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Abstract. In this study, the Weather Research and Forecasting model with online coupled chemistry (WRF-Chem) is applied to simulate an intense Saharan dust outbreak event that took place over the Mediterranean in May 2014. Comparison of a simulation using a physics-based desert dust emission scheme with a numerical experiment using a simplified (minimal) emission scheme is included to highlight the advantages of the former. The model was found to reproduce well the synoptic meteorological conditions driving the dust outbreak: an omega-like pressure configuration associated with a cyclogenesis in the Atlantic coasts of Spain. The model performances in reproducing the atmospheric desert dust load were evaluated using a multi-platform observational dataset of aerosol and desert dust properties, including optical properties from satellite and ground-based sun photometers and lidars, plus in situ particulate matter mass concentration (PM) data. This comparison allowed us to investigate the model ability in reproducing both the horizontal and the vertical displacement of the dust plume, as well as its evolution in time.The comparison with satellite (MODIS-Terra) and sun photometers (AERONET) showed that the model is able to reproduce well the horizontal field of the aerosol optical depth (AOD) and its evolution in time (temporal correlation coefficient with AERONET of 0.85). On the vertical scale, the comparison with lidar data at a single site (Rome, Italy) confirms that the desert dust advection occurs in several, superimposed "pulses" as simulated by the model. Crossanalysis of the modeled AOD and desert dust emission fluxes further allowed for the source regions of the observed plumes to be inferred. The vertical displacement of the modeled dust plume was in rather good agreement with the lidar soundings, with correlation coefficients among aerosol extinction profiles up to 1 and mean discrepancy of about 50 %.The model-measurement comparison for PM 10 and PM 2.5 showed a good temporal matching, although it revealed a marked overestimation of PM 10 and PM 2.5 (of the order of 70 % during the dust peak). For PM 10 , it was also possible to investigate the accordance between the model-and the measurement-based dust PM 10 , this confirming the model PM 10 overestimation to be related to over-predicted dust mass up to a factor of 140 %. In all the model-measurement comparisons performed, the enhanced capabilities of the physics-based emission scheme with respect to its simplified, minimal version were evident and are documented.

Some studies suggested a role of the atmospheric particulate matter (PM) and of its oxidative potential (OP) in determining adverse health effects. Several works have focused on characterisation of source contributions to PM OP, mainly using three approaches: correlation between OP and chemical markers of specific sources; use of OP as input variable in source apportionment with receptor models; and multi-linear regression (MLR) between OP and source contributions to PM obtained from receptor models. Up to now, comparison of results obtained with different approaches on the same dataset is scarce. This work aims to perform a OP study of PM2.5 collected in an industrial site, located near a biogas production and combustion plant (in southern Italy), comparing different approaches to investigate the contributions of the different sources to OP. The PM2.5 samples were analysed for determining ions, metals, carbonaceous components, and OP activity with the DTT (dithiotreitol) assay. Results showed that OP normalised in volume (DTTV) is correlated with carbonaceous components and some ions (NO3−, and Ca2+) indicating that PM of combustion, secondary, and crustal origin could contribute to the OP activity. The source apportionment, done with the Environmental Protection Agency (EPA)—Positive Matrix Factorization (PMF5.0) model, identified six sources: secondary sulphate; biomass burning; industrial emissions; crustal; vehicle traffic and secondary nitrate; and sea spray. A MLR analysis between the source’s daily contributions and the daily DTTV values showed a reasonable agreement of the two approaches (PMF and MLR), identifying the biomass burning and the vehicle traffic and secondary nitrate as the main sources contributing to DTTV activity.