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Urban air consists of a complex mixture of gaseous and particulate species from anthropogenic and biogenic sources that are further processed in the atmosphere. This study investigated the characteristics and sources of volatile organic compounds (VOCs) and submicron organic aerosol (OA) in a traffic environment in Helsinki, Finland, in late summer. The anthropogenic VOCs (aVOCs; aromatic hydrocarbons) and biogenic VOCs (bVOCs; terpenoids) relevant for secondary-organic-aerosol formation were analyzed with an online gas chromatograph mass spectrometer, whereas the composition and size distribution of submicron particles was measured with a soot particle aerosol mass spectrometer. This study showed that aVOC concentrations were significantly higher than bVOC concentrations in the traffic environment. The largest aVOC concentrations were measured for toluene (campaign average of 1630 ng m−3) and p/m xylene (campaign average of 1070 ng m−3), while the dominating bVOC was α-pinene (campaign average of 200 ng m−3). For particle-phase organics, the campaign-average OA concentration was 2.4 µg m−3. The source apportionment analysis extracted six factors for OA. Three OA factors were related to primary OA sources – traffic (24 % of OA, two OA types) and a coffee roastery (7 % of OA) – whereas the largest fraction of OA (69 %) consisted of oxygenated OA (OOA). OOA was divided into less oxidized semi-volatile OA (SV-OOA; 40 % of OA) and two types of low-volatility OA (LV-OOA; 30 %). The focus of this research was also on the oxidation potential of the measured VOCs and the association between VOCs and OA in ambient air. Production rates of the oxidized compounds (OxPR) from the VOC reactions revealed that the main local sources of the oxidation products were O3 oxidation of bVOCs (66 % of total OxPR) and OH radical oxidation of aVOCs and bVOCs (25 % of total OxPR). Overall, aVOCs produced a much smaller portion of the oxidation products (18 %) than bVOCs (82 %). In terms of OA factors, SV-OOA was likely to originate from biogenic sources since it correlated with an oxidation product of monoterpene, nopinone. LV-OOA consisted of highly oxygenated long-range or regionally transported OA that had no correlation with local oxidant concentrations as it had already spent several days in the atmosphere before reaching the measurement site. In general, the main sources were different for VOCs and OA in the traffic environment. Vehicle emissions impacted both VOC and OA concentrations. Due to the specific VOCs attributed to biogenic emissions, the influence of biogenic emissions was more clearly detected in the VOC concentrations than in OA. In contrast, the emissions from the local coffee roastery had a distinctive mass spectrum for OA, but they could not be seen in the VOC measurements due to the measurement limitations for the large VOC compounds. Long-range transport increased the OA concentration and oxidation state considerably, while its effect was observed less clearly in the VOC measurements due to the oxidation of most VOC in the atmosphere during the transport. Overall, this study revealed that in order to properly characterize the impact of different emission sources on air quality, health, and climate, it is of importance to describe both gaseous and particulate emissions and understand how they interact as well as their phase transfers in the atmosphere during the aging process.

