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We present an analysis of modelled trends of PM10, NO2 and O3 airborne concentrations over the Italian territory in the period 2003–2010. Our analysis shows a general downward simulated trend for all pollutants, with good agreement between observed and modelled values and the model widening both coverage and significance of air concentration trends. Due to the complex atmospheric dynamics, emission reductions do not always result in decreasing concentrations, especially for secondary pollutants.
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https://doi.org/10.5194/acp-2020-1058
https://doi.org/10.5194/acp-2020-1058

  04 Jan 2021

04 Jan 2021

Review status: this preprint is currently under review for the journal ACP.

Measured and modelled air quality trends in Italy over the period 2003–2010

Ilaria D'Elia, Gino Briganti, Lina Vitali, Antonio Piersanti, Gaia Righini, Massimo D'Isidoro, Andrea Cappelletti, Mihaela Mircea, Mario Adani, Gabriele Zanini, and Luisella Ciancarella Ilaria D'Elia et al.
  • Laboratory of Atmospheric Pollution - Italian National Agency for New Technologies, Energy and Sustainable Economic Development - ENEA, Rome, 00123, Italy

Abstract. Air pollution harms human health and the environment. Several regulatory efforts and different actions have been taken in the last decades by authorities. Air quality trend analysis represents a valid tool in assessing the impact of these actions taken both at national and local levels. This paper presents for the first time the capability of the Italian national chemical transport model, AMS-MINNI, in capturing the observed concentration trends of three air pollutants, NO2, inhalable particles having diameter less than 10 micrometres (PM10) and O3, in Italy over the period 2003–2010. We firstly analyse the model performance finding it in line with the state of the art of regional models applications. The modelled trends result in a general significant downward trend for the three pollutants and, in comparison with observations, the values of the simulated slopes show the same magnitude for NO2 (in the range −3.0 ÷ −0.5 ug m−3 yr−1), while a smaller variability is detected for PM10 (−1.5 ÷ −0.5 ug m−3 yr−1) and O3-maximum daily 8-hour average concentration (−2.0 ÷ −0.5 ug m−3 yr−1). As a general result, we find a good agreement between modelled and observed trends; moreover, the model allowed to extend both the spatial coverage and the statistical significance of pollutants' concentrations trends with respect to observations, in particular for NO2. We also conduct a qualitative attempt to correlate the temporal concentration trends to meteorological and emission variability. Since no clear tendency in yearly meteorological anomalies (temperature, precipitation, geopotential height) was observed for the period investigated, we focus the discussion of concentrations trends on emissions variations. We point out that, due to the complex links between precursors emissions and air pollutants concentrations, emission reductions do not always result in a corresponding decrease in atmospheric concentrations, especially for those pollutants that are formed in the atmosphere such as O3 and the major fraction of PM10. These complex phenomena are still uncertain and their understanding is of the utmost importance in planning future policies for reducing air pollution and its impacts on health and ecosystems.

Ilaria D'Elia et al.

Status: open (until 01 Mar 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Ilaria D'Elia et al.

Ilaria D'Elia et al.

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Short summary
We present an analysis of modelled trends of PM10, NO2 and O3 airborne concentrations over the Italian territory in the period 2003–2010. Our analysis shows a general downward simulated trend for all pollutants, with good agreement between observed and modelled values and the model widening both coverage and significance of air concentration trends. Due to the complex atmospheric dynamics, emission reductions do not always result in decreasing concentrations, especially for secondary pollutants.
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