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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.

Atmospheric emissions, including particle number and size distribution, from a 726 MWth coal-fired power plant were studied experimentally from a power plant stack and flue-gas plume dispersing in the atmosphere. Experiments were conducted under two different flue-gas cleaning conditions. The results were utilized in a plume dispersion and dilution model taking into account particle formation precursor (H2SO4 resulted from the oxidation of emitted SO2) and assessment related to nucleation rates. The experiments showed that the primary emissions of particles and SO2 were effectively reduced by flue-gas desulfurization and fabric filters, especially the emissions of particles smaller than 200 nm in diameter. Primary pollutant concentrations reached background levels in 200–300 s. However, the atmospheric measurements indicated that new particles larger than 2.5 nm are formed in the flue-gas plume, even in the very early phases of atmospheric ageing. The effective number emission of nucleated particles were several orders of magnitude higher than the primary particle emission. Modelling studies indicate that regardless of continuing dilution of the flue gas, nucleation precursor (H2SO4 from SO2 oxidation) concentrations remain relatively constant. In addition, results indicate that flue-gas nucleation is more efficient than predicted by atmospheric aerosol modelling. In particular, the observation of the new particle formation with rather low flue-gas SO2 concentrations changes the current understanding of the air quality effects of coal combustion. The results can be used to evaluate optimal ways to achieve better air quality, particularly in polluted areas like India and China.

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.

Changes in vehicle emission reduction technologies significantly affect traffic-related emissions in urban areas. In many densely populated areas the amount of traffic is increasing, keeping the emission level high or even increasing. To understand the health effects of traffic-related emissions, both primary (direct) particulate emission and secondary particle formation (from gaseous precursors in the exhaust emissions) need to be characterized. In this study, we used a comprehensive set of measurements to characterize both primary and secondary particulate emissions of a Euro 5 level gasoline passenger car. Our aerosol particle study covers the whole process chain in emission formation, from the tailpipe to the atmosphere, and also takes into account differences in driving patterns. We observed that, in mass terms, the amount of secondary particles was 13 times higher than the amount of primary particles. The formation, composition, number and mass of secondary particles was significantly affected by driving patterns and engine conditions. The highest gaseous and particulate emissions were observed at the beginning of the test cycle when the performance of the engine and the catalyst was below optimal. The key parameter for secondary particle formation was the amount of gaseous hydrocarbons in primary emissions; however, also the primary particle population had an influence.

As evidence of adverse health effects due to air pollution continues to increase, the World Health Organization (WHO) recently published its latest edition of the global air quality guidelines (World Health Organization, 2021). Although not legally binding, the guidelines aim to provide a framework in which policymakers can combat air pollution by formulating evidence-based air quality management strategies. In the light of this, the European Union has stated its intent to revise the current ambient air quality directive (2008/50/EC) to more closely resemble the newly published WHO guidelines (European Commission, 2020). This article provides an informed opinion on selected features of the air quality directive that we believe would benefit from a reassessment. The selected features include discussion about (1) air quality sensors as a part of a hierarchical observation network, (2) the number of minimum sampling points and their siting criteria, and (3) new target air pollution parameters for future consideration.

The effect of fuel ethanol content (10, 85 and 100 %) on primary emissions and on subsequent secondary aerosol formation was investigated for a Euro 5 flex-fuel gasoline vehicle. Emissions were characterized during a New European Driving Cycle (NEDC) using a comprehensive set-up of high time-resolution instruments. A detailed chemical composition of the exhaust particulate matter (PM) was studied using a soot particle aerosol mass spectrometer (SP-AMS), and secondary aerosol formation was studied using a potential aerosol mass (PAM) chamber. For the primary gaseous compounds, an increase in total hydrocarbon emissions and a decrease in aromatic BTEX (benzene, toluene, ethylbenzene and xylenes) compounds was observed when the amount of ethanol in the fuel increased. In regard to particles, the largest primary particulate matter concentrations and potential for secondary particle formation was measured for the E10 fuel (10 % ethanol). As the ethanol content of the fuel increased, a significant decrease in the average primary particulate matter concentrations over the NEDC was found. The PM emissions were 0.45, 0.25 and 0.15 mg m−3 for E10, E85 and E100, respectively. Similarly, a clear decrease in secondary aerosol formation potential was observed with a larger contribution of ethanol in the fuel. The secondary-to-primary PM ratios were 13.4 and 1.5 for E10 and E85, respectively. For E100, a slight decrease in PM mass was observed after the PAM chamber, indicating that the PM produced by secondary aerosol formation was less than the PM lost through wall losses or the degradation of the primary organic aerosol (POA) in the chamber. For all fuel blends, the formed secondary aerosol consisted mostly of organic compounds. For E10, the contribution of organic compounds containing oxygen increased from 35 %, measured for primary organics, to 62 % after the PAM chamber. For E85, the contribution of organic compounds containing oxygen increased from 42 % (primary) to 57 % (after the PAM chamber), whereas for E100 the amount of oxidized organics remained the same (approximately 62 %) with the PAM chamber when compared to the primary emissions.

