Academic literature on the topic 'Air Traffic Organization (U.S.)'

This study reports on the harvesting, ingestion, and contamination of American Indian tea Thelesperma megapotamicum grown on the Navajo Reservation in New Mexico. Uranium (U) and co-metal(loid)s (As, Cd, Cs, Mo, Pb, Se, Th, and V) have contaminated local soil and plants. Tea plants were gathered for analysis near U mining impacted areas. The study collected samples of wild tea plants (n = 14), roots (n = 14), and soil (n = 12) that were analyzed with inductively coupled plasma mass spectrometry. Tea harvesting activities, behavior, and ingestion information were collected via questionnaires. Harvesting took place in community fields and near roadways. Results indicate edible foliage concentration levels for Cd exceeded the World Health Organization (WHO) raw medicinal plant permissible level guidelines. Tea samples collected near high traffic areas had significantly greater Cd and Mo concentrations than those collected near low traffic areas (p < 0.001). Tea sample metal(loid) concentration levels ranged from 0.019–7.916 mg/kg. When compared to established food guidelines including the WHO provisional tolerable weekly intake (PTWI), reference dietary intake, recommended dietary allowance, and the tolerable upper limit (UL), Cd exceeded the WHO guidelines but none exceeded the PTWI nor the UL. These findings warrant improved standardization and establishment of universal guidelines for metal(loid) intake in food.

Abstract Road traffic noise is a widely studied environmental risk factor for ischaemic heart disease and myocardial infarction in particular. Given that myocardial infarction is a leading disability and mortality cause in Bulgaria and that a significant proportion of the urban population is exposed to high noise levels, quantification of the burden of disease attributable to traffic noise is essential for environmental health policy making and noise control engineering. This study aimed at estimating the burden of the myocardial infarction cases attributable to road traffic noise in the Bulgarian urban population. We used the methodology for estimating the burden of disease attributable to environmental noise outlined by the World Health Organization. Risk data were extracted from a recently published meta-analysis providing updated exposureresponse relationship between traffic noise and the risk for myocardial infarction. Based on these data we calculated the fraction of myocardial infarction cases attributable to traffic noise, loss of quality-adjusted life-years (QALYs), and the economic burden, assuming € 12,000 per QALY. About 2.9 % or 101 of all myocardial infarction cases could be attributed to road traffic noise. Fifty-five of these were fatal. Nine hundred and sixty-eight QALYs were lost to these cases. The monetary value of these QALYs was about € 11.6 million. Although the measures used in this study are crude and give only an approximation of the real burden of disease from road traffic noise, they are indicative of the important social and economic aspect of noise pollution in Bulgaria. Hopefully, these results will direct the attention of epidemiologists, environmental hygienists, and health economists to this pivotal environmental issue.

Finding a solution to collect, analyze, and share, in near real-time, data acquired by heterogeneous sensors, such as traffic, air pollution, soil moisture, or weather data, represents a great challenge. This paper describes the solution developed at Eurac Research to automatically upload data, in near real-time, by adopting Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) standards to guarantee interoperability. We set up a methodology capable of ingesting heterogeneous datasets to automatize observation uploading and sensor registration, with minimum interaction required of the user. This solution has been successfully tested and applied in the Long Term (Socio-)Ecological Research (LT(S)ER) Matsch-Mazia initiative, and the code is accessible under the CC BY 4.0 license.

In this paper, two multielement analysis techniques, Neutron Activation Analysis (NAA) and Total Reflection X-ray Fluorescence (TXRF), are combined to detect elemental concentrations in Barbula indica moss collected at Dalat, Vietnam. Combining these two techniques has improved the qualitative detection of elements due to atmospheric deposition on moss samples. The concentrations of 40 elements, including Na, Mg, Al, Si, P, S, Cl, K, Ar, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Br, Kr, Rb, Y, Sb, I, Cs, Ba, La, Ce, Sm, Eu, Tb, Dy, Yb, Hf, Ta, Pb, Th, and U in the Barbula indicamoss samples collected at 19 locations at Dalat have been determined. It is shown that the air in Dalat is suspected of contamination by Na, Mg, Si, P, S, V, Mn, Cu, Se, Br, and U; slightly contaminated by Mg, Cl, K, Cr, Ni, S, and Ni; moderately contaminated by Sc, Fe, Co, Zn, As, Kr, Rb, Y, Sb, Cs, Ba, La, Ce, Sm, Eu, Dy, Yb, Ta, Pb, and Th; and seriously contaminated by Tb. Factor analysis has been used to explain the contamination sources of these elements, including V, As, Fe, Zn, Se, Rb, Sb, Cs, Al, Cu, and Pb in the investigated area. Four factors have been extracted that can explain 86% of the total variance, and the results suggest that the main sources of atmospheric pollution in Dalat originate from traffic and windblown dust.

