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Social life cycle assessment (S-LCA) is a tool to address social impacts from a life cycle perspective. This work presents the first S-LCA of the Portuguese pulp and paper sector. It presents the social risks and the positive social impacts of the sector, which have not been extensively covered in the scientific literature. Two different approaches were used to compile the social life cycle inventory based on input-output analysis. Approach 1 was based on impact analysis of changes in added value. Approach 2 was based on structural path analysis, supporting the disaggregation by production levels (or tiers). The functional unit was the production of 1 United States Dollar in the Portuguese pulp and paper sector. In the social life cycle impact assessment, the social risk assessment was conducted using the Social Hotspots Database. The positive impacts were addressed considering indicators of employment and remuneration, namely total employment, number of employees, labor compensation, and compensation of employees. Based on Approach 1, the results of the social risks showed the Portuguese pulp and paper industry, commerce and forestry sectors, and the Spanish forestry sector as the critical sectors. Based on Approach 2, Portugal and Spain continued to be the countries having the greatest social risks. Other contributions of countries were identified at different production levels, particularly China in tier 3. In both approaches, the stakeholder category 'worker' was the most critical. Regarding the positive social impacts, Approach 1 showed that Portugal and Spain contribute to the highest employment level linked to the pulp and paper, commerce, and forestry sectors. Approach 2 supported the differentiation of employment and remuneration at different production levels. Tier 2 (direct suppliers) presented the highest employment level and persons engaged and the lowest remuneration level. The results showed that Approach 1 provides a faster and more aggregated assessment of social risks and positive social impacts. Furthermore, Approach 2 allowed a more detailed evaluation, providing a better understanding of the social risks and positive social impacts on the supply chain. Thus, Approach 2 is encouraged for different applications since it produces a better understanding of where social risks and positive social impacts occur, providing better guidance for corrective measures and decision-making. [ABSTRACT FROM AUTHOR]

• GNSS-PWV time series are computed using 44 GNSS stations in Andalucía and ERA5 temperature and pressure data. • GNSS-PWV and ERA5-PWV are used to compute SPCI time series. • The SPCI generally shows high correlation with the SPEI, with better results for ERA5-SPCI than GNSS-SPCI. • The SPCI outperforms SPI for a limited geographical region and 24-month timescale. In this paper the Standardized Precipitation Conversion Index (SPCI), a PWV-based drought index, has been computed using GNSS and ERA5 PWV and its performance has been tested with respect to the Standardized Precipitation Evapotranspiration Index (SPEI) in Southern Spain. One of the climatic features of this area is the low correlation level between PWV and precipitation, in contrast with other areas in which SPCI has been previously tested. The GNSS-SPCI has been derived from validated ZTD time series estimated from local GNSS permanent stations' data. All the needed meteorological values were derived from ERA5, excepting precipitation values and SPEI-SPI values which were extracted from a national high-resolution dataset. The resulting SPCI time series have shown high correlation coefficients with respect to the SPEI. The use of longer SPCI time series allowed by ERA5 model has provided the most coherent results, suggesting that the ERA5-PWV data can be interesting to overcome problems caused by the short timespan of GNSS time series in SPCI computation. In general, high correlation coefficients have been obtained compared to global results from previous studies. This shows that, even for regions with low correlation levels between PWV and precipitation, the SPCI can have an interesting potential for drought monitoring. The SPCI was found to perform better on higher timescales (12 and 24 months). The performance of SPCI has also been compared that of the SPI: SPCI is able to outperform SPI for the 24-month timescale for a limited geographical region. This supports that the inclusion of PWV data in drought monitoring indices could be promising and is worth keeping to be investigated. [ABSTRACT FROM AUTHOR]

• Mining has a high level of serious accidents compared to all other sectors. The analysis is based on 15,032 accidents. • 12 variables are studied and the influence of each variable on the severity, which is divided into three types (light, serious and fatal). • The results make it possible to design prevention measures for the situations that have led to the most serious accidents. Introduction: The aim of this paper is to understand the causes of occupational accidents in Spain's mining sector in order to propose action plans and improve future accident rates. Method: This research analyzed a pool of data on 15,032 accidents occurring in the mining sector and reported to authorities between 2013 and 2018. Accidents are divided into three levels of severity: light, serious, and fatal. We study the influence of 12 variables on the accident severity rate in our sample. Results: The results show that accident severity is related to age, gender, nationality, length of service, economic activity, company size, accident location, days of injury leave, day of the week, deviation, injury, and specific Spanish region. This sector produces a high rate of serious accidents compared to all other sectors; has a male-dominated, older and experienced workforce; and employs mainly Spanish workers. Its activity is concentrated in larger companies and the work involves the use of heavy machinery and dangerous materials. We offer conclusions and future lines of research to help regulators, companies and workers to improve worker safety. [ABSTRACT FROM AUTHOR]

• Analysis of several factors that may be behind an undue extension of the injured worker's recovery time. • Comprehensive empirical analysis of more than 400,000 non-fatal work accidents in manufacturing industries. • Women, workers with more seniority, higher wages, and those employed in larger companies take longer to return to work after an occupational injury. • The duration of sick leave for immigrant workers is lower than for native-born workers. • Improving working conditions and supervision mechanisms during the recovery process is recommended to increase attendance motivation and reduce moral hazard exposure. Introduction : Despite the significant economic impact of occupational injuries on companies and society, studies focused on analyzing the determinants of workdays lost due to sick leave remain scarce and incomplete. This paper contributes to this issue by (a) analyzing the drivers of sick leave duration, distinguishing factors that explain the health recovery time from those that could lead workers to a voluntary extension of the absence period, and (b) formulating and empirically testing the effect of gender, citizenship, temporary work, job tenure, amount of disability benefit, and size of the injured worker's firm on the number of days the employee is off work after the injury. Method: Hypotheses are tested on a comprehensive dataset that includes all nonfatal occupational injuries causing sick leave that occurred in the manufacturing sector in Spain during 2015–2019, with more than 400,000 injuries. We conduct ordinary least squares and count data regression models in which the number of days off work is regressed on employees and work characteristics while accounting for a set of variables to control the injury's nature and severity. Results: The results show that after considering the intrinsic characteristics of the injury and the severity of the worker's injuries, women, native workers, workers with more seniority, workers with higher salaries, and those working in larger companies have longer periods of sick leave. The results suggest that moral hazard considerations significantly impact the time to return to work after an occupational injury. Practical applications: Based on the findings, several insights for company managers and public decision-makers are discussed. Specifically, interventions aimed at improving the organization of work and the working conditions of workers in manufacturing industries are highlighted, as well as the need to improve control and supervision mechanisms during the recovery process of injured workers. [ABSTRACT FROM AUTHOR]

• Mechanisms to encourage clean energy in Spain from 2005 to the present are analyzed. • FiT and FiP mechanisms can be effective in promoting new renewable projects. • Experience suggests that excessive support can jeopardize the entire market. • A transitional retribution scheme has proven to be effective if correctly supervised. This research paper delves into the central mechanisms implemented in Spain to foster the adoption of clean energy projects. The study scrutinizes various mechanisms established from 2005 onwards, evaluating their efficacy in achieving the desired outcome of supporting clean energy project implementation. The aim is to comprehensively assess the associated costs, effectiveness, and attainment of renewable energy objectives stemming from implementing diverse support schemes. This paper explores the effectiveness of feed-in tariffs and feed-in premium mechanisms in promoting the integration of new renewable energy generation into the power system. However, the Spanish experience suggests that exhaustive planning control is imperative to prevent excessive support costs that could jeopardize the entire market and cause a significant rise in final energy costs. The research studies the support schemes and associated market distortion and presents the roadmap from regulated tariffs to a "total market" for wind and solar energy. The findings demonstrate that, under proper supervision and planning, a transitional retribution scheme effectively promotes renewable energy plant integration. Finally, the analysis examines the need to regulate the excess benefits that non-emitting power sources receive in a marginal price market, as this can lead to market imbalances and a substantial increase in final costs. These plans serve as a crucial tool in achieving the decarbonization objectives of the European Power System. [ABSTRACT FROM AUTHOR]

• Pioneer Spain: first country to include in grid code the synthetic inertia capability. • Synthetic inertial response of a PV inverter studied based on the Spanish grid code. • Analyzed the role of power plant controller in the dynamic response of the facility. • PV system able to contribute to frequency control providing an inertial response. As the large-scale integration of renewable energy grows, inertia in power systems is reduced, which causes the rate of change of frequency to increase and worsens the frequency nadir during disturbances. There is, therefore, an urgent need to accelerate the development of international standards and regulations that require solar PV systems and other renewable installations to also contribute to stability in the network by emulating inertia. Spain is a pioneer in this field: it is currently the only country that includes the synthetic inertia technical requirement in its grid code. In this context, the present paper analyzes the synthetic inertial response of a PV system model that forms part of an actual PV power plant in Spain under two scenarios, according to its grid code: i) when measurements are conducted at the power conversion PV system level and hence only its control algorithms are executed; and ii) when measurements are conducted at the power plant level and the power plant controller also influences the response of the entire installation. Among its aims, this paper seeks to thoroughly assess the response of an actual PV power conversion system that has the capability of actively contributing to frequency regulation in a network and to critically analyze the suitability of having a power plant controller when the PV facility contributes to power-frequency control by means of an inertial response. The results reveal, at the power conversion PV system level, that the inertia emulation module significantly reduces the response times of the installation to changes in frequency -by more than 68% in the worst case under frequency increases, and by 73% under frequency decreases, thus contributing to more rapid power-frequency regulation in the network. However, at the power plant level, the power plant controller disturbs the PV power conversion system's behavior and causes the opposite effect: response times increase -up to more than 7% in the most unfavorable case when the inertia module is enabled. [ABSTRACT FROM AUTHOR]

