Datasets that include only the PM elemental composition and no other important constituents such as ions and OC, should be treated carefully when used for source apportionment. This work is ...demonstrating how a source apportionment study utilizing PMF 5.0 enhanced diagnostic tools can achieve an improved solution with documented levels of uncertainty for such a dataset. The uncertainty of the solution is rarely reported in source apportionment studies or it is reported partially. Reporting the uncertainty of the solution is very important especially in the case of small datasets. PM2.5 samples collected in Patras during the year 2011 were used. The concentrations of 22 elements (Z=11–33) were determined using PIXE. Source apportionment analysis revealed that PM2.5 emission sources were biomass burning (11%), sea salt (8%), shipping emissions (10%), vehicle emissions (33%), mineral dust (2%) and secondary sulfates (33%) while unaccounted mass was 3%. Although Patras city center is located in a very close proximity to the city's harbor, the contribution of shipping originating emissions was never before quantified. As rotational stability is hard to be achieved when a small dataset is used the rotational stability of the solution was thoroughly evaluated. A number of constraints were applied to the solution in order to reduce rotational ambiguity.
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•Results showed that the new tools in PMF 5.0 are very useful in the case of small datasets•The application of the appropriate constraints may be needed to reduce the rotational ambiguity of the solution for small datasets•The case of biomass burning revealed that sources with high seasonal variability are vulnerable to resampling techniques in small datasets•Biomass burning (11%), shipping (10%), sea salt (9%), sulfates (34%), mineral dust (2%) and vehicle emissions (34%)
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
The present study highlights the importance of examining the contribution of Saharan dust (SD) sources not only in terms of overall mass contribution but also in terms of composition, size ...distribution and inhaled dose. The effect of SD intrusions on PM and the respective major and trace metals mass concentrations and size distributions was investigated in a suburban site in Athens, Greece. SD events were associated, on average, with lower boundary layer heights (BLH) compared to the non-Sahara (nSD) dust days. During SD events, PM1-10 concentrations showed an increasing trend with increasing atmospheric BLH, in contrary to the fine PM (PM1). Generally, increased PM1 and CO (i.e. anthropogenic origin) levels were observed for BLH lower than around 500 m. The average contribution of SD to PM10 and PM2.5 mass concentration was roughly equal to 30.9% and 19.4%, respectively. The mass size distributions of PM and specific major and trace elements (Na, Al, Si, S, Cl, K, Ca, Fe, and Zn) displayed a somewhat different behavior with respect to the mass origin (Algeria-Tunisia vs Libya-Egypt), affecting in turn the regional deposition of inhaled aerosol in the human respiratory tract (HRT). The average PM deposited mass in the upper and lower HRT was 80.1% (Head) and 26.9% (Lung; Tracheobronchial and Pulmonary region) higher for SD days than for nSD days. Higher doses were estimated in the upper and lower HRT for the majority of the elements, when SD intrusions occurred, supporting the increasingly growing interest in exploring the health effects of SD. Only the mass deposition for S, and Na in the lower HRT and Zn in the upper HRT was higher in the case of nSD.
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•Mass/Elemental size distributions displayed different structure with air mass origin.•The size limit separating the fine and coarse mode was at 1 μm.•Increase of 80.1% (Head) and 26.9% (Lung) in PM deposition mass during SD events.•The majority of the SD events were associated with an apparent shallow boundary layer.•Health impacts from allergens, pollen and heavy metals need to be assessed further.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This work presents the results of a PM2.5 source apportionment study conducted in urban background sites from 16 European and Asian countries. For some Eastern Europe and Central Asia cities this was ...the first time that quantitative information on pollution source contributions to ambient particulate matter (PM) has been performed. More than 2200 filters were sampled and analyzed by X-Ray Fluorescence (XRF), Particle-Induced X-Ray Emission (PIXE), and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) to measure the concentrations of chemical elements in fine particles. Samples were also analyzed for the contents of black carbon, elemental carbon, organic carbon, and water-soluble ions. The Positive Matrix Factorization receptor model (EPA PMF 5.0) was used to characterize similarities and heterogeneities in PM2.5 sources and respective contributions in the cities that the number of collected samples exceeded 75. At the end source apportionment was performed in 11 out of the 16 participating cities. Nine major sources were identified to have contributed to PM2.5: biomass burning, secondary sulfates, traffic, fuel oil combustion, industry, coal combustion, soil, salt and “other sources”. From the averages of sources contributions, considering 11 cities 16% of PM2.5 was attributed to biomass burning, 15% to secondary sulfates, 13% to traffic, 12% to soil, 8.0% to fuel oil combustion, 5.5% to coal combustion, 1.9% to salt, 0.8% to industry emissions, 5.1% to “other sources” and 23% to unaccounted mass. Characteristic seasonal patterns were identified for each PM2.5 source. Biomass burning in all cities, coal combustion in Krakow/POL, and oil combustion in Belgrade/SRB and Banja Luka/BIH increased in Winter due to the impact of domestic heating, whereas in most cities secondary sulfates reached higher levels in Summer as a consequence of the enhanced photochemical activity. During high pollution days the largest sources of fine particles were biomass burning, traffic and secondary sulfates.
