Particle Number Size Distribution Data Research Articles

A comprehensive study on the validation and application of multi-lognormal distribution models for atmospheric particles

The particle number size distribution (PNSD) is crucial for evaluating air quality and mitigating environmental pollution, as particles of different sizes have diverse effects on human health and climate. However, obtaining a comprehensive understanding of PNSD is challenging due to its inherent complexities and variability. Multi-lognormal distribution models are employed to fit PNSD, as seen in climate models, but discrepancies between model fits and observed PNSD persist. This study adopts hourly data from 2017 to 2020 across eight monitoring sites in diverse environments—rural, urban, mountainous, and polar, and compares the observed PNDS with those simulated by multi-lognormal distribution models. The results demonstrated that the model generally achieved a high correlation with observed PNSD data (r2 > 0.75), effectively capturing key characteristics of nucleation and Aitken mode particles. However, the model had a tendency to overestimate the total number concentration by approximately 1.09 times, particularly noticeable under conditions of high concentrations of smaller particles. The model successfully represented prevalent bimodal size distribution patterns in urban areas with high ultrafine particle concentrations, though its performance was slightly less accurate in scenarios involving trimodal distributions. Despite these strong correlations and the model's ability to reflect diurnal and seasonal variations, which suggests its broad applicability and utility, there were notable limitations on smaller time scales and in specific particle size ranges. These limitations were particularly evident in capturing detailed phenomena relevant to new particle formation events, indicating areas where model refinement is necessary. The results highlighted the importance of investigating discrepancies between model predictions and actual observations, which is crucial for refining climate models that utilize PNDS. The uniform comparison facilitated a detailed exploration of particle properties from model results, offering deeper insights into aerosol behavior and its environmental impacts.

Characterization, Sources, and Chemical Processes of Submicron Aerosols at a Mountain Site in Central China

AbstractA field campaign (29 May to 14 June 2018) was conducted at a mountain site in central China. The chemical composition of non‐refractory submicron particulate matter (NR‐PM1) and the particle number size distribution (PNSD) were measured, respectively. The mean NR‐PM1 mass concentration was 20.94 ± 10.14 μg m−3, among which organics (47%) was the most abundant component, followed by sulfate (37%), ammonium (11%), nitrate (4%), and chloride (1%). Notably, sulfate accounted for more than 70% of the secondary inorganic aerosols. Positive matrix factorization (PMF) analysis of the composition data resulted in three organic aerosol (OA) factors: hydrocarbon‐like OA (HOA), oxygenated OA I (OOA‐I), and oxygenated OA II (OOA‐II). The secondary organic aerosol (SOA) composed of the latter two factors (SOA: OOA‐I + OOA‐II) was dominant in OA (80.7%). The PMF analysis of the PNSD data yielded three factors: new particle formation related mode, growth mode, and accumulation mode, among which the last factor dominated both number and volume ratios. The sulfate formation was characterized by the sulfur oxidation ratio (SOR), heterogeneous sulfate production rate (Phet), and gaseous sulfuric acid concentration, representing secondary sulfate transformation, heterogeneous reactions, and gas phase reactions, respectively. The results showed that Phet was well correlated with both sulfate concentrations and SOR, especially during the polluted periods. Our study demonstrates that photochemically‐driven heterogeneous reactions contribute dominantly to the sulfate formation and SOA is formed predominantly by photochemical oxidation of volatile organic compounds under high temperatures and ultraviolet (UV) intensities, and NOX concentrations on Mt. Wudang during the campaign period.

Estimation of hygroscopic growth properties of source-related sub-micrometre particle types in a mixed urban aerosol

Knowledge of hygroscopic properties is essential to prediction of the role of aerosol in cloud formation and lung deposition. Our objective was to introduce a new approach to classify and predict the hygroscopic growth factors (Gfs) of specific atmospheric sub-micrometre particle types in a mixed aerosol based on measurements of the ensemble hygroscopic growth factors and particle number size distribution (PNSD). Based on a non-linear regression model between aerosol source contributions from PMF applied to the PNSD data set and the measured Gf values (at 90% relative humidity) of ambient aerosols, the estimated mean Gf values for secondary inorganic, mixed secondary, nucleation, urban background, fresh, and aged traffic-generated particle classes at a diameter of 110 nm were found to be 1.51, 1.34, 1.12, 1.33, 1.09 and 1.10, respectively. It is found possible to impute (fill) missing HTDMA data sets using a Random Forest regression on PNSD and meteorological conditions.

