Inversion algorithm of black carbon mixing state based on machine learning

The radiative properties of black carbon (BC) are significantly influenced by its mixing state. The single-particle soot photometer (SP2) is a widely recognized instrument for quantifying BC mixing state. However, the derivation of BC mixing state from SP2 is quite challenging. Since the SP2 records...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Atmospheric measurement techniques Jg. 18; H. 5; S. 1149 - 1162
Hauptverfasser:	Tian, Zeyuan, Wang, Jiandong, Wang, Jiaping, Liu, Chao, Xing, Jia, Wang, Jinbo, Zhang, Zhouyang, Jin, Yuzhi, Shen, Sunan, Wang, Bin, Nie, Wei, Huang, Xin, Ding, Aijun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Katlenburg-Lindau Copernicus GmbH 06.03.2025 Copernicus Publications
Schlagworte:	Aerosols Algorithms Analysis Black carbon Carbon Data analysis Data mining Data processing Decision making Ensemble learning Lasers Leading edges Learning algorithms Light Machine learning Optical properties Particle size Photometers Physical properties Protective coatings Radiation Real time Sensors Signal to noise ratio
ISSN:	1867-8548, 1867-1381, 1867-8548
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	The radiative properties of black carbon (BC) are significantly influenced by its mixing state. The single-particle soot photometer (SP2) is a widely recognized instrument for quantifying BC mixing state. However, the derivation of BC mixing state from SP2 is quite challenging. Since the SP2 records individual particle signals, it requires complex data processing to convert raw signals into particle size and mixing states. Furthermore, the rapid accumulation of substantial data volumes impedes real-time analysis of BC mixing states. This study employs the Light Gradient-Boosting Machine (LightGBM), an advanced tree-based ensemble learning algorithm, to establish an inversion model that directly correlates SP2 signals with the mixing state of BC-containing particles. Our model achieves high accuracy for both particle size inversion and optical cross-section inversion of BC-containing particles, with a coefficient of determination R2 higher than 0.98. We further employ the SHapley Additive exPlanation (SHAP) method to analyze the importance of input features from SP2 signals in the inversion model of the entire particle diameter (Dp) and explore their underlying physical significance. Compared to the widely used leading-edge-only (LEO) fitting method, the machine learning (ML) method utilizes a larger coverage of signals encompassing the peak of scattering signal rather than the leading-edge data. This allows for more accurate capture of the diverse characteristics of particles. Moreover, the ML method uses signals with a high signal-to-noise ratio, providing better noise resistance. Our model is capable of accurately and efficiently acquiring the single-particle information and statistical results of the BC mixing state, which provides essential data for BC aging mechanism investigation and the assessment of further BC radiative effects.
AbstractList	The radiative properties of black carbon (BC) are significantly influenced by its mixing state. The single-particle soot photometer (SP2) is a widely recognized instrument for quantifying BC mixing state. However, the derivation of BC mixing state from SP2 is quite challenging. Since the SP2 records individual particle signals, it requires complex data processing to convert raw signals into particle size and mixing states. Furthermore, the rapid accumulation of substantial data volumes impedes real-time analysis of BC mixing states. This study employs the Light Gradient-Boosting Machine (LightGBM), an advanced tree-based ensemble learning algorithm, to establish an inversion model that directly correlates SP2 signals with the mixing state of BC-containing particles. Our model achieves high accuracy for both particle size inversion and optical cross-section inversion of BC-containing particles, with a coefficient of determination R2 higher than 0.98. We further employ the SHapley Additive exPlanation (SHAP) method to analyze the importance of input features from SP2 signals in the inversion model of the entire particle diameter (Dp) and explore their underlying physical significance. Compared to the widely used leading-edge-only (LEO) fitting method, the machine learning (ML) method utilizes a larger coverage of signals encompassing the peak of scattering signal rather than the leading-edge data. This allows for more accurate capture of the diverse characteristics of particles. Moreover, the ML method uses signals with a high signal-to-noise ratio, providing better noise resistance. Our model is capable of accurately and efficiently acquiring the single-particle information and statistical results of the BC mixing state, which provides essential data for BC aging mechanism investigation and the assessment of further BC radiative effects. The radiative properties of black carbon (BC) are significantly influenced by its mixing state. The single-particle soot photometer (SP2) is a widely recognized instrument for quantifying BC mixing state. However, the derivation of BC mixing state from SP2 is quite challenging. Since the SP2 records individual particle signals, it requires complex data processing to convert raw signals into particle size and mixing states. Furthermore, the rapid accumulation of substantial data volumes impedes real-time analysis of BC mixing states. This study employs the Light Gradient-Boosting Machine (LightGBM), an advanced tree-based ensemble learning algorithm, to establish an inversion model that directly correlates SP2 signals with the mixing state of BC-containing particles. Our model achieves high accuracy for both particle size inversion and optical cross-section inversion of BC-containing particles, with a coefficient of determination R.sup.2 higher than 0.98. We further employ the SHapley Additive exPlanation (SHAP) method to analyze the importance of input features from SP2 signals in the inversion model of the entire particle diameter (D.sub.p) and explore their underlying physical significance. Compared to the widely used leading-edge-only (LEO) fitting method, the machine learning (ML) method utilizes a larger coverage of signals encompassing the peak of scattering signal rather than the leading-edge data. This allows for more accurate capture of the diverse characteristics of particles. Moreover, the ML method uses signals with a high signal-to-noise ratio, providing better noise resistance. Our model is capable of accurately and efficiently acquiring the single-particle information and statistical results of the BC mixing state, which provides essential data for BC aging mechanism investigation and the assessment of further BC radiative effects.
