Recommendations for reporting equivalent black carbon (eBC) mass concentrations based on long-term pan-European in-situ observations.

A reliable determination of equivalent black carbon (eBC) mass concentrations derived from filter absorption photometers (FAPs) measurements depends on the appropriate quantification of the mass absorption cross-section (MAC) for converting the absorption coefficient (babs) to eBC. This study investigates the spatial-temporal variability of the MAC obtained from simultaneous elemental carbon (EC) and babs measurements performed at 22 sites. We compared different methodologies for retrieving eBC integrating different options for calculating MAC including: locally derived, median value calculated from 22 sites, and site-specific rolling MAC. The eBC concentrations that underwent correction using these methods were identified as LeBC (local MAC), MeBC (median MAC), and ReBC (Rolling MAC) respectively. Pronounced differences (up to more than 50 %) were observed between eBC as directly provided by FAPs (NeBC; Nominal instrumental MAC) and ReBC due to the differences observed between the experimental and nominal MAC values. The median MAC was 7.8 ± 3.4 m2 g-1 from 12 aethalometers at 880 nm, and 10.6 ± 4.7 m2 g-1 from 10 MAAPs at 637 nm. The experimental MAC showed significant site and seasonal dependencies, with heterogeneous patterns between summer and winter in different regions. In addition, long-term trend analysis revealed statistically significant (s.s.) decreasing trends in EC. Interestingly, we showed that the corresponding corrected eBC trends are not independent of the way eBC is calculated due to the variability of MAC. NeBC and EC decreasing trends were consistent at sites with no significant trend in experimental MAC. Conversely, where MAC showed s.s. trend, the NeBC and EC trends were not consistent while ReBC concentration followed the same pattern as EC. These results underscore the importance of accounting for MAC variations when deriving eBC measurements from FAPs and emphasize the necessity of incorporating EC observations to constrain the uncertainty associated with eBC.

Inter-annual trends of ultrafine particles in urban Europe.

Ultrafine particles (UFP, those with diameters ≤ 100 nm), have been reported to potentially penetrate deeply into the respiratory system, translocate through the alveoli, and affect various organs, potentially correlating with increased mortality. The aim of this study is to assess long-term trends (5-11 years) in mostly urban UFP concentrations based on measurements of particle number size distributions (PNSD). Additionally, concentrations of other pollutants and meteorological variables were evaluated to support the interpretations. PNSD datasets from 12 urban background (UB), 5 traffic (TR), 3 suburban background (SUB) and 1 regional background (RB) sites in 15 European cities and 1 in the USA were evaluated. The non-parametric Theil-Sen's method was used to detect monotonic trends. Meta-analyses were carried out to assess the overall trends and those for different environments. The results showed significant decreases in NO, NO2, BC, CO, and particle concentrations in the Aitken (25-100 nm) and the Accumulation (100-800 nm) modes, suggesting a positive impact of the implementation of EURO 5/V and 6/VI vehicle standards on European air quality. The growing use of Diesel Particle Filters (DPFs) might also have clearly reduced exhaust emissions of BC, PM, and the Aitken and Accumulation mode particles. However, as reported by prior studies, there remains an issue of poor control of Nucleation mode particles (smaller than 25 nm), which are not fully reduced with current DPFs, without emission controls for semi-volatile organic compounds, and might have different origins than road traffic. Thus, contrasting trends for Nucleation mode particles were obtained across the cities studied. This mode also affected the UFP and total PNC trends because of the high proportion of Nucleation mode particles in both concentration ranges. It was also found that the urban temperature increasing trends might have also influenced those of PNC, Nucleation and Aitken modes.

Constructing transferable and interpretable machine learning models for black carbon concentrations.

