euro 6 d-TEMP diesel car equipped with dual lean-NOx traps. Results for the City route also showed that the road infrastructure (crossroads and speed limitations) can have a noticeable effect on promoting driving that leads to higher NOx emissions even with a Euro 6 d-TEMP car. Estimations of NOx emissions with modelling based on continuous NOx concentration monitoring suggested that Euro 6 b diesel cars can provide NOx emissions close to the current RDE legislation. In addition, the modelling suggested that the Euro 6 b car with updated ECU software and the Euro 6 d-TEMP diesel car are capable of extremely low daily average NOx emissions, even close to 20 mg/km, in normal daily usage. Nevertheless, the monitoring results and model also suggest that cold ambient temperature has a high effect on the NOx emissions reduction performance of these vehicles, occasionally increasing their daily average emissions to as high as 900 mg/km.

Urbanisation and industrialisation led to the increase of ambient particulate matter (PM) concentration. While subsequent regulations may have resulted in the decrease of some PM matrices, the simultaneous changes in climate affecting local meteorological conditions could also have played a role. To gain an insight into this complex matter, this study investigated the long-term trends of two important matrices, the particle mass (PM2.5) and particle number concentrations (PNC), and the factors that influenced the trends. Mann-Kendall test, Sen's slope estimator, the generalised additive model, seasonal decomposition of time series by LOESS (locally estimated scatterplot smoothing) and the Buishand range test were applied. Both PM2.5 and PNC showed significant negative monotonic trends (0.03-0.6 mg m(-3).yr(-1) and 0.40-3.8 x 10(3) particles. cm(-3). yr(-1), respectively) except Brisbane (+0.1 mg m(-3). yr(-1) and +53 particles. cm(-3). yr(-1), respectively). For the period covered in this study, temperature increased (0.03-0.07 degrees C.yr(-1)) in all cities except London; precipitation decreased (0.02-1.4 mm.yr(-1)) except in Helsinki; and wind speed was reduced in Brisbane and Rochester but increased in Helsinki, London and Augsburg. At the change-points, temperature increase in cold cities influenced PNC while shifts in precipitation and wind speed affected PM2.5. Based on the LOESS trend, extreme events such as dust storms and wildfires resulting from changing climates caused a positive step-change in concentrations, particularly for PM2.5. In contrast, among the mitigation measures, controlling sulphur in fuels caused a negative step-change, especially for PNC. Policies regarding traffic and fleet management (e.g. low emission zones) that were implemented only in certain areas or in a progressive uptake (e.g. Euro emission standards), resulted to gradual reductions in concentrations. Therefore, as this study has clearly shown that PM2.5 and PNC were influenced differently by the impacts of the changing climate and by the mitigation measures, both metrics must be considered in urban air quality management. (C) 2020 Elsevier Ltd. All rights reserved.

Reliable and self-consistent data on air quality are needed for an extensive period of time for conducting long-term, or even lifetime health impact assessments. We have modelled the urban-scale concentrations of fine particulate matter (PM2.5) in the Helsinki Metropolitan Area for a period of 35 years, from 1980 to 2014. The regional background concentrations were evaluated based on reanalyses of the atmospheric composition on global and European scales, using the SILAM model. The high-resolution urban computations included both the emissions originated from vehicular traffic (separately exhaust and suspension emissions) and those from small-scale combustion, and were conducted using the road network dispersion model CAR-FMI and the multiple-source Gaussian dispersion model UDM-FMI. The modelled concentrations of PM2.5 agreed fairly well with the measured data at a regional background station and at four urban measurement stations, during 1999–2014. The modelled concentration trends were also evaluated for earlier years, until 1988, using proxy analyses. There was no systematic deterioration of the agreement of predictions and data for earlier years (the 1980s and 1990s), compared with the results for more recent years (2000s and early 2010s). The local vehicular emissions were about 5 times higher in the 1980s, compared with the emissions during the latest considered years. The local small-scale combustion emissions increased slightly over time. The highest urban concentrations of PM2.5 occurred in the 1980s; these have since decreased to about to a half of the highest values. In general, regional background was the largest contribution in this area. Vehicular exhaust has been the most important local source, but the relative shares of both small-scale combustion and vehicular non-exhaust emissions have increased in time. The study has provided long-term, high-resolution concentration databases on regional and urban scales that can be used for the assessment of health effects associated with air pollution.