Effects of marine fuel sulfur restrictions on particle number concentrations and size distributions in ship plumes in the Baltic Sea

Abstract. Exhaust emissions from shipping are a major contributor to particle concentrations in coastal and marine areas. Previously, the marine fuel sulfur content (FSC) was restricted globally to 4.5 m/m%, but the limit was changed to 3.5 m/m% at the beginning of 2012 and further down to 0.5 m/m% in January 2020. In sulfur emission control areas (SECA), the limits are stricter: the FSC restriction was originally 1.50 m/m%, but it decreased to 1.00 m/m% in July 2010 and again to 0.10 m/m% in January 2015. In this work, the effects of the FSC restrictions on particle number concentrations (PNCs) and particle number size distributions (NSDs) are studied in the Baltic Sea SECA. Measurements were made on a small island (Utö, Finland; 59∘46′50 N, 21∘22′23 E) between 2007 and 2016. Ship plumes were extracted from the particle number size distribution data, and the effects of the FSC restrictions on the observed plumes as well as on the ambient concentrations were investigated. Altogether, 42 322 analyzable plumes were identified during the 10-year measurement period. The results showed that both changes in the FSC restrictions reduced the PNCs of the plumes. The latter restriction (to 0.10 m/m% in January 2015) also decreased the ambient particle number concentrations, as a significant portion of particles in the area originated from ship plumes that were diluted beyond the plume detection limits. The overall change in the PNCs of the plumes and ambient air was 27 and 32 %, respectively, for the total FSC change from 1.50 m/m% to 0.10 m/m%. The decrease in the plume particle number concentration was caused mostly by a decrease in the concentration of particle sizes of between approximately 33 and 144 nm. The latter restriction also reduced the geometric mean diameter of the particles, which was probably caused by the fuel type change from residual oil to distillates during the latter restriction. The PNC was larger for the plumes measured at daytime than for those measured at nighttime, likely because of the photochemical aging of particles due to UV light. The difference decreased with decreasing FSC, indicating that a lower FSC also has an impact on the atmospheric processing of ship plumes.

Statistical modelling of spatial and temporal variation in urban particle number size distribution at traffic and background sites

Ultrafine particles (UFP) pose a risk to human health, but due to the multitude of sources and fast transformation in the urban atmosphere, quantifying the exposure is challenging. Furthermore, physical properties of aerosol particles depend on the particle size. Statistical models are used to quantify spatial and temporal variation of UFP, but rarely used for particle number size distribution (PNSD). The aim of the study was to establish an interpretable statistical model capturing spatial and temporal variation of urban PNSDs using generalized additive models (GAM) and multivariate adaptive regression spline models (MARS). These algorithms automatically fit interpretable, non-linear marginal function to represent relationships between explanatory and response variables. Three different approaches were evaluated to cope with the multidimensionality of the PNSD data (20–800 nm, 34 size bins): a generalized additive model for the particle number concentration (PNC) of every individual size bin (GAMbins), a generalized additive model for the parameters of the PNSD function (GAMpams) and a multivariate adaptive regression spline model for the PNC of every size bin (MARSbins). Reanalysis data of meteorological quantities, urban geometry parameters and approximated traffic counts were used as explanatory variables. Marginal functions of the final models could be attributed to major processes that contribute to spatial and temporal variation of the PNSD, i.e. emissions from vehicle traffic, transport, dilution, accumulation, deposition and new particle formation. Cross-validation coefficients of determination ranged between 0.27 and 0.48 for most size bins. Nonetheless, the modelling approaches resulted in similar root mean square errors (RMSE) and mean absolute error (MAE). Though direct spatial transferability of the models is limited, the presented approaches may be useful for estimating ambient exposure to particles.

Particle Formation in a Complex Environment

A field aerosol measurement campaign as part of the Measurements of Urban, Marine and Biogenic Air (MUMBA) campaign was conducted between 16 January 2013 and 15 February 2013 in the coastal city of Wollongong, Australia. The objectives of this research were to study the occurrence frequency, characteristics and factors that influence new particle formation processes. Particle formation and growth events were observed from particle number size distribution data in the range of 14 nm–660 nm measured using a scanning particle mobility sizer (SMPS). Four weak Class I particle formation and growth event days were observed, which is equivalent to 13% of the total observation days. The events occurred during the day, starting after 8:30 Australian Eastern Standard time with an average duration of five hours. The events also appeared to be positively linked to the prevailing easterly to north easterly sea breezes that carry pollutants from sources in and around Sydney. This suggests that photochemical reactions and a combination of oceanic and anthropogenic air masses are among the factors that influenced these events.

