Size Distribution of Dry Matter of Particles in the Surface Atmospheric Layer in the Suburban Region of Tomsk within the Empirical Classification of Aerosol Weather Types
Abstract Based on the integrated monitoring of aerosol characteristics in the suburban region of Tomsk (2000–2017), a version of the classification of states of the surface atmospheric layer with respect to “aerosol weather” types is suggested. The principle of separate study of the processes of var...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Panchenko, M. V. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Anmerkung: |
© Pleiades Publishing, Ltd. 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Atmospheric and oceanic optics - Dordrecht [u.a.] : Springer Science + Business Media B.V, 2009, 32(2019), 6 vom: Nov., Seite 655-662 |
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| Übergeordnetes Werk: |
volume:32 ; year:2019 ; number:6 ; month:11 ; pages:655-662 |
| Links: |
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| DOI / URN: |
10.1134/S1024856019060113 |
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| Katalog-ID: |
SPR026326051 |
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| 100 | 1 | |a Panchenko, M. V. |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Size Distribution of Dry Matter of Particles in the Surface Atmospheric Layer in the Suburban Region of Tomsk within the Empirical Classification of Aerosol Weather Types |
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| 520 | |a Abstract Based on the integrated monitoring of aerosol characteristics in the suburban region of Tomsk (2000–2017), a version of the classification of states of the surface atmospheric layer with respect to “aerosol weather” types is suggested. The principle of separate study of the processes of variation in the “dry matter” of aerosol particles and their condensation activity is utilized as a basis of the measurement method used. The corresponding aerosol weather types were identified in the coordinates ($ σ_{d} $; Р), where $ σ_{d} $ is the scattering coefficient of the dry matter of aerosol (λ = 0.51 μm); P is the ratio of the mass concentration of the absorbing substance to the mass concentration of submicron particles, which reflects the “blackening” degree of the particles. With respect to the value of the scattering coefficient $ σ_{d} $ = 100 $ Mm^{−1} $, the dataset is divided into two classes: “atmospheric hazes” ($ σ_{d} $ < 100 $ Mm^{−1} $) and “haze” ($ σ_{d} $ > 100 $ Mm^{−1} $). Then, the observation dataset is divided according to the value P = 0.05. In each calendar season, in accordance with the parameters specified, four types of aerosol weather are identified, which are conventionally designated as background (P < 0.05, $ σ_{d} $ < 100 $ Mm^{−1} $), haze-S (P > 0.05, $ σ_{d} $ < 100 $ Mm^{−1} $), smog (P > 0.05, $ σ_{d} $ > 100 $ Mm^{−1} $), and smoke haze (P < 0.05, $ σ_{d} $ > 100 $ Mm^{−1} $). It is shown that the main aerosol weather types are reliably different in the ratio of the contents of submicron and coarse particles in all seasons. | ||
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| 700 | 1 | |a Pol’kin, Vas. V. |4 aut | |
| 700 | 1 | |a Kozlov, V. S. |4 aut | |
| 700 | 1 | |a Yausheva, E. P. |4 aut | |
| 700 | 1 | |a Shmargunov, V. P. |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Atmospheric and oceanic optics |d Dordrecht [u.a.] : Springer Science + Business Media B.V, 2009 |g 32(2019), 6 vom: Nov., Seite 655-662 |w (DE-627)599674695 |w (DE-600)2493873-7 |x 2070-0393 |7 nnns |
