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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]

Historically, the olive grove has been one of the most emblematic ecosystems in Mediterranean countries. Currently, in Andalusia, Spain, the land under olive grove cultivation exceeds 1.5 million hectares, approximately 17% of the regional surface. Its exploitation has traditionally been based on the use of the available land and heterogeneous plantations, with different species adapted to southern Mediterranean climatic conditions, and to the management of the traditional olive cultivation culture. The objective of this work is to characterize the mechanical behavior of the atmospheric surface boundary layer (SBL) (under neutral stability) interacting with different olive grove configurations. Experimental tests were carried out in the Boundary Layer Wind Tunnel (BLWT) of the Andalusian Institute for Earth System Research (IISTA), University of Granada. Three representative configurations of olive groves under neutral atmospheric conditions were tested. The wind flow time series were recorded at several distances and heights downwind the olive plantation models with a cross hot wire anemometry system. Herein, this paper shows the airflow streamwise, including the mean flow and the turbulent characteristics. The spatial variability of these two mechanical magnitudes depends on, among others, the size, the agro-forest length, the layout of the tree rows, the porosity, the tree height, the crown shape and the surface vegetation cover. The aerodynamic diameter and Reynolds number for each agro-forest management unit are proposed as representative variables of the system response, as these could be related to olive grove management. The plantation, in turn, conforms to a windbreak, which affects the microclimate and benefits the elements of the ecosystem. Detailed knowledge of these variables and the interaction between the ecosystem and the atmosphere is relevant to optimize the resources management, land use and sustainability of the overall crop. Thus, this paper presents preliminary work to relate atmospheric variables to environmental variables, some of which could be humidity, erosion, evapotranspiration or pollen dispersion. [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]

As part of a contract with ENRESA (National Radioactive Waste Company S.A. is a Spanish public company responsible for the management of radioactive waste), after the closure of the uranium mill factory in Andújar, Spain, continuous measurements of the radon flux have been carried out on an annual basis using activated carbon detectors following a methodology established in our laboratory (ISO 11665-7, 2012). The results obtained and their usefulness are presented from the point of view of control of the closure conditions established by the competent authority in order to minimize the impact of the site on the environment. [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]

This work presents a full statistical analysis and accurate prediction of low-visibility events due to fog, at the A-8 motor-road in Mondoñedo (Galicia, Spain). The present analysis covers two years of study, considering visibility time series and exogenous variables collected in the zone affected the most by extreme low-visibility events. This paper has then a two-fold objective: first, we carry out a statistical analysis for estimating the fittest probability distributions to the fog event duration, using the Maximum Likelihood method and an alternative method known as the L-moments method. This statistical study allows association of the low-visibility depth with the event duration, showing a clear relationship, which can be modeled with distributions for extremes such as Generalized Extreme Value and Generalized Pareto distributions. Second, we apply a neural network approach, trained by means of the ELM (Extreme Learning Machine) algorithm, to predict the occurrence of low-visibility events due to fog, from atmospheric predictive variables. This study provides a full characterization of fog events at this motor-road, in which orographic fog is predominant, causing important traffic problems during all year. We also show how the ELM approach is able to obtain highly accurate low-visibility events predictions, with a Pearson correlation coefficient of 0.8 , within a half-hour time horizon, enough to initialize some protocols aiming at reducing the impact of these extreme events in the traffic of the A-8 motor road. [ABSTRACT FROM AUTHOR]

This paper provides scientific results from a European LIFE project carried out in the Valencian region of Spain during the 2017 to 2018 time frame. In 2018, more than 60,000 tons of pesticides were commercialized in Spain, with approximately 15% destined for Valencian crops. In order to improve the air quality in the agricultural areas of this region, an innovative cropping system based on irrigation was developed and compared to conventional treatments based on hand-spray and turbo application. After applying conventional treatments to five types of crops (citrus, persimmon, nectarine, watermelon, and other stone fruits), a total of 13 active substances were detected in the air. The same active substances were applied to crops using the novel irrigation system, and no pesticide was detected in the air. Moreover, applicator and bystander populations in the region were assessed for their risk of inhalation exposure to pesticides, and no risk was found when either of the techniques, the innovative and the conventional agricultural one, were applied. [ABSTRACT FROM AUTHOR]