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•Data on source apportionment in Eastern European and Central Asian countries is sparse.•The average PM2.5 concentrations measured in the 16 cities exceeded the WHO guidelines.•Biomass burning, traffic and secondary sulfates were the main sources in high pollution days.•Key targets towards healthy cities: clean energy and sustainable transports.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The elemental composition of water-soluble and acid-soluble fractions of PM2.5 samples from two different Greek cities (Patras and Megalopolis) was investigated. Patras and Megalopolis represent ...different environments. Specifically, Patras is an urban environment with proximity to a large port, while Megalopolis is a small city located close to lignite power plants. Both cities can serve as a representative example of European cities with similar characteristics. The concentration of 14 elements (As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Fe, Sr, Ti, V and Zn) was determined in each fraction by ICP-MS. Microwave assisted digestion was used to digest the samples using a mixture of HNO3 and HF. For the determination of the water soluble fraction, water was chosen as the simplest and most universal extraction solvent. For the validation of the extraction procedure, the recoveries were tested on two certified reference materials (NIST SRM 1648 Urban Particulate Matter and NIST 1649a Urban Dust). Results showed that Zn has the highest total concentration (273 and 186ng/m3) and Co the lowest (0.48 and 0.23ng/m3) for Patras and Megalopolis samples, respectively. Nickel with 65% for Patras and As with 49% for Megalopolis displayed the highest solubility, whereas Fe (10%) and Ti (2%) the lowest ones, respectively.
•Elemental analysis of PM2.5 samples from two Greek cities was performed by ICP-MS.•The study has given information on the dissolution behavior of 14 elements.•Concentration of Cd in Patras exceeds European Commission’s assessment threshold.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Exposure to Particulate Matter (PM) has been associated with adverse health effects. The main objective of this study is to quantify children's exposure to PM and the respective inhaled dose in ...Lisbon. For that, a time activity pattern survey was performed with the participation of 1189 children. In addition, PM was sampled inside 5 schools, 40 homes, 4 modes of transportation and in the respective outdoor environments. Time-activity pattern records showed that children spent 86% of their time indoors, especially at home and in the classroom. The PM2.5 and PM10 concentrations in classrooms (35.3 μg/m3 and 65.4 μg/m3, respectively) were more than double than in homes (14.5 μg/m3 and 18.2 μg/m3, respectively) and highly exceeded the limit values established by the Portuguese legislation for indoor air quality. The high indoor-to-outdoor concentration ratios (I/O) calculated in schools for PM2.5 (1.8) and PM10 (2.1) suggest that a substantial fraction of particles was generated by indoor sources. PM daily patterns for classrooms showed the importance of occupancy, resuspension of dust and cleaning activities for the elevated levels of particles. The average daily children exposure was 20.6 μg/m3 for PM2.5 and 31.5 μg/m3 for PM10. During weekdays, the classrooms contributed with 42% and 50% to the PM2.5 and PM10 daily exposure, and with 36% and 41% to the PM2.5 and PM10 inhaled dose, respectively. This work quantitatively demonstrated that indoor microenvironments (MEs) are the main contributors to personal exposure to PM and respective inhaled dose.
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•Children spend more than 86% of their time indoors.•PM2.5 and PM10 concentrations in classrooms is significantly higher than in homes.•PM I/O ratios are higher in schools than in homes.•The classroom is the ME that mostly contributes to children exposure to PM.•Indoor MEs are the main contributors to the personal exposure and inhaled dose.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Average daily contributions (90% CI black bar) to PM10 in relative (%, left pie charts), OPAA and OPDTT per source’s mass in absolute (OPm in nmol/min m−3, middle bar plot) and OPAA and OPDTT per ...volume in relative (OPv in %, right bar plot) values grouped according to sources common activities. Arrows indicate (additional) associations between the sources.
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•Concentrations of PM10 at the site are comparable to other Alpine valleys, however, OP levels are higher compared to European sites.•Extensive tests with PMF were performed including organic tracers.•The most important sources of PM10, OP per µg of source as well as OP per m3 at the site are biomass burning and activities related to cement production.•Sources with important contributions to PM10 do not necessarily have high OP.•An unusual chloride-rich source was identified with high OP per µg of source.
