No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Source apportionment of wide range particle size spectra and black carbon collected at the airport of Venice (Italy)
Abstract
Atmospheric particles are of high concern due to their toxic properties and effects on climate, and large airports are known as significant sources of particles. This study investigates the contribution of the Airport of Venice (Italy) to black carbon (BC), total particle number concentrations (PNC) and particle number size distributions (PNSD) over a large range (14 nm–20 μm). Continuous measurements were conducted between April and June 2014 at a site located 110 m from the main taxiway and 300 m from the runway. Results revealed no significantly elevated levels of BC and PNC, but exhibited characteristic diurnal profiles. PNSD were then analyzed using both k-means cluster analysis and positive matrix factorization. Five clusters were extracted and identified as midday nucleation events, road traffic, aircraft, airport and nighttime pollution. Six factors were apportioned and identified as probable sources according to the size profiles, directional association, diurnal variation, road and airport traffic volumes and their relationships to micrometeorology and common air pollutants. Photochemical nucleation accounted for ∼44% of total number, followed by road + shipping traffic (26%). Airport-related emissions accounted for ∼20% of total PNC and showed a main mode at 80 nm and a second mode beyond the lower limit of the SMPS (<14 nm). The remaining factors accounted for less than 10% of number counts, but were relevant for total volume concentrations: nighttime nitrate, regional pollution and local resuspension. An analysis of BC levels over different wind sectors revealed no especially significant contributions from specific directions associated with the main local sources, but a potentially significant role of diurnal dynamics of the mixing layer on BC levels. The approaches adopted in this study have identified and apportioned the main sources of particles and BC at an international airport located in area affected by a complex emission scenario. The results may underpin measures for improving local and regional air quality, and health impact assessment studies.
... The effect of large-size and busy airports on ambient air quality was assessed based on in-situ measurements by different studies in the US (e. g., Hudda et al., 2020;Hudda et al., 2016;Riley et al., 2016;Shirmohammadi et al., 2018;Westerdahl et al., 2008) and also several European countries (e.g., Carslaw et al., 2006;Keuken et al., 2015;Lopes et al., 2019;Masiol and Harrison, 2015;Masiol et al., 2016;Stafoggia et al., 2016;Ungeheuer et al., 2021;Vichi et al., 2016). However, only a small fraction of these studies measured the PNSD including the entire range of nucleation mode particles that can pass through the respiratory tract due to their small size (Kwon et al., 2020). ...
... On the other hand, the PNSD measurements of Brilke et al. (2020) at Paphos airport covered only a few days and lack measurements of other air pollutants. Air quality at Venice airport was investigated by Masiol et al. (2016), which also had medium number of passengers in 2018/19 (about 11 million per year), but the site is influenced by large emissions from surrounding industrial areas and by a land-sea breeze circulation. ...
... The measurements with the EMV were made from June 25, 2018 to September 05, 2018 (IOP summer period) and from February 25, 2019 to May 05, 2019 (IOP winter/spring period) at location 1 in Birelerhaff in a distance of ~330 m lateral to the southern edge of the runway (Fig. 1), which is outside of the fenced airport area. The selected location represents the best compromise between rigorous airport safety measures and the achievement of scientific objectives (see Masiol et al., 2016). The EMV was powered externally and continuously measured PM10, PM2.5, PM1.0 as well as NO + NO 2 (NO x ) and O 3 , complemented by meteorological quantities (wind speed & direction (7 m), global radiation, air temperature and humidity (3 m)) with a time resolution of 1 min. ...
... Harrison et al. (2011) reported that using wide-range particle sizes in source apportionment was extremely successful for the identification of the separate contributions of on-road emission, including brake wear and resuspension. Although there are a few studies of wide-range particle characterization in Beijing (Jing et al., 2014) and source apportionment in Venice, Italy (Masiol et al., 2016), there has been no previous wide-range PNSD study in Delhi. In this study, we aimed to interpret particulate matter size distributions over a wide range (15 nm to 10 µm) in the winter and postmonsoon and pre-monsoon seasons in Delhi. ...
... In the literature, PNCs measuring below 1 µm are frequently split into the following three ranges: nucleation, Aitken, and accumulation . Nucleation size ranges have variously been described as being either below 30 nm (Masiol et al., 2016), below 25 nm , or below 20 nm (Wu and Boor, 2021). Some studies have evaluated the wide-range PNSDs and split them into four ranges (nucleation, Aitken, accumulation, and coarse; Masiol et al., 2016;Harrison et al., 2011). ...
... Nucleation size ranges have variously been described as being either below 30 nm (Masiol et al., 2016), below 25 nm , or below 20 nm (Wu and Boor, 2021). Some studies have evaluated the wide-range PNSDs and split them into four ranges (nucleation, Aitken, accumulation, and coarse; Masiol et al., 2016;Harrison et al., 2011). In this study, the modes have been aggregated into five differently Figure 2. Average diurnal variation in particle number counts for nucleation, Aitken, accumulation, large fine, and coarse modes and PM 2.5 , BC, NO, and NO 2 in autumn, summer, and winter. ...
Delhi is one of the world's most polluted cities, with very high concentrations of airborne particulate matter. However, little is known about the factors controlling the characteristics of wide-range particle number size distributions. Here, new measurements are reported from three field campaigns conducted in winter and pre-monsoon and post-monsoon seasons at the Indian Institute of Technology campus in the south of the city. Particle number size distributions were measured simultaneously, using a scanning mobility particle sizer and a GRIMM optical particle monitor, covering 15 nm to >10 µm diameter. The merged, wide-range size distributions were categorized into the following five size ranges: nucleation (15–20 nm), Aitken (20–100 nm), accumulation (100 nm–1 µm), large fine (1–2.5 µm), and coarse (2.5–10 µm) particles. The ultrafine fraction (15–100 nm) accounts for about 52 % of all particles by number (PN10 is the total particle number from 15 nm to 10 µm) but just 1 % by PM10 volume (PV10 is the total particle volume from 15 nm to 10 µm). The measured size distributions are markedly coarser than most from other parts of the world but are consistent with earlier cascade impactor data from Delhi. Our results suggest substantial aerosol processing by coagulation, condensation, and water uptake in the heavily polluted atmosphere, which takes place mostly at nighttime and in the morning hours. Total number concentrations are highest in winter, but the mode of the distribution is largest in the post-monsoon (autumn) season. The accumulation mode particles dominate the particle volume in autumn and winter, while the coarse mode dominates in summer. Polar plots show a huge variation between both size fractions in the same season and between seasons for the same size fraction. The diurnal pattern of particle numbers is strongly reflective of a road traffic influence upon concentrations, especially in autumn and winter, although other sources, such as cooking and domestic heating, may influence the evening peak. There is a clear influence of diesel traffic at nighttime, when it is permitted to enter the city, and also indications in the size distribution data of a mode
... This contribution is lower than reported in previous studies: Masiol et al. (2017) found the contribution of an airport associated factor to be 32-33% near Heathrow airport, measured in two seasons. In a previous source apportionment study near Venice airport (Masiol et al. 2016) the factor associated with the airport contributed 20% to the total PNC. ...
... Fresh Traffic has one size mode; this was of 24.1 nm and 25 nm at Horley and Poles Lane, respectively. This was lower than in a study near Venice airport, where the factor associated with traffic had a size range between 35 and 40 nm (Masiol et al. 2016), but compared well with a source apportionment study near Heathrow airport where a factor associated with fresh traffic was found in the size range 18-35 nm and 20-35 nm in the cold and warm season, respectively (Masiol et al. 2017). A study in Rochester, NY found a factor in the size range of 20-30 nm, which they associated with traffic (Kasumba et al. 2009), and a study looking at SMPS data from a number of cities across Europe found fresh traffic was associated with size ranges between 13 and 37 nm (Rivas et al 2020). ...
... This factor correlated moderately with NO 2 at both sites, which is a primary road traffic tracer but showed no or only weak correlations with other primary road traffic tracers, such as NO X and BC 880 (Supplement Table S2). This has been observed in other studies ((Masiol et al. 2016;Harrison et al. 2011;Vu et al. 2016), where these correlations were weak even though other information is consistent with traffic sources. ...
There is increasing evidence of potential health impacts from both aircraft noise and aircraft-associated ultrafine particles (UFP). Measurements of noise and UFP are however scarce near airports and so their variability and relationship are not well understood. Particle number size distributions and noise levels were measured at two locations near Gatwick airport (UK) in 2018–19 with the aim to characterize particle number concentrations (PNC) and link PNC sources, especially UFP, with noise. Positive Matrix Factorization was used on particle number size distribution to identify these sources. Mean PNC (7500–12,000 p cm⁻³) were similar to those measured close to a highly trafficked road in central London. Peak PNC (94,000 p cm⁻³) were highest at the site closer to the runway. The airport source factor contributed 17% to the PNC at both sites and the concentrations were greatest when the respective sites were downwind of the runway. However, the main source of PNC was associated with traffic emissions. At both sites noise levels were above the recommendations by the WHO (World Health Organisation). Regression models of identified UFP sources and noise suggested that the largest source of noise (LAeq-1hr) above background was associated with sources of fresh traffic and urban UFP depending on the site. Noise and UFP correlations were moderate to low suggesting that UFP are unlikely to be an important confounder in epidemiological studies of aircraft noise and health. Correlations between UFP and noise were affected by meteorological factors, which need to be considered in studies of short-term associations between aircraft noise and health.
... In turn, the specific shapes and modal structures of PNSD contain valuable clues to sector sources, geographic origins, and particle formation mechanisms (Charron et al., 2008;Tunved et al., 2004;Vu et al., 2015). Some scientific and realistic fundamentals of source apportionment based on PNSD include: 1) PNSD have tempo-spatial quasi-stability Mcelroy et al., 1982;Ogulei et al., 2006;Zhou et al., 2005;Zhou et al., 2004); 2) advanced measurements can capture short-term dramatic changes in PNSD (Hameri et al., 2004;Morawska et al., 1999;Weber et al., 2006;Wegner et al., 2012); 3) the particle size spans multiple orders of magnitude so the PNSD contain rich manifest (Charron et al., 2008;Hussein et al., 2014) and latent (Masiol et al., 2017a;Masiol et al., 2016) fingerprints (Morawska and Zhang, 2002) for distinguishing sources; 4) PNC have large spatial heterogeneity and are very sensitive to emission sources (Lianou et al., 2007;Price et al., 2014;Rodriguez et al., 2007;Weber et al., 2013); 5) source apportionments of PNC are indispensable to know the source contributions of PNC (Ogulei et al., 2007); 6) number concentration supplements mass concentration (Pey et al., 2010); 7) source apportionments of PNC are cost-effective (Thimmaiah et al., 2009;Yue et al., 2008). ...
... Researchers preprocessed their raw data by simply deleting abnormal (wrong) observations (Wiedensohler et al., 2012) and replacing missing data by mean values (Gu et al., 2011) or temporal linear interpolation (Liu et al., 2014). Masiol et al. (2017a) and Masiol et al. (2016) further stated using the top 0.5% percentile to remove outliers and nearest size bins for NA interpolation. Except new particle formation (NPF) events (Carnerero et al., 2019;Dal Maso et al., 2005;Kulmala et al., 2004;Sun et al., 2016;Wu et al., 2007), there are few descriptions of PNC pollution events (Rodriguez et al., 2007) and their classification methods. ...
... The general law of the sources of PNC is summarized in Table 1 (details in Table S4) based on the 30 results (29 publications): 24 of PMF (Al-Dabbous and Kumar, 2015;Beddows et al., 2015;Dall'Osto et al., 2012;Friend et al., 2013;Friend et al., 2012;Gu et al., 2011;Harrison et al., 2011;Kasumba et al., 2009;Krecl et al., 2008;Krecl et al., 2015;Z. Liu et al., 2017;Liu et al., 2014;Liu et al., 2016;Masiol et al., 2017a;Masiol et al., 2016;Ogulei et al., 2007;Sowlat et al., 2016;Squizzato et al., 2019;Thimmaiah et al., 2009;Wang et al., 2013;Yue et al., 2008;Zhou et al., 2005;Zhou et al., 2004;Zong et al., 2019); 4 of k-means clustering (Charron et al., 2008;Dall'Osto et al., 2012;Hussein et al., 2014;Wegner et al., 2012); 2 of PCA (Cusack et al., 2013;Pey et al., 2009). ...
Number concentration is an important index to measure atmospheric particle pollution. However, tailored methods for data preprocessing and characteristic and source analyses of particle number concentrations (PNC) are rare and interpreting the data is time-consuming and inefficient. In this method-oriented study, we develop and investigate some techniques via flexible conditions, C++ optimized algorithms, and parallel computing in R (an open source software for statistics and graphics) to tackle these challenges. The data preprocessing methods include deletions of variables and observations, outlier removal, and interpolation for missing values (NA). They do better in cleaning data and keeping samples and generate no new outliers after interpolation, compared with previous methods. Besides, automatic division of PNC pollution events based on relative values suites PNC properties and highlights the pollution characteristics related to sources and mechanisms. Additionally, basic functions of k-means clustering, Principal Component Analysis (PCA), Factor Analysis (FA), Positive Matrix Factorization (PMF), and a newly-introduced model NMF (Non-negative Matrix Factorization) were tested and compared in analyzing PNC sources. Only PMF and NMF can identify coal heating and produce more explicable results, meanwhile NMF apportions more distinctly and runs 11–28 times faster than PMF. Traffic is interannually stable in non-heating periods and always dominant. Coal heating's contribution has decreased by 40%–86% in recent 5 heating periods, reflecting the effectiveness of coal burning control.
... Venice [49]. The noon/afternoon peak is driven by the enhanced photochemistry whereas the 214 morning peak is likely vehicular emissions that nucleate as the emissions dilute and cool [29,44]. ...
