In this work, the source of ambient particulate matter (PM10 ) collected over a one-year period at an urban background site in Lens (France) was determined and investigated using a positive matrix ...factorization receptor model (US EPA PMF v3.0). In addition, a potential source contribution function (PSCF) was performed by means of the Hybrid Single-Particle Lagrangian Integrated Trajectory (Hysplit) v4.9 model to assess prevailing geographical origins of the identified sources. A selective iteration process was followed for the qualification of the more robust and meaningful PMF solution. Components measured and used in the PMF included inorganic and organic species: soluble ionic species, trace elements, elemental carbon (EC), sugar alcohols, sugar anhydride, and organic carbon (OC). The mean PM10 concentration measured from March 2011 to March 2012 was about 21 μg m-3 with typically OM, nitrate and sulfate contributing to most of the mass and accounting respectively for 5.8, 4.5 and 2.3 μg m-3 on a yearly basis. Accordingly, PMF outputs showed that the main emission sources were (in decreasing order of contribution) secondary inorganic aerosols (28% of the total PM10 mass), aged marine emissions (19%), with probably predominant contribution of shipping activities, biomass burning (13%), mineral dust (13%), primary biogenic emissions (9%), fresh sea salts (8%), primary traffic emissions (6%) and heavy oil combustion (4%). Significant temporal variations were observed for most of the identified sources. In particular, biomass burning emissions were negligible in summer but responsible for about 25% of total PM10 and 50% of total OC in wintertime. Conversely, primary biogenic emissions were found to be negligible in winter but to represent about 20% of total PM10 and 40% of total OC in summer. The latter result calls for more investigations of primary biogenic aerosols using source apportionment studies, which quite usually disregard this type of source. This study further underlines the major influence of secondary processes during daily threshold exceedances. Finally, apparent discrepancies that could be generally observed between filter-based studies (such as the present one) and aerosol mass spectrometer-based PMF analyses (organic fractions) are also discussed.
The emission of organic aerosols (OA) in the ambient air by residential wood burning is nowadays a subject of great scientific concern and a growing number of studies aim at apportioning the ...influence of such emissions on urban air quality. In the present study, results obtained using two commonly-used source apportionment models, i.e., Chemical Mass Balance (CMB, performed with off-line filter measurements) and Positive Matrix Factorization (PMF, applied to Aerosol Mass Spectrometer measurements), as well as using the recently-proposed Aethalometer model (based on the measurement of the aerosol light absorption at different wavelengths) are inter-compared. This work is performed using field data obtained during the winter season (14 to 29 January 2009) at an urban background site of a French Alpine city (Grenoble). Converging results from the different models indicate a major contribution of wood burning organic aerosols (OMwb) to the ambient aerosol organic fraction, with mean OMwb contributions to total OA of 68%, 61% and 37% for the CMB, the Aethalometer and the AMS-PMF models respectively, during the period when the three modelling studies overlapped (12 days). Quantitative discrepancies might notably be due to the overestimation of OMwb calculated by the CMB due to the loss of semi-volatile compounds from sources to receptor site, as well as to the accounting of oxidized primary wood burning organic (OPOAwb) aerosols within the Oxygenated Organic Aerosol (OOA) PMF-factor. This OOA factor accounts on average for about 50% of total OM, while non-combustion sources contribute to about 25% and 28% of total OM according to the CMB and Aethalometer models respectively. Each model suggests a mean contribution of fossil fuel emissions to total OM of about 10%. A good agreement is also obtained for the source apportionment of elemental carbon (EC) by both the CMB and the Aethalometer models, with fossil fuel emissions representing on average more than 80% of total EC.
The chemical characterization of PM2.5 was conducted at 5 rural background sites in France for the year 2013. Chemical analysis of daily samples every sixth day included the measurements of organic ...carbon (OC), elemental carbon (EC), ionic species and several specific primary and secondary organic tracers such as levoglucosan, polyols, methane sulfonic acid (MSA) and oxalate. The sampling sites were spatially distributed in order to be representative of the French atmospheric background. The results showed well identified temporal variations common to all the 5 sampling sites, covering a large fraction of France. During winter, concentrations of the biomass burning marker levoglucosan are significantly increased with high synchronous temporal pattern, indicating the strong impact of this source at a regional scale. During summer, concentrations of primary biogenic markers such as polyols (arabitol, mannitol) increase due to higher biological activities while oxalate contributions to OC also increases, attributed to ageing processes. The sources of primary organic aerosol are investigated using mono-tracer approaches based on these compounds. Results indicate that the relative contributions of wood burning to OC are very high, reaching an average value of 90% during winter for some of the rural sites. Terrestrial primary biogenic organic fraction is significant in summer and fall with a monthly contribution ranging from 4.5 to 9.5% of OC in PM2.5. A synchronous increase is also observed for secondary organic tracers (MSA, oxalic acid) during warm period confirming the influence on the large scale of these compounds that can account for 10–20% and 5–7% of the OC mass, respectively.