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10.1134/S1024856019060113 doi (DE-627)SPR026326051 (SPR)S1024856019060113-e DE-627 ger DE-627 rakwb eng Panchenko, M. V. verfasserin aut Size Distribution of Dry Matter of Particles in the Surface Atmospheric Layer in the Suburban Region of Tomsk within the Empirical Classification of Aerosol Weather Types 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2019 Abstract Based on the integrated monitoring of aerosol characteristics in the suburban region of Tomsk (2000–2017), a version of the classification of states of the surface atmospheric layer with respect to “aerosol weather” types is suggested. The principle of separate study of the processes of variation in the “dry matter” of aerosol particles and their condensation activity is utilized as a basis of the measurement method used. The corresponding aerosol weather types were identified in the coordinates ($ σ_{d} $; Р), where $ σ_{d} $ is the scattering coefficient of the dry matter of aerosol (λ = 0.51 μm); P is the ratio of the mass concentration of the absorbing substance to the mass concentration of submicron particles, which reflects the “blackening” degree of the particles. With respect to the value of the scattering coefficient $ σ_{d} $ = 100 $ Mm^{−1} $, the dataset is divided into two classes: “atmospheric hazes” ($ σ_{d} $ < 100 $ Mm^{−1} $) and “haze” ($ σ_{d} $ > 100 $ Mm^{−1} $). Then, the observation dataset is divided according to the value P = 0.05. In each calendar season, in accordance with the parameters specified, four types of aerosol weather are identified, which are conventionally designated as background (P < 0.05, $ σ_{d} $ < 100 $ Mm^{−1} $), haze-S (P > 0.05, $ σ_{d} $ < 100 $ Mm^{−1} $), smog (P > 0.05, $ σ_{d} $ > 100 $ Mm^{−1} $), and smoke haze (P < 0.05, $ σ_{d} $ > 100 $ Mm^{−1} $). It is shown that the main aerosol weather types are reliably different in the ratio of the contents of submicron and coarse particles in all seasons. Pol’kin, V. V. aut Pol’kin, Vas. V. aut Kozlov, V. S. aut Yausheva, E. P. aut Shmargunov, V. P. aut Enthalten in Atmospheric and oceanic optics Dordrecht [u.a.] : Springer Science + Business Media B.V, 2009 32(2019), 6 vom: Nov., Seite 655-662 (DE-627)599674695 (DE-600)2493873-7 2070-0393 nnns volume:32 year:2019 number:6 month:11 pages:655-662 https://dx.doi.org/10.1134/S1024856019060113 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2019 6 11 655-662 |
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10.1134/S1024856019060113 doi (DE-627)SPR026326051 (SPR)S1024856019060113-e DE-627 ger DE-627 rakwb eng Panchenko, M. V. verfasserin aut Size Distribution of Dry Matter of Particles in the Surface Atmospheric Layer in the Suburban Region of Tomsk within the Empirical Classification of Aerosol Weather Types 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2019 Abstract Based on the integrated monitoring of aerosol characteristics in the suburban region of Tomsk (2000–2017), a version of the classification of states of the surface atmospheric layer with respect to “aerosol weather” types is suggested. The principle of separate study of the processes of variation in the “dry matter” of aerosol particles and their condensation activity is utilized as a basis of the measurement method used. The corresponding aerosol weather types were identified in the coordinates ($ σ_{d} $; Р), where $ σ_{d} $ is the scattering coefficient of the dry matter of aerosol (λ = 0.51 μm); P is the ratio of the mass concentration of the absorbing substance to the mass concentration of submicron particles, which reflects the “blackening” degree of the particles. With respect to the value of the scattering coefficient $ σ_{d} $ = 100 $ Mm^{−1} $, the dataset is divided into two classes: “atmospheric hazes” ($ σ_{d} $ < 100 $ Mm^{−1} $) and “haze” ($ σ_{d} $ > 100 $ Mm^{−1} $). Then, the observation dataset is divided according to the value P = 0.05. In each calendar season, in accordance with the parameters specified, four types of aerosol weather are identified, which are conventionally designated as background (P < 0.05, $ σ_{d} $ < 100 $ Mm^{−1} $), haze-S (P > 0.05, $ σ_{d} $ < 100 $ Mm^{−1} $), smog (P > 0.05, $ σ_{d} $ > 100 $ Mm^{−1} $), and smoke haze (P < 0.05, $ σ_{d} $ > 100 $ Mm^{−1} $). It is shown that the main aerosol weather types are reliably different in the ratio of the contents of submicron and coarse particles in all seasons. Pol’kin, V. V. aut Pol’kin, Vas. V. aut Kozlov, V. S. aut Yausheva, E. P. aut Shmargunov, V. P. aut Enthalten in Atmospheric and oceanic optics Dordrecht [u.a.] : Springer Science + Business Media B.V, 2009 32(2019), 6 vom: Nov., Seite 655-662 (DE-627)599674695 (DE-600)2493873-7 2070-0393 nnns volume:32 year:2019 number:6 month:11 pages:655-662 https://dx.doi.org/10.1134/S1024856019060113 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2019 6 11 655-662 |