Abstract. Urban aerosol sources are important due to the health effects of particles and their potential impact on climate. Our aim has been to quantify and parameterise the urban aerosol source number flux F (particles m−2 s−1), in order to help improve how this source is represented in air quality and climate models. We applied an aerosol eddy covariance flux system 118.0 m above the city of Stockholm. This allowed us to measure the aerosol number flux for particles with diameters >11 nm. Upward source fluxes dominated completely over deposition fluxes in the collected dataset. Therefore, the measured fluxes were regarded as a good approximation of the aerosol surface sources. Upward fluxes were parameterised using a traffic activity (TA) database, which is based on traffic intensity measurements. The footprint (area on the surface from which sources and sinks affect flux measurements, located at one point in space) of the eddy system covered road and building construction areas, forests and residential areas, as well as roads with high traffic density and smaller streets. We found pronounced diurnal cycles in the particle flux data, which were well correlated with the diurnal cycles in traffic activities, strongly supporting the conclusion that the major part of the aerosol fluxes was due to traffic emissions. The emission factor for the fleet mix in the measurement area EFfm=1.4±0.1×1014 veh−1 km−1 was deduced. This agrees fairly well with other studies, although this study has an advantage of representing the actual effective emission from a mixed vehicle fleet. Emission from other sources, not traffic related, account for a F0=15±18×106 m−2 s−1. The urban aerosol source flux can then be written as F=EFfmTA+F0. In a second attempt to find a parameterisation, the friction velocity U* normalised with the average friction velocity has been included, F=EF . This parameterisation results in a somewhat reduced emission factor, 1.3×1014 veh−1 km−1. When multiple linear regression have been used, two emission factors are found, one for light duty vehicles EFLDV=0.3±0.3×1014 veh−1 km−1 and one for heavy-duty vehicles, EFHDV=19.8±4.0×1014 veh−1 km−1, and F0=19±16×106 m−2 s−1. The results show that during weekdays ~70–80% of the emissions came from HDV.

Abstract. Urban aerosol sources are important due to the health effects of particles and their potential impact on climate. Our aim has been to quantify and parameterise the urban aerosol source number flux F (particles m-2 s-1), in order to help improve how this source is represented in air quality and climate models. We applied an aerosol eddy covariance flux system 118.0 m above the city of Stockholm. This allowed us to measure the aerosol number flux for particles with diameters >11 nm. Upward source fluxes dominated completely over deposition fluxes in the collected dataset. Therefore, the measured fluxes were regarded as a good approximation of the aerosol surface sources. Upward fluxes were parameterised using a traffic activity (TA) database, which is based on traffic intensity measurement. The footprint (area on the surface from which sources and sinks affect flux measurements, located at one point in space) of the eddy system covered road and building construction areas, forests and residential areas, as well as roads with high traffic density and smaller streets. We found pronounced diurnal cycles in the particle flux data, which were well correlated with the diurnal cycles in traffic activities, strongly supporting the conclusion that the major part of the aerosol fluxes was due to traffic emissions. The emission factor for the fleet mix in the measurement area EFfm=1.4±0.1×1014 veh-1 km-1 was deduced. This agrees fairly well with other studies, although this study has an advantage of representing the actual effective emission from a mixed vehicle fleet. Emission from other sources, not traffic related, account for a F0=14±18×106 m-2 s-1. The urban aerosol source flux can then be written as F=EFfmTA+F0. In a second attempt to find a parameterisation, the friction velocity U* normalised with the average friction velocity has been included, F=EF. This parameterisation results in a somewhat reduced emission factor, 1.3×1014 veh-1 km-1. When multiple linear regression have been used, two emission factors are found, one for light duty vehicles EFLDV=0.3±0.3×1014 veh-1 km-1 and one for heavy-duty vehicles, EFHDV=19.8±4.0×1014 veh-1 km-1, and F0=18±16×106 m-2 s-1. The results show that during weekdays ~70–80% of the emissions came from HDV.

