Madrid-Barajas Airport Research Articles

International airport emissions and their impact on local air quality: chemical speciation of ambient aerosols at Madrid–Barajas Airport during the AVIATOR campaign

Abstract. Madrid–Barajas Airport (MAD) is the fourth-busiest airport in Europe. The aerosol chemical composition and the concentrations of other key pollutants were measured at the airport perimeter during October 2021 to assess the impact of airport emissions on local air quality. A high-fidelity ambient instrumentation system was deployed at Madrid–Barajas Airport to measure the following: concentrations of organic aerosols (with their composition), black carbon (eBC), carbon dioxide (CO2), carbon monoxide (CO), nitrogen dioxide (NOx), sulfur dioxide (SO2), particulate matter (PM2.5, PM10), total hydrocarbon (THC), and total particle number. The average concentrations of eBC, NOx, SO2, PM2.5, PM10, CO, and THC at the airport for the entire campaign were 1.07 µg m−3, 22.7 µg m−3, 4.10 µg m−3, 9.35 µg m−3, 16.43 µg m−3, 0.23 mg m−3, and 2.30 mg m−3, respectively. The source apportionment analysis of the non-refractory organic aerosol (OA) using positive matrix factorisation (PMF) allowed us to discriminate between different sources of pollution, namely less oxidised oxygenated organic aerosol (LO-OOA), alkane organic aerosol (AlkOA), and more oxidised oxygenated organic aerosol (MO-OOA). The results showed that LO-OOA and MO-OOA account for more than 80 % of the total organic particle mass measured near the runway. Trace gases correlate better with the AlkOA factor than LO-OOA and MO-OOA, indicating that AlkOA is mainly related to primary combustion emissions. Bivariate polar plots were used for pollutant source identification. Significantly higher concentrations of the obtained factors were observed at low wind speeds (<3 m s−1) from the southwest, where two of the runways and all terminals are located. Higher SO2/NOx and CO/eBC ratios were observed when the winds originated from the northeast, where the two northern runways are located. These elevated ratios are attributed to the aircraft activity being the major pollutant source in the northeast area.

Estimation of NO2 emission strengths over Riyadh and Madrid from space from a combination of wind-assigned anomalies and a machine learning technique

Abstract. Nitrogen dioxide (NO2) air pollution provides valuable information for quantifying NOx (NOx = NO + NO2) emissions and exposures. This study presents a comprehensive method to estimate average tropospheric NO2 emission strengths derived from 4-year (May 2018–June 2022) TROPOspheric Monitoring Instrument (TROPOMI) observations by combining a wind-assigned anomaly approach and a machine learning (ML) method, the so-called gradient descent algorithm. This combined approach is firstly applied to the Saudi Arabian capital city of Riyadh, as a test site, and yields a total emission rate of 1.09×1026 molec. s−1. The ML-trained anomalies fit very well with the wind-assigned anomalies, with an R2 value of 1.0 and a slope of 0.99. Hotspots of NO2 emissions are apparent at several sites: over a cement plant and power plants as well as over areas along highways. Using the same approach, an emission rate of 1.99×1025 molec. s−1 is estimated in the Madrid metropolitan area, Spain. Both the estimate and spatial pattern are comparable with the Copernicus Atmosphere Monitoring Service (CAMS) inventory. Weekly variations in NO2 emission are highly related to anthropogenic activities, such as the transport sector. The NO2 emissions were reduced by 16 % at weekends in Riyadh, and high reductions were found near the city center and in areas along the highway. An average weekend reduction estimate of 28 % was found in Madrid. The regions with dominant sources are located in the east of Madrid, where residential areas and the Madrid-Barajas airport are located. Additionally, due to the COVID-19 lockdowns, the NO2 emissions decreased by 21 % in March–June 2020 in Riyadh compared with the same period in 2019. A much higher reduction (62 %) is estimated for Madrid, where a very strict lockdown policy was implemented. The high emission strengths during lockdown only persist in the residential areas, and they cover smaller areas on weekdays compared with weekends. The spatial patterns of NO2 emission strengths during lockdown are similar to those observed at weekends in both cities. Although our analysis is limited to two cities as test examples, the method has proven to provide reliable and consistent results. It is expected to be suitable for other trace gases and other target regions. However, it might become challenging in some areas with complicated emission sources and topography, and specific NO2 decay times in different regions and seasons should be taken into account. These impacting factors should be considered in the future model to further reduce the uncertainty budget.