Urban air consists of a complex mixture of gaseous and particulate species from anthropogenic and biogenic sources that are further processed in the atmosphere. This study investigated the features and sources of volatile organic compounds (VOCs) and submicron organic aerosol (OA) at a street canyon in Helsinki, Finland in late summer. The main anthropogenic VOCs (aVOCs) and biogenic VOCs (bVOCs) were analyzed with an online gas chromatograph mass spectrometer, whereas the composition and size distribution of submicron particles was measured with a soot particle aerosol mass spectrometer. This study showed that aVOC concentrations were significantly higher than bVOC concentrations at the street canyon. The largest aVOC concentrations were measured for toluene (campaign-average 1630 ng m-3) and p/m xylene (campaign-average 1070 ng m-3), while the dominating bVOCs was α-pinene (campaign-average 200 ng m-3). The special focus of this research was also on the oxidation of VOCs and the association between VOCs and OA in ambient air. Production rates of the oxidized compounds (OxPR) from the VOC reactions revealed that the main local sources of the oxidation products were O3 oxidation of bVOCs (66 % of total OxPR) and OH radical oxidation of aVOCs and bVOCs (25 % of total OxPR). Overall, aVOCs produced much smaller portion of the oxidation products (18 %) than bVOCs (82 %). For particle phase organics, the source apportionment analysis extracted six factors for OA. Three OA factors were related to primary OA sources, traffic (24 % of OA, two OA types) and coffee roastery (7 % of OA), whereas the largest fraction of OA (69 %) consisted of oxygenated OA (OOA). OOA was divided into less oxidized semi-volatile OA (SV-OOA; 40 % of OA) and two types of low-volatility OA (LV-OOA; 30 %). SV-OOA was likely to originate from biogenic sources since it correlated with an oxidation product of monoterpene, nopinone. LV-OOA consisted of highly oxygenated long-range or regionally transported OA that had no correlation with local oxidant concentrations as it had already spent several days in the atmosphere before reaching the measurement site. By investigating specific air quality cases, it was noticed that the influence of main pollutant sources was different for VOCs and OA at the street canyon. Vehicle emissions impacted both VOC and OA concentrations, whereas the influence of biogenic emissions was more clearly detected in the VOC concentrations than in OA due to the specific VOCs attributed to biogenic emissions. In contrast, the emissions from the local coffee roastery had a distinctive mass spectrum for OA but they could not be seen in the VOC measurements due to the measurement limitations for the large VOC compounds. Long-range transport increased the OA concentrations and oxidation state considerably, while its effect was observed less clearly in the VOC measurements due to the oxidation of most VOC in the atmosphere during the transport. Overall, this study revealed that in order to properly characterize the impact of different emission sources to air quality, health and climate, it is of importance to describe both gaseous and particulate emissions and understand how they interact as well as their phase transfers in the atmosphere during the aging process.

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.

Particle size distribution is a major factor in the health and climate effects of ambient aerosols, and it shows a large variation depending on the prevailing atmospheric emission sources. In this work, the particle number size distributions of ambient air were investigated at a suburban detached housing area in northern Helsinki, Finland, during a half-year period from winter to summer of 2020. The measurements were conducted with a scanning mobility particle sizer (SMPS) with a particle size range of 16–698 nm (mobility diameter), and the events with a dominant particle source were identified systematically from the data based on the time of the day and different particle physical and chemical properties. During the measurement period, four different types of events with a dominant contribution from either wood-burning (WB), traffic (TRA), secondary biogenic (BIO), or long-range transported (LRT) aerosol were observed. The particle size was the largest for the LRT events followed by BIO, WB, and TRA events with the geometric mean diameters of 72, 62, 57, and 41 nm, respectively. BIO and LRT produced the largest particle mode sizes followed by WB, and TRA with the modes of 69, 69, 46, and 25 nm, respectively. Each event type had also a noticeably different shape of the average number size distribution (NSD). In addition to the evaluation of NSDs representing different particle sources, also the effects of COVID-19 lockdown on specific aerosol properties were studied as during the measurement period the COVID-19 restrictions took place greatly reducing the traffic volumes in the Helsinki area in the spring of 2020. These restrictions had a significant contribution to reducing the concentrations of NOx and black carbon originating from fossil fuel combustion concentration, but insignificant effects on other studied variables such as number concentration and size distribution or particle mass concentrations (PM1, PM2.5, or PM10). publishedVersion

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.

In this study, we present results of 12 years of black carbon (BC) measurements at 14 different measurement 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 fireplaces and sauna stoves. All the BC measurements were conducted optically and therefore we refer to the measured BC as equivalent BC (eBC). Measurement stations were located at different types of environments that represented traffic environment (six sites), detached housing area (five sites), urban background (two sites), and regional background (two sites). The measurements of eBC were conducted during 2007–2018; however, the time period and the length of the time series varied from site to site. As expected, the largest annual mean eBC concentrations were measured at the traffic sites (0.67–2.64 μg m−3) and the lowest at the regional background sites (0.16–0.29 μg m−3). The annual mean eBC concentrations at the detached housing sites varied in the range of 0.64–0.80 μg m−3 and the annual mean eBC concentrations at the urban background sites varied in the range of 0.42–0.68 μg m−3. The clearest seasonal variation was observed at the detached housing sites, where the residential wood combustion increased the eBC concentrations during the cold season. Traffic rates and wood burning influenced the diurnal and weekly variations of eBC concentration in different types of environments. The dependency was not so clear for the other air pollutants, which were here NOx and mass of particles smaller than 2.5 μm (PM2.5). At four sites, which had at least four-year-long time series available, we observed that the eBC concentrations had statistically significant decreasing trends, which varied in the range of −10.4–−5.9 % yr−1. Compared to the trends determined at the urban and regional background sites, the absolute trends decreased the fastest at the traffic sites and especially during the morning rush hour. The relative long-term trends of eBC and NOx were similar to each other, and their concentrations decreased more rapidly than the concentration of PM2.5. The results indicate that especially the emissions from traffic have decreased in the HMA during the last decade. This shows that air pollution control, new emission standards and newer fleet of vehicles really have an effect in the air quality.