Tourism has become a very active ecosystem to deploy solutions based on Information and Communication Technologies. Indeed, it is now possible to analyse the mobility behaviour of tourists in great detail. However, current solutions aimed at anticipating tourist flows usually follow a limited approach based on the local (e.g., to predict the next landmark to visit) or regional (e.g., to predict the incoming number of tourists in a city) level. This paper states a novel approach to solve the problem of tourist inflow forecasting on a broader nationwide scale by defining it as an edge prediction task. To do so, we model the tourist mobility of a country as a graph which fuses heterogeneous tourism data obtained from multiple sources related to the country's mobility and infrastructure features. Then, as a major contribution, an ensemble of Graph Neural Networks are fed with the graph models to provide the final prediction. The proposed solution has been tested in Spain showing a F1 score higher than 0.7. • A mechanism to anticipate the flows of tourists in Spain is proposed. • The prediction is solved as an edge prediction between Spanish regions. • A Graph Neural Network has been used as underlying model. • The model is fed with touristic infrastructure data of Spain. • The human mobility is extracted from Call Detail Records and geo-tagged Twitter posts. [ABSTRACT FROM AUTHOR]

This paper analyzes impoliteness-related language in response to a season's greeting posted by the Prime Ministers of Spain and the UK on December 2022 to wish Twitter users a Felices Fiestas/Merry Christmas. A corpus made up of the first 500 tweet events mentioning Sánchez and Johnson with the @ feature was analyzed through the combination of qualitative and quantitative tools and an impoliteness2 approach to compare the most commonly deployed impoliteness strategies in response to what is felt as an offense by respondents from the two cultures. Previous impoliteness taxonomies inform the analysis (Culpeper, 1996 , 2011). Findings reveal a preference for on-record strategies vs. off-record ones in both corpora. Whereas the English respondents oriented themselves towards attacking the negative face of the Prime Minister and that of other participants with sarcasm and implicated impoliteness, the Spanish group deployed impoliteness-related language to disparage the Prime Minister's positive face through insults and the rectification of his words. The hostile acts and explicit metalanguage about emotional states in the replies to the expressive speech act corroborate the perceived breach of some aspects of the moral order on the part of the Twitter users in the out-group, hence aiding the already observed polarization of political discussions. • Impoliteness is analyzed in replies to the season's greetings of Pedro Sánchez and Boris Johnson on Twitter. • Findings indicate that on-record impoliteness strategies are more frequent than off-record ones in both groups. • English respondents use negative-face attacking strategies, sarcasm, and implicated impoliteness in their replies. • Spanish respondents deploy insults and attacks on the positive public face of the Prime Minister as favored strategies. • Impoliteness-related discourse is multifunctional and aims to restore the moral order that is perceived as altered. [ABSTRACT FROM AUTHOR]

Wind power is one of the most important renewable energy sources worldwide. However, integrating these technologies in electricity markets faces challenges due to the intermittent wind speed, resulting in potential imbalances within power grids and subsequent inefficiencies. Traditional approaches for modeling the uncertainty simulate fixed wind power values in operational and planning models, which work well for the short term but are inadequate for medium-term decision-making due to lower forecasting accuracy and the inability to differentiate the imbalance risk faced in each simulated value. This paper proposes a novel methodology for incorporating wind energy imbalance risk into medium-term generation planning models. For this, we first simulate realistic wind power scenarios and perform a fitting distribution process to characterize the hourly wind power behavior. Then, we model the imbalance risk using quadratic-uncertainty cost functions for each simulated value. Subsequently, we conduct a temporal aggregation to reduce the computational burden, preserving the conceptual framework of the imbalance risk modeling. Finally, we propose a medium-term generation planning model that includes the imbalance risk model and the temporal aggregation strategy. Our approach is evaluated in a real-size case study formed by Spain, Portugal, and France's electricity markets, demonstrating accurate wind power imbalance risk modeling and lowered planning costs. • A new methodology to include wind energy imbalance risk in generation planning models. • Introducing a fitting process for modeling the distribution asymmetries of wind power. • Proposing a temporal aggregation method for efficient imbalance risk modeling. • The methodology is validated in Spanish, Portuguese, and French electricity markets. [ABSTRACT FROM AUTHOR]

This paper compares the performance of conventional and organic fruit farms in Spain, using a set of base indicators to assess their economic and environmental performance on a per hectare basis. Composite indicators are also calculated to measure the overall economic and environmental performance of both production systems. Comparisons are made using propensity score matching to minimize the non-randomization biases caused by structural differences between the samples of conventional (n = 552) and organic (n = 127) fruit farms sourced from the Spanish Farm Accountancy Data Network (RECAN). The results based on per hectare metrics point to modest changes in the performance of farms that converted to organic farming. This is mainly because most converted farms were former conventional farms characterized by lower profitability and less intensive input use, consequently minimizing the effects of the conversion process. Nevertheless, the conversion outcomes exhibit a degree of variability depending on the specific type of fruit production. Economic gains are only discernible in the case of nut farms, whereas fruit and tropical fruit farms tend to yield the most favorable results per hectare from an environmental perspective. • Changes in per hectare performance are assessed when fruit farms convert to organic. • A complete set of economic and environmental base and composite indicators is used. • A matching procedure is implemented to obtain unbiased difference estimators. • Small changes are found in the per hectare performance of farms after conversion. • Mixed results depending on the type of fruit production subsector considered. [ABSTRACT FROM AUTHOR]

The increased penetration of renewable energy sources (RESs) as an effective mechanism to reduce carbon emissions leads to an increased weather dependency for power and energy systems. This has created dynamic operation and degradation phenomena, which affect the lifetime estimation of the assets operated with RESs. For the reliable and efficient operation of RES it is crucial to monitor the health of its constituent components and feature selection is a crucial step for building robust and accurate health monitoring approaches. In this context, this paper presents a probabilistic feature selection approach, which probabilistically weights and selects features through a heuristic and iterative process for an improved asset lifetime estimation. Power transformers are key power grid assets and they are used to demonstrate the validity and impact of the proposed approach. The approach is tested on two different photovoltaic power plants operated in Spain and Australia. Results consistently show that the proposed feature-selection approach reduces the prediction error and consistently selects relevant features. The approach has been applied to transformer lifetime estimation, but it can be generally applied to assist in the lifetime estimation of other components operated in RESs. Part of the studies presented here as well as source codes are all open-source under the GitHub repository https://github.com/iramirezg/FeatureSelection. • Probabilistic feature selection approach for improved asset lifetime estimation. • Integration of environmental features for improved renewable-operated asset lifetime. • Systematic and robust feature weighting methodology. • Improved transformer lifetime estimation including sensor and environmental data. • Validated on two real photovoltaic power plant case studies. [ABSTRACT FROM AUTHOR]

Identifying the main drivers of the household carbon footprint (HCF) is a priority to reduce greenhouse gas emissions and move towards a more sustainable economy. Among the multiple factors that explain the HCF, some previous research has confirmed the relevance of gender. In this paper, we calculate the HCF of individual Spanish households using an environmentally extended multi-regional input-output model (EEMRIO) that quantifies the emissions embedded in the supply chain of goods and services that households consume. We assess the differences in consumption and emissions patterns, measuring the gender effect by a new variable, the female share of the household. This represents a remarkable improvement over previous literature that used simpler approaches. Moreover, we assess the partial effect of gender on HCF using multivariate regressions for total and sectoral emissions. Our results show that households with a majority of men have a higher HCF and carbon intensity. Even when controlling for economic, sociodemographic, and regional factors there are differences in the sectoral emissions according to gender. Female households spend more (and generate more emissions) on housing and food products, while male households show that pattern for restaurants and transport. These results can help orientate environmental campaigns and demand-side mitigation policies. • We calculate HCF for individual Spanish households using HBS data and EEMRIO. • We introduce the gender effect through the new indicator "female share in households." • Households with a majority of men have a higher HCF and carbon intensity. • The higher the proportion of women in a household, the higher the HCF in housing and food. • The higher the proportion of women in a household, the lower the carbon footprint in restaurants and transportation. [ABSTRACT FROM AUTHOR]