Lung-deposited surface area (LDSA) has been considered to be a better metric to explain nanoparticle toxicity instead of the commonly used particulate mass concentration. LDSA concentrations can be obtained either by direct measurements or by calculation based on the empirical lung deposition model and measurements of particle size distribution. However, the LDSA or size distribution measurements are neither compulsory nor regulated by the government. As a result, LDSA data are often scarce spatially and temporally. In light of this, we developed a novel statistical model, named the input-adaptive mixed-effects (IAME) model, to estimate LDSA based on other already existing measurements of air pollutant variables and meteorological conditions. During the measurement period in 2017–2018, we retrieved LDSA data measured by Pegasor AQ Urban and other variables at a street canyon (SC, average LDSA = 19.7 ± 11.3 µm2 cm−3) site and an urban background (UB, average LDSA = 11.2 ± 7.1 µm2 cm−3) site in Helsinki, Finland. For the continuous estimation of LDSA, the IAME model was automatised to select the best combination of input variables, including a maximum of three fixed effect variables and three time indictors as random effect variables. Altogether, 696 submodels were generated and ranked by the coefficient of determination (R2), mean absolute error (MAE) and centred root-mean-square difference (cRMSD) in order. At the SC site, the LDSA concentrations were best estimated by mass concentration of particle of diameters smaller than 2.5 µm (PM2.5), total particle number concentration (PNC) and black carbon (BC), all of which are closely connected with the vehicular emissions. At the UB site, the LDSA concentrations were found to be correlated with PM2.5, BC and carbon monoxide (CO). The accuracy of the overall model was better at the SC site (R2=0.80, MAE = 3.7 µm2 cm−3) than at the UB site (R2=0.77, MAE = 2.3 µm2 cm−3), plausibly because the LDSA source was more tightly controlled by the close-by vehicular emission source. The results also demonstrated that the additional adjustment by taking random effects into account improved the sensitivity and the accuracy of the fixed effect model. Due to its adaptive input selection and inclusion of random effects, IAME could fill up missing data or even serve as a network of virtual sensors to complement the measurements at reference stations.

Oxidation flow reactors (OFRs) or environmental chambers can be used to estimate secondary aerosol formation potential of different emission sources. Emissions from anthropogenic sources, such as vehicles, often vary on short timescales. For example, to identify the vehicle driving conditions that lead to high potential secondary aerosol emissions, rapid oxidation of exhaust is needed. However, the residence times in environmental chambers and in most oxidation flow reactors are too long to study these transient effects ( ∼  100 s in flow reactors and several hours in environmental chambers). Here, we present a new oxidation flow reactor, TSAR (TUT Secondary Aerosol Reactor), which has a short residence time ( ∼  40 s) and near-laminar flow conditions. These improvements are achieved by reducing the reactor radius and volume. This allows studying, for example, the effect of vehicle driving conditions on the secondary aerosol formation potential of the exhaust. We show that the flow pattern in TSAR is nearly laminar and particle losses are negligible. The secondary organic aerosol (SOA) produced in TSAR has a similar mass spectrum to the SOA produced in the state-of-the-art reactor, PAM (potential aerosol mass). Both reactors produce the same amount of mass, but TSAR has a higher time resolution. We also show that TSAR is capable of measuring the secondary aerosol formation potential of a vehicle during a transient driving cycle and that the fast response of TSAR reveals how different driving conditions affect the amount of formed secondary aerosol. Thus, TSAR can be used to study rapidly changing emission sources, especially the vehicular emissions during transient driving.

Natural gas usage in the traffic and energy production sectors is a growing trend worldwide; thus, an assessment of its effects on air quality, human health and climate is required. Engine exhaust is a source of primary particulate emissions and secondary aerosol precursors, which both contribute to air quality and can cause adverse health effects. Technologies, such as cleaner engines or fuels, that produce less primary and secondary aerosols could potentially significantly decrease atmospheric particle concentrations and their adverse effects. In this study, we used a potential aerosol mass (PAM) chamber to investigate the secondary aerosol formation potential of natural gas engine exhaust. The PAM chamber was used with a constant UV-light voltage, which resulted in relatively long equivalent atmospheric ages of 11 days at most. The studied retro-fitted natural gas engine exhaust was observed to form secondary aerosol. The mass of the total aged particles, i.e., particle mass measured downstream of the PAM chamber, was 6–268 times as high as the mass of the emitted primary exhaust particles. The secondary organic aerosol (SOA) formation potential was measured to be 9–20 mg kgfuel−1. The total aged particles mainly consisted of organic matter, nitrate, sulfate and ammonium, with the fractions depending on exhaust after-treatment and the engine parameters used. Also, the volatility, composition and concentration of the total aged particles were found to depend on the engine operating mode, catalyst temperature and catalyst type. For example, a high catalyst temperature promoted the formation of sulfate particles, whereas a low catalyst temperature promoted nitrate formation. However, in particular, the concentration of nitrate needed a long time to stabilize – more than half an hour – which complicated the conclusions but also indicates the sensitivity of nitrate measurements on experimental parameters such as emission source and system temperatures. Sulfate was measured to have the highest evaporation temperature, and nitrate had the lowest. The evaporation temperature of ammonium depended on the fractions of nitrate and sulfate in the particles. The average volatility of the total aged particles was measured to be lower than that of primary particles, indicating better stability of the aged natural gas engine-emitted aerosol in the atmosphere. According to the results of this study, the exhaust of a natural gas engine equipped with a catalyst forms secondary aerosol when the atmospheric ages in a PAM chamber are several days long. The secondary aerosol matter has different physical characteristics from those of primary particulate emissions.

CITYZER develops new digital services and products to support decision-making processes related to weather and air quality in cities. This includes, for example, early warnings and forecasts (0–24 h), which allow for avoiding weather-related accidents, mitigate human distress and costs from weather-related damage and bad air quality, and generally improve the resilience and safety of the society. The project takes advantage of the latest scientific know-how and directly exploits the expertise obtained from earlier projects. Central to the project is the Observation Network Manager NM10 developed by Vaisala, on which CITYZER defines and builds new commercial services and connects new sensor networks, for example, for air quality measurements, as well as the ENFUSER local-scale air quality modelling system developed by the Finnish Meteorological Institute, for real-time air quality forecasts and nowcasts.