... The highest contributions of PNC were observed during the daytime, with two peaks, one the morning and one around noon during summer, and in the morning and the afternoon during the winter and transition months. Similar profiles and daily patterns have been also reported in London [44][45][46]; Los Angeles [47,48], and Venice [49]. The noon/afternoon peak is driven by the enhanced photochemistry whereas the morning peak is likely vehicular emissions that nucleate as the emissions dilute and cool [29,44]. ...
... This source was identified only during winter and transition months with the highest contributions during nighttime and early morning when conditions are best for particulate nitrate formation and low contributions to PNC (3 ± 2% on average), again similar to what was observed in Pittsburgh [51]. As previously observed by Dall'Osto et al. [55,56] and Masiol et al. [49], increased contributions during the night are consistent with the heterogeneous reactions of N 2 O 5 and NO 3 on the particle surfaces [11]. The particles in the nucleation and Aitken mode were likely associated with the gas-to-particle conversion of local NO x emissions [51] whereas the accumulation mode particles could be formed during the transport and reactions of its precursor gases, NH 3 and NO x [30]. ...
The northeastern United States has experienced significant emissions reductions in the last two decades leading to a decrease in PM2.5, major gaseous pollutants (SO2, CO, NOx) and ultrafine particles (UFPs) concentrations. Emissions controls were implemented for coal-fired power plants, and new heavy-duty diesel trucks were equipped with particle traps and NOx control systems, and ultralow sulfur content is mandatory for both road and non-road diesel as well as residual oil for space heating. At the same time, economic changes also influenced the trends in air pollutants. Investigating the influence of these changes on ultrafine particle sources is fundamental to determine the success of the mitigation strategies and to plan future actions. Particle size distributions have been measured in Rochester, NY since January 2002. The particle sources were investigated with positive matrix factorization (PMF) of the size distributions (11–470 nm) and measured criteria pollutants during five periods (2002–2003, 2004–2007, 2008–2010, 2011–2013, and 2014–2016) and three seasons (winter, summer, and transition). Monthly, weekly, and hourly source contributions patterns were evaluated.
... For the NSD PMF we tested time resolution from 5 min to 2-h and determined 15-min resolution was optimal as these data are the best compromise to maintain a high time resolution but avoid unwanted noise. Additionally, the input data were handled by merging three consecutive bins, in order to (i) reduce the noise of the raw data, (ii) to decrease the variables number, (iii) to reduce the number of zeroes found in the coarse SMPS and APS bins (Masiol et al., 2016). Since the size segregation of SMPS is based on particle electrical mobility while the APS and 8DRUM impactor use particle aerodynamic properties, the mobility diameter was converted into aerodynamic using a standard ambient aerosol density (1.5 g cm À3 , Hinds, 1999). ...
... Other studies reported multi-source factors in the ultrafine size range (Gu et al., 2011;Masiol et al., 2016). Nanoparticles can originate from various sources, and it is sometimes not possible to attribute to this factor only to one source. ...
... Gu et al., 2011 observed a peak in the NSD between 2 and 8.8 mm associated with natural crustal dust and resuspended road dust. Masiol et al., 2016, observed a peak in the volume concentration at 5 mm attributed to resuspension of crustal dust. ...
Ostrava in the Moravian-Silesian region (Czech Republic) is a European air pollution hot spot for airborne particulate matter (PM), polycyclic aromatic hydrocarbons (PAHs), and ultrafine particles (UFPs). Air pollution source apportionment is essential for implementation of successful abatement strategies. UFPs or nanoparticles of diameter <100 nm exhibit the highest deposition efficiency in human lungs. To permit apportionment of PM sources at the hot-spot including nanoparticles, Positive Matrix Factor-ization (PMF) was applied to highly time resolved particle number size distributions (NSD, 14 nm-10 mm) and PM 0.09-1.15 chemical composition. Diurnal patterns, meteorological variables, gaseous pollutants, organic markers, and associations between the NSD factors and chemical composition factors were used to identify the pollution sources. The PMF on the NSD reveals two factors in the ultrafine size range: industrial UFPs (28%, number mode diameter-NMD 45 nm), industrial/fresh road traffic nanoparticles (26%, NMD 26 nm); three factors in the accumulation size range: urban background (24%, NMD 93 nm), coal burning (14%, volume mode diameter-VMD 0.5 mm), regional pollution (3%, VMD 0.8 mm) and one factor in the coarse size range: industrial coarse particles/road dust (2%, VMD 5 mm). The PMF analysis of PM 0.09-1.15 revealed four factors: SIA/CC/BB (52%), road dust (18%), sinter/steel (16%), iron production (16%). The factors in the ultrafine size range resolved with NSD have a positive correlation with sinter/ steel production and iron production factors resolved with chemical composition. Coal combustion factor resolved with NSD has moderate correlation with SIA/CC/BB factor. The organic markers homohopanes correlate with coal combustion and the levoglucosan correlates with urban background. The PMF applications to NSD and chemical composition datasets are complementary. PAHs in PM 1 were found to be associated with coal combustion factor.
... Generally, studies performed within or close to airports have reported increases in particles ranging from 4 to 100 nm in diameter and mostly distributed in the nucleation range (< 30 nm). For example, Mazaheri et al. (2009) showed a main nucleation mode and an accumulation mode (40-100 nm) more evident during take-offs; Keuken et al. (2015) reported PNSD dominated by 10-20 nm particles in an area affected by emissions from Schiphol airport (the Netherlands); Hudda and Fruin (2016) found strong increases in particles smaller than 40 nm downwind of Los Angeles International Airport; Ren et al. (2016) showed that particles peaking at 16 nm dominate the PNSD at various distances from the runway of Tianjin International Airport, China; Masiol et al. (2016) reported that the fingerprint of aircraft emissions sampled under real ambient conditions at the airport of Venice (Italy) has a main mode at approx. 80 nm and a second mode in the nucleation range below 14 nm. ...
... However, this study is the first one carried out in south-west London to characterise and quantitatively apportion the impacts of LHR under real ambient conditions. Moreover, only one earlier study (Masiol et al., 2016) has used both cluster analysis and PMF to directly assess the airport contributions to UFPs. In addition, this study also investigated the effects of a regional nucleation event on the results of the two source apportionment methods. ...
... The SMPS operated at a sheath-air-to-aerosol flow ratio of 10 : 1 (sheath and sample air flow rates were 3.0 and 0.3 L min −1 , respectively; voltage, 10-9591 V; density, 1.2 g cm −3 ; scan time, 120 s; retrace, 15 s; number of scans, 2) while the CPC operated at low flow rate (0.3 L min −1 ). The use of 5 min resolved spectra has already been used successfully for source apportionment purposes at an airport (Masiol et al., 2016). ...
The international airport of Heathrow is a major source of nitrogen oxides, but its contribution to the levels of sub-micrometre
particles is unknown and is the objective of this study. Two sampling campaigns were carried out during warm and cold seasons at
a site close to the airfield (1.2 km). Size spectra were largely dominated by ultrafine particles: nucleation particles ( < 30 nm) were found to be ∼ 10 times higher than those commonly measured in urban background environments of London. Five
clusters and six factors were identified by applying k means cluster analysis and positive matrix factorisation (PMF), respectively, to
particle number size distributions; their interpretation was based on their modal structures, wind directionality, diurnal patterns,
road and airport traffic volumes, and on the relationship with weather and other air pollutants. Airport emissions, fresh and aged
road traffic, urban accumulation mode, and two secondary sources were then identified and apportioned. The fingerprint of Heathrow has
a characteristic modal structure peaking at < 20 nm and accounts for 30–35 % of total particles in both the
seasons. Other main contributors are fresh (24–36 %) and aged (16–21 %) road traffic emissions and urban accumulation from
London (around 10 %). Secondary sources accounted for less than 6 % in number concentrations but for more than 50 % in
volume concentration. The analysis of a strong regional nucleation event showed that both the cluster categorisation and PMF
contributions were affected during the first 6 h of the event. In 2016, the UK government provisionally approved the construction of
a third runway; therefore the direct and indirect impact of Heathrow on local air quality is expected to increase unless mitigation
strategies are applied successfully.
... Our results are comparable to Masiol et al. (2017), who calculated a 30% contribution of London Heathrow (LHR) airport to total PNC in the vicinity. The observed contribution of the airport to total PNC is higher at LHR and TXL than for London Gatwick Airport (17%) (Tremper et al., 2022) or Venice airport (20%) (Masiol et al., 2016). ...
... Our measurements during the closure of the airport allowed for a direct attribution of changes in PNCs to flight operations. In contrast, during the ongoing operation of an airport, it is possible, for example, to assign particles or emissions to an airport by including different particle sizes (Masiol et al., 2016;Psanis et al., 2017), other air quality parameters like black carbon (Keuken et al., 2012;Austin et al., 2021) or chemical analyses of the collected particles (Ungeheuer et al., 2021). The impact of an airport on PNCs can furthermore be contrasted from other UFP sources statistically, by including regression analyses (i.e., Hsu et al., 2012;Fritz et al., 2022), k-means clustering or positive matrix factorisation (PMF) (Masiol et al., 2017;Tremper et al., 2022) and principal component analyses (Austin et al., 2021). ...
Airports contribute substantially to ultrafine particle (UFP; <100 nm) concentrations on a local scale. These UFPs, which derive mainly from combustion processes, are generated during takeoff and landing of aircraft, during aircraft movements on the tarmac, when engines and turbines are started, and by vehicles transporting goods and people on the airfield. UFPs are considered particularly harmful to human health as their small size enables them to pass far into the human body. This study investigates the extent to which particle number concentrations (PNCs) sized 7-2,000 nm respond to the cessation of air traffic due to the closure of a major airport. PNCs and wind data were monitored with a 5 s resolution at one location on the airfield of Berlin-Tegel Airport (TXL). The station was located 600-640 m east of the runways and thus downwind of the runways for the predominant wind direction. Observations took place 24 h per day every day for about 3 weeks before and 3 weeks after the closure of the airport. During the measurement campaign, a total of 2,507 takeoffs and landings took place. Including all wind directions, this study shows 30 %-40% lower PNCs on average, 2.5-fold lower maximum values as well as a 3-fold lower PNC spread after the closure of the airport. These differences are evident only during the day with active flight operations, and not during the nighttime flight ban. Downwind of the airfield, differences are even higher. After the closure of the airport, average PNCs drop by 70%, maxima by 85%, and variability is reduced by almost 90%. 70% lower and 30% less frequent PNC peaks occur downwind of the airport after flight operations are discontinued. This unique natural experiment allows for relatively clear conclusions about the relevance of airport operations on PNCs in the airfield area. The measurements carried out before and after the closure allow a direct comparison of the PNCs during airport operations and without any. Thus, our study reveals the change in UFP concentration that can be achieved through a reduction in flight operations.
... It is hard to measure wide size ranges such as 2-20,000 nm by using one single aerosol instrument (Kulkarni and Baron, 2011). Consequently, two or more instruments such as scanning mobility particle sizer (SMPS), aerodynamic particle sizer (APS), and optical particle spectrometer/sizer (OPS) have been jointly used in studies on particle number size distributions (PNSD) containing PNC with wide size ranges (Harrison et al., 2011;Masiol et al., 2016;Spielvogel et al., 2010;Wu and Boor, 2020;Xia et al., 2020). Second, the practical PNC source apportionment operations often involve combing receptor models and supplementary information such as related pollutants, meteorology, and dispersion models (Beddows et al., 2015;Harrison et al., 2011;Masiol et al., 2017;Vu et al., 2015). ...
... where Min is the minimum particle number concentration of a size bin, Q B is the bottom quantile (percentile) with a default of 0.0025 (0.25 %), E B is the bottom allowable error coefficient with a default of 0.2, M BQ is the order of magnitude of Q B , and C BM is the coefficient (default is 0.4) of M BQ . The different parameter values are chosen based on the improvement (Liang et al., 2020) from previous work (Masiol et al., 2017;Masiol et al., 2016). Allowable error coefficient and order of magnitude of quantiles and its coefficient were added as constraints to improve the traditional percentile-based outlier removal method. ...
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.
... Application of PMF on PNSD is commonly adopted in source apportionment studies since by investigating particles in various size ranges, it is possible to more clearly identify and apportion contributions from those sources that contributed more to the particle number than to the particle mass (e.g. Beddows et al., 2015;Masiol et al., 2016;Sowlat et al., 2016;Leoni et al., 2018;Pokorná et al., 2020;Zíková et al., 2020). Episodes in which the factor contributions to the total particle number concentrations were higher than 80 % were chosen for the subsequent particle density calculations. ...
Seasonal variability of non-refractory PM1 (NR-PM1) was studied at a rural background site (National Atmospheric Observatory Košetice – NAOK) in the Czech Republic to investigate the effect of regional and long-range atmospheric transport in central Europe. NR-PM1 measurements were performed by compact time-of-flight aerosol mass spectrometry (C-ToF-AMS), and the chemically speciated mass size distributions, density, shape, and origin were discussed. Average PM1 concentrations, calculated as the sum of the NR-PM1 and the equivalent black carbon (eBC) concentrations measured by an aethalometer (AE), were 8.58 ± 3.70 µg m−3 in summer and 10.08 ± 8.04 µg m−3 in winter. Organics were dominant during both campaigns (summer/winter: 4.97 ± 2.924.55 ± 4.40 µg m−3), followed by SOin summer (1.68 ± 0.81/1.36 ± 1.38 µg m−3) and NO in winter (0.67 ± 0.38/2.03 ± 1.71 µg m−3). The accumulation mode dominated the average mass size distribution during both seasons, with larger particles of all species measured in winter (mode diameters: Org: 334413 nm, NO: 377/501 nm, SO: 400547 nm, and NH: 489515 nm) indicating regional and long-range transport. However, since the winter aerosols were less oxidized than the summer aerosols (comparing fragments f44 and f43), the importance of local sources in the cold part of the year was still enough to be considered. Although aged continental air masses from the south-east (SE) were rare in summer (7 %), they were related to the highest concentrations of PM1, eBC, and all NR-PM1 species, especially SO and NH. In winter, slow continental air masses from the south-west (SW) (44 %) were linked to inversion conditions over central Europe and were associated with the highest concentrations among all NR-PM1 species as well as PM1 and eBC. Average PM1 material density (ρm) corresponded to higher inorganic contents in both seasons (summer: ∼ 1.30 g cm−3 and winter: ∼ 1.40 g cm−3). During episodes of higher mass concentrations ρm ranged from 1.30–1.40 g cm−3 in summer and from 1.30–1.50 g cm−3 in winter. The dynamic shape factors (χ) decreased slightly with particle mobility diameter (Dm) in both seasons. This study provides insights into the seasonal effects and air mass variability on aerosol particles, focusing on episodes of high mass and number concentrations measured at a central European rural background site.
