Ambient air pollution remains a major public health concern, contributing to millions of premature deaths worldwide according to the World Health Organization. Regional air quality assessments are commonly performed using chemical-transport models that require substantial computational resources due to their detailed representation of atmospheric processes. This study explores the feasibility of applying the second-generation dispersion model URBAIR (R) as a computationally efficient alternative for long-term regional air quality simulations. URBAIR (R) was implemented for three European case studies within the DISTENDER project to simulate particulate matter (PM10) and nitrogen dioxide (NO2) concentrations for 2018 under different spatial and temporal resolutions. Model performance was assessed against background monitoring stations and compared across grid configurations. The results show that the model successfully reproduces annual mean concentration patterns, particularly in urban areas, with R2 values ranging mostly between 0.2-0.6, RMSE between 16-36 & micro;g.m-3, and mean bias from -8 to 5 & micro;g.m-3, indicating overall acceptable statistical performance. Within the specific configurations evaluated in this study, increasing spatial resolution was not consistently associated with improved model performance. However, because spatial resolution covaried with other factors including meteorological temporal resolution, domain characteristics, and monitoring station density, the present analysis does not allow the independent effect of spatial resolution to be isolated. Moreover, a key limitation of the modeling approach is the absence of chemical transformation processes, which may affect the representation of secondary pollutants. Overall, the dispersion-based modeling framework substantially reduces computational demand and input complexity, proving suitable for long-term exposure and climate-related applications when annual average concentrations are the primary objective. In future studies, the modeling approach should be applied to other case studies to consolidate the findings of this exploratory work so that it may contribute to sustainability-oriented decision making by facilitating regional assessments of air quality and potential health impacts related to climate change.

Air pollution and meteorological variability are key drivers of short-term fluctuations in hospital admissions (HAs) for respiratory diseases, yet most existing studies focus mainly on retrospective associations with past hospitalizations or on same-day predictions, which limits their operational value for proactive healthcare planning. This study evaluates different machine-learning (ML) and deep-learning (DL) forecasting frameworks to predict daily HAs up to three days ahead using routinely available air-quality and meteorological data. Three preprocessing scenarios are evaluated to assess the role of temporal and environmental information: three-day sequence forecasting (S1), independent same-day modeling without historical context (S2), where three consecutive days are modeled as independent observations rather than as a temporal sequence, and three-day sequence forecasting with one-week historical information (S3). The input feature space includes air quality, multiple meteorological variables, and calendar-based indicators, together with lagged and rolling-window features designed to capture short-term environmental memory. The sequence-based HGB and RNN framework (S1 and S3) consistently delivered strong three-day-ahead forecasts, whereas the same-day model without historical context (S2) showed substantially weaker generalization, highlighting the importance of short-term temporal memory for predicting HAs. The multi-day recurrent models demonstrated high stability and accuracy across consecutive forecast horizons, with close agreement between predicted and observed admissions under normal operating conditions and increased uncertainty only during rare peak-demand events. These results demonstrate the strength of the proposed framework in capturing the dynamic, environmentally driven patterns of respiratory hospital demand and its suitability for operational healthcare forecasting.

Wildfire smoke significantly perturbs atmospheric composition and radiative balance, with implications for air quality, weather, and climate. Accurately simulating smoke-radiation-convection interactions remains a scientific challenge, particularly at meso- to local scales. This study presents developments in the BRAMS v6.0 modelling system, including the integration of crown fire spread into SFIRE and dynamic coupling of fire-emitted smoke fluxes. These enhancements enable physically consistent simulations of wildfire behaviour, smoke emissions, and their radiative impacts. Fire spread and heat release are used to compute Fire Radiative Power, which drives smoke emissions in real time. These emissions are fully integrated with aerosol-radiation interactions and atmospheric dynamics. The system was applied to the 15 October 2017 wildfire in central Portugal using high-resolution simulations. Model performance was evaluated by comparing a diagnostic Smoke Optical Depth (SOD), computed offline from BRAMS-simulated PM2.5 using a Mie-based framework, with MERRA-2 Aerosol Optical Depth (AOD). Statistical comparison shows that SOD and MERRA-2 AOD share a coherent spatiotemporal structure, with correspondence maximised during the active fire phase. Peak extinction reached similar to 67 m(-1) at 400 nm and absorption approached similar to 5 m(-1) at 550 nm in the near-source plume core, consistent with an OC-dominated scattering regime and localized BC-driven shortwave absorption. The resulting radiative heating contributed to the upward displacement of the CIN layer ( approximate to 100-200 m) and to the partial erosion of low-level inversions, producing transient stability modifications. Although the model occasionally produces very high near-source PM2.5 and optical-depth values confined to a small number of grid cells, additional diagnostics show that plume-integrated mass and optical properties remain physically consistent and are not dominated by boundary effects. These results demonstrate that the enhanced BRAMS system captures the coupled fire-atmosphere-radiation feedbacks of intense wildfires, improving the interpretation and prediction of smoke-induced thermodynamic and radiative perturbations.