In the field of civil engineering, specifically within railway projects, the adoption of Building Information Modeling (BIM) has become imperative. However, the sector's adherence to traditional methods for infrastructure design and management has delayed the widespread adoption of BIM. This paper describes a BIM-centered methodology applied to a real-world High-Speed Railway Line (HSRL) project. Through an extensive examination of HSRL design parameters and the integration of various BIM-based tools, a 3D parametric design model is generated. This model serves as the foundation for evaluating up to five design alternatives, considering technical and economic aspects. Furthermore, a degradation model is proposed to assess alternative performance over the infrastructure's full lifecycle under specific rail traffic assumptions. This facilitates a rapid evaluation of alignment design's impact on maintenance costs. The paper draws two main conclusions with potential benefits for both private companies and railway administrations: Firstly, BIM proves feasible and advantageous despite some interoperability challenges, highlighting the need for continued advancements in BIM software compatibility. Secondly, the proposed approach can potentially lead to up to 40% cost savings in maintenance for railway administrations. [Display omitted] • The influence of the horizontal alignment design of a High-Speed Railway Line (HSRL) on its maintenance period is analyzed. • A BIM-centered method has been proposed to evaluate both construction and tamping costs. • The importance of considering the maintenance costs during the design phase has been demonstrated. • The interoperation between an alignment design tool and a budget management software has been tested. • The method is applied to a real HSRL project in Spain. [ABSTRACT FROM AUTHOR]

In this paper, a climatological study focused on the analysis of atmospheric aerosols properties over Burjassot and Aras de los Olmos AERONET sites, located at Eastern Spain, is performed. Computed mean values for Aerosol Optical Depth (AOD 440), Ångström exponent (α 440 − 870) and columnar water vapor (w) in Burjassot (Aras de los Olmos) are 0.19 ± 0.08 (0.10 ± 0.02), 1.21 ± 0.16 (1.18 ± 0.25) and 1.87 ± 0.68 cm (0.94 ± 0.10 cm), respectively. From the monthly statistics of w and AOD , it is concluded that summer months are characterised by higher humidity and turbidity, being the latter favoured by particle stagnation, recirculation, secondary aerosol formation, hygroscopic processes and North African dust intrusions. Regarding the volume size distribution, well developed fine and coarse modes are found in summer. On the other hand, the spectral dependence of the asymmetry parameter (g) describes a decrease in λ up to 870 nm , and then a slight increase due to the influence of dust particles. The computed mean values for the asymmetry parameter (g 440) are 0.700 ± 0.013 (0.709 ± 0.016) for Burjassot (Aras de los Olmos). With respect to the Single Scattering Albedo (SSA) and the complex refractive index, an additional analysis using Level 1.5 data is performed, as relatively few data points pass the filters imposed by the Level 2.0 conditions. In the case of SSA, which is found to be larger in spring and summer months, the obtained mean annual values at 440 nm are 0.94 ± 0.03 (0.95 ± 0.03) for Burjassot (Aras de los Olmos). Finally, regarding the complex refractive index, the mean values of the real part (m 440) are 1.50 ± 0.03 (1.49 ± 0.03), and for the imaginary part (∣ k 440 ∣) are 0.007 ± 0.004 (0.004 ± 0.003) in Burjassot (Aras de los Olmos). The present study is complemented with three analyses based on the distinction between the different aerosol types. To begin with, these are classified according to their nature, being the most abundant continental clean aerosols. Next, Gobbi diagrams are plotted for both sites, depicting a clustered region of coarse aerosols, as well as particle growth traces. Finally, the different air masses affecting the measurement sites are studied, reaching to the conclusion that these originated in the European and the African continents are the most turbid ones. To conclude this study, a seasonal Mann-Kendall test is applied to AOD, α , w and SSA measurements in order to study possible trends. Hence, a statistically significant (> 80 %) decreasing trend in AOD is observed for most months; whereas α , w and SSA do not show statistically significant long-term trends, only for a few months of the year. • 20 (6) years AERONET data studied for Burjassot (Aras de los Olmos) in Eastern Spain. • Continental clean aerosols are dominant over Burjassot and Aras de los Olmos. • AOD, α and ω higher at Burjassot than Aras de los Olmos due to its urban character. • European and African air masses are responsible for high turbidity measurements. • AOD decreases with 80% confidence level; but α and SSA trends are not statistically significant. [ABSTRACT FROM AUTHOR]

This study presents the characterization and chronology of the Quaternary terrace sequence developed in the confluence zone of the Júcar and Cabriel river valleys. The study area covers a radius of 10 km from the confluence of the two valleys near the locality of Cofrentes (Valencia). It is located in the northern zone of the Ayora-Cofrentes Graben in the northeastern sector of the External Prebetic Zone adjacent to southern Iberian Chain. This N-S graben is an inherited tectonic feature with an axial salt wall subject to different periods of fluvial dissection and refilling since at least the end of the Messinian Salinity Crisis (MSC). Regional isostatic uplift and local uplift and subsidence processes related to salt flow and dissolution during the Quaternary period, favored by fluvial entrenchment and terrace development. The terrace system and the previous pre-incision erosional and depositional surfaces are analyzed from a geomorphological point of view. The terrace system only develops within the soft Mio-Pliocene sedimentary fill of the graben, whilst upstream and downstream this zone the studied valleys develop important gorges (canyons) carved in the Cretaceous tablelands (Caroch Massif) and Mio-Pliocene deposits (Llanura Manchega). The geochronological control is established from 20 numerical ages obtained by different dating methods, such us Electron Spin Resonance (ESR), Optically Stimulated Luminescence (OSL) in detrital sequences and Th/U series in calcareous tufa deposits. Additionally, four K/Ar dates available for volcanic materials disrupting the upper and oldest terrace have been considered. A total of 14 terrace levels were identified between +130–135 m (T1) and +3–4 m (T14) of relative height above the river thalwegs. The T1 has an approximate age of c. 1.6–2.0 M.a. as indicate the age of the volcanic materials from the Agras Volcano intruded in the terrace, marking the onset of Quaternary fluvial dissection in the zone. The obtained geochronological data indicate that the Lower-Middle Pleistocene boundary is slightly above T6 (+60–65 m), which has an ESR age of 577 ± 43 ka. This terrace also documents the onset of the most important period of rise of the salt within the graben interfering drainage development. The geochronological data indicate the occurrence of a second eruptive event during the second half of the Middle Pleistocene related to the terraces T7 (+50 m) and T8 (+35–40 m), updating the volcanic activity in the zone. The beginning of the Upper Pleistocene is recorded by the top sequences of the T9 terrace (+25–30 m) where numerous OSL and Th/U data have been obtained with ages between ca. 105 and 81 ka (MIS 5). However, these young terraces (T8 to T13) are thickened (8–10 m) recording Middle Pleistocene sediments in their basal sequences with ages between ca. 193 and 137 ka (MIS 6). The study provides important data on valley evolution under the interference of volcanic activity, salt –related deformation (diapirism, solution subsidence) and fluvial incision fostered by the rise of the axial diapir (salt wall) protruding the graben since at least the Brunhes-Matuyama boundary. The analyzed fluvial evolution documents interesting cases of river capture, drainage antecedence and tectonic adaptation of the drainage throughout the Quaternary Period. The paper also poses interesting geochronological data on the long-term Quaternary evolution of Mediterranean valleys in the Iberian Peninsula, poorly explored to the date. [Display omitted] • The evolution of the Júcar Fluvial system (Spain) is addressed for first time supported. • The catchment contains a complex sequence of 14 terrace levels up to +135 m • Terrace sequence is dated within the last 2.0 Ma using K/Ar, Th/U, OSL and ESR data. • Phreatomagmatic activity disrupts valley evolution during the Lower-Middle Pleistocene • Cases of river capture, drainage antecedence and tectonic superposition are documented [ABSTRACT FROM AUTHOR]

In this paper, an innovative framework is introduced for assessing the impact of climate change on coastal structures, with a primary emphasis on modeling future climate conditions. The framework encompasses several key components: the development of a new climate emulator to facilitate multivariate extreme value analysis; a hybrid statistical–numerical wave propagation strategy; and the integration of a novel compound modeling approach specific to coastal structure sites. These advancements are designed to effectively manage multiple climate scenarios and models within the context of high uncertainty. The methodology is applied and validated for a coastal structure located on the Mediterranean coast in Spain. Focusing on compound wave and sea-level events, the projections indicate an increase in the frequency and severity of extreme conditions, exacerbating coastal impacts such as wave overtopping. For instance, the present-day 500-year coastal flooding level would be associated with a return period of 50 years by mid-century under a stabilization scenario and would be further reduced to 25 years under a high-emission global warming scenario, resulting in important consequences for structure performance if no adaptation is implemented. • Develops a framework for assessing the impact of climate change on coastal structures. • It develops a new climate emulator to facilitate multivariate extreme value analysis. • It encompasses a hybrid statistical–numerical wave propagation strategy. • It integrates a novel compound modeling approach specific to coastal structures sites. • It has been applied and validated for a coastal structure in Spain. [ABSTRACT FROM AUTHOR]