... The Dunn index (DI; Masiol et al., 2016;Agudelo-Castañeda et al., 2019) is a metric for evaluating clustering algorithms using an internal evaluation scheme based on the clustering results. The higher the Dunn index, the better is the clustering. ...
The important role of traffic emissions in air pollution has been widely recognized. However, the corresponding contributions to the ultrafine particle distribution and new particle formation (NPF) are poorly understood due to the mixed influences of various sources. Measurements of particle evolution and NPF were made at a heavy traffic site near the 5th Ring Road of Beijing from August 2017 to October 2019. A positive matrix factorization receptor model (version 5.0) consisting of the log-normal fit method and the k-means clustering method was used to identify and quantify the major sources of particle number concentrations. Based on the characteristics of NPF, additional analyses were performed to identify formation sources, including back-trajectory clustering and correlations between auxiliary variables and the relative contributions of all resolved factors. Traffic emissions mainly influenced Aitken mode (25–100 nm) particles (> 60%), and NPF significantly contributed to the ultrafine particle distribution (47.2%). Additionally, regional transport notably affected the occurrence of NPF at the observation site, with northwest airmass transport influencing the nucleation mode the most and southeast airmass transport influencing the Aitken mode the most.
... Application of PMF on PNSD is commonly adopted in source apportionment studies since by investigating particles in various size ranges, it is possible to more clearly identify and apportion contributions from those sources that contributed more to the particle number than to the particle mass (e.g. Beddows et al., 2015;Masiol et al., 2016;Sowlat et al., 2016;Leoni et al., 2018;Pokorná et al., 2020;Zíková et al., 2020). Episodes in which the factor contributions to the total particle number concentrations were higher than 80 % were chosen for the subsequent particle density calculations. ...
Seasonal variability of non-refractory PM1 (NR-PM1) was studied at a rural background site (National Atmospheric Observatory Košetice – NAOK) in the Czech Republic to investigate the effect of regional and long-range atmospheric transport in central Europe. NR-PM1 measurements were performed by compact time-of-flight aerosol mass spectrometry (C-ToF-AMS), and the chemically speciated mass size
distributions, density, shape, and origin were discussed. Average PM1
concentrations, calculated as the sum of the NR-PM1 and the equivalent black carbon (eBC) concentrations measured by an aethalometer (AE), were 8.58 ± 3.70 µg m−3 in summer and 10.08 ± 8.04 µg m−3 in winter. Organics were dominant during both campaigns (summer/winter: 4.97 ± 2.92/4.55 ± 4.40 µg m−3),
followed by SO42-in summer (1.68 ± 0.81/1.36 ± 1.38 µg m−3)
and NO3- in winter (0.67 ± 0.38/2.03 ± 1.71 µg m−3). The accumulation mode dominated the average mass size distribution during both seasons, with larger particles of all species measured in winter (mode diameters: Org: 334/413 nm, NO3-: 377/501 nm, SO42-: 400/547 nm, and NH4+: 489/515 nm) indicating regional and long-range transport. However, since the winter aerosols were less oxidized than the summer aerosols (comparing fragments f44 and f43), the importance of local sources in the cold part of the year was still enough to be considered. Although aged continental air masses from the south-east (SE) were rare in summer (7 %), they were related to the highest concentrations of PM1, eBC, and all NR-PM1 species, especially SO42- and NH4+. In winter, slow continental air masses from the south-west (SW) (44 %) were linked to inversion conditions over central Europe and were associated with the highest concentrations among all NR-PM1 species as well as PM1 and eBC. Average PM1 material density (ρm) corresponded to higher inorganic contents in both seasons (summer: ∼ 1.30 g cm−3 and winter: ∼ 1.40 g cm−3).
During episodes of higher mass concentrations ρm ranged
from 1.30–1.40 g cm−3 in summer and from 1.30–1.50 g cm−3 in winter. The dynamic shape factors (χ) decreased slightly with particle mobility diameter (Dm) in both seasons. This study provides insights into the seasonal effects and air mass variability on aerosol particles, focusing on episodes of high mass and number concentrations measured at a central European rural background site.
... To our knowledge, this work is significant and novel in that: 1 mobile monitoring was simultaneously performed at a significant distance (∼10 km) both upwind and downwind of a major airport to examine the relative impacts of takeoffs versus landings (Stacey et al. 40 and Keuken et al. 38 used near-source fixed-site sampling at one location coupled with the wind direction; Lopes et al. 41 used fixed-site sampling at both locations but did not sample simultaneously upwind and downwind; and Shirmohammadi et al. 39 and Hudda et al. 33 did not sample upwind at LAX); 2 multivariate analysis was conducted on purely mobile monitoring data to separate traffic sources from aircraft sources (Tessum et al. 42 conducted PCA in the Los Angeles study, but relied on both mobile and fixed-site data; others using only fixed-site data include Rivas et al. 43 and Masiol et al., 37,44 who conducted PMF and analyses on fixed-site data); and 3 PCA-based predictions were used to derive spatially resolved independent estimates of fuel-based emission factors (Shirmohammadi et al. 39 assessed emissions based on a more spatially limited sampling scheme), demonstrating clear separation in emissions between roadway traffic and aircraft, as well as between landing and takeoff conditions. ...
The Mobile ObserVations of Ultrafine Particles study was a two-year project to analyze potential air quality impacts of ultrafine particles (UFPs) from aircraft traffic for communities near an international airport. The study assessed UFP concentrations within 10 miles of the airport in the directions of aircraft flight. Over the course of four seasons, this study conducted a mobile sampling scheme to collect time-resolved measures of UFP, CO2, and black carbon (BC) concentrations, as well as UFP size distributions. Primary findings were that UFPs were associated with both roadway traffic and aircraft sources, with the highest UFP counts found on the major roadway (I-5). Total concentrations of UFPs alone (10–1000 nm) did not distinguish roadway and aircraft features. However, key differences existed in the particle size distribution and the black carbon concentration for roadway and aircraft features. These differences can help distinguish between the spatial impact of roadway traffic and aircraft UFP emissions using a combination of mobile monitoring and standard statistical methods.
... 'an insoluble, refractory aggregate of small carbon spherules [that] strongly absorb visible light' (Bond et al., 2013). The principle sources of particulate BC in the atmosphere are biomass burning (Chen et al., 2017;Xu et al., 2018) and the incomplete combustion of fossil fuels within various anthropogenic processes, including road (Cross et al., 2010;Laborde et al., 2012;Vignati et al., 2010); air (Dodson et al., 2009;Masiol et al., 2016;Shirmohammadi et al., 2017) and water transport (Comer, 2015). Estimates suggest that the global BC emission rate is of the order 7500-17,000 Gg yr − 1 , with source contributions varying significantly by region (Bond et al., 2013). ...
Particulate black carbon has a range of negative impacts on health, the environment and climate, however despite this there are relatively few long-term studies on its ambient distribution as a tropospheric air pollutant. In order to address this lack of data, to help to provide greater insight into the spatio-temporal distribution of particulate black carbon and to assess potential influencing factors, a new, permanent suburban monitoring network was established with sites in four northwest European cities: London (UK), Leicester (UK), Amsterdam (the Netherlands) and Antwerp (Belgium). We report here an analysis of the first measurements made by the network over a twenty-seven-month period (January 01, 2013–April 01, 2015), alongside data from pre-existing comparator urban roadside (AURN Marylebone Road, London, UK) and rural background (AURN Auchencorth Moss, Scotland) sites. The temporal evolution of black carbon was investigated at each site, as were associations with other commonly monitored pollutants (e.g. O3, NOx, PM2.5, PM10) and wind fields. Results showed clear anthropogenic signatures across the diurnal, weekly and annual timeframes, and positive correlations were obtained between black carbon measurements and other common traffic-related pollutants, highlighting the importance of vehicle emissions as a major contributor to ambient black carbon concentration in northwest Europe. Average black carbon concentrations varied from 6.6 μg m⁻³ at the urban roadside, to 0.2 μg m⁻³ in the rural background, with suburban and urban background sites having average concentrations in the range of 1.0–2.4 μg m⁻³. Wind field analysis further highlighted the importance of road traffic as a source of black carbon and demonstrated the importance of local emission sources at the various receptor locations. Statistical analysis of data between sites generally indicated a weak correlation (rs = −0.03 to 0.68, COD = 0.32–0.91), further highlighting the importance of local emissions in determining ambient black carbon concentration. It was also found that black carbon comprised a significant portion of total ambient particulate matter (PM), particularly at sites with the larger traffic volumes and during rush-hour (e.g. ∼45% of PM2.5 at Marylebone Road), however, its contribution to total PM was found to decrease on days of high pollution, indicating the importance of other PM components when air quality is particularly poor.
... Particulate matter toxicity depends on its chemical and physical characteristics: composition, size, morphology or solubility (Heal et al. 2012). Thus, source apportionment studies focus not only on chemical composition but also on particle number size distribution data (Kim et al. 2004;Zhou et al. 2004Zhou et al. , 2005aOgulei et al. 2007a, b;Kasumba et al. 2009;Pey et al. 2009;Harrison et al. 2011;Cusack et al. 2013;Beddows et al. 2015;Sowlat et al. 2016;Masiol et al. 2016;Squizzato et al. 2019). By investigating particles in various size ranges, it is possible to more clearly identify and apportion contributions from those sources that contributed more to the particle number than to the particle mass (Vu et al. 2015). ...
Polychlorinated biphenyls (PCBs), a group of 209 congeners that differ in the number and position of chlorines on the biphenyl ring, are anthropogenic chemicals that belong to the persistent organic pollutants (POPs). For many years, PCBs have been a topic of interest because of their biomagnification in the food chain and their environmental persistence. PCBs with fewer chlorine atoms, however, are less persistent and more susceptible to metabolic attack, giving rise to chemicals characterized by the addition of one or more hydroxyl groups to the chlorinated biphenyl skeleton, collectively known as hydroxylated PCBs (OH-PCBs). In animals and plants, this biotransformation of PCBs to OH-PCBs is primarily carried out by cytochrome P-450-dependent monooxygenases. One of the reasons for infrequent detection of lower chlorinated PCBs in serum and other biological matrices is their shorter half-lives, and their metabolic transformation, resulting in OH-PCBs or their conjugates, such as sulfates and glucuronides, or macromolecule adducts. Recent biomonitoring studies have reported the presence of OH-PCBs in human serum. The occurrence of OH-PCBs, the size of this group (there are 837 mono-hydroxyl PCBs alone), and their wide spectra of physical characteristics (pKa’s and log P’s ranging over 5 to 6 orders of magnitude) give rise to a multiplicity of biological effects. Among those are bioactivation to electrophilic metabolites that can form covalent adducts with DNA and other macromolecules, interference with hormonal signaling, inhibition of enzymes that regulate cellular concentrations of active hormones, and interference with the transport of hormones. This new information creates an urgent need for a new perspective on these often overlooked metabolites.
... When the diurnal variation of PM 2.5 and wind speed are compared (Fig. 5 and Fig. S9), they seem to be closely related to each other. The higher concentrations of PM 2.5 in the night can be caused in part by a dispersion effect due to lower wind speeds, and also the reduced height of the mixing layer (Soni et al., 2019) and probably also due to formation of nitrate on aerosol surfaces (Masiol et al., 2016), and condensation of other semivolatile pollutants. According to Aksoyoglu et al., 2017 road transport is the most important source for particulate nitrate during the cold season. ...
This study aimed to investigate the variability of Black Carbon (BC) at one traffic site in Istanbul as a function of other pollutants, traffic density and meteorological parameters in order to identify the source and determinants of concentration. This was carried out through the statistical analyses of data measured in the period of May 2016–December 2018. The mean concentration of BC was observed as 6.5 ± 3.5 μg m⁻³. Daytime BC was observed to be higher than nighttime during the weekdays but slightly lower at the weekend, similar to oxides of nitrogen (NOx). However, nighttime traffic during the weekend was found to be higher than the weekdays. The diurnal pattern showed two peaks of BC corresponding to the traffic rush hours in the morning and evening. Since the traffic density did not change during the day, the sharp decrease after the morning rush hour is attributed to enhanced atmospheric mixing, due to an increased wind speed and boundary layer depth. In addition, polar plots indicated that the only major source of BC is in the vicinity of the monitoring site, consistent with road traffic. Moreover, episodic north-easterly atmospheric transport events from the ship activity in the Bosphorus in summer can contribute to elevated BC concentrations. These results suggest that vehicular emissions heavily impact the environmental concentrations of BC and affect NOx concentrations more than other pollutants. BC concentrations are highly correlated with NOx, reflecting a common source in diesel vehicle emissions. Since the proportion of diesels in total vehicles in Istanbul is increasing every year, there may be future increases in BC and NOx unless exhaust after-treatment works effectively on newer vehicles entering the fleet.
... Stacey [9] provided a most recent review of UFP related studies conducted at or near the airport with a focus on aircraft emission. Other studies have looked at particle size distributions in the ambient air near the airports, such as in Hudda et al. [10], Masiol et al. [11,12], and Fanning et al. [13]. However, indoor air quality studies inside the airport terminal buildings are still limited for good reasons. ...