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10.1134/S1024856019060113 doi (DE-627)SPR026326051 (SPR)S1024856019060113-e DE-627 ger DE-627 rakwb eng Panchenko, M. V. verfasserin aut Size Distribution of Dry Matter of Particles in the Surface Atmospheric Layer in the Suburban Region of Tomsk within the Empirical Classification of Aerosol Weather Types 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2019 Abstract Based on the integrated monitoring of aerosol characteristics in the suburban region of Tomsk (2000–2017), a version of the classification of states of the surface atmospheric layer with respect to “aerosol weather” types is suggested. The principle of separate study of the processes of variation in the “dry matter” of aerosol particles and their condensation activity is utilized as a basis of the measurement method used. The corresponding aerosol weather types were identified in the coordinates ($ σ_{d} $; Р), where $ σ_{d} $ is the scattering coefficient of the dry matter of aerosol (λ = 0.51 μm); P is the ratio of the mass concentration of the absorbing substance to the mass concentration of submicron particles, which reflects the “blackening” degree of the particles. With respect to the value of the scattering coefficient $ σ_{d} $ = 100 $ Mm^{−1} $, the dataset is divided into two classes: “atmospheric hazes” ($ σ_{d} $ < 100 $ Mm^{−1} $) and “haze” ($ σ_{d} $ > 100 $ Mm^{−1} $). Then, the observation dataset is divided according to the value P = 0.05. In each calendar season, in accordance with the parameters specified, four types of aerosol weather are identified, which are conventionally designated as background (P < 0.05, $ σ_{d} $ < 100 $ Mm^{−1} $), haze-S (P > 0.05, $ σ_{d} $ < 100 $ Mm^{−1} $), smog (P > 0.05, $ σ_{d} $ > 100 $ Mm^{−1} $), and smoke haze (P < 0.05, $ σ_{d} $ > 100 $ Mm^{−1} $). It is shown that the main aerosol weather types are reliably different in the ratio of the contents of submicron and coarse particles in all seasons. Pol’kin, V. V. aut Pol’kin, Vas. V. aut Kozlov, V. S. aut Yausheva, E. P. aut Shmargunov, V. P. aut Enthalten in Atmospheric and oceanic optics Dordrecht [u.a.] : Springer Science + Business Media B.V, 2009 32(2019), 6 vom: Nov., Seite 655-662 (DE-627)599674695 (DE-600)2493873-7 2070-0393 nnns volume:32 year:2019 number:6 month:11 pages:655-662 https://dx.doi.org/10.1134/S1024856019060113 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2019 6 11 655-662 |
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10.1134/S1024856019060113 doi (DE-627)SPR026326051 (SPR)S1024856019060113-e DE-627 ger DE-627 rakwb eng Panchenko, M. V. verfasserin aut Size Distribution of Dry Matter of Particles in the Surface Atmospheric Layer in the Suburban Region of Tomsk within the Empirical Classification of Aerosol Weather Types 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2019 Abstract Based on the integrated monitoring of aerosol characteristics in the suburban region of Tomsk (2000–2017), a version of the classification of states of the surface atmospheric layer with respect to “aerosol weather” types is suggested. The principle of separate study of the processes of variation in the “dry matter” of aerosol particles and their condensation activity is utilized as a basis of the measurement method used. The corresponding aerosol weather types were identified in the coordinates ($ σ_{d} $; Р), where $ σ_{d} $ is the scattering coefficient of the dry matter of aerosol (λ = 0.51 μm); P is the ratio of the mass concentration of the absorbing substance to the mass concentration of submicron particles, which reflects the “blackening” degree of the particles. With respect to the value of the scattering coefficient $ σ_{d} $ = 100 $ Mm^{−1} $, the dataset is divided into two classes: “atmospheric hazes” ($ σ_{d} $ < 100 $ Mm^{−1} $) and “haze” ($ σ_{d} $ > 100 $ Mm^{−1} $). Then, the observation dataset is divided according to the value P = 0.05. In each calendar season, in accordance with the parameters specified, four types of aerosol weather are identified, which are conventionally