A Method of Aerosol Particle Number Size Distribution Inversed by PM2.5 Mass Concentration in PRD

The aerosol particle number size distribution(PNSD) is of great importance in calculating atmospheric radiation and optics. It can effectively supplement the inadequate observation of PNSD using the widely known aerosol mass concentration (PM2.5) measurement to invert PNSD. It would be valuable for research that needs PNSD data, like atmospheric visibility calculation. This paper created a PNSD inversed method based on the statistics and parameterization of the dry aerosol PM2.5 and PNSD dataset from the Guangzhou urban site's simultaneous measurements from November 2014 to January 2015. The inversed results appeared good in the accumulation mode, whereas more differences showed with higher PM2.5 loading. The applicability and stability of this method makes it preferable. It would provide advanced technical support for the visibility calculation and application in PRD.

Emission characterization of size-resolved particles in a pre-school classroom in relation to children’s activities

The objective of this study was to investigate the effect of children’s daily activity patterns on the size-resolved indoor particles in a pre-school classroom, qualitatively and quantitatively. Real-time particle number size distribution data in conjunction with activity were collected experimentally in a pre-school classroom during two seasons: late spring and early winter. We divided daily activities into six groups: Arrival, Large Group, Small Group, Naptime, Dismissal and Unoccupied. The concentration variation forms of coarse particles were compared qualitatively among activity groups. The results indicated that noteworthy coarse particle concentration variations were observed during each activity group. These variations were caused by four factors: presence/absence of children, variation in the number of children, activity intensity and presence of sources. The emission rates were estimated to quantify the impact of activities. Time series emission rates were deduced from the piecewise-constant inputs and outputs model. The piecewise-constant emission rates were averaged for sequential parts to reduce noise and ensure daily repeated peaks were not missed. The sequential part-constant emission rates were consistent with previously described particle number concentration variation results and the predicted indoor particle number concentration data with sequential part-constant emission rates agreed well with measured data.

What are the Driving Factors Influencing the Size Distribution of Airborne Synthetic Clay Particles Emitted from a Jet Milling Process?

In the field of workplace air quality, measuring and analyzing the size distribution of airborne particles to identify their sources and apportion their contribution has become widely accepted, however, the driving factors that influence this parameter, particularly for nanoparticles (< 100 nm), have not been thoroughly determined. Identification of driving factors, and in turn, general trends in size distribution of emitted particles would facilitate the prediction of nanoparticles’ emission behavior and significantly contribute to their exposure assessment. In this study, a comprehensive analysis of the particle number size distribution data, with a particular focus on the ultrafine size range of synthetic clay particles emitted from a jet milling machine was conducted using the multi-lognormal fitting method. The results showed relatively high contribution of nanoparticles to the emissions in many of the tested cases, and also, that both surface treatment and feed rate of the machine are significant factors influencing the size distribution of the emitted particles of this size. In particular, applying surface treatments and increasing the machine feed rate have the similar effect of reducing the size of the particles, however, no general trend was found in variations of size distribution across different surface treatments and feed rates. The findings of our study demonstrate that for this process and other activities, where no general trend is found in the size distribution of the emitted airborne particles due to dissimilar effects of the driving factors, each case must be treated separately in terms of workplace exposure assessment and regulations.

Intra-urban variation of ultrafine particles as evaluated by process related land use and pollutant driven regression modelling

The microscale intra-urban variation of ultrafine particle concentrations (UFP, diameter Dp<100nm) and particle number size distributions was studied by two statistical regression approaches. The models were applied to a 1km2 study area in Braunschweig, Germany. A land use regression model (LUR) using different urban morphology parameters as input is compared to a multiple regression type model driven by pollutant and meteorological parameters (PDR). While the LUR model was trained with UFP concentration the PDR model was trained with measured particle number size distribution data. The UFP concentration was then calculated from the modelled size distributions. Both statistical approaches include explanatory variables that try to address the ‘process chain’ of particle emission, dilution and deposition.LUR explained 74% and 85% of the variance of UFP for the full data set with a root mean square error (RMSE) of 668cm−3 and 1639cm−3 in summer and winter, respectively. PDR explained 56% and 74% of the variance with RMSE of 4066cm−3 and 6030cm−3 in summer and winter, respectively. Both models are capable to depict the spatial variation of UFP across the study area and in different outdoor microenvironments. The deviation from measured UFP concentrations is smaller in the LUR model than in PDR.The PDR model is well suited to predict urban particle number size distributions from the explanatory variables (total particle number concentration, black carbon and wind speed). The urban morphology parameters in the LUR model are able to resolve size dependent concentration variations but not as adequately as PDR.