Accurate modeling of aerosol optical properties is critical to simulate aerosol radiative effects. However, uncertainties regarding the simulation of aerosol-intensive optical properties are still significant. Therefore, the use of observations to constrain aerosol optical properties in models has been indicated as an option. Also, explicit computations of optical properties are still too costly for operational models, which makes observation-based prescriptions a convenient solution. We developed an observation-based prescription of aerosol optical properties driven by machine-learning techniques that can be applied in models. The Iberian Peninsula (IP) was taken as the reference domain, and the aerosol products from the AERONET sites across the IP were the main dataset. First, clustering was applied to define the typical aerosol optical regimes affecting the IP atmosphere. Five typical regimes were identified. Two of them were dominated by coarse mode, which was associated with Saharan dust. One was found to be close to pure dust, while the other indicated a mixed scenario of dust and pollution. Two of the non-dust regimes, strongly and moderately absorbing, were found to be associated with smoke. The remaining non-dust regime, with no clear association, occurs mostly in the eastern portion of the IP. Afterward, using aerosol-type columnar mass density from MERRA-2, a model was trained as a predictor of the optical regimes using the Random Forest method. The model was tested under distinct aerosol scenarios. Predictions' accuracy ranged from 60 % to 75 %, depending on the regime, while presenting an average accuracy of 70 %.

Air pollution remains a critical environmental and public health threat, particularly in highly populated urban areas such as the Lisbon Metropolitan Area (LMA). This study provides a refined and detailed assessment of the spatial distribution of air pollution and associated attributable mortality across the LMA. High-resolution (1 km2) annual mean concentrations of key pollutants (PM2.5, PM10 and NO2) for 2022 and 2023 were estimated by integrating outputs from the URBAIR dispersion model with ground-based monitoring observations using advanced geostatistical data-fusion techniques. Air pollutant concentrations were combined with gridded population data and age-stratified baseline mortality rates within a Geographic Information System framework to quantify spatial variations in health impacts. Using the World Health Organization AirQ+ framework and established concentration-response functions, we estimated a total of 3195 air-pollution-attributable deaths across the Lisbon Metropolitan Area (LMA) in 2022, increasing to 4010 deaths in 2023. Fine particulate matter (PM2.5) was identified as the dominant contributor, accounting for more than 40% of the total health burden. At a high spatial resolution (1 km2 grid), estimated mortality exhibited substantial variability, ranging from 0 to 29 deaths per cell in 2022 and from 0 to 36 deaths per cell in 2023. These results highlight the importance of fine-scale spatial analysis, revealing intra-urban disparities that are not captured by aggregated estimates of total attributable mortality. The proposed methodological framework, integrating dispersion modelling, data fusion, and spatially explicit health impact assessment at fine spatial scales, provides a robust and transferable approach to support evidence-based air quality management and urban health policy development in European metropolitan contexts. This integrated approach enhances comparability, improves exposure assessment accuracy, and strengthens the scientific basis for designing targeted mitigation strategies that could prevent hundreds of premature deaths annually while addressing documented spatial inequalities in pollution exposure.