•Annual chemical composition and seasonal variability of rural PM2.5.•Synchronous variability of primary and secondary organic compounds.•OC source apportionment using primary molecular markers.•30% of the total OC attributed to the fungal spore during harvesting periods.•PSCF analysis highlighted a potential terrestrial additional source of MSA in continental sites.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Major contributors to the organic aerosol include water-soluble macromolecular compounds (e.g. HULISWS: Water Soluble Humic LIke Substances). The nature and sources of HULISWS are still largely ...unknown. This work is based on a monitoring in six different French cities performed during summer and winter seasons. HULISWS analysis was performed with a selective method of extraction complemented by carbon quantification. UV spectroscopy was also applied for their chemical characterisation. HULISWS carbon represent an important contribution to the organic aerosol mass in summer and winter, as it accounts for 12–22% of Organic Carbon and 34–40% of Water Soluble Organic Carbon. We found strong differences in the optical properties (specific absorbance at 250, 272, 280 nm and E2/E3 ratio) and therefore in the chemical structure between HULISWS from samples of summer- and wintertime. These differences highlight different processes responsible for emissions and formation of HULISWS according to the season, namely biomass burning in winter, and secondary processes in summer. Specific absorbance can also be considered as a rapid and useful indicator of the origin of HULISWS in urban environment.
A comprehensive aerosol characterization was conducted at Marseille during summer, including organic (OC) and elemental carbon (EC), major ionic species, radiocarbon (14C), water-soluble OC and HULIS ...(HUmic LIke Substances), elemental composition and primary and secondary organic markers. This paper is the second paper of a two-part series that uses this dataset to investigate the sources of Organic Aerosol (OA). While the first paper investigates the primary sources (El Haddad et al., 2010), this second paper focuses on the secondary fraction of the organic aerosol. In the context of overall OC mass balance, primary OC (POC) contributes on average for only 22% and was dominated by vehicular emissions accounting on average for 17% of OC. As a result, 78% of OC mass cannot be attributed to the major primary sources and remains un-apportioned. Radiocarbon measurements suggest that more than 70% of this fraction is of non-fossil origin, assigned predominantly to biogenic secondary organic carbon (BSOC). Therefore, contributions from three traditional BSOC precursors, isoprene, α-pinene and β-caryophyllene, were considered. These were estimated using the ambient concentrations of Secondary Organic Aerosol (SOA) markers from each precursor and laboratory-derived marker mass fraction factors. Secondary organic markers derived from isoprene photo-oxidation (ie: 2-methylglyceric acid and 2-methyltetrols) do not exhibit the same temporal trends. This variability was assigned to the influence of NOx concentration on their formation pathways and to their potential decay by further processing in the atmosphere. The influence of changes in isoprene chemistry on assessment of isoprene SOC contribution was evaluated explicitly. The results suggest a 60-fold variation between the different estimates computed using different isoprene SOC markers, implying that the available profiles do not reflect the actual isoprene SOC composition observed in Marseille. Using the marker-based approach, the aggregate contribution from traditional BSOC was estimated at only 4.2% of total OC and was dominated by α-pinene SOC accounting on average for 3.4% of OC. As a result, these estimates underpredict the inexplicably high loadings of OC. This underestimation can be associated with (1) uncertainties underlying the marker-based approach, (2) presence of other SOC precursors and (3) further processing of fresh SOC, as indicated by organosulfates (RSO4H) and HUmic LIke Substances (HULIS) measurements.
The level of nitrated and oxygenated derivatives of polycyclic aromatic hydrocarbons (PAHs) in the ambient air of two French alpine valleys was assessed. Two types of high volume samplers were used ...to collect PAHs and their derivatives. During both summer and winter campaigns conducted at Chamonix valley (Cv) and Maurienne valley (Mv) in 2002-2003, oxygenated PAH (OPAH) concentration levels were of the same order of magnitude as PAHs while nitrated PAH (NPAH) concentrations were one to two orders of magnitude lower. The fraction of PAHs, OPAHs and NPAHs associated with the particle phase was strongly dependent on their vapour pressure and on the ambient conditions.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
In order to identify and quantify key species associated with non-exhaust
emissions and exhaust vehicular emissions, a large comprehensive dataset of
particulate species has been obtained thanks to ...simultaneous near-road and
urban background measurements coupled with detailed traffic counts and
chassis dynamometer measurements of exhaust emissions of a few in-use
vehicles well-represented in the French fleet. Elemental carbon, brake-wear
metals (Cu, Fe, Sb, Sn, Mn), n-alkanes (C19-C26), light-molecular-weight
polycyclic aromatic hydrocarbons
(PAHs; pyrene, fluoranthene, anthracene) and two hopanes (17α21βnorhopane and
17α21βhopane) are strongly associated with the
road traffic. Traffic-fleet emission factors have been determined for all of
them and are consistent with most recent published equivalent data. When
possible, light-duty- and heavy-duty-traffic emission factors are also
determined. In the absence of significant non-combustion emissions, light-duty-traffic
emissions are in good agreement with emissions from chassis
dynamometer measurements. Since recent measurements in Europe including those
from this study are consistent, ratios involving copper (Cu∕Fe and Cu∕Sn)
could be used as brake-wear emissions tracers as long as brakes with Cu
remain in use. Near the Grenoble ring road, where the traffic was largely
dominated by diesel vehicles in 2011 (70 %), the OC∕EC ratio estimated for
traffic emissions was around 0.4. Although the use of quantitative data for
source apportionment studies is not straightforward for the identified
organic molecular markers, their presence seems to well-characterize fresh
traffic emissions.