Urban air pollution has been a global challenge, and continuous air quality measurement is important to understand the nature of the problem. However, missing data has often been an issue in air quality measurement. In this study, we presented a modified method to impute missing data by input-adaptive proxy. We used black carbon (BC) concentration data in Mäkelänkatu traffic site (TR) and Kumpula urban background site (BG) in Helsinki, Finland in 2017–2018 as training sets. The input-adaptive proxy selected input variables of other air quality variables based on their Pearson correlation coefficients with BC. In order to avoid overfitting, this proxy used the algorithm of least squares model with a bisquare weighting function and allowed a maximum of three input variables. The generated models were then evaluated and ranked by adjusted coefficient of determination (adjR2), mean absolute error and root mean square error. BC concentration was first estimated by the best model. In case of missing data in the input variables in the best model, the input-adaptive proxy then used the second-best model until all the missing data gaps were filled up.The input-adaptive proxy managed to fill up 100% of the missing voids while traditional proxy filled only 20–80% of missing BC data. Furthermore, the overall performance of the input-adaptive proxy is reliable both in TR (adjR2=0.86–0.94) and in BG (adjR2=0.74–0.91). TR has a generally better regression performance because the level of BC can be mostly explained by traffic count, nitrogen oxides and accumulation mode. On the contrary, the source of BC in BG is more heterogeneous, which includes traffic emission and residential combustion, and the concentration of BC is influenced by meteorological parameters; therefore, the rule of including maximum three input variables might lead to the lower adjR2. The proxy works slightly better for workdays scenario than in weekends in both sites. In TR, the proxy works similarly in all seasons, while in BG, the proxy performance is better in winter and autumn than in the other seasons. The simplicity, full coverage and high reliability of the input-adaptive proxy make it sound to further estimate other air quality parameters. Moreover, it can act as an air quality virtual sensor alongside with on-site instruments.

In this study, the NO

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 (PM

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.

The main chemical composition of PM1 (total organics, black carbon, sulphate, nitrate and ammonium), mass concentrations of PM2.5 and PM2.5–10 and concentration of specific trace gases were measured in a high temporal resolution from May 2013 to April 2015 in the city centre of Helsinki, Finland. On average, the concentrations of PM2.5 and PM2.5–10 were 9.1 µg m–3 and 16 µg m–3, respectively, during a two-year campaign. PM1 consisted mostly of organics (60%), followed by sulphate (12%), black carbon (11%), nitrate (9.8%) and ammonium (6.5%). The particle and gas data were combined with the meteorological data in order to obtain information on how local meteorology affects concentrations of air pollutants. Two meteorological parameters that mostly affected the pollutant concentrations were the wind speed and temperature, while sulphate and PM2.5–10 were also impacted by the relative humidity. The highest concentrations of the measured PM1 components were observed when the wind was calm or the temperature was either very cold or very warm. PM2.5–10 concentrations were at the highest during calm or very windy conditions, due to local street and construction dust. The seasonal-and diurnal-varying mixing height did not seem to affect markedly the concentrations of pollutants. Overall, air quality in terms of the aerosol mass was governed by three different main pollution sources in the Helsinki city centre: 1) local sources, of which traffic-related emissions were the most important; 2) long-range or regional transport of pollutants; and 3) local sources of organic aerosol. © 2019, Finnish Environment Institute. All rights reserved.

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).