Nature-based solutions (NBS), are known for their multiple benefits such as their potential to improve air quality and reduce temperature. Thus, NBS can provide answers to challenges that urban areas are facing, associated with urban densification and climate change. This study aims to assess how NBS can contribute to climate change adaptation, focusing on temperature attenuation and air quality improvement. To this end the following research questions were addressed: How is an urban area characterized in terms of meteorology, atmospheric emissions, and air quality? How is climate change foreseen at the city level? and What will be the impact of NBS under foreseen climate change? The state-of-the-art WRF-CHEM air quality modelling system was applied with a spatial resolution of 1 km2 for representative years of the recent past and of the medium-term future based on the RCP4.5 scenario. The considered NBS were co-defined with city stakeholders considering the municipalities’ aspirations and challenges. Model results indicate that: (i) the NBS impact on temperature reduction is stronger than the impact on air quality; (ii) NBS promote an increase in NO2 and a decrease in O3 concentrations; (iii) NBS have a stronger impact during warmer months; and (iv) NBS can help adapt to climate change by offsetting projected temperature increases. These results corroborate that NBS can contribute to climate change adaptation and reinforce the importance of accessing case-specific solutions, considering environmental characteristics and challenges.

Air pollution is a major factor influencing hospital admissions worldwide, highlighting the need for robust predictive tools to support healthcare planning and public health measures. Machine learning (ML) has been widely employed to simulate the intricate relationships between pollution and health outcomes. This paper examines publications indexed in the Scopus database, from 2010 to 2024 focusing on using ML techniques to forecast outcomes related to air pollution and hospital admissions. A bibliometric study of the 89 identified papers was also conducted to determine dominant research themes, commonly employed methodologies, and the geographical distribution of publications. The results indicate that research activity increased notably after 2020, with the United States of America, China, and Brazil contributing the highest number of publications. Moreover, the findings indicate that approximately 83% of the reviewed research applied predictive models appropriately, suggesting that ML techniques can effectively forecast healthcare outcomes. Random Forest was the most frequently used method (33 studies), followed by Neural Networks (18 studies). Extreme Gradient Boosting (XGBoost) algorithm, although less frequent, showed the highest reported accuracy, with values ranging from 87% to 95%. The most studied pollutants were particulate matter (PM2.5), nitrogen dioxide (NO2), and coarse particulate matter (PM10). Demographic and meteorological data were the most frequently used complementary (71% and 65%, respectively), followed by temporal (46%) and socioeconomic factors (20%). The combination of several variable categories not only enhanced understanding of how environmental exposure affects health outcomes but also improved the accuracy and reliability of the reviewed ML models.

This paper unveils the VESPRA Tool (Vulnerable Elements in Spain and Portugal and Risk Assessment), a cutting-edge digital open-source Geographic Information System (GIS) designed to provide an essential overview of potential hazards in the transboundary region between Spain and Portugal. To enhance risk management for local threats, such as wildfires, industrial accidents, nuclear incidents, and extreme weather, VESPRA addresses the urgent need for timely and accurate information about vulnerable elements. Emergency managers cannot afford to operate without the latest data in a world where extreme events are becoming increasingly frequent and intense. The VESPRA Tool offers a dynamic platform that guarantees easy access for users and civil protection agents, equipping them with essential digital information on various hazards. Aligned with contemporary risk-science perspectives, this tool integrates quantitative hazard likelihoods and qualitative assessments of uncertainty in the underlying data, in line with the current evolving definition of risk as knowledge-dependent. This dual quantitative-qualitative approach not only enhances transparency and improves communication of uncertainty among decision-makers, but also supports evidence-based disaster management. This innovative tool is valuable throughout all phases of emergency management prevention, preparation, and response empowering decision-makers to plan effectively and respond swiftly in critical situations. Furthermore, the practical application of the VESPRA Tool is illustrated through a real-world case study of wildfires along the central Portuguese-Spanish border, demonstrating its potential to transform risk management and improve resilience in vulnerable areas.