Estimating the contribution of photochemical particle formation to ultrafine particle number averages in an urban atmosphere

Ultrafine particles (UFPs, diameter<100nm) have gained major attention in the environmental health discussion due to a number of suspected health effects. Observations of UFPs in urban air reveal the presence of several, time-dependent particle sources. In order to attribute measured UFP number concentrations to different source type contributions, we analyzed observations collected at a triplet of observation sites (roadside, urban background, rural) in the city of Leipzig, Germany. Photochemical new particle formation (NPF) events can be the overwhelming source of UFP particles on particular days, and were identified on the basis of characteristic patterns in the particle number size distribution data. A subsequent segmentation of the diurnal cycles of UFP concentration yielded a quantitative contribution of NPF events to daily, monthly, and annual mean values. At roadside, we obtained source contributions to the annual mean UFP number concentration (diameter range 5–100nm) for photochemical NPF events (7%), local traffic (52%), diffuse urban sources (20%), and regional background (21%). The relative contribution of NPF events rises when moving away from roadside to the urban background and rural sites (14 and 30%, respectively). Their contribution also increases when considering only fresh UFPs (5–20nm) (21% at the urban background site), and conversely decreases when considering UFPs at bigger sizes (20–100nm) (8%). A seasonal analysis showed that NPF events have their greatest importance on UFP number concentration in the months May–August, accounting for roughly half of the fresh UFPs (5–20nm) at the urban background location. The simplistic source apportionment presented here might serve to better characterize exposure to ambient UFPs in future epidemiological studies.

Assessment and application of clustering techniques to atmospheric particle number size distribution for the purpose of source apportionment

Abstract. Long-term measurements of particle number size distribution (PNSD) produce a very large number of observations and their analysis requires an efficient approach in order to produce results in the least possible time and with maximum accuracy. Clustering techniques are a family of sophisticated methods that have been recently employed to analyse PNSD data; however, very little information is available comparing the performance of different clustering techniques on PNSD data. This study aims to apply several clustering techniques (i.e. K means, PAM, CLARA and SOM) to PNSD data, in order to identify and apply the optimum technique to PNSD data measured at 25 sites across Brisbane, Australia. A new method, based on the Generalised Additive Model (GAM) with a basis of penalised B-splines, was proposed to parameterise the PNSD data and the temporal weight of each cluster was also estimated using the GAM. In addition, each cluster was associated with its possible source based on the results of this parameterisation, together with the characteristics of each cluster. The performances of four clustering techniques were compared using the Dunn index and Silhouette width validation values and the K means technique was found to have the highest performance, with five clusters being the optimum. Therefore, five clusters were found within the data using the K means technique. The diurnal occurrence of each cluster was used together with other air quality parameters, temporal trends and the physical properties of each cluster, in order to attribute each cluster to its source and origin. The five clusters were attributed to three major sources and origins, including regional background particles, photochemically induced nucleated particles and vehicle generated particles. Overall, clustering was found to be an effective technique for attributing each particle size spectrum to its source and the GAM was suitable to parameterise the PNSD data. These two techniques can help researchers immensely in analysing PNSD data for characterisation and source apportionment purposes.

Sources of ultrafine particles and chemical species along a traffic corridor: comparison of the results from two receptor models

Environmental context Identifying the sources responsible for air pollution is crucial for reducing the effect of the pollutants on human health. The sources of the pollutants were found here by applying two mathematical models to data consisting of particle size distribution and chemical composition data. The identified sources could be used as the basis for controlling or reducing emissions of air pollution into the atmosphere. Abstract Particulate matter is common in our environment and has been linked to human health problems particularly in the ultrafine size range. In this investigation, the sources of particles measured at two sites in Brisbane, Australia, were identified by analysing particle number size distribution data, chemical species concentrations and meteorological data with two source apportionment models. The source apportionment results obtained by positive matrix factorisation (PMF) and principal component analysis–absolute principal component scores (PCA–APCS) were compared with information from the gaseous chemical composition analysis. Although PCA–APCS resolved more sources, the results of the PMF analysis appear to be more reliable. Six common sources were identified by both methods and these include: traffic 1, traffic 2, local traffic, biomass burning and two unassigned factors. Thus motor vehicle related activities had the greatest effect on the data with the average contribution from nearly all sources to the measured concentrations being higher during peak traffic hours and weekdays. Further analyses incorporated the meteorological measurements into the PMF results to determine the direction of the sources relative to the measurement sites, and this indicated that traffic on the nearby road and intersection was responsible for most of the factors. The described methodology that utilised a combination of three types of data related to particulate matter to determine the sources and combination of two receptor models could assist future development of particle emission control and reduction strategies.

