Search Results (47)

The COVID-19 pandemic severely disrupted tourism globally, with considerable variation in impact and recovery across regions. This study examines how tourism demand, revenue, employment, and transport evolved in the Visegrád Four (V4) countries—Czechia, Hungary, Poland, and Slovakia—between 2015 and 2025, assessing recovery speed and sectoral resilience across nine indicators. The modelled window varies by indicator: 2015–2023 for revenue indicators, 2015–2024 for visitor-flow indicators, and 2015–2025 for employment and air passenger indicators. Annual data from Eurostat and the Hungarian Central Statistical Office were analysed using descriptive comparative methods, linear mixed-effects models, and interrupted time series analysis with counterfactual projections. All indicators showed sustained pre-pandemic growth followed by a statistically significant 2020 shock. International demand fell roughly twice as sharply as domestic demand, and air passenger traffic suffered the largest absolute collapse. Revenue recovered fastest, surpassing pre-pandemic levels by 2023–2024 in all countries, while guest-volume recovery is projected only by 2025–2027. Part-time employment remained below 2015 levels in three of four countries despite volume and revenue recovery, indicating structural labour market adjustment. Slovakia consistently showed non-significant post-shock recovery slopes in seven of nine indicators, emerging as the region’s weakest performer. These findings indicate that COVID-19 produced uneven, revenue-led recovery across V4, and the employment paradox and Slovakia’s underperformance underscore the need for diversified, evidence-based tourism crisis management. Full article

Although prior research focused on Baghdad has identified variability in fine particulate matter concentrations (PM_2.5) and their origins, there remains uncertainty in the identification of the relative importance of local and long-range PM_2.5 sources. This study analysed hourly air pollutant concentrations and meteorological data from three monitoring sites over the year 2019 in Baghdad, namely Al-Wazeriya (WZ), Al-Andalus Square (AS), and Al-Saiydiya (SA) sites, to determine the nature of PM_2.5 sources. Multi-stage statistical models were utilised to address inherent data limitations and varying sampling dates caused by limitations on power supplies to monitoring equipment, thus improving the identification of urban particulate sources. Bivariate polar plots, concentration ratios, and conditional bivariate probability function (CBPF) plots were used to identify local sources of PM_2.5. Potential Source Contribution Function (PSCF) and concentration weighted trajectory (CWT) methods were employed for distant and regional source apportionment. Domestic diesel generators are suggested to be the primary local source of PM_2.5 pollutants in Baghdad’s WZ area (categorised as residential with significant traffic volumes). Gasoline- and diesel-fueled motor vehicles significantly contribute to PM_2.5 concentrations in the AS and SA areas, which are commercial areas with the latter having close proximity to motorway sources. Additional impacts result from gas flaring and thermal power plants in these regions. Long-range PM_2.5 transport may be attributed to the combustion of low-quality heavy fuel oils from several potential sources, including Nahrawan brick factories, oil fields, and Al-Musayyab thermal power plants, primarily towards the northeast, east, and southeast of Baghdad. Transboundary contributions to PM_2.5 concentrations in Baghdad were also identified, from industrial sources in western Iran and eastern Syria, as well as dust particulates, and oil and gas production from southwestern Iran’s Khuzestan Province, Kuwait, and the Arabian Gulf. Low to medium wind speeds (1–4 ms⁻¹) were linked with the highest source contributions, suggesting local emission sources to be the most significant contributors to high PM_2.5 at the studied sample locations. Full article

