Ambient Particle Number Concentrations Research Articles

Contrail formation on ambient aerosol particles for aircraft with hydrogen combustion: a box model trajectory study

Abstract. Future air traffic using (green) hydrogen (H2) promises zero carbon emissions, but the effects of contrails from this new technology have hardly been investigated. We study contrail formation behind aircraft with H2 combustion by means of the particle-based Lagrangian Cloud Module (LCM) box model. Assuming the absence of soot and ultrafine volatile particle formation, contrail ice crystals form solely on atmospheric background particles mixed into the plume. While a recent study extended the original LCM with regard to the contrail formation on soot particles, we further advance the LCM to cover the contrail formation on ambient particles. For each simulation, we perform an ensemble of box model runs using the dilution along 1000 different plume trajectories. The formation threshold temperature of H2 contrails is around 10 K higher than for conventional contrails (which form behind aircraft with kerosene combustion). Then, contrail formation becomes primarily limited by the homogeneous freezing temperature of the water droplets such that contrails can form at temperatures down to around 234 K. The number of ice crystals formed varies strongly with ambient temperature even far away from the contrail formation threshold. The contrail ice crystal number clearly increases with ambient aerosol number concentration and decreases significantly for ambient particles with mean dry radii ⪅ 10 nm due to the Kelvin effect. Besides simulations with one aerosol particle ensemble, we analyze contrail formation scenarios with two co-existing aerosol particle ensembles with different mean dry sizes or hygroscopicity parameters. We compare them to scenarios with a single ensemble that is the average of the two aerosol ensembles. We find that the total ice crystal number can differ significantly between the two cases, in particular if nucleation-mode particles are involved. Due to the absence of soot particle emissions, the ice crystal number in H2 contrails is typically reduced by more than 80 %–90 % compared to conventional contrails. The contrail optical thickness is significantly reduced, and H2 contrails either become visible later than kerosene contrails or are not visible at all for low ambient particle number concentrations. On the other hand, H2 contrails can form at lower flight altitudes where conventional contrails would not form.

Modelling the dispersion of particle number concentrations in the West Midlands, UK using the ADMS-Urban model

Ultrafine particles (UFPs) are respirable particles with a diameter less than 100 nm, which some studies have associated with adverse effects upon health. UFPs are currently not regulated as the health evidence is insufficient and very few observational data are available in most cities. The 2021 WHO Global Air Quality Guidelines highlighted the pressing issue of UFPs and provided a good practice statement for UFPs, which recommends that more measurement and modelling studies are implemented in future. Particle number concentrations (PNC) are the most common metric for UFPs as this fraction normally dominates the total ambient PNC in urban environments. This study simulates the dispersion of particle number concentrations in the West Midlands (a metropolitan area), UK using the local scale ADMS-Urban model, which is an advanced quasi-Gaussian plume dispersion modelling system. ADMS-Urban implements a physics-based approach to represent the characteristics of the atmospheric boundary layer and has been widely used in the dispersion modelling of air pollutants. It can represent a variety of source types (such as road and grid emissions) occurring in urban environments and requires a range of input data. Particle number was used as a passive scalar, with no inclusion of aerosol microphysics within the model, as a first implementation in the ADMS-Urban model for the West Midlands, UK. Evaluation was conducted by comparing the modelled (from a receptor run) and measured data at the Birmingham Air Quality Supersite. Overall, the model performed well although there was a slight underestimation for PNC. Based on the modelling output from a contour run, PNC maps at a variety of spatial scales (i.e. street scale, ward level and local authority level) and temporal resolutions (i.e. annual, 24-hour, and 1-hour) were generated. PNC mapping could be linked to local population and health data for potential epidemiological studies.

