Statistical patterns of contaminants distribution in the atmosphere of manned space stations
Автор: Ozerov Dmitry Sergeevich, Nosovsky Andrey Maksimovich, Mukhamedieva Lana Nizamovna, Tsarkov Dmitry Sergeevich, Pakhomova Anna Andreevna, Guzenberg Arkady Samuilovich, Yurgin Alexey Viktorovich
Журнал: Космическая техника и технологии @ktt-energia
Статья в выпуске: 1 (12), 2016 года.
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The paper provides a statistical analysis of contaminants concentrations measured in the atmosphere of the International Space Station (ISS) over the period 2001...2014. Air samples were taken and delivered to Earth in solid sorbent samplers AK-1M, jointly developed by IBMP and RSC Energia. The samples were analyzed at IBMP using the methods of gas chromatography and combined gas chromatography mass-spectrometry. A statistical analysis of the identified chemical substances established the following features of their distribution in the ISS air: positive asymmetry, peaked density distribution, approaching lognormal distribution pattern, correspondence of the arithmetic mean value of the distribution to 70th percentile. It is demonstrated that the distribution of substances in the ISS air is valid for contaminants belonging to various classes of chemical compounds and for various averaging times. The distribution of contaminants in the ISS atmosphere is consistent with patterns established for chemical substances in the air of the working area and atmospheric air. The correspondence of the arithmetic mean value of the distribution to 70th percentile will allow to use general hygienical methodology for accelerated setting of environmental standards for contaminants content in the atmosphere of manned spacecraft, for validating a math model of accelerated setting of environmental standards for contaminants content in the atmospheres of air-tight rooms and manned spacecraft.
Chemical substances in the iss air, lognormal substance distribution pattern, accelerated setting of environmental standards
Короткий адрес: https://sciup.org/14343501
IDR: 14343501