The air quality inside airport terminal buildings is a lesser studied area compared to ambient air quality at the airport. The contribution of outdoor particulate matter (PM), aircraft traffic, and passenger traffic to indoor PM concentration is not well understood. Using the largest airport in Southeast Asia as the study site (extends 17.9 square kilometers), the objective of this paper is to conduct a preliminary analysis to examine the mass concentrations of fine particles, including PM1 and PM2.5, and coarse particles PM2.5–10 inside a four-story terminal building spanning 400,000 square meters in Jakarta, Indonesia. The results showed the indoor/outdoor (I/O) ratio of 0.42 for PM1 with 15-min time lag and 0.33 for PM2.5 with 30-min time lag. The aircraft traffic appeared to have a significant impact on indoor PM1 and PM2.5, whereas the passenger traffic showed an influence on indoor PM2.5–10.
... Previous positive matrix factorization (PMF) source apportionment models were able to resolve only one factor attributed to airport emissions (Masiol et al., 2017(Masiol et al., , 2016. The quantitative contribution of specific major airport activities (e.g., aircraft departures, aircraft arrivals, and GSE) to the PNCs in the nearby urban areas is essential in order to fully understand the sources of increased PNCs in these areas. ...
... As a receptor-based model, PMF has not only been widely used to apportion the sources of particulate chemical composition from filter sampling, but also for resolving sources of PNC and particle number size distribution (PNSD) data. [88][89][90][91] Once particulate matter is emitted from a given source, its size, number, and chemical composition may change through multiple mechanisms (for example, by interactions with other chemical species in the atmosphere) until eventually removed by natural processes. 92 Additionally, filter-sampling typically lasts for 24 hours or longer, obscuring dynamic changes in particle size, number and chemical composition, and resulting in a lack of available emissions information. ...
A bibliometric analysis of published papers with the key words “positive matrix factorization” and “source apportionment” in ‘Web of Science’, reveals that more than 1000 papers are associated with this research and that approximately 50% of these were produced in Asia. As a receptor-based model, positive matrix factorization (PMF) has been widely used for source apportionment of various environmental pollutants, such as persistent organic pollutants (POPs), heavy metals, volatile organic compounds (VOCs) as well as inorganic cations and anions in the last decade. In this review, based on the papers mainly from 2008 to 2018 that focused on source apportionment of pollutants in different environmental media, we provide a comparison and summary of the source categories of typical environmental pollutants, with a special focus on polycyclic aromatic hydrocarbons (PAHs), apportioned using PMF. Based on the statistical average, coal combustion and vehicular emission, are shown to be the two most common sources of PAHs, and contribute much more to emissions than other sources, such as biomass burning, biogenic sources and waste incineration. Heavy metals were mainly from agricultural activities, industrial and vehicular emissions and mining activities. Quantitative source apportionment on pollutants such as VOCs and particulate matter were also apportioned, showing a prominent contribution from fossil-fuel combustion. We conclude that, aside from natural sources, abatement strategies should be focused on changes in energy structure and industrial activities, especially in China. Source apportionment of typical POPs including polychlorinated dibenzo-p-dioxins/dibenzofurans (PCDD/Fs), polychlorinated biphenyls (PCBs), halogenated flame retardants (HFRs) and perfluorinated compounds (PFCs) is less comprehensive and further study is required.
... Wind speed was not significantly correlated with any of the pollutants, but there was a negative tendency for all pollutants in ferry and at the piers. Wind speed is a significant factor for dilution and transport of exhaust emissions [53]. Knibbs et al. [25] reported that there were no statistically significant correlations between wind speed and UFP or between wind speed and PM2.5 in ferry. ...
This paper presents measurements and analyses of the concentrations of black carbon (BC), particle number concentration (PNC), and PM2.5 (≤2.5 μm) while commuting by ferries in Istanbul. In this context, exposures to the mentioned pollutants were estimated for car ferry, fast ferry, and at the piers, and for two travel routes, for a total of 89 trips. BC, PNC, and PM2.5 measurements were simultaneously performed in a ferry and at the piers, and the correlation between pollutant concentrations, meteorological parameters, and environmental factors were analyzed. The mean concentrations for all pollutants in car ferry were lower than the average concentrations in fast ferry. The concentration ratios of fast ferry to car ferry for BC, PNC, and PM2.5 were 6.4, 1.2, and 1.3, respectively. High variability in the concentrations was observed at the piers and in ferry during berthing. The highest mean concentrations (±standard deviation) of BC (14.3 ± 10.1 µg m−3) and PNC (42,005 ± 30,899 pt cm−3) were measured at Yalova pier. The highest mean concentration (±standard deviation) of PM2.5 (26.1 ± 11.5) was measured at Bostancı pier. It was observed that the main external sources of BC, PNC, and PM2.5 at the piers were road transport, residential heating, and shipping activity. There were no significant correlations between BC, PNC, and PM2.5 in fast ferry, while BC was positively correlated with PNC (r = 0.61, p < 0.01) and PM2.5 (r = 0.76, p < 0.01) in car ferry. At the piers, significant relations between pollutants and meteorological variables were observed. It was noticed that there was no significant difference between summer and winter in ferry and at the pier concentrations of BC, PNC, and PM2.5 except for Yenikapı pier and Bakırköy pier. The highest total exposure to PNC and PM2.5 was in car ferry mode, while the highest total exposure to BC was in fast ferry mode.
... PMF is one of the most widely used receptor models used for source apportionment of PM mass, PM number, and specific constituents of ambient aerosols, such as particulate OC and polycyclic aromatic hy- drocarbons (PAHs) (Beddows et al., 2015;Cesari et al., 2016;Dall'Osto et al., 2013;Jang et al., 2013;Kim and Hopke, 2004b;Masiol et al., 2016;Sowlat et al., 2016a;Taghvaee et al., 2018). The PMF receptor ...
In this study, we used the positive matrix factorization (PMF) model to apportion the sources of organic carbon volatility fractions (OC x ) as well as total OC concentrations in five different locations across the Los Angeles Basin, including West Long Beach, Anaheim, central Los Angeles (CELA), Rubidoux, and Fontana over the period from July 2012 to June 2013. Total OC as well as OC volatility fractions (OC 1 -OC 4 ), measured with the thermal-optical analysis as part of the fourth Multiple Air Toxics Exposure Study (MATES IV) by the South Coast Air Quality Management District (SCAQMD), in combination with gaseous and particulate source tracers (such as NO x , O 3 , particulate sulfate, K ⁺ /K ratio, and biomass-burning originated black carbon (BC bb )) were used as inputs to the PMF model. A 3-factor solution, including traffic, secondary organic aerosols (SOA), and biomass burning, was found to be the most physically interpretable solution. Average total OC concentrations showed an upward trend from the sites closer to the coast (i.e., 3.7 ± 1.9 μg m ⁻³ at West Long Beach) to inland downwind sites (i.e., 4.8 ± 1.8 μg m ⁻³ at Fontana), especially in the warm season, suggesting the major impact of SOA formation on total OC concentrations. Source apportionment results indicated that traffic is the dominant contributor to OC 2 and OC 3 fractions, especially at the sites that are near major primary sources such as CELA and West Long Beach, with corresponding contributions of 60 ± 1.0% and 79 ± 1.7% to OC 2 and 53 ± 0.9% and 64 ± 1.3% to OC 3 , respectively. On the other hand, SOA was found to be the dominant contributor to OC 4 fraction, especially at the receptor sites located further inland, with corresponding contributions of 66 ± 1.0% in Rubidoux and 56 ± 0.7% in Fontana. Our results also indicated that traffic is the dominant source of total OC concentrations, with an average contribution of 53 ± 2.4% at all the sites, followed by SOA formation and biomass burning, contributing to 40 ± 1.8% and 7 ± 0.8% of total OC concentrations, respectively. The contribution of traffic and biomass burning to total OC concentrations increased during the cold season, while that of SOA became more significant during the warm season when photochemical activities peak. Results from the present study provided important insight on the sources and spatio-temporal variations of OC volatility fractions as well as total OC concentrations in PM 2.5 across the Los Angeles Basin.
... M. Lopes, et al. Atmospheric Pollution Research xxx (xxxx) xxx-xxx (2012) and Masiol et al. (2016) had also concluded. ...
Airports have been identified as a significant source of ultrafine particulate matter (UFP, particulate matter with diameter less than 0.1 μm), which may induce or aggravate pulmonary or cardio-respiratory health conditions, if prolonged exposure to high concentrations of UFP occur. Thus, assessing its impacts is vital to estimate UFP contribution to air quality degradation within the city and the degree of population exposure. However, there is lack of information regarding UFP concentrations in the vicinity of airports. This work aims to study the influence of air traffic and ground activities of Lisbon Airport (LA), in the surrounding urban area, focusing on the UFP concentrations. An UFP monitoring campaign was carried out in 2017 and 2018, for a 19 non-consecutive days period. The monitoring network was designed to include several sampling sites in the vicinity of LA and a set of sites further away of the LA, under the landing or take-off path. Based on the information collected, correlation analysis between air traffic activity and UFP concentrations was conducted. The results show the occurrence of high UFP concentrations in LA vicinity. Considering 10-min means, the particle counting increased 18–26-fold at locations near the airport, downwind, and 4-fold at locations up to 1 km distance to LA. Adverse orographic conditions leads to UFP punctual and average high concentrations. Results show that particle number increases with the number of flights and decreases with the distance to LA.
... More sophisticated source-oriented models include chemical transport models such as the Community Multiscale Air Quality (CMAQ) model that generally have lower spatial resolution but account for all sources and emissions in an urban area and fully model fate and transport with proper treatment of chemistry and particle dynamics and typically larger spatial domains that can capture communities further downwind (Arunachalam et al., 2011;Kukkonen et al., 2016;Levy et al., 2008;Levy et al., 2015;Levy et al., 2012). Receptor-oriented source apportionment models such as PMF or PCA used in our study have proven valuable for determining source impacts at affected communities and disentangling the airport signal from other potentially correlated UFP sources in the air pollution mixture (Masiol et al., 2016). ...
Background:
Exposure to ultrafine particles (UFP, particles with aerodynamic diameter < 100 nm) is associated with reduced lung function and airway inflammation in individuals with asthma. Recently, elevated UFP number concentrations (PN) from aircraft landing and takeoff activity were identified downwind of the Los Angeles International Airport (LAX) but little is known about the health impacts of airport-related UFP exposure.
Methods:
We conducted a randomized crossover study of 22 non-smoking adults with mild to moderate asthma in Nov-Dec 2014 and May-Jul 2015 to investigate short-term effects of exposure to LAX airport-related UFPs. Participants conducted scripted, mild walking activity on two occasions in public parks inside (exposure) and outside (control) of the high UFP zone. Spirometry, multiple flow exhaled nitric oxide, and circulating inflammatory cytokines were measured before and after exposure. Personal UFP PN and lung deposited surface area (LDSA) and stationary UFP PN, black carbon (BC), particle-bound PAHs (PB-PAH), ozone (O3), carbon dioxide (CO2) and particulate matter (PM2.5) mass were measured. Source apportionment analysis was conducted to distinguish aircraft from roadway traffic related UFP sources. Health models investigated within-subject changes in outcomes as a function of pollutants and source factors.
Results:
A high two-hour walking period average contrast of ~34,000 particles·cm-3 was achieved with mean (std) PN concentrations of 53,342 (25,529) and 19,557 (11,131) particles·cm-3 and mean (std) particle size of 28.7 (9.5) and 33.2 (11.5) at the exposure and control site, respectively. Principal components analysis differentiated airport UFPs (PN), roadway traffic (BC, PB-PAH), PM mass (PM2.5, PM10), and secondary photochemistry (O3) sources. A standard deviation increase in the 'Airport UFPs' factor was significantly associated with IL-6, a circulating marker of inflammation (single-pollutant model: 0.21, 95% CI = 0.08-0.34; multi-pollutant model: 0.18, 0.04-0.32). The 'Traffic' factor was significantly associated with lower Forced Expiratory Volume in 1 s (FEV1) (single-pollutant model: -1.52, -2.28 to -0.77) and elevated sTNFrII (single-pollutant model: 36.47; 6.03-66.91; multi-pollutant model: 64.38; 6.30-122.46). No consistent associations were observed with exhaled nitric oxide.
Conclusions:
To our knowledge, our study is the first to demonstrate increased acute systemic inflammation following exposure to airport-related UFPs. Health effects associated with roadway traffic exposure were distinct. This study emphasizes the importance of multi-pollutant measurements and modeling techniques to disentangle sources of UFPs contributing to the complex urban air pollution mixture and to evaluate population health risks.
Considering the adverse effect of fine particulate matter (PM) on human health, the number concentration of PM is considered superior to the mass concentration. CO, NO, NO2, SO2, O3, and size segregated PM number concentration were collected during May 2018-July 2018 in Chennai city to examine the possible sources of fine particles. A total of 8640 number concentration samples covering different particle sizes ranging from 0.265 to 2.75 µm and gaseous species were analysed using positive matrix factorization (PMF) model. Additional analyses such as determining the directional locations of the factors using conditional bivariate probability function (CBPF) and examining the diurnal variations of the factor’s contribution were performed to aid the interpretations of the sources. Three major sources were identified at the study site which includes local traffic, regional traffic, and secondary nitrate. Nearly one-half of the total number concentrations at the study site were attributed to local traffic (36%) and regional traffic (21%) emissions.
Submicron and ultrafine particle (UFP) exposure may be epidemiologically and toxicologically linked to pulmonary, neurodegenerative, and cardiovascular diseases. This study explores UFP and fine particle sources using a positive matrix factorization (PMF) model based on PM2.5 chemical compositions and particle number size distributions (PNSDs). The particle chemical composition and size distribution contributions are simultaneously identified to evaluate lung deposition and excess cancer risks. High correlations between the PNSD and chemical composition apportionment results were observed. Fresh and aged traffic particles dominated the number concentrations, while heterogeneous, photochemical reactions and/or regional transport may have resulted in secondary aerosol formation. The fresh and aged road traffic particle sources mostly contributed to the lung deposition dosage in the pulmonary region (~53%), followed by the tracheobronchial (~30.4%) and head regions (~16.6%). However, lung-deposited surface area (LDSA) concentrations were dominated by aged road traffic (~39.2%) and secondary aerosol (~33.2%) sources. The excess cancer risks caused by Cr6+, Ni, and As were also mainly contributed to by aged road traffic (~31.7%) and secondary aerosols (~67%). The source apportions based on the physical and chemical properties of aerosol particles are complementary, offering a health impact benchmark of UFPs in a southeast Asia urban city.
