Terra Scenica

Sea and estuarine breezes (SEB) play a critical role for cooling air temperatures and enhancing thermal comfort during summer in coastal urban areas. This research investigates the dynamics and impacts of SEB in Lisbon (Portugal) by combining data from a mesoscale meteorological network comprising 80 stations with high-resolution wind field simulations during the thermal summer of 2022 (June 10 to October 8). SEB episodes were identified using criteria based on wind direction shifts: the disruption of prevailing northerly winds in the morning (Nortada), transitions to easterly or southerly directions (rotation between 22,6 and 292,5º), and the resumption of regional flow by late afternoon. Wind fields were modeled using GRAMM-SCI, initialized with ERA5 reanalysis data. Finally, air temperature, absolute humidity and thermal comfort (Universal Thermal Climate Index) anomalies (ΔT/Ha/UTCIurb) were calculated to a reference site (the airport weather station) according to the distance to the riverfront area. SEB at 10 m height, occurred on 31% of summer days, peaking in July and August, with an average duration of six hours (10:00AM – 4:00PM). Considering only the roughness effect of the city and the terrain, these breezes reduced air temperatures up to 4 km inland, with maximum cooling effects (-1.7°C) observed within 500m of the Tagus estuary. Absolute humidity increased by 4.2 g/m³ near the estuary, and UTCI values dropped by 2.2°C on typical breeze days, reaching 5.1°C during heatwave events, substantially reducing heat stress within 1.5 km of the riverfront. However, some riverside areas still experienced moderate heat stress, highlighting the importance of preserving SEB corridors through climate-sensitive urban planning. This study reinforces the importance of integrating natural ventilation processes into urban design to foster cooler and more liveable cities.

Urban air pollution and microclimatic conditions significantly influence public health. This study validates pollutant dispersion and microclimatic conditions simulated in ENVI-met using lichen biomonitoring data. The case study focuses on Avenida da Liberdade, an urban canyon in Lisbon, Portugal, characterized by high traffic and dense tree coverage. ENVI-met simulations of NO₂ and PM10 dispersion, along with microclimatic conditions (wind speed, solar radiation, and humidity), were performed under current conditions. To validate the simulations, changes in lichen species composition on Celtis australis trees were assessed using a European standard methodology. A total of 82 trees were surveyed, recording lichen abundance and species diversity across four cardinal orientations. Non-metric multidimensional scaling (NMDS) was applied to identify gradients in lichen species composition associated with pollution and microclimatic factors. The ordination analysis revealed significant correlations between NMDS axes and simulated environmental variables, such as NO₂, PM10 concentrations, and wind speed. The spatial distribution of NMDS scores closely mirrored the ENVI-met outputs, with axis 3 reflecting pollution gradients and axis 2 aligning with wind patterns. This integrated approach demonstrates the efficacy of combining CFD-based simulations with biological indicators to assess urban microclimates and pollutant dispersion. The results highlight the potential of lichen biomonitoring as a cost-effective tool for validating environmental models, contributing to more accurate urban environmental management strategies. Future research may explore extending this methodology to diverse urban contexts and a broader range of climatic conditions and pollutant types.

The thermal impact of vehicle surfaces on urban climate is a critical yet understudied component, with the potential to influence local microclimates and the urban energy balance. This research quantified the thermal effects of vehicle surfaces in Lisbon, Portugal, through a combination of in situ measurements and parametric modeling. Using a compact mobile weather station equipped with precise sensors, we conducted both mobile and stationary measurements to capture detailed data on the radiative properties of vehicle surfaces. Results revealed significant variations in surface temperatures based on vehicle color. Black vehicles reached a median surface temperature of approximately 71°C, about 20°C higher than white vehicles, which had a median surface temperature of 45°C. This difference in heat absorption directly affected local air temperatures: black vehicles increased surrounding air temperatures by around 2.5°C on a summer day, while white vehicles caused a more moderate rise of 1°C. Black vehicles also exhibited the highest median net all-wave radiative balance (752.8 W/m²), while white vehicles showed much lower values (515 W/m²), illustrating darker vehicles’ greater thermal load and localized heating effects. When exposed to direct sunlight, dark grey vehicles showed the highest radiative balance values (median 680 W/m²), while white vehicles demonstrated the lowest values, confirming their reflective nature. To complement field measurements, we conducted thermal simulations using EnergyPlus integrated with Honeybee for Grasshopper. These simulations, calibrated with site-specific meteorological data, were used to assess whether the 3D EnergyPlus models could replicate real-world conditions. The results validated observed trends and deepened understanding of vehicle characteristics’ role in influencing surface and air temperatures. This study bridges the gap between empirical data and simulations, offering valuable insights for urban climate research and informing mitigation strategies to reduce urban heat island effects.