Achieving sustainable maritime transport requires comprehensive understanding of port-related emissions and evidence-based mitigation strategies. Maritime shipping significantly contributes to air pollution in port cities, threatening environmental sustainability and public health, yet comprehensive emission inventories remain scarce for major ports in developing economies. This study presents the first bottom-up emission inventory for Ambarlı Port, Turkey’s largest container port, utilizing AIS data from Global Fishing Watch for calendar year 2025. Emissions of CO₂, NO_x, SO₂, PM₁₀, PM_2.5, CO, and NMVOC were quantified using EMEP/EEA activity-based methodology with IMO Tier II emission factors and vessel type-specific load factors (75% for passenger, 45% for cargo) from ENTEC guidelines. Non-commercial vessels (tugs, service craft, fishing vessels) and lay-up vessels exceeding six months continuous berthing were excluded to focus on active commercial shipping operations, resulting in a validated dataset of 10,267 port visits from commercial cargo, passenger, and bunker vessels. Annual emissions from active commercial vessels totaled 404,766 tonnes CO₂, 8487 tonnes NO_x, 6724 tonnes SO₂, 914 tonnes PM₁₀, and 849 tonnes PM_2.5. Passenger vessels dominated the inventory (93.3% of CO₂) due to high auxiliary power demands for hotel services and elevated load factors, while cargo vessels contributed 6.5% despite representing 61.4% of port visits. Turkish-flagged vessels accounted for the majority of domestic ferry traffic. These findings provide baseline data for air quality management in the Istanbul metropolitan area and support policy development regarding shore power implementation, with particular emphasis on reducing emissions from passenger vessels with extended berth times. From a policy perspective, prioritized shore power investment at passenger ferry terminals emerges as the most cost-effective emission reduction strategy, with potential to eliminate over 90% of port-related air pollutant emissions through public-private partnership models. Full article

Vehicular emissions are a significant anthropogenic source of air pollutants in South Africa, driven by urbanisation and industrialisation. Thulamela Municipality in Limpopo Province faces increasing air quality challenges associated with rising vehicle kilometres travelled (VKT) and population growth. A reliable baseline emission inventory is therefore required to inform effective air quality management. This study quantified emissions and developed a vehicular emission inventory (VEI) for Thulamela Municipality using a bottom-up approach for the period 2012–2021. VKT was estimated using odometer readings obtained through a questionnaire-based seven-day vehicle survey, together with registered vehicle population data from the National Traffic Information System (NaTIS). Results indicate that VKT increased over the study period, with light-duty vehicles (LDVs) contributing the most, followed by passenger cars (PCs), heavy-duty vehicles (HDVs), and heavy-passenger vehicles (HPVs). Cumulative emissions of CO, NO_x, PM₁₀, PM_2.5, and SO₂ over the 10 years were 32,781.1, 22,326.0, 1367.8, 1291.7, and 547.2 tons, respectively, with growth rates ranging from 39% to 41%. In 2021, total vehicular emissions reached 6647.6 tons, dominated by CO (56%) and NO_x (38%), with PM₁₀ (3%), PM_2.5 (2%), and SO₂ (1%). LDVs contributed 82% of total emissions, followed by PCs (9%), HDVs (6%), and HPVs (3%). A positive correlation between vehicle numbers and Gross Domestic Product (GDP) further suggests that economic growth is associated with higher emissions. These findings show that vehicular emissions are a key contributor to air pollution in the area and highlight the need for targeted mitigation strategies to improve air quality and protect public health. Full article

Air transport networks function as strategic infrastructure whose structural evolution reflects broader geopolitical and economic forces. This study introduces a network-based interpretive framework for Geoeconomics in Air Transport by integrating complex network analysis with geoeconomic perspectives. It conceptualizes air transport networks as strategic economic infrastructure in which network topology encodes market access, power asymmetries, and resilience under geopolitical uncertainty. Using global civil aviation data, this paper constructs air transport networks at both the global level and across major regions—including the United States, Europe, the Middle East, ASEAN, China, and Japan—and compares passenger and cargo connectivity before (2019) and after (2023) the COVID-19 pandemic. Standard network metrics, such as centrality, topology, and connectivity, are used to quantify structural changes, which are subsequently interpreted through a geoeconomic lens. Global connectivity increased by approximately 8% in the post-pandemic period. In contrast, the United States—maintaining the most structurally resilient national air transport network—expanded by about 12%, while connectivity across Asian countries contracted, either domestically, internationally, or both. These patterns reflect a combination of intentional strategic responses and unintended structural adjustments. North American and European networks remain large-scale, meshed, and structurally resilient, whereas regions outside these core areas exhibit stronger hub-and-spoke dependence, both internally and in their connections with core regions. Such dependence signals persistent geoeconomic asymmetries and increased exposure to external shocks, despite higher traffic volumes per route. Betweenness centrality shifted markedly from European and North American hubs toward the Middle East, indicating the emergence of a geoeconomic intermediary region capable of sustaining connectivity across increasingly fragmented markets. The findings further demonstrate that, despite the United Kingdom’s withdrawal from the European Union, institutional and strategic realignments can enhance air transport network resilience in ways not anticipated by conventional geoeconomic interpretations of regional integration. By linking quantitative network outcomes with geoeconomic interpretation, this study provides reproducible insights into the strategic reconfiguration of global air transport systems under rising geopolitical uncertainty. Full article

