Modal aerosol dynamics model for Europe

Ackermann, I.J.; Hass, H.; Memmesheimer, M.; Ebel, A.; Binkowski, Francis S.; Shankar, Uma

doi:10.1016/s1352-2310(98)00006-5

Cited by 645 publications

(178 citation statements)

References 25 publications

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“…RADM2 is a widely used mechanism over the European domain and it is coupled with the Modal Aerosol Dynamics for Europe (MADE) (Ackermann et al, 1998). MADE uses a modal approach for aerosol treatment and is coupled with the Secondary Organic Aerosol Model (SORGAM) (Schell et al, 2001).…”

Section: Radm2-made/sorgam (Rms)mentioning

confidence: 99%

Air quality modelling in the summer over the eastern Mediterranean using WRF-Chem: chemistry and aerosol mechanism intercomparison

et al. 2018

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Abstract. We employ the WRF-Chem model to study summertime air pollution, the intense photochemical activity and their impact on air quality over the eastern Mediterranean. We utilize three nested domains with horizontal resolutions of 80, 16 and 4 km, with the finest grid focusing on the island of Cyprus, where the CYPHEX campaign took place in July 2014. Anthropogenic emissions are based on the EDGAR HTAP global emission inventory, while dust and biogenic emissions are calculated online. Three simulations utilizing the CBMZ-MOSAIC, MOZART-MOSAIC, and RADM2-MADE/SORGAM gas-phase and aerosol mechanisms are performed. The results are compared with measurements from a dense observational network of 14 ground stations in Cyprus. The model simulates T 2 m , P surf , and WD 10 m accurately, with minor differences in WS 10 m between model and observations at coastal and mountainous stations attributed to limitations in the representation of the complex topography in the model. It is shown that the south-eastern part of Cyprus is mostly affected by emissions from within the island, under the dominant (60 %) westerly flow during summertime. Clean maritime air from the Mediterranean can reduce concentrations of local air pollutants over the region during westerlies. Ozone concentrations are overestimated by all three mechanisms (9 % ≤ NMB ≤ 23 %) with the smaller mean bias (4.25 ppbV) obtained by the RADM2-MADE/SORGAM mechanism. Differences in ozone concentrations can be attributed to the VOC treatment by the three mechanisms. The diurnal variability of pollution and ozone precursors is not captured (hourly correlation coefficients for O 3 ≤ 0.29). This might be attributed to the underestimation of NO x concentrations by local emissions by up to 50 %. For the fine particulate matter (PM 2.5 ), the lowest mean bias (9 µg m −3 ) is obtained with the RADM2-MADE/SORGAM mechanism, with overestimates in sulfate and ammonium aerosols. Overestimation of sulfate aerosols by this mechanism may be linked to the SO 2 oxidation in clouds. The MOSAIC aerosol mechanism overestimates PM 2.5 concentrations by up to 22 µg m −3 due to a more pronounced dust component compared to the other two mechanisms, mostly influenced by the dust inflow from the global model. We conclude that all three mechanisms are very sensitive to boundary conditions from the global model for both gas-phase and aerosol pollutants, in particular dust and ozone.

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Section: Radm2-made/sorgam (Rms)mentioning

confidence: 99%

Air quality modelling in the summer over the eastern Mediterranean using WRF-Chem: chemistry and aerosol mechanism intercomparison

et al. 2018

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“…Figure 1 shows the simulation regions, and Table 1 summarises the most important information on these simulations. WRF-Chem (Weather Research and Forecasting model coupled with Chemistry) (Grell et al, 2005;Fast et al, 2006) was run for three regions using the MADE/SORGAM aerosol module (Ackermann et al, 1998;Schell et al, 2001), and one region using the GOCART bulk aerosol scheme. The meteorology was nudged to NCEP-FNL operational analysis data.…”

Section: The Regional Modelsmentioning

confidence: 99%

Will a perfect model agree with perfect observations? The impact of spatial sampling

et al. 2016

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Abstract. The spatial resolution of global climate models with interactive aerosol and the observations used to evaluate them is very different. Current models use grid spacings of ∼ 200 km, while satellite observations of aerosol use so-called pixels of ∼ 10 km. Ground site or airborne observations relate to even smaller spatial scales. We study the errors incurred due to different resolutions by aggregating high-resolution simulations (10 km grid spacing) over either the large areas of global model grid boxes ("perfect" model data) or small areas corresponding to the pixels of satellite measurements or the field of view of ground sites ("perfect" observations). Our analysis suggests that instantaneous rootmean-square (RMS) differences of perfect observations from perfect global models can easily amount to 30-160 %, for a range of observables like AOT (aerosol optical thickness), extinction, black carbon mass concentrations, PM 2.5 , number densities and CCN (cloud condensation nuclei). These differences, due entirely to different spatial sampling of models and observations, are often larger than measurement errors in real observations. Temporal averaging over a month of data reduces these differences more strongly for some observables (e.g. a threefold reduction for AOT), than for others (e.g. a twofold reduction for surface black carbon concentrations), but significant RMS differences remain (10-75 %). Note that this study ignores the issue of temporal sampling of real observations, which is likely to affect our present monthly error estimates. We examine several other strategies (e.g. spatial aggregation of observations, interpolation of model data) for reducing these differences and show their effectiveness. Finally, we examine consequences for the use of flight campaign data in global model evaluation and show that significant biases may be introduced depending on the flight strategy used.

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“…Brownian coagulation (Lee and Chen, 1984), condensation (Söderlund et al, 1998), or nucleation (Ackermann et al, 1998). Lognormal functions are generally used to describe aerosol particle dimensions (William C. Hinds, 1999), and also in specific cases such as marine aerosol (Smith et al, 1993) or biomass burning aerosol emissions from vegetation fires (Janhäll et al, 2009).…”

Section: Scanning Electron Microscopy and Energy-dispersive X-ray Spementioning

confidence: 99%

Characterization of leaf-level particulate matter for an industrial city using electron microscopy and X-ray microanalysis

Sgrigna

Baldacchini

Esposito

et al. 2016

Science of The Total Environment

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This study reports application of monitoring and characterization protocol for particulate matter (PM) deposited on tree leaves, using Quercus ilex as a case study species. The study area is located in the industrial city of Terni in central Italy, with high PM concentrations. Four trees were selected as representative of distinct pollution environments based on their proximity to a steel factory and a street. Wash off from leaves onto cellulose filters were characterized using scanning electron microscopy and energy dispersive X-ray spectroscopy, inferring the associations between particle sizes, chemical composition, and sampling location.Modeling of particle size distributions showed a tri-modal fingerprint, with the three modes centered at 0.6 (factory related), 1.2 (urban background), and 2.6 μm (traffic related). Chemical detection identified 23 elements abundant in the PM samples. Principal component analysis recognized iron and copper as sourcespecific PM markers, attributed mainly to industrial and heavy traffic pollution respectively. Upscaling these results on leaf area basis provided a useful indicator for strategic evaluation of harmful PM pollutants using tree leaves.

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Modal aerosol dynamics model for Europe

Cited by 645 publications

References 25 publications

Air quality modelling in the summer over the eastern Mediterranean using WRF-Chem: chemistry and aerosol mechanism intercomparison

Air quality modelling in the summer over the eastern Mediterranean using WRF-Chem: chemistry and aerosol mechanism intercomparison

Will a perfect model agree with perfect observations? The impact of spatial sampling

Characterization of leaf-level particulate matter for an industrial city using electron microscopy and X-ray microanalysis

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