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- Crippa, M., et al.
(författare)
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Organic aerosol components derived from 25 AMS data sets across Europe using a consistent ME-2 based source apportionment approach
- 2014
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Ingår i: Atmospheric Chemistry and Physics. - : Copernicus GmbH. - 1680-7324. ; 14:12, s. 6159-6176
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Tidskriftsartikel (refereegranskat)abstract
- Organic aerosols (OA) represent one of the major constituents of submicron particulate matter (PM1) and comprise a huge variety of compounds emitted by different sources. Three intensive measurement field campaigns to investigate the aerosol chemical composition all over Europe were carried out within the framework of the European Integrated Project on Aerosol Cloud Climate and Air Quality Interactions (EUCAARI) and the intensive campaigns of European Monitoring and Evaluation Programme (EMEP) during 2008 (May-June and September-October) and 2009 (February-March). In this paper we focus on the identification of the main organic aerosol sources and we define a standardized methodology to perform source apportionment using positive matrix factorization (PMF) with the multilinear engine (ME-2) on Aerodyne aerosol mass spectrometer (AMS) data. Our source apportionment procedure is tested and applied on 25 data sets accounting for two urban, several rural and remote and two high altitude sites; therefore it is likely suitable for the treatment of AMS-related ambient data sets. For most of the sites, four organic components are retrieved, improving significantly previous source apportionment results where only a separation in primary and secondary OA sources was possible. Generally, our solutions include two primary OA sources, i.e. hydrocarbon-like OA (HOA) and biomass burning OA (BBOA) and two secondary OA components, i.e. semi-volatile oxygenated OA (SV-OOA) and low-volatility oxygenated OA (LV-OOA). For specific sites cooking-related (COA) and marine-related sources (MSA) are also separated. Finally, our work provides a large overview of organic aerosol sources in Europe and an interesting set of highly time resolved data for modeling purposes.
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| 2. |
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| 3. |
- Knote, C., et al.
(författare)
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Towards an online-coupled chemistry-climate model: evaluation of trace gases and aerosols in COSMO-ART
- 2011
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Ingår i: Geoscientific Model Development. - : Copernicus GmbH. - 1991-959X .- 1991-9603. ; 4:4, s. 1077-1102
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Tidskriftsartikel (refereegranskat)abstract
- The online-coupled, regional chemistry transport model COSMO-ART is evaluated for periods in all seasons against several measurement datasets to assess its ability to represent gaseous pollutants and ambient aerosol characteristics over the European domain. Measurements used in the comparison include long-term station observations, satellite and ground-based remote sensing products, and complex datasets of aerosol chemical composition and number size distribution from recent field campaigns. This is the first time these comprehensive measurements of aerosol characteristics in Europe are used to evaluate a regional chemistry transport model. We show a detailed analysis of the simulated size-resolved chemical composition under different meteorological conditions. Mean, variability and spatial distribution of the concentrations of O-3 and NOx are well reproduced. SO2 is found to be overestimated, simulated PM2.5 and PM10 levels are on average underestimated, as is AOD. We find indications of an overestimation of shipping emissions. Time evolution of aerosol chemical composition is captured, although some biases are found in relative composition. Nitrate aerosol components are on average overestimated, and sulfates underestimated. The accuracy of simulated organics depends strongly on season and location. While strongly underestimated during summer, organic mass is comparable in spring and autumn. We see indications for an overestimated fractional contribution of primary organic matter in urban areas and an underestimation of SOA at many locations. Aerosol number concentrations compare well with measurements for larger size ranges, but overestimations of particle number concentration with factors of 2-5 are found for particles smaller than 50 nm. Size distribution characteristics are often close to measurements, but show discrepancies at polluted sites. Suggestions for further improvement of the modeling system consist of the inclusion of a revised secondary organic aerosols scheme, aqueous-phase chemistry and improved aerosol boundary conditions. Our work sets the basis for subsequent studies of aerosol characteristics and climate impacts with COSMO-ART, and highlights areas where improvements are necessary for current regional modeling systems in general.
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| 4. |
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| 5. |
- Falchi, M., et al.
(författare)
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Low copy number of the salivary amylase gene predisposes to obesity
- 2014
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Ingår i: Nature Genetics. - : Nature Publishing Group. - 1061-4036 .- 1546-1718. ; 46:5, s. 492-497
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Tidskriftsartikel (refereegranskat)abstract
- Common multi-allelic copy number variants (CNVs) appear enriched for phenotypic associations compared to their biallelic counterparts. Here we investigated the influence of gene dosage effects on adiposity through a CNV association study of gene expression levels in adipose tissue. We identified significant association of a multi-allelic CNV encompassing the salivary amylase gene (AMY1) with body mass index (BMI) and obesity, and we replicated this finding in 6,200 subjects. Increased AMY1 copy number was positively associated with both amylase gene expression (P = 2.31 × 10-14) and serum enzyme levels (P < 2.20 × 10-16), whereas reduced AMY1 copy number was associated with increased BMI (change in BMI per estimated copy =-0.15 (0.02) kg/m 2; P = 6.93 × 10-10) and obesity risk (odds ratio (OR) per estimated copy = 1.19, 95% confidence interval (CI) = 1.13-1.26; P = 1.46 × 10-10). The OR value of 1.19 per copy of AMY1 translates into about an eightfold difference in risk of obesity between subjects in the top (copy number > 9) and bottom (copy number < 4) 10% of the copy number distribution. Our study provides a first genetic link between carbohydrate metabolism and BMI and demonstrates the power of integrated genomic approaches beyond genome-wide association studies. © 2014 Nature America, Inc. All rights reserved.
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| 6. |
- Fountoukis, C., et al.
(författare)
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Organic aerosol concentration and composition over Europe: insights from comparison of regional model predictions with aerosol mass spectrometer factor analysis
- 2014
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Ingår i: Atmospheric Chemistry and Physics. - : Copernicus GmbH. - 1680-7324. ; 14:17, s. 9061-9076
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Tidskriftsartikel (refereegranskat)abstract
- A detailed three-dimensional regional chemical transport model (Particulate Matter Comprehensive Air Quality Model with Extensions, PMCAMx) was applied over Europe, focusing on the formation and chemical transformation of organic matter. Three periods representative of different seasons were simulated, corresponding to intensive field campaigns. An extensive set of AMS measurements was used to evaluate the model and, using factor-analysis results, gain more insight into the sources and transformations of organic aerosol (OA). Overall, the agreement be-tween predictions and measurements for OA concentration is encouraging, with the model reproducing two-thirds of the data (daily average mass concentrations) within a factor of 2. Oxygenated OA (OOA) is predicted to contribute 93% to total OA during May, 87% during winter and 96% during autumn, with the rest consisting of fresh primary OA (POA). Predicted OOA concentrations compare well with the observed OOA values for all periods, with an average fractional error of 0.53 and a bias equal to -0.07 (mean error = 0.9 mu g m(-3), mean bias =-0.2 mu g m(-3)). The model systematically underpredicts fresh POA at most sites during late spring and autumn (mean bias up to -0.8 mu g m(-3)). Based on results from a source apportionment algorithm running in parallel with PMCAMx, most of the POA originates from biomass burning (fires and residential wood combustion), and therefore biomass burning OA is most likely underestimated in the emission inventory. The sensitivity of POA predictions to the corresponding emissions' volatility distribution is discussed. The model performs well at all sites when the Positive Matrix Factorization (PMF)-estimated low-volatility OOA is compared against the OA with saturation concentrations of the OA surrogate species C* <= 0.1 mu g m(-3) and semivolatile OOA against the OA with C* > 0.1 mu g m(-3).
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