On the Estimation of Vector Wind Profiles Using Aircraft-Derived Data and Gaussian Process Regression

This work addresses the problem of vertical wind profile online estimation at a given location. Specifically, the north and east components of the wind are continuously estimated as functions of time and altitude at two waypoints used for landing on the Adolfo Suarez Madrid-Barajas airport. A continuous nowcast of the wind profile is performed in which wind observations are derived from the aircraft states and assimilated into the model. It is well known that wind is one of the utmost contributors to uncertainties in the current and future paradigm of Air Traffic Management. Accurate wind information is key in continuous climb and descent operations, spacing, four dimensional trajectory-based operations, and aircraft performance studies, among others. In this work, wind data are obtained indirectly from the aircraft’s states broadcast by the Mode S and ADS-B aircraft surveillance systems. The Gaussian process regression is adapted to this framework and used to solve the problem. The presented method allows to construct a complete vector wind profile at any specific position that is continuous in time and altitude; namely, there is no need for grid points and time discretisation. The Gaussian process regression is a very flexible estimator which is statistically consistent under general conditions, meaning that it converges to the underground truth when more and more data are dispensed. In addition, the Gaussian process regression approach provides the whole probability distribution of any particular estimation, allowing confidence intervals to be computed naturally. In the case study presented in this paper, in which the wind is constantly estimated, the Gaussian process regression model is iteratively updated every 15 min to capture possible changes in the wind behaviour and give an estimation of the wind profile every half a minute. The method has been validated using a test dataset, achieving a reduction of 50% of the prediction uncertainty in comparison to a baseline model. Moreover, two popular wind profile estimators based on the Kalman filter are also implemented for the sake of comparison. The Kalman filter outperforms the baseline model, but it does not outperform the Gaussian process regression with errors higher by around 35%, in comparison. The obtained results show that the Gaussian process regression of aircraft-derived data reliably nowcast the wind state, which is key in Air Traffic Management.

Ability of the WRF-ARW and HARMONIE-AROME models to detect turbulence related to mountain waves over Central Iberia

Aircraft turbulence is one of the most dangerous meteorological phenomena that can affect aviation safety. This study is focused on the turbulence associated to mountain lee waves in the vicinity of Adolfo Suárez Madrid-Barajas airport (Spain). Sixty-eight mountain lee waves events are selected to simulate the turbulence with the Weather Research and Forecasting (WRF-ARW) and the HARMONIE-AROME numerical weather prediction models. To study and characterize the turbulence associated, the vertical wind speeds are selected as an important variable and the Eddy Dissipation Rate is estimated. Both models have properly simulated the turbulence and the clear air turbulence, obtaining higher values of turbulence intensity by WRF-ARW than HARMONIE-AROME in the mountain lee waves events. Finally, these results are used to enhance a mountain wave warning decision tree, including the turbulence warning which is validated through several turbulence reports.

A methodology for understanding passenger flows combining mobile phone records and airport surveys: Application to Madrid-Barajas Airport after the COVID-19 outbreak

This paper presents a methodology to extract a series of passenger indicators from anonymized mobile phone records, with the aim of improving the available knowledge on passenger needs, behaviors and characteristics. The methodology is validated against the information provided by passenger surveys at the Madrid-Barajas Airport. The research shows the potential of anonymized mobile phone records to complement and enrich the information provided by passenger surveys regarding airport catchment areas, intermodality and passenger behavior. This methodology is then applied to the study of air travel demand in Madrid-Barajas Airport in July 2020, two weeks after the COVID-19 lockdown period in Spain. Finally, we discuss how the information extracted from the fusion of mobile phone records and passenger surveys can be leveraged for the modelling and evaluation of novel intermodal solutions.