We have evaluated numerically how effective a few selected measures would be for reducing road dust. The selected measures included the reduction of the use of studded tyres in light-duty vehicles and phasing-out of salt or sand in traction control. We have evaluated these measures for a street canyon location in central Helsinki, for four years (2007–2009 and 2014). Air quality measurements were conducted in the street canyon for two years, 2009 and 2014. Two road dust emission models, NORTRIP and FORE, were applied in combination with the street canyon dispersion model OSPM to compute the street increments of PM10 within the street canyon. The predicted concentrations were compared with the air quality measurements. Both models reproduced the seasonal variability of the PM10 concentrations but under-predicted the yearly mean values. It was found that the largest reductions of concentrations could potentially be achieved by reducing the fraction of vehicles that use studded tyres. For instance, a 30 % percent decrease in the number of vehicles using studded tyres would result in an average decrease of the non-exhaust increment of PM10 from 10 to 22 %, depending on the model used and the year considered. The corresponding decrease after removal of sanding and salting would be from 4 % and 20 % and from 0.1 % to 4 %, respectively. The results can be used for finding optimal strategies for reducing the high springtime particulate matter concentrations originated from road dust.

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.

Reliable and self-consistent data on air quality is 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. These high resolution 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 regional background concentrations were evaluated based on reanalyses of the atmospheric composition on global and European scales, using the SILAM model. The modelled concentrations of PM2.5 agreed fairly well or well with the measured data at a regional background station and at four urban measurement stations, during 1999–2014. There was no systematic deterioration of the agreement of predictions and data for earlier years (the 1980's and 1990's), compared with the results for more recent years (2000's and early 2010's). The local vehicular emissions were about five-folds higher in the 1980's, compared with the emissions during the latest considered years. However, the local small-scale combustion emissions increased slightly over time. The highest urban concentrations of PM2.5 occurred in the 1980's; these have since decreased to about to a half of the highest values. However, there is only a very slight decreasing trend of the PM2.5 concentrations during the last decade. Regional background is 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 suspension emissions have increased. The study provides long-term, high-resolution concentration databases on regional and urban scales that have been used for the assessment of health effects.

Exhaust emissions of 23 individual city buses at Euro III, Euro IV and EEV (Enhanced Environmentally Friendly Vehicle) emission levels were measured by the chasing method under real-world conditions at a depot area and on the normal route of bus line 24 in Helsinki. The buses represented different technologies from the viewpoint of engines, exhaust after-treatment systems (ATS) and fuels. Some of the EEV buses were fueled by diesel, diesel-electric, ethanol (RED95) and compressed natural gas (CNG). At the depot area the emission factors were in the range of 0.3-21 × 10(14) # (kg fuel)(-1), 6-40 g (kg fuel)(-1), 0.004-0.88 g (kg fuel)(-1), 0.004-0.56 g (kg fuel)(-1), 0.01-1.2 g (kg fuel)(-1), for particle number (EFN), nitrogen oxides (EFNOx), black carbon (EFBC), organics (EFOrg), and particle mass (EFPM1), respectively. The highest particulate emissions were observed from the Euro III and Euro IV buses and the lowest from the ethanol and CNG-fueled buses, which emitted BC only during acceleration. The organics emitted from the CNG-fueled buses were clearly less oxidized compared to the other bus types. The bus line experiments showed that lowest emissions were obtained from the ethanol-fueled buses whereas large variation existed between individual buses of the same type indicating that the operating conditions by drivers had large effect on the emissions.

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.

We have developed a deterministic urban scale dispersion modelling system further by adding a road dust suspension model. The system includes both vehicular exhaust emissions and suspended road dust. The modelling system was combined with a regional scale chemical transport model for calculations of concentrations in an urban area for the year 2008, and for the year 2010 measured regional background concentration was used. The time series’ were modelled for a spatial area more extensive than before using the FORE road dust suspension model. The predictions were compared against observed concentrations of PM2.5 and PM10. The use of the index of determination (r2) is discussed. We criticize the use of r2 alone as well as in addition to an index of agreement—type measure of agreement, and review the underlying data assumptions for the use of both measures. We then suggest a strategy to develop model evaluation statistical understanding, practice and nomenclature.

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.