The operation of solar photovoltaic installations for many years may lead to problems such as reduced energy efficiency and aging of components. Under this framework, replacing certain components in the facility to extend the service life of the power plant is viewed, in some cases, as a feasible strategy. However, despite its enormous strategic interest for the solar PV industry, we find that revamping PV installations has been the focus of very few scientific publications, and even less so when it comes to analyzing real facilities. Thus, this paper performs a techno-economic analysis of three revamping strategies at a real PV power plant located in the south-east of Spain. The strategies consist of substituting, in each case, the PV modules, the inverter, and both components. The simulated results show that the highest production value by installed power is obtained when both the modules and the inverter are replaced. In the same vein, the economic analysis reveals this scenario as the most favorable in terms of profitability. Based on the results obtained, an interesting future line of research would be focused on the extension of the analysis at country level to study the joint impact of numerous revamping actions. • Techno-economic analysis of revamping projects of a real solar PV power plant. • Replacing modules and inverter emerges as the most feasible strategy. • Study's applicability extends to other revamping initiatives and geographical regions. • Economic outcomes allow the most cost-effective alternatives to be determined. • This research can contribute to efficient and sustainable energy planning. [ABSTRACT FROM AUTHOR]

In smart agriculture, the accurate prediction of evapotranspiration plays a crucial role in optimizing water usage and maximizing crop yield. However, the increasing adoption of IoT sensor technologies has resulted in the accumulation of large amounts of data, which are frequently contaminated by noise and pose a significant challenge to extract reliable knowledge through data modeling. This research addresses the problem of noisy IoT sensor data and its impact on evapotranspiration prediction, an essential aspect of agricultural practices. The effect of noise on sensor variables and evapotranspiration is extensively analyzed by simulating different noise levels in evapotranspiration datasets collected from various agricultural areas in Spain, enabling a comprehensive evaluation of its impact on the performance of data science models. Despite the potential consequences of this type of errors, a noise preprocessing stage is often overlooked in existing literature in this field, which is necessary to improve data quality prior to modeling. In order to address this challenge, this paper proposes the usage of regression noise filters as approach to mitigate the detrimental effects of noisy IoT sensor data on evapotranspiration prediction. Additionally, we introduce the rgnoisefilt R package, which offers a practical and efficient implementation of noise filtering techniques for regression datasets, providing a valuable solution for handling noisy data in smart agriculture applications. The experimental results obtained emphasize the negative impacts of noise on evapotranspiration prediction performance and highlight the importance of an appropriate data treatment to mitigate system deterioration. Furthermore, the findings of this research emphasize the efficacy of the regression noise filters implemented in the rgnoisefilt software, enhancing the performance of the models built and providing a valuable tool for improving data quality in smart agriculture. • Noisy IoT sensor data in evapotranspiration prediction are addressed. • This research analyzes the impact of different types of noise in the data. • Regression noise filters are proposed as a tool to improve data quality. • The rgnoisefilt R package provides efficient solutions for noisy sensor data. • The effectiveness of filters with noisy evapotranspiration data is shown. [ABSTRACT FROM AUTHOR]

[Display omitted] • Inclusion of temporal data can improve detection F 1 -Score by 8% • Infrared bands provide about 4% more F 1 -Score than visible bands despite lower resolution • Using over 80 features provides an increase of about 12% F 1 -Score over using less than 10. • The proposed method successfully detects all test plots with nearly 90% F 1 -Score • A dataset of recent manure application, verified through on-site validation, is made public. The use of manure in agricultural fields during the wet season can lead to environmental pollution by releasing nitrates into nearby water sources. To address this issue, authorities may impose closed periods during which manure application is prohibited. However, ensuring compliance with these regulations can be challenging, as it is difficult to monitor all fields in a country. To tackle this problem, a solution has been proposed that involves employing machine learning techniques in conjunction with satellite imagery to automatically identify freshly manured fields. This paper investigates the relationship and effectiveness of the Sentinel-2 satellite bands and 51 frequently utilized multispectral indices in the context of precision agriculture, by exploring different feature selection methods. The proposed method achieves nearly 90% F 1 -Score and detects all test plots of the northern Spanish region, showing its potential for large-scale use in precision agriculture and environmental monitoring. This method incorporates temporal data, resulting in an 8% improvement in the detection F 1 -Score. Despite their lower spatial resolution, infrared bands have proven to be more effective than visible bands, enhancing the F 1 -Score by 4%. Furthermore, the use of over 80 features contributes to a 12% increase in the F 1 -Score compared to using fewer than 10 features. For further research and future studies, a dataset of recently manured plots, verified on-site, has been developed and made publicly available. [ABSTRACT FROM AUTHOR]

Efficiency is a fundamental element of the transition towards a sustainable use of natural resources among which water has a prominent role. Spain is among the European countries facing considerable water shortage problems, particularly within its southern regions. The current paper introduces an efficiency evaluation model based on Data Envelopment Analysis (DEA). The model incorporates a subset of outputs as undesirable inputs to account for the circularity of the production process by means of treated and reused water. The main differences existing between the circular approach proposed, a standard two-stage DEA model and a dynamic Slacks-Based Measure process will be carefully illustrated. In particular, the drawbacks faced by these latter techniques to consistently incorporate circularity into their optimization structures will be demonstrated both formally and numerically. The malleability of the current approach and its capacity to generate consistent identifiable categories among the regions evaluated within a circular production setting will be studied in detail. The analysis performed demonstrates significant structural differences in production capacity and efficiency across regions, with the inefficient ones displaying a considerable subpar use of resources through their production processes. The prevalence of inefficiency in several northern regions is partially compensated through their treatment and reutilization of water. However, this is not the case for the southern ones, which face stricter constraints and more inefficient production processes. The subsequent implications for efficiency are examined and policies aimed at enhancing the structural quality of the corresponding production systems suggested. [Display omitted] • We define a circular Data Envelopment Analysis (DEA) efficiency evaluation model. • Undesirable inputs implement the production circularity generated by treated water. • Two-stage and dynamic Slacks-Based Measure DEA are inapplicable to our setting. • Significant differences in production efficiency are found across Spanish regions. • Southern regions remain inefficient even with water treatment and reutilization. [ABSTRACT FROM AUTHOR]

• Urbanization causes biodiversity loss in rivers. • Benthic macroinvertebrates can be used as indicators of land use changes. • Land uses changes modify the trophic structure of benthic communities. • Riparian forests act as a buffer zone important to preserve riverine biodiversity. • Management politics must highlight the importance of biological indicators. Since the second half of the 20th century, when monitoring programmes were implemented as a measure to improve the water quality of rivers, numerous advantages have been achieved. one of the most remarkable advances was the integration of bioindicators as a suitable and quick tool to complement the assessment based on the evaluation of physical and chemical parameters. This paper evaluated which of the already used water quality indices based on macroinvertebrates would be more suitable to assess the ecological status of 6 rivers (Barxas, Deva, Tea, Manco, Louro and Furnia rivers) within the Miño River international basin (NW Spain and N Portugal) applying the methodology proposed by the Water Framework Directive (WFD). In addition, the relationship between the water quality and the land uses within a buffer zone of 100 along the watershed of each river was studied. Our findings demonstrate that the IBMWP (Iberian Biomonitoring Working Party) is the most suitable index to evaluate the water quality of rivers from this geographical zone. Nevertheless it is necessary to use other more sensitive index like EPT and PT indices to identify any potential pressures that might be concealed by IBMWP. The tributaries of the Miño River generally presented a good ecological status according to the IBMWP. Nevertheless, the Louro River had the lowest score for all the indices being the worst preserved among all within the basin. On the contrary Barxas, Deva and Furnia rivers showed the higher values. The results indicated that the most urbanised river was the Louro River (13 %) followed, to a lesser extent, by Manco River (7 %) and Tea River (2 %). Consequently, the Louro River had the worst water quality (poor quality) and a less diverse benthic macroinvertebrate community, in which more generalist taxa such as Chironomids, Ceratopogonids, or Crustaceans were found. On the other hand, the rivers with the best water quality were the Barxas, Deva, and Furnia, all of them with a negligible proportion of artificial cover surface (<1 %) within the delimited buffer zone. This would suggest a negative influence of the proportion of artificial areas over diversity and quality indices. [ABSTRACT FROM AUTHOR]

Controlling solar furnace systems presents significant challenges due to their nonlinear dynamics and uncertainties in model parameters. Therefore, this paper provides a comprehensive study of four predictive control strategies specifically tailored for solar furnaces: linear generalized predictive control (GPC), nonlinear GPC (NGPC), nonlinear model predictive control (NMPC), and practical NMPC (PNMPC). The primary objective is to address practical issues in solar furnaces, including nonlinear behavior, measured and unmeasured disturbances, and optimal control actions to enhance control performance and reliability in thermal resistance trials. Using real data from an actual solar furnace facility, the control strategies are evaluated in a simulation environment, considering various aspects such as control performance, computational burden, and robustness. Among the strategies, PNMPC proves to be the most promising, attending a compromise between control performance and computational cost. It exhibits a small error-index and significantly shorter processing time (20 times less) compared to NMPC in the simulated test. Consequently, PNMPC is implemented in the existing solar furnace SF60 in Plataforma Solar de Almería, Spain. Real-world results demonstrate the effectiveness of PNMPC in controlling the sample temperature during thermal stress trials in the solar furnace. The controller successfully handles system constraints and performs exceptionally with no steady-state error. As a result, the research outcomes provide suitable solutions to meet high-criteria requirements in thermal stress experiments in solar furnace systems. Furthermore, this study's findings advance the control engineering field in solar furnace systems, facilitating the transition towards sustainable and efficient use of solar energy. • Comprehensive study of four control strategies for solar furnaces is conducted. • Model validation and linear model parameter analysis are performed. • Practical NMPC balances performance and computational cost to enhance thermal stress trials. • Real-world tests validate the selected PNMPC in meeting high-criteria requirements. [ABSTRACT FROM AUTHOR]