Comparison of the WRF and HARMONIE models ability for mountain wave warnings

Mountain lee waves usually involve aircraft icing and turbulence events. These weather phenomena, in turn, are a threat to aviation safety. For this reason, mountain lee waves are an interesting subject of study for the scientific community. This paper analyses several mountain lee waves events in the south-east of the Guadarrama mountain range, near the Adolfo Suarez Madrid-Barajas airport (Spain), using the Weather Research and Forecasting (WRF) and the HARMONIE-AROME high-resolution numerical models. For this work, simulated brightness temperature from the optimum WRF parametrization schemes and from the HARMONIE are validated using satellite observations to evaluate the performance of the models in reproducing the lenticular clouds associated to mountain lee waves. The brightness temperature probability density shows interesting differences between both models. Following, a mountain wave characterization is performed simulating some atmospheric variables (wind direction, wind speed, atmospheric stability, liquid water content and temperature) in several grid points located in the leeward, windward and over the summit of the mountains. The characterization results are compared for both numerical models and a decision tree is developed for each to forecast and warn the mountain lee waves, lenticular clouds and icing events with a 24 to 48 h lead time. These warnings are validated using several skill scores, revealing similar results for both models.

Mountain Waves Analysis in the Vicinity of the Madrid-Barajas Airport Using the WRF Model

Turbulence and aircraft icing associated with mountain waves are weather phenomena potentially affecting aviation safety. In this paper, these weather phenomena are analysed in the vicinity of the Adolfo Suárez Madrid-Barajas Airport (Spain). Mountain waves are formed in this area due to the proximity of the Guadarrama mountain range. Twenty different weather research and forecasting (WRF) model configurations are evaluated in an initial analysis. This shows the incompetence of some experiments to capture the phenomenon. The two experiments showing the best results are used to simulate thirteen episodes with observed mountain waves. Simulated pseudosatellite images are validated using satellite observations, and an analysis is performed through several skill scores applied to brightness temperature. Few differences are found among the different skill scores. Nevertheless, the Thompson microphysics scheme combined with the Yonsei university PBL scheme shows the best results. The simulations produced by this scheme are used to evaluate the characteristic variables of the mountain wave episodes at windward and leeward and over the mountain. The results show that north-northwest wind directions, moderate wind velocities, and neutral or slightly stable conditions are the main features for the episodes evaluated. In addition, a case study is analysed to evidence the WRF ability to properly detect turbulence and icing associated with mountain waves, even when there is no visual evidence available.

Prediction of convective clouds formation using evolutionary neural computation techniques

The prediction of convective clouds formation is a very important problem in different areas such as agriculture, natural hazards prevention or transport-related facilities. In this paper, we evaluate the capacity of different types of evolutionary artificial neural networks to predict the formation of convective clouds, tackling the problem as a classification task. We use data from Madrid-Barajas airport, including variables and indices derived from the Madrid-Barajas airport radiosonde station. As objective variable, we use the cloud information contained in the METAR and SPECI meteorological reports from the same airport and we consider a prediction time horizon of 12 h. The performance of different types of evolutionary artificial neural networks has been discussed and analysed, including three types of basis functions (sigmoidal unit, product unit and radial basis function) and two types of models, a mono-objective evolutionary algorithm with two objective functions and a multi-objective evolutionary algorithm optimised by the two objective functions simultaneously. We show that some of the developed neuro-evolutionary models obtain high quality solutions to this problem, due to its high unbalance characteristic.

Ordinal regression algorithms for the analysis of convective situations over Madrid-Barajas airport

Abstract In this paper we tackle a problem of convective situations analysis at Adolfo-Suarez Madrid-Barajas International Airport (Spain), based on Ordinal Regression algorithms. The diagnosis of convective clouds is key in a large airport like Barajas, since these meteorological events are associated with strong winds and local precipitation, which may affect air and land operations at the airport. In this work, we deal with a 12-h time horizon in the analysis of convective clouds, using as input variables data from a radiosonde station and also from numerical weather models. The information about the objective variable (convective clouds presence at the airport) has been obtained from the Madrid-Barajas METAR and SPECI aeronautical reports. We treat the problem as an ordinal regression task, where there exist a natural order among the classes. Moreover, the classification problem is highly imbalanced, since there are very few convective clouds events compared to clear days. Thus, a process of oversampling is applied to the database in order to obtain a better balance of the samples for this specific problem. An important number of ordinal regression methods are then tested in the experimental part of the work, showing that the best approach for this problem is the SVORIM algorithm, based on the Support Vector Machine strategy, but adapted for ordinal regression problems. The SVORIM algorithm shows a good accuracy in the case of thunderstorms and Cumulonimbus clouds, which represent a real hazard for the airport operations.