Black carbon (BC) has received increasing attention from researchers due to its adverse health effects. However, in-situ BC measurements are often not included as a regulated variable in air quality monitoring networks. Machine learning (ML) models have been studied extensively to serve as virtual sensors to complement the reference instruments. This study evaluates and compares three white-box (WB) and four black-box (BB) ML models to estimate BC concentrations, with the focus to show their transferability and interpretability. We train the models with the long-term air pollutant and weather measurements in Barcelona urban background site, and test them in other European urban and traffic sites. Despite the difference in geographical locations and measurement sites, BC correlates the strongest with particle number concentration of accumulation mode (PNacc, r = 0.73-0.85) and nitrogen dioxide (NO2, r = 0.68-0.85) and the weakest with meteorological parameters. Due to its similarity of correlation behaviour, the ML models trained in Barcelona performs prominently at the traffic site in Helsinki (R2 = 0.80-0.86; mean absolute error MAE = 3.90-4.73 %) and at the urban background site in Dresden (R2 = 0.79-0.84; MAE = 4.23-4.82 %). WB models appear to explain less variability of BC than BB models, long short-term memory (LSTM) model of which outperforms the rest of the models. In terms of interpretability, we adopt several methods for individual model to quantify and normalize the relative importance of each input feature. The overall static relative importance commonly used for WB models demonstrate varying results from the dynamic values utilized to show local contribution used for BB models. PNacc and NO2 on average have the strongest absolute static contribution; however, they simultaneously impact the estimation positively and negatively at different sites. This comprehensive analysis demonstrates that the possibility of these interpretable air pollutant ML models to be transfered across space and time.

Particle lung deposited surface area (LDSAal) size distributions in different urban environments and geographical regions: Towards understanding of the PM2.5 dose-response.

Recent studies indicate that monitoring only fine particulate matter (PM2.5) may not be enough to understand and tackle the health risk caused by particulate pollution. Health effects per unit PM2.5 seem to increase in countries with low PM2.5, but also near local pollution sources (e.g., traffic) within cities. The aim of this study is to understand the differences in the characteristics of lung-depositing particles in different geographical regions and urban environments. Particle lung deposited surface area (LDSAal) concentrations and size distributions, along with PM2.5, were compared with ambient measurement data from Finland, Germany, Czechia, Chile, and India, covering traffic sites, residential areas, airports, shipping, and industrial sites. In Finland (low PM2.5), LDSAal size distributions depended significantly on the urban environment and were mainly attributable to ultrafine particles (<100 nm). In Central Europe (moderate PM2.5), LDSAal was also dependent on the urban environment, but furthermore heavily influenced by the regional aerosol. In Chile and India (high PM2.5), LDSAal was mostly contributed by the regional aerosol despite that the measurements were done at busy traffic sites. The results indicate that the characteristics of lung-depositing particles vary significantly both within cities and between geographical regions. In addition, ratio between LDSAal and PM2.5 depended notably on the environment and the country, suggesting that LDSAal exposure per unit PM2.5 may be multiple times higher in areas having low PM2.5 compared to areas with continuously high PM2.5. These findings may partly explain why PM2.5 seems more toxic near local pollution sources and in areas with low PM2.5. Furthermore, performance of a typical sensor based LDSAal measurement is discussed and a new LDSAal2.5 notation indicating deposition region and particle size range is introduced. Overall, the study emphasizes the need for country-specific emission mitigation strategies, and the potential of LDSAal concentration as a health-relevant pollution metric.

Sub-23 nm Particles Dominate Non-Volatile Particle Number Emissions of Road Traffic.

Ultrafine particles (<100 nm) in urban air are a serious health hazard not yet fully understood. Therefore, particle number concentration monitoring was recently included in the WHO air quality guidelines. At present, e.g., the EU regulates particle number only regarding the emissions of solid particles larger than 23 nm emitted by vehicles. The aim of this study was to examine the non-volatile fraction of sub-23 nm particles in a traffic-influenced urban environment. We measured the number concentration of particles larger than 1.4, 3, 10, and 23 nm in May 2018. Volatile compounds were thermally removed in the sampling line and the line losses were carefully determined. According to our results, the sub-23 nm particles dominated the non-volatile number concentrations. Additionally, based on the determined particle number emission factors, the traffic emissions of non-volatile sub-10 nm particles can be even 3 times higher than those of particles larger than 10 nm. Yet, only a fraction of urban sub-10 nm particles consisted of non-volatiles. Thus, while the results highlight the role of ultrafine particles in the traffic-influenced urban air, a careful consideration is needed in terms of future particle number standards to cover the varying factors affecting measured concentrations.