This study applied positive matrix factorization (PMF) to identify the sources of size-resolved submicrometer (10–1000 nm) particles and quantify their contributions to impaired visibility based on the particle number size distributions (PNSDs), aerosol light extinction (bp), air pollutants (PM10, PM2.5, SO2, O3, and NO), and meteorological parameters (temperature, relative humidity, wind speed, wind direction, and ultraviolet index) measured hourly over an urban basin in central Taiwan between 2017 and 2021. The transport of source-specific PNSDs was evaluated with wind and back trajectory analyses. The PMF revealed six sources to the total particle number (TPN), surface (TPS), volume (TPV), and bp. Factor 1 (F1), the key contributor to TPN (35.0 %), represented nucleation (<25 nm) particles associated with fresh traffic emission and secondary new particle formation, which were transported from the west-southwest by stronger winds (>2.2 m s⁻¹). F2 represented the large Aitken (50–100 nm) particles transported regionally via northerly winds, whereas F3 represented large accumulation (300–1000 nm) particles, which showed elevated concentrations under stagnant conditions (<1.1 m s⁻¹). F4 represented small Aitken (25–50 nm) particles arising from the growth and transport of the nucleation particles (F1) via west-southwesterly winds. F5 represented large Aitken particles originating from combustion-related SO2 sources and carried by west-northwesterly winds. F6 represented small accumulation (100–300 nm) particles emitted both by local sources and by the remote SO2 sources found for F5. Overall, large accumulation particles (F3) played the greatest role in determining the TPV (66.4 %) and TPS (34.8 %), and their contribution to bp increased markedly from 17.3 % to 40.7 % as visibility decreased, indicating that TPV and TPS are better metrics than TPN for estimating bp. Furthermore, slow-moving air masses—and therefore stagnant conditions—facilitate the build-up of accumulation mode particles (F3 + F6), resulting in the poorest visibility.
Airports are identified hotspots for air pollution, notably for fine particles (PM2.5) that are pivotal in aerosol-cloud interaction processes of climate change and human health. We herein studied the field observation and statistical analysis of 10-year data of PM2.5 and selected emitted co-pollutants (CO, NOx, and O3), in the vicinity of three major Canadian airports, with moderate to cold climates. The decadal data analysis indicated that in colder climate airports, pollutants like PM2.5 and CO accumulate disproportionally to their emissions in fall and winter, in comparison to airports in milder climates. Decadal daily averages and standard errors of PM2.5 concentrations were as follows: Vancouver, 5.31 ± 0.017; Toronto, 6.71 ± 0.199; and Montreal, 7.52 ± 0.023 μg/m³. The smallest and the coldest airport with the least flights/passengers had the highest PM2.5 concentration. QQQ-ICP-MS/MS and HR-S/TEM analysis of aerosols near Montreal Airport indicated a wide range of emerging contaminants (Cd, Mo, Co, As, Ni, Cr, and Pb) ranging from 0.90 to 622 μg/L, which were also observed in the atmosphere. During the lockdown, a pronounced decrease in the concentrations of PM2.5 and submicron particles, including nanoparticles, in residential areas close to airports was observed, conforming with the recommended workplace health thresholds (~ 2 × 10⁴ cm⁻³), while before the lockdown, condensable particles were up to ~ 1 × 10⁵ cm⁻³. Targeted reduction of PM2.5 emission is recommended for cold climate regions.
Graphical abstract
Many studies revealed the rapid decline of atmospheric PM2.5 in Beijing due to the emission control measures. The variation of particle number concentration (PN) which has important influences on regional climate and human health, however, was rarely reported. This study measured the particle number size distributions (PNSD) in 3–700 nm in winter of Beijing during 2013–2019. It was found that PN decreased by 58% from 2013 to 2017, but increased by 29% from 2017 to 2019. By Positive matrix factorization (PMF) analysis, five source factors of PNSD were identified as Nucleation, Fresh traffic, Aged traffic + Diesel, Coal + biomass burning and Secondary. Overall, factors associated with primary emissions were found to decrease continuously. Coal + biomass burning dominated the reduction (65%) among the three primary sources during 2013–2017, which resulted from the great efforts on emission control of coal combustion and biomass burning. Fresh traffic and Aged traffic + Diesel decreased by 43% and 66%, respectively, from 2013 to 2019, as a result of the upgrade of the vehicle emission standards in Beijing-Tianjin-Hebei area. On the other hand, the contribution from Nucleation and Secondary decreased with the reduction of gaseous precursors in 2013–2017, but due to the increased intensity of new particle formation (NPF) and secondary oxidation, they increased by 56% and 70%, respectively, from 2017 to 2019, which led to the simultaneously increase of PN and particle volume concentration. This study indicated that NPF may play an important role in urban atmosphere under continuous air quality improvement.
To estimate the pollution impact of seven sample airports in California on the surrounding schools, routine high-quality observations of 1-min average wind direction and speed at the airports were used to calculate the time each school within a 10-km radius was downwind of each airport. We demonstrate 0.87 correlation between pollution levels and downwind time at Los Angeles International Airport (LAX). Depending on the airport size, between 10 and 15 years of data was collected and downwind times calculated for the school hours throughout the school-months of August to June. By comparing this data with the 2015–2016 student enrollment size and demographics of the neighboring schools, we have demonstrated that an estimated 65,409 (8.7%)students spend 1–6 h per day at school throughout the academic year with exposure to airport-related air pollution. However, a higher proportion of economically disadvantaged students (10.6%)experience aforementioned levels of exposure. The level of exposure does not appear to be affected by the proposed 1-h delay in the school starting time that is intended to reduce student fatigue.
Equatorial warming conditions in urban areas can influence the particle number concentrations (PNCs), but studies assessing such factors are limited. The aim of this study was to evaluate the level of size-resolved PNCs, their potential deposition rate in the human respiratory system, and probable local and transboundary inputs of PNCs in Kuala Lumpur. Particle size distributions of a 0.34 to 9.02 μm optical-equivalent size range were monitored at a frequency of 60 s between December 2016 and January 2017 using an optical-based compact scanning mobility particle sizer (SMPS). Diurnal and correlation analysis showed that traffic emissions and meteorological confounding factors were potential driving factors for changes in the PNCs (Dp ≤1 μm) at the modeling site. Trajectory modeling showed that a PNC <100/cm3 was influenced mainly by Indo-China region air masses. On the other hand, a PNC >100/cm3 was influenced by air masses originating from the Indian Ocean and Indochina regions. Receptor models extracted five potential sources of PNCs: industrial emissions, transportation, aged traffic emissions, miscellaneous sources, and a source of secondary origin coupled with meteorological factors. A respiratory deposition model for male and female receptors predicted that the deposition flux of PM1 (particle mass ≤1 μm) into the alveolar (AL) region was higher (0.30 and 0.25 μg/h, respectively) than the upper airway (UA) (0.29 and 0.24 μg/h, respectively) and tracheobronchial (TB) regions (0.02 μg/h for each). However, the PM2.5 deposition flux was higher in the UA (2.02 and 1.68 μg/h, respectively) than in the TB (0.18 and 0.15 μg/h, respectively) and the AL regions (1.09 and 0.91 μg/h, respectively); a similar pattern was also observed for PM10.
Major airports are often located within or close to large cities; their impacts on the deterioration of air quality at ground level are amply recognised. The international airport of Heathrow is a major source of nitrogen oxides in the Greater London area, but its contribution to the levels of submicrometre particles is unknown, and is the objective of this study. Two sampling campaigns were carried out during warm and cold seasons at a site close to the airfield (1.2 km). Size spectra were largely dominated by ultrafine particles: nucleation particles (k-means cluster analysis and positive matrix factorization (PMF) respectively to particle number size distributions; their interpretation was based on their modal structures, wind directionality, diurnal patterns, road and airport traffic volumes and on the relationship with weather and other air pollutants. Airport emissions, fresh and aged road traffic, urban accumulation mode and two secondary sources were then identified and apportioned. The comparison of cluster and PMF analyses allowed extraction of further information. The analysis of a strong regional nucleation event was also performed to detect its effect upon concentrations. The fingerprint of Heathrow has a characteristic modal structure peaking at
Nitrate aerosols make a very major contribution to PM2.5 and PM10 in western Europe, but their sources and pathways have not been fully elucidated. An Aerosol Time-Of-Flight Mass Spectrometer (ATOFMS) and a Compact Time of Flight Aerosol Mass Spectrometer (C-ToF-AMS) were deployed in an urban background location in London, UK, collecting data as part of the REPARTEE-I experiment. During REPARTEE-I, daily PM10 concentrations ranged up to 43.6 µg m−3, with hourly nitrate concentrations (measured by AMS) of up to 5.3 µg m−3. The application of the ART-2a neural network algorithm to the ATOFMS data characterised the nitrate particles as occurring in two distinct clusters (i.e. particle types). The first (33.6% of particles by number) appeared to be locally produced in urban locations during nighttime, whilst the second (22.8% of particles by number) was regionally transported from continental Europe. Nitrate in locally produced aerosol was present mainly in particles smaller than 300 nm, whilst the regional nitrate presented a coarser mode, peaking at 600 nm. In both aerosol types, nitrate was found to be internally mixed with sulphate, ammonium, elemental and organic carbon. Nitrate in regional aerosol appeared to be more volatile than that locally formed. During daytime, a core of the regionally transported nitrate aerosol particle type composed of organic carbon and sulphate was detected.
New-particle formation in the plumes of coal-fired power plants and other anthropogenic sulfur sources may be an important source of particles in the atmosphere. It remains unclear, however, how best to reproduce this formation in global and regional aerosol models with grid-box lengths that are 10s of kilometers and larger. The predictive power of these models is thus limited by the resultant uncertainties in aerosol size distributions. In this paper, we focus on sub-grid sulfate aerosol processes within coal-fired power plant plumes: the sub-grid oxidation of SO<sub>2</sub> with condensation of H<sub>2</sub>SO<sub>4</sub> onto newly-formed and pre-existing particles. We have developed a modeling framework with aerosol microphysics in the System for Atmospheric Modelling (SAM), a Large-Eddy Simulation/Cloud-Resolving Model (LES/CRM). The model is evaluated against aircraft observations of new-particle formation in two different power-plant plumes and reproduces the major features of the observations. We show how the downwind plume aerosols can be greatly modified by both meteorological and background aerosol conditions. In general, new-particle formation and growth is greatly reduced during polluted conditions due to the large pre-existing aerosol surface area for H<sub>2</sub>SO<sub>4</sub> condensation and particle coagulation. The new-particle formation and growth rates are also a strong function of the amount of sunlight and NO<sub>x</sub> since both control OH concentrations. The results of this study highlight the importance for improved sub-grid particle formation schemes in regional and global aerosol models.
Nanoparticles emitted from road traffic are the largest source of respiratory exposure for the general public living in urban areas. It has been suggested that adverse health effects of airborne particles may scale with airborne particle number, which if correct, focuses attention on the nanoparticle (less than 100 nm) size range which dominates the number count in urban areas. Urban measurements of particle size distributions have tended to show a broadly similar pattern dominated by a mode centred on 20–30 nm diameter emitted by diesel engine exhaust. In this paper we report the results of measurements of particle number concentration and size distribution made in a major London park as well as on the BT Tower, 160 m aloft. These measurements taken during the REPARTEE project (Regents Park and BT Tower experiment) show a remarkable shift in particle size distributions with major losses of the smallest particle class as particles are advected away from the traffic source. In the Park, the traffic related mode at 20–30 nm diameter is much reduced with a new mode at <10 nm. Size distribution measurements also revealed higher number concentrations of sub-50 nm particles at the BT Tower during days affected by higher turbulence as determined by Doppler Lidar measurements and are indicative of loss of nanoparticles from air aged during less turbulent conditions. These results are suggestive of nanoparticle loss by evaporation, rather than coagulation processes. The results have major implications for understanding the impacts of traffic-generated particulate matter on human health.
The EPA PMF (Environmental Protection Agency positive matrix
factorization)
version 5.0 and the underlying multilinear engine-executable ME-2 contain
three methods for estimating uncertainty in factor analytic models: classical
bootstrap (BS), displacement of factor elements (DISP), and bootstrap
enhanced by displacement of factor elements (BS-DISP). The goal of these
methods is to capture the uncertainty of PMF analyses due to random errors
and rotational ambiguity. It is shown that the three methods complement each
other: depending on characteristics of the data set, one method may provide
better results than the other two. Results are presented using synthetic data
sets, including interpretation of diagnostics, and recommendations are given
for parameters to report when documenting uncertainty estimates from EPA PMF
or ME-2 applications.