Abstract. Sources of airborne particulate matter and their seasonal variation in urban areas in Sub-Sahara Africa are poorly understood due to lack of long-term measurement data. In view of this, airborne fine particles matter (particle diameter ≤ 2.5 μm, PM2.5) were collected between May 2008 and April 2010 at two sites (urban background site and suburban site) within the Nairobi metropolitan area. A total of 780 samples were collected and analyzed for particulate mass, black carbon (BC) and thirteen trace elements. The average PM2.5 concentration at the urban background site was 20 ± 8 μg m−3 whereas the concentration at the suburban site was 13 ± 8 μg m−3. The daily PM2.5 concentrations exceeded 25 μg m−3 (the World Health Organization 24 h guideline value) 29% of the days at the urban background site and 7% of the days at the suburban site. At both sites, BC, Fe, S and Cl accounted for approximately 80% of all detected elements. Positive Matrix Factorization analysis identified five source factors that contribute to PM2.5 in Nairobi; traffic, mineral dust, secondary aerosol, industrial and combustion. Mineral dust and traffic factors were related to approximately 74% of PM2.5. Identified source factors exhibited seasonal variation though traffic factor was prominently consistent throughout the sampling period. The results provide information that can be exploited for policy formulation and mitigation strategies to control air pollution in Sub-Sahara African cities.

In Air Traffic Control (ATC), aircraft altitude data is used to keep an aircraft within a specified minimum distance vertically from other aircraft, terrain and obstacles to reduce the risk of collision. Two types of altitude data are downlinked by radar; actual flight level (Mode C) and selected altitude (Mode S). Flight level indicates pressure altitude, also known as barometric altitude used by controllers for aircraft vertical separation. ‘Selected altitude’ presents intent only, and hence cannot be used for separation purposes. The emergence of Global Navigation Satellite Systems (GNSSs) has enabled geometric altitude on board and to the controllers via the Automatic Dependent Surveillance-Broadcast (ADS-B) system. In addition, ADS-B provides quality indicator parameters for both geometric and barometric altitudes. Availability of this information will enhance Air Traffic Management (ATM) safety. For example, incidents due to Altimetry System Error (ASE) may potentially be avoided with this information. This work investigates the use and availability of these parameters and studies the characteristics of geometric and barometric data and other data that complement the use of these altitude data in the ADS-B messages. Findings show that only 8·7% of the altitude deviation is < 245 feet (which is a requirement of the International Civil Aviation Organization (ICAO) to operate in Reduced Vertical Separation Minimum (RVSM) airspace). This work provides an alert/guidance for future ground or airborne applications that may utilise geometric/barometric altitude data from ADS-B, to include safety barriers that can be found or analysed from the ADS-B messages itself to ensure ATM safety.