Analyzing door-to-door travel times through mobile phone data

A strategic objective of the European transport policy is the so-called 4-h door-to-door target, according to which, by 2050, 90% of travelers within Europe should be able to complete their journey, door-to-door, within 4 h. However, information on door-to-door travel times is scarce and difficult to obtain, which make it difficult to assess the level of accomplishment of this ambitious target. In this paper, we extract door-to-door travel times based on the analysis of opportunistically collected data generated by mobile phones. Anonymized mobile phone records are combined with data from the Google Maps Directions API to reconstruct the different legs of the trip and estimate the travel times for the door-to-kerb, kerb-to-gate, gate-to-gate, gate-to-kerb and kerb-to-door segments. The travel times of these legs have been extracted for different scenarios, all of them focusing on the Adolfo Suarez Madrid-Barajas airport, with the aim of exploring their influence on total door-to-door travel time. Results show that the methodology presented is able to measure door-to-door travel times and that these travel times measured are far from the 4-h door-to-door target. We finish by outlining future research directions.

Is the reusable tableware the best option? Analysis of the aviation catering sector with a life cycle approach

Annually, around 7.7 billion passengers travel by plane. The menus served during the flight are quite similar between different airlines and are composed of the food itself, packaging (paper envelopes, film, etc.) and tableware (mainly trays, plates, glasses, cups and cutlery). In 2016, 1522 tonnes of tourist class menus were served in Iberia aircrafts landing at Madrid Barajas airport in Spain. From this amount, 51% by weight was packaging and tableware, and the remaining 49% food. As changes in the food has little room for maneuver, since the same amount would be delivered regardless how it is served, this study focus on the possibilities of packaging and tableware to reduce GHG emissions. The assessment has been done using life cycle assessment methodology (LCA) in order to identify the hotspots along the whole life cycle of packaging and tableware items. The case study chosen was the catering service of Iberia, the national airline of Spain. The functional unit used was “the service of 1,000 tourist class menus on Iberia flights that landed in Madrid in 2016”.The results show that the impacts of reusable and single use items take place at different stages of their life cycles. For reusable ones, 76% of the impact is produced during the flight phase, meanwhile, for single use ones, 53% of the impact comes from the production stage.Variables such as material, weight and the number of reuses can greatly influence greenhouse gas (GHG) emissions. From the results of the analysis some eco-design strategies has been proposed and analysed. The paper reveals that the lighter single-use packaging and tableware for airline catering are less harmful under a life cycle perspective become.

Improving aircraft approach operations taking into account noise and fuel consumption

While air transport brings very significant economic and social benefits to the cities and regions served by airports, aircraft noise is the single major cause of community opposition to airport operations, becoming a critical issue that affects the sustainability of future traffic growth. However, planning operations exclusively focusing on noise impact may result in an increase of fuel consumption or delays. This paper develops a suitable bi-objective model for landing aircraft, which finds a schedule that minimises noise impact, total fuel consumption and delays, under wake vortex separation and Constrained Position Shifting restrictions. The results of this model are compared with real operations in a major European airport to assess the potential level of improvements. By comparing with real data from Madrid-Barajas airport, the research shows potential improvements of up to 4.5% reduction of total fuel consumption (without increasing noise levels) only by modifying the sequence of arrivals, and up to 43% (without extra fuel consumption) of reduction in noise impact over the populations under study.

Analyzing the Departure Runway Capacity Effects of Integrating Optimized Continuous Climb Operations

Performing Continuous Climb Operation (CCO) procedures enable the reduction of the environmental footprint and the improvement of the trajectory efficiency when individually operated. However, its operation may affect negatively the overall operational efficiency at Terminal Manoeuvring Areas (TMAs). The estimation of capacity is a matter of paramount importance to all airport planning and analyzing the capacity effects of this particular operational technique on a certain scenario will definitely help on evaluating its potential applicability. In this paper, departure runway capacity at the Adolfo Suárez Madrid-Barajas airport was operationally evaluated when introducing CCOs. The considered trajectories consisted of multiobjective optimized CCOs based on the optimal control theory, using the pseudospectral direct numerical method. These scenarios allowed addressing of the incremental variations of CCOs versus conventional departures, through fast time simulation, with the objective to assess the effects on the operations.