Abstract To achieve the goals of the 2030 Global Biodiversity Framework, the European Biodiversity Strategy, and the EU Green Deal, biodiversity monitoring is critical. Monitoring efforts in Europe, however, suffer from gaps and biases in taxonomy, spatial coverage, and temporal resolution, resulting in fragmented and disconnected data. To assess user and policy needs in biodiversity monitoring, we employed a four‐step user‐centered stakeholder engagement process with over 300 stakeholders including a public stakeholder workshop, online survey, interviews, and a meeting with experts from 18 EU member states, the European Commission, and the European Environment Agency. The stakeholders identified policy needs, current challenges, and potential solutions. Based on the policy and stakeholder assessment, we recommend establishing a European Biodiversity Observation Coordinating Centre to optimize existing observation efforts, harmonize data, and enhance our ability to predict and respond to key challenges related to biodiversity loss in Europe.

This study investigates microclimate changes induced by urbanization, with a focus on the Urban Heat Island (UHI) phenomenon and the crucial role of Land Surface Temperature (LST). Using the Landsat 8 Land Surface Temperature and Reflectance dataset obtained from the USGS Earth Explorer platform, the research evaluates LST and exoatmospheric albedo characteristics within Lisbon's Metropolitan Area across a defined set of thermal periods from 2015 to 2020. Among the findings, the Bare Soil or Sand LCZ consistently exhibits heightened LST values, while the Large Low Rise urban LCZ consistently records the highest temperatures, exceeding 54 °C and 55 °C in spring and summer and 43.8 °C and 37.8 °C in autumn and winter, respectively. These insights, derived from a meticulous examination of the Landsat 8 dataset and advanced processing methods, bear critical implications for climate change adaptation strategies, providing valuable insights to mitigate the adverse effects of the Urban Heat Island and foster sustainable urban development practices.

Abstract The vast volume of currently available unstructured text data, such as research papers, news, and technical report data, shows great potential for ecological research. However, manual processing of such data is labour-intensive, posing a significant challenge. In this study, we aimed to assess the application of three state-of-the-art prompt-based large language models (LLMs), GPT 3.5, GPT 4, and LLaMA-2-70B, to automate the identification, interpretation, extraction, and structuring of relevant ecological information from unstructured textual sources. We focused on species distribution data from two sources: news outlets and research papers. We assessed the LLMs for four key tasks: classification of documents with species distribution data, identification of regions where species are recorded, generation of geographical coordinates for these regions, and supply of results in a structured format. GPT 4 consistently outperformed the other models, demonstrating a high capacity to interpret textual data and extract relevant information, with the percentage of correct outputs often exceeding 90% (average accuracy across tasks: 87–100%). Its performance also depended on the data source type and task, with better results achieved with news reports, in the identification of regions with species reports and presentation of structured output. Its predecessor, GPT 3.5, exhibited reasonably low accuracy across all tasks and data sources (average accuracy across tasks: 81–97%), whereas LLaMA-2-70B showed the worst performance (37– 73%). These results demonstrate the potential benefit of integrating prompt-based LLMs into ecological data assimilation workflows as essential tools to efficiently process large volumes of textual data.

Introdução: O sono é um importante determinante de saúde que contribui para o desenvolvimento da criança e para o bem-estar das famílias. A escola é um contexto privilegiado para a promoção da saúde, devendo a abordagem ocorrer nos primeiros anos de escolaridade. A intervenção da enfermagem comunitária deve incidir sobre a literacia em saúde, desenvolvendo estratégias de promoção de hábitos de sono saudáveis. Objetivos: Identificar e mapear a literatura científica existente sobre as intervenções de enfermagem no âmbito da promoção da literacia em saúde sobre hábitos de sono saudáveis, das crianças dos 6 aos 12 anos, em contexto de saúde escolar. Material e Métodos: Foi realizada uma revisão scoping que seguiu a metodologia recomendada pelo Joanna Briggs Institute. A pesquisa foi realizada entre 25 e 30 de maio de 2023, nas bases de dados CINAHL e MEDLINE da plataforma EBSCOhost e na PubMed, sendo utilizados os descritores: crianças, sono, hábitos de sono, enfermagem e escola, por meio do operador booleano AND. Foram considerados como critérios de inclusão o acesso a texto completo, publicações com friso cronológico até 5 anos, artigos em que a idade das crianças se situa entre os 6 e os 12 anos. Resultados: A pesquisa resultou em sete estudos: 2 da Turquia, 1 do Japão, 1 da Austrália, 1 do Irão, 1 da Finlândia e 1 de Portugal. Os resultados evidenciaram que os problemas de sono são frequentes nas crianças, podendo afetar a sua saúde física e mental e que a enfermagem em contexto escolar é fundamental no desenvolvimento de programas para melhorar o sono das crianças, com o envolvimento da comunidade escolar (Shamsaei et al., 2019, Silva et al, 2018; Top et al., 2022). Conclusões: Todos os estudos destacam a importância do sono no crescimento e desenvolvimento das crianças e salientam a relevância da promoção da literacia em saúde, para capacitar a comunidade escolar (crianças, famílias, professores). Neste sentido é destacado o papel da enfermagem comunitária em contexto escolar, uma vez que em muito pode contribuir para a capacitação da comunidade escolar, através de intervenções promotoras de hábitos de sono saudáveis.