A two weeks measurement campaign by a mobile laboratory van was performed at downtown Helsinki, Finland, in winter 2010. The characteristics of air pollutants such as fine particles in the size ranges of 7-40 nm (N_(p40)) and 40-1000 nm (N_(p1000)), black carbon (BC), fine particle mass (PM_(2.5)), as well as gaseous compounds NO, NO_2, NO_x, CO and CO_2 were studied. The statistical analysis showed that the air pollution conditions strongly depended on the traffic flow area; therefore, the street sections were classified as high traffic flow areas (HTF1-HTF4), low traffic flow areas (LTF1-LTF5), and urban background areas (BG1-BG3). Large variation of particle emissions was observed, and the momentary peak particle concentrations were 8 × 10^5 cm^(-3). At the HTF areas exhaust emissions followed clearly daily average heavy duty vehicle counts rather than the average daily vehicle counts. Higher correlation coefficients were found between CO_2 and NO than between CO_2 and N_(tot). The dispersion studies indicated that the air pollution conditions strongly improved from high traffic flow areas to low traffic flow areas. Therefore, proper city planning and locating, for example, cycle tracks, schools and hospitals farther from busy city streets might significantly reduce exposure risk for humans.

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.

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

Most epidemiological studies have been conducted based on relations between pollution concentrations measured at fixed ambient air quality monitoring sites, or modelled values using land-use regression models, and various health indicators. However, such simplistic modelling ignores several crucial factors, such as, (i) the activity patterns of individuals, i.e. people’s day-to-day movements, and (ii) the differences between indoor and outdoor air. We have developed a mathematical model for the determination of human exposure to ambient air pollution in an urban area, called EXPAND (EXposure model for Particulate matter And Nitrogen oxiDes). The model combines (i) predicted concentrations, and (ii) 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. In particular, the model takes into account the movements of the population and the infiltration from outdoor to indoor air. We present fine-resolution numerical results on annual spatial concentration, time activity and population exposures to PM2.5 in London and in the Helsinki Metropolitan Area, for 2008 and 2009. We have shown that the effect of neglecting the movements of the population, which is the currently commonly applied procedure, can result in an underprediction of exposure by several tens of per cent.

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.

The intake fraction (iF) gives a measure of the portion of a source's emissions that is inhaled by an exposed population over a defined period of time. This study examines spatial and population-based iF distributions of a known human carcinogen, benzene, from a ubiquitous urban source, local vehicular traffic, in the Helsinki Metropolitan Area using three computational methods. The first method uses the EXPAND model (EXPosure to Air pollution, especially to Nitrogen Dioxide and particulate matter), which incorporates spatial and temporal information on population activity patterns as well as urban-scale and street canyon dispersion models to predict spatial population exposure distributions. The second method uses data from the personal monitoring study EXPOLIS (Air Pollution Exposure Distributions of Adult Urban Populations in Europe) to estimate the intake fractions for individuals in the study. The third method, a one-compartment box model provides estimates within an order-of-magnitude or better for non-reactive agents in an urban area. Population intake fractions are higher using the personal monitoring data method (median iF 30 per million, mean iF 39 per million) compared with the spatial model (annual mean iF 10 per million) and the box model (median iF 4 per million, mean iF 7 per million). In particular, this study presents detailed intake fraction distributions on several different levels (spatial, individual, and generic) for the same urban area.

We present a modelling system that contains a treatment of the emissions and atmospheric dispersion of fine particulate matter (PM2.5) on an urban scale, combined with a statistical model for estimating the contribution of long-range transported aerosols. The model of PM2.5 emissions includes exhaust emissions, cold starts and driving, as well as, non-exhaust emissions originated from urban vehicular traffic. The influence of primary vehicular emissions from the road and street network was evaluated using a roadside emission and dispersion model, CAR-FMI, in combination with a meteorological pre-processing model, MPP-FMI. We have computed hourly sequential time series of the PM2.5 concentrations in 2002 in a numerical grid in the Helsinki Metropolitan Area. The predicted results were compared against measured data at two locations in central Helsinki: urban roadside station of Vallila and urban background station of Kallio. The predicted daily average PM2.5 concentrations agreed well with the measured values; e.g., the index of agreement values were 0.83 and 0.86 at Vallila and Kallio, respectively, and the absolute values of fractional bias ⩽0.13. As expected, the scatter of data points is substantially wider for the hourly concentration values; e.g., the index of agreement values were 0.69 and 0.74. We also computed the spatial concentration distributions of PM2.5. The predicted contribution from long-range transport to the street level PM2.5 varied spatially from 40% in the most trafficked areas to nearly 100% in the outskirts of the area. The emissions originated from cold starts and driving were responsible for

