In response to the COVID-19 pandemic, governments declared severe restrictions throughout 2020, presenting an unprecedented scenario of reduced anthropogenic emissions of air pollutants derived mainly from traffic sources. To analyze the effect of these restrictions derived from COVID-19 pandemic on air quality levels, relative changes in NO, NO 2 , O 3 , PM10 and PM2.5 concentrations were calculated at urban traffic sites in the most populated Spanish cities over different periods with distinct restrictions in 2020. In addition to the changes calculated with respect to the observed air pollutant levels of previous years (2013–2019), relative changes were also calculated using predicted pollutant levels for the different periods over 2020 on a business-as-usual scenario using MLR models with meteorological and seasonal predictors. MLR models were selected among different data mining techniques (MLR RF, KNN), based on their higher performance and accuracy obtained from a leave-one-year-out cross-validation scheme using 2013–2019 data. A q-q mapping post-correction was also applied in all cases in order to improve the reliability of the predictions to reproduce the observed distributions and extreme events. This approach allows us to estimate the relative changes in the studied air pollutants only due to COVID-19 restrictions. The results obtained from this approach show a decreasing pattern for NO x , with the largest reduction in the lockdown period above −50%, whereas the increase observed for O 3 contrasts with the NO x patterns with a maximum increase of 23.9%. The slight reduction in PM10 (−4.1%) and PM2.5 levels (−2.3%) during lockdown indicates a lower relationship with traffic sources. The developed methodology represents a simple but robust framework for exploratory analysis and intervention detection in air quality studies.

The global COVID-19 pandemic that began in late December 2019 led to unprecedented lockdowns worldwide, providing a unique opportunity to investigate in detail the impacts of restricted anthropogenic emissions on air quality. A wide range of strategies and approaches exist to achieve this. In this paper, we use the “deweather” R package, based on Boosted Regression Tree (BRT) models, first to remove the influences of meteorology and emission trend patterns from NO, NO2, PM10 and O3 data series, and then to calculate the relative changes in air pollutant levels in 2020 with respect to the previous seven years (2013–2019). Data from a northern Spanish region, Cantabria, with all types of monitoring stations (traffic, urban background, industrial and rural) were used, dividing the calendar year into eight periods according to the intensity of government restrictions. The results showed mean reductions in the lockdown period above −50% for NOx, around −10% for PM10 and below −5% for O3. Small differences were found between the relative changes obtained from normalised data with respect to those from observations. These results highlight the importance of developing an integrated policy to reduce anthropogenic emissions and the need to move towards sustainable mobility to ensure safer air quality levels, as pre-existing concentrations in some cases exceed the safe threshold.

Cantabria, a small coastal region of Northern Spain, is one of the biggest producers of gourmet tuna cans in Europe. The fish capture in the Cantabrian Sea and the subsequent transformation in a local processing plant give distinction to this product, which is widely marketed in cans of 105 g of net weight. This work evaluates for the first time the environmental profile of the whole supply chain of this product, from fishing, processing, and waste valorization to inter-stage transport and packaging management in the end-of-life. To this end, the life cycle assessment methodology was applied considering primary data from the stakeholders involved in the supply chain and analyzing the seven most studied categories in this sector. Results revealed that fishing and processing accounted for the majority of the environmental impacts, while valorization and end-of-life treatments only avoid less than 10% of the burdens. The most important findings are focused on the high dependence on fuel use, identified as a hotspot in most stages although low compared to other fisheries, and on the intensive use of resources, especially sunflower oil, which contributes more than 70% of the impact on the global warming potential of the processing. This current framework forces the enhancement of the efficiency of a sector that attempts to engage the challenge of societal sustainability, by identifying the critical points and guiding policy makers on the path to sustainable development.

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