Exhaust particle number and composition for diesel and gasoline passenger cars under transient driving conditions: Real-world emissions down to 1.5 nm

Recent recommendations given by WHO include systematic measurements of ambient particle number concentration and black carbon (BC) concentrations. In India and several other highly polluted areas, the air quality problems are severe and the need for air quality related information is urgent. This study focuses on particle number emissions and BC emissions of passenger cars that are technologically relevant from an Indian perspective. Particle number and BC were investigated under real-world conditions for driving cycles typical for Indian urban environments. Two mobile laboratories and advanced aerosol and trace gas instrumentation were utilized. Our study shows that passenger cars without exhaust particle filtration can emit in real-world conditions large number of particles, and especially at deceleration a significant fraction of particle number can be even in 1.5–10 nm particle sizes. The mass concentration of exhaust plume particles was dominated by BC that was emitted especially at acceleration conditions. However, exhaust particles contained also organic compounds, indicating the roles of engine oil and fuel in exhaust particle formation. In general, our study was motivated by serious Indian air quality problems, by the recognized lack of emission information related to Indian traffic, and by the recent WHO air quality guidance; our results emphasize the importance of monitoring particle number concentrations and BC also in Indian urban areas and especially in traffic environments where people can be significantly exposed to fresh exhaust emissions.

Assessing the impact of aircraft arrival on ambient ultrafine particle number concentrations in near-airport communities in Boston, Massachusetts

Aircraft emissions contribute to overall ambient air pollution, including ultrafine particle (UFP) concentrations. However, accurately ascertaining aviation contributions to UFP is challenging due to high spatiotemporal variability along with intermittent aviation emissions. The objective of this study was to evaluate the impact of arrival aircraft on particle number concentration (PNC), a proxy for UFP, across six study sites 3–17 km from a major arrival aircraft flight path into Boston Logan International Airport by utilizing real-time aircraft activity and meteorological data. Ambient PNC at all monitoring sites was similar at the median but had greater variation at the 95th and 99th percentiles with more than two-fold increases in PNC observed at sites closer to the airport. PNC was elevated during the hours with high aircraft activity with sites closest to the airport exhibiting stronger signals when downwind from the airport. Regression models indicated that the number of arrival aircraft per hour was associated with measured PNC at all six sites, with a maximum contribution of 50% of total PNC at a monitor 3 km from the airport during hours with arrival activity on the flight path of interest (26% across all hours). Our findings suggest strong but intermittent contributions from arrival aircraft to ambient PNC in communities near airports.

Racial-ethnic exposure disparities to airborne ultrafine particles in the United States

Ultrafine particles (‘UFP’; <100 nm in diameter) are a subset of fine particulate matter (PM2.5); they have different sources and spatial patterns. Toxicological studies suggest UFP may be more toxic per mass than PM2.5. Racial-ethnic exposure disparities for PM2.5 are well documented; national exposure disparities for UFP remain unexplored due to a lack of national exposure estimates. Here, we combine high-spatial-resolution (census block level) national-scale estimates of long-term, ambient particle number concentrations (PNC; a measure of UFP) with publicly available demographic data (census block-group level) to investigate exposure disparities by race-ethnicity and income across the continental United States. PNC exposure for racial-ethnic minorities (Asian, Black, Hispanic) is 35% higher than the overall national mean. The magnitudes of exposure disparities vary spatially. Disparities are generally larger in densely populated metropolitan areas. The magnitudes of disparities are much larger for PNC than for PM2.5; PM2.5 exposure for racial-ethnic minorities is 9% higher than the overall national mean. Our analysis shows that PNC exposure disparities cannot be explained by differences in income. Whites of all incomes, including low-income Whites, have substantially lower average PNC exposures than people of color of all incomes. A higher proportion of traffic and other PNC sources are located near many minority communities. This means that the exposure disparities are structural and strongly tied to where certain subsets of the population live and that simply reducing PNC emissions nationwide will not reduce these disparities.