A two-week measurement campaign by a mobile laboratory van was performed in urban environments in the Helsinki metropolitan area, Finland, in winter 2012, to obtain a comprehensive view on aerosol properties and sources. The abundances and physico-chemical properties of particles varied strongly in time and space, depending on the main sources of aerosols. Four major types of winter aerosol were recognized: 1) clean background aerosol with low particle number (Ntot) and lung deposited surface area (LDSA) concentrations due to marine air flows from the Atlantic Ocean; 2) long-range transported (LRT) pollution aerosol due to air flows from eastern Europe where the particles were characterized by the high contribution of oxygenated organic aerosol (OOA) and inorganic species, particularly sulphate, but low BC contribution, and their size distribution possessed an additional accumulation mode; 3) fresh smoke plumes from residential wood combustion in suburban small houses, these particles were characterized by high biomass burning organic aerosol (BBOA) and black carbon (BC) concentrations; and 4) fresh emissions from traffic while driving on busy streets in the city centre and on the highways during morning rush hours. This aerosol was characterized by high concentration of Ntot, LDSA, small particles in the nucleation mode, as well as high hydrocarbon-like organic aerosol (HOA) and BC concentrations. In general, secondary components (OOA, NO3, NH4, and SO4) dominated the PM1 chemical composition during the LRT episode accounting for 70–80% of the PM1 mass, whereas fresh primary emissions (BC, HOA and BBOA) dominated the local traffic and wood burning emissions. The major individual particle types observed with electron microscopy analysis (TEM/EDX) were mainly related to residential wood combustion (K/S/C-rich, soot, other C-rich particles), traffic (soot, Si/Al-rich, Fe-rich), heavy fuel oil combustion in heat plants or ships (S with V-Ni-Fe), LRT pollutants (S/C-rich secondary particles) and sea salt (Na/Cl-rich). Tar balls from wood combustion were also observed, especially (∼5%) during the LRT pollution episode. acceptedVersion

Even though emission inventories indicate that wood combustion is a major source of polycyclic aromatic hydrocarbons (PAHs), estimating its impacts on PAH concentration in ambient air remains challenging. In this study the effect of local small-scale wood combustion on the benzo[a]pyrene (BaP) concentrations in ambient air in the Helsinki metropolitan area in Finland is evaluated, using ambient air measurements, emission estimates, and dispersion modeling. The measurements were conducted at 12 different locations during the period from 2007 to 2015. The spatial distributions of annual average BaP concentrations originating from wood combustion were predicted for four of those years: 2008, 2011, 2013, and 2014. According to both the measurements and the dispersion modeling, the European Union target value for the annual average BaP concentrations (1 ng m−3) was clearly exceeded in certain suburban detached-house areas. However, in most of the other urban areas, including the center of Helsinki, the concentrations were below the target value. The measured BaP concentrations highly correlated with the measured levoglucosan concentrations in the suburban detached-house areas. In street canyons, the measured concentrations of BaP were at the same level as those in the urban background, clearly lower than those in suburban detached-house areas. The predicted annual average concentrations matched with the measured concentrations fairly well. Both the measurements and the modeling clearly indicated that wood combustion was the main local source of ambient air BaP in the Helsinki metropolitan area.

Missing data has been a challenge in air quality measurement. In this study, we develop an input-adaptive proxy, which selects input variables of other air quality variables based on their correlation coefficients with the output variable. The proxy uses ordinary least squares regression model with robust optimization and limits the input variables to a maximum of three to avoid overfitting. The adaptive proxy learns from the data set and generates the best model evaluated by adjusted coefficient of determination (adjR2). In case of missing data in the input variables, the proposed adaptive proxy then uses the second-best model until all the missing data gaps are filled up. We estimated black carbon (BC) concentration by using the input-adaptive proxy in two sites in Helsinki, which respectively represent street canyon and urban background scenario, as a case study. Accumulation mode, traffic counts, nitrogen dioxide and lung deposited surface area are found as input variables in models with the top rank. In contrast to traditional proxy, which gives 20&ndash, 80% of data, the input-adaptive proxy manages to give full continuous BC estimation. The newly developed adaptive proxy also gives generally accurate BC (street canyon: adjR2 = 0.86&ndash, 0.94, urban background: adjR2 = 0.74&ndash, 0.91) depending on different seasons and day of the week. Due to its flexibility and reliability, the adaptive proxy can be further extend to estimate other air quality parameters. It can also act as an air quality virtual sensor in support with on-site measurements in the future.