Due to the exponential growth of the SARS-CoV-2 pandemic in Spain (2020), the Spanish Government adopted lockdown measures as mitigating strategies to reduce the spread of the pandemic from 14 March. In this paper, we report the results of the change in air quality at two Atlantic Coastal European cities (Northwest Spain) during five lockdown weeks. The temporal evolution of gaseous (nitrogen oxides, comprising NOx, NO, and NO2; sulfur dioxide, SO2; carbon monoxide, CO; and ozone, O3) and particulate matter (PM10; PM2.5; and equivalent black carbon, eBC) pollutants were recorded before (7 February to 13 March 2020) and during the first five lockdown weeks (14 March to 20 April 2020) at seven air quality monitoring stations (urban background, traffic, and industrial) in the cities of A Coruña and Vigo. The influences of the backward trajectories and meteorological parameters on air pollutant concentrations were considered during the studied period. The temporal trends indicate that the concentrations of almost all species steadily decreased during the lockdown period with statistical significance, with respect to the pre-lockdown period. In this context, great reductions were observed for pollutants related mainly to fossil fuel combustion, road traffic, and shipping emissions (−38 to −78% for NO, −22 to −69% for NO2, −26 to −75% for NOx, −3 to −77% for SO2, −21% for CO, −25 to −49% for PM10, −10 to −38% for PM2.5, and −29 to −51% for eBC). Conversely, O3 concentrations increased from +5 to +16%. Finally, pollutant concentration data for 14 March to 20 April of 2020 were compared with those of the previous two years. The results show that the overall air pollutants levels were higher during 2018–2019 than during the lockdown period. [ABSTRACT FROM AUTHOR]

Background: Heatwaves have become a public health emergency raising great public health concerns. Correspondingly, due to climate change, the increase in extreme weather events, such as heatwaves, floods and hurricanes, is predicted through state-of-the-art models and robust statistical analyses. Since the Mediterranean basin is recognized as the most prominent global climate change hot spot, further scientific research on the future impacts of heatwaves on human mortality, as well as human health and well-being, in the Mediterranean region is critical. Methods: The aim of the present study is to compare the relationship between three different causes of mortality (i.e., cardiological, respiratory and cardiorespiratory) and temperature between two countries (Spain and Greece) and five cities (i.e., Madrid, Barcelona, Valencia, Attica and Thessaloniki). To compare the five cities under examination, a robust statistical methodological framework (i.e., Threshold Regression Analysis (TRA)) was applied so as to examine the critical value above which the mean temperature affects cardiological, respiratory and cardiorespiratory mortality. Results: Our results prove that the relationship between mortality and temperature is a nonlinear relationship shaping a U- or J-shaped curve, meaning that mortality is affected by temperature in a non-constant way, indicating that mortality increases during both low and high temperatures. Conclusions: By calculating different temperature thresholds for the five cities under examination, we show that different temperature thresholds are more accurate for different climatic conditions. Hence, climatic conditions should be taken into account when examining the impacts of temperature on human health. [ABSTRACT FROM AUTHOR]

