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.
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.
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•Both PM2.5 and PNC had a monotonic downward trend in all cities except Brisbane.•Extreme events due to changing climates caused positive step-changes to PM2.5.•Negative step-changes in PNC were observed upon regulation of sulphur in fuels.•Gradual reduction of PM2.5 and PNC was achieved by traffic and fleet management.
Aerosol particles cool the climate by scattering solar radiation and by acting as cloud condensation nuclei. Higher temperatures resulting from increased greenhouse gas levels have been suggested to ...lead to increased biogenic secondary organic aerosol and cloud condensation nuclei concentrations creating a negative climate feedback mechanism. Here, we present direct observations on this feedback mechanism utilizing collocated long term aerosol chemical composition measurements and remote sensing observations on aerosol and cloud properties. Summer time organic aerosol loadings showed a clear increase with temperature, with simultaneous increase in cloud condensation nuclei concentration in a boreal forest environment. Remote sensing observations revealed a change in cloud properties with an increase in cloud reflectivity in concert with increasing organic aerosol loadings in the area. The results provide direct observational evidence on the significance of this negative climate feedback mechanism.
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 (adjR
). 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: adjR
= 0.86-0.94; urban background: adjR
= 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.
Air pollution is a contributor to approximately one in every nine deaths annually. Air quality monitoring is being carried out extensively in urban environments. Currently, however, city air quality ...stations are expensive to maintain resulting in sparse coverage and data is not readily available to citizens. This can be resolved by city-wide participatory sensing of air quality fluctuations using low-cost sensors. We introduce new concepts for participatory sensing: a voluntary community-based monitoring data forum for stakeholders to manage air pollution interventions; an automated system (cyber-physical system) for monitoring outdoor air quality and indoor air quality; programmable platform for calibration and generating virtual sensors using data from low-cost sensors and city monitoring stations. To test our concepts, we developed a low-cost sensor to measure particulate matter (PM
2.5
), nitrogen dioxide (NO
2
), carbon monoxide (CO), and ozone (O
3
) with GPS. We validated our approach in Helsinki, Finland, with participants carrying the sensor for 3 months during six data campaigns between 2019 and 2021. We demonstrate good correspondence between the calibrated low-cost sensor data and city’s monitoring station measurements. Data analysis of their personal exposure was made available to the participants and stored as historical data for later use. Combining the location of low cost sensor data with participants public profile, we generate proxy concentrations for black carbon and lung deposition of particles between districts, by age groups and by the weekday.
In this study, we present results from 12 years of black
carbon (BC) measurements at 14 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 BC measurements were conducted optically, and therefore we refer
to the measured BC as equivalent BC (eBC). Measurement stations were located
in different environments that represented traffic environment, detached
housing area, urban background, and regional background. The measurements of
eBC were conducted from 2007 through 2018; however, the times and the
lengths of the time series varied at each site. The largest annual mean eBC
concentrations were measured at the traffic sites (from 0.67 to 2.64 µg m−3) and the lowest at the regional background sites (from 0.16 to
0.48 µg m−3). The annual mean eBC concentrations at the detached
housing and urban background sites varied from 0.64 to 0.80 µg m−3 and from 0.42 to 0.68 µg m−3, respectively. The
clearest seasonal variation was observed at the detached housing sites
where residential wood combustion increased the eBC concentrations during
the cold season. Diurnal variation in eBC concentration in different urban
environments depended clearly on the local sources that were traffic and
residential wood combustion. The dependency was not as clear for the typically measured air quality parameters, which were here NOx concentration and mass concentration of particles smaller that 2.5 µm in diameter (PM2.5). At four sites which had at least a 4-year-long time series available, the
eBC concentrations had statistically significant decreasing trends that
varied from −10.4 % yr−1 to −5.9 % yr−1. Compared to trends determined at
urban and regional background sites, the absolute trends decreased fastest
at traffic sites, especially during the morning rush hour. Relative
long-term trends in eBC and NOx were similar, and their concentrations
decreased more rapidly than that of PM2.5. The results indicated that
especially emissions from traffic have decreased in the HMA during the last
decade. This shows that air pollution control, new emission standards, and a
newer fleet of vehicles had an effect on air quality.