Finding alternative phosphorus sources is imperative to address negative environmental and societal impacts caused by its current inefficient use. However, the direct use of phosphorus in sewage sludge in agriculture is controversial. This paper uses Denmark, Germany, and Spain as case examples to assess relevant legislation and boundary conditions in agricultural production to identify opportunities and barriers for the utilisation of recycled phosphorus from wastewater in agriculture on a regional level. Only five out of 22 phosphorus recycling technologies considered were in full compliance with legislation across all three countries, and these five were then assessed for their potential to supply phosphorus to major crops within countries. We considered the application of technologies across four scenarios: 1) struvite; 2) vivianite as iron supply; 3) vivianite for calcium phosphate precipitation; and 4) ashes for calcium phosphate precipitation. The most suitable scenario identified for Denmark was vivianite for calcium phosphate precipitation, whereas in Spain vivianite as iron supply was identified as most suitable, and ashes for calcium phosphate in Germany. We found that in 2018, the potential phosphorus supply from recycling technologies was on average 0.38, 0.29 and 0.05 kg of phosphorus per capita for Danish, German, and Spanish regions. These quantities could meet 9.1, 21.7, and 10.0 percent of the phosphorus required to produce major cereals in each country (specifically wheat, barley, and rye). Given current legal constraints, wastewater treatment plant connections and agronomic context, the potential contribution of recycled phosphorus is non-negligible in many sub-national regions. Still, to access the full potential of phosphorus circularity clear product specifications and transport and logistics among regions will be necessary. [ABSTRACT FROM AUTHOR]

Despite the relevant potential of offshore resource to substantially mitigate the effects of climate change through the generation of renewable electricity, the full exploitation of such wind resource currently remains incomplete. To promote offshore wind energy projects, it is crucial to engage in comprehensive planning processes that encompass technical, social, environmental, stakeholder, and political considerations. Within this framework, this paper presents a combined approach based on Geographical Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) methodology to optimize the selection of offshore wind farm locations. The proposed methodology includes annual net electricity production as a critical factor in the decision-making process, which has been previously optimized with regard to the wake effect. Subsequently, the determination of potential locations involves key criteria such as technical feasibility, environmental impact, economic viability, and power generation potential. This MCDM-GIS approach was applied and evaluated in a case study along the Spanish coastline, considering 92 initial alternatives. The results indicate that the offshore wind energy targets established for Spain in 2030 and 2050 should be reevaluated, as the optimal utilization of the available area represents only 16% of the total potential area. Based on the aforementioned criteria, a suitability map was generated by integrating all relevant map layers, including their respective buffer zones. [Display omitted] • Net electricity production criterion included in the alternative decision matrix. • GIS-MCDM synergy methodology at territorial level to optimize offshore wind sites. • Multi-criteria definition includes technical, environmental, and economic factors. • 92 potential offshore wind plant alternatives in a Spanish case study are considered. • Results give different ranking of alternatives depending on each MCDM approach. [ABSTRACT FROM AUTHOR]

IEC 61400-21-1 describes the procedure for measuring the flicker produced by a wind turbine. This is a complex procedure, as it involves the processing of voltage and current time series recorded in a grid connected wind turbine for its entire operating range. The standard assumes that the flicker produced by a wind turbine is related to wind variations and switching operations. The voltage and current signals recorded in a 2 MW Type III wind turbine located at a 32 MW wind power plant in Spain were used for the study. The work shows that most of the flicker produced by the wind turbine is due to the power consumption of the motor used by the yaw control system to orient the nacelle. These operations generate voltage changes whose amplitude is independent of the generated power. As the generated power increases, so does the number of yaw control operations and thus the flicker emitted. A simple analytical method is proposed to estimate the flicker produced by yaw control operations. The results confirm that incorporation of this strategy into the standard could considerably simplify the current flicker measurement procedure. • The paper quantifies the effect of yaw operations on flicker in a 2 MW Type III WT. • Each yaw operation generates three times as much flicker as a WT switching operation. • During continuous operation the 80% of generated flicker is due to yaw operations. • Flicker due to yaw operations can be simply estimated from the P s t =1 curve. • The estimation would simplify the flicker measurements, reducing economic costs. [ABSTRACT FROM AUTHOR]

The consumption of plastic raw materials has been exponentially growing throughout the world in the last decade and so has, in the same proportion, their associated waste. Recycling through the mechanical treatment of the heavy fraction of these plastics is the most economic and the safest alternative to use this waste and to recirculate it for reuse within the same industry or for other related industries located near the place where the waste is generated. In this paper, a study of characterization and post-processing of recycled high density polyethylene received by a hazardous waste manager in southern Spain is presented, an area in which containers, Intermediate Bulk Containers, and drums, originally used for oil products from refineries and petrochemical industries nearby, are employed. These high density polyethylene materials are validated to meet the requirements of the harmonized standards to be used as self-supply in the production of manhole covers. EN 124–6:2015 standard allows the use of plastic materials such as PE or PP, even using material externally reprocessed, such as material derived from thermoplastic products that have not been previously used as lids or manhole covers. Class A-15 and B-125 of the standard are oriented for the construction of these types of elements to cover or close in certain urban spaces such as pedestrian areas, parking areas or multi-story car parks for vehicles. The study shows that the recycled material fully complies with the density; it keeps unalterable during fire test after application of the coating, there are no adverse effects on artificial ageing; the decrease of the average value of the tensile impact strength by 21.84%; and achieves 6 mm of maximum deflection against the test load Fp (45.3 MPa). Therefore, it can be considered a valid scenario for the effective use of solid plastic waste in products that make up part of the urban or industrial areas, as an example of complete circularity material and offers a valid alternative to the end-of-waste condition. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper presents IH-LANSloc (IH-Long-term Anthropized coastlines Simulation tool for local-scale studies), a numerical model that solves shoreline and nearshore evolution at short and long-term time scales and in a wide variety of sandy coastal settings. IH-LANSloc results from the two-way fully coupling of a data-assimilated shoreline evolution model with a process-based wave propagation model and a rules-based profile translation tool. In contrast to shoreline evolution models, IH-LANSloc is able to reproduce the nearshore evolution in areas with complex bathymetries, considering the effects of human interventions (e.g., groynes, seawalls, breakwaters and nourishments) and natural ecosystems (e.g., coral reefs, seagrasses, and vegetation patches). This nearshore evolution model is less computationally expensive and is applicable to larger time scales than most process-based models and provides more accurate and richer results than present-day physics-based models. Model validation is carried out at an embayed beach in northern Spain showing great agreement with satellite-derived shoreline observations (RMSE<7m). • A physics-based nearshore evolution model for sandy coasts is presented. • It is composed of a multi-model coupling including a spectral wave model. • It can assess the nearshore evolution considering complex bathymetries. • The model accounts for the effects of hard structures and ecosystems. • Model outputs can be directly used for flood modelling. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of reducing the number of values required to characterize an electricity demand profile, which is usually known as its dimensionality. This reduction may have a significant impact on the computational efforts and storage capacities required to analyze and process high volumes of electricity load curves. Also, the reduction to 2 or even 1 component enables its graphic representation. Specifically, this work is mainly focused on profiles defined by their monthly demand values, and where the clients are aggregated by locations and/or economic activities. This approach is of great interest for marketing analysis and decision-making of electricity retailers. In this sense, the use of dimensionality reduction techniques based on knowledge (calendar and temperature) along with the application of data-driven procedures (Principal Component Analysis and autoencoders), are explored in the paper. The results of this research show that autoencoders clearly outperform the other techniques, yielding errors in the reduction process between 15% to 40% lower and preserving distances between profiles in the low-dimensional spaces, with a correlation of 0.93 with the distances in high dimensional space. Additionally, the bidimensional graphical representation of a profile can easily be interpreted in a polar way, where the angle denotes the shape of the profile, and the radius reveals its scale. To reach these results, a very large dataset has been employed, with about half a million aggregated profiles corresponding to the electricity consumption during 3 years of more than 27 million clients in Spain. [Display omitted] • Autoencoders are the best option to reduce the dimensionality of demand profiles. • Data-driven techniques outperform knowledge-based models by reducing components. • Low-dimensional characterization preserves the relative distances between profiles. • The graphic representation of bidimensional profiles admits a polar interpretation. [ABSTRACT FROM AUTHOR]