This paper presents the first outcomes of the “FAIRMODE pilot” activity, aiming at improving the way in which air quality models are used in the frame of the European “Air Quality Directive”. Member States may use modelling, combined with measurements, to “assess” current levels of air quality and estimate future air quality under different scenarios. In case of current and potential exceedances of the Directive limit values, it is also requested that they “plan” and implement emission reductions measures to avoid future exceedances. In both “assessment” and “planning”, air quality models can and should be used; but to do so, the used modelling chain has to be fit-for-purpose and properly checked and verified. FAIRMODE has developed in the recent years a suite of methodologies and tools to check if emission inventories, model performance, source apportionment techniques and planning activities are fit-for-purpose. Within the “FAIRMODE pilot”, these tools are used and tested by regional/local authorities, with the two-fold objective of improving management practices at regional/local scale, and providing valuable feedback to the FAIRMODE community. Results and lessons learnt from this activity are presented in this paper, as a showcase that can potentially benefit other authorities in charge of air quality assessment and planning., Highlights • Air quality is still a challenge in various areas in Europe. • FAIRMODE provides methodologies and tools for supporting air quality improvement. • Specifically, a new FAIRMODE initiative for better management practices has been recently launched. • The initiative provides a “live tutorial” to interested authorities. • First results on improved emission inventories and concentrations are presented.

Lung deposited surface area (LDSA) is a relatively new metric that has been argued to be more accurate at predicting health effects from aerosol exposure. For typical atmospheric aerosol, the LDSA concentration depends mainly on the concentration of ultrafine particles (e.g. vehicular exhaust emissions and residential wood combustion) and therefore optical methods cannot be used to measure and quantify it. The objective of this study was to investigate and describe typical characteristics of LDSA under different urban environments and evaluate how a diffusion charging-based Pegasor AQ Urban sensor (Pegasor Ltd., Finland) can be used as an alternative to optical sensors when assessing local combustion emissions and respective LDSA concentrations. Long-term (12 months) sensor measurements of LDSA were carried out at three distinctly different measurement sites (four sensor nodes) in the Helsinki metropolitan area, Finland. The sites were affected mainly by vehicular exhaust emission (street canyon and urban background stations) and by residential wood combustion (two detached housing area stations). The results showed that the accuracy of the AQ Urban was good (R2 = 0.90) for the measurement of LDSA when compared to differential mobility particle sizer. The mean concentrations of LDSA were more than twice as high at the street canyon (mean 22 µm2 cm−3) site when compared to the urban background site (mean 9.4 µm2 cm−3). In the detached housing area, the mean concentrations were 12 µm2 cm−3, and wood combustion typically caused high LDSA peaks in the evenings. High correlations and similar diurnal cycles were observed for the LDSA and black carbon at street canyon and urban background stations. The utilization of a small-scale sensor network (four nodes) showed that the cross-station variability in hourly LDSA concentrations was significant in every site, even within the same detached housing area (distance between the two sites ∼670 m).

