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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.

Low-cost particulate matter (PM) sensors have been under investigation as it has been hypothesized that the use of low-cost and easy-to-use sensors could allow cost-efficient extension of the currently sparse measurement coverage. While the majority of the existing literature highlights that low-cost sensors can indeed be a valuable addition to the list of commonly used measurement tools, it often reiterates that the risk of sensor misuse is still high and that the data obtained from the sensors are only representative of the specific site and its ambient conditions. This implies that there are underlying reasons for inaccuracies in sensor measurements that have yet to be characterized. The objective of this study is to investigate the particle-size selectivity of low-cost sensors. Evaluated sensors were Plantower PMS5003, Nova SDS011, Sensirion SPS30, Sharp GP2Y1010AU0F, Shinyei PPD42NS, and Omron B5W-LD0101. The investigation of size selectivity was carried out in the laboratory using a novel reference aerosol generation system capable of steadily producing monodisperse particles of different sizes (from ∼0.55 to 8.4 µm) on-line. The results of the study show that none of the low-cost sensors adhered to the detection ranges declared by the manufacturers; moreover, cursory comparison to a mid-cost aerosol size spectrometer (Grimm 1.108, 2020) indicates that the sensors can only achieve independent responses for one or two size bins, whereas the spectrometer can sufficiently characterize particles with 15 different size bins. These observations provide insight into and evidence of the notion that particle-size selectivity has an essential role in the analysis of the sources of errors in sensors.

Particulate black carbon (BC) affects global warming by absorbing the solar radiation, by affecting cloud formation, and by decreasing ground albedo when deposited to snow or ice. BC has also a wide variety of adverse effects on human population health. In this article we reviewed the BC emission factors (EFs) of major anthropogenic sources, i.e. traffic (incl. marine and aviation), residential combustion, and energy production. We included BC EFs measured directly from individual sources and EFs derived from ambient measurements. Each source category was divided into sub-categories to find and demonstrate systematical trends, such as the potential influence of fuel, combustion technologies, and exhaust/flue gas cleaning systems on BC EFs. Our review highlights the importance of society level emission regulation in BC emission mitigation; a clear BC emission reduction was observed in ambient studies for road traffic as well as in direct emission measurements of diesel-powered individual vehicles. However, the BC emissions of gasoline vehicles were observed to be higher for vehicles with direct fuel injection techniques (gasoline direct injection) than for vehicles with port-fueled injection, indicating potentially negative trend in gasoline vehicle fleet BC EFs. In the case of shipping, a relatively clear correlation was seen between the engine size and BC EFs so that the fuel specific BC EFs of the largest engines were the lowest. Regarding the BC EFs from residential combustion, we observed large variation in EFs, indicating that fuel type and quality as well as combustion appliances significantly influence BC EFs. The largest data gaps were in EFs of large-scale energy production which can be seen crucial for estimating global radiative forcing potential of anthropogenic BC emissions. In addition, much more research is needed to improve global coverage of BC EFs. Furthermore, the use of existing data is complicated by different EF calculation methods, different units used in reporting and by variation of results due to different experimental setups and BC measurement methods. In general, the conducted review of BC EFs is seen to significantly improve the accuracy of future emission inventories and the evaluations of the climate, air quality, and health impacts of anthropogenic BC emissions.

Elevated ambient concentrations of sub-3 nm particles (nanocluster aerosol, NCA) are generally related to atmospheric new particle formation events, usually linked with gaseous sulfuric acid (H2SO4) produced via photochemical oxidation of sulfur dioxide. According to our measurement results of H2SO4 and NCA concentrations, traffic density, and solar irradiance at an urban traffic site in Helsinki, Finland, the view of aerosol formation in traffic-influenced environments is updated by presenting two separate and independent pathways of traffic affecting the atmospheric NCA concentrations: by acting as a direct nanocluster source and by influencing the production of H2SO4. As traffic density in many areas is generally correlated with solar radiation, it is likely that the influence of traffic-related nanoclusters has been hidden in the diurnal variation and is thus underestimated because new particle formation events also follow the diurnal cycle of sunlight. Urban aerosol formation studies should, therefore, be updated to include the proposed formation mechanisms. The formation of H2SO4 in urban environments is here separated into two routes: primary H2SO4 is formed in hot vehicle exhaust and is converted rapidly to the particle phase; secondary H2SO4 results from the combined effect of emitted gaseous precursors and available solar radiation. A rough estimation demonstrates that ∼85 % of the total NCA and ∼68 % of the total H2SO4 in urban air at noontime at the measurement site are contributed by traffic, indicating the importance of traffic emissions.