Toxicity of particulate matter (PM) depends on its sources, size and composition. We identified PM10 sources and determined their contribution to oxidative potential (OP) as a health proxy for PM exposure in an Alpine valley influenced by cement industry. PM10 filter sample chemical analysis and equivalent black carbon (eBC) were measured at an urban background site from November 2020 to November 2021. Using an optimized Positive Matrix Factorization (PMF) model, the source chemical fingerprints and contributions to PM10 were determined. The OP assessed through two assays, ascorbic acid (AA) and dithiothreitol (DTT), was attributed to the PM sources from the PMF model with a multiple linear regression (MLR) model. Ten factors were found at the site, including biomass burning (34, 40 and 38% contribution to annual PM10, OPAA and OPDDT, respectively), traffic (14, 19 and 7%), nitrate- and sulphate-rich (together: 16, 5 and 8%), aged sea salt (2, 2 and 0%) and mineral dust (10, 12 and 17%). The introduction of innovative organic tracers allowed the quantification of the PM primary and secondary biogenic fractions (together: 13, 8 and 21%). In addition, two unusual factors due to local features, a chloride-rich factor and a second mineral dust-rich factor (named the cement dust factor) were found, contributing together 10, 14 and 8%. We associate these two factors to different processes in the cement plant. Despite their rather low contribution to PM10 mass, these sources have one of the highest OPs per µg of source. The results of the study provide vital information about the influence of particular sources on PM10 and OP in complex environments and are thus useful for PM control strategies and actions.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In an attempt to investigate the traffic-impacted vertical aerosols profile and its relationship with potential carcinogenicity and/or mutagenicity, samples of different sized airborne particles were ...collected in parallel at the 1st and 5th floor of a 19 m high building located next to one of the busiest roads of Athens. The maximum daily concentrations were 65.9, 42.5 and 38.5 μg/m3, for PM10, PM2.5 and PM1, respectively. The vertical concentration ratio decreased with increasing height verifying the role of the characteristics of the area (1st/5th floor: 1.21, 1.13, 1.09 for PM10, PM2.5 and PM1, respectively). Chemically, strengthening the previous hypothesis, the collected particles were mainly carbonaceous (68%–93%) with the maximum budget of the polyaromatic hydrocarbons being recorded near the surface (1st/5th floor: 1.84, 1.07, 1.15 for PM10, PM2.5 and PM1, respectively). The detected PM-bound PAHs along with the elements as well as the carbonaceous and ionic constituents were used in a source apportionment study. Exhaust and non-exhaust emissions, a mixed source of biomass burning and high temperature combustion processes (natural gas, gasoline/diesel engines), sea salt, secondary and soil particles were identified as the major contributing sources to the PM pollution of the investigated area. With respect to the health hazards, the calculation of the BenzoaPyrene toxicity equivalency factors underlined the importance of the height of residence in buildings for the level of the exposure (1st/5th floor: BaPTEQ: 1.82, 1.12, 1.10, BaPMEQ: 1.85, 1.13, 1.09 for PM10, PM2.5 and PM1, respectively). Finally, despite its verified significance as a surrogate compound for the mixture of the hydrocarbons (its contribution up to 72%, 79% on the level of the 1st and 5th floor, respectively), the importance of the incorporation of PAH species in addition to BaP when assessing PAH toxicity was clearly documented.
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•The vertical distance from the street level was determinant.•The maxima of the PM mass, the PAHs and the PM toxicity/mutagenicity occurred near the surface.•OC being followed by EC was the main contributor to the PM mass.•As many as possible PAH species should be incorporated in addition to BaP when assessing PAH toxicity.•The influence of the anthropogenic input was more pronounced at the smaller fraction.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Assessment of Arctic pollution is hampered by a lack of aerosol studies in Northern Siberia. Black carbon observations were carried out at the Hydrometeorological Observatory Tiksi, a coast of Laptev ...sea, from September 2014 to September 2016. Aerosol sampling was accompanied by physico-chemical characterization. BC climatology showed a seasonal variation with highest concentrations from January to March (up to 450ng/m3) and lowest ones for June and September (about 20ng/m3). Stagnant weather and stable atmosphere stratification resulted in accumulation of pollution, in dependence on the wind direction and air mass transportation. Carbon fractions, functionalities, ions, and elements are associated to marine, biogenic, and continental sources. In September low OC, aliphatic, carbonyls, amines, and hydroxyls characterize background aerosols. Na+/Cl− ratio much higher than in sea-salt indicates a strong Cl depletion. Increased OC, aromatic, carbonyls, and nitrocompounds as well as waste burning markers K+, Cl−, and PO42− confirm impacts from Tiksi landfill burns. BC pollution episodes are differentiated through increased EBC and sulfates, related to gas flaring, industrial and residential emissions transported from Western Siberia while the increase of carbonyls, hydroxyl, and aromatic indicate emissions sources from Yakutia and Tiksi urban area. Arctic Haze aerosols are characterized by increased concentrations of SO42− in comparison with OC, much higher abundance of oxygenated compounds with respect to alkanes of anthropogenic origin. In summer rich organic chemistry indicates impacts of biogenic, local urban, and shipping sources as well as secondary aerosol formation influenced by emissions from low latitude Siberia.