The analysis of aerosol size distributions is a useful tool for
understanding the sources and the processes influencing particle number
concentrations (N) in urban areas. Hence, during the one-month SAPUSS
campaign (Solving Aerosol Problems by Using Synergistic Strategies, EU Marie
Curie Action) in autumn 2010 in Barcelona (Spain), four SMPSs (Scanning
Mobility Particle Sizer) were simultaneously deployed at four monitoring
sites: a road side (RSsite), an urban background site located in the
city (UBsite), an urban background site located in the nearby hills of the
city (Torre Collserola, TCsite) and a regional background site located
about 50 km from the Barcelona urban areas (RBsite). The spatial
distribution of sites allows study of the aerosol temporal variability as
well as the spatial distribution, progressively moving away from urban
aerosol sources. In order to interpret the data sets collected, a k-means
cluster analysis was performed on the combined SMPS data sets. This resulted
in nine clusters describing all aerosol size distributions from the four
sites. In summary there were three main categories (with three clusters in
each category): "Traffic" (Traffic 1, "Tclus_1" – 8%;
Traffic 2, "Tclus_2" – 13%; and Traffic 3,
"Tclus_3" – 9%) "Background Pollution" (Urban
Background 1, "UBclus_1" – 21%; Regional Background 1,
"RBclus_1" – 15%; and Regional Background 2,
"RBclus_2" – 18%) and "Special Cases" (Nucleation,
"NUclus" – 5%; Regional Nitrate, "NITclus" – 6%; and
Mix, "MIXclus" – 5%). As expected, the frequency of traffic clusters
(Tclus_1–3) followed the order RSsite, UBsite,
TCsite, and RBsite. These showed typical traffic modes mainly
distributed at 20–40 nm. The urban background sites (UBsite and
TCsite) reflected also as expected urban background number
concentrations (average values, N = 1.0 × 104 cm−3
and N = 5.5 × 103 cm−3, respectively, relative to
1.3 × 104 cm−3 seen at RSsite). The cluster
describing the urban background pollution (UBclus_1)
could be used to monitor the sea breeze circulation towards the regional
background study area. Overall, the RBsite was mainly characterised by
two different regional background aerosol size distributions: whilst both
exhibited low N (2.7 × 103 for RBclus_1
and 2.2 × 103 cm−3 for RBclus_2),
RBclus_1 had average PM10 concentrations higher
than RBclus_2 (27 vs. 23 μg m−3). As regards
the minor aerosol size distribution clusters, the "Nucleation" cluster was
observed during daytime, whilst the "Regional Nitrate" was mainly seen at
night. The ninth cluster ("Mix") was the least well defined and likely
composed of a number of aerosol sources.
When correlating averaged values of N, NO2 and PM (particulate mass)
for each k-means cluster, a linear correlation between N and NO2 with
values progressively increasing from the regional site RBsite to the
road site RSsite was found. This points to vehicular traffic as the
main source of both N and NO2. By contrast, such an association does
not exist for the case of the nucleation cluster, where the highest N is
found with low NO2 and PM.
Finally, the clustering technique allowed study of the impact of
meteorological parameters on the traffic N emissions. This study confirms
the shrinking of freshly emitted particles (by about 20% within 1 km in
less than 10 min; Dall'Osto et al., 2011a) as particles
are transported from the traffic hot spots towards urban background
environments. Additionally, for a given well-defined aerosol size
distribution (Tclus_2) associated with primary aerosol
emissions from road traffic we found that N5–15 nm concentrations can
vary up to a factor of eight.
Within our measurement range of SMPSs (N15–228 nm) and Condensation
Particle Counters (CPCs, N>5 nm), we found that ultrafine particles
within the range 5–15 nm in urban areas are the most dynamic, being a
complex ensemble of primary evaporating traffic particles, traffic tailpipe
new particle formation and non-traffic new particle formation.
Elevated particle number concentration (PNC) observed during nucleation events could make a significant contribution to the total particle load and thus air pollution in urban environments. Therefore, a field measurement study of PNC was conducted to investigate the temporal and spatial variations of PNC within the urban airshed of Brisbane, Australia. PNC was monitored at urban (QUT), roadside (WOO) and semi-urban (ROC) areas around the Brisbane region during 2009. The results showed that morning traffic exhaust emissions were the main contributor to high PNCs at QUT and WOO which contributed 5.5% and 5.1 5 during the week, respectively, with a less significant contribution on weekends. PNC peaks were observed around noon, which correlated with the highest solar radiation levels at all three stations, thus suggesting that high PNC levels were likely to be associated with new particle formation caused by photochemical reactions. Wind rose plots showed relatively higher PNC for the NE direction, which was associated with industrial pollution, accounting for 12%, 9% and 14% of overall PNC at QUT, WOO and ROC, respectively. Although there was no significant correlation between PNC at each station, the variation of PNC was well correlated among three stations during regional nucleation events. In addition, PNC at ROC was significantly influenced by upwind urban pollution during the nucleation burst events, with the average enrichment factor of 15.4. This study provides an insight into the influence of regional nucleation events on PNC in the Brisbane region and is the first study to quantify the effect of urban pollution on semi-urban PNC through the nucleation events.
Differential mobility particle sizer (DMPS) aerosol concentrations ( N <sub>13–800</sub>) were collected over a one-year-period (2004) at an urban background site in Barcelona, North-Eastern Spain. Quantitative contributions to particle number concentrations of the nucleation (33–38%), Aitken (39–49%) and accumulation mode (18–22%) were estimated. We examined the source and time variability of atmospheric aerosol particles by using both K-means clustering and Positive Matrix Factorization (PMF) analysis. Performing clustering analysis on hourly size distributions, nine K-means DMPS clusters were identified and, by directional association, diurnal variation and relationship to meteorological and pollution variables, four typical aerosol size distribution scenarios were identified: traffic (69% of the time), dilution (15% of the time), summer background conditions (4% of the time) and regional pollution (12% of the time). According to the results of PMF, vehicle exhausts are estimated to contribute at least to 62–66% of the total particle number concentration, with a slightly higher proportion distributed towards the nucleation mode (34%) relative to the Aitken mode (28–32%). Photochemically induced nucleation particles make only a small contribution to the total particle number concentration (2–3% of the total), although only particles larger than 13 nm were considered in this study. Overall the combination of the two statistical methods is successful at separating components and quantifying relative contributions to the particle number population.
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.
Earlier work has demonstrated the potential for volatilisation of nanoparticles emitted by road traffic as these are advected downwind from the source of emissions, but there have been few studies and the processes have yet to be elucidated in detail. Using a dataset collected at paired sampling sites located respectively in a street canyon and in a nearby park, an in depth analysis of particle number size distributions has been conducted in order to better understand the size reduction of the semi-volatile nanoparticles. By sorting the size distributions according to wind direction and fitting log normal modes, it can be seen that the mode peaking at around 22 nm at the street canyon site is on average shrinking to 6.2 nm diameter at the park site which indicates a mean shrinkage rate for these particles of 0.13 nm s-1 with temperatures within the range 12-18ºC. The diurnal variation of the shrunken mode in the park reflects the diurnal pattern of particle concentrations at the street canyon site taken as the main source area. An analysis of peak diameter for the smallest mode at the downwind park site shows an inverse relationship to wind speed suggesting that dilution rather than travel time is the main determinant of the particle shrinkage rate. An evaluation of previously collected C10 to C35 n-alkane data from a different urban location shows a good fit to Pankow partitioning theory reflecting the semi-volatility of compounds believed to be representative of the composition of diesel exhaust nanoparticles, hence confirming the feasibility of an evaporative mechanism for particle shrinkage.
Road traffic emissions are often considered the main source of ultrafine
particles (UFP, diameter smaller than 100 nm) in urban environments.
However, recent studies worldwide have shown that – in high-insolation urban
regions at least – new particle formation events can also contribute to UFP.
In order to quantify such events we systematically studied three cities
located in predominantly sunny environments: Barcelona (Spain), Madrid
(Spain) and Brisbane (Australia). Three long-term data sets (1–2 years) of
fine and ultrafine particle number size distributions (measured by SMPS,
Scanning Mobility Particle Sizer) were analysed. Compared to total particle
number concentrations, aerosol size distributions offer far more information
on the type, origin and atmospheric evolution of the particles. By applying
k-means clustering analysis, we categorized the collected aerosol size
distributions into three main categories: "Traffic" (prevailing 44–63% of
the time), "Nucleation" (14–19%) and "Background pollution and
Specific cases" (7–22%). Measurements from Rome (Italy) and Los Angeles
(USA) were also included to complement the study. The daily variation
of the average UFP concentrations for a typical nucleation day at each site
revealed a similar pattern for all cities, with three distinct particle
bursts. A morning and an evening spike reflected traffic rush hours, whereas
a third one at midday showed nucleation events. The photochemically
nucleated particles' burst lasted 1–4 h, reaching sizes of 30–40 nm. On
average, the occurrence of particle size spectra dominated by nucleation
events was 16% of the time, showing the importance of this process as a
source of UFP in urban environments exposed to high solar radiation. Nucleation events lasting for 2 h or more occurred on 55% of
the days, this extending to > 4 h in 28% of the days,
demonstrating that atmospheric conditions in urban environments are not
favourable to the growth of photochemically nucleated particles. In summary,
although traffic remains the main source of UFP in urban areas, in developed
countries with high insolation urban nucleation events are also a main
source of UFP. If traffic-related particle concentrations are reduced in the
future, nucleation events will likely increase in urban areas, due to the
reduced urban condensation sinks.
The new version of EPA's positive matrix factorization (EPA PMF) software, 5.0, includes three error estimation (EE) methods for analyzing factor analytic solutions: classical bootstrap (BS), displacement of factor elements (DISP), and bootstrap enhanced by displacement (BS-DISP). These methods capture the uncertainty of PMF analyses due to random errors and rotational ambiguity. To demonstrate the utility of the EE methods, results are presented for three data sets: (1) speciated PM2.5 data from a chemical speciation network (CSN) site in Sacramento, California (2003-2009); (2) trace metal, ammonia, and other species in water quality samples taken at an inline storage system (ISS) in Milwaukee, Wisconsin (2006); and (3) an organic aerosol data set from high-resolution aerosol mass spectrometer (HR-AMS) measurements in Las Vegas, Nevada (January 2008). We present an interpretation of EE diagnostics for these data sets, results from sensitivity tests of EE diagnostics using additional and fewer factors, and recommendations for reporting PMF results. BS-DISP and BS are found useful in understanding the uncertainty of factor profiles; they also suggest if the data are over-fitted by specifying too many factors. DISP diagnostics were consistently robust, indicating its use for understanding rotational uncertainty and as a first step in assessing a solution's viability. The uncertainty of each factor's identifying species is shown to be a useful gauge for evaluating multiple solutions, e.g., with a different number of factors.
Published by Elsevier B.V.
Particles in the atmosphere are of concern due to their toxic properties and effects on climate. In coastal areas ship emissions can be a significant anthropogenic source. In this study we investigated the contribution from ship emissions to the total particle number and mass concentrations at a remote location. We studied the particle number concentration (12 to 490 nm in diameter), the mass concentration (12 to 150 nm in diameter) and number and volume size distribution of aerosol particles in ship plumes for a period of four and a half months at Høvsøre, a coastal site on the western coast of Jutland in Denmark. During episodes of western winds the site is about 50 km downwind of a major shipping lane and the plumes are approximately one hour aged when they arrive at the site. We have used a sliding percentile based method for separating the plumes from the measured background values and to calculate the ship plume contribution to the total particle number and PM0.15 mass concentration (mass of particles below 150 nm in diameter, converted from volume assuming sphericity) at the site. The method is not limited to particle number or volume concentration, but can also be used for different chemical species in both particle and gas phase. The total number of analyzed ship plumes was 726, covering on average 19% of the time when air masses were arriving to the site over the shipping lane. During the periods when plumes were present, the particle concentration exceeded the background values on average by 790 cm-3 by number and 0.10 g/m3 by mass. The corresponding daily average values were 170 cm-3 and 0.023 g/m3, respectively. This means that the ship plumes contributed between 11 and 19% to the particle number concentration, and between 9 and 18% to PM0.15 during days when air was arriving over the shipping lane. The estimated annual contribution from ship plumes, where all wind directions were included, was in the range of 5-8% in particle number concentration and 4-8% in PM0.15.
We measured the spatial pattern of particle number (PN) concentrations downwind from the Los Angeles International Airport (LAX) with an instrumented vehicle that enabled us to cover larger areas than allowed by traditional stationary measurements. LAX emissions adversely impacted air quality much farther than reported in previous airport studies. We measured at least a 2-fold increase in PN concentrations over unimpacted baseline PN concentrations during most hours of the day in an area of about 60 km(2) that extended to 16 km (10 miles) downwind and a 4- to 5-fold increase to 8-10 km (5-6 miles) downwind. Locations of maximum PN concentrations were aligned to eastern, downwind jet trajectories during prevailing westerly winds and to 8 km downwind concentrations exceeded 75 000 particles/cm(3), more than the average freeway PN concentration in Los Angeles. During infrequent northerly winds, the impact area remained large but shifted to south of the airport. The freeway length that would cause an impact equivalent to that measured in this study (i.e., PN concentration increases weighted by the area impacted) was estimated to be 280-790 km. The total freeway length in Los Angeles is 1500 km. These results suggest that airport emissions are a major source of PN in Los Angeles that are of the same general magnitude as the entire urban freeway network. They also indicate that the air quality impact areas of major airports may have been seriously underestimated.
Cluster analysis of particle number size distributions from background sites across Europe is presented. This generated a total of nine clusters of particle size distributions which could be further combined into two main groups, namely: a south-to-north category (four clusters) and a west-to-east category (five clusters). The first group was identified as most frequently being detected inside and around northern Germany and neighbouring countries, showing clear evidence of local afternoon nucleation and growth events that could be linked to movement of air masses from south to north arriving ultimately at the Arctic contributing to Arctic haze. The second group of particle size spectra proved to have narrower size distributions and collectively showed a dependence of modal diameter upon the longitude of the site (west to east) at which they were most frequently detected. These clusters indicated regional nucleation (at the coastal sites) growing to larger modes further inland. The apparent growth rate of the modal diameter was around 0.6–0.9 nm* h^(−1)
.
Four specific air mass back-trajectories were successively taken as case studies to examine in real time the evolution of aerosol size distributions across Europe. While aerosol growth processes can be observed as aerosol traverses Europe, the processes are often obscured by the addition of aerosol by emissions en route. This study revealed that some of the 24 stations exhibit more complex behaviour than others, especially when impacted by local sources or a variety of different air masses. Overall, the aerosol size distribution clustering analysis greatly simplifies the complex data set and allows a description of aerosol aging processes, which reflects the longer-term average development of particle number size distributions as air masses advect across Europe.