Inexpensive aerosol sensors have been considered as a complementary option to address the issue of expensive but low spatial coverage air quality monitoring networks. However, the accuracy and response characteristics of these sensors is poorly documented. In this study, inexpensive Shinyei PPD42NS and PPD60PV sensors were evaluated using a novel laboratory evaluation method. A continuously changing monodisperse size distribution of particles was generated using a Vibrating Orifice Aerosol Generator. Furthermore, the laboratory results were validated in a field experiment. The laboratory tests showed that both of the sensors responded to particulate mass (PM) concentration stimulus, rather than number concentration. The highest detection efficiency for the PPD42NS was within particle size range of 2.5–4 µm, and the respective optimal size range for the PPD60PV was 0.7–1 µm. The field test yielded high PM correlations (R2 = 0.962 and R2 = 0.986) for viable detection ranges of 1.6–5 and 0.3–1.6 µm, when compared to a medium cost optical dust monitor. As the size distribution of atmospheric particles tends to be bimodal, it is likely that indicatively valid results could be obtained for the PM10–2.5 size fraction (particulate mass in size range 2.5–10 µm) with the PPD42NS sensor. Respectively, the PPD60PV could possibly be used to measure the PM2.5 size fraction (particulate mass in size below 2.5 µm).

Ultrafine particles produced by diesel-powered vehicles in underground mines are largely unaccounted for in mass-based air quality metrics. The Lung Deposited Surface Area concentration (LDSA) is an alternative to describe the harmfulness of particles. We aim to study concentrations and size distributions of LDSA at various locations in an underground mine as well as to evaluate the applicability of sensor-type measurement of LDSA. This study was conducted in an underground mine in Kemi, Finland, in 2017. Our main instrument was an electrical low-pressure impactor (ELPI+) inside a mobile laboratory. Additionally, five diffusion-charging based sensors were tested. The environment was challenging for the sensors as the particle size distribution was often outside the optimum range (20–300 nm) and dust accumulated inside the instruments. Despite this, the correlations with the ELPI+ were decent (R 2 from 0.53 to 0.59). With the ELPI+ we determined that the maintenance area had the lowest mean LDSA concentration (79 ± 38 µm 2 cm −3 ) of the measured locations. At the other locations, concentrations ranged from 137 to 405 µm 2 cm −3 . The mode particle size for the LDSA distribution was around 100 nm at most locations, with the blasting site as a notable exception (mode size closer to 700 nm). Diffusion-charging based sensors—perhaps aided by optical sensors—are potential solutions for long-term monitoring of LDSA if dust accumulation is taken care of. Our research indicates worker exposure could be reduced with the implementation of a sensor network to show which locations need either protective gear or increased ventilation.

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.

Residential wood burning emits a complex mixture of particulate and gaseous compounds. In this article we show an in-depth chemical characterization of particulate matter evidencing the impact of biomass burning on the urban air quality in Greater Temuco, the capital city of the Araucanía Region, Chile. The measurements were carried out at two sites, Las Encinas and Padre Las Casas, in spring and winter. Extremely high fine particulate matter (PM 2.5 ) concentrations (up to 700 µg m –3 ) were frequently observed at both stations in the wintertime, while in spring, PM 2.5 concentrations were significantly lower (campaign-average 6.4 and 8.6 µg m –3 in Las Encinas and Padre Las Casas, respectively). Chemical composition of submicron PM was dominated by organics (average 87%) followed by inorganic ions (10–30%) and a minor contribution of black carbon (< 5%). In the wintertime, atmospheric levels of biomass burning tracers, such as levoglucosan, potassium and chloride, were elevated and their diurnal profiles showed a significant concentration increase in the evening. Diurnal profiles combined with the in-depth chemical analysis clearly indicated that in the wintertime local biomass burning was the main source of air pollutants in the region. Furthermore, in winter, most of the high concentration events correlated with the periods with high surface pressure, low temperature and low wind speed. These events matched with higher temperatures at high altitude than at the surface characterizing the typical profile of a vertical inversion that prevents the dilution of air pollutants.

High-resolution modelling is needed to understand urban air quality and pollutant dispersion in detail. Recently, the PALM model system 6.0, which is based on large-eddy simulation (LES), was extended with the detailed Sectional Aerosol module for Large Scale Applications (SALSA) v2.0 to enable studying the complex interactions between the turbulent flow field and aerosol dynamic processes. This study represents an extensive evaluation of the modelling system against the horizontal and vertical distributions of aerosol particles measured using a mobile laboratory and a drone in an urban neighbourhood in Helsinki, Finland. Specific emphasis is on the model sensitivity of aerosol particle concentrations, size distributions and chemical compositions to boundary conditions of meteorological variables and aerosol background concentrations. The meteorological boundary conditions are taken from both a numerical weather prediction model and observations, which occasionally differ strongly. Yet, the model shows good agreement with measurements (fractional bias <0.67, normalised mean squared error <6, fraction of the data within a factor of 2 >0.3, normalised mean bias factor <0.25 and normalised mean absolute error factor <0.35) with respect to both horizontal and vertical distribution of aerosol particles, their size distribution and chemical composition. The horizontal distribution is most sensitive to the wind speed and atmospheric stratification, and vertical distribution to the wind direction. The aerosol number size distribution is mainly governed by the flow field along the main street with high traffic rates and in its surroundings by the background concentrations. The results emphasise the importance of correct meteorological and aerosol background boundary conditions, in addition to accurate emission estimates and detailed model physics, in quantitative high-resolution air pollution modelling and future urban LES studies.