• Energy Communities analysis and review in Europe and, more specifically, in Spain. • Promoting energy poverty reduction and cooperation through Energy Communities. • Real case study focused on reducing energy poverty in the city of Getafe (Spain). • Selection of public buildings to implement energy community in two neighborhoods. • Significant electricity bill reductions for energy poverty households. Advancements in renewable energy technologies, a growing awareness of the need for sustainable energy solutions, and the emergence of new business models in the energy sector, have contributed to the growth of Energy Communities. Public entities can find in energy communities today an opportunity to tackle energy poverty, to increase the cooperation among citizens and to empower them to be a more active player within the energy sector. This paper analyzes energy communities in Europe and focuses specifically on Spain; considering the three beneficial aspects previously mentioned. It aims to obtain ground truth about the applications of these new citizens' arrangements. A citizen-led initiative at the city of Getafe (Madrid, Spain) is included and discussed in detail. This case study focused on reducing energy poverty of its participants. An energy community was designed and implemented as a collective PV solar self-consumption infrastructure. The roofs of various public buildings in the two case study neighborhoods were selected for locating the PV installations. Real metered data were used to evaluate the solution. A public building was selected for the energy community, considering generation on roof and dwellings proximity. The results provided that out of the 77% PV generations could be used to contribute to the demand of 100 residences; while the resulting 23% would provide approximately 60% of the energy demand of the public building. The setting up of the Energy Community not only had the technical part but also included workshops seminars and information sessions, to ensure that people prone to benefit and maximize its impact. [ABSTRACT FROM AUTHOR]

The low energy efficiency of the European building stock is both a social and environmental issue. However, plans and studies on energy retrofitting interventions do not usually evaluate the impact of these measures on households' energy affordability. This paper analyses and compares the effect of alternative retrofitting strategies on thermal energy services' affordability in Spanish vulnerable households. This analysis was carried out by applying the Required Thermal Energy Expenditure model proposed in previous studies to several 2030 residential building stock scenarios, setting 2020 as a baseline. Therefore, this work proposes a methodological approach to assess the impact of the considered energy efficiency interventions on energy poverty in winter (WEP) and summer (SEP) and carries out both a 'social cost' and a cost-benefit analysis. The dwelling's thermal-enclosure retrofitting stands out as a very WEP-reduction and social-cost effective measure. Instead, replacing old thermal systems with more efficient ones would have a higher impact on SEP. Combining both kinds of retrofitting measures could reduce heating and cooling required expenditures by, respectively, 49% and 59%, and decrease the WEP and SEP share, correspondingly, by 35% and 63%. These results might inform policymakers to enhance the targeting and design of energy efficiency programs. [ABSTRACT FROM AUTHOR]

The spiraling growth of the world's population and unregulated urbanization have resulted in many environmental problems, including poor quality of air, which is associated with a wide range of health issues. Machine learning approaches have been extensively employed to predict air quality, attracting the attention of the scientific community worldwide. Bibliometric studies provide a useful means by which to visualize and analyze published works, helping researchers to make novel scientific contributions by filling existing knowledge gaps in the research. To acquire an in-depth understanding of the topic, this paper presents a bibliometric analysis of all published articles on the use of machine learning networks to predict air quality found in the Web of Science (WoS) search engine from 1992 to 2021. S-curve analysis and social network analysis were used to identify the temporal distribution of articles, productivity by countries/continents, research institutions, and scientific metrics of journal productivity. This study indicated that maximum expansion of the literature witnessed during 2017–2021 (second phase) which represents an expansion or growth stage of machine learning and air quality prediction research. The number of published works increased significantly with 1432 articles accounting for 68.51% of all publications. As a result of the increased interest in machine learning-based prediction tools, the number of articles grew 2.17-fold compared to the 1992–2016 (first phase). In terms of international collaboration impact, Italy emerged as the most successful country (43.44), followed by Greece (31.22) and Spain (23.29). Author keywords analysis was employed to explore and evaluate the emerging research trends on the subject of air quality using machine learning models. Keywords that appear most frequently in this study are 'air pollution', 'air quality', 'machine learning', and 'forecasting'. Citation burst analysis, research productivity analysis, highly influential and highly cited works were also employed to examine various research themes and questions. In this study we also discussed how conventional methods were transformed into machine learning approaches. It is expected that this paper will provide technical guidelines, research priorities, and future opportunities for the precise prediction of air quality and emergency management of air pollution globally. • Interest in air quality prediction through machine learning model is expanding. • China and US were found productive countries in terms of countries collaboration. • Big data, Internet of Things, and Smart city are emerging topic for future research. • Significant difference between citations, funded and non-funded research. • Ensemble modeling is popular in air quality prediction research. [ABSTRACT FROM AUTHOR]

The spiraling growth of the world's population and unregulated urbanization have resulted in many environmental problems, including poor quality of air, which is associated with a wide range of health issues. Machine learning approaches have been extensively employed to predict air quality, attracting the attention of the scientific community worldwide. Bibliometric studies provide a useful means by which to visualize and analyze published works, helping researchers to make novel scientific contributions by filling existing knowledge gaps in the research. To acquire an in-depth understanding of the topic, this paper presents a bibliometric analysis of all published articles on the use of machine learning networks to predict air quality found in the Web of Science (WoS) search engine from 1992 to 2021. S-curve analysis and social network analysis were used to identify the temporal distribution of articles, productivity by countries/continents, research institutions, and scientific metrics of journal productivity. This study indicated that maximum expansion of the literature witnessed during 2017–2021 (second phase) which represents an expansion or growth stage of machine learning and air quality prediction research. The number of published works increased significantly with 1432 articles accounting for 68.51% of all publications. As a result of the increased interest in machine learning-based prediction tools, the number of articles grew 2.17-fold compared to the 1992–2016 (first phase). In terms of international collaboration impact, Italy emerged as the most successful country (43.44), followed by Greece (31.22) and Spain (23.29). Author keywords analysis was employed to explore and evaluate the emerging research trends on the subject of air quality using machine learning models. Keywords that appear most frequently in this study are 'air pollution', 'air quality', 'machine learning', and 'forecasting'. Citation burst analysis, research productivity analysis, highly influential and highly cited works were also employed to examine various research themes and questions. In this study we also discussed how conventional methods were transformed into machine learning approaches. It is expected that this paper will provide technical guidelines, research priorities, and future opportunities for the precise prediction of air quality and emergency management of air pollution globally. • Interest in air quality prediction through machine learning model is expanding. • China and US were found productive countries in terms of countries collaboration. • Big data, Internet of Things, and Smart city are emerging topic for future research. • Significant difference between citations, funded and non-funded research. • Ensemble modeling is popular in air quality prediction research. [ABSTRACT FROM AUTHOR]

Software testing is the most used technique for quality assurance in industry. However, in computer science education software testing is still treated as a second-class citizen and students are unable to test their software well enough. One reason for this is that teaching the subject of software testing is difficult as it is a complex intellectual activity for which students need to allocate multiple cognitive resources at the same time. A myriad of primary and secondary studies have tried to solve this problem in education, however still with very limited results. Before we can design interventions to improve our pedagogical approaches, we need to gain more in-depth understanding and recognition of sensemaking as it is happening when students design test cases. An initial exploratory study identified four different sensemaking approaches used by students while creating test models. In this paper we present a follow-up study with 50 students from a large university in Spain. The used methodology was based on the previous study with the improvements that originated from its evaluation. We asked the participants to create a test model based on a description of a test problem using a specialized web-based tool for modeling test cases. We measured how well these models fit the test problem, the sensemaking process that students went through when creating the models, and the students' perception of the modeling task. The participants received no compensation for their efforts, and we scheduled the experiment during a regular class. Apart from the created models and their metadata, we also collected recordings of the students' computer screens made during the experiment and used a questionnaire to study their perspectives on the assignment. All the collected textual, graphical, and video data was analyzed using an iterative inductive analysis process to allow new information about the different sensemaking approaches to emerge. We gained better insights into the sensemaking processes of students while modeling test cases for a problem. The results enabled us to refine our previous findings, and we identified new sensemaking approaches. Based on these results, we can further investigate ways to influence the sensemaking process in education, the possible misconceptions that have a negative influence on it, and the desired mental model we want our students to have to design test cases. [ABSTRACT FROM AUTHOR]