The variability of mass concentrations and source apportionment analysis of equivalent black carbon across urban Europe.

This study analyzed the variability of equivalent black carbon (eBC) mass concentrations and their sources in urban Europe to provide insights into the use of eBC as an advanced air quality (AQ) parameter for AQ standards. This study compiled eBC mass concentration datasets covering the period between 2006 and 2022 from 50 measurement stations, including 23 urban background (UB), 18 traffic (TR), 7 suburban (SUB), and 2 regional background (RB) sites. The results highlighted the need for the harmonization of eBC measurements to allow for direct comparisons between eBC mass concentrations measured across urban Europe. The eBC mass concentrations exhibited a decreasing trend as follows: TR > UB > SUB > RB. Furthermore, a clear decreasing trend in eBC concentrations was observed in the UB sites moving from Southern to Northern Europe. The eBC mass concentrations exhibited significant spatiotemporal heterogeneity, including marked differences in eBC mass concentration and variable contributions of pollution sources to bulk eBC between different cities. Seasonal patterns in eBC concentrations were also evident, with higher winter concentrations observed in a large proportion of cities, especially at UB and SUB sites. The contribution of eBC from fossil fuel combustion, mostly traffic (eBCT) was higher than that of residential and commercial sources (eBCRC) in all European sites studied. Nevertheless, eBCRC still had a substantial contribution to total eBC mass concentrations at a majority of the sites. eBC trend analysis revealed decreasing trends for eBCT over the last decade, while eBCRC remained relatively constant or even increased slightly in some cities.

Ambient air particulate total lung deposited surface area (LDSA) levels in urban Europe.

This study aims to picture the phenomenology of urban ambient total lung deposited surface area (LDSA) (including head/throat (HA), tracheobronchial (TB), and alveolar (ALV) regions) based on multiple path particle dosimetry (MPPD) model during 2017-2019 period collected from urban background (UB, n = 15), traffic (TR, n = 6), suburban background (SUB, n = 4), and regional background (RB, n = 1) monitoring sites in Europe (25) and USA (1). Briefly, the spatial-temporal distribution characteristics of the deposition of LDSA, including diel, weekly, and seasonal patterns, were analyzed. Then, the relationship between LDSA and other air quality metrics at each monitoring site was investigated. The result showed that the peak concentrations of LDSA at UB and TR sites are commonly observed in the morning (06:00-8:00 UTC) and late evening (19:00-22:00 UTC), coinciding with traffic rush hours, biomass burning, and atmospheric stagnation periods. The only LDSA night-time peaks are observed on weekends. Due to the variability of emission sources and meteorology, the seasonal variability of the LDSA concentration revealed significant differences (p = 0.01) between the four seasons at all monitoring sites. Meanwhile, the correlations of LDSA with other pollutant metrics suggested that Aitken and accumulation mode particles play a significant role in the total LDSA concentration. The results also indicated that the main proportion of total LDSA is attributed to the ALV fraction (50 %), followed by the TB (34 %) and HA (16 %). Overall, this study provides valuable information of LDSA as a predictor in epidemiological studies and for the first time presenting total LDSA in a variety of European urban environments.

Snapshots of wintertime urban aerosol characteristics: Local sources emphasized in ultrafine particle number and lung deposited surface area.