(1) Background: In agro-ecosystems, the success of the crops has a strong connection to biodiversity in the landscape. In the face of climate change, it is important to understand the response to environmental variation of weed species by means of their distribution. In the last century, biodiversity has been impacted due to a variety of stresses related to climate change. Although the composition of vegetation tends to change at a slower rate than climate change, we hypothesize species present in weed communities are distributed in diverse patterns as a response to the climate. Therefore, the general aim of this paper is to investigate the effect of temperature, using latitude as an indicator, on the composition and distribution of weed communities in agro-ecosystems. (2) Methods: Weeds were monitored in georeferenced cereal fields which spanned south and central Spanish regions. The graphic representation according to latitude allowed us to identify groups of weeds and associate them to a temperature range. We classified weeds as generalist, regional, or local according to the range of distribution. (3) Results: The monitoring of species led to the classification of weeds as generalist, regional or local species according to latitude and associated temperature ranges. Three weed species that were present in all latitude/temperature regions, were classified as generalist (Linaria micrantha (Cav) Hoffmanns & Link, Sonchus oleraceous L., and Sysimbrium irium L.). The species were classified as regional or local when their presence was limited to restricted latitude/temperature ranges. One weed, Stellaria media (L.) Vill., was considered a local species and its distribution dynamics can be considered an indicator of temperature. (4) Conclusions: The novel methodology used in this study to assign weed distribution as an indicator of climatic conditions could be applied to evaluate climate gradients around the world. [ABSTRACT FROM AUTHOR]

To understand soil erosion processes, it is vital to know how the weather types and atmospheric situations, and their distribution throughout the year, affect the soil erosion rates. This will allow for the development of efficient land management practices to mitigate water-induced soil losses. Vineyards are one of the cultivated areas susceptible to high soil erosion rates. However, there is a lack of studies that link weather types and atmospheric conditions to soil erosion responses in viticultural areas. Thus, the main aim of this research is to assess the impacts of weather types and atmospheric conditions on soil erosion processes in a conventional vineyard with tillage in eastern Spain. To achieve this goal, rainfall events from 2006 to 2017 were monitored and the associated runoff and soil loss were collected from experimental plots. Our results showed that the highest volume of runoff and soil erosion is linked to rainfall associated with the eastern winds that accounted for 59.7% of runoff and 63.9% of soil loss, while cold drops in the atmospheric situation classifications emerged as the highest contributor of 40.9% in runoff and 44.1% in soil loss. This paper provides new insights into the development of soil erosion control measures that help to mitigate the negative impact of extreme rainfall and runoff considering atmospheric conditions. [ABSTRACT FROM AUTHOR]

In sun-and-beach destinations, weather has a decisive influence on the variability of the daily flow of tourists. Uncertainty in demand flows directly affects businesses and employment. This work aims to improve understanding of the behaviour of tourists in response to changes in weather conditions. The analysis is carried out in the Rías Baixas, a sun-and-beach destination in north-west Spain. The paper analyses the relationship of weather conditions with daily flows during the high season at the main tourist beaches in the area, also considering two beach typologies. The density of beach use is measured three times a day through the analysis of webcam images in combination with real-time weather, and an online survey is conducted among tourists who have visited these beaches. The results show that the hours of sunshine are the most influential weather factor. Weather forecast greatly or totally influenced the decision to go to the beach for almost 70% of respondents and about 80% of the respondents checked on the weather before visiting a beach. [ABSTRACT FROM AUTHOR]

Physical activity is an increasingly frequent part of our leisure time. Within this context, hiking is a popular form of tourism which has a positive impact on the quality of life. In spite of the importance of climate conditions for this recreational activity, relatively little research has been done on hiking from the perspective of climate and tourism. With this in mind in this paper we make the first detailed extensive assessment of climate preferences for the practice of hiking tourism in Spain. To this end a review of the theoretical/methodological body of literature on tourism climatology was conducted together with a survey aimed at evaluating the stated climate preferences of hiking tourists. The results are offered within the framework of international research on climate preferences for a range of tourism activities. The comparative analysis of these results with regard to those obtained in previous research highlights various similarities but also certain factors specific to hiking in Spain. Overall, the climate preferences of hiking tourists are similar to those of other segments of the tourism market in terms of the aesthetic and physical aspects of the climate, although they also have certain specific preferences as regards thermal aspects, especially regarding the optimal daily thermal conditions for hiking. The results obtained are useful for assessing the suitability of the climate for the practice of hiking tourism in Spain and for promoting proper management and planning of this leisure activity in tourist destinations, including the development of climate calendars detailing the most suitable times of the year for hiking at these destinations. These issues will be addressed in future research studies. [ABSTRACT FROM AUTHOR]

