Comparison of aerosol number size distribution and new particle formation in summer at alpine and urban regions in the Guanzhong Plain, Northwest China

Ultrafine particles play a crucial role in understanding climate change, mitigating adverse health effects, and developing strategies for air pollution control. However, the factors influencing the occurrence and development of new particle formation (NPF) events, as well as the underlying chemical mechanisms, remain inadequately explained. This study compared number concentrations and size distributions of atmospheric ultrafine particles at Xi'an (urban area) and the summit of Mt. Hua (alpine region) in summer to investigate the NPF mechanism and particle growth in both clean and polluted areas of the Guanzhong Plain. The average particle number concentration in Xi'an was significantly higher than that at Mt. Hua. The diurnal variation of total particle number concentration differed between Xi'an and Mt. Hua indicating a divergence in influencing factors. The size distributions in Xi'an varied across different timescales and weather conditions, whereas Mt. Hua exhibited little variation. This stability at Mt. Hua is attributed to its cleaner background atmosphere and the steady influx of aging particles with larger diameters transported from the free atmosphere. In both areas, geometric mean diameters (GMDs) were inversely proportional to particle number concentrations suggesting that increase in particle numbers were primarily due to the generation of smaller particles. The potential governing factors for NPF events differed somewhat between the urban and mountainous stations. In the urban area, intense local stationary and mobile emission sources promoted the growth of newly formed nanoparticles, with ozone-oxidized condensable vapors serving as key precursors. In contrast, at the mountainous station, NPF process were significantly influenced by anthropogenic precursors from long-range transport and locally emitted biogenic organics. The rapid increase in ultrafine particle concentrations primarily poses serious health risks and degrades air quality in urban areas, while also contributing to climate-related effects in alpine regions.

Impact of urban aerosols on the cloud condensation activity using a clustering model

The indirect effect of aerosols on climate through aerosol-cloud-interactions is still highly uncertain and limits our ability to assess anthropogenic climate change. The foundation of this uncertainty is in the number of cloud condensation nuclei (CCN), which itself mainly stems from uncertainty in aerosol sources and how particles evolve to become effective CCN. We analyze particle number size distribution (PNSD) and CCN measurements from an urban site in a two-step method: (1) we use an unsupervised clustering model to classify the main aerosol categories and processes occurring in the urban atmosphere and (2) we explore the influence of the identified aerosol populations on the CCN properties. According to the physical properties of each cluster, its diurnal timing, and additional air quality parameters, the clusters are grouped into five main aerosol categories: nucleation, growth, traffic, aged traffic, and urban background. The results show that, despite aged traffic and urban background categories are those with lower total particle number concentrations (N_tot) these categories are the most efficient sources in terms of contribution to the overall CCN budget with activation fractions (AF) around 0.5 at 0.75 % supersaturation (SS). By contrast, road traffic is an important aerosol source with the highest frequency of occurrence (32 %) and relatively high N_tot, however, its impact in the CCN activity is very limited likely due to lower particle mean diameter and hydrophobic chemical composition. Similarly, nucleation and growth categories, associated to new particle formation (NPF) events, present large N_tot with large frequency of occurrence (22 % and 28 %, respectively) but the CCN concentration for these categories is about half of the CCN concentration observed for the aged traffic category, which is associated with their small size. Overall, our results show that direct influence of traffic emissions on the CCN budget is limited, however, when these particles undergo ageing processes, they have a significant influence on the CCN concentrations and may be an important CCN source. Thus, aged traffic particles could be transported to other environments where clouds form, triggering a plausible indirect effect of traffic emissions on aerosol-cloud interactions and consequently contributing to climate change.

Source apportionment of atmospheric particle number concentrations with wide size range by nonnegative matrix factorization (NMF)

