Study on the temporal and spatial relationship between public health events and the development of air transport scale: A case of the Southwest China.

The spread of the COVID-19 had profoundly affected the development of the air transportation. In order to determine the changes in air transportation volume associated with the development of the epidemic, this paper takes Southwest China as the study area. Monthly data and methods, such as the coefficient of variation, rank-size analysis and spatial matching index, were applied. The results found that: (1) during 2020-2022, there was a positive relationship between passenger volume and epidemic development, while freight volume increased for most airports in the first quarter of 2020-2022, particularly in the eastern region; (2) From the perspective of changes in air transportation volume under the development of the COVID-19, among various types of airports, the changes in transportation volume of main trunk airports were more significant than those of regional feeder airports in remote areas; (3) however, under the influence of the epidemic, main trunk airports still exhibited stronger attraction in passenger volume. That is to say, the passengers who chose to travel by air still tended to choose the main trunk airports and formed the agglomeration distribution pattern which around high-level airports in the provincial capital. Whereas the freight volume had a tendency of equalization among airports in Southwest China; (4) Over the course of time, the consistency of the spatial distribution of the number of cases and the passenger or freight volume in southwest China gradually increased. Among them, the spatial matching rate of the passenger volume and the number of COVID-19 cases was always higher than that of the cases and freight volume, which might indicate that there was a stronger correlation relationship. Therefore, it is proposed that the construction of multi-center airport system should be strengthened, the resilience of the route network for passenger transportation should be moderately enhanced, and the risk-resistant capacity of mainline airports and airports in tourist cities should be upgraded, so as to provide references for the orderly recovery of civil aviation and regional development.

Quantifying the contributions of road and air traffic to ambient ultrafine particles in two urban communities.

Traffic-related activities are widely acknowledged as a primary source of urban ambient ultrafine particles (UFPs). However, a notable gap exists in quantifying the contributions of road and air traffic to size-resolved and total UFPs in urban areas. This study aims to delineate and quantify the traffic's contributions to size-resolved and total UFPs in two urban communities. To achieve this, stationary sampling was conducted at near-road and near-airport communities in Seattle, Washington State, to monitor UFP number concentrations during 2018-2020. Comprehensive correlation analyses among all variables were performed. Furthermore, a fully adjusted generalized additive model, incorporating meteorological factors, was developed to quantify the contributions of road and air traffic to size-resolved and total UFPs. The study found that vehicle emissions accounted for 29% of total UFPs at the near-road site and 13% at the near-airport site. Aircraft emissions contributed 14% of total UFPs at the near-airport site. Notably, aircraft predominantly emitted UFP sizes below 20 nm, while vehicles mainly emitted UFP sizes below 50 nm. These findings reveal the variability in road and air traffic contributions to UFPs in distinct areas. Our study emphasizes the pivotal role of traffic layout in shaping urban UFP exposure.

Collaborative optimization model and algorithm for airport capacity and traffic flow allocation.

How to efficiently utilize the existing airport capacity without physical expansion and considerable economic inputs to meet air traffic needs is one of the important tasks of air traffic management. To improve the efficiency of capacity utilization, it is necessary to find the actual airport capacity properly. In this work, taking Shuangliu International Airport as an example, a methodology for capacity estimation is proposed that combines the empirical method with an analytical approach that uses historical performance data from the airport to construct a capacity envelope to approximate the airport's actual capacity to the greatest extent, establishes a collaborative optimization model that reflects the inherent relations between airport capacity and arrival and departure traffic demand, adopts an improved optimization algorithm to solve the model, and generates an optimal flight allocation scheme. Priority ratio is introduced to dynamically adjust management preferences for arrival and departure traffic demand to further reveal the synergy mechanism between departure and arrival traffic flow demand and the airport capacity. The result shows that the Flight On-time Performance rate is lifted by 6% in the case study which proves the feasibility of the proposed method, demonstrating its value for maximizing airport capacity and traffic flow demand without requiring expansions on airport scales.

Understanding the impact of network structure on air travel pattern at different scales.

