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• W3097810896 abstract "Abstract In order to evaluate the airport's comprehensive service capabilities, this paper considers the impact of air quality and noise on the airport environment under the big data of air traffic activities. In this study, the concept of environmental traffic capacity and big data are applied to the air traffic field. Recently, the airport air and noise pollution has been widely investigated and has become one of the major concerns of the potentially exposed people. This study explores the usage of governmental ambient air quality and noise standards to evaluate the airport operation capacities in the context of the era of big data. The first step is to analyze the typical airport operation scenario as the evaluation scenario. The second step is to use the air and noise emission assessment model for calculating the airport maximum air pollutant concentration and noise level. The final step is to establish a complete airport environment traffic capacity (AETC) evaluation process. As a case study, the capacity evaluation of Nanjing Lukou international airport (NKG) is performed using the above steps. In this case, significant associations between the pollutant concentrations/noise level and the air traffic volume were observed. The AETC of NKG was calculated with the established evaluation process successfully. The results show that the NKG maximum hourly air traffic volume is 120, daily air traffic volume is 770, and annual air traffic volume is 365,805, meeting the China Ambient Air Quality and Noise Standards. Although different air pollutants were investigated in this research, only the NO x was found to be the species that approaching the China governmental standards in this case. Thus, the airport NO x concentration was selected as the AETC limitation factor." @default.
• W3097810896 created "2020-11-09" @default.
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• W3097810896 date "2020-11-02" @default.
• W3097810896 modified "2023-10-15" @default.
• W3097810896 title "Airport capacity evaluation based on air traffic activities big data" @default.
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• W3097810896 doi "https://doi.org/10.1186/s13638-020-01851-w" @default.
• W3097810896 hasPublicationYear "2020" @default.
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