Audience	Academic
Author	Tian, Zeyuan Liu, Chao Nie, Wei Xing, Jia Wang, Jiaping Jin, Yuzhi Wang, Bin Huang, Xin Wang, Jiandong Shen, Sunan Wang, Jinbo Zhang, Zhouyang Ding, Aijun
Author_xml	– sequence: 1 givenname: Zeyuan surname: Tian fullname: Tian, Zeyuan – sequence: 2 givenname: Jiandong orcidid: 0000-0003-3000-622X surname: Wang fullname: Wang, Jiandong – sequence: 3 givenname: Jiaping surname: Wang fullname: Wang, Jiaping – sequence: 4 givenname: Chao surname: Liu fullname: Liu, Chao – sequence: 5 givenname: Jia surname: Xing fullname: Xing, Jia – sequence: 6 givenname: Jinbo surname: Wang fullname: Wang, Jinbo – sequence: 7 givenname: Zhouyang surname: Zhang fullname: Zhang, Zhouyang – sequence: 8 givenname: Yuzhi surname: Jin fullname: Jin, Yuzhi – sequence: 9 givenname: Sunan surname: Shen fullname: Shen, Sunan – sequence: 10 givenname: Bin surname: Wang fullname: Wang, Bin – sequence: 11 givenname: Wei orcidid: 0000-0002-6048-0515 surname: Nie fullname: Nie, Wei – sequence: 12 givenname: Xin orcidid: 0000-0003-0922-5014 surname: Huang fullname: Huang, Xin – sequence: 13 givenname: Aijun orcidid: 0000-0003-4481-5386 surname: Ding fullname: Ding, Aijun
BookMark	eNptks2PFCEQxYlZE3dX7x478eShV6BhgONm48eYTUz8OJOCLnoZu5sVGLP-9zKOUScxHCCvfvVSFd4FOVvTioQ8Z_RKMiNewVJ7pnvGhOk55fIROWd6o3othT775_2EXJSyo3QjmOLn5P12_Y65xLR2ME8px3q3dCl0bgb_tfOQXass8SGuU1cqVOwcFBy7gwr-Lq7YzQh5bfWn5HGAueCz3_cl-fLm9eebd_3th7fbm-vb3otB1n50ZgQlAwpOwYH3ypg2nfTCS6rQwOioMqAFV15yyhQGjabxlFGhdRguyfboOybY2fscF8g_bIJofwkpTxZyjX5Ga0aHwY8KFQjBtdEDInCUbOOcYgGa14uj131O3_ZYqt2lfV7b-HZgqs0r-cD_UhM007iGVDP4JRZvrzU3WhqlaKOu_kO1M-ISffutEJt-0vDypKExFR_qBPtS7PbTx1OWHlmfUykZw5_FGbWHANgWAMu0PQTAHgIw_AQcUKOc
Cites_doi	10.1080/02786820500421521 10.1364/AO.42.003726 10.1080/02786826.2010.484450 10.1093/nsr/nwaa307 10.1016/j.jaerosci.2007.12.002 10.1080/02786826.2023.2202243 10.5194/acp-21-7863-2021 10.1029/2021JD036055 10.5194/acp-10-219-2010 10.3390/jmse9050496 10.1109/ACCESS.2019.2897754 10.5194/amt-8-1701-2015 10.1371/journal.pcbi.0030116 10.1016/j.agwat.2019.105758 10.1126/science.aaa8415 10.1109/ACCESS.2022.3165792 10.1029/2008JD010680 10.1038/s41467-018-05635-1 10.1103/RevModPhys.91.045002 10.1029/2006JD007076 10.1016/j.accre.2024.06.007 10.1080/02786820701199728 10.1029/2021GL096437 10.1016/j.frl.2018.12.032 10.1029/1998JD100069 10.1002/jgrd.50171 10.1080/02786826.2015.1074978 10.1016/j.apr.2020.02.011 10.1016/j.atmosres.2022.106238 10.5194/amt-5-1031-2012 10.1126/science.1223447 10.1029/2007JD009042 10.1038/ngeo2901 10.1038/35055518 10.5194/amt-9-1833-2016 10.1080/02786820701197078 10.1029/2005JD006046 10.1016/j.jenvman.2020.111061 10.1021/acs.estlett.7b00418 10.1029/2008JD010546 10.1080/02786820601118398 10.5194/gmd-12-1209-2019 10.3390/s18082674 10.1029/2012GL050905 10.1038/ngeo156 10.5194/acp-14-10061-2014 10.1029/2021JD034620 10.1073/pnas.1919723117
ContentType	Journal Article
