Assessing the spatial representativeness area of air quality monitoring stations is essential to ensure that measured concentrations adequately reflect surrounding pollutant levels. However, traditional spatial representativeness area assessments often rely on deterministic thresholds and simplified geometric assumptions, without explicitly accounting for uncertainty. This study proposes an uncertainty-aware, street-scale framework for evaluating the spatial representativeness area of urban traffic air quality monitoring stations and applies it to NO2 concentrations during a two-week period (May 13-31, 2023) in Barcelona, Spain. Spatial representativeness is assessed using observational and modeled NO2 data, complemented by a data-fusion field that integrates both sources. Using a 15% tolerance consistent with FAIRMODE recommendations, circular spatial representativeness areas ranged between 20 and 24 m, indicating highly localized representativeness in the dense street-canyon environment of l'Eixample. Binary maps derived from modeled and fused concentration fields revealed irregular, non-circular patterns and highlighted the influence of microscale variability. To explicitly incorporate prediction uncertainty, a probabilistic SRA is derived from the Universal Kriging field, quantifying the likelihood that concentrations fall within the representativeness interval. Probability-based maps demonstrate that representativeness depends strongly on the required confidence level and decreases substantially under stricter thresholds. The proposed framework provides a nuanced and uncertainty-informed characterization of air quality monitoring stations' representativeness, supporting improved interpretation of urban monitoring data in heterogeneous environments.

Residential solid fuel combustion is a major global source of air pollutants, with significant implications for both public health and climate. Accurate emission factors (EFs) are essential for developing reliable emission inventories, air quality models, and exposure assessments. Standardised laboratory-derived EFs provide data for regulatory compliance testing and technology benchmarking, whereas field-derived EFs better capture the variability of real-world appliance operation required for representative emission inventories. However, because comprehensive field datasets remain limited, laboratory-derived EFs continue to be widely used for inventory development, contributing to uncertainties in estimates of residential combustion emissions. This review critically assesses the methods currently used to estimate real-world EFs under both laboratory and field settings, with the aim of identifying key methodological gaps and advancing harmonisation efforts. Evidence shows that field-based studies provide more representative data but remain methodologically heterogeneous (e.g., sampling design, measurement duration, and dilution methods) and geographically uneven. The review highlights the tendency of laboratory-based protocols to underestimate emissions under real-world conditions and examines how updated EFs can improve the accuracy of emission inventories. Gaps in current data coverage, the underrepresentation of emergent pollutants such as ultrafine particles, and key areas for future research are also identified and discussed.

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.

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.

The open burning of agricultural residues is a widespread practice with significant environmental implications. This study explores the potential of satellite remote sensing to detect and analyze small-scale agricultural fires in Portugal, focusing on their spatial and temporal characteristics. Using active fire detection products from various satellite platforms, including VIIRS, MODIS, SLSTR, and SEVIRI, we conducted a detailed analysis across two local case studies and a national-scale assessment. This study evaluates both active fire detections and post-fire burned area estimations, using high-resolution satellite imagery to overcome the limitations associated with the small size and low intensity of these fires. The results indicate that while active fire detections are feasible for larger-scale burning, challenges remain for smaller fires due to resolution constraints. A systematic comparison with an agricultural burning request database further highlights the need for the enhancement of temporal and spatial precision in data to improve detection reliability. Despite these limitations, this work underscores the importance of remote sensing tools in monitoring agricultural burning practices and enhancing environmental management efforts.

Climate change (CC) and air pollution are closely interlinked environmental challenges that significantly affect human health and quality of life, especially in urban and industrialized regions. This study conducted a comprehensive investigation on how future climate scenarios may affect air quality and related human impacts, using a Southern European country (Portugal) for illustration. The study employed the most up-to-date future climate projections (Shared Socioeconomic Pathways-SSP) that were dynamically downscaled for Portugal. High-resolution simulations were carried out using the Weather Research & Forecasting (WRF) model, providing data for relevant meteorological variables that most affect air quality, for three future climate scenarios: fossil-fueled development (SSP5-8.5), regional inequality (SSP3-7.0), and a middle-of-the-road future (SSP2-4.5). Current and future air quality was simulated with the CHIMERE chemical transport model driven by WRF downscaled data and future emissions from the SSP v2.0 database. Results show that CC will impact nitrogen oxides (NO2), ozone (O3), and particulate matter (PM) concentrations over Portugal, with only agricultural emissions increasing in all scenarios. PM and NO2 will decrease in urban areas, over the short and long term, mainly for more conservative scenarios (SSP2-4.5 and SSP3-7.0), while O3 will increase over mainland Portugal (except for coastal/urban areas). Regarding human health, premature deaths are expected to be highest in urban areas, with reductions projected for NO2 and PM2.5 under SSP2-4.5 and increases in O3-related mortality under SSP5-8.5. Overall, SSP2-4.5 presents the most sustainable outcomes, highlighting the importance of integrating air quality management and health impact assessments into climate adaptation strategies to promote long-term environmental sustainability in southern Europe, consistent with the United Nations Sustainable Development Goals (SDGs).