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•Chemical characterization of East Siberian Arctic aerosols•Insight on source-influenced and season-dependent composition•Black carbon indicates arctic background and pollution episodes.•Source markers by carbon fractions, functionalities and ions•Marine, biogenic, continent local and long–term transportation sources
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
The study aims to assess the differences between the chemical profiles of the major anthropogenic and natural PM sources in two areas with different levels of urbanization and traffic density within ...the same urban agglomeration. A traffic site and an urban background site in the Athens Metropolitan Area have been selected for this comparison. For both sites, eight sources were identified, with seven of them being common for the two sites (Mineral Dust, non-Exhaust Emissions, Exhaust Emissions, Heavy Oil Combustion, Sulfates & Organics, Sea Salt and Biomass Burning) and one, site-specific (Nitrates for the traffic site and Aged Sea Salt for the urban background site). The similarity between the source profiles was quantified using two statistical analysis tools, Pearson correlation (PC) and Standardized Identity Distance (SID). According to Pearson coefficients five out of the eight source profiles present high (PC > 0.8) correlation (Mineral Dust, Biomass Burning, Sea Salt, Sulfates and Heavy Oil Combustion), one presented moderate (0.8 > PC > 0.6) correlation (Exhaust) and two low/no (PC < 0.6) correlation (non-Exhaust, Nitrates/Aged Sea Salt). The source profiles that appear to be more correlated are those of sources that are not expected to have high spatial variability because there are either natural/secondary and thus have a regional character or are emitted outside the urban agglomeration and are transported to both sites. According to SID four out of the eight sources have high statistical correlation (SID < 1) in the two sites (Mineral Dust, Sea salt, Sulfates, Heavy Oil Combustion). Biomass Burning was found to be the source that yielded different results from the two methodologies. The careful examination of the source profile of that source revealed the reason for this discrepancy. SID takes all the species of the profile equally into account, while PC might be disproportionally affected by a few numbers of species with very high concentrations. It is suggested, based on the findings of this work, that the combined use of both tools can lead the users to a thorough evaluation of the similarity of source profiles. This work is, to the best of our knowledge, the first time a study is focused on the quantitative comparison of the source profiles for sites inside the same urban agglomeration using statistical indicators.
•Comparison between the sources of two sites inside an urban agglomeration.•Two statistical indicators PC and SID can be used for source profile comparison.•Source profiles with low spatial varability are more consistent in an urban area.•PC and SID provide complementary information in source profile comparison.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The unprecedented growth in aviation during the last years has resulted in a notable increase of local air pollution related to airports. The impacts of aviation on air quality can be extremely high ...particularly around airports serving remote insular regions with pristine atmospheric environments. Here we report measurements that show how the atmospheric aerosol is affected by the activity at a small airport in a remote region. More specifically, we provide measurements performed at the airport of Mytilene, Greece, a regional yet international airport that serves the entire island of Lesvos; the third largest island of the country. The measurements show that the activity during landing, taxiing and take-off of the aircrafts accounted for up to a 10-fold increase in particulate matter (PM) mass concentration in the vicinity of the airport. The number concentration of particles having diameters from 10 to 500nm also increased from ca. 4×102 to 8×105particlescm−3, while the mean particle diameter decreased to 20nm when aircrafts were present at the airport. Elemental analysis on particle samples collected simultaneously at the airport and at a remote site 3km away, showed that the former were significantly influenced by combustion sources, and specifically from the engines of the aircrafts. Our results show that despite their small size, local airports serving remote insular regions should be considered as important air pollution hotspots, raising concerns for the exposure of the people working and leaving in their vicinities to hazardous pollutants.
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•Aviation activity accounted for up to a 10-fold increase TSP mass concentration.•Elemental analysis showed that aircraft emissions are the dominant source of PM.•Particle number concentrations increased by 2 orders of magnitude during take-offs.•Mean particle size dropped from 70 to 20nm during due to aircraft activity.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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