The frequency of extreme wildfire events (EWEs) is expected to increase due to climate change, leading to higher levels of atmospheric pollutants being released into the air, which could cause significant short-term impacts on human health (both for the population and firefighters) and on visibility. This study aims to gain a better understanding of the effects of EWEs’ smoke on air quality, its short-term impacts on human health, and how it reduces visibility by applying a modelling system to the Portuguese EWEs of October 2017. The Weather Research and Forecasting Model was combined with a semi-empirical fire spread algorithm (WRF-SFIRE) to simulate particulate matter smoke dispersion and assess its impacts based on up-to-date numerical approaches. Hourly simulated particulate matter values were compared to hourly monitored values, and the WRF-SFIRE system demonstrated accuracy consistent with previous studies, with a correlation coefficient ranging from 0.30 to 0.76 and an RMSE varying between 215 µg/m3 and 418 µg/m3. The estimated daily particle concentration levels exceeded the European air quality limit value, indicating a potential strong impact on human health. Health indicators related to exposure to particles were estimated, and their spatial distribution showed that the highest number of hospital admissions (>300) during the EWE, which occurred downwind of the fire perimeters, were due to the combined effect of high smoke pollution levels and population density. Visibility reached its worst level at night, when dispersion conditions were poorest, with the entire central and northern regions registering poor visibility levels (with a visual range of less than 2 km). This study emphasises the use of numerical models to predict, with high spatial and temporal resolutions, the population that may be exposed to dangerous levels of air pollution caused by ongoing wildfires. It offers valuable information to the public, civil protection agencies, and health organisations to assist in lessening the impact of wildfires on society.

Air pollution causes damage and imposes risks on human health, especially in cities, where the pollutant load is a major concern, although the extent of these effects is still largely unknown. Thus, taking the busiest road traffic area in Portugal as a local case study (600 m × 600 m domain, 4 m2 spatial resolution), the objective of this work was to investigate two health risk methodologies (linear and nonlinear), which were applied for estimating short-term health impacts related to daily variations of high-resolution ambient nitrogen dioxide (NO2) concentrations modelled for winter and summer periods. Both approaches are based on the same general equation and health input metrics, differing only in the relative risk calculation. Health outcomes, translated into the total number of cases and subsequent damage costs, were compared, and their associated uncertainties and challenges for health impact modelling were addressed. Overall, for the winter and summer periods, health outcomes considering the whole simulation domain were lower using the nonlinear methodology (less 27

Green infrastructures have been pointed out as innovative solutions to deal with current and future challenges related to air pollution and climate change. Although the potential of green infrastructures, such as green walls and green roofs, to mitigate air pollution has been documented, evidence at a local scale is still limited. This work aims to increase knowledge about the potentialities of green infrastructures in improving local air quality, focusing on particulate matter, nitrogen dioxide and ozone pollutants, and by using a local-scale computational fluid dynamics model. The ENVI-met model was applied to a particular hour of a summer day over a built-up environment centred on a main avenue in the city of Lisbon (Portugal). The dimensions of the computational domain are 618 m × 594 m × 143 m, and it contains 184 buildings, with the tallest building being 56 m. In addition to the baseline simulation, modelling was also done considering the application of green walls and green roofs to specific buildings located near the main avenue, together with a green corridor. The overall results show no disturbances exerted by green walls on the turbulent flow dynamics and on the air quality levels when compared to the baseline scenario (without green walls). The integrated scenario, which includes green walls, green roofs and a green corridor, will lead to potential local benefits of green infrastructures on O3 concentrations, followed by variable impacts on NO2 and particulate matter concentrations.

Wildfire ignitions are often linked to environmental and climatic factors, but human behavior plays a critical role, particularly in rural southern Europe. However, tools to quantify the probability of human-caused ignitions are lacking. This study addresses this by developing a human behavior wildfire ignition probability index focused on mainland Portugal, a region historically vulnerable to wildfires. Statistical analyses, including multicollinearity checks and a Generalized Linear Model, were used to analyze ignition data, while geospatial analyses estimated the ignition probabilities for 2021 and 2022. Inputs included human activity indicators, land use types, and proximity to residential roads. The resulting probability maps identified high-risk areas, particularly in forested zones and near residential roads. These maps closely aligned with documented human-caused ignitions, confirming the model’s reliability. The index is a robust tool for identifying high-risk areas and has significant potential to improve fire prevention strategies by targeting the most vulnerable regions. Future research should explore its integration into forecasting systems for real-time fire prevention and response strategies as well as its adaptation to other regions with similar wildfire risks.