Modelling ultrafine particle number concentrations at address resolution in Denmark from 1979 to 2018 - Part 2: Local and street scale modelling and evaluation

Modelling of ambient particle number concentrations (PNC) has been implemented in the Danish air quality modelling system DEHM/UBM/AirGIS and evaluated with long-term measurements. We implemented particle dynamical processes in the regional scale model DEHM using the M7 aerosol dynamics module (presented in the accompanying article by Frohn et al., 2021), and we developed models for PNC at the local scale (UBM) and street scale (OSPM), in a first approximation without including the particle dynamics as presented in this article.Outdoor concentration estimates are provided at the front door of all residential address locations in Denmark for the past 40 years (1979–2018) with a spatial resolution of 1 km × 1 km taking all emission sectors in Denmark into account and additionally at the street location, with significant traffic (>500 vehicles/day).We evaluated our model with up to 18-year long measurement time series of particle number size distributions (PNSD) at Danish street, urban and rural background stations. Two particle size ranges were used for evaluation: PNC>10 (count of particles with diameter larger than 10 nm) and PNC30_250 (diameter range 30–250 nm), in order to exclude nucleation events from the measurements and to obtain a consistent long-term measured time series.When comparing our model estimates with PNC30_250 measurements, we obtain Pearson correlation coefficients (Rp) in the range 0.39–0.95 depending on station location (street, urban background, rural) and averaging time (hour, day, month, year). The highest correlations were found for yearly averages at a monitoring station located at a street with dense traffic (Rp = 0.95) whereas shorter time averages and comparisons with monitoring stations at urban and rural background locations provided lower correlations. The model performance for PNC in terms of correlation coefficients with respect to measurements is comparable to the performance for other pollutants such as NOX, PM2.5 and better than the performance for PM10.The model generally overestimated the observed concentrations, Normalized Mean Bias (NMB) was in the range 6%–190% compared to PNC>10 and 90%–290% compared to PNC30_250. These relatively high NMBs are probably caused by uncertainties in the modelling process, especially the estimation of particle number emissions, which largely determine the ambient concentrations of PNC. Furthermore, uncertainties might as well originate from the complexity of modelling particle dynamical processes accurately and the great challenges in performing long-term PNC measurements.The presented model can estimate PNC at all Danish addresses over the last 40 years with a 1-h time resolution. The data seem to provide a good indication of the relative differences in PNC at Danish addresses.

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.

Can respirator face masks in a developing country reduce exposure to ambient particulate matter?

Respirator face masks (RFMs) as a personal-level intervention is increasingly being utilized to reduce ambient particulate matter (PM) exposure, globally. We tested the effectiveness of 50 commercially available ones in reducing the exposure of ambient particle number concentrations (PNC), PM10, PM2.5, and PM1 (PM ≤ 10, 2.5, and 1 μm in diameter, respectively) in a traffic-affected urban site in Tehran. To examine the efficiency of RFMs, we applied a specific experimental setup including vacuum pumps, dummy heads, connecting tubes, glass chambers, and GRIMM Aerosol Spectrometer to measure all metrics after dummy heads. The average effectiveness of RFMs was in the range of 0.7-83.5%, 3.5-68.1%, 0.8-46.1%, and 0.4-32.2% in reducing ambient PNC, PM10, PM2.5, and PM1, respectively. Considering all metrics, the highest effectiveness was observed always for Biomask, followed by 3 M 9332, due to their well-designed physical characteristics (e.g., adjustable nose clip for any face/nose shape, and size, soft inner material in the nose panel to provide a secure seal against leakage, adjustable or elasticated straps/ear loops to better adjust on any face). Biomask reduced ambient PM10 with a mean value of 94.6 μg m-3 (minimum-maximum: 51.7-100.3 μg m-3), whereas it filtered on average just 29.0 μg m-3 (25.7-43.5 μg m-3) of ambient PM2.5 and 18.2 μg m-3 (14.7-21.8 μg m-3) of PM1. A fuzzy analytical hierarchy process to find the most important design-related factors of RFMs affecting their effectiveness, which showed the exhalation valve and its diaphragm (20.4%), nose clip (19.7%), and cheek flaps (18.6%) are ranked as the main design-related variables. The fuzzy technique for order preference by similarity to ideal solution indicated that Biomask and 3M 9332 had scores of 1 and 0.97, the highest scores compared with other RFMs. This study provides crucial evidence-based results to elucidate the effectiveness and design-related factors of RFMs in real-environmental circumstances.