In Spain, due to its important mining past, there are many abandoned mine waste facilities. This study evaluates the potential of Spanish mine wastes for the recovery of critical and strategic raw materials and other elements of economic relevance. For this purpose, 20 mine waste facilities have been selected in different parts of Spain based on criteria such as tonnage, element content or metal market price. Surface samples were taken at the facilities, considered representative of these, for subsequent granulometric, mineralogical and chemical analysis. The grain size data were obtained by standard sieving (ASTM 5000), while the mineralogy was obtained by X-ray Diffraction (XRD). For chemical characterization, Wavelength Dispersion X-ray Fluorescence Spectrometry (WDXRF) and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) were used to analyze 38 chemical elements (Be, Sc, V, Cr, Co, Ni, Cu, Zn, Ga, Ge, As, Se, Br, Rb, Sr, Y, Zr, Nb, Mo, Ag, Cd, Sn, Sb, I, Cs, Ba, La, Ce, Nd, Sm, Hf, Ta, W, Tl, Pb, Bi, Th and U), many of them included in the European Union's list of Critical Raw Materials (CRMs). In addition, leachate data from mine wastes in the Iberian Pyritic Belt were analyzed to assess their bioleaching potential. The calculated enrichment factors (EF) indicate that some critical elements, as well as others with strategic and economic interest, are highly enriched in the wastes, including Sb, Bi, As, Pb, Cu, Ag, Zn, Cd, Sn, Se and Th. The most promising facilities with the highest recovery potential are located in the historic Riotinto and Tharsis mines (both in Huelva, Iberian Pyrite Belt) and the Rubiais mine in Lugo, whose tailings could contribute up to 3.2 billion dollars gross, according to the current market metal prices. This paper aims to provide a first step to promote mine waste recycling and circular mining in Spain, an initiative that would also help to mitigate the environmental damage derived from abandoned mine wastes. • First national-scale assessment of Critical Raw Materials from mine waste facilities in Spain. • Mine waste facilities enriched with critical and strategic metals in terms of enrichment factors. • Most promising facilities evaluated in terms of metal tonnages and estimated gross benefit. [ABSTRACT FROM AUTHOR]

In this paper, we explore two strategies for reducing the cash flow uncertainty of wind energy producers ascribed to variable weather conditions. The first strategy is based on the idea of aggregating the output of geographically-dispersed generating units. The second strategy employs financial instruments that compensate producers for unanticipated declines in the power delivery. Using a blend of advanced weather modeling, time series analysis, simulation and optimization techniques, we empirically access the ability of the two risk management approaches to control volumetric risk in Spain. With the aid of factor analysis techniques, we proceed with the decomposition of the remaining risk levels and assess the exposure of each strategy's revenue to systematic risk factors. Motivated by the results of this analysis, we propose new financial contracts and mixed-style strategies that are better suited to the risk position of each market player. • We introduce various strategies for managing the wind volumetric risk in Spain. • We quantify their ability to reduce the revenue variance of wind power producers. • Spatial aggregation of resources is not a cure for wind energy stochasticity. • Commercial wind power futures cannot hedge away all risk dimensions. • New contract designs are needed to control systematic variations in producers' income. [ABSTRACT FROM AUTHOR]

This paper analyses the impacts and water policy implications of an increase of perennial crops on irrigation water requirements and on the vulnerability of the agricultural system to climatic events in the case of the Guadalquivir River basin. As indicated by the EU Water Framework Directive (WFD), the characterisation of a river basin requires information on the major economic drivers and pressures at river basin scale. Guadalquivir River basin (southern Spain) can be considered a representative Mediterranean case study that has faced basin closure and continues the trend towards increased crop intensification and greater water-use efficiency. Our result shows an increased vulnerability to drought and water scarcity in the basin due to the expansion of irrigated perennial crops and a decrease of RIS (relative irrigation supply) from 0.70 to 0.56 in the period 2000–2021, thereby forcing farmers to adopt deficit irrigation techniques. The potential increase in the frequency of droughts and the growth of irrigation water requirements (IWR) due to higher temperatures, raise the vulnerability of the system to extreme climate events. Such findings can be employed to support the efficient allocation of water resources in order to improve water policy through its adaptation to increasing risks related to droughts and water scarcity. • The evolution of irrigated area in Guadalquivir River basin is characterised by an increasing share of perennial crops. • IWR have increased continuously due to changes in crop patterns (intensification). • Since IWR exceed availability, the irrigation system applies deficit irrigation as a response, with a decreasing RIS. [ABSTRACT FROM AUTHOR]

This paper evaluates a parametric analysis towards compliance with the nZEB standard, which in Spain is defined by the latest update of the Technical Building Code. This regulation is critically assessed regarding its ability to promote the concept of cost optimization, promote renewable energy sources and minimize primary energy consumption in the residential sector. To this end, a virtual building was defined and multiple designs were evaluated using DesignBuilder software. A set of 170 alternative scenarios was established and parametrically evaluated for five cities representing the five climatic zones of inland Spain (Bilbao, Burgos, Seville, Madrid and Almeria). The results were evaluated focusing on cost-cost effectiveness and primary energy consumption values for the different scenarios, evaluating them in relation to the minimum requirements set by the regulation. The great potential of photovoltaic energy is highlighted, which allows negative values to be obtained for the two Equivalent Uniform Annual Costs due to the cost avoided through self-consumption. This fact makes the optimal designs tend to electrification, specifically through air and ground source heat pumps. It is worth mentioning that the new Code has only established a minimum target for renewable self-consumption in the residential sector, which should be reinforced in future updates. [ABSTRACT FROM AUTHOR]

Depopulation is a major challenge for many rural inland areas, as is the case in a considerable number of rural municipalities in Galicia (north-western Spain). The pandemic had a considerable impact on internal migration in 2020, but it was far from being anywhere near high enough to reverse the ongoing trend of population decline, highlighting the need for further action. Thus, this paper has a twofold objective: firstly, to propose an index of rural sustainable development for the rural Galician municipalities, and secondly, to identify the factors related to it. In so doing, a composite index stemming from the Benefit of the Doubt methodology has been proposed in the first stage, an index built from four-dimension factors– economic, demographic, social, and environmental. The empirical evidence of this index has been compared with three additional ones (namely, common weights, super-efficiency, and geometric mean and logistic normalization models). In the second stage, the relationship between the composite index and a set of exogenous variables that affect rural development has been analyzed, the data confirming that the rates of female immigration and occupancy in the hospitality sector favors sustainable development, whereas the distance from the provincial capital hinders it. This paper paves the way for an assessment of the potentialities of each territory, and the evidence found allows actions to be supported that generate sustainable progress in rural Galician municipalities and that are useful for tackling this complex problem. • A new rural sustainable development index based on four dimensions is proposed. • Strengths and weaknesses for each Galician rural municipality are identified. • Environmental factors constitute a driver of rural development in Galicia. • Social aspects (i.e., health, education, services) stand out as main weaknesses. • Policies to boost social care and attract childbearing-age inhabitants are paramount. [ABSTRACT FROM AUTHOR]

Parabolic-trough solar collector fields are large-scale systems, so the application of centralized optimization-based control methods to these systems is often not suitable for real-time control. As such, this paper formulates a novel coalitional control approach as an appropriate alternative to the centralized scheme. The key idea is to split the overall solar collector field into smaller subsystems, each of them governed by a local controller. Then, controllers are clustered into coalitions to solve a local optimization-based problem related to the corresponding subset of subsystems, so that an approximate solution of the original centralized problem can be obtained in a decentralized fashion. However, the operational constraints of the solar collector field couple the optimization problems of the multiple coalitions, thus limiting the ability to solve them in a fully decentralized manner. To overcome this issue, a novel population-dynamics-assisted resource allocation strategy is proposed as a mechanism to decouple the local optimization problems of the multiple coalitions. The proposed coalitional methodology allows to solve the multiple local subproblems in parallel, hence reducing the overall computational burden, while guaranteeing the satisfaction of the operational constraints and without significantly compromising the overall performance. The effectiveness of proposed approach is shown through numerical simulations of a 10- and 100-loop version of the ACUREX solar collector field of Plataforma Solar de Almería , Spain. • Decentralized control of large-scale parabolic-trough solar collector fields. • Reducing the computational burden of model predictive controllers for solar plants. • Splitting large-scale solar plants by clustering model predictive controllers. • Improving the scalability of model predictive control for large-scale solar plants. • Evolutionary game theoretical assistance for model predictive control in solar plants. [ABSTRACT FROM AUTHOR]

• Self-consumption in residential sector is studied under Spanish regulatory framework. • An average-day model versus a whole-year model is benchmarked. • Scheduling household appliances is analysed in a residential building. • Ex-ante and ex-post renewable energy sharing is investigated. Studies on self-consumption in residential use are constantly evolving, and society claims new solutions in response to changes to new paradigms. This paper aims to optimise the sizing of a self-consumption installation for collective self-consumption in a residential building under the Spanish regulatory framework. The study considers the current price volatility in the retail electricity Spanish market and focuses on the techno-economic analysis of self-consumption in a building consisting of 12 dwellings using photovoltaic energy and the possible support of a storage system. The three aspects analysed are the sizing of the self-consumption facility, the use of smart appliances to shift their consumption to more cost-effective times and the sharing of the renewable energy generated by the facility among the consumers participating in the collective self-consumption. This study employs two models, an average-day model and a whole-year model. The techno-economic analysis yields that between the two self-consumption modalities for residential consumption, the modality with surplus simplified compensation is more profitable than the one without surpluses. Finally, the study focuses on sharing the total renewable energy generated among the participants. The results indicate that both ex-ante and ex-post dynamic sharing brings few benefits. [ABSTRACT FROM AUTHOR]