Urban air fine particles are a major health-relating problem. However, it is not well understood how the health-relevant features of fine particles should be monitored. Limitations of PM2.5 (mass concentration of sub 2.5 µm particles), which is commonly used in the health effect estimations, have been recognized and, e.g., World Health Organization (WHO) has released good practice statements for particle number (PN) and black carbon (BC) concentrations (2021). In this study, a characterization of urban wintertime aerosol was done in three environments: a detached housing area with residential wood combustion, traffic-influenced streets in a city centre and near an airport. The particle characteristics varied significantly between the locations, resulting different average particle sizes causing lung deposited surface area (LDSA). Near the airport, departing planes had a major contribution on PN, and most particles were smaller than 10 nm, similarly as in the city centre. The high hourly mean PN (>20 000 1/cm3) stated in the WHO's good practices was clearly exceeded near the airport and in the city centre, even though traffic rates were reduced due to a SARS-CoV-2-related partial lockdown. In the residential area, wood combustion increased both BC and PM2.5, but also PN of sub 10 and 23 nm particles. The high concentrations of sub 10 nm particles in all the locations show the importance of the chosen lower size limit of PN measurement, e.g., WHO states that the lower limit should be 10 nm or smaller. Furthermore, due to ultrafine particle emissions, LDSA per unit PM2.5 was 1.4 and 2.4 times higher near the airport than in the city centre and the residential area, respectively, indicating that health effects of PM2.5 depend on urban environment as well as conditions, and emphasizing the importance of PN monitoring in terms of health effects related to local pollution sources.

Modeling non-linear changes in an urban setting: From pro-environmental affordances to responses in behavior, emissions and air quality.

Interactions in urban environment were investigated using a multidisciplinary model combination, with focus on traffic, emissions and atmospheric particles. An agent-based model was applied to simulate the evolution of unsustainable human behavior (usage of combustion-based personal vehicles) as a function of pro-environmental affordances (opportunities for sustainable choices). Scenarios regarding changes in multi-pollutant emissions were derived, and the non-linear implications to atmospheric particles were simulated with a box model. Based on the results for a Nordic city, increasing pro-environmental affordances by 10%, 50% or 100% leads to emission reductions of 15%, 30% and 40% within 2 years. To reduce ambient particle mass, emissions from traffic should decrease by > 15%, while the lung deposited surface area decreases in all scenarios ([Formula: see text], [Formula: see text] and [Formula: see text], correspondingly). The presented case is representative of one season, but the approach is generic and applicable to simulating a full year, given meteorological and pollution data that reflects seasonal variation. This work emphasizes the necessity to consider feedback mechanisms and non-linearities in both human behavior and atmospheric processes, when predicting the outcomes of changes in an urban system.

Phenomenology of ultrafine particle concentrations and size distribution across urban Europe.

The 2017-2019 hourly particle number size distributions (PNSD) from 26 sites in Europe and 1 in the US were evaluated focusing on 16 urban background (UB) and 6 traffic (TR) sites in the framework of Research Infrastructures services reinforcing air quality monitoring capacities in European URBAN & industrial areaS (RI-URBANS) project. The main objective was to describe the phenomenology of urban ultrafine particles (UFP) in Europe with a significant air quality focus. The varying lower size detection limits made it difficult to compare PN concentrations (PNC), particularly PN10-25, from different cities. PNCs follow a TR > UB > Suburban (SUB) order. PNC and Black Carbon (BC) progressively increase from Northern Europe to Southern Europe and from Western to Eastern Europe. At the UB sites, typical traffic rush hour PNC peaks are evident, many also showing midday-morning PNC peaks anti-correlated with BC. These peaks result from increased PN10-25, suggesting significant PNC contributions from nucleation, fumigation and shipping. Site types to be identified by daily and seasonal PNC and BC patterns are: (i) PNC mainly driven by traffic emissions, with marked correlations with BC on different time scales; (ii) marked midday/morning PNC peaks and a seasonal anti-correlation with PNC/BC; (iii) both traffic peaks and midday peaks without marked seasonal patterns. Groups (ii) and (iii) included cities with high insolation. PNC, especially PN25-800, was positively correlated with BC, NO2, CO and PM for several sites. The variable correlation of PNSD with different urban pollutants demonstrates that these do not reflect the variability of UFP in urban environments. Specific monitoring of PNSD is needed if nanoparticles and their associated health impacts are to be assessed. Implementation of the CEN-ACTRIS recommendations for PNSD measurements would provide comparable measurements, and measurements of <10 nm PNC are needed for full evaluation of the health effects of this size fraction.

