Transport of Po Valley aerosol pollution to the northwestern Alps – Part 2: Long-term impact on air quality

• Main Authors: H. Diémoz, G. P. Gobbi, T. Magri, G. Pession, S. Pittavino, I. K. F. Tombolato, M. Campanelli, F. Barnaba
• Format: Article
• Language: English
• Published: Copernicus Publications 2019-08-01
• Series: Atmospheric Chemistry and Physics
• Online Access: https://www.atmos-chem-phys.net/19/10129/2019/acp-19-10129-2019.pdf

<p>This work evaluates the impact of trans-regional aerosol transport from the Po basin on particulate matter levels (<span class="inline-formula">PM₁₀</span>) and physico-chemical characteristics in the northwestern Alps. To this purpose, we exploited a multi-sensor, multi-platform database over a 3-year 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), vertically integrated 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 <span class="inline-formula">PM₁₀</span> chemical speciation analyses and particle size distributions. In a first companion study, 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. 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 <span class="inline-formula">PM₁₀</span> air-quality levels in this alpine environment. Based on an original ALC-derived classification, we found that an advected aerosol layer is observed at the receptor site (Aosta) in 93&thinsp;% of days characterized by easterly winds (i.e. from the Po basin) and that the longer the time spent by air masses over the Po plain the higher this probability. Frequency of these advected aerosol layers was found to be rather stable over the seasons with about 50&thinsp;% of the days affected. Duration of these advection events ranges from few hours up to several days, while aerosol layer thickness ranges from 500 up to 4000&thinsp;<span class="inline-formula">m</span>. Our results confirm this phenomenon to be related to non-local emissions, to act at the regional scale and to largely impact both surface levels and column-integrated aerosol properties. In Aosta, <span class="inline-formula">PM₁₀</span> and aerosol optical depth (AOD) values increase respectively up to factors of 3.5 and 4 in dates under the Po Valley influence. 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&thinsp;% of the <span class="inline-formula">PM₁₀</span> measured in the urban site of Aosta, and adding up to over 50&thinsp;<span class="inline-formula">µg m⁻³</span> during specific episodes, thus exceeding alone the EU established daily limit. 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 markedly underestimates its impact on <span class="inline-formula">PM₁₀</span> 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 <span class="inline-formula">PM₁₀</span> field. This simplified exercise indicated that an increase in such “external” emissions by a factor of 4 in the model is needed to halve the model <span class="inline-formula">PM₁₀</span> maximum deviations and to significantly reduce the <span class="inline-formula">PM₁₀</span> normalised mean bias forecasts error (from <span class="inline-formula">−35</span>&thinsp;% to 5&thinsp;%).</p>