Taxi planning: Branch and price decomposition

Taxi planning problem studies aircraft routing and scheduling on the airport ground. Taxi planning has been formulated using a binary multicommodity flow model in a space-time airport network. The flow capacity constraints are used to represent the conflicts among aircraft, given an airport’s capacity. Branch and price methodology has been adapted to take advantage of the integer model structures. The computational tests have been run with real data from Adolfo Suárez Madrid-Barajas airport. The tests were oriented toward comparing the new adapted branch and price with the classic Branch and Bound algorithm, trying to obtain conclusions that are useful for airport managers.

Uncertainty Management at the Airport Transit View

Air traffic networks, where airports are the nodes that interconnect the entire system, have a time-varying and stochastic nature. An incident in the airport environment may easily propagate through the network and generate system-level effects. This paper analyses the aircraft flow through the Airport Transit View framework, focusing on the airspace/airside integrated operations. In this analysis, we use a dynamic spatial boundary associated with the Extended Terminal Manoeuvring Area concept. Aircraft operations are characterised by different temporal milestones, which arise from the combination of a Business Process Model for the aircraft flow and the Airport Collaborative Decision-Making methodology. Relationships between factors influencing aircraft processes are evaluated to create a probabilistic graphical model, using a Bayesian network approach. This model manages uncertainty and increases predictability, hence improving the system’s robustness. The methodology is validated through a case study at the Adolfo Suárez Madrid-Barajas Airport, through the collection of nearly 34,000 turnaround operations. We present several lessons learned regarding delay propagation, time saturation, uncertainty precursors and system recovery. The contribution of the paper is two-fold: it presents a novel methodological approach for tackling uncertainty when linking inbound and outbound flights and it also provides insight on the interdependencies among factors driving performance.

Characterization and prediction of the airport operational saturation

This paper develops a functional analysis of the aircraft flow through the airport operational framework, focusing on the airspace-airside integrated system. In this analysis, we use a dynamic spatial boundary associated with the Extended Terminal Maneuvering Area (E-TMA) concept, so inbound and outbound timestamps can be considered. Aircraft operations are characterized by several temporal milestones, which arise from the combination of a Business Process Model (BPM) for the aircraft flow and the Airport Collaborative Decision Making (A-CDM) methodology. This timestamp approach allows us to study the successive hierarchical tasks. The objective is to establish a taxonomy that classifies the system's capacity to “receive and transmit” aircraft streams with adherence to the expected schedule. By considering the accumulated delay across the different processes and its evolution, several indicators are proposed to evaluate the system's level of saturation and its ability to ensure an appropriate aircraft flow in terms of time-efficiency. Finally, the relationships between the factors that influence the aircraft flow are evaluated to create a probabilistic graphical model, using a Bayesian Network (BN) approach. This model predicts outbound delays given the probability of having different values at the causal control variables. The methodology is developed and validated through a case study at Adolfo Suárez Madrid-Barajas Airport (LEMD): a collection of nearly 34,000 turnaround operations (registered at the peak traffic months of 2016) is used to statistically determine the aircraft path characteristics. The contribution of the paper is twofold: it presents a novel methodological approach to evaluate and predict the system's state at the rotation stage and it also provides insights on the interdependencies between factors influencing performance.

TACKLING INTERNATIONAL AIRLINE CATERING WASTE MANAGEMENT: LIFE ZERO CABIN WASTE PROJECT. STATE OF THE ART AND FIRST STEPS

Concerns about cabin waste date back more than two decades. However, in the past few years, several airlines and stakeholders (notably catering companies) have increased their efforts to tackle this issue. A number of factors such as low landfill disposal rates, particularly for inorganic fractions, lack of appropriate facilities and restrictive regulations had traditionally discouraged airlines and other actors to proactively look for solutions. LIFE+ Zero Cabin Waste aims to create a sustainable model to reduce, re-use and recycle (including energy recovery) waste recollected in Iberia airplane cabins in Madrid-Barajas Airport (Spain) and set the basis for its replication by other airlines and related stakeholders in the future. The objectives of this project are: properly separate the cabin waste on board, demonstrate that waste can be dealt with in a more environmentally friendly way contributing to the reduction of the high carbon footprint associated to the generation and the current inadequate management of cabin waste and set the basis for replication through standard protocols. The project, although it is in the first stage, has already been laid the bases and recommendations for future implementations and improvements that will allow reaching the proposed objectives.​