Relative contributions of a major international airport activities and other urban sources to the particle number concentrations (PNCs) at a nearby monitoring site

In this study, the positive matrix factorization (PMF) source apportionment model was employed to quantify the contributions of airport activities to particle number concentrations (PNCs) at Amsterdam Schiphol. Time-resolved particle number size distributions in parallel with the concentrations of auxiliary variables, including gaseous pollutants (NOx and CO), black carbon, PM2.5 mass, and number of arrivals/departures were measured for 32 sampling days over a 6-month period near Schiphol airport to be used in the model. PMF results revealed that airport activities, cumulatively, accounted for around 79.3% of PNCs and our model segregated them into three major groups: (i) aircraft departures, (ii) aircraft arrivals, and (iii) ground service equipment (GSE) (with some contributions of local road traffic, mostly from airport parking lots). Aircraft departures and aircraft arrivals showed mode diameters <20 nm and contributed, respectively, to 46.1% and 26.7% of PNCs. The factor GSE/local road traffic, with a mode diameter of around 60–80 nm, accounted for 6.5% of the PNCs. Road traffic related mainly to the surrounding freeways was characterized with a mode diameter of 30–40 nm; this factor contributed to 18.0% of PNCs although its absolute PNCs was comparable with that of areas heavily impacted by traffic emissions. Lastly, urban background with a mode diameter at 150–225 nm, had a minimal contribution (2.7%) to PNCs while dominating the particle volume/mass concentrations with a contribution of 58.2%. These findings illustrate the dominant role of the airport activities in ambient PNCs in the surrounding areas. More importantly, the quantification of the contributions of different airport activities to PNCs is a useful tool to better control and limit the increased PNCs near the airports that could adversely impact the health of the adjacent urban communities.

New insights into the spatial distribution of particle number concentrations by applying non-parametric land use regression modelling

Ambient particle number concentration (PNC) varies significantly in time and space within cities, yet complexity and cost prohibit large-scale routine monitoring; as a consequence, there is not enough data for assessment of human exposure to, or risk from the particles. The quality of assessments can be augmented by modelling; however, models are generally less capable of predicting PNC spatial variation than predicting variations in other ambient pollutants. To advance modelling of PNC, we aimed to develop and compare the performance of parametric and non-parametric machine learning land-use regression (LUR) models to predict hourly average PNC. We used data from 25 short-term stationary campaigns and five long-term sites during 2009–2012 in the Brisbane Metropolitan Area, Australia. We analysed three particle size ranges of total PNC (<30 nm, <414 nm and <3000 nm) as response variables, and over 150 independent variables, including land use, roads and traffic, population, distance, elevation, meteorology and time of day as potential predictors of PNC. The LUR models were developed separately for All Days, Nuc Days (when particle nucleation occurred), and No-nuc Days (when no particle nucleation occurred). We selected two algorithms to develop LUR models for PNC: a random forest (RF) model, and a generalised additive model (GAM) based on the least angle regression (LARS). The best LARS model for <30 nm, <414 nm and <3000 nm explained 30%, 31%, and 34%, respectively, whereas the best RF models were significantly better, explaining 73%, 64%, and 88%, respectively. Using this novel approach, we provided new insights into spatial variation in PNC and also demonstrated that the non-parametric RF model is a better choice for developing a LUR model for PNCs because of its robust predictive performance in comparison with the LARS parametric regression model.

Sex differences in the interaction of short-term particulate matter exposure and psychosocial stressors on C-reactive protein in a Puerto Rican cohort.