This study examines the global air travel demand pattern using complex network analysis. Using the data for the top 50 airports based on passenger volume rankings, we investigate the relationship between network measures of nodes (airports) in the global flight network and their passenger volume. The analysis explores the network measures at various spatial scales, from individual airports to metropolitan areas and countries. Different attributes, such as flight route length and the number of airlines, are considered in the analysis. Certain attributes are found to be more relevant than others, and specific network measure models are found to better capture the dynamics of global air travel demand than others. Among the models, PageRank is found to be the most correlated with total passenger volume. Moreover, distance-based measures perform worse than the ones emphasising the number of airlines, particularly those counting the number of airlines operating a route, including codeshare. Using the PageRank score weighted by the number of airlines, we find that airports in Asian cities tend to have more traffic than expected, while European and North American airports have the potential to attract more passenger volume given their connectivity pattern. Additionally, we combine the network measures with socio-economic variables such as population and GDP to show that the network measures could greatly augment the traditional approaches to modelling and predicting air travel demand. We'll also briefly discuss the implications of the findings in this study for airport planning and airline industry strategy.

Influence of a large commercial airport on the ultrafine particle number concentration in a distant residential area under different wind conditions and the impact of the COVID-19 pandemic.

Exposure to ultrafine particles has a significant influence on human health. In regions with large commercial airports, air traffic and ground operations can represent a potential particle source. The particle number concentration was measured in a low-traffic residential area about 7 km from Frankfurt Airport with a Condensation Particle Counter in a long-term study. In addition, the particle number size distribution was determined using a Fast Mobility Particle Sizer. The particle number concentrations showed high variations over the entire measuring period and even within a single day. A maximum 24 h-mean of 24,120 cm-3 was detected. Very high particle number concentrations were in particular measured when the wind came from the direction of the airport. In this case, the particle number size distribution showed a maximum in the particle size range between 5 and 15 nm. Particles produced by combustion in jet engines typically have this size range and a high potential to be deposited in the alveoli. During a period with high air traffic volume, significantly higher particle number concentrations could be measured than during a period with low air traffic volume, as in the COVID-19 pandemic. A large commercial airport thus has the potential to lead to a high particle number concentration even in a distant residential area. Due to the high particle number concentrations, the critical particle size, and strong concentration fluctuations, long-term measurements are essential for a realistic exposure analysis.

Investigation of an airport-associated cluster of falciparum malaria in Frankfurt, Germany, 2022.

Airport malaria is uncommon but increasing in Europe and often difficult to diagnose. We describe the clinical, epidemiological and environmental investigations of a cluster of airport malaria cases and measures taken in response. Three Frankfurt International Airport employees without travel histories to malaria-endemic areas were diagnosed with Plasmodium falciparum malaria in Germany in 2022. Two cases were diagnosed within 1 week, and the third one after 10 weeks. Two cases had severe disease, all three recovered fully. The cases worked in separate areas and no specific location for the transmissions could be identified. No additional cases were detected among airport employees. In June and July, direct flights from Equatorial Guinea, Nigeria and Angola and one parcel originating in Ghana arrived at Frankfurt airport. No vector-competent mosquitoes could be trapped to identify the source of the outbreak. Whole genome sequencing of P. falciparum genomes showed a high genetic relatedness between samples of the three cases and suggested the geographical origin closest to Ghana. A diagnosis of airport malaria should prompt appropriate and comprehensive outbreak investigations to identify the source and to prevent severe forms of falciparum malaria.

Characterization of PAHs bound to ambient ultrafine particles around runways at an international airport.

The impact of airport activities on air quality, is not sufficiently documented. In order to better understand the magnitude and properly assess the sources of emissions in the sector, it is necessary to establish databases with real data on those pollutants that could have the greatest impact on both health and the environment. Particulate matter (PM), especially ultrafine particles, are a research priority, not only because of its physical properties, but also because of its ability to bind highly toxic compounds such as polycyclic aromatic hydrocarbons (PAHs). Samples of PM were collected in the ambient air around the runways at Barajas International Airport (Madrid, Spain) during October, November and December 2021. Samples were gathered using three different sampling systems and analysed to determine the concentration of PAHs bound to PM. A high-volume air sampler, a Berner low-pressure impactor, and an automated off-line sampler developed in-house were used. The agreement between the samplers was statistically verified from the PM and PAH results. The highest concentration of PM measured was 31 µg m-3, while the concentration of total PAH was 3 ng m-3, both comparable to those recorded in a semi-urban area of Madrid. The PAHs showed a similar profile to the particle size distribution, with a maximum in the 0.27-0.54 µm size range, being preferentially found in the submicron size fractions, with more than 84% and around 15-20% associated to UFPs. It was found that the ratio [PAHs(m)/PM(m)] was around 10-4 in the warmer period (October), whereas it more than doubled in the colder months (November-December). It is significant the shift in the relative distribution of compounds within these two periods, with a notable increase in the 5 and 6 ring proportions in the colder period. This increase was probably due to the additional contribution of other external sources, possibly thermal and related to combustion processes, as supported by the PAH diagnostic ratios.