Air pollution remains a critical environmental threat, particularly in developing countries like Brazil, where social vulnerability and insufficient environmental controls exacerbate its impact. This study presents a modelbased framework aimed at supporting the development of effective air quality management systems in datascarce contexts. Using the case of Santa Catarina, Brazil, we offer a systematic approach that combines emissions inventories and air quality data from the Community Multiscale Air Quality (CMAQ) model, as provided in the Brazilian Atmospheric Inventory (BRAIN) at a 4 km x 4 km spatial resolution. The framework identifies nonattainment areas, pinpoints major emission sources, and determines the first-order emission reductions necessary to meet regulatory standards for criteria pollutants (PM10, PM2.5, NO2, and O3). The highest concentration exceedances for PM10, PM2.5, and NO2 are observed in densely populated coastal areas, while elevated surface O3 levels are found in neighboring inland regions. By employing a quadrant-based classification method, we stratify areas based on emission intensity and concentration exceedances, thus revealing priority zones for targeted emission control while accounting for interactions between local and adjacent emission sources. The inclusion of health metrics, such as hospitalization and mortality rates from cardiorespiratory diseases, further refines priority areas into two tiers of cities for targeted intervention. This framework provides a valuable tool for guiding practical policy implementation and optimizing resource allocation for emission controls. Its adaptability also makes it particularly useful for other data-scarce regions seeking to develop cost-effective, evidence-based air quality management strategies under constrained resources.

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.

In many places across the world, air pollution is a serious problem. Citizen-collaborative air quality monitoring and forecasting systems are gaining popularity as a potential solution. Community platforms with online air quality maps made by individuals offer a way to include people in environmental monitoring, increase public awareness of air pollution exposure, and support the growth of smart cities.The notion of smart cities places significant emphasis on the utility of technology in enhancing urban planning and management, as well as data-driven decision-making. Systems for citizen collaborative air quality monitoring and forecasting support this idea by giving residents of the city a means of gathering and evaluating data on air quality in real time.The article explores the role of social innovation techniques, such as co-creation and participatory sensing, in engaging citizens and raising awareness. Furthermore, these systems can contribute to the creation of smart cities that are more long-term sustainable and responsive to the demands of their residents by involving citizens and making use of technology.In conclusion, the combination of tools and social innovation significantly advances the concept of smart collaboration between citizens, researchers, and local authorities, fostering a stronger commitment to addressing air quality, and climate change challenges within smart communities.

Agricultural residue burning is a common practice in various regions of the world, which may have several environmental impacts, including on air quality, and the potential for triggering wildfires. In Portugal, this practice is particularly prevalent during the wet season, spanning from October to April. It involves open field burning of pruning residues and extensive burning to clear shrubbery, creating pastures for livestock. This research, conducted within the framework of the PRUNING project - Mapping open burning of agricultural residues from Earth Observations and modelling of air quality impacts- aims to explore the potential for detecting such events through satellite remote sensing. The primary focus of this study is to assess the limitations of satellite remote sensing detection, with the overarching aim of integrating these findings into a systematic monitoring framework for open burning of agricultural residues. Additionally, the study aims to predict pollutant emissions and assess their impacts on air quality, providing valuable insights for environmental management and sustainable agricultural practices. To achieve this goal, an in-depth analysis of known burning events was conducted using infrared thermal sensors. Multiple products, including Fire Radiative Power and fire masks from various sensors (e.g., MODIS, VIIRS, and Sentinel 3), were employed to characterize these known open field burning events. The results of this work allow verifying the tradeoffs effects associated with spatial, spectral, and temporal resolutions for each sensor, elucidating their impacts on the precision and accuracy of event detections. In parallel, this study evaluated the accuracy of the MINDED-FBA method in characterizing these known events. This automatic detection method, allows incorporating data from higher spatial resolution sensors (e.g., Sentinel-1, Sentinel-2, Landsat), for determining the extent of burned areas through multiple multispectral indices. In this context, the MINDED-FBA method may also be used to validate thermal anomalies detection products. Finally, the results of this work have also been compared to a national level register database of open burning, provided by the ICNF (Institute for Nature Conservation and Forests).

In urban areas, the complex nature of the built environment is critical in shaping the local microclimate, strongly influencing heat transfer mechanisms and solar radiation dynamics. In this study, a dedicated module was developed within the open-source Computational Fluid Dynamics model OpenFOAM to evaluate the local urban microclimate. A set of simulations were performed for the University of Aveiro campus (Portugal), during a heat wave. This work aims to compare two distinct approaches for simulating wall thermal behaviour: one assumes surfaces and walls as adiabatic, while the other incorporates convective and radiative processes at surfaces and building walls. The latter approach enables a more accurate representation of spatial temperature variations in urban areas, particularly when also considering solar radiation input. The overall results emphasize the role of accounting for heat transfer mechanisms and solar radiation dynamics. Large differences were found in the modelled near-wall air temperatures, mainly at the leeward side of buildings and during the hours of higher solar radiation. This paper's novelty stems from the development of the OpenFOAM CFD model, coupling surface heat transfer mechanisms with solar radiation dynamics. We demonstrate its capabilities through testing and evaluating its performance in assessing the local microclimate.