Literature New capabilities to modernize the U.S. National Airspace System (NAS) include support of real-time information streams derived from many data sources across the NAS. As an emergent property, safety of the NAS arises from interactions between many elements at different levels, ranging from those attributable to humans, technology, and the environment. Each component in the NAS needs to interact with other components, exchange resources and information, and operate under broad regulations to achieve overall system objectives (Harris & Stanton, 2010). Sometimes, incidents and accidents result from insufficient interaction (communication and coordination) between humans (e.g., pilot-controller). The content of communication provides value and supports understanding with a multitude of individual, group, team, and data sets within air traffic research. In addition, another dimension to communication with a potentially rich source of understanding is everything outside of its explicit meaning. Cooke and Gorman (2009) describe methods of communication flow between teams (considered to be a system) that have proven insightful. The first is a ratio of team members speech quantity, which can indicate the degree of influence one member has over others. Another is the communication required and passed score, or how much variation there is in actual team communication from expectations. Flow quantity represents how much speech each member of the team produces. Gorman et al.’s (2012) study applied discrete Recurrence Quantification Analysis (RQA) to team communication flow data in order to visualize and measure coordination dynamics of Unnamed Aircraft Vehicle (UAV) teams, both mixed teams (i.e., team members changed) and intact teams (i.e., team members stayed the same over successive experimental sessions). Interestingly, mixed teams were better able to adjust to unexpected perturbations; this ability was linked to team level coordination dynamics. That is, mixed teams adopted a globally stable pattern of communication while exhibiting strong temporal dependence (Gorman, Cooke, Amazeen, & Fouse, 2012). Similarly, Demir, Cooke, & Amazeen (2018) applied discrete RQA on human-robot interaction in an Urban Search and Rescue task and multivariate extension of RQA on human-synthetic team in a UAV task. They underline that metastable team coordination (not too stable nor too flexible) between team members is associated with the ability to successfully overcome novel events (i.e., team situation awareness) in a dynamic task environment. The current project addresses the question of how human factors related to air traffic control (ATC), specifically situation awareness and cognitive load, interact with other factors in the NAS to affect ATC performance and a result in a safe and effective NAS? One way to answer this question is focusing on ATC-pilot communication as a chief performance indicator. In the current study, we investigate the potential of dynamical systems perspectives to capture the differential dynamics of three cases between controller-pilot communication flow during incidents and accidents. Method One of the approaches for investigating interaction patterns between system components (in the controller-pilot case) and their change over time involves looking at communication flow using discrete Recurrence Plot (RP) and corresponding Recurrence Quantification Analysis (RQA), which quantifies how many recurrences with a certain length are present by multidimensional space (phase space) trajectory in a dynamical system (Marwan, Carmen Romano, Thiel, & Kurths, 2007). RP is the basis of discrete RQA (Eckmann, Kamphorst, & Ruelle, 1987), which is a visual tool for demonstrating a system’s recurrent structure in the phase space when a system revisits specific states or sequences of states within a region of phase space over a period of time. In the case of two or more systems, discrete RP displays the times when two or more separate dynamical systems show a recurrence simultaneously (Marwan et al., 2007). Three cases of controller-pilot audio transmissions with their communication time stamps were obtained from “Cockpit Voice Recorder Transcripts” (2019), visualized using RP, and analyzed via discrete RQA. The cases represent situations of particular interest, communication, and coordination. Discrete RQA quantifies not only the effect of interventions (such as unexpected events) on instability, but also the dyad interaction processes and the dynamics that contribute to that process. The RQA was used to produce several measures, including percent recurrence rate, percent determinism (DET), longest diagonal line, longest vertical line, entropy, and laminarity. Of these, the focal variable was determinism (Marwan et al., 2007), which indicates the amount of organization in the communication of a system. DET is derived from the recurrence plot by examining how the recurrent points are distributed. Dyads with high determinism tend to repeat sequences of states many times, while a controller-pilot with low determinism rarely repeats a sequence of states, producing few diagonal lines. Results and discussion One of the objectives of this study is to monitor human performance indicators in real-time in the NAS to make predictions about risk. The current exploratory paper presents an idea about how to model human interaction between two or more roles with the larger purpose of developing NAS risk prognostics. We have presented three controller-pilot communication flows via discrete RP and RQA methods that differentiate three real cases based on discrete interaction sequences. The measures extracted from the RQA and visualizations of the interaction patterns show that effective communication and coordination is needed for effective situation awareness, i.e., overcoming the failures. Based on previous studies (Demir et al., 2018), we expected that the rigidity of the coordination dynamics between controller and pilot in one of the cases would associated with a fatal accident as well as lack of communication (confusion during the landing), resulting in a lack of situation awareness. On the other hand, two other incidents demonstrated more flexible behavior across the roles (controller-pilot) to adapt to the dynamic environment. In this case, the key lies in the dynamic transition between interaction and the environment. The controller and pilot are compelled to adjust their interaction patterns (flexibility) to adapt to changes in the environment and maintain a stable trajectory toward meeting their goals, such as landing safely. Thus, there are three crucial states for effective interaction in both temporal and spatial states: what needs to be communicated, when it needs to be coordinated, and how it needs to be communicated and coordinated”.

Abstract. Sources of airborne particulate matter and their seasonal variation in urban areas in Sub-Saharan Africa are poorly understood due to lack of long-term measurement data. In view of this, filter samples of airborne particulate matter (particle diameter ≤2.5 μm, PM2.5) were collected between May 2008 and April 2010 at two sites (urban background site and suburban site) within the Nairobi metropolitan area. A total of 780 samples were collected and analyzed for particulate mass, black carbon (BC) and 13 trace elements. The average PM2.5 concentration at the urban background site was 21±9.5 μg m−3, whereas the concentration at the suburban site was 13±7.3 μg m−3. The daily PM2.5 concentrations exceeded 25 μg m−3 (the World Health Organization 24 h guideline value) on 29% of the days at the urban background site and 7% of the days at the suburban site. At both sites, BC, Fe, S and Cl accounted for approximately 80% of all detected elements. Positive matrix factorization analysis identified five source factors that contribute to PM2.5 in Nairobi, namely traffic, mineral dust, industry, combustion and a mixed factor (composed of biomass burning, secondary aerosol and aged sea salt). Mineral dust and traffic factors were related to approximately 74% of PM2.5. The identified source factors exhibited seasonal variation, apart from the traffic factor, which was prominently consistent throughout the sampling period. Weekly variations were observed in all factors, with weekdays having higher concentrations than weekends. The results provide information that can be exploited for policy formulation and mitigation strategies to control air pollution in Sub-Saharan African cities.