NO, NO₂, and O₃ were measured for 1 year at a suburban site in the southeast Mediterranean. NO preserved no seasonality, but significant seasonal variations were obtained for NO₂ and O₃. These pollutants exhibited inverse trends with higher NO₂ levels measured during wintertime, whilst higher O₃ levels were measured during summertime. Photochemistry was the primary reason for the opposing variations in both pollutants, although O₃ levels were frequently increased due to O₃-rich plumes travelling from northeast Europe, highlighting the impact of regional contributions in the measured concentrations. Nevertheless, anthropogenic sources were identified and contributed to both NO and NO₂. Diurnal variations analysis showed that NO increased usually in the early morning and was linked with primary emissions from traffic. NO₂ increased simultaneously with NO in the early morning, and besides primary vehicle emissions, it was associated with secondary formation from the emitted NO. Moreover, a significant contribution from domestic heating emissions on NO₂ was identified in the late evening during wintertime. Overall, a relative burden of weekdays was associated with NO (morning rush hours) and NO₂ (morning rush hours, evening), whereas weekends were burdened by O₃ due to the weekend effect. Comparison with European Union air quality standards showed that NO₂ was considerably lower than the limit values, but a significant number of exceedances were identified for O₃, especially during the warmer months. This finding suggested the relative burden of the study site from O₃. In conclusion, NO at the study site was influenced by primary traffic emissions, whereas NO₂ had both primary and secondary contributions, and together with photochemistry, both pollutants governed O₃ diurnal and seasonal cycles. Full article

Bulgaria continues to face serious challenges related to air quality. To mitigate traffic-related air pollution and in line with the European regulations, the Metropolitan Municipal Council of Sofia has adopted and introduced low-emission zones (LEZs) in the city centre. The goal of this study is to address the specific needs of urban planning in the city in support of local decision-making. A bespoke emission inventory was developed for the LEZs in Sofia, and high-resolution numerical simulations (100 m resolution) were carried out to assess the effect of the measures implemented to reduce emissions in the central part of the city. The results show a decrease in nitrogen dioxide concentrations along major roads and intersections, but projected concentrations will still be high. No significant improvement is expected for particulate matter pollution due to the limitations of this study. High-resolution (100 m) emission inventories of domestic heating, minor roads, and bare soil surfaces, the major sources of particulate matter pollution, are not included in this study. An integrated model is needed to analyse and compare different scenarios for the development of the transport system, and the gradual introduction of LEZs must be accompanied by a number of other additional measures and actions. Full article

This study investigates the variability of major air pollutants, such as nitrogen oxides (NO_x, including nitric oxide (NO) and nitrogen dioxide (NO₂)), ozone (O₃), and particulate matter with a diameter ≤ 10 µm (PM10), in Thessaloniki over the period 2001–2022, highlighting their evolution in response to vehicle technology adoption and the COVID-19 pandemic. Four monitoring stations representing urban traffic, urban background, urban industrial, and suburban industrial environments were analyzed. PM10 concentrations generally decreased until 2015 but rose thereafter, mainly due to increased petrol car usage, with the highest levels recorded at the urban traffic station during colder months, influenced by domestic heating and local wind patterns. NO and NO₂ concentrations peaked at urban traffic and industrial sites, closely linked to vehicle emissions and industrial activities, respectively, with notable reductions during the 2020 COVID-19 lockdown. O₃ levels showed steady trends with diurnal and seasonal variability inversely related to NO_x concentrations and positively correlated with temperature. Despite some pollutant reductions, air quality issues persist in Thessaloniki. The findings emphasize the need for robust governmental policies promoting cleaner heating alternatives; two policy scenarios are presented in this respect with the corresponding air pollutant concentrations estimates up to 2035. Full article