Four measurement campaigns were performed in two different environments – inside the harbour areas in the city centre of Helsinki, and along the narrow shipping channel near the city of Turku, Finland – using a mobile laboratory van during winter and summer conditions in 2010–2011. The characteristics of gaseous (CO, CO2, SO2, NO, NO2, NOx) and particulate (number and volume size distributions as well as PM2.5) emissions for 11 ships regularly operating on the Baltic Sea were studied to determine the emission parameters. The highest particle concentrations were 1.5 × 106 and 1.6 × 105 cm−3 in Helsinki and Turku, respectively, and the particle number size distributions had two modes. The dominating mode peaked at 20–30 nm, and the accumulation mode at 80–100 nm. The majority of the particle mass was volatile, since after heating the sample to 265 °C, the particle volume of the studied ship decreased by around 70%. The emission factors for NOx varied in the range of 25–100 g (kg fuel)−1, for SO2 in the range of 2.5–17.0 g (kg fuel)−1, for particle number in the range of (0.32–2.26) × 1016 # (kg fuel)−1, and for PM2.5 between 1.0–4.9 g (kg fuel)−1. The ships equipped with SCR (selective catalytic reduction) had the lowest NOx emissions, whereas the ships with DWI (direct water injection) and HAMs (humid air motors) had the lowest SO2 emissions but the highest particulate emissions. For all ships, the averaged fuel sulphur contents (FSCs) were less than 1% (by mass) but none of them was below 0.1% which will be the new EU directive starting 1 January 2015 in the SOx emission control areas; this indicates that ships operating on the Baltic Sea will face large challenges.

Chemical characterization of non-refractory submicron particles (NR-PM1) and source apportionment of organic aerosols (OA) were carried out at four different sites in the Helsinki metropolitan area, Finland, using an Aerodyne Aerosol Chemical Speciation Monitor (ACSM). Two of the sites represented suburban residential areas, whereas the other two were traffic sites, one in a curbside in downtown and the other one in a suburban highway edge. The residential and the curbside measurements were conducted during the winter, but the highway campaign was carried out in the autumn. NR-PM1 were composed mainly of organics (40–68% in the different sites), followed by sulphate (11–34%), nitrate (12–16%), ammonium (7.8–8.5%) and chloride (0.24–1.3%). The mean concentrations of NR-PM1 were quite similar during the winter campaigns (10.1–12.5 µg/m3), but NR-PM1 was clearly lower during the autumn campaign at the highway site (6.0 µg/m3) due to the meteorology (favourable mixing conditions), small concentrations of long-range transported particles and non-intensive heating period locally and regionally. Using a multilinear engine algorithm (ME-2) and the custom software tool Source Finder (SoFI), the organic fraction was divided into two or three types of OA representing hydrocarbon-like organic aerosol (HOA), oxygenated organic aerosol (OOA), and in three sites, biomass burning organic aerosol (BBOA). At the downtown traffic site (Curbside), BBOA could not be found, probably because most of the local wood burning occurs in the suburban areas of the Helsinki region. OOA had the largest contribution to OA at all the sites (50–67%). The contribution of HOA was higher at the traffic sites (25–32%) than at the residential sites (15–18%). At the suburban residential and highway sites, the contribution of BBOA was high (25–30%). Especially during cold periods, very high BBOA contributions (~50%) were observed at the residential sites.

In densely populated areas, traffic is a significant source of atmospheric aerosol particles. Owing to their small size and complicated chemical and physical characteristics, atmospheric particles resulting from traffic emissions pose a significant risk to human health and also contribute to anthropogenic forcing of climate. Previous research has established that vehicles directly emit primary aerosol particles and also contribute to secondary aerosol particle formation by emitting aerosol precursors. Here, we extend the urban atmospheric aerosol characterization to cover nanocluster aerosol (NCA) particles and show that a major fraction of particles emitted by road transportation are in a previously unmeasured size range of 1.3–3.0 nm. For instance, in a semiurban roadside environment, the NCA represented 20–54% of the total particle concentration in ambient air. The observed NCA concentrations varied significantly depending on the traffic rate and wind direction. The emission factors of NCA for traffic were 2.4·1015 (kgfuel)−1 in a roadside environment, 2.6·1015 (kgfuel)−1 in a street canyon, and 2.9·1015 (kgfuel)−1 in an on-road study throughout Europe. Interestingly, these emissions were not associated with all vehicles. In engine laboratory experiments, the emission factor of exhaust NCA varied from a relatively low value of 1.6·1012 (kgfuel)−1 to a high value of 4.3·1015 (kgfuel)−1. These NCA emissions directly affect particle concentrations and human exposure to nanosized aerosol in urban areas, and potentially may act as nanosized condensation nuclei for the condensation of atmospheric low-volatile organic compounds. acceptedVersion acceptedVersion