Mass Casualty Incident Preparedness for Airport Emergencies: Report From an Aeroplane Crash Simulation at Guglielmo Marconi Airport, Bologna (Italy).

BACKGROUND: Airport emergencies are rare but potentially catastrophic; therefore, system preparedness is crucial. Airport emergency plans include the organization of emergency drills on a regular basis, including full-scale exercises, to train and test the entire rescue organization. OBJECTIVE: This report describes a full-scale simulation at Bologna International Airport, Italy, in October 2022, involving local EMS resources. METHODS: A full-scale aeroplane crash was simulated on the airport ground, activating the Airport emergency plan, and requiring the intervention of supplementary resources (ambulances, medical cars, and other emergency vehicles). RESULTS: Twenty-seven simulated patients were evaluated by EMS: START triage assessment was correct for 81.48% of patients; 11.11% were over-triaged and 7.41% were under-triaged. All patients were transported to the hospitals of the area. The simulation ended 2 hours and 28 minutes after the initial alarm. CONCLUSION: The response time proved a good response. Triage accuracy was correct in more than 80% of simulated patients. The availability of a trauma centre within 6 kilometres allowed the transportation of a quota of patients directly from the event, without affecting transportation times. Areas for improvement were identified in the communication within the different agencies and in moving ambulances within the airport runway without airport personnel guidance.

Reconstructing the COVID-19 incidence in India using airport screening data in Japan.

BACKGROUND: A major epidemic of COVID-19 caused by the Delta variant (B.1.617.2) occurred in India from March to July 2021, resulting in 19 million documented cases. Given the limited healthcare and testing capacities, the actual number of infections is likely to have been greater than reported, and several modelling studies and excess mortality research indicate that this epidemic involved substantial morbidity and mortality. METHODS: To estimate the incidence during this epidemic, we used border entry screening data in Japan to estimate the daily incidence and cumulative incidence of COVID-19 infection in India. Analysing the results of mandatory testing among non-Japanese passengers entering Japan from India, we calculated the prevalence and then backcalculated the incidence in India from February 28 to July 3, 2021. RESULTS: The estimated number of infections ranged from 448 to 576 million people, indicating that 31.8% (95% confidence interval (CI): 26.1, 37.7) - 40.9% (95% CI: 33.5, 48.4) of the population in India had experienced COVID-19 infection from February 28 to July 3, 2021. In addition to obtaining cumulative incidence that was consistent with published estimates, we showed that the actual incidence of COVID-19 infection during the 2021 epidemic in India was approximately 30 times greater than that based on documented cases, giving a crude infection fatality risk of 0.47%. Adjusting for test-negative certificate before departure, the quality control of which was partly questionable, the cumulative incidence can potentially be up to 2.3-2.6 times greater than abovementioned estimates. CONCLUSIONS: Our estimate of approximately 32-41% cumulative infection risk from February 28 to July 3, 2021 is roughly consistent with other published estimates, and they can potentially be greater, given an exit screening before departure. The present study results suggest the potential utility of border entry screening data to backcalculate the incidence in countries with limited surveillance capacity owing to a major surge in infections.

Systematic Genomic Surveillance of SARS-CoV-2 at Xiamen International Airport and the Port of Xiamen Reveals the Importance of Incoming Travelers in Lineage Diversity.

Sever Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is still a threat to human health globally despite the World Health Organization (WHO) announcing the end of the COVID-19 pandemic. Continued surveillance of SARS-CoV-2 at national borders would be helpful in understanding the epidemics of novel imported variants and updating local strategies for disease prevention and treatment. This study focuses on the surveillance of imported SARS-CoV-2 variants among travelers entering Xiamen International Airport and the Port of Xiamen from February to August 2023. A total of 97 imported SARS-CoV-2 sequences among travelers from 223 cases collected from 12 different countries and regions were identified by real-time RT-PCR. Next-generation sequencing was used to generate high-quality complete sequences for phylogenetic and population dynamic analysis. The study revealed a dominant shift in variant distribution, in which the XBB subvariant (XBB.1.5, XBB.1.16, XBB.1.9, XBB.2.3, and EG.5.1) accounted for approximately 88.8% of the sequenced samples. In detail, clades 23D and 23E accounted for 26.2% and 21.4% of the sequenced samples, respectively, while clades 23B (13.6%) and 23F (10.7%) took the third and fourth spots in the order of imported sequences, respectively. Additionally, the XBB.2.3 variants were first identified in imported cases from the mainland of Xiamen, China on 27 February 2023. The spatiotemporal analyses of recent viral genome sequences from a limited number of travelers into Xiamen provide valuable insights into the situation surrounding SARS-CoV-2 and highlight the importance of sentinel surveillance of SARS-CoV-2 variants in the national border screening of incoming travelers, which serves as an early warning system for the presence of highly transmissible circulating SARS-CoV-2 lineages.

