Retraction Note: Ambient air quality prediction and unbounded variational continuous function based on big data

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 NOx was found to be the species that approaching the China governmental standards in this case. Thus, the airport NOx concentration was selected as the AETC limitation factor.

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APPLICATION OF OPEN AIR MODEL (R PACKAGE) TO ANALYZE AIR POLLUTION DATA

INDONESIAN JOURNAL OF URBAN AND ENVIRONMENTAL TECHNOLOGY ◽

10.25105/urbanenvirotech.v1i1.2430 ◽

2017 ◽

Vol 1 (1) ◽

pp. 94 ◽

Cited By ~ 2

Author(s):

Intan Agustine ◽

Hernani Yulinawati ◽

Endro Suswantoro ◽

Dodo Gunawan

Keyword(s):

Air Pollution ◽

Big Data ◽

Air Quality ◽

Meteorological Data ◽

Ambient Air ◽

Quality Data ◽

Fluctuation Analysis ◽

Ambient Air Quality ◽

Long Time ◽

Air Quality Data

Air pollution problem is faced by many countries in the world. Ambient air quality studies and monitoring need a long time period of data to cover various atmospheric conditions, which create big data. A tool is needed to make easier and more effective to analyze big data. Aims: This study aims to analyze various application of openair model, which is available in open-source, for analyzing urban air quality data. Methodology and results: Each pollutant and meteorological data were collected through their sampling-analysis methods (active, passive or real-time) from a certain period of time. The data processed and imported in the openair model were presented in comma separated value (csv) format. The input data must consist of date-time, pollutant, and meteorological data. The analysis is done by selecting six functions: theilSen for trend analysis, timeVariation for temporal variations, scatterPlot for linear correlation analysis, timePlot for fluctuation analysis, windRose for wind rose creation, and polarPlot for creating pollution rose. Results from these functions are discussed. Conclusion, significance and impact study: Openair model is capable of analyzing a long time air quality data. Application of openair model is possible to cities in Indonesia that already monitor ambient air quality but have not analyzed the data yet

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Air Quality Prediction: Big Data and Machine Learning Approaches

International Journal of Environmental Science and Development ◽

10.18178/ijesd.2018.9.1.1066 ◽

2018 ◽

Vol 9 (1) ◽

pp. 8-16 ◽

Cited By ~ 22

Author(s):

Gaganjot Kaur Kang ◽

◽

Jerry Zeyu Gao ◽

Sen Chiao ◽

Shengqiang Lu ◽

...

Keyword(s):

Machine Learning ◽

Big Data ◽

Air Quality ◽

Quality Prediction ◽

Learning Approaches ◽

Air Quality Prediction

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Application of Electron Optics to Characterization of Particulate Pollutants

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010009508x ◽

1972 ◽

Vol 30 ◽

pp. 364-365

Author(s):

J. B. Moran ◽

J. L. Miller

Keyword(s):

Air Quality ◽

Quality Standards ◽

Ambient Air ◽

Hazardous Substances ◽

Fuel Additives ◽

Ambient Air Quality ◽

Air Quality Standards ◽

Emission Standards ◽

Stationary Sources ◽

Ambient Air Quality Standards

The Clean Air Act Amendments of 1970 provide the basis for a dramatic change in Federal air quality programs. The Act establishes new standards for motor vehicles and requires EPA to establish national ambient air quality standards, standards of performance for new stationary sources of pollution, and standards for stationary sources emitting hazardous substances. Further, it establishes procedures which allow states to set emission standards for existing sources in order to achieve national ambient air quality standards. The Act also permits the Administrator of EPA to register fuels and fuel additives and to regulate the use of motor vehicle fuels or fuel additives which pose a hazard to public health or welfare.National air quality standards for particulate matter have been established. Asbestos, mercury, and beryllium have been designated as hazardous air pollutants for which Federal emission standards have been proposed.

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