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

In the EXPOLIS study personal exposures and microenvironment levels of air pollutants from 50–201 urban adult (25–55 yr) participants were measured in six European cities during 1 yr from autumn 1996 to winter 1997–98. This paper presents the associations between the personal PM2.5 exposures, microenvironment (residential indoor, residential outdoor and workplace indoor) and ambient fixed site concentrations measured in Helsinki (Finland), Basel (Switzerland), Prague (Czech Republic) and Athens (Greece). Considering the whole chain from ambient fixed site to residential outdoor, residential indoor and personal leisure time (non-working hours) exposure, the correlations were highest between personal leisure time exposures and residential indoor concentrations (non-environmental tobacco smoke (ETS): Pearson r=0.72−0.92, ETS included: r=0.82−0.86) except in Athens, where the correlation between residential indoor and outdoor air was highest (non-ETS: r=0.82, ETS included: r=0.68)). Unfortunately, ambient fixed site PM2.5 concentrations were measured continuously only in Helsinki. Ambient fixed site PM2.5 concentrations correlated quite well with residential outdoor concentrations (r=0.90), and also with residential indoor (non-ETS) concentrations (r=0.80), but concentrations measured at ambient fixed site monitors were poor predictors of personal exposures to PM2.5. They were particularly poor predictors of personal workday exposures (non-ETS: r=0.34, ETS included: r=0.25), but considerably better for personal leisure time exposures (non-ETS: r=0.69, ETS included: r=0.54). According to log-linear regression models combined from all centres of non-ETS-exposed participants, residential indoor concentrations explained 76% of personal leisure time PM2.5 exposure variation and workplace indoor concentrations explained 66% of the workday exposure variation.

A mathematical model is presented for the determination of human exposure to ambient air pollution in an urban area. The main objective was to evaluate the spatial and temporal variation of average exposure of the urban population to ambient air pollution in different microenvironments with reasonable accuracy, instead of analysing in detail personal exposures for specific individuals. We have utilised a previously developed modelling system for predicting the traffic flows and emissions, emissions originating from stationary sources, and atmospheric dispersion of pollution in an urban area. A model was developed for combining the predicted concentrations, information on people's activities (such as the time spent at home, in the workplace and at other places of activity during the day) and location of the population. Time-microenvironment activity data for the working-age population was obtained from the EXPOLIS study (air pollution distributions within adult urban populations in Europe). Information on the location of homes and workplaces was obtained from local municipalities. The concentrations of NO2 were modelled over the Helsinki Metropolitan Area for 1996 and 1997. The computed results were processed and visualised using the geographical information system (GIS) MapInfo. The utilisation of the modelling system has been illustrated by presenting numerical results for the Helsinki Metropolitan Area. The results show the spatial and temporal (diurnal) variation of the ambient air NO2 concentrations, the population density and the corresponding average exposure. The model developed has been designed to be utilised by municipal authorities in urban planning, e.g., for evaluating the impacts of traffic planning and land use scenarios.

Mass differences less than 100 microg must be correctly measured in gravimetric analysis of particles collected on filters. Even small variations in mass measurement may contribute significant errors to calculated concentrations. In addition to the collected particles, a number of other factors affect the observed mass difference between the measurements before and after sampling. The most often controlled of these factors are static charge, temperature, and humidity. Using 951 laboratory blank filter weights, we have statistically analyzed these and other factors that affect the observed filter weight. Some of these are controllable or correctable; others are not and enter into the final results as errors. The standard deviation of differential blank filter weighing after applying all corrections was 2.7 microg. The most important correctable factors are air buoyancy variation and filter storage time. When weighing blank Teflon filters at relative humidity50%, these are an order of magnitude more important than weighing-room humidity. Using field blank filters in each weighing batch could control these three factors but also doubles the errors caused by balance random variation and filter handling contamination, because four weighing measurements and the handling of two filters are needed to obtain one corrected differential mass result.