The management of the COVID-19 pandemic, which required global lockdowns and mobility restrictions, positively impacted air quality (especially the concentrations of primary pollutants). With the progressive return to normal activity during the last few months of 2020 and 2021, air pollutant concentrations were expected to rise again. In this work, together with meteorological conditions and traffic intensity, the concentrations of NO, NO 2 , O 3 , PM 10 , and SO 2 measured at three different locations (urban, industrial, and rural) in southeast Spain, from January 2016 to December 2021, were analyzed. For the three locations, PM 10 concentrations did not change significantly in 2020 or 2021. SO 2 concentrations did not experience changes at the industrial site in 2020 or 2021, but SO 2 concentrations did slightly increase in five months of 2021 in the urban location (with a maximum monthly average concentration of 11 μ g·m − 3 ). In 2020, NO and NO 2 concentrations were, in general, lower than those measured in previous years (2016–2019). Interestingly, at the urban site (San Basilio), the levels of NO and NO 2 were significantly lower in 2021 than in 2020 despite the higher traffic intensity in 2021. While the concentrations of NO and NO 2 decreased, in VOC-limited areas, O 3 concentrations increased. This was clearly observed at the industrial and the urban sites. Despite the increase, O 3 concentrations were still below the European O 3 target value for the protection of human health. An analysis of new registrations of EVs and HEVs in the latter years and their impact on the vehicle fleet of Murcia and, subsequently, on road emissions was carried out. Regardless of the steep increase in environmentally friendly vehicles in 2021, their share of the vehicle fleet is still negligible; thus, the lower concentrations of NO and NO 2 are likely due to more favorable weather conditions in 2021 than in 2020. [ABSTRACT FROM AUTHOR]

Flash drought is the result of strong precipitation deficits and/or anomalous increases in atmospheric evaporative demand (AED), which triggers a rapid decline in soil moisture and stresses vegetation over short periods of time. However, little is known about the role of precipitation and AED in the development of flash droughts. For this paper, we compared the standardized precipitation index (SPI) based on precipitation, the evaporative demand drought index (EDDI) based on AED, and the standardized evaporation precipitation index (SPEI) based on the differences between precipitation and AED as flash drought indicators for mainland Spain and the Balearic Islands for 1961–2018. The results show large differences in the spatial and temporal patterns of flash droughts between indices. In general, there was a high degree of consistency between the flash drought patterns identified by the SPI and SPEI, with the exception of southern Spain in the summer. The EDDI showed notable spatial and temporal differences from the SPI in winter and summer, while it exhibited great coherence with the SPEI in summer. We also examined the sensitivity of the SPEI to AED in each month of the year to explain its contribution to the possible development of flash droughts. Our findings showed that precipitation is the main driver of flash droughts in Spain, although AED can play a key role in the development of these during periods of low precipitation, especially in the driest areas and in summer. [ABSTRACT FROM AUTHOR]

The presence of missing data in hydrometeorological datasets is a common problem, usually due to sensor malfunction, deficiencies in records storage and transmission, or other recovery procedures issues. These missing values are the primary source of problems when analyzing and modeling their spatial and temporal variability. Thus, accurate gap-filling techniques for rainfall time series are necessary to have complete datasets, which is crucial in studying climate change evolution. In this work, several machine learning models have been assessed to gap-fill rainfall data, using different approaches and locations in the semiarid region of Andalusia (Southern Spain). Based on the obtained results, the use of neighbor data, located within a 50 km radius, highly outperformed the rest of the assessed approaches, with RMSE (root mean squared error) values up to 1.246 mm/day, MBE (mean bias error) values up to −0.001 mm/day, and R2 values up to 0.898. Besides, inland area results outperformed coastal area in most locations, arising the efficiency effects based on the distance to the sea (up to an improvement of 63.89% in terms of RMSE). Finally, machine learning (ML) models (especially MLP (multilayer perceptron)) notably outperformed simple linear regression estimations in the coastal sites, whereas in inland locations, the improvements were not such significant. [ABSTRACT FROM AUTHOR]

