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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]

In this paper, we propose different explicable forecasting approaches, based on inductive and evolutionary decision rules, for extreme low-visibility events prediction. Explicability of the processes given by the rules is in the core of the proposal. We propose two different methodologies: first, we apply the PRIM algorithm and evolution to obtain induced and evolved rules, and subsequently these rules and boxes of rules are used as a possible simpler alternative to ML/DL classifiers. Second, we propose to integrate the information provided by the induced/evolved rules in the ML/DL techniques, as extra inputs, in order to enrich the complex ML/DL models. Experiments in the prediction of extreme low-visibility events in Northern Spain due to orographic fog show the good performance of the proposed approaches. [ABSTRACT FROM AUTHOR]

This paper assesses the health impact, in terms of the reduction of premature deaths associated with changes in air pollutant exposure, resulting from double-aim strategies for reducing emissions of greenhouse gases and air pollutants from the transport sector for the year 2030 in Spain. The impact on air quality of selected measures for reducing emissions from the transport sector (increased penetration of biofuel and electric car use) was assessed by air quality modeling. The estimation of population exposure to NO2, particulate matter (PM) and O3 allows for estimation of associated mortality and external costs in comparison with the baseline scenario with no measures. The results show that the penetration of the electric vehicle provided the largest benefits, even when the emissions due to the additional electricity demand were considered. [ABSTRACT FROM AUTHOR]

This paper analyses surface ozone measurements at five stations in an urban area (Valladolid) in the upper Spanish plateau over the period 2002–2020. Temporal evolutions, the relationship between ozone and other pollutants such as nitrogen oxides, and the assessment of the ozone concentration trend during the study period were analysed. Daily evolution of ozone at all the stations showed mean maximum concentrations in the afternoon, 15:00 GMT, with values ranging between 73.8 and 80.9 µg m−3, evidencing the influence of photochemical processes favoured by solar radiation in ozone formation. The lowest levels were recorded at night and in the early morning, 7:00 GMT, and were between 23.4 and 32.3 µg m−3, related with the reduction by NO reactions and deposition processes. A broad spring–summer peak between May and July was seen, with the highest values in the latter, with a mean value of up to 73.8 µg m−3. The variation in the monthly mean ozone concentrations of the different percentiles was analysed using a harmonic model. The empirical equation described the experimental values satisfactorily, with a confidence level of 95% and coefficients of determination above 80%, confirming the major decreasing trend in the ozone peak values over the study period. [ABSTRACT FROM AUTHOR]

The aim of this paper is to statistically characterize gross beta activity concentrations in ground-level air in Bilbao (northern Spain) by analysing five years (2014–2018) worth of weekly measurements in aerosols collected in filters to analyse the impact of local meteorological parameters on concentrations. In addition, synoptic meteorological scenarios associated with anomalous beta surface activity concentrations were identified. Over this five-year period, beta activity concentrations ranged from 35.45 µBq/m3 to 1778 µBq/m3 with a mean of 520.12 ± 281.77 µBq/m3. A positive correlation was found with the alpha concentrations (0.67), with an average of 0.138 for the alpha/beta ratio, and a low correlation was found with 7Be (0.16). Statistical analysis identified a seasonal component in the time series, increasing, on average, beta activity concentrations from winter to autumn. The highest beta activity concentrations were measured under the arrival of southerly land winds with low wind speeds, while the wind analysis (surface winds and air masses) of two different seasonal periods (autumn 2015 and winter 2017) have highlighted how small variations in synoptic and local winds highly influence beta activity concentrations. These results are relevant to understand the meteorological factors affecting beta activity concentrations in this area and hence to define meteorological scenarios that are in favour to high/anomalous surface activity concentrations that are harmful to the environmental and public health. [ABSTRACT FROM AUTHOR]

More than 50% of the world's population lives in cities. Its buildings consume more than a third of the energy and generate 40% of the emissions. This makes cities in general and their buildings in particular priority points of attention for policymakers and utilities. This paper uses population density as a variable to know its influence on energy consumption and emissions produced in buildings. Furthermore, to show its effect more clearly, the influence of the climate was eliminated. The usual energy consumption in buildings is thermal and electrical. The study was carried out at the city level, both per inhabitant and per household. The area actually occupied by the city was considered. The proposed method was applied to the case of Spanish cities with more than 50,000 inhabitants. The results show that the higher the population density, the higher the energy consumption per inhabitant and household in buildings. The consumption of thermal energy is elastic, while that of electrical energy is inelastic, varying more than 100% between extreme groups. Regarding CO2 emissions, the higher the population density, the higher the emissions. Emissions of electrical origin barely vary by 2% and are greater than those of thermal origin. In addition, the proportion of emissions of electrical origin, with respect to the total, decreases with increasing population density from 74% to 55%. This research aims to help policymakers and utilities to take the appropriate measures that favor the use of renewable energies and reduce CO2 emissions. [ABSTRACT FROM AUTHOR]