designated as background (P < 0.05, $ σ_{d} $ < 100 $ Mm^{−1} $), haze-S (P > 0.05, $ σ_{d} $ < 100 $ Mm^{−1} $), smog (P > 0.05, $ σ_{d} $ > 100 $ Mm^{−1} $), and smoke haze (P < 0.05, $ σ_{d} $ > 100 $ Mm^{−1} $). It is shown that the main aerosol weather types are reliably different in the ratio of the contents of submicron and coarse particles in all seasons. Pol’kin, V. V. aut Pol’kin, Vas. V. aut Kozlov, V. S. aut Yausheva, E. P. aut Shmargunov, V. P. aut Enthalten in Atmospheric and oceanic optics Dordrecht [u.a.] : Springer Science + Business Media B.V, 2009 32(2019), 6 vom: Nov., Seite 655-662 (DE-627)599674695 (DE-600)2493873-7 2070-0393 nnns volume:32 year:2019 number:6 month:11 pages:655-662 https://dx.doi.org/10.1134/S1024856019060113 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2019 6 11 655-662 |
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10.1134/S1024856019060113 doi (DE-627)SPR026326051 (SPR)S1024856019060113-e DE-627 ger DE-627 rakwb eng Panchenko, M. V. verfasserin aut Size Distribution of Dry Matter of Particles in the Surface Atmospheric Layer in the Suburban Region of Tomsk within the Empirical Classification of Aerosol Weather Types 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2019 Abstract Based on the integrated monitoring of aerosol characteristics in the suburban region of Tomsk (2000–2017), a version of the classification of states of the surface atmospheric layer with respect to “aerosol weather” types is suggested. The principle of separate study of the processes of variation in the “dry matter” of aerosol particles and their condensation activity is utilized as a basis of the measurement method used. The corresponding aerosol weather types were identified in the coordinates ($ σ_{d} $; Р), where $ σ_{d} $ is the scattering coefficient of the dry matter of aerosol (λ = 0.51 μm); P is the ratio of the mass concentration of the absorbing substance to the mass concentration of submicron particles, which reflects the “blackening” degree of the particles. With respect to the value of the scattering coefficient $ σ_{d} $ = 100 $ Mm^{−1} $, the dataset is divided into two classes: “atmospheric hazes” ($ σ_{d} $ < 100 $ Mm^{−1} $) and “haze” ($ σ_{d} $ > 100 $ Mm^{−1} $). Then, the observation dataset is divided according to the value P = 0.05. In each calendar season, in accordance with the parameters specified, four types of aerosol weather are identified, which are conventionally designated as background (P < 0.05, $ σ_{d} $ < 100 $ Mm^{−1} $), haze-S (P > 0.05, $ σ_{d} $ < 100 $ Mm^{−1} $), smog (P > 0.05, $ σ_{d} $ > 100 $ Mm^{−1} $), and smoke haze (P < 0.05, $ σ_{d} $ > 100 $ Mm^{−1} $). It is shown that the main aerosol weather types are reliably different in the ratio of the contents of submicron and coarse particles in all seasons. Pol’kin, V. V. aut Pol’kin, Vas. V. aut Kozlov, V. S. aut Yausheva, E. P. aut Shmargunov, V. P. aut Enthalten in Atmospheric and oceanic optics Dordrecht [u.a.] : Springer Science + Business Media B.V, 2009 32(2019), 6 vom: Nov., Seite 655-662 (DE-627)599674695 (DE-600)2493873-7 2070-0393 nnns volume:32 year:2019 number:6 month:11 pages:655-662 https://dx.doi.org/10.1134/S1024856019060113 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 32 2019 6 11 655-662 |
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Panchenko, M. V. @@aut@@ Pol’kin, V. V. @@aut@@ Pol’kin, Vas. V. @@aut@@ Kozlov, V. S. @@aut@@ Yausheva, E. P. @@aut@@ Shmargunov, V. P. @@aut@@ |
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The principle of separate study of the processes of variation in the “dry matter” of aerosol particles and their condensation activity is utilized as a basis of the measurement method used. The corresponding aerosol weather types were identified in the coordinates ($ σ_{d} $; Р), where $ σ_{d} $ is the scattering coefficient of the dry matter of aerosol (λ = 0.51 μm); P is the ratio of the mass concentration of the absorbing substance to the mass concentration of submicron particles, which reflects the “blackening” degree of the particles. With respect to the value of the scattering coefficient $ σ_{d} $ = 100 $ Mm^{−1} $, the dataset is divided into