Abstract. This work evaluates the impact of trans-regional aerosol transport from the polluted Po basin on particulate matter levels (PM10) and physico-chemical characteristics in the northwestern Alps. To this purpose, we exploited a multi-sensor, multiplatform database over a 3-years period (2015–2017) accompanied by a series of numerical simulations. The experimental setup included operational (24/7) vertically-resolved aerosol profiles by an Automated LiDAR-Ceilometer (ALC), verticallyintegrated aerosol properties by a sun/sky photometer, and surface measurements of aerosol mass concentration, size distribution and chemical composition. This experimental set of observations was then complemented by modelling tools, including Numerical Weather Prediction (NWP), Trajectory Statistical (TSM) and Chemical Transport (CTM) models, plus Positive Matrix Factorisation (PMF) on both the PM10 chemical speciation analyses and size distributions. In a first companion study (Diémoz et al., 2019), we showed and discussed through detailed case studies the 4-D phenomenology of recurrent episodes of aerosol transport from the polluted Po basin to the northwestern Italian Alps, and particularly to the Aosta Valley. Here we draw more general and statistically significant conclusions on the frequency of occurrence of this phenomenon, and on the quantitative impact of this regular, wind-driven, aerosol-rich atmospheric tide on PM10 air quality levels in this alpine environment. Combining vertically-resolved ALC measurements with wind information, we found that an advected aerosol layer is observed at the receptor site (Aosta) in 93 % of days characterized by easterly winds (thermally-driven winds from the plain or synoptic circulation regimes), and that the longer the time spent by air masses over the Po plain the higher this probability. On a seasonal basis, frequency of advected aerosol layers from the Po basin maximises in summer (70 % of the days classified using the ALC profiles) and minimises in winter and spring (57 % of the classified days). Duration of these advection events ranges from few hours up to several days, while aerosol layer thickness ranges from 500 up to 4000 m. This phenomenon was found to largely impact both surface levels and column-integrated aerosol properties, with PM10 and AOD values respectively increasing up to a factor of 3.5 and 4 in dates under the Po Valley influence. Similar variations in PM10 values observed at different stations within the Aosta Valley also indicated the phenomenon to act at the regional scale and to be related to non-local emissions. Pollution transport events were also shown to modify the mean chemical composition and typical size of particles in the target region. In fact, increase in secondary species, and mainly nitrate- and sulfate-rich components, were found to be effective proxies of the advections, with the transported aerosol responsible for at least 25 % of the PM10 measured in the urban site of Aosta, and adding up to over 50 μg m−3 during specific episodes, thus exceeding alone the EU established daily limit. This percentage is expected to be higher in the rural, pristine areas on the northwestern Alps, where chemical data were not available and trans-boundary contribution to PM10 might thus exceed the local one. Advected aerosols were also found to be on average finer, more light-scattering and more hygroscopic than the locally-produced ones. From a modelling point of view, our CTM simulations performed over a full year showed that the model is able to reproduce the phenomenon but underestimates its impact on PM10 levels. As a sensitivity test, we employed the ALC-derived identification of aerosol advections to re-weight the emissions from outside the boundaries of the regional domain in order to match the observed PM10 field. This simplified exercise indicated that an increase of such external emissions by a factor of 4 in the model would reduce the PM10 mean bias forecasts error (MBE) from −10 μg m−3 to less than 2 μg m−3, the normalised mean standard deviation (NMSD) from over −50 % to less than −10 % and would halve the model PM10 maximum deviations.
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