Particle formation events measured at a semirural background site in Denmark

The particle formation and growth events observed at a semirural background site in Denmark were analyzed based on particle number size distribution data collected during the period from February 2005 to December 2010. The new particle formation (NPF) events have been classified visually in detail according to 3D daily plots in combination with an automatic routine. A clear seasonal variation was found in the way that events occurred more frequently during the warm season from May to September and especially in June. The mean values of the apparent 6 nm particle formation rates, the growth rate and the condensation sink were about 0.36 cm(-3) s(-1), 2.6 nm h(-1), 4.3 × 10(-3) s(-1), respectively. A positive relationship of oxidation capacity (OX = O3 + NO2) of the atmosphere and the appearance of NPF events was found indicating that the oxidation of the atmosphere was linked to the formation of new particles. An analysis of a 3-day backward trajectories revealed that NW air masses from the North Sea were giving the highest probability of NPF events, namely between 20 and 40 %.

Tracheobronchial and alveolar dose of submicrometer particles for different population age groups in Italy

Exposure to ultrafine particles (diameter less than 100 nm) is an important topic in epidemiological and toxicological studies. This study used the average particle number size distribution data obtained from our measurement survey in major microenvironments, together with the people activity pattern data obtained from the Italian Human Activity Pattern Survey to estimate the tracheobronchial and alveolar dose of submicrometer particles for different population age groups in Italy. We developed a numerical methodology based on Monte Carlo method, in order to estimate the best combination from a probabilistic point of view. More than 10 6 different cases were analyzed according to a purpose built sub-routine and our results showed that the daily alveolar particle number and surface area deposited for all of the age groups considered was equal to 1.5 × 10 11 particles and 2.5 × 10 15 μm 2 , respectively, varying slightly for males and females living in Northern or Southern Italy. In terms of tracheobronchial deposition, the corresponding values for daily particle number and surface area for all age groups was equal to 6.5 × 10 10 particles and 9.9 × 10 14 μm 2 , respectively. Overall, the highest contributions were found to come from indoor cooking (female), working time (male) and transportation (i.e. traffic derived particles) (children). ► We estimate particle number and surface area deposition for different age groups and lifestyles through an indirect approach. ► Particle number distribution range for each microenvironment was obtained from our measurement survey data. ► Deposition increases as a function age group, with the maximum value observed for 41–65 years old. ► Major contributions arise from indoor cooking and eating times (female), working time (male) and transportation (children).

A 17 month climatology of the cloud condensation nuclei number concentration at the high alpine site Jungfraujoch

[1] Between May 2008 and September 2009 the cloud condensation nuclei (CCN) number concentration, NCCN, was measured at the high alpine site Jungfraujoch, which is located in the free troposphere most of the time. Measurements at 10 different supersaturations (0.12%–1.18%) were made using a CCN counter (CCNC). The monthly median NCCN values show a distinct seasonal variability with ∼5–12 times higher values in summer than in winter. The major part of this variation can be explained by the seasonal amplitude of total aerosol number concentration (∼4.5 times higher values in summer), but it is further amplified (factor of ∼1.1–2.6) by a shift of the particle number size distribution toward slightly larger sizes in summer. In contrast to the extensive properties, the monthly median of the critical dry diameter, above which the aerosols activate as CCN, does not show a seasonal cycle (relative standard deviations of the monthly median critical dry diameters at the different supersaturations are 4–9%) or substantial variability (relative standard deviations of individual data points at the different supersaturations are less than 18–37%). The mean CCN-derived hygroscopicity of the aerosol corresponds to a value of the hygroscopicity parameter κ of 0.20 (assuming a surface tension of pure water) with moderate supersaturation dependence. NCCN can be reliably predicted throughout the measurement period with knowledge of the above-mentioned averaged κ value and highly time-resolved (∼5 min) particle number size distribution data. The predicted NCCN was within 0.74 to 1.29 times the measured value during 80% of the time (94,499 data points in total at 10 different supersaturations).