The size distribution of polycyclic aromatic hydrocarbons (PAHs) and PAH derivatives was determined during the intensive sampling campaigns of the POVA (Pollution des Vallées Alpines) research ...programme, in two French alpine valleys, in winter and summer. The size distributions of PAHs, oxygenated PAHs (OPAHs) and nitrated PAHs (NPAHs) present large variations with year time and site type (traffic; suburban and rural). In general, these compounds were mainly associated (60–90%) with fine particles (
D
p<1.3
μm) in agreement with their release from sources (primary and/or secondary). The pollutant distributions with particle size were unimodal and centred at 0.85
μm both in the Chamonix and Maurienne valleys. The summer size distribution of NPAHs was centred at
D
p=2.75
μm. PAH, OPAH and NPAH super micrometre fractions were significantly larger in summer for most sites suggesting the existence of a second mode in that particle size range. A possible reason to explain this phenomenon is that aerosol was locally polluted and characterised by fine particles in winter whereas in summer, aerosol was more mixed and older with possibilities of adsorption of gaseous organic compounds at the surface of the pre-exiting particles. In summer, NPAHs were associated to a greater degree with the super micrometre fraction of the aerosol than the other categories of compounds.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
During the past years, actions implemented for the reduction of particulate matter emissions have in many European countries focused on road traffic emissions. Much less attention was paid to ...emissions from domestic wood combustion though the importance of residential wood burning as a source of atmospheric particulate matter (PM) in the Alpine region has been shown in many studies.
Here we review the current knowledge about the contribution of wood burning emissions to ambient concentrations of elemental carbon (EC), organic carbon (OC) and PM in the Alpine region. The published results obtained by different approaches (e.g. macro-tracer method, multivariate receptor modeling, chemical mass balance modelling, and so-called Aethalometer modeling) are used in an ambient mono-tracer approach to estimate representative relationships between wood burning tracers (levoglucosan and mannosan) and EC, OC and PM from wood burning. The relationships found are applied to available ambient measurements of levoglucosan and mannosan at Alpine sites for estimation of the contributions of wood burning emissions to average levels of carbonaceous aerosols and PM at these sites. Our results imply that PM from wood burning alone adds often up to 50% and more of the EU daily limit value for PM10 in several alpine valleys during days in winter. Concentrations of carbonaceous aerosols in these valleys are often up to six times higher than in urban or rural sites at the foothills of the Alps.
•Review of current knowledge about wood burning emissions in the alpine region.•Ambient mono-tracer approach combining data from source apportionment studies.•Compared concentrations of EC, OC and PM from wood burning at 23 measurement sites.•50% and more of EU daily limit value for PM10 reached due to wood burning emissions.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
In this study, the results of source apportionment of particulate matter (PM10), organic carbon (OC), and elemental carbon (EC) — as obtained through different approaches at different types of sites ...(urban background, urban roadside, and two rural sites in Switzerland) — are compared. The methods included in this intercomparison are positive matrix factorisation modelling (PMF, applied to chemical composition data including trace elements, inorganic ions, OC, and EC), molecular marker chemical mass balance modelling (MM-CMB), and the aethalometer model (AeM).
At all sites, the agreement of the obtained source contributions was reasonable for OC, EC, and PM10. Based on an annual average, and at most of the considered sites, secondary organic carbon (SOC) is the component with the largest contribution to total OC; the most important primary source of OC is wood combustion, followed by road traffic. Secondary aerosols predominate in PM10. All considered techniques identified road traffic as the dominant source of EC, while wood combustion emissions are of minor importance for this constituent.
The intercomparison of different source apportionment approaches is helpful to identify the strengths and the weaknesses of the different methods. Application of PMF has limitations when source emissions have a strong temporal correlation, or when meteorology has a strong impact on PM variability. In these cases, the use of PMF can result in mixed source profiles and consequently in the under- or overestimation of the real-world sources. The application of CMB models can be hampered by the unavailability of source profiles and the non-representativeness of the available profiles for local source emissions.
This study also underlines that chemical transformations of molecular markers in the atmosphere can lead to the underestimation of contributions from primary sources, in particular during the summer period or when emission sources are far away from the receptor sites.
► Source apportionment results for PM10, OC and EC by PMF, MM-CMB and AeM are compared. ► Generally good agreement of source contributions as estimated with the different approaches is obtained. ► The strengths and the limitations of the different approaches are investigated. ► PMF: correlated source activities are an obstacle for a correct identification of PM source. ► MM-CMB: degradation of organic tracers can lead to an underestimation of source impacts.
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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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