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

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

An operational urban air quality model ENFUSER, based on dispersion modelling and data assimilation.

An operational urban air quality modelling system ENFUSER is presented with an evaluation against measured data. ENFUSER combines several dispersion modelling approaches, uses data assimilation, and continuously extracts information from online, global open-access sources. The modelling area is described with a combination of geographic datasets. These GIS datasets are globally available with open access, and therefore the model can be applied worldwide. Urban scale dispersion is addressed with a combination of Gaussian puff and Gaussian plume modelling, and long-range transport of pollutants is accounted for via a separate regional model. The presented data assimilation method, which supports the use of AQ sensors and incorporates a longer-term learning mechanism, adjusts emission factors and the regional background values on an hourly basis. The model can be used with reasonable accuracy also in urban areas, for which detailed emissions inventories would not be available, due to the data assimilation capabilities.

Improving the current air quality index with new particulate indicators using a robust statistical approach.

To convey the severity of ambient air pollution level to the public, air quality index (AQI) is used as a communication tool to reflect the concentrations of individual pollutants on a common scale. However, due to the enhanced air pollution control in recent years, air quality has improved, and the roles of some air pollutant species included in the existing AQI as urban air pollutants have diminished. In this study, we suggest the current AQI should be revised in a way that new air pollution indicators would be considered so that it would better represent the health effects caused by local combustion processes from traffic and residential burning. Based on the air quality data of 2017-2019 in three different sites in Helsinki metropolitan area, we assumed the statistical distributions of the current indicators (NO2 and PM2.5) and the proposed particulate indicators (BC, LDSA and PNC) were related as they have similar sources in urban regions despite the varying correlations between the current and proposed indicators (NO2: r = 0.5-0.85, PM2.5: r = 0.28-0.72). By fitting the data to an optimal distribution function, together with expert opinions, we improved the current Finnish AQI and determined the AQI breakpoints for the proposed indicators where this robust statistical approach is transferrable to other cities. The addition of the three proposed indicators to the current AQI would decrease the number of good air quality hours in all three environments (largest decrease in urban traffic site, ~22 %). The deterioration of air quality class appeared more severe during peak hours in the urban traffic site due to vehicular emission and evenings in the detached housing site where domestic wood combustion often takes place. The introduction of the AQI breakpoints of the three new indicators serve as a first step of improving the current AQI before further air quality guideline levels are updated.

Long-term trends in PM2.5 mass and particle number concentrations in urban air: The impacts of mitigation measures and extreme events due to changing climates.

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 µg m-3. yr-1 and 0.40-3.8 × 103 particles. cm-3. yr-1, respectively) except Brisbane (+0.1 µg 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 °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.

Source apportionment of particle number size distribution in urban background and traffic stations in four European cities.

Ultrafine particles (UFP) are suspected of having significant impacts on health. However, there have only been a limited number of studies on sources of UFP compared to larger particles. In this work, we identified and quantified the sources and processes contributing to particle number size distributions (PNSD) using Positive Matrix Factorization (PMF) at six monitoring stations (four urban background and two street canyon) from four European cities: Barcelona, Helsinki, London, and Zurich. These cities are characterised by different meteorological conditions and emissions. The common sources across all stations were Photonucleation, traffic emissions (3 sources, from fresh to aged emissions: Traffic nucleation, Fresh traffic - mode diameter between 13 and 37 nm, and Urban - mode diameter between 44 and 81 nm, mainly traffic but influenced by other sources in some cities), and Secondary particles. The Photonucleation factor was only directly identified by PMF for Barcelona, while an additional split of the Nucleation factor (into Photonucleation and Traffic nucleation) by using NOx concentrations as a proxy for traffic emissions was performed for all other stations. The sum of all traffic sources resulted in a maximum relative contributions ranging from 71 to 94% (annual average) thereby being the main contributor at all stations. In London and Zurich, the relative contribution of the sources did not vary significantly between seasons. In contrast, the high levels of solar radiation in Barcelona led to an important contribution of Photonucleation particles (ranging from 14% during the winter period to 35% during summer). Biogenic emissions were a source identified only in Helsinki (both in the urban background and street canyon stations), that contributed importantly during summer (23% in urban background). Airport emissions contributed to Nucleation particles at urban background sites, as the highest concentrations of this source took place when the wind was blowing from the airport direction in all cities.