Half of the world's population lives in cities. In addition, more than 40% of greenhouse gas emissions are produced in buildings in the residential and tertiary sectors. Therefore, cities, and in particular their buildings, have a great influence on these emissions. In fact, they are reflected in several of the United Nations' Sustainable Development Goals. Any measure taken to reach these goals has a significant impact from the point of view of reducing greenhouse gases. An understanding of these goals is the basis of greenhouse gas mitigation. This work analyzed the CO2 emissions from the buildings in cities as a function of the economic income of their inhabitants. For this, databases published by official sources were used. The origins of the CO2 are usually emitted by buildings were analyzed—electrical and thermal, in the form of natural gas. Another variable that influences these emissions is climate. To study only the income variable, the influence of climate has been eliminated. Also, to facilitate analysis, an index has been introduced. As an example of application of the proposed methodology, Spanish cities with more than 50,000 inhabitants were studied. The analysis was carried out by household and by inhabitant. The results showed the following: the higher the income of the citizens, the higher the total and thermal emissions; thermal consumption is elastic, while electrical consumption is inelastic; emissions of electrical origin are almost constant; emissions from electrical energy are greater than those from thermal energy; as income increases, the ratio between emissions of electrical and thermal origin decreases. [ABSTRACT FROM AUTHOR]

A Bayesian hierarchical framework with a Gaussian copula and a generalized extreme value (GEV) marginal distribution is proposed for the description of spatial dependencies in data. This spatial copula model was applied to extreme summer temperatures over the Extremadura Region, in the southwest of Spain, during the period 1980–2015, and compared with the spatial noncopula model. The Bayesian hierarchical model was implemented with a Monte Carlo Markov Chain (MCMC) method that allows the distribution of the model's parameters to be estimated. The results show the GEV distribution's shape parameter to take constant negative values, the location parameter to be altitude dependent, and the scale parameter values to be concentrated around the same value throughout the region. Further, the spatial copula model chosen presents lower deviance information criterion (DIC) values when spatial distributions are assumed for the GEV distribution's location and scale parameters than when the scale parameter is taken to be constant over the region. [ABSTRACT FROM AUTHOR]

This work presents the analysis of weekly 7Be activity concentrations in the air measured in Bilbao (northern Spain) by collecting aerosols in filters over a ten years period (2009–2018). 7Be level is in the 0.35–7.3 mBq/m3 range, with a mean of 3.20 ± 1.12 mBq/m3. The trend, cycle, seasonal and monthly variability are evaluated using time series analysis techniques. The results indicate the impact of sunspots (24th solar cycle) on interannual 7Be activity concentrations, and a significant seasonal and monthly variation, with maximum concentrations occurring in spring-summer and minimum in the winter. The correlation of different 7Be ranges with local meteorological parameters, such as precipitation, temperature, relative humidity, and pressure, is also addressed, with precipitation having the greatest impact on 7Be activity values. The analysis of synoptic airflows, by calculating the back-trajectory clusters, and local winds at surface level reveals the important influence of the arrival of slow northwest Atlantic flows and the development of breezes on reaching high 7Be activity concentrations in this area. [ABSTRACT FROM AUTHOR]

A yellow fever epidemic occurred in Cádiz and other areas of southern Spain during the last months of 1800. An anonymous author attributed this disease to the contrast between the cold and rainy winter and spring, and the subsequent very hot summer. However, the physician J.M. Aréjula published a report in 1806 where he refuted this conclusion after a detailed analysis of the meteorological conditions in the area. This controversy is a good example of the discussion about the relationships between meteorological conditions and public health. In this work, this "scientific" controversy is studied. Although the arguments of both authors were inspired by the neo-Hippocratic medical paradigm, the anonymous author put forth a simple cause–effect hypothesis, while Aréjula recognized the complexity of the problem, introducing the concept of "concause" to explain the confluence of environmental and contagious effects. [ABSTRACT FROM AUTHOR]

