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.

Monitoring Euro 6 diesel passenger cars NOx emissions for one year in various ambient conditions with PEMS and NOx sensors.

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.

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.

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.

Traffic is a major source of atmospheric nanocluster aerosol.

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.

Physical and Chemical Characterization of Real-World Particle Number and Mass Emissions from City Buses in Finland.

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.

Quantitative analysis of environmental factors in differential weighing of blank Teflon filters.

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 humidity < 50%, 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.