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One of the main identified causes of energy poverty (EP) is the low energy efficiency of housing. In this line, since 2018, public administrations and NGOs collaborating with the Naturgy Foundation's Energy Renovation Solidarity Fund have implemented several shallow renovation interventions in 3660 Spanish vulnerable households. However, the effects of these measures on domestic energy affordability were not evaluated before because of a lack of a proper method. This paper presents a methodology to objectively assess the impact of these interventions on EP. In particular, this work proposes calculating a hidden EP indicator using data from a primary survey and applies it to a local case study (54 vulnerable households in Catalonia—10% of dwellings renovated by the Fund in the region) by processing their characteristics and energy bills before and after the implementation of the interventions. Considering the whole sample of households analysed, the hidden EP indicator drops by 10% in absolute terms (11.2% in relative terms) after the retrofit, and the average EP gap goes from 423 €/year to 313 €/year, thus marking a significant positive effect of the analysed interventions on the EP situation of this population. Eventually, extrapolating the results to the vulnerable population in Spain, this paper points out a series of recommendations that could be useful for decision-makers and organisations when designing and implementing shallow renovation interventions. [ABSTRACT FROM AUTHOR]

Sideritis royoi is found in the rocky limestone habitats of the Port Massif (southern Catalonia, Spain). The species was first collected by the local botanist Lluís de Torres in the late part of the 20th century, but the specimens have remained unidentified positively in herbaria for over 40 years. Sideritis royoi likely belongs to section Sideritis subsection Hyssopifoliae and shows some morphological affinities with the relatively widespread South European species S. hyssopifolia L., but it differs from this species because it has subspinescent upper leaves, the main surfaces of its leaves are glabrous or glabrescent, the main abaxial surface of its bracts is without eglandular hairs, and due to the fact that it has shorter inflorescences. Weaker similarities have also been observed with some species belonging to S. subsection Fruticulosae Obón & D.Rivera. In this paper, a description for the new orophilous species is provided, along with a detailed illustration, field photographs, and a comparison with closely related species. We include an assessment of its conservation status and a dichotomous key for the identification of all the species of Sideritis subsection Hyssopifoliae. [ABSTRACT FROM AUTHOR]

Aerobiological predictive model development is of increasing interest, despite the distribution and variability of data and the limitations of statistical methods making it highly challenging. The use of concentration thresholds and models, where a binary response allows one to establish the occurrence or non-occurrence of the threshold, have been proposed to reduce difficulties. In this paper, we use logistic regression (logit) and regression trees to predict the daily concentration thresholds (low, medium, high, and very high) of six airborne fungal spore taxa (Alternaria, Cladosporium, Agaricus, Ganoderma, Leptosphaeria, and Pleospora) in eight localities in Catalonia (NE Spain) using data from 1995 to 2014. The predictive potential of these models was analyzed through sensitivity and specificity. The models showed similar results regarding the relationship and influence of the meteorological parameters and fungal spores. Ascospores showed a strong relationship with precipitation and basidiospores with minimum temperature, while conidiospores did not indicate any preferences. Sensitivity (true-positive) and specificity (false-positive) presented highly satisfactory validation results for both models in all thresholds, with an average of 73%. However, seeing as logit offers greater precision when attempting to establish the exceedance of a concentration threshold and is easier to apply, it is proposed as the best predictive model. [ABSTRACT FROM AUTHOR]