Quantifying the sources of atmospheric particles is essential to air quality control but remains challenging, especially for the source apportionment of particles based on number concentration with wide size range. Here, particle number concentrations (PNC) with size range 19-20,000 nm involving four modes Nucleation, Aitken, Accumulation, and Coarse are used to do source apportionment of PNC at the Guangdong Atmospheric Supersite (Heshan) during July-October 2015 by nonnegative matrix factorization (NMF) with 6 factors. For July 2015, separated source apportionments for three different size ranges from collocated instruments nano scanning mobility particle sizer (NSMPS), SMPS, and aerodynamic particle sizer (APS) and for two different size ranges (below and above 100 nm) show similar quantitative source information with that for the one whole size range. The mean absolute difference of contribution percentages of total particle number concentrations (TPNC) based on 5 unique apportioned sources is 5.6 % (4.3-7.6 %) for the instrument segregated apportionment and 4.2 % (0-5.3 %) for the size range segregated apportionment respectively, relative to the one whole apportionment. Moreover, the contribution percentages of TPNC are close to the weighted sum of contribution percentages of all size bins, with a mean absolute difference of 1.1 % (0-3.4 %). In both these two aspects, the consistency among different technical paths proves the matrix factorization by NMF is practically desirable and the simplicity of reducing some steps or calculations saves time. Besides, dust can be identified with the wide size range including larger than 3000 nm. Six apportioned sources in the 4 months are Accumulation (32.4 %), Nucleation (20.0 %), Aitken (15.2 %), traffic (14.6 %), dust (10.6 %), and Coarse (7.1 %). Therefore, NMF would serve as a promising tool for PNC source apportionment with wide size range and conducting the apportionment with the whole size range in one matrix factorization procedure and using the single TPNC contribution percentage are feasible.

Submicron and Ultrafine Particles in Downtown Rome: How the Different Euro Engines Have Influenced Their Behavior for Two Decades

Today, submicron particles are recognized as the new target in environmental sciences and human health issues as well. Their level in urban air is strongly affected by anthropogenic sources, i.e., domestic heating and autovehicular traffic, but the availability of large datasets represents a limit in the knowledge both of the behavior and of the relative levels. This paper would like to highlight the role of these two anthropogenic sources in a big city such as Rome in the particle formation/removal processes in the range 18–750 nm using a Scanning Mobility Particle Analyser (SMPS). The investigation starts from data collected in the previous decade (2010) and analyzes the role played by different Euro (0–6) engines on the particle levels as well as the responsibility of different biomass burning in this issue. Furthermore, a chemometric approach (Cluster Analysis, CA, and Principal Component Analysis, PCA) has allowed the identification of three different clusters, strongly dependent on the accumulation and nucleation modes of the Ultrafine Particles. On the other hand, the PCA demonstrated a scatter distribution in December larger than that in October, justified by the different sources present in these periods.

An overview of experimental results and dispersion modelling of nanoparticles in the wake of moving vehicles

Understanding the transformation of nanoparticles emitted from vehicles is essential for developing appropriate methods for treating fine scale particle dynamics in dispersion models. This article provides an overview of significant research work relevant to modelling the dispersion of pollutants, especially nanoparticles, in the wake of vehicles. Literature on vehicle wakes and nanoparticle dispersion is reviewed, taking into account field measurements, wind tunnel experiments and mathematical approaches. Field measurements and modelling studies highlighted the very short time scales associated with nanoparticle transformations in the first stages after the emission. These transformations strongly interact with the flow and turbulence fields immediately behind the vehicle, hence the need of characterising in detail the mixing processes in the vehicle wake. Very few studies have analysed this interaction and more research is needed to build a basis for model development. A possible approach is proposed and areas of further investigation identified.

Cluster analysis of rural, urban, and curbside atmospheric particle size data

Particle size is a key determinant of the hazard posed by airborne particles. Continuous multivariate particle size data have been collected using aerosol particle size spectrometers sited at four locations within the UK: Harwell (Oxfordshire); Regents Park (London); British Telecom Tower (London); and Marylebone Road (London). These data have been analyzed using k-means cluster analysis, deduced to be the preferred cluster analysis technique, selected from an option of four partitional cluster packages, namelythe following: Fuzzy; k-means; k-median; and Model-Based clustering. Using cluster validation indices k-means clustering was shown to produce clusters with the smallest size, furthest separation, and importantly the highest degree of similarity between the elements within each partition. Using k-means clustering, the complexity of the data set is reduced allowing characterization of the data according to the temporal and spatial trends of the clusters. At Harwell, the rural background measurement site, the cluster analysis showed that the spectra may be differentiated by their modal-diameters and average temporal trends showing either high counts during the day-time or night-time hours. Likewise for the urban sites, the cluster analysis differentiated the spectra into a small number of size distributions according their modal-diameter, the location of the measurement site, and time of day. The responsible aerosol emission, formation, and dynamic processes can be inferred according to the cluster characteristics and correlation to concurrently measured meteorological, gas phase, and particle phase measurements.