Ultrafine particles and PM2.5 in the air of cities around the world: Are they representative of each other?

Can mitigating only particle mass, as the existing air quality measures do, ultimately lead to reduction in ultrafine particles (UFP)? The aim of this study was to provide a broader urban perspective on the relationship between UFP, measured in terms of particle number concentration (PNC) and PM2.5 (mass concentration of particles with aerodynamic diameter < 2.5 µm) and factors that influence their concentrations. Hourly average PNC and PM2.5 were acquired from 10 cities located in North America, Europe, Asia, and Australia over a 12-month period. A pairwise comparison of the mean difference and the Kolmogorov-Smirnov test with the application of bootstrapping were performed for each city. Diurnal and seasonal trends were obtained using a generalized additive model (GAM). The particle number to mass concentration ratios and the Pearson's correlation coefficient were calculated to elucidate the nature of the relationship between these two metrics. Results show that the annual mean concentrations ranged from 8.0 × 103 to 19.5 × 103 particles·cm-3 and from 7.0 to 65.8 µg·m-3 for PNC and PM2.5, respectively, with the data distributions generally skewed to the right, and with a wider spread for PNC. PNC showed a more distinct diurnal trend compared with PM2.5, attributed to the high contributions of UFP from vehicular emissions to PNC. The variation in both PNC and PM2.5 due to seasonality is linked to the cities' geographical location and features. Clustering the cities based on annual median concentrations of both PNC and PM2.5 demonstrated that a high PNC level does not lead to a high PM2.5, and vice versa. The particle number-to-mass ratio (in units of 109 particles·µg-1) ranged from 0.14 to 2.2, >1 for roadside sites and <1 for urban background sites with lower values for more polluted cities. The Pearson's r ranged from 0.09 to 0.64 for the log-transformed data, indicating generally poor linear correlation between PNC and PM2.5. Therefore, PNC and PM2.5 measurements are not representative of each other; and regulating PM2.5 does little to reduce PNC. This highlights the need to establish regulatory approaches and control measures to address the impacts of elevated UFP concentrations, especially in urban areas, considering their potential health risks.

Particle emissions of Euro VI, EEV and retrofitted EEV city buses in real traffic.

Exhaust emissions from traffic significantly affect urban air quality. In this study, in-traffic emissions of diesel-fueled city buses meeting enhanced environmentally friendly vehicle (EEV) and Euro VI emission limits and the effects of retrofitting of EEV buses were studied on-road by chasing the buses with a mobile laboratory in the Helsinki region, Finland. The average emission factors of particle number (PN), particle mass (PM1) and black carbon mass (BC) were 0.86·1015 1/kgfuel, 0.20 g/kgfuel and 0.10 g/kgfuel, respectively, for EEV buses. For Euro VI buses, the emissions were below 0.5·1015 1/kgfuel (PN), 0.07 g/kgfuel (PM1) and 0.02 g/kgfuel (BC), and the exhaust plume concentrations of these pollutants were close to the background concentrations. The emission factors of PM1 and BC of retrofitted EEV buses were at the level of Euro VI buses, but their particle number emissions varied significantly. On average, the EEV buses were observed to emit the largest amounts of nanocluster aerosol (NCA) (i.e., the particles with size between 1.3 and 3 nm). High NCA emissions were linked with high PN emissions. In general, results demonstrate that advanced exhaust aftertreatment systems reduce emissions of larger soot particles but not small nucleation mode particles in all cases.

Input-Adaptive Proxy for Black Carbon as a Virtual Sensor.

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 20-80% 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 = 0.86-0.94; urban background: adjR2 = 0.74-0.91) 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.