MECCO: A method to estimate concentrations of condensing organics—Description and evaluation of a Markov chain Monte Carlo application

The development of a new method to estimate concentrations of condensing organics (MECCO) is described. A Markov chain Monte Carlo method is applied, and by using measured particle size distribution and random vapor concentrations as input, the predicted changes in particle population by an aerosol dynamics model are utilized. The method provides the ambient vapor concentrations required for the observed particle growth in particle number size distribution data, assuming all growth can be attributed to net condensation of super-saturated vapors. In this paper, MECCO was coupled with the UHMA box-model to provide aerosol dynamics. With few changes, MECCO could be applied to study other input parameters, and coupled with other dynamics models as well. Evaluation of the method was carried out with simulated output from the UHMA model using the assumption of three organic vapors, and MECCO-UHMA was able to estimate their concentrations with great accuracy. However, the condensation of vapors is currently considered irreversible, since the used particle size distribution data do not provide information on the composition of particles. The distinguishing between the vapors is based on few vapor parameters, which limits the possibilities of identifying actual vapors. An example of atmospheric application is also presented. This revealed the importance of quality control of the input particle concentrations: instrumental noise and changes in the observed air mass pose challenges for the presented method. Data need to be smoothed in a reasonable way so that the point-like measurements can be utilized, but also so that the important information on particle growth is conserved. MECCO is a useful tool to approximate vapor concentrations and may be applied to estimate vapor properties as well. However, a computationally efficient and physically accurate aerosol dynamics model is essential for MECCO's performance.

Characterization of particulate matter concentrations during controlled indoor activities

Indoor sources have been identified as a major contributor to the increase of particle concentration in indoor environments. The work presented here is a study of the characteristics of particulate matter number size distribution and mass concentration under controlled indoor activities in a laboratory room. The objective is to characterize particulate matter concentrations indoors resulted under the influence of specific sources. Measurements were performed in an empty laboratory (period September–October 2006) using a GRIMM SMPS+C system (particle size range between 11.1 and 1083.3 nm), a DustTrak Aerosol Monitor (TSI) and a P-Trak Ultrafine Particle Counter (TSI). The studied indoor activities included candle burning, hot plate heating, water boiling, onion frying, vacuuming, hair drying, hair spraying, smoking and burning of incense stick. The AMANpsd computer algorithm was used to evaluate the modal structure of measured particle number size distribution data. Furthermore, the change of the particle number size distribution shape under the influence of different emission sources was studied versus time. Finally the particle emission rates were computed. High particle number concentrations were observed during smoking, onion frying, candle burning and incense stick burning. The highest particle mass concentrations were measured during smoking and hair spraying. The shift of the particle size distribution to larger diameters suggests the presence of strong coagulation effect during candle burning, incense stick burning, smoking and onion frying. The size distribution was mainly bimodal during onion frying and candle burning, whereas the size distribution remained unimodal during incense stick burning and smoking experiments.

Trends in size classified particle number concentration in subtropical Brisbane, Australia, based on a 5 year study

Particle number size distribution data in the range from 0.015 to 0.630 μm were collected over a 5-year period in the central business district (CBD) of Brisbane, Australia. Particle size distribution was summarised by total number concentration and number median diameter (NMD) as well as the number concentration of the 0.015–0.030 ( N 15–30), 0.030–0.050 ( N 30–50), 0.050–0.100 ( N 50–100), 0.100–0.300 ( N 100–300) and 0.300–0.630 ( N 300–630) μm size classes. Morning (6:00–10:00) and afternoon (16:00–19:00) measurements, the former representing fresh traffic emissions (based on the local meteorological conditions) and the latter well-mixed emissions from the CBD, during weekdays were extracted and the respective monthly mean values were estimated for time series analysis. For all size fractions, average morning concentrations were about 1.5 higher than in the afternoon whereas NMD did not vary between the morning and afternoon. The trend and seasonal components were extracted through weighted linear regression models, using the monthly variance as weights. Only the morning measurements exhibited significant trends. During this time of the day, total particle number increased by 105.7% and the increase was greater for larger particles, resulting in a shift in NMD by 7.9%. Although no seasonal component was detected the evidence against it remained weak due to the limitations of the database.