Concerns about hydrological drought risk and irrigation water supply reliability have grown in recent years due to the increasing demand for water for irrigation and other uses, and the decline in water availability due to climate change. Hydrological drought insurance hedging against water supply gaps can be a key instrument for adapting irrigated agriculture to this new scenario, since it improves the resilience of the irrigation sector, which is having to cope with increasing uncertainty and vulnerability. The objective of this paper is to assess farmers' willingness to pay (WTP) for index-based hydrological drought insurance under different policy designs, considering several different amounts of insured capital, insurance deductibles, and contract terms. To that end, it uses a discrete choice experiment as the valuation method and the Sector BXII irrigation district (southern Spain) as a case study. The results show that farmers would be willing to pay for the proposed hydrological drought insurance, stating a higher preference for policy designs with lower amounts of insured capital, lower deductibles, and shorter contract terms. Moreover, the results also show the existence of heterogeneity among farmers' preferences, depending on their socio-economic characteristics. Finally, we compare the distribution of farmers' WTP for different policy design options of the proposed insurance with the commercial premium estimated using actuarial analysis. The comparison confirms that only the options with lower levels of insured capital present a mean WTP below the commercial premium, while the rest of the policy design options would need to be subsidized like other agricultural insurance schemes to make them attractive to most farmers. • Farmers are willing to pay for hydrological drought insurance. • Farmers prefer policies with low levels of insured capital, low deductibles, and short contract terms. • WTP is heterogeneous depending on farmers' socio-economic characteristics. • Only policy designs with low levels of insured capital are currently commercially viable. • Once the new drought insurance is implemented, it will become more attractive for farmers. [ABSTRACT FROM AUTHOR]

• A new hybrid prediction system is proposed to predict wind speed. • Simplex and chaotic mapping optimization cuckoo algorithm are proposed. • An uncertainty prediction model based on Monte Carlo method is proposed. • The energy theory is proposed to optimize the decomposition mode number. • Theories proofs and experiments verify the effectiveness of the model. Wind energy has strong volatility and intermittent. Accurate wind speed prediction can not only improve the safety of the system, but also optimize dispatch and reduce economic losses. However, previous studies tend to ignore the influence of virtual components and lack effective identification of wind speed characteristics and a robust interval prediction scheme, resulting in poor results. To bridge these gaps, this paper proposes an energy theory method to solve the problem of modal over-decomposition. The study also combines effective modal recognition, uses different prediction methods according to modal characteristics and proposes a set of new optimization algorithms to improve nonlinear prediction capabilities. Finally, based on Monte Carlo theory, a set of interval prediction schemes that can adapt to different error characteristics are proposed. Under the verification of wind speed data in Changma, China and Sotavento, Spain. The mean absolute percentage error of wind speed deterministic prediction reaches 4.22% and 5.82%, respectively. The coverage rate of wind speed uncertainty prediction meets different confidence requirements, and the average interval width is still less than 2.5 m/s at 90% confidence. The results show that the forecasting system proposed in this paper is significantly better than all the comparative forecasting schemes, which can reduce the risk of fluctuations and improve the stability and safety of the wind power system. [ABSTRACT FROM AUTHOR]

The rapid expansion of photovoltaics within the global energy market requires increasing use of land, a limited resource where different uses compete. In this respect, photovoltaic systems that combine electricity generation with land use for crops are becoming increasingly important. This article proposes an analytical model validated in an operating bifacial photovoltaic plant during the summer months. This analytical model considers view factors and heat transfer calculations, as well as an electrical model of the photovoltaic modules. A study was carried out on a commercial solar power plant (11.24 MWp) in Seville, southern Spain, where different vegetal species were planted in two strings, and string performance was monitored. Nine crop species with different irrigation and light requirements, offering different albedo parameters and thermal characteristics. The results show advantages from the point of view of module temperature and power gain. This paper provides regressions for the characterisation of the photovoltaic agricultural plant, with R2 higher than 85%, both for module temperature and energy exported in the string with crops. [Display omitted] [ABSTRACT FROM AUTHOR]

Studies in the agro-industry sector have shown the importance to evaluate the feasibility of an IBLC (a multi-product transformation plant). This paper develops a model to simulate the performance of an IBLC in Spain, using a combined approach: AHP, TOPSIS, and a hybrid simulation model. Results show that profits can be incremented by about 55.7% and environmental emissions of KgCO2e per tonne of bio-commodity produced, reduced by 24.2%. The scientific contribution covers the development of a simulation model that dynamically select suppliers based on a combination of social, environmental, and economic criteria. In addition, the model can be used to evaluate the economic and environmental feasibility of the IBLC by integrating the suppliers selection with the operations of a multi-product transformation plant. [Display omitted] • We provide a list of criteria that are relevant to choose suppliers of wheat. • We introduce the concept of ÍBLC and test its economic feasibility. • We develop an approach to integrate AHP and TOPSIS in a simulation environment. • We show, based on a case study in Spain, that the IBLC can be profitably run. • Profits increase by 55% while KgCO2e emissions reduces by 24.2%. [ABSTRACT FROM AUTHOR]

Wine is the main economic resource in the 'Area of Jerez', located in the south of Spain. It is one of the most important wine tourism destinations in the world. However, its sustainable production process, so strongly influenced by climatic conditions, can be clearly affected by the phenomenon of global warming. The ageing process of "Sherry wine" takes place in buildings known as 'Cathedral warehouses', an example of Nearly-Zero Energy Buildings (NZEBs). For centuries, they have been used to provide specific ecological conditions for biological ageing without any type of mechanical heating/cooling system. The purpose of this paper is to assess the impact of climate change on this production model. For this, the annual thermal behavior of a representative winery was monitored during several years. The first energy simulation model of this type of buildings was developed and validated (daily R2 = 99%; hourly R2 = 97%). Finally, the model was simulated by modifying the outdoor climatic conditions (9 years training data sets) for a climate change scenario, applying the 'morphing' method for the transformation of the weather data. The results show that, by the middle of this century, the average outdoor temperature will increase by 2.3 °C (in summer up to 3.6 °C). As a consequence, the temperature in the ageing zone would exceed the comfort limit for 19% of the year, (up to 34% in hot years). For 2080, the prediction is even worse (average value of 27%). As a result, the microorganisms responsible for this unique process could not maintain their activity. This fact will force the sector to face a harsh reconversion through the adoption of new mechanisms and strategies, which endangers the economic sustainability of an entire region dedicated to this practice. Beyond the specific case, the work highlights the need to evaluate the future behavior of NZEBs through simulation. [Display omitted] • Assess the impact of climate change in a Near Zero Energy Building. • "Cathedral warehouses" will lose their usefulness by the middle of this century. • Comfort limits will be exceeded 19% of the year, interrupting biological ageing. • The economic sustainability of an entire region would be endangered. • The need to evaluate the future behavior of Near Zero Energy Building is highlighted. [ABSTRACT FROM AUTHOR]

This paper assesses the feasibility and robustness of an index-based insurance scheme against hydrological droughts under climate change. To this end, we develop a grand ensemble that samples both modeling and scenario uncertainty in the estimation of the insurance risk premium, so to reveal potential unfavorable surprises and minimize regret in the design of the proposed insurance scheme. The grand ensemble combines four microeconomic models and seven GAMLSS models, which are run for three alternative climate change scenarios: stationary climate/no climate change, RCP 2.6, and RCP 8.5. Methods are illustrated with an application to the Cega River Sub-basin (CRS) in central Spain. Results indicate that for a conventional deductible of 30%, the proposed index-based insurance scheme would be actuarially feasible and affordable under all models for the stationary climate scenario (i.e., robust). For climate change scenarios RCP 2.6 and 8.5 and a 30% deductible, the suggested index-based insurance would be actuarially feasible under most models, albeit some outliers point towards potential unfavorable surprises. Lower deductibles decrease feasibility, particularly for deductibles <10%. • For a deductible of 30% and stationary climate, the proposed index-based insurance is actuarially feasible under all models. • Under climate change (RCPs 2.6, 8.5), the insurance is feasible for all deductibles and the median model (except PMAUP WGP). [ABSTRACT FROM AUTHOR]

Many types of research have been carried out with the aim of combating the COVID-19 pandemic since the first outbreak was detected in Wuhan, China. Anticipating the evolution of an outbreak helps to devise suitable economic, social and health care strategies to mitigate the effects of the virus. For this reason, predicting the SARS-CoV-2 transmission rate has become one of the most important and challenging problems of the past months. In this paper, we apply a two-stage mid and long-term forecasting framework to the epidemic situation in eight districts of Andalusia, Spain. First, an analytical procedure is performed iteratively to fit polynomial curves to the cumulative curve of contagions. Then, the extracted information is used for estimating the parameters and structure of an evolutionary artificial neural network with hybrid architectures (i.e., with different basis functions for the hidden nodes) while considering single and simultaneous time horizon estimations. The results obtained demonstrate that including polynomial information extracted during the training stage significantly improves the mid- and long-term estimations in seven of the eight considered districts. The increase in average accuracy (for the joint mid- and long-term horizon forecasts) is 37.61% and 35.53% when considering the single and simultaneous forecast approaches, respectively. • Estimated polynomial model coefficients serve as COVID19 contagion rate descriptors. • EANNs are used to build valid predictors for different pandemic stages. • Considering curve polynomial descriptors significantly improve the model performances. • Simultaneous Multi-Task EANN forecast perform better in Málaga with simpler models. [ABSTRACT FROM AUTHOR]