The impact of Chinese airport infrastructure on airline pollutant emissions: A hybrid stochastic-neural network approach based on utility functions.

With China being the world's largest emitter of greenhouse gases and its aviation sector burgeoning, the environmental performance of Chinese airlines has global significance. Amidst rising demands for eco-friendly practices from both customers and regulators, the interplay between airport infrastructure and environmental performance becomes pivotal. This research offers an innovative methodology to gauge the environmental performance of Chinese airlines, emphasizing the distance traveled between airports using weighted additive utility functions. Leveraging neural networks, the study investigates the impact of various airport infrastructural characteristics on environmental performance. Noteworthy findings indicate that ground control measures, automatic information services at origin airports, surface concrete on runways at both ends, and a centerline lighting system in destination airports positively influence environmental performance. In contrast, longer and wider runways at origin airports, increased distances to control towers, and asphalt runways at destination airports adversely affect it. These insights not only underscore the importance of strategic infrastructure enhancements for reducing carbon footprints but also hold profound policy implications. As global climate change remains at the forefront, fostering sustainable airport infrastructure in China can significantly contribute to worldwide mitigation efforts.

AIRPORT: A Data Consistency Constrained Deep Temporal Extrapolation Method To Improve Temporal Resolution In Contrast Enhanced CT Imaging.

Typical tomographic image reconstruction methods require that the imaged object is static and stationary during the time window to acquire a minimally complete data set. The violation of this requirement leads to temporal-averaging errors in the reconstructed images. For a fixed gantry rotation speed, to reduce the errors, it is desired to reconstruct images using data acquired over a narrower angular range, i.e., with a higher temporal resolution. However, image reconstruction with a narrower angular range violates the data sufficiency condition, resulting in severe data-insufficiency-induced errors. The purpose of this work is to decouple the trade-off between these two types of errors in contrast-enhanced computed tomography (CT) imaging. We demonstrated that using the developed data consistency constrained deep temporal extrapolation method (AIRPORT), the entire time-varying imaged object can be accurately reconstructed with 40 frames-per-second temporal resolution, the time window needed to acquire a single projection view data using a typical C-arm cone-beam CT system. AIRPORT is applicable to general non-sparse imaging tasks using a single short-scan data acquisition.

The Berlin-Brandenburg Air Study-A Methodological Study Paper of a Natural Experiment Investigating Health Effects Related to Changes in Airport-Related Exposures.

Objectives: This paper presents the study design of the Berlin-Brandenburg Air study (BEAR-study). We measure air quality in Berlin and Brandenburg before and after the relocation of aircraft (AC) traffic from Tegel (TXL) airport to the new Berlin-Brandenburg airport (BER) and investigate the association of AC-related ultrafine particles (UFP) with health outcomes in schoolchildren. Methods: The BEAR-study is a natural experiment examining schoolchildren attending schools near TXL and BER airports, and in control areas (CA) away from both airports and associated air corridors. Each child undergoes repeated school-based health-examinations. Total particle number concentration (PNC) and meteorological parameters are continuously monitored. Submicrometer particle number size distribution, equivalent black carbon, and gas-phase pollutants are collected from long-term air quality monitoring stations. Daily source-specific UFP concentrations are modeled. We will analyze short-term effects of UFP on respiratory, cardiovascular, and neurocognitive outcomes, as well as medium and long-term effects on lung growth and cognitive development. Results: We examined 1,070 children (as of 30 November 2022) from 16 schools in Berlin and Brandenburg. Conclusion: The BEAR study increases the understanding of how AC-related UFP affect children's health.

A new approach for sensitivity analysis of the StormWater Management Model applied in an airport.