This study evaluates the performance of statistical models applied to the output of numerical models for short-term (1–24 h) hourly wind forecasts at three locations in the Basque Country. The target variables are horizontal wind components and the maximum wind gust at 3 h intervals. Statistical approaches such as persistence, analogues, linear regression, and random forest (RF) are used. The verification statistics used are coefficient of determination (R2) and root mean square error (RMSE). Statistical models use three inputs: (1) Local wind observations; (2) extended EOFs (empirical orthogonal functions) derived from past local observations and ERA-Interim variables in a previous 24-h period covering a domain around the area of study; and (3) wind forecasts provided by ERA-Interim. Results indicate that, for horizons less than 1–4 h, persistence is the best model. For longer predictions, RF provides the best forecasts. For horizontal components at 4–24 h horizons, RF slightly outperformed ERA-Interim wind forecasts. For gust, RF performs better than ERA-Interim for all the horizons. Persistence is the most influential factor for 2–5 h. Beyond this horizon, predictors from the ERA-Interim wind forecasts led the contribution. Hybrid numerical–statistical methods can be used to improve short-term wind forecasts. [ABSTRACT FROM AUTHOR]

Heat waves pose additional risks to urban spaces because of the additional heat provided by urban heat islands (UHIs) as well as poorer air quality. Our study focuses on the analysis of UHIs, human thermal comfort, and air quality for the city of Madrid, Spain during heat waves. Heat wave periods are defined using the long-term records from the urban station Madrid-Retiro. Two types of UHI were studied: the canopy layer UHI (CLUHI) was evaluated using air temperature time-series from five meteorological stations; the surface UHI (SUHI) was derived from land surface temperature (LST) images from MODIS (Moderate Resolution Imaging Spectroradiometer) products. To assess human thermal comfort, the Physiological Equivalent Temperature (PET) index was applied. Air quality was analyzed from the records of two air quality networks. More frequent and longer heat waves have been observed since 1980; the nocturnal CLUHI and both the diurnal and nocturnal SUHI experience an intensification, which have led to an increasing number of tropical nights. Conversely, thermal stress is extreme by day in the city due to the lack of cooling by winds. Finally, air quality during heat waves deteriorates because of the higher than normal amount of particles arriving from Northern Africa. [ABSTRACT FROM AUTHOR]

SCALA© (Sampling Campaigns for Aerosols in the Low Atmosphere) is a web-based software system that was developed in a multidisciplinary manner to integrally support the documentation and the management and analysis of atmospheric aerosol data from sampling campaigns. The software development process applied considered the prototyping and the evolutionary approaches. The software product (SCALA©) allows for the comprehensive management of the sampling campaigns' life cycle (management of the profiles and processes involved in the start-up, development and closure of a campaign) and provides support for both intra- and inter-campaigns data analysis. The pilot deployment of SCALA© considers the Spanish Network on Environmental Differential Mobility Analysers (DMAs) (REDMAAS) and the PROACLIM project. This research project involves, among other objectives, the study of temporal and spatial variations of the atmospheric aerosol through a set of microphysical properties (size distribution, optical properties, hygroscopicity, etc.) measured in several locations in Spain. The main conclusions regarding size distribution are presented in this work. These have been have been extracted through SCALA© from the data collected in the REDMAAS 2015 and 2019 intercomparison campaigns and two years (2015 and 2016) of measurements with two Scanning Mobility Particle Sizers (SMPS) at CIEMAT (Madrid, central Spain) and UDC (A Coruña, NW of Spain) sites. [ABSTRACT FROM AUTHOR]