The deterioration of stone heritage in urban environments is mainly the product of sources of air pollution like vehicular traffic and domestic heating. The results of these phenomena usually manifest as acid rain and particulate patinas, acting on the surface of stone monuments to form the so-called “black crusts”, a typical stone degradation product, mainly composed of gypsum. The aims of this study were to investigate the extent of these phenomena on the decorative apparatus of the frontal façade of Gesù Nuovo Church, in the historical centre of Naples (Italy). Preliminary diagnostics consisted of XRD and FTIR to analyse the composition of stone materials and inquire about previous restorations. The chemical characterization of black crusts was performed, using a diverse array of techniques, to highlight how different compounds are distributed along a vertical gradient and considering the proximity of specific sources of pollution (vehicle engine ignition, incense combustion, domestic heating products). Finally, molecular biology techniques were employed to identify the organisms which typically dwell in this formation and speculate about their contribution to the degradation of stone. Full article

With increasing global concerns related to global warming, air pollution, and environmental health, South Korea is actively implementing various particulate matter (PM) reduction policies to improve air quality. Accurate data analysis, including the investigation of weather phenomena, monitoring, and integrated prediction, is essential for effective PM reduction. However, the factors influencing the PM generated from domestic road sections have not yet been systematically analyzed, and currently, no predictive models utilize weather and traffic data. This study analyzed the correlations among factors influencing PM to develop models for estimating fine and coarse PM (PM_2.5 and PM₁₀, respectively) concentrations in road sections. Regression analysis models were used to assess the sensitivity of PM_2.5 and PM₁₀ concentrations to the traffic volume, whereas machine learning-based models, including linear regression, convolutional neural networks, and random forest models, were constructed and compared. The random forest models outperformed the other models, with coefficients of determination of 0.74 and 0.71 and mean absolute errors of 5.78 and 9.60 for PM_2.5 and PM₁₀, respectively. These results indicate that the random forest model provides the most accurate PM concentration estimates for road sections. The practical applications of the developed models were considered to inform effective transportation policies aimed at reducing PM. The developed model has practical applications in the formulation of transportation policies aimed at reducing PM. In particular, the model will play an important role in data-driven policymaking for sustainable urban development and environmental protection. By analyzing the correlation between traffic volume and weather conditions, policymakers can formulate more effective and sustainable strategies for reducing air pollution. Full article

The global air transportation system continues to be greatly impacted by operational changes induced by the COVID-19 pandemic. As air traffic management (ATM) focuses on balancing system capacity with demand, many facets of ATM and system operations more broadly were subjected to dramatic changes that deviate from pre-pandemic procedures. Since the start of the COVID-19 pandemic when air travel became one of the first transport modes to be impacted by lockdown procedures and travel restrictions, a geographically diverse cohort of researchers began investigating the impacts of the COVID-19 pandemic on air navigation service providers, airline and airport operations, on-time performance, as well as airline network structure, connectivity, crew scheduling, and service impacts due to pilot and crew shortages. In this study, we provide a comprehensive review of this aforementioned body of research literature published during one of the most tumultuous times in the history of aviation, specifically as it relates to air traffic management and air traffic control. We first organize the reviewed literature into three broad categories: strategic air traffic management and response, air traffic control and airport operational changes, and air traffic system resilience. Then, we highlight the main takeaways from each category. We emphasize specific findings that describe how various aspects of the air transportation systems could be improved in the domestic and global airline industry post-COVID. Lastly, we identify specific changes in operational procedures due to the COVID-19 pandemic and suggest future industry trends as informed by the literature. We anticipate this review article to be of interest to a broad swath of aviation industry and intercity transportation audiences. Full article