Sensitivity analysis of urban flood model parameters is important for efficient and accurate flood simulation. In order to explore the problems of large sampling parameters and nonlinear correlation between input and output variables, this paper proposed a new correlation analysis approach. The type, strength, and the order of sensitive parameters to the four outputs are analyzed using the proposed approach. The results show that the R values of Manning-N are biggest, its distribution is linear in heat maps, and the Manning-N has a strong linear correlation with Average Depth, Hour of Maximum Flooding, and Time to Peak. For Average Depth, the second sensitive parameter is Conductivity. For Hour of Maximum Flooding, the second and third more sensitive parameters are Conductivity and N-perv; however, there are certain nonlinear correlations from heat maps. For Total Inflow, the R values of each parameter are between 0.021 and 0.534. Most sensitive parameters are none; however, the more sensitive parameters are Conductivity, N-perv, and initial deficit. For Time to Peak, the second and third more sensitive parameters are N-perv and N-Imperv; however, there are certain non-linear correlations from heat maps. The results can provide theoretical guidance for application and parameter calibration of SWMM in airport.

Ambient air quality assessment at the airports based on a meteorological perspective.

Natural mineral dust episodes elevate particle concentrations and eventually decrease air quality. Air pollutant emissions from aircraft, airport ground operations, and long-range dust transport are producing problems for the aviation sector. Dust transport from the Sahara Desert, one of the primary dust sources globally, significantly affects the eastern Mediterranean basin, including Türkiye. This study investigates the effect of long-range dust transport on particulate matter (PM) concentrations at Turkish airports. Three different approaches were used to analyse dust episodes in the study area. Firstly, routine reports of meteorological conditions at the airports were investigated. For this purpose, airport routine meteorological reports (Metar) and aviation-selected special weather report (Speci) codes, recorded at 11 airports from 2012 to 2021, were used to determine the dust episode days. Secondly, the hourly PM measurement dataset was analyzed from the closest air quality monitoring stations to the airports. Finally, regional atmospheric model results and backward-trajectory analysis were used to validate the dust episodes and determine the dust origin. Results showed that 163 dust episodes occurred during the study period, 17% from North Africa and 12% from the Mediterranean region.

"We are unique": organizational stressors, peer support and attitudes toward mental health treatment among airport firefighters. / « Nous sommes uniques ¼ : facteurs de stress organisationnels, soutien par les pairs et attitudes à l'égard des soins de santé mentale chez les pompiers d'aéroport.

INTRODUCTION: Airport firefighters are responsible for providing emergency responses to aviation incidents on a runway or in the vicinity of an airport, including airplane crashes, mass casualty events, emergency landings and many other concerns on airport grounds. While data exist on the occupational stressors of firefighters and public safety personnel in general, there is a gap in knowledge regarding the experiences of airport firefighters, particularly in relation to their organizational stressors, peer supports and attitudes toward mental health treatment. METHODS: We conducted two focus groups with 10 career firefighters working at an airport in Atlantic Canada in 2019. Focus groups were recorded; the recordings were transcribed and later coded using thematic analysis, which took an inductive, iterative, narrative approach. RESULTS: Airport firefighters face unique challenges, and operational stressors are overshadowed by organizational stressors. Additionally, peer support is an integral aspect of coping with both organizational stressors and critical incidents. Firefighters were found to have positive attitudes toward mental health treatment in general, but several barriers still remain, such as stigma, fear of being placed on leave and fear of confidentiality breach. CONCLUSIONS: Specialized treatment options for public safety personnel and airport firefighters who engage in serious incidents outside of their regular duties are needed.

An EMS Response to Refugees Arriving at an International Airport: A Report From the Field.

Following Afghanistan's fall in August 2021, many refugees were settled in the United States as part of Operation Allies Welcome. They were flown from Kabul to the Middle East and Europe before continuing to the U.S. By late September Philadelphia was the sole destination. From there refugees were transported to Safe Haven military bases around the country. Philadelphia International Airport became the site of a months-long operation involving city, state, federal, and private agencies engaged in processing, medical screening, and COVID-testing of arriving refugees. The Philadelphia Fire Department played an integral role. Minor medical conditions were treated onsite. Higher acuity patients were transported to nearby hospitals. The goal was to maintain flow of refugees to their next destination while addressing acute medical issues. Between August 28, 2021, and March 1, 2022, the airport processed 29,713 refugees. Philadelphia's experience may serve as a guide for planning future such refugee operations.