Buildings have become a key source of greenhouse gas (GHG) emissions due to the consumption of primary energy, especially when used to achieve thermal comfort conditions. In addition, buildings play a key role for adapting societies to climate change by achieving more energy efficiency. Therefore, buildings have become a key sector to tackle climate change at the local level. However, public decision-makers do not have tools with enough spatial resolution to prioritise and focus the available resources and efforts in an efficient manner. The objective of the research is to develop an innovative methodology based on a geographic information system (GIS) for mapping primary energy consumption and GHG emissions in buildings in cities according to energy efficiency certificates. The developed methodology has been tested in a representative medium-sized city in Spain, obtaining an accurate analysis that shows 32,000 t of CO₂ emissions due to primary energy consumption of 140 GWh in residential buildings with high spatial resolution at single building level. The obtained results demonstrate that the majority of residential buildings have low levels of energy efficiency and emit an average of 45 kg CO₂/m². Compared to the national average in Spain, this obtained value is on the average, while it is slightly better at the regional level. Furthermore, the results obtained demonstrate that the developed methodology is able to directly identify city districts with highest potential for improving energy efficiency and reducing GHG emissions. Additionally, a data model adapted to the INSPIRE regulation has been developed in order to ensure interoperability and European-wide application. All these results have allowed the local authorities to better define local strategies towards a low-carbon economy and energy transition. In conclusion, public decision-makers will be supported with an innovative and user-friendly GIS-based methodology to better define local strategies towards a low-carbon economy and energy transition in a more efficient and transparent way based on metrics of high spatial resolution and accuracy.

Indoor air quality is a characteristic that depends on air pollutants inside a building and that can be affected by different ventilation strategies. There is strong evidence linking poor indoor air quality (IAQ) and harmful health effects, especially on vulnerable collectives, such as children in schools. Due to this concern, this work aims to provide guidance on the design of highly efficient ventilation strategies to improve the air quality of schools’ classrooms. For this, IAQ monitoring has been carried out in eight educational in real conditions centres using CO₂ concentration as an IAQ indicator. Variables such as the presence of students and their number, activity developed in the classroom and ventilation strategy used together with break time duration have been also recorded to analyse their influence on CO₂ concentration levels. Concluding results have allowed us to determine the maximum number of students allowed in a closed room to maintain CO₂ levels at normal concentrations and the time needed to reduce these CO₂ levels depending on the ventilation strategy adopted. Moreover, it has been discussed how surrounding school conditions (pollution or noise) and the building isolation are impacting the final IAQ in the classrooms studied.

Today, about 55% of the world population lives in cities and this is foreseen to increase to 68% by 2050. The urban activities of such a large number of people in relatively small spaces can make the air quality levels in cities harmful to human health. For this reason, the European Union (EU) has established a regulatory framework to control and improve air quality levels in cities (Directive 2008/50/EC) by defining a number of fixed stations and other requirements. The aim of this work is to evaluate the air quality reported by the official fixed stations via the installation of a complementary mobile network of air quality based on passive dosimetry of NO₂ measurement during the period 2017–2019. In this study, Valencia (Spain) is selected as a representative European city with seven fixed stations and a network of 424 passive dosimetry sensors distributed throughout the city. In addition, an index of impact of pollutant on population is developed to optimize the locations of air quality stations among neighbourhoods across the city based on the levels of pollution measured by mobile sensors and the population directly affected. The results obtained show that 43.7% of mobile sensors in Valencia exceeded the limit value established by the EU Directive as well as by the World Health Organization during the assessment period. This indicates that the air quality levels offered by the fixed stations are neither representative nor reliable for the air quality monitoring of the city. Thus, the fixed stations currently operating do not provide reliable information on the areas of the city where the majority of the population breathes air with the highest level of pollution. Specifically, the results show that 34.6% of citizens live in areas with an average annual value above the limit recommended for the protection of human health.

Urban traffic congestion is a critical issue with significant implications for mobility and urban planning. In this study, we develop a real-time predictive alarm system capable of forecasting whether a street is likely to experience unusually high traffic within the next 30 min. The system classifies road segments into three alert levels based on traffic data updated every 10 min, providing timely information that can support decision-making in traffic management. The prediction model is built using deep learning techniques trained on a whole year of traffic data in the city of Valencia, and tested with the following year’s data. We evaluated different neural network architectures, including long short-term memory (LSTM) networks, an extended LSTM variant (xLSTM), and Graph Neural Networks (GNNs). Our results show that LSTM provides the best balance between accuracy and computational efficiency, making it the most suitable model for real-time deployment. In addition to traffic data, we incorporate meteorological variables such as wind speed, wind direction, and precipitation to explore their potential impact on traffic dynamics. Although the relationship between traffic and environmental conditions warrants further study, this work demonstrates the feasibility of using real-time predictions to improve urban mobility strategies. The proposed system offers a data-driven approach that can be integrated into broader traffic management frameworks to improve efficiency and responsiveness.

During the period of the COVID-19 pandemic, the air quality reached the best levels to be recorded in large cities in Spain. To analyze and demonstrate this improvement in air quality levels, the evolution of the average nitrogen dioxide (NO₂) levels in 78 Spanish cities with more than 50,000 inhabitants during the pre-COVID-19 years (2017–2019), the period of the COVID-19 lockdown, and the post-COVID-19 year (2021) was analyzed. The results show an improvement in the air quality in most of the cities analyzed for 2020 due to the COVID-19 restrictions. In addition, in 2021, without the COVID-19 restrictions, the air quality levels of the largest cities in Spain showed important improvements in terms of NO₂ concentration compared to the levels in the pre-COVID-19 years (2017–2019). Nevertheless, in 2021, only 11 cities were below the average annual limit of 10 µg/m³ NO₂ established by the World Health Organization (WHO). In addition, no cities with more than 500,000 inhabitants achieved NO₂ levels below the WHO limit. Finally, a detailed monthly analysis indicated that the pre-COVID-19 levels were reached again during the last months of the monitored period.

This study analyses the environmental sustainability of using wooden boxes (WBs) compared to corrugated cardboard boxes (CCBs) for the transport of fruit and vegetable products, where the same box size between WB and CCB is assessed and compared. The Life Cycle Assessment (LCA) followed ISO 14040:2006 and ISO 14044:2006 standards, using the ReCiPe 2016 MidPoint methodology. The following impact categories analysed for sustainability impact, evaluation and monitoring are included: global warming potential, acidification, eutrophication, human toxicity and abiotic resource depletion. The study covered all the stages of packaging life through cradle-to-grave analysis. The study results show that CCBs have a higher environmental impact across most categories despite being single-use packaging systems. The comparison between both packaging systems shows that WBs are a more sustainable alternative, with lower overall environmental impacts in fruit and vegetable packaging and transport. As a general conclusion, WBs have a lower overall environmental impact than CCBs, especially in the key impact criteria of global warming potential, acidification, eutrophication, human non-carcinogenic toxicity, fossil resource depletion and water consumption. Due to that, the wooden box is a more sustainable material for fruit and vegetable packaging, logistics and transport than the corrugated cardboard box, considering the scope and destination analysed.