Evaluation of the effects of the COVID-19 lockdown and the meteorology on surface ozone variability with machine learning techniques.&#160;&#160;
Authors: Gagliardi Roberta Valentina , Andenna Claudio 
Published: July 2022

&lt;p&gt;The Mediterranean Basin, which is considered an hot-spot region in term of air-quality and climate change, frequently experiences surface ozone (O&lt;sub&gt;3&lt;/sub&gt;) exceedances to European legislated values for human health protection. Indeed, its peculiar meteorological conditions, such as frequent clear sky and high solar radiation in summer, as well as the availability of natural and anthropogenic precursors, enhance the formation of photochemical ozone so increasing the risk of O&lt;sub&gt;3 &lt;/sub&gt;pollution events in the area.&lt;/p&gt;
&lt;p&gt;O&lt;sub&gt;3&lt;/sub&gt; is one of the air pollutants of most concern for public health, as it can damage the respiratory and circulatory systems. O&lt;sub&gt;3 &lt;/sub&gt;can also deteriorate the vegetation, ecosystems, and building materials. Moreover, it is also one of the most important greenhouse gases as a so-called short-lived climate factor. Being a secondary pollutant, the variability of O&lt;sub&gt;3&lt;/sub&gt; is strongly dependent on the precursors, the meteorological parameters and the interactions among them through a series of complex and non-linear functions. Such complexity makes the definition of the O&lt;sub&gt;3 &lt;/sub&gt;control strategies not immediate; this is why the improvement of the extensive networks of continuous O&lt;sub&gt;3&lt;/sub&gt; measurements as well as of appropriate methods to analyze O&lt;sub&gt;3&lt;/sub&gt; levels and to predict future changes is required.&lt;/p&gt;
&lt;p&gt;The abrupt and unplanned anthropogenic emission reductions determined by the COVID-19 lockdown in 2020 produced an extraordinary real-world opportunity to assess the air quality response to changes in emissions. Taking advantage of this circumstance, the present study aims to characterize the influence of both the precursor emissions and the local meteorological conditions during 2020 on the surface ozone concentrations measured in the Basilicata region, (Southern Italy), which is located in the center of the Mediterranean basin.&lt;/p&gt;
&lt;p&gt;To these ends, eXtreme Gradient Boosting (XGBoost) machine learning (ML) models were built to provide both the business-as-usual (BAU) and the meteorological normalized O&lt;sub&gt;3&lt;/sub&gt; time series. The former allows the estimates of the impact of the lockdown restrictive measures on O&lt;sub&gt;3&lt;/sub&gt; by means of the comparison with the observed concentrations, the latter accounts for the meteorological variability and other time features for more reliable assessment of O&lt;sub&gt;3&lt;/sub&gt; trend/changes. Moreover, thanks to the non-black box nature of the developed ML models, the partial dependence of the observed O&lt;sub&gt;3&lt;/sub&gt; concentrations on each explanatory variables used in the models can be analysed, shedding light on the role of local meteorological processes in the observed O&lt;sub&gt;3&lt;/sub&gt; variability.&lt;/p&gt;
&lt;p&gt;The results obtained made it possible to evaluate the different contribution of emissions and meteorology on the detected changes in the levels of O&lt;sub&gt;3&lt;/sub&gt;&lt;em&gt; &lt;/em&gt;and to provide deeper insights of major drivers of surface ozone concentrations in the studied area. This knowledge could help in defining strategies effective in reducing the negative impacts associated with O&lt;sub&gt;3&lt;/sub&gt; exposure as well as in optimizing the potential synergies between O&lt;sub&gt;3&lt;/sub&gt; reduction policies and climate change policies.&lt;/p&gt;
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