two classes: “atmospheric hazes” ($ σ_{d} $ < 100 $ Mm^{−1} $) and “haze” ($ σ_{d} $ > 100 $ Mm^{−1} $). Then, the observation dataset is divided according to the value P = 0.05. In each calendar season, in accordance with the parameters specified, four types of aerosol weather are identified, which are conventionally designated as background (P < 0.05, $ σ_{d} $ < 100 $ Mm^{−1} $), haze-S (P > 0.05, $ σ_{d} $ < 100 $ Mm^{−1} $), smog (P > 0.05, $ σ_{d} $ > 100 $ Mm^{−1} $), and smoke haze (P < 0.05, $ σ_{d} $ > 100 $ Mm^{−1} $). It is shown that the main aerosol weather types are reliably different in the ratio of the contents of submicron and coarse particles in all seasons.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pol’kin, V. V.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pol’kin, Vas. V.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kozlov, V. S.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yausheva, E. 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Panchenko, M. V. |
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Panchenko, M. V. Size Distribution of Dry Matter of Particles in the Surface Atmospheric Layer in the Suburban Region of Tomsk within the Empirical Classification of Aerosol Weather Types |
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Size Distribution of Dry Matter of Particles in the Surface Atmospheric Layer in the Suburban Region of Tomsk within the Empirical Classification of Aerosol Weather Types |
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Size Distribution of Dry Matter of Particles in the Surface Atmospheric Layer in the Suburban Region of Tomsk within the Empirical Classification of Aerosol Weather Types |
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Size Distribution of Dry Matter of Particles in the Surface Atmospheric Layer in the Suburban Region of Tomsk within the Empirical Classification of Aerosol Weather Types |
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Panchenko, M. V. |
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Atmospheric and oceanic optics |
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Panchenko, M. V. Pol’kin, V. V. Pol’kin, Vas. V. Kozlov, V. S. Yausheva, E. P. Shmargunov, V. P. |
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Panchenko, M. V. |
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10.1134/S1024856019060113 |
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size distribution of dry matter of particles in the surface atmospheric layer in the suburban region of tomsk within the empirical classification of aerosol weather types |
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Size Distribution of Dry Matter of Particles in the Surface Atmospheric Layer in the Suburban Region of Tomsk within the Empirical Classification of Aerosol Weather Types |
| abstract |
Abstract Based on the integrated monitoring of aerosol characteristics in the suburban region of Tomsk (2000–2017), a version of the classification of states of the surface atmospheric layer with respect to “aerosol weather” types is suggested. The principle of separate study of the processes of variation in the “dry matter” of aerosol particles and their condensation activity is utilized as a basis of the measurement method used. The corresponding aerosol weather types were identified in the coordinates ($ σ_{d} $; Р), where $ σ_{d} $ is the scattering coefficient of the dry matter of aerosol (λ = 0.51 μm); P is the ratio of the mass concentration of the absorbing substance to the mass concentration of submicron particles, which reflects the “blackening” degree of the particles. With respect to the value of the scattering coefficient $ σ_{d} $ = 100 $ Mm^{−1} $, the dataset is divided into two classes: “atmospheric hazes” ($ σ_{d} $ < 100 $ Mm^{−1} $) and “haze” ($ σ_{d} $ > 100 $ Mm^{−1} $). Then, the observation dataset is divided according to the value P = 0.05. In each calendar season, in accordance with the parameters specified, four types of aerosol weather are identified, which are conventionally designated as background (P < 0.05, $ σ_{d} $ < 100 $ Mm^{−1} $), haze-S (P > 0.05, $ σ_{d} $ < 100 $ Mm^{−1} $), smog (P > 0.05, $ σ_{d} $ > 100 $ Mm^{−1} $), and smoke haze (P < 0.05, $ σ_{d} $ > 100 $ Mm^{−1} $). It is shown that the main aerosol weather types are reliably different in the ratio of the contents of submicron and coarse particles in all seasons. © Pleiades Publishing, Ltd. 2019 |