Climate change is expected to influence urban living conditions, challenging cities to adopt mitigation and adaptation measures. This paper assesses climate change projections for different urban areas in Europe –Eindhoven (The Netherlands), Genova (Italy) and Tampere (Finland)—and discusses how nature-based solutions (NBS) can help climate change adaptation in these cities. The Weather Research and Forecasting Model was used to simulate the climate of the recent past and the medium-term future, considering the RCP4.5 scenario, using nesting capabilities and high spatial resolution (1 km2). Climate indices focusing on temperature-related metrics are calculated for each city: Daily Temperature Range, Summer Days, Tropical Nights, Icing Days, and Frost Days. Despite the uncertainties of this modelling study, it was possible to identify some potential trends for the future. The strongest temperature increase was found during winter, whereas warming is less distinct in summer, except for Tampere, which could experience warmer summers and colder winters. The warming in Genova is predicted mainly outside of the main urban areas. Results indicate that on average the temperature in Eindhoven will increase more than in Genova, while in Tampere a small reduction in annual average temperature was estimated. NBS could help mitigate the increase in Summer Days and Tropical Nights projected for Genova and Eindhoven in the warmer months, and the increase in the number of Frost Days and Icing Days in Eindhoven (in winter) and Tampere (in autumn). To avoid undesirable impacts of NBS, proper planning concerning the location and type of NBS, vegetation characteristics and seasonality, is needed.

The grapevine is a key crop for Mediterranean environments and is both sensitive to climate warming and air pollutants, of which ozone is the most damaging to crop yield and quality. Ambient ozone effects on the grapevine have been noticed since the late fifties but risk assessments are still impaired by the lack of information concerning differences in cultivar sensitivity, and adaptation capacity to environmental factors including drought conditions. This study develops a specific parametrization for autochthonous grape cultivars within a leaf-level stomatal flux model, the DO3SE model, coupled with a meteorological and atmospheric chemical transport modelling system, the WRF-CHIMERE, by using a renowned wine producing area, the Douro wine region of Portugal, as case study. The DO3SE model parametrization introduced in this study included phenology, photosynthetic active radiation, air temperature, air vapour pressure deficit, and leaf water potential as a proxy of soil water content. The modelling experiments, which included simulations with the current default Convention on Long-Range Transboundary Air Pollution DO3SE parametrization and with the proposed parametrization, covered a reference grapevine growing season (from April to September 2017), during which a measuring campaign was carried out. Simulation results show that the proposed parametrization succeeded to replicate the observed grapevine leaf-level stomatal flux gradient in the region. Both field and modified DO3SE model values indicate that considerable areas in the Douro wine region of Portugal can exceed critical phytotoxic ozone dose (POD) values, although with a lower and different spatial extent when compared to the default DO3SE parametrization for the grapevine. However, under irrigated conditions, the POD values increase, and the values are close to those obtained with the default parametrization. Overall, the research results indicate that air quality management, in particular the reduction of ozone levels in the ambient air, must also be considered to define sustainable grape and wine production strategies in the context of climate and wine production management change.

Background Air quality deteriorates significantly during wildfire events, which poses a risk for the health of affected human populations. The Mediterranean Basin was strongly impacted by wildfires during the 2021 fire season, particularly in Greece. Aims This work aims at estimating the impact of the Greek wildfires of August 2021 on the air quality in Athens. Methods The numerical modelling system WRF-APIFLAME-CHIMERE, which comprises a meteorological model, a smoke emissions model and a chemical transport model, was employed in estimating the hourly three-dimensional distribution of particulate matter (PM), CO and O3 concentrations during the wildfires. The performance of the modelling system was evaluated by comparing modelled results with air quality observations and atmospheric optical depth measurements. Key results Good agreement between measured data and model results was found, with results obtained with a higher-resolution computational grid performing the best. Conclusions The calculated values indicate concerning hourly and daily levels of air pollution, above the limit values for human health protection, during the analysed days within and around Athens. Implications The results highlight the importance of implementing a strategy for human health protection during wildfire events affecting populated areas. This modelling approach could be a basis for a smoke forecasting system.