We have studied particulate matter (PM) concentrations,PM10 and PM2.5, measured in an urban air qualitymonitoring network in the Helsinki Metropolitan Area during1997–1999. The data includes PM10 concentrationsmeasured at five locations (two urban traffic, one suburbantraffic, one urban background and one regional backgroundsite) and PM2.5 concentrations measured at twolocations (urban traffic and urban background sites). Theconcentrations of PM10 show a clear diurnal variation,as well as a spatial variation within the area. Bycontrast, both the spatial and temporal variation of thePM2.5 concentrations was moderate. We have analysedthe evolution of urban PM concentrations in terms of therelevant meteorological parameters in the course of oneselected peak pollution episode during 21–31 March, 1998.The meteorological variables considered included wind speedand direction, ambient temperature, precipitation, relativehumidity, atmospheric pressure at the ground level,atmospheric stability and mixing height. The elevated PMconcentrations during the 1998 March episode were clearlyrelated to conditions of high atmospheric pressure,relatively low ambient temperatures and low wind speeds inpredominantly stable atmospheric conditions. The resultsprovide indirect evidence indicating that the PM10concentrations originate mainly from local vehiculartraffic (direct emissions and resuspension), while thePM2.5 concentrations are mostly of regionally andlong-range transported origin.

During April 1996–June 1997 size-segregated atmospheric aerosol particles were collected at an urban and a rural site in the Helsinki area by utilising virtual impactors (VI) and Berner low-pressure impactors (BLPI). In addition, VI samples were collected at a semi-urban site during October 1996–May 1997. The average PM2.3 (fine particle) concentrations at the urban and rural sites were 11.8 and 8.4 μg/m3, and the PM2.3−15 (coarse particle) concentrations were 12.8 and about 5 μg/m3, respectively. The difference in fine particle mass concentrations suggests that on average, more than one third of the fine mass at the urban site is of local origin. Evaporation of fine particle nitrate from the VI Teflon filters during sampling varied similarly at the three sites, the average evaporation being about 50–60%. The average fine particle concentrations of the chemical components (25 elements and 13 ions) appeared to be fairly similar at the three sites for most components, which suggests that despite the long-range transport, the local emissions of these components were relatively evenly distributed in the Helsinki area. Exceptions were the average fine particles Ba, Fe, Sb and V concentrations that were clearly highest at the urban site pointing to traffic (Ba, Fe, Sb) and to combustion of heavy fuel oil (V) as the likely local sources. The average coarse particle concentrations for most components were highest at the urban site and lowest at the rural site. Average chemical composition of fine particles was fairly similar at the urban and rural sites: non-analysed fraction (mainly carbonaceous material and water) 43% and 37%, sulphate 21% and 25%, crustal matter 12% and 13%, nitrate 12% and 11%, ammonium 9% and 10% and sea-salt 2.5% and 3.2%, respectively. At the semi-urban site also, the average fine particle composition was similar. At the urban site, the year round average composition of coarse particles was dominated by crustal matter (59%) and the non-analysed components (28%, mainly carbonaceous material and water), while the other contributions were much lower: sea-salt 7%, nitrate 4% and sulphate 2%. At the rural site, the coarse samples were collected in spring and summer and the percentage was clearly lower for crustal matter (37%) and sea-salt (3%) but higher for the not-analysed fraction (51%). At the semi-urban site, the average composition of coarse particles was nearly identical to that at the urban site. Correlations between the chemical components were calculated separately for fine and coarse particles. In urban fine particles sulphate, ammonium, Tl, oxalate and PM2.3 mass correlated with each other and originated mainly from long-range transport. The sea-salt ions Na+, Cl− and Mg2+ formed another group and still another group was formed by the organic anions oxalate, malonate, succinate, glutarate and methane sulphonate. Ni and V correlated strongly pointing to combustion of heavy fuel oil as the likely source. In addition, some groups with lower correlations were detected. At the rural and semi-urban sites, the correlating components were rather similar to those at the urban site, although differences were also observed.