The Station for Measuring Ecosystem–Atmosphere Relations (SMEAR) II is well known among atmospheric scientists due to the immense amount of observational data it provides of the Earth–atmosphere ...interface. Moreover, SMEAR II plays an important role for the large European research infrastructure, enabling the large scientific community to tackle climate- and air-pollution-related questions, utilizing the high-quality long-term data sets recorded at the site. So far, this well-documented site was missing the description of the seasonal variation in aerosol chemical composition, which helps understanding the complex biogeochemical and physical processes governing the forest ecosystem. Here, we report the sub-micrometer aerosol chemical composition and its variability, employing data measured between 2012 and 2018 using an Aerosol Chemical Speciation Monitor (ACSM). We observed a bimodal seasonal trend in the sub-micrometer aerosol concentration culminating in February (2.7, 1.6, and 5.1 µg m−3 for the median, 25th, and 75th percentiles, respectively) and July (4.2, 2.2, and 5.7 µg m−3 for the median, 25th, and 75th percentiles, respectively). The wintertime maximum was linked to an enhanced presence of inorganic aerosol species (ca. 50 %), whereas the summertime maximum (ca. 80 % organics) was linked to biogenic secondary organic aerosol (SOA) formation. During the exceptionally hot months of July of 2014 and 2018, the organic aerosol concentrations were up to 70 % higher than the 7-year July mean. The projected increase in heat wave frequency over Finland will most likely influence the loading and chemical composition of aerosol particles in the future. Our findings suggest strong influence of meteorological conditions such as radiation, ambient temperature, and wind speed and direction on aerosol chemical composition. To our understanding, this is the longest time series reported describing the aerosol chemical composition measured online in the boreal region, but the continuous monitoring will also be maintained in the future.
Aerosol optical properties (AOPs) describe the ability of
aerosols to scatter and absorb radiation at different wavelengths. Since
aerosol particles interact with the sun's radiation, they impact the
...climate. Our study focuses on the long-term trends and seasonal variations
of different AOPs measured at a rural boreal forest site in northern Europe.
To explain the observed variations in the AOPs, we also analyzed changes in
the aerosol size distribution. AOPs of particles smaller than 10 µm
(PM10) and 1 µm (PM1) have been measured at SMEAR II, in southern
Finland, since 2006 and 2010, respectively. For PM10 particles, the median
values of the scattering and absorption coefficients, single-scattering
albedo, and backscatter fraction at λ=550 nm were 9.8
Mm−1, 1.3 Mm−1, 0.88, and 0.14. The median values of scattering and
absorption Ångström exponents at the wavelength ranges 450–700
and 370–950 nm were 1.88 and 0.99, respectively. We found statistically
significant trends for the PM10 scattering and absorption coefficients,
single-scattering albedo, and backscatter fraction, and the slopes of these
trends were −0.32 Mm−1, −0.086 Mm−1, 2.2×10-3, and
1.3×10-3 per year. The tendency for the extensive AOPs to decrease
correlated well with the decrease in aerosol number and volume
concentrations. The tendency for the backscattering fraction and
single-scattering albedo to increase indicates that the aerosol size
distribution consists of fewer larger particles and that aerosols absorb less
light than at the beginning of the measurements. The trends of the
single-scattering albedo and backscattering fraction influenced the aerosol
radiative forcing efficiency, indicating that the aerosol particles are
scattering the radiation more effectively back into space.
Atmospheric aerosol particle concentrations are strongly affected by various wet processes, including below and in-cloud wet scavenging and in-cloud aqueous-phase oxidation. We studied how wet ...scavenging and cloud processes affect particle concentrations and composition during transport to a rural boreal forest site in northern Europe. For this investigation, we employed air mass history analysis and observational data. Long-term particle number size distribution (â¼15 years) and composition measurements (â¼8 years) were combined with air mass trajectories with relevant variables from reanalysis data. Some such variables were rainfall rate, relative humidity, and mixing layer height. Additional observational datasets, such as temperature and trace gases, helped further evaluate wet processes along trajectories with mixed effects models.
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 Mäkelänkatu and from an urban background site in Kumpula, in Helsinki, Finland. At the street canyon site, WB models performed (R2 = 0.81–0.87) in a similar way as the BB models did (R2 = 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 (R2 = 0.44–0.60) and BB models (R2 = 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.
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.
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•Reliable black carbon models give additional insights to air quality index advance.•Models at street canyon show better performance than that at urban background.•No significant difference in white-box and black-box models in estimating BC.•White-box models are preferred due to their transparency in the model architecture.•The developed white-box BC estimation model could serve as a virtual sensor.
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