In West African cities, the impacts of the air quality on the health of the population is expected to increase significantly in the near future. For the first time to our knowledge, we conducted a high-resolution modeling study over Abidjan (Côte d’Ivoire) using the WRF-Chem model and the simplified GOCART model to simulate carbonaceous aerosols BC and OC, sulfate, dust, sea salt, PM_2.5, and PM₁₀. The simulations were carried out during January and February 2019, a period over which there are databases of observations available. The DACCIWA inventory provided anthropogenic emissions at the regional scale, whereas a new emission inventory has been developed for the city of Abidjan. In 2019, the emissions were 4986.8 Gg for BC, 14,731.4 Gg for OC, and 7751.6 Gg for SO₂. Domestic fires were the primary OC source (7719.5 Gg), while road traffic was the largest BC emitter (2198.8 Gg). Our modeling results generally overestimate urban particle concentrations, despite having a better agreement for those based on the inventory of the city of Abidjan. Modeled concentrations of BC are higher in administrative centers due to road traffic, while OC concentrations are significant in densely populated neighborhoods. Full article

In this study, we describe the development of seasonal winter and summer (heating and non-heating season) land use regression (LUR) models for PM_2.5 mass concentration for the city of Novi Sad, Serbia. The PM_2.5 data were obtained through an extensive seasonal measurement campaign conducted at 21 locations in urban, urban/industrial, industrial and background areas in the period from February 2020–July 2021. At each location, PM_2.5 samples were collected on quartz fibre filters for 10 days per season using a reference gravimetric pump. The developed heating season model had two predictors, the first can be associated with domestic heating over a larger area and the second with local traffic. These predictors contributed to the adjusted R² of 0.33 and 0.55, respectively. The developed non-heating season model had one predictor which can be associated with local traffic, which contributed to the adjusted R² of 0.40. Leave-one-out cross-validation determined RMSE/mean absolute error for the heating and non-heating season model were 4.04/4.80 μg/m³ and 2.80/3.17 μg/m³, respectively. For purposes of completeness, developed LUR models were also compared to a simple linear model which utilizes satellite aerosol optical depth data for PM_2.5 estimation, and showed superior performance. The developed LUR models can help with quantification of differences between seasonal levels of air pollution, and, consequently, air pollution exposure and association between seasonal long-term exposure and possible health risk implications. Full article

Urban science plays a pivotal role in understanding the complex interactions between fireworks, air quality, and urban environments. Dense firework smoke worsens air quality and poses a health hazard to the public. In this study, we show a situation where extremely foggy meteorological conditions coincided with intense anthropogenic emissions, including fireworks, in an urban area. For the first time, the chemical composition and sources of non-refractory submicron aerosol (NR-PM₁) in outdoor and indoor air were characterized in Vilnius (Lithuania) using an aerosol chemical speciation monitor (ACSM) and Positive Matrix Factorization for the period before the fireworks, on New Year’s Eve, and after the fireworks in 2020/2021; thus, typical changes were assessed. Due to stagnant weather conditions and increased traffic, the highest concentrations of black carbon (BC) (13.8 μg/m³) were observed before the fireworks display. The contribution of organic (Org) fraction to the total NR-PM₁ mass concentration, in the comparison of the values of a typical night and New Year’s Eve (from 9 p.m. to 6 a.m.), increased from 43% to 70% and from 47% to 60% in outdoor and indoor air, respectively. Biomass-burning organic aerosol (BBOA, 48% (44%)) and hydrocarbon-like organic aerosol (HOA, 35% (21%)) dominated the organic fraction indoors and outdoors, respectively. HOA was likely linked to increased traffic during the event, while BBOA may have been related to domestic heating and fireworks. Full article

The need to use renewable sources and matrices with energy potential is widely recognized. The development of innovative technologies aimed at the improvement of energy conversion processes and reducing environmental impacts is currently receiving increasing attention from the scientific community and policymakers. The presence of sugars in airborne particle materials is attributed to biomass combustion. For this reason, these compounds are considered markers of biomass burning. The purpose of this work was to evaluate the emissions produced by agroforestry biomass burning (citrus pruning) by simultaneously sampling both stack emissions and atmospheric particulates in the area around a biomass boiler to understand the real contribution of biomass burning to atmospheric pollution. The combustion tests were carried out by comparing the processes with and without particulate abatement system to see how biomass combustion’s contribution to particulate emission can be controlled and reduced. During the tests, the focus was on particulate matter (PM) speciation in terms of sugar marker identification and determination. This study aims to increase knowledge to better understand the contribution of biomass plants to air pollution and differentiate it from the contributions of other sources, such as vehicular traffic or domestic heating. Full article