Understanding the fingerprints of urban aerosols is very important in urban model development. Cluster analysis combined with visual classification, air mass back-trajectories, and local meteorology form a comprehensive analysis tool to understand the fingerprints of urban aerosol particles and relate them to their source origin as local or regional. Here we identified seven fingerprints of urban aerosols in Helsinki during 2006. The fingerprints of fresh emissions (Clusters 1-2) from local sources including traffic are characterized by a dominant nucleation mode (GMD < 25 nm and 62%-82% of the submicron particle number concentration). Cluster 3 is characterized by aged ultrafne particle modes with a dominant Aitken mode (diameter 25-100 nm). The fingerprint (Cluster 0) of New Particle Formation (NPF) events is characterized by a second nucleation mode (GMD < 10 nm and a fraction more than 65% of the submicron particle number concentration); the inclusion of particles with Dp < 7 nm in the analysis is important to identify this unique fingerprint. The fingerprints (Clusters 4-5) of aerosols originated via Short-Range or Long-Range Transport (SRT/LRT) from Russia; middle Europe and the Baltic Sea are characterized by dominant Aitken and accumulation modes (as high as 70% of the submicron particle number concentration). Cluster 6 emerged from a mixture between locally emitted aerosols and those originated via SRT/LRT with roughly 50% contribution of the nucleation mode in the submicron particle number concentration. While the data used in this analysis were for the year 2006 only, we foresee the fingerprints are generally valid for the Helsinki Metropolitan Area.
Sources and evolution of ultrafine particles were investigated both
horizontally and vertically in the large urban agglomerate of Barcelona,
Spain. Within the SAPUSS project (Solving Aerosol Problems by Using
Synergistic Strategies), a large number of instruments was deployed
simultaneously at different monitoring sites (road, two urban
background, regional background, urban tower 150 m a.s.l., urban
background tower site 80 m a.s.l.) during a 4 week period in
September-October 2010. Particle number concentrations (N>5
nm) are highly correlated with black carbon (BC) at all sites only
under strong vehicular traffic influences. By contrast, under cleaner
atmospheric conditions (low condensation sink, CS) such correlation
diverges towards much higher N/BC ratios at all sites, indicating
additional sources of particles including secondary production of
freshly nucleated particles. Size-resolved aerosol distributions
(N10-500) as well as particle number concentrations
(N>5 nm) allow us to identify three types of nucleation
and growth events: (1) a regional type event originating in the whole
study region and impacting almost simultaneously the urban city of
Barcelona and the surrounding urban background area; (2) a regional type
event impacting only the regional background area but not the urban
agglomerate; (3) an urban type event which originates only within the
city centre but whose growth continues while transported away from the
city to the regional background. Furthermore, during these clean air
days, higher N are found at tower level than at ground level only in the
city centre whereas such a difference is not so pronounced at the remote
urban background tower. In other words, this study suggests that the
column of air above the city ground level possesses the optimal
combination between low CS and high vapour source, hence enhancing the
concentrations of freshly nucleated particles. By contrast, within
stagnant polluted atmospheric conditions, higher N and BC concentrations
are always measured at ground level relative to tower level at all
sites. Our study suggests that the city centre of Barcelona is a source
of non-volatile traffic primary particles (29-39% of N>5
nm), but other sources, including secondary freshly nucleated
particles contribute up to 61-71% of particle number (N>5
nm) at all sites. We suggest that organic compounds evaporating
from freshly emitted traffic particles are a possible candidate for new
particle formation within the city and urban plume.
An elevated particle number concentration (PNC) observed during
nucleation events could play a significant contribution to the total
particle load and therefore to the air pollution in the urban
environments. Therefore, a field measurement study of PNC was commenced
to investigate the temporal and spatial variations of PNC within the
urban airshed of Brisbane, Australia. PNC was monitored at urban (QUT),
roadside (WOO) and semi-urban (ROC) areas around the Brisbane region
during 2009. During the morning traffic peak period, the highest
relative fraction of PNC reached about 5% at QUT and WOO on weekdays.
PNC peaks were observed around noon, which correlated with the highest
solar radiation levels at all three stations, thus suggesting that high
PNC levels were likely to be associated with new particle formation
caused by photochemical reactions. Wind rose plots showed relatively
higher PNC for the NE direction, which was associated with industrial
pollution, accounting for 12%, 9% and 14% of overall PNC at QUT, WOO and
ROC, respectively. Although there was no significant correlation between
PNC at each station, the variation of PNC was well correlated among
three stations during regional nucleation events. In addition, PNC at
ROC was significantly influenced by upwind urban pollution during the
nucleation burst events, with the average enrichment factor of 15.4.
This study provides an insight into the influence of regional nucleation
events on PNC in the Brisbane region and it the first study to quantify
the effect of urban pollution on semi-urban PNC through the nucleation
events.
We study the combined effects of land surface conditions, atmospheric
boundary layer dynamics and chemistry on the diurnal evolution of
biogenic secondary organic aerosol in the atmospheric boundary layer,
using a model that contains the essentials of all these components.
First, we evaluate the model for a case study in Hyytiälä,
Finland, and find that it is able to satisfactorily reproduce the
observed dynamics and gas-phase chemistry. We show that the exchange of
organic aerosol between the free troposphere and the boundary layer
(entrainment) must be taken into account in order to explain the
observed diurnal cycle in organic aerosol (OA) concentration. An
examination of the budgets of organic aerosol and terpene concentrations
show that the former is dominated by entrainment, while the latter is
mainly driven by emission and chemical transformation. We systematically
investigate the role of the land surface, which governs both the surface
energy balance partitioning and terpene emissions, and the large-scale
atmospheric process of vertical subsidence. Entrainment is especially
important for the dilution of organic aerosol concentrations under
conditions of dry soils and low terpene emissions. Subsidence suppresses
boundary layer growth while enhancing entrainment. Therefore, it
influences the relationship between organic aerosol and terpene
concentrations. Our findings indicate that the diurnal evolution of
secondary organic aerosols (SOA) in the boundary layer is the result of
coupled effects of the land surface, dynamics of the atmospheric
boundary layer, chemistry, and free troposphere conditions. This has
potentially some consequences for the design of both field campaigns and
large-scale modeling studies.
Recent studies have shown very high frequencies of atmospheric new
particle formation in different environments in South Africa. Our aim
here was to investigate the causes for two or three consecutive daytime
nucleation events, followed by subsequent particle growth during the
same day. We analysed 108 and 31 such days observed in a polluted
industrial and moderately polluted rural environments, respectively, in
South Africa. The analysis was based on two years of measurements at
each site. After rejecting the days having notable changes in the air
mass origin or local wind direction, i.e. two major reasons for observed
multiple nucleation events, we were able to investigate other factors
causing this phenomenon. Clouds were present during, or in between most
of the analysed multiple particle formation events. Therefore, some of
these events may have been single events, interrupted somehow by the
presence of clouds. From further analysis, we propose that the first
nucleation and growth event of the day was often associated with the
mixing of a residual air layer rich in SO2 (oxidized to
sulphuric acid) into the shallow surface-coupled layer. The second
nucleation and growth event of the day usually started before midday and
was sometimes associated with renewed SO2 emissions from
industrial origin. However, it was also evident that vapours other than
sulphuric acid were required for the particle growth during both events.
This was especially the case when two simultaneously growing particle
modes were observed. Based on our analysis, we conclude that the
relative contributions of estimated H2SO4 and
other vapours on the first and second nucleation and growth events of
the day varied from day to day, depending on anthropogenic and natural
emissions, as well as atmospheric conditions.
Photochemically driven nucleation bursts, which typically occur in a few hours after sunrise, often produce strong aerosol number concentration (ANC) fluctuations. The causes of such ANC spikes were investigated using a detailed aerosol model running in the parcel mode. Two potential mechanisms for the ANC spikes are proposed and simulated. The blocking of actinic flux by scattered clouds can significantly influence new particle production, but this does not cause strong fluctuations in the number of aerosols within sizes greater than the detection limit of our measurements. A more plausible mechanism is the turbulence eddy effect. Strong aerosol nucleation may occur in both updrafts and downdrafts, while the cloud formation at the boundary layer top strongly reduces the number of aerosols. As the number of aerosols is sensitive to turbulence eddy and cloud formation properties, a changing turbulence condition would result in large fluctuations in the evolution of ANC similar to that observed at the surface.
Efforts have been made to relate measured concentrations of airborne constituents to their origins for more than 50 years. During this time interval, there have been developments in the measurement technology to gather highly time resolved, detail chemical compositional data. Similarly, the improvements in computers have permitted a parallel development of data analysis tools that permit the extraction of information from these data. There is now a substantial capability to provide useful insights into the sources of pollutants and their atmospheric processing that can help inform air quality management options. Efforts have been made to combine receptor and chemical transport models to provide improved apportionments. Tools are available to utilize limited numbers of known profiles with the ambient data to obtain more accurate apportionments for targeted sources. In addition, tools are in place to allow more advanced models to be fit to the data based on conceptual models of the nature of the sources and the sampling/analytical approach. Each of the approaches has its strengths and weaknesses. However, the field as a whole suffers from a lack of measurements of source emission compositions. There has not been an active effort to develop source profiles for stationary sources for a long time and with many significant sources built in developing countries, the lack of local profiles is a serious problem in effective source apportionment. The field is now relatively mature in terms of its methods and its ability to adapt to new measurement technologies so that we can be assured of a high likelihood of extracting the maximal information from the collected data. Implications Efforts have been made over the past 50 years to use air quality data to estimate the influence of air pollution sources. These methods are now relatively mature and many are readily accessible through publically available software. This review examines the development of receptor models and the current state-of-the-art in extracting source identification and apportionments from ambient air quality data.
An emissions factor model (WATERBUS) is developed to estimate boats emissions in Venice, Italy. Waterbuses and small private boats undertake most of the internal transport with the city and provide connections with other islands in the Venice lagoon and the mainland. Even though the engines of these boats are smaller, sailing conditions do not correspond to those of larger vessels. Consequently, emission factors typically associated with large boats are not readily transferrable. We thus estimate specific emissions for small boats and waterbus in the lagoon area. The WATERBUS model is used to assess emission changes that would occur if the fleet were fitted with electronic engines.
The risk of air quality degradation is of considerable concern particularly for those airports that are located near urban areas. The ability to quantitatively predict the effects of air pollutants originated by airport operations is important for assessing air quality and the related impacts on human health. Current emission regulations have focused on local air quality in the proximity of airports. However, an integrated study should consider the effects of meteorological events, at both regional and local level, that can affect the dispersion and the deposition of exhausts. Rigorous scientific studies and extensive experimental data could contribute to the analysis of the impacts of airports expansion plans. This paper is focused on the analysis of the effects of meteorology on aircraft emission for the Marco Polo Airport in Venice. This is the most important international airport in the eastern part of the Po' Valley, one of the most polluted area in Europe. Air pollution is exacerbated by meteorology that is a combination of large and local scale effects that do not allow significant dispersion. Moreover, the airport is located near Venice, a city of noteworthy cultural and architectural relevance, and nearby the lagoon that hosts several areas of outstanding ecological importance at European level (Natura 2000 sites). Dispersion and deposit of the main aircraft exhausts (NOx, HC and CO) have been evaluated by using a Lagrangian particle model. Spatial and temporal aircraft exhaust dispersion has been analyzed for LTO cycle. Aircraft taxiing resulted to be the most impacting aircraft operation especially for the airport working area and its surroundings, however occasionally peaks may be observed even at high altitudes when cruise mode starts. Mixing height can affect concentrations more significantly than the concentrations in the exhausts themselves. An increase of HC and CO concentrations (15-50%) has been observed during specific meteorological events.
The air quality is influenced by the potential effects of meteorology at meso- and synoptic scales. While local weather and mixing layer dynamics mainly drive the dispersion of sources at small scales, long-range transports affect the movements of air masses over regional, transboundary and even continental scales. Long-range transport may advect polluted air masses from hot-spots by increasing the levels of pollution at nearby or remote locations or may further raise air pollution levels where external air masses originate from other hot-spots. Therefore, the knowledge of ground-wind circulation and potential long-range transports is fundamental not only to evaluate how local or external sources may affect the air quality at a receptor site but also to quantify it.
This review is focussed on establishing the relationships among PM2.5 sources, meteorological condition and air mass origin in the Po Valley, which is one of the most polluted areas in Europe. We have chosen the results from a recent study carried out in Venice (Eastern Po Valley) and have analysed them using different statistical approaches to understand the influence of external and local contribution of PM2.5 sources. External contributions were evaluated by applying Trajectory Statistical Methods (TSMs) based on back-trajectory analysis including (i) back-trajectories cluster analysis, (ii) potential source contribution function (PSCF) and (iii) concentration weighted trajectory (CWT). Furthermore, the relationships between the source contributions and ground-wind circulation patterns were investigated by using (iv) cluster analysis on wind data and (v) conditional probability function (CPF). Finally, local source contribution have been estimated by applying the Lenschow' approach.
In summary, the integrated approach of different techniques has successfully identified both local and external sources of particulate matter pollution in an European hot-spot affected by the worst air quality.
Current merging algorithms for particle size spectral data collected with electrical mobility and aerodynamic time-of-flight instruments either require a priori knowledge of densities and shape factors, or use alignment of the number spectra alone to determine an optimal fit and effective density. In this work, an enhanced algorithm is described in which the best fit between the two instrument datasets is achieved for the number, surface area, and volume spectra, also yielding estimated values of transition-regime effective density. When applied to data collected at a kerbside site, integrated aerosol mass calculated from the merged data correlates highly with independently measured PM10 mass data. Typical merged data from the site are shown and used to examine the diurnal and wind direction dependence of the estimated values of transition-regime effective density derived from the merging procedure.