There is substantial evidence linking particulate matter air pollution with cardiovascular morbidity and mortality. However, health disparities between populations may exist due to imprecisely defined non-innate susceptibility factors. Psychosocial stressors are associated with cardiovascular disease and may increase non-innate susceptibility to air-pollution. We investigated whether the association between short-term changes in ambient particulate matter and cardiovascular health risk differed by psychosocial stressors in a Puerto Rican cohort, comparing women and men. We used data from the Boston Puerto Rican Health Study (BPRHS), a longitudinal study of cardiovascular health among adults, collected between 2004 and 2013. We used mixed effect models to estimate the association of current-day ambient particle number concentration (PNC) on C-reactive protein (CRP), a marker of systemic inflammation, and effect modification by psychosocial stressors (depression, acculturation, perceived stress, discrimination, negative life events and a composite score). Point estimates of percent difference in CRP per interquartile range change in PNC varied among women with contrasting levels of stressors: negative life events (15.7% high vs. 6.5% low), depression score (10.6% high vs. 4.6% low) and composite stress score (16.2% high vs. 7.0% low). There were minimal differences among men. For Puerto Rican adults, cardiovascular non-innate susceptibility to adverse effects of ambient particles may be greater for women under high stress. This work contributes to understanding health disparities among minority ethnic populations.

A field study to estimate inhalation rates for use in a particle inhalation rate exposure metric

Particle inhalation rate (PIR) is an air pollution exposure metric that relies on age-, sex-, and physical activity-specific estimates of minute respiratory volume (MRV; L/min-kg) to account for personal inhalation patterns. United States Environmental Protection Agency (USEPA)-generated MRV estimates derive primarily from relatively homogenous populations without substantial cardiorespiratory challenges. To determine if these MRV estimates are relevant to populations in generally poor cardiorespiratory health (e.g., the Boston Puerto Rican Health Study (BPRHS) population) or whether population-specific estimates are needed, we 1) estimated population-specific MRVs and compared them to USEPA MRV estimates, and 2) compared exposure distributions and health effect estimates using PIR with population-specific MRVs, PIR with USEPA MRVs, and ambient particle number concentration (PNC). We recruited 40 adults (80% Puerto Rican, mean age = 60.2 years) in Boston with health characteristics similar to the BPRHS population. We measured pulse, oxygen saturation, respiration rate, and inspiratory volume while participants walked, stood, sat, and lay down. Pulse, respiration rate, inspiratory volume, and MRV were greater when participants were walking/standing compared to sitting or lying down. We then calculated MRVs adjusted for age, sex, measured body weight, and physical activity using data from 19 Puerto Rican participants who wore a nose clip or held their nostrils closed. We applied the population-specific and USEPA MRVs to estimate ultrafine particle exposure for participants in the BPRHS (n = 781). We compared exposure distributions and health effect estimates using the PIR with population-specific MRV estimates, PIR with USEPA MRV estimates, and ambient concentrations. We found that while population-specific MRVs differed from USEPA MRVs, particularly for unhealthy participants, PIR exposure distributions and health effect estimates were similar using each exposure metric. Confidence intervals were narrower using the PIR metrics than ambient PNC, suggesting increased statistical efficiency. Even in our understudied population, using USEPA MRVs did not meaningfully change PIR estimates.

Short-and medium-term associations of particle number concentration with cardiovascular markers in a Puerto Rican cohort

Air pollution has been linked to adverse cardiovascular outcomes; however, susceptibility may vary by population. Puerto Rican adults living in the US may be a susceptible group due to a high rate of adverse cardiovascular events. We evaluated the effect of changes in ambient particle number concentration (PNC, a measure of ultrafine particles) and effects on biomarkers of cardiovascular risk in the Boston Puerto Rican Health Study (BPRHS), a longitudinal cohort (n = 1499). Ambient PNC was measured at a fixed site between 2004 and 2013 and daily mean concentrations were used to construct PNC metrics, including lags of 0, 1 and 2 days and moving averages (MAs) of 3, 7 and 28 days. We examined the association of each metric with C-reactive protein (CRP) and blood pressure. Each model included subject-specific random intercepts to account for multiple measurements. An interquartile range (IQR) increase in PNC was associated with CRP for all metrics, notably a 3-day increase in PNC was associated with a 7.1% (95% CI: 2.0%, 12.2%) increase in CRP. Significant associations with CRP were seen in women, but not men; with current and former (but not non-) smokers; participants younger (but not older) than 65 y; those without diabetes (but not with), and those with (but not without), hypertension. Our study extends knowledge about the health effects of air pollution to a vulnerable population that has been understudied.