Detailed chemical characterization of exhaust particles from 23 individual city buses was performed in Helsinki, Finland. Investigated buses represented different technologies in terms of engines, exhaust after-treatment systems (e.g., diesel particulate filter, selective catalytic reduction, and three-way catalyst) and fuels (diesel, diesel-electric (hybrid), ethanol, and compressed natural gas). Regarding emission standards, the buses operated at EURO III, EURO IV, and EEV (enhanced environmentally friendly vehicle) emission levels. The chemical composition of exhaust particles was determined by using a soot particle aerosol mass spectrometer (SP-AMS). Based on the SP-AMS results, the bus emission particles were dominated by organics and refractory black carbon (rBC). The mass spectra of organics consisted mostly of hydrocarbon fragments (54-86% of total organics), the pattern of hydrocarbon fragments being rather similar regardless of the bus type. Regarding oxygenated organic fragments, ethanol-fueled buses had unique mass-to-charge ratios (m/z) of 45, 73, 87, and 89 (mass fragments of C2H5OC, C3H5O2+, C4H7O2+, and C4H9O2+, respectively) that were not detected for the other bus types at the same level. For rBC, there was a small difference in the ratio of C-4(+) and C-5(+) to C-3(+) for different bus types but also for the individual buses of the same type. In addition to organics and rBC, the presence of trace metals in the bus emission particles was investigated.

A mathematical model is presented for the determination of human exposure to ambient air pollution in an urban area; the model is a refined version of a previously developed mathematical model EXPAND (EXposure model for Particulate matter And Nitrogen oxiDes). The model combines predicted concentrations, information on people's activities and location of the population to evaluate the spatial and temporal variation of average exposure of the urban population to ambient air pollution in different microenvironments. The revisions of the modelling system containing the EXPAND model include improvements of the associated urban emission and dispersion modelling system, an improved treatment of the time use of population, and better treatment for the infiltration coefficients from outdoor to indoor air. The revised model version can also be used for estimating intake fractions for various pollutants, source categories and population subgroups. We present numerical results on annual spatial concentration, time activity and population exposures to PM2.5 in the Helsinki Metropolitan Area and Helsinki for 2008 and 2009, respectively. Approximately 60% of the total exposure occurred at home, 17% at work, 4% in traffic and 19% in other microenvironments in the Helsinki Metropolitan Area. The population exposure originating from the long-range transported background concentrations was responsible for a major fraction, 86 %, of the total exposure in Helsinki. The largest local contributors were vehicular emissions (12 %) and shipping (2 %). © Author(s) 2014. CC Attribution 3.0 License.

To estimate spatial–temporal variations of ultrafine particles (UFP) in Helsinki, we measured particle total number concentrations (PNC) continuously in a busy street and an urban background site for six months, using condensation particle counters (CPC). We also evaluated the effects of temperature, wind speed and wind direction on PNC, as well as the correlation between PNC and PM2.5, PM10 and black carbon (BC) at the street. We found that on weekdays, hourly median PNC were highly correlated with BC (r = 0.88), moderately correlated with PM2.5 (r = 0.59) and weakly correlated with PM10 (r = 0.22). Number concentrations at the street were inversely proportional to temperature and wind speed, and highly dependent on wind direction. The highest PNC occurred during northeastern winds while the lowest occurred during southwestern winds. As these wind directions are nearly perpendicular to the street axis, the formation of wind vortices may have influenced the dispersion of UFP in the site. Although the temporal correlation for PNC was moderately high between the sites (r = 0.71), the median concentration at the street was 3 times higher than the urban background levels. The results indicate that people living or passing by the busy street are exposed to UFP concentrations well above the urban background levels. Thus, the study suggests that urban microenvironments should be considered in epidemiological studies. In addition the results emphasize that regulations based solely on PM2.5 and PM10 concentrations may be insufficient for preventing the adverse health effects of airborne particles.