BACKGROUND
ICU readmissions are associated with increased mortality and costs; however, it is unclear whether these outcomes are caused by readmissions or by residual confounding by illness severity. An assessment of temporal changes in ICU readmission in response to a specific policy change could help disentangle these possibilities. We sought to determine whether ICU readmission rates changed after 2003 Accreditation Council for Graduate Medical Education Resident Duty Hours reform (“reform”) and whether there were temporally corresponding changes in other ICU outcomes.
METHODS
We used a difference-in-differences approach using Project IMPACT (Improved Methods of Patient Information Access of Core Clinical Tasks). Piecewise regression models estimated changes in outcomes immediately before and after reform in 274,491 critically ill medical and surgical patients in 151 community and academic US ICUs. Outcome measures included ICU readmission, ICU mortality, and in-hospital post-ICU-discharge mortality.
RESULTS
In ICUs with residents, ICU readmissions increased before reform (OR, 1.5; 95% CI, 1.22-1.84;P< .01), and decreased after (OR, 0.85; 95% CI, 0.73-0.98;P= .03). This abrupt decline in ICU readmissions after reform differed significantly from an increase in readmissions observed in ICUs without residents at this time (difference-in-differences P< .01). No comparable changes in mortality were observed between ICUs with vs without residents.
CONCLUSIONS
The changes in ICU readmission rates after reform, without corresponding changes in mortality, suggest that ICU readmissions are not causally related to other untoward patient outcomes. Instead, ICU readmission rates likely reflect operational aspects of care that are not patient-centered, making them less useful indicators of ICU quality.
This study investigates the chemical speciation of fine particulate matter (PM2.5) collected at three sites in the Venice area, eastern Po Valley (Italy). This area is one of the few hot spots left in Europe where levels of PM2.5 frequently breach EU target values and cause a serious risk for public health. Elemental composition, inorganic ions and polycyclic aromatic hydrocarbon concentrations were quantified in 448 PM2.5 samples and the multiple-site PMF receptor model was based on the elemental and inorganic ion data. Six factors associated with potential sources were quantified, namely, secondary sulfate, ammonium nitrate and combustions, fossil fuels, traffic, industrial and glassmaking. Source apportionment results were further processed using a series of chemometric tools for returning additional information about the seasonal and spatial changes of factors extracted by the PMF analysis. In addition, PMF results were also studied in combination with weather conditions and PAH concentrations revealing that sources of secondary nitrate and sulfate are homogeneously distributed throughout the area, while remaining pollutant sources may have a distinct origin. PMF results were cluster analyzed to sort out samples with similar source profiles and then the wind roses of grouped samples were examined to assess the role of wind speed and direction on PM2.5 pollution and chemistry. The tested tools and the results obtained can be used for air quality assessment studies and air pollution reduction strategies.
Concurrent measurements of ultra-fine (r<5 nm) particle (UFP) formation, OH and SO2 concentrations in the coastal environment are examined to further elucidate the processes leading to tidal-related homogeneous heteromolecular nucleation. During almost daily nucleation events, UFP concentration approached ≈300,000 cm−3 under conditions of solar radiation and low tide. Simultaneous measurements of OH illustrate that, as well as occurring during low tide, these events occur during conditions of peak OH concentration, suggesting that at least one of the nucleating species is photochemically produced. Derived H2SO4 production also exhibited remarkable coherence, although phase-lagged, with UFP formation, thus suggesting its involvement, although binary nucleation of H2SO4 and H2O can be ruled out as a plausible mechanism. Ternary nucleation involving NH3 seems most likely as a trigger mechanism, however, at least a fourth condensable species, X, is required for growth to detectable sizes. Since UFP are only observed during low tide events, it is thought that species X, or it's parent, is emitted from the shore biota - without which, no nucleation is detected. Species X remains to be identified. Model simulations indicate that, in order to reproduce the observations, a nucleation rate of 107 cm−3 s−1, and a condensable vapour concentration of 5 × 107 cm−3, are required.
Thirteen air pollutant concentrations were measured hourly for 13 years (2000–2013) at an urban background site of a large city in the eastern Po Valley (Italy) and results were chemometrically analysed. The pollutant list includes CO, NO, NO2, NOx, O3, SO2, benzene, toluene, ethylbenzene, o-, m- and p-xylenes and PM10, all known or suspected of having adverse effects on human health. The hourly data were statistically processed to detect the long-term trends in relation to the changes in the emission scenarios occurred in the last decade. The most probable emission sources and atmospheric photochemical processes were investigated by analyzing the seasonal, weekly, diurnal cycles of pollutants and the lagged correlations amongst pollutants. The role of micro-meteorological factors upon the air quality was assessed by analyzing the relationships with key weather parameters, while the location of the potential sources was studied by matching atmospheric circulation and pollution data through bivariate polar plots and conditional probability functions. In addition, a new statistical procedure is presented and tested to analyze the periods when common mitigation measures were adopted in the city (e.g., the total stop of traffic and car-free days) and to evaluate their real effect upon the air quality. By providing direct information on the levels and trends of key pollutants, this study finally enables some general considerations about air pollution in an important hotspot of Southern Europe, the eastern Po Valley, where the levels of some key pollutants are still far from meeting the EC limit and target values. It may help policy-makers to take successful mitigation measures.
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.
Civil aviation is fast-growing (about +5% every year), mainly driven by the developing economies and globalization. Its impact on the environment is heavily debated, particularly in relation to climate forcing attributed to emissions at cruising altitudes and the noise and the deterioration of air quality at ground-level due to airport operations. This latter environmental issue is of particular interest to the scientific community and policymakers, especially in relation to the breach of limit and target values for many air pollutants, mainly nitrogen oxides and particulate matter, near the busiest airports and the resulting consequences for public health. Despite the increased attention given to aircraft emissions at ground-level and air pollution in the vicinity of airports, many research gaps remain. Sources relevant to air quality include not only engine exhaust and non-exhaust emissions from aircraft, but also emissions from the units providing power to the aircraft on the ground, the traffic due to the airport ground service, maintenance work, heating facilities, fugitive vapours from refuelling operations, kitchens and restaurants for passengers and operators, intermodal transportation systems, and road traffic for transporting people and goods in and out to the airport. Many of these sources have received inadequate attention, despite their high potential for impact on air quality. This review aims to summarise the state-of-the-art research on aircraft and airport emissions and attempts to synthesise the results of studies that have addressed this issue. It also aims to describe the key characteristics of pollution, the impacts upon global and local air quality and to address the future potential of research by highlighting research needs.
In this paper, PM1 elemental composition and relative sources have been investigated near Venice (Eastern Po Valley, Italy). Considering the lack of information on PM1 composition in this area and the general poor knowledge of emission profiles, this study can give useful information to better understand the source profile and the dispersion of finer particles. A sampling campaign was carried out in two sampling sites located inside the “Marco Polo” international airport of Venice. Elemental composition on PM1 samples was characterized by inductively coupled plasma optical emission spectroscopy. Five sources were identified and quantified by using the positive matrix factorization receptor model: heavy oil combustion, road traffic, airplane tire-wear, glassmaking and mixed combustions. The role of local atmospheric circulation was investigated by studying the source contributions as a function of wind velocity. Days characterized by low dispersion (lower wind speed and higher wind calm with respect to full period rose) showed a higher contribution for all sources, and a glassmaking contribution increase linked to wind blowing from South, where the artistic glass district of Venice is located.
On-road emission measurements of gasoline- and diesel-fueled vehicles
were conducted by a portable emission measurement system (PEMS) in
Shanghai, China. Horiba OBS 2200 and TSI EEPS 3090 were employed to
detect gaseous and ultrafine particle emissions during the tests. The
driving-based emission factors of gaseous pollutants and particle mass
and number were obtained on various road types. The average
NOx emission factors of the diesel bus, diesel car, and
gasoline car were 8.86, 0.68, and
0.17 g km‑1, all of which were in excess of
their emission limits. The particle number emission factors were
7.06 × 1014,
6.08 × 1014, and
1.57 × 1014 km‑1,
generally higher than the results for similar vehicle types reported in
the previous studies. The size distributions of the particles emitted
from the diesel vehicles were mainly concentrated in the accumulation
mode, while those emitted from the gasoline car were mainly distributed
in the nucleation mode. Both gaseous and particle emission rates exhibit
significant correlations with the change in vehicle speed and power
demand. The lowest emission rates for each vehicle type were produced
during idling. The highest emission rates for each vehicle type were
generally found in high-VSP bins. The particle number emission rates of
the gasoline car show the strongest growth trend with increasing VSP and
speed. The particle number emission for the gasoline car increased by 3
orders of magnitude from idling to the highest VSP and driving speed
conditions. High engine power caused by aggressive driving or heavy
loads is the main contributor to high emissions for these vehicles in
real-world situations.
Few studies on long-term exposure to air pollution and mortality have been reported from Europe. Within the multicentre European Study of Cohorts for Air Pollution Effects (ESCAPE), we aimed to investigate the association between natural-cause mortality and long-term exposure to several air pollutants.
We used data from 22 European cohort studies, which created a total study population of 367 251 participants. All cohorts were general population samples, although some were restricted to one sex only. With a strictly standardised protocol, we assessed residential exposure to air pollutants as annual average concentrations of particulate matter (PM) with diameters of less than 2·5 μm (PM2·5), less than 10 μm (PM10), and between 10 μm and 2·5 μm (PMcoarse), PM2.5 absorbance, and annual average concentrations of nitrogen oxides (NO2 and NOx), with land use regression models. We also investigated two traffic intensity variables-traffic intensity on the nearest road (vehicles per day) and total traffic load on all major roads within a 100 m buffer. We did cohort-specific statistical analyses using confounder models with increasing adjustment for confounder variables, and Cox proportional hazards models with a common protocol. We obtained pooled effect estimates through a random-effects meta-analysis.
The total study population consisted of 367 251 participants who contributed 5 118 039 person-years at risk (average follow-up 13·9 years), of whom 29 076 died from a natural cause during follow-up. A significantly increased hazard ratio (HR) for PM2·5 of 1·07 (95% CI 1·02-1·13) per 5 μg/m(3) was recorded. No heterogeneity was noted between individual cohort effect estimates (I(2) p value=0·95). HRs for PM2·5 remained significantly raised even when we included only participants exposed to pollutant concentrations lower than the European annual mean limit value of 25 μg/m(3) (HR 1·06, 95% CI 1·00-1·12) or below 20 μg/m(3) (1·07, 1·01-1·13).
Long-term exposure to fine particulate air pollution was associated with natural-cause mortality, even within concentration ranges well below the present European annual mean limit value.
European Community's Seventh Framework Program (FP7/2007-2011).
A cluster analysis algorithm was applied to reduce the amount and complexity of 30 min aerosol number size distributions in a three year data set (2006–2008) at a urban background station in Helsinki, Finland. Only after objective validity tests to determine the appropriate number of clusters, a k-means cluster algorithm was applied to extract seven characteristic size distributions from the data set. The average total number concentrations of the clustered size distributions range from 6067 cm−3 to 12,818 cm−3 with modal diameters between 5 and 193 nm. The clustered size distributions were analyzed in terms of their physical properties (shape, log-normal modes, mode diameter), temporal occurrence (e.g. time of day, season) and their relation to local meteorology.Three different types of cluster distributions being represented by either three or two log-normal modes (in only one case) were characterized at the site: four clusters that were indicative for urban-type size distributions with different influence of anthropogenic and traffic activities occurring 69% of the study time, two maritime-type distributions (29% occurrence) and one nucleation-type size distribution (2% occurrence). We were able to relate the clusters to characteristic modal diameters, different temporal occurrence on the daily and annual cycle, e.g. urban clusters that occurred year round and those that were attributed to winter daytime situations specifically. Analysis of the daily patterns clearly reveals the influence of local traffic activity on three of the four urban-type cluster size distributions. The method offers the chance for a simple kind of source apportionment by establishing signature size distributions based on the physical properties of the aerosol size spectra.
The heterogeneous reaction of N2O5 on mixed organic-inorganic aerosol particles was investigated using an entrained aerosol flow tube coupled to a custom-built chemical ionization mass spectrometer. Laboratory results on aqueous particles confirm a strong dependence of the reactive uptake coefficient (gamma) on particle liquid water, for particle water concentrations below 15 M, and the molar ratio of particle water to nitrate. Measurements of gamma(N2O5) on mixed chloride-nitrate particles indicate that the presence of trace chloride can negate the suppression of gamma(N2O5) at high nitrate loadings with implications for polluted coastal regions. These results are used to construct a new parameterization for gamma(N2O5), that when coupled to an aerosol thermodynamics model, can be used within regional and/or global chemical transport models.
In these experiments size-resolved emission factors for particle number (EFPN) and mass (EFPM) have been determined for 734 individual ship passages for real-world dilution. The method used is an extractive sampling method of the passing ship plumes where particle number/mass and CO2 were measured with high time resolution (1 Hz). The measurements were conducted on a small island located in the entrance to the port of Gothenburg (N57.6849, E11.838), the largest harbor in Scandinavia. This is an emission control area (ECA) and in close vicinity to populated areas. The average EFPN and EFPM were 2.55 0.11 × 10 16 (kg fuel)-1 and 2050 110 mg (kg fuel)-1, respectively. The determined EF for ships with multiple passages showed a great reproducibility. Size-resolved EFPN were peaking at small particle sizes ∼35 nm. Smaller particle sizes and hence less mass were observed by a gas turbine equipped ship compared to diesel engine equipped ships. On average 36 to 46% of the emitted particles by number were non-volatile and 24% by mass (EFPN 1.16 0.19 × 1016 [kg fuel]-1 and EFPM 488 73 mg [kg fuel]-1, respectively). This study shows a great potential to gain large data-sets regarding ship emission determining parameters that can improve current dispersion modeling for health assessments on local and regional scales. The global contributions of total and non-volatile particle mass from shipping using this extensive data-set from an ECA were estimated to be at least 0.80 Tgy-1 and 0.19 Tgy -1.