Managing turnaround performance through Collaborative Decision Making

The purpose of this paper is to explore turnaround performance as a resultant from both Collaborative Decision Making (CDM) processes and collaborative measures. This paper presents how CDM operates in the Turnaround Process (TAP) to propose a new method for managing the collaborative turnaround performance of all actors by predicting the most critical indicators. To achieve this, data from a CDM airport is used. Sample data of 6500 observations, taken from turnaround movements handled in 2014 at Madrid-Barajas Airport, were obtained from three separate databases and analyzed separately (in three databases). To predict turnaround performance, this paper also introduces a predictor dependent variable called “star values” as a measure of minimal delay conditions in order to predict time performance. The analysis shows that the proposed method unveils a new approach in determining how collaborative performance can be measured in the TAP and the predicted key performance indicators, which shows variations in the predicted CDM indicators. Results challenge managers and policymakers to find which improvements can be enacted for better usage of airport infrastructures and resources for optimum use as well as enhanced TAP. In terms of theory use and extension, the study reveals how CDM is an essential element in the literature on air traffic management.

Mathematical model for optimising the sequence for clearing snow from the manoeuvring area during winter operations

SummaryThis article considers the optimisation of the sequence for clearing snow from stretches of the manoeuvring area of an airport. This issue involves the optimisation of limited resources to remove snow from taxiways and runways thereby leaving them in an acceptable condition for operating aircraft. The airfield is divided into subsets of significant stretches for the purpose of operations and target times are established during which these are open to aircraft traffic. The document contains several mathematical models each with different functions, such as the end time of the process, the sum of the end times of each stretch and gap between the estimated and the real end times. During this process, we introduce different operating restrictions on partial fulfilment of the operational targets as applied to zones of special interest, or relating to the operation of the snow‐clearing machines. The problem is solved by optimisation based on linear programming. The article gives the results of the computational tests carried out on five distinct models of the manoeuvring area, which cover increasingly complex situations and larger areas. The mathematical model is particularised for the case of the manoeuvring area of Adolfo Suarez Madrid—Barajas Airport. Copyright © 2016 John Wiley & Sons, Ltd. Highlights Optimal sequence for clearing snow from the manoeuvring area of an airport. Contains optimising algorithms solved using CPLEX LP‐based tree search. Restrictions on partial fulfilment of operational targets applied to subsets of significant stretches, used for planning the operation of snow‐clearing machines. Model applied to the case of the manoeuvring area of Adolfo Suárez Madrid Barajas Airport. Conclusions are given on the results of the computational tests carried out. There are five models of the manoeuvring area which cover increasingly complex situations and larger areas.

Annual noise assessment in the vicinity of airports with different flights’ intensity

The noise metric recognized as universal to describe aircraft flight noise events and community noise pollution as a whole has not been yielded yet. Among a set of factors that impacts aircraft noise perception is a number of flights – number of aircraft noise events. This article deals with the number of flights’ influence on applicability of the usually used environmental noise metrics: equivalent Leq and maximum Lmax sound level for the long-term interval of their observation. The correlation between Leq and Lmax levels is considered against duration of observation interval. As a result the definition of representative duration is obtained. Noise measurements provided within representative time interval allow to assess the annually averaged value of equivalent sound level adequately. The adequacy or representativeness of measurements results is based on accuracy criterion taking into account the ‘noisiest’ events only for long-term Leq evaluation procedure under consideration. The long term noise descriptors are considered as the parameters of averaged ‘virtual’ flight noise event, for which the both Leq,avg and Lmax,avg are interrelated through averaged sound exposure energy for all events in observation. The results show that for airports with low intensity of flights (around 5 events per hour during the day) the long term equivalent sound level is heavily changing in relation with the long term maximum sound level, but for high intensity flight traffic this interrelation is quite stable. In the vicinity of airports with low flight intensity the maximum sound level as a noise impact metric is more sensitive than the equivalent level. Inside the areas, where the value of mandatory descriptors Lden (Ldn) is in conformity with established limits for environmental noise, proposed statistical post-processing analysis provides to recognize some additional reasons of community annoyances. The proposed in article assessment scheme was applied for data sets collected at the points of noise control closely to airports with low (about 30 flight noise events per day – Vilnius airport) and high (200–250 flight noise events per day – Madrid-Barajas airport) flight intensity.