Methodological guidelines for the calculation of a Water-Energy-Food nexus index for seafood products.

Indicators from life cycle assessment methodologies (i.e., footprints) have emerged as useful tools for identifying and communicating the environmental impacts of a system thanks to they are accessible and intuitive and easy to understand to non-expert public. However, the focus on a single environmental problem is one of their main drawbacks. From this idea arises the concept of Water-Energy-Food (WEF) nexus, with the aim of raising awareness of the connections between the universal rights to water supply, energy security and food provision. Regarding the latter, the fisheries sector stands out as a fundamental pillar in the fight against malnutrition. In this sense, the European project "blue growth" aims to ensure that the development of the marine sector is not linked to the degradation of its ecosystems. However, although producers and authorities are willing to communicate the sustainability of products, there is still no standard methodology for reporting it. With the purpose of remedying this current situation, this paper aims to provide technical guidance to calculate a single WEF nexus index for ecolabelling seafood products in the European framework (Atlantic area). Therefore, through this, it is expected to create a useful communication channel between producers and consumers through an easy-to-read ecolabel. Nonetheless, certain aspects, such as the footprints selected or the calculation procedures selected have to be reconsidered to refine the methodology proposed, apart from broadening the approach to other food sectors with the aim that the proposed eco-certification can be present in major supply and retail chains.

Environmental performance of Cantabrian (Northern Spain) pelagic fisheries: Assessment of purse seine and minor art fleets under a life cycle approach.

The perpetuation of fishing activity from an environmentally, socially and economically sustainable approach is essential to guarantee not only the future of coastal populations, but also the supply of high-value seafood for society and the safeguarding of cultural heritage. This article aims to assess the environmental performance associated with fishing fleet operations in Cantabria (northern Spain) under a life cycle thinking from a holistic approach. Thus, the Life Cycle Assessment (LCA) methodology was applied under a 'cradle-to-port' approach, setting the functional unit as 1 kg of fresh fish landed. Inventory data on the main inputs and outputs were collected from a sample of 57 vessels covering for the first time the main techniques, purse seine and minor art fisheries. The results identified that the vessel use stage was the responsible of most of the impacts. In line with the literature, diesel consumption stood as the chief hotspot in six of the seven impact categories analysed. Purse seiners got a value of 0.25 kg of fuel per kg of fish landed, while the performance of the minor art fleet showed significantly lower consumption (0.07). Regarding impacts on climate change, this study found a quantity of 1.00 and 0.34 kg CO2 eq. per FU, for purse seine and minor arts, respectively. These figures were consistent with the expected results for pelagic fisheries. For the remaining indicators, purse seiners generally performed worse. The LCA methodology provided outcomes that allow the proposal of potential improvements and measures to foster the transition towards a more sustainable smart-fishing sector. Further research efforts should focus on the development and implementation of renewable energy and low-carbon vessel propulsion technologies.

Estimating changes in air pollutant levels due to COVID-19 lockdown measures based on a business-as-usual prediction scenario using data mining models: A case-study for urban traffic sites in Spain.

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, NO2, O3, 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 Multiple Linear Regression (MLR) models with meteorological and seasonal predictors. MLR models were selected among different data mining techniques (MLR, Random Forest (RF), K-Nearest Neighbors (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 NOx, with the largest reduction in the lockdown period above -50%, whereas the increase observed for O3 contrasts with the NOx 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.

Meteorological Normalisation Using Boosted Regression Trees to Estimate the Impact of COVID-19 Restrictions on Air Quality Levels.

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.