A high percentage of the world's population lives in areas where air pollutant concentrations exceed the World Health Organization guidelines. This work aims to develop and test, a high-resolution multi-scale air pollution modelling system by integrating a set of adequate tools. This system is able to provide detailed air pollutant concentrations in urban areas and support air quality management strategies through a better identification of different atmospheric processes. It also allows furthering the design and assessment of air pollution control measures for a specific area. To evaluate its performance and suitability, the system was applied to the Macau Special Administrative Region (SAR), China, one of the most densely populated areas on earth, during a winter period when this area is affected by high levels of Particulate Matter (PM). Although the developed system tends to underestimate the PM concentrations, it revealed a good performance in reproducing the temporal and spatial air pollution patterns. Several exceedances of the Chinese air quality standards were calculated and high population exposure to PM pollution was estimated. The tested urban atmospheric emission reduction scenarios have shown air quality improvements, indicating that emission reduction measures at urban level should focus on the domestic sector. However, it is crucial to implement joint pollution prevention strategies with neighbouring regions to improve the air quality in Macau SAR. The approach developed in this work can support policymakers in defining new strategies to reduce atmospheric pollution in urban areas.

Air pollution is nowadays a serious public health problem worldwide, especially in urban areas, due to high population density and intense anthropogenic activity. This paper aims to present the development of a modelling tool suitable for simulating multiscale air quality and health impacts -the modair4health system, and its application to an urban case study. The modair4health system includes the online model WRF-Chem, which provides meteorological and air quality fields from regional to urban scales, and the computational fluid dy-namics model VADIS, which uses the urban WRF-Chem outputs to simulate the flow and pollutant dispersion in urban built-up areas. A health module based on World Health Organization (WHO) methodologies was also integrated into the system to quantify physical and economic health impacts resulting from air quality changes. The system was applied over a local case study, which represents one of the busiest road traffic areas of the city of Coimbra in Portugal, to assess its operationality in estimating NO2 concentrations and health impacts, by testing two traffic management scenarios. This scenario analysis considered a 4-domain nesting approach, with the finer resolution (4 m) domain focusing on the local case study and on two simulation periods, for which short-term health impacts were estimated. Spatially, the air quality and health greatest benefits were simulated around roads, where higher emission reductions were estimated, but they were also strongly influenced by the urban structure, local weather and population affected. The modair4health system has revealed to be an important multiscale modelling tool for integrated air quality and health assessment, able to support decision makers by facilitating the choice of cost-effective air quality and health management strategies and decisions. Moreover, its user-friendly interface allows to quickly test other urban air pollution control policies and the easy adaptation and application to other case studies considering regional to local atmospheric influences.

Within the scope of the Aveiro STEAM City project, an air quality monitoring network was installed in the city of Aveiro (Portugal), to evaluate the potential of sensors to characterize spatial and temporal patterns of air quality in the city. The network consists of nine sensors stations with air quality sensors (PM10, PM2.5, NO2, O3 and CO) and two meteorological stations, distributed within selected locations in the city of Aveiro. The analysis of the data was done for a one-year measurement period, from June 2020 to May 2021, using temporal profiles, statistical comparisons with reference stations and Air Quality Indexes (AQI). The analysis of sensors data indicated that air quality variability exists for all pollutants and stations. The majority of the study area is characterized by good air quality, but specific areas—associated with hotspot traffic zones—exhibit medium, poor and bad air quality more frequently. The daily patterns registered are significantly different between the affected and non-affected road traffic sites, mainly for PM and NO2 pollutants. The weekly profile, significative deltas are found between week and weekend: NO2 is reduced on the weekends at traffic sites, but PM10 is higher in specific areas during winter weekends, which is explained by residential combustion sources.