| abstractGer |
Abstract Based on the integrated monitoring of aerosol characteristics in the suburban region of Tomsk (2000–2017), a version of the classification of states of the surface atmospheric layer with respect to “aerosol weather” types is suggested. The principle of separate study of the processes of variation in the “dry matter” of aerosol particles and their condensation activity is utilized as a basis of the measurement method used. The corresponding aerosol weather types were identified in the coordinates ($ σ_{d} $; Р), where $ σ_{d} $ is the scattering coefficient of the dry matter of aerosol (λ = 0.51 μm); P is the ratio of the mass concentration of the absorbing substance to the mass concentration of submicron particles, which reflects the “blackening” degree of the particles. With respect to the value of the scattering coefficient $ σ_{d} $ = 100 $ Mm^{−1} $, the dataset is divided into two classes: “atmospheric hazes” ($ σ_{d} $ < 100 $ Mm^{−1} $) and “haze” ($ σ_{d} $ > 100 $ Mm^{−1} $). Then, the observation dataset is divided according to the value P = 0.05. In each calendar season, in accordance with the parameters specified, four types of aerosol weather are identified, which are conventionally designated as background (P < 0.05, $ σ_{d} $ < 100 $ Mm^{−1} $), haze-S (P > 0.05, $ σ_{d} $ < 100 $ Mm^{−1} $), smog (P > 0.05, $ σ_{d} $ > 100 $ Mm^{−1} $), and smoke haze (P < 0.05, $ σ_{d} $ > 100 $ Mm^{−1} $). It is shown that the main aerosol weather types are reliably different in the ratio of the contents of submicron and coarse particles in all seasons. © Pleiades Publishing, Ltd. 2019 |
| abstract_unstemmed |
Abstract Based on the integrated monitoring of aerosol characteristics in the suburban region of Tomsk (2000–2017), a version of the classification of states of the surface atmospheric layer with respect to “aerosol weather” types is suggested. The principle of separate study of the processes of variation in the “dry matter” of aerosol particles and their condensation activity is utilized as a basis of the measurement method used. The corresponding aerosol weather types were identified in the coordinates ($ σ_{d} $; Р), where $ σ_{d} $ is the scattering coefficient of the dry matter of aerosol (λ = 0.51 μm); P is the ratio of the mass concentration of the absorbing substance to the mass concentration of submicron particles, which reflects the “blackening” degree of the particles. With respect to the value of the scattering coefficient $ σ_{d} $ = 100 $ Mm^{−1} $, the dataset is divided into two classes: “atmospheric hazes” ($ σ_{d} $ < 100 $ Mm^{−1} $) and “haze” ($ σ_{d} $ > 100 $ Mm^{−1} $). Then, the observation dataset is divided according to the value P = 0.05. In each calendar season, in accordance with the parameters specified, four types of aerosol weather are identified, which are conventionally designated as background (P < 0.05, $ σ_{d} $ < 100 $ Mm^{−1} $), haze-S (P > 0.05, $ σ_{d} $ < 100 $ Mm^{−1} $), smog (P > 0.05, $ σ_{d} $ > 100 $ Mm^{−1} $), and smoke haze (P < 0.05, $ σ_{d} $ > 100 $ Mm^{−1} $). It is shown that the main aerosol weather types are reliably different in the ratio of the contents of submicron and coarse particles in all seasons. © Pleiades Publishing, Ltd. 2019 |
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Size Distribution of Dry Matter of Particles in the Surface Atmospheric Layer in the Suburban Region of Tomsk within the Empirical Classification of Aerosol Weather Types |
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Pol’kin, V. V. Pol’kin, Vas. V. Kozlov, V. S. Yausheva, E. P. Shmargunov, V. P. |
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Pol’kin, V. V. Pol’kin, Vas. V. Kozlov, V. S. Yausheva, E. P. Shmargunov, V. P. |
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2024-07-03T20:16:07.088Z |
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| score |
7.3984175 |