Airborne ultrafine particles in a Pacific Island country: Characteristics, sources and implications for human exposure

The Pacific Islands carry a perception of having clean air, yet emissions from transport and burning activities are of concern in regard to air quality and health. Ultrafine particle number concentrations (PNCs), one of the best metrics to demonstrate combustion emissions, have not been measured either in Suva or elsewhere in the Islands. This work provides insight into PNC variation across Suva and its relationship with particle mass (PM) concentration and composition. Measurements over a short monitoring campaign provide a vignette of conditions in Suva. Ambient PNCs were monitored for 8 day at a fixed location, and mobile PNC sampling for two days. These were compared with PM concentration (TSP, PM10, PM2.5, PM1) and are discussed in relation to black carbon (BC) content and PM2.5 sources, determined from elemental concentrations; for the October 2015 period and longer-term data. Whilst Suva City PM levels remained fairly low, PM2.5 = 10–12 μg m−3, mean PNC (1.64 ± 0.02 × 104 cm−3) was high compared to global data. PNCs were greater during mobile sampling, with means of 10.3 ± 1.4 × 104 cm−3 and 3.51 ± 0.07 × 104 cm−3 when travelling by bus and taxi, respectively. Emissions from road vehicles, shipping, diesel and open burning were identified as PM sources for the October 2015 period. Transport related ultrafine particle emissions had a significant impact on microscale ambient concentrations, with PNCs near roads being 1.5 to 2 times higher than nearby outdoor locations and peak PNCs occurring during peak traffic times. Further data, particularly on transport and wet-season exposures, are required to confirm results. Understanding PNC in Suva will assist in formulating effective air emissions control strategies, potentially reducing population exposure across the Islands and in developing countries with similar emission characteristics.Suva's PNC was high in comparison to global data; high exposures were related to transport and combustion emissions, which were also identified as significant PM2.5 sources.

Estimate of main local sources to ambient ultrafine particle number concentrations in an urban area

Quantifying and apportioning the contribution of a range of sources to ultrafine particles (UFPs, D<100nm) is a challenge due to the complex nature of the urban environments. Although vehicular emissions have long been considered one of the major sources of ultrafine particles in urban areas, the contribution of other major urban sources is not yet fully understood. This paper aims to determine and quantify the contribution of local ground traffic, nucleated particle (NP) formation and distant non-traffic (e.g. airport, oil refineries, and seaport) sources to the total ambient particle number concentration (PNC) in a busy, inner-city area in Brisbane, Australia using Bayesian statistical modelling and other exploratory tools. The Bayesian model was trained on the PNC data on days where NP formations were known to have not occurred, hourly traffic counts, solar radiation data, and smooth daily trend. The model was applied to apportion and quantify the contribution of NP formations and local traffic and non-traffic sources to UFPs. The data analysis incorporated long-term measured time-series of total PNC (D≥6nm), particle number size distributions (PSD, D=8 to 400nm), PM2.5, PM10, NOx, CO, meteorological parameters and traffic counts at a stationary monitoring site. The developed Bayesian model showed reliable predictive performances in quantifying the contribution of NP formation events to UFPs (up to 4×104particlescm−3), with a significant day to day variability. The model identified potential NP formation and no-formations days based on PNC data and quantified the sources contribution to UFPs. Exploratory statistical analyses show that total mean PNC during the middle of the day was up to 32% higher than during peak morning and evening traffic periods, which were associated with NP formation events. The majority of UFPs measured during the peak traffic and NP formation periods were between 30–100nm and smaller than 30nm, respectively. To date, this is the first application of Bayesian model to apportion different sources contribution to UFPs, and therefore the importance of this study is not only in its modelling outcomes but in demonstrating the applicability and advantages of this statistical approach to air pollution studies.

Atmospheric deposition of particles at a sensitive alpine lake: Size-segregated daily and annual fluxes from passive sampling techniques.