The multi-mode transport sector plays a crucial role in economic, social, and political development, but it also contributes to global energy consumption, greenhouse gas emissions, and atmospheric pollution. To effectively address these issues, accurate and detailed emission inventories are essential. This study introduces a comprehensive approach (the BigAir concept), which integrates open-source datasets with traditional methods to estimate multi-mode transport emissions with high spatial (< 500 m) and hourly temporal resolutions. The accuracy, replicability, and scalability of the BigAir approach were evaluated through multi-scale air quality modelling simulations using Portugal as a case study. According to the results obtained, three main conclusions emerged: i) differences in emission magnitudes between BigAir and available inventories were mainly due to the use of outdated datasets; ii) notable spatial distribution differences were observed, particularly for road transport and civil aviation activities; iii) BigAir demonstrated greater accuracy in simulating PM10 levels, with higher correlation coefficients (0.23-0.47, p-value <0.01), and lower errors (7.82-14.0 mu g.m(-3)) while no significant improvement was observed for NO2.. Overall, the BigAir approach strengthens the reliability of the developed methodology and emerges as a powerful tool to guide policymakers in promoting sustainable, clean, resilient, and healthy cities.

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.

As urban areas grow with the increase in population, so do the problems associated with these areas, such as an increase in atmospheric emissions. Since urban morphology has an effect on the environment, it is necessary to design future urban morphologies to accommodate the expected growth and mitigate the associated problems. By employing an emission distribution methodology based on the relationship between land use and emission activity sectors, including transport and road traffic emissions modelling (with PTV-VISUM and TREM, respectively), this study aims to identify urban morphologies that have the potential to minimize atmospheric emissions for future multi-core regions. This study assesses three urban morphology scenarios, focused on Aveiro, Portugal, where two represent urban compaction - Focused City scenario and Independent City scenario -, and one represents an extreme version of the current urban dispersion. The impact of urban scenarios was compared against the current urban morphology. Results indicate that, for the compact urban morphologies, the Focused City scenario showed a small increase in emissions, and the Independent City scenario led to a decrease in emissions, especially for NOx (-16 %), as it is the pollutant most affected by road traffic emissions. As for the Disperse City scenario, it showed the highest overall increase, as it greatly increased the vehicle volume and total distance travelled. These results highlight the need for policy and behavioral changes to accompany the changes to urban morphology, and for special attention to be paid to the location of activity sectors when designing the different urban morphologies. This study contributes novel insights by applying a comprehensive methodology that integrates land use, activity sectors, and road traffic emissions modelling. By assessing the urban morphology's impact on air pollutant emissions, it is possible to inform urban planners of future urban planning strategies.

The reduction in vehicle exhaust emissions achieved in the last two decades is offset by the growth in traffic, as well as by changes in the composition of emitted pollutants. The present investigation illustrates the emissions of eight in-use gasoline and diesel passenger cars using the official European driving cycle and the ARTEMIS real-world driving cycles. Measurements comprised gaseous regulated pollutants (CO, CO2, NOx and hydrocarbons), particulate matter and its carbonaceous content (organic and elemental carbon, OC and EC), as well as about 20 different volatile organic compounds (VOCs) in the C6–C11 range. It was observed that some of the vehicles do not comply with the corresponding regulations. Significant differences in emissions were registered between driving cycles. Not all regulated pollutants showed a tendency to decrease from Euro 3 to Euro 5. The particulate carbon emission factors were significantly lower under the ARTEMIS Road compared with the ARTEMIS Urban driving cycle with cold start. In general, cold start-up driving conditions produced the highest emission factors. A tendency to the decline of carbonaceous emissions from Euro 3 to Euro 5 diesel vehicles was observed, whilst this trend was not registered for petrol-powered cars. The fraction of total carbon composed of EC was much lower in particles emitted by petrol vehicles (< 10

Carbon neutrality, sustainable development and reducing our impact on the environment is the top priority in future measures. The COVID-19 pandemic brought challenges to every sector at a global scale but can provide valuable insight to reach these goals. The main objective of this work is to provide an integrated analysis of the impact of the COVID-19 pandemic, focused on energy and its related aspects, i.e., environment and costs. Mainland Portugal was used as a case study and two years were analysed, one pre pandemic (2019) and another post pandemic (2020). In 2020, the majority of sectors -Transport, Services, Industry and Agriculture & Fisheries - show a reduction of energy consumption, atmospheric emissions, carbon footprint and related monetary and social costs. In contrast, the Domestic sector presents an overall increase, with maximums of 25.4% in electricity consumption (during Spring), 0.72% in the PM10 (particulate matter) and NOx (nitrogen dioxides) emissions (in Summer), and 2.9% in carbon footprint (in Spring). The integrated analysis proposed in this work was crucial to identify the paths to a post pandemic world focused on the different aspects of sustainability - new concepts of mobility and workplace, as well as increased investment in energy performance and renewable energy sources. This study showed that changing our energy consumption patterns could significantly affect future greenhouse gas emissions, and contribute to the sustainable growth of the economy, while maintaining good progress to-wards climate-neutral goals.

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.