A measurement campaign by a mobile laboratory van was performed in urban microenvironments bounded by a busy street Mannerheimintie in the city center of Helsinki, Finland. The characteristics of spatiotemporally high-resolution pollutant concentrations were studied such as ultrafine particles in the size range of 3–414 nm, black carbon BC, fine particle mass PM2.5, as well as nitrogen oxides NO and NO2. In addition, the effects of street geometry and roadside structure on the local dispersion of traffic emissions were analyzed as well. Meteorological conditions stayed stable and the wind direction was perpendicular to Mannerheimintie during the campaign. The highest particle concentrations were ∼8 × 105 cm−3, of which around 94% was smaller than 40 nm. At the pavement, the average concentration was in maximum 5 × 104 cm−3; around 80% of the particles was smaller than 40 nm. The volatility fraction was 75% by number. Due to the street canyon effect by the surrounding buildings, the downwind concentrations were around 24% of the upwind concentrations for particle number, 28% of NO, 39% of BC and 70% of NO2 concentrations. Furthermore, the upwind concentrations were higher than the simultaneously measured concentrations within the traffic flow. In fact, the particle count was around 3-fold, BC 2.5-fold, PM2.5 and NO2 1.5-fold compared to the concentrations while driving. Thus, for this measurement site and under these meteorological conditions, the exposure to pedestrians and cyclist on the upwind pavement is even higher than the driver's exposure. If the downwind buildings were parallel to Mannerheimintie, the concentrations dropped significantly at the pavement and continued decreasing slightly in the courtyards. When the downwind buildings were perpendicular to Mannerheimintie, a gradual reduction in the concentrations between the buildings was observed. However, at a distance of approximately a hundred meters a parallel side street which was a street canyon, started to affect the concentrations resulting in an increased exposure risk for pedestrians and cyclists. Understanding the local transport and the dispersion of traffic emissions are important for city planning and air quality assessment.

Fine particulate matter (PM2.5) and particle number concentrations were monitored in the Helsinki subway system during a two-weeks’ measurement campaign in March 2004. The PM2.5 samples was analysed for elemental composition and carbon fraction. The average daytime PM2.5 concentrations were 47 (±4) and 60 (±18) μg m−3 at the two underground subway stations and 19 (±6) and 21 (±4) μg m−3 at a ground level station and in subway cars, respectively. For the same measurement period, the corresponding PM2.5 concentrations at the urban background and street canyon monitoring sites were 10 (±7) and 17 (±10) μg m−3. The particle number (D

We have slightly refined, evaluated and tested a mathematical model for predicting the vehicular suspension emissions of PM10. The model describes particulate matter generated by the wear of road pavement, traction sand, and the processes that control the suspension of road dust particles into the air. However, the model does not address the emissions from the wear of vehicle components. The performance of this suspension emission model has been evaluated in combination with the street canyon dispersion model OSPM. We used data from a measurement campaign that was conducted in the street canyon Runeberg Street in Helsinki from 8 January to 2 May, 2004. The model reproduced fairly well the seasonal variation of the PM10 concentrations, also during the time periods, when studded tyres and anti-skid treatments were commonly in use. For instance, the index of agreement (IA) was 0.83 for the time series of the hourly predicted and observed concentrations of PM10. The predictions of the model were found to be sensitive to precipitation and street traction sanding. The main uncertainties in the predictions are probably caused by (i) the cleaning processes of the streets, which are currently not included in the model, (ii) the uncertainties in the estimation of the sanding days, and (iii) the uncertainties in the evaluation of precipitation. This study provides more confidence that this model could potentially be a valuable tool of assessment to evaluate and forecast the suspension PM10 emissions worldwide. However, a further evaluation of the model is needed against other datasets in various vehicle fleet, speed and climatic conditions.

Air pollution causes significant excess mortality and health effects. Action plans required by the EU legislation should optimally account for the complex interactions of population distributions, time-activity and the sources of air pollution. This paper presents FUMAPEX study results on probabilistic exposure modelling for evaluation of population exposure distributions during selected episodes in Helsinki. The air quality model characterised best the episodes of local origin, while highest exposures occurred during a long-range transported episode (average annual exposures exceeded three-fold). Fixed monitoring data represented different population exposure percentiles (from 65th to 95th) depending on the episode type and monitoring station.