Lake Tahoe, a North American alpine lake long appreciated for its clear water and geographic setting, has experienced a trend of declining water clarity due to increasing nutrient and particle inputs. Contributions from atmospheric deposition of particulate matter (PM) could be important, yet they are inadequately quantified. This study established a yearlong deposition monitoring network in the northern Lake Tahoe Basin. Dry deposition was quantified on surrogate surfaces while wet deposition was based on particles suspended in precipitation at 24-hour resolution. The particle size ranges by these passive techniques were 1-64μm and 0.5-20μm in diameter for dry and wet deposition, respectively. Dry deposition of submicrometer (0.5-1μm) particles was also estimated by extrapolation of a lognormal size distribution. Higher daily number deposition fluxes (NDFdry and NDFwet) were found at a near-shore site, confirming substantial impacts of commercial and tourist activities. The two more isolated sites indicated a uniform regional background. On average, daily NDFdry is about one order of magnitude lower than daily NDFwet. Dry deposition velocities increased rapidly with particle size, as evidenced by collocated measurements of NDFdry and ambient particle number concentrations, though it seems less so for wet deposition due to different scavenging mechanisms. Despite fewer "wet" days than "dry" days during the monitoring period, wet processes dominated seasonal particle deposition, particularly in winter and spring when most precipitation occurred. Adopting sediment (insoluble, inorganic) particle fraction estimates from the literature, this study reports an annual particle flux of 2.9-5.2×1010#m-2yr-1 for sediment particles with 1-20μm diameter and 6.1-11×1010#m-2yr-1 for those with 0.5-20μm diameter. Implications of these findings to the current knowledge of atmospheric deposition in the Lake Tahoe Total Maximum Daily Load (TMDL) are discussed.

Source apportionment of ambient particle number concentrations in central Los Angeles using positive matrix factorization (PMF)

Abstract. In this study, the positive matrix factorization (PMF) receptor model (version 5.0) was used to identify and quantify major sources contributing to particulate matter (PM) number concentrations, using PM number size distributions in the range of 13 nm to 10 µm combined with several auxiliary variables, including black carbon (BC), elemental and organic carbon (EC/OC), PM mass concentrations, gaseous pollutants, meteorological, and traffic counts data, collected for about 9 months between August 2014 and 2015 in central Los Angeles, CA. Several parameters, including particle number and volume size distribution profiles, profiles of auxiliary variables, contributions of different factors in different seasons to the total number concentrations, diurnal variations of each of the resolved factors in the cold and warm phases, weekday/weekend analysis for each of the resolved factors, and correlation between auxiliary variables and the relative contribution of each of the resolved factors, were used to identify PM sources. A six-factor solution was identified as the optimum for the aforementioned input data. The resolved factors comprised nucleation, traffic 1, traffic 2 (with a larger mode diameter than traffic 1 factor), urban background aerosol, secondary aerosol, and soil/road dust. Traffic sources (1 and 2) were the major contributor to PM number concentrations, collectively making up to above 60 % (60.8–68.4 %) of the total number concentrations during the study period. Their contribution was also significantly higher in the cold phase compared to the warm phase. Nucleation was another major factor significantly contributing to the total number concentrations (an overall contribution of 17 %, ranging from 11.7 to 24 %), with a larger contribution during the warm phase than in the cold phase. The other identified factors were urban background aerosol, secondary aerosol, and soil/road dust, with relative contributions of approximately 12 % (7.4–17.1), 2.1 % (1.5–2.5 %), and 1.1 % (0.2–6.3 %), respectively, overall accounting for about 15 % (15.2–19.8 %) of PM number concentrations. As expected, PM number concentrations were dominated by factors with smaller mode diameters, such as traffic and nucleation. On the other hand, PM volume and mass concentrations in the study area were mostly affected by sources with larger mode diameters, including secondary aerosols and soil/road dust. Results from the present study can be used as input parameters in future epidemiological studies to link PM sources to adverse health effects as well as by policymakers to set targeted and more protective emission standards for PM.

Variability in exposure to ambient ultrafine particles in urban schools: Comparative assessment between Australia and Spain

Ambient ultrafine particle number concentrations (PNC) have inhomogeneous spatio-temporal distributions and depend on a number of different urban factors, including background conditions and distant sources. This paper quantitatively compares exposure to ambient ultrafine particles at urban schools in two cities in developed countries, with high insolation climatic conditions, namely Brisbane (Australia) and Barcelona (Spain). The analysis used comprehensive indoor and outdoor air quality measurements at 25 schools in Brisbane and 39 schools in Barcelona. PNC modes were analysed with respect to ambient temperature, land use and urban characteristics, combined with the measured elemental carbon concentrations, NOx (Brisbane) and NO2 (Barcelona). The trends and modes of the quantified weekday average daily cycles of ambient PNC exhibited significant differences between the two cities. PNC increases were observed during traffic rush hours in both cases. However, the mid-day peak was dominant in Brisbane schools and had the highest contribution to total PNC for both indoors and outdoors. In Barcelona, the contribution from traffic was highest for ambient PNC, while the mid-day peak had a slightly higher contribution for indoor concentrations. Analysis of the relationships between PNC and land use characteristics in Barcelona schools showed a moderate correlation with the percentage of road network area and an anti-correlation with the percentage of green area. No statistically significant correlations were found for Brisbane. Overall, despite many similarities between the two cities, school-based exposure patterns were different. The main source of ambient PNC at schools was shown to be traffic in Barcelona and mid-day new particle formation in Brisbane. The mid-day PNC peak in Brisbane could have been driven by the combined effect of background and meteorological conditions, as well as other local/distant sources. The results have implications for urban development, especially in terms of air quality mitigation and management at schools.

Atmospheric particle number concentration and size distribution in a traffic–impacted area

This study measured ambient particle number concentrations (PNC) and the particle number distributions (PND) in the urban area of Porto Alegre, Rio Grande do Sul, Brazil. The samples were analyzed using a NanoScan model 3910 from TSI (diameters between 10 and 420 nm) and were taken from sites with high density of vehicular traffic, including two roadsides, two traffic intersections, one street canyon and one urban background. Association of meteorological variables (temperature, relative humidity, solar radiation, wind direction, and wind speed) on nanoparticle concentrations was examined. The results indicated PNC averages between 4.85×104 cm–3 and 1.80×105 cm–3 for locations affected by vehicular traffic, wherein highest concentrations were observed at sites corresponding to traffic intersections. In addition, all sites studied showed a trimodal average PND, with the modes centered at ~14 nm, ~30 nm, and ~105 nm. PND was dominated by nucleation (44.9%) and Aitken (42.0%) modes being representative at the studied sites of the pollution originating from urban traffic, except at the urban background. Meteorological parameters and synoptic meteorological conditions contributed to the variation in the results between the sampled days in the same location.

Modelling particle number concentrations in a typical street canyon in Germany and analysis of future trends

An aerosol box model and a gas-phase chemistry box model were coupled to simulate particle number (PN) concentrations, both solid and volatile, in a typical street canyon with a high traffic volume in Germany. The simulation accounts for emission, nucleation and aerosol aging processes while dilution is parameterised by a simple two-stage process. Calculations were performed for the years 2010, 2015, 2020 and 2025, and for a vehicle fleet consisting of electric vehicles only (“electric mobility”). Projections including a high fraction of Euro-6 vehicles in the fleet suggest that PN emissions will reduce by 90% in 2025 compared to 2010. Ambient PN concentrations are, however, expected to reduce by merely 29% over the same period. Apart from contributions of urban background air, reductions in primary particles are partially offset by secondary particle formation by nucleating exhaust gases. In the “electric mobility” scenario omitting tailpipe emissions, PN concentrations are expected to reduce by 60% from 2010 to 2025. For an aerosol assumed to be mixed externally only, PN of elemental carbon (EC) was calculated to reduce by 76% from 2010 to 2025, in the “electric mobility” scenario by 87%. Overall, the contribution of solid PN emissions from gasoline vehicles to PN concentration in the street canyon is expected to be approximately 4% in 2025.