Copyright	COPYRIGHT 2025 Copernicus GmbH 2025. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: COPYRIGHT 2025 Copernicus GmbH – notice: 2025. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID	AAYXX CITATION ISR 7QH 7TG 7TN 7UA 8FD 8FE 8FG ABUWG AEUYN AFKRA ARAPS AZQEC BENPR BFMQW BGLVJ BHPHI BKSAR C1K CCPQU DWQXO F1W H8D H96 HCIFZ KL. L.G L7M P5Z P62 PCBAR PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS DOA
DOI	10.5194/amt-18-1149-2025
DatabaseName	CrossRef Gale In Context: Science Aqualine Meteorological & Geoastrophysical Abstracts Oceanic Abstracts Water Resources Abstracts Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) ProQuest One Sustainability (subscription) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central Continental Europe Database Technology collection Natural Science Collection Earth, Atmospheric & Aquatic Science Collection Environmental Sciences and Pollution Management ProQuest One ProQuest Central ASFA: Aquatic Sciences and Fisheries Abstracts Aerospace Database Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources SciTech Premium Collection Meteorological & Geoastrophysical Abstracts - Academic Aquatic Science & Fisheries Abstracts (ASFA) Professional Advanced Technologies Database with Aerospace Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Earth, Atmospheric & Aquatic Science Database ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef Publicly Available Content Database Aquatic Science & Fisheries Abstracts (ASFA) Professional Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Central China Water Resources Abstracts Environmental Sciences and Pollution Management Earth, Atmospheric & Aquatic Science Collection ProQuest Central ProQuest One Applied & Life Sciences Aerospace Database ProQuest One Sustainability Meteorological & Geoastrophysical Abstracts Oceanic Abstracts Natural Science Collection ProQuest Central Korea ProQuest Central (New) Advanced Technologies Database with Aerospace Advanced Technologies & Aerospace Collection ProQuest One Academic Eastern Edition Earth, Atmospheric & Aquatic Science Database ProQuest Technology Collection Continental Europe Database ProQuest SciTech Collection Aqualine Advanced Technologies & Aerospace Database Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources ProQuest One Academic UKI Edition ASFA: Aquatic Sciences and Fisheries Abstracts ProQuest One Academic Meteorological & Geoastrophysical Abstracts - Academic ProQuest One Academic (New)
DatabaseTitleList	Publicly Available Content Database CrossRef
Database_xml	– sequence: 1 dbid: DOA name: Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: ProQuest Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Meteorology & Climatology
EISSN	1867-8548
EndPage	1162
ExternalDocumentID	oai_doaj_org_article_9dbefcd7e7a4428983eea2e516bb71fa A829859770 10_5194_amt_18_1149_2025
GroupedDBID	23N 5VS 8FE 8FG 8FH 8R4 8R5 AAFWJ AAYXX ABDBF ABUWG ACGFO ACUHS ADBBV AEGXH AENEX AEUYN AFFHD AFKRA AFPKN AFRAH AHGZY AIAGR ALMA_UNASSIGNED_HOLDINGS ARAPS BCNDV BENPR BFMQW BGLVJ BHPHI BKSAR BPHCQ CCPQU CITATION D1K E3Z ESX GROUPED_DOAJ H13 HCIFZ IAO IEA ISR ITC K6- KQ8 LK5 M7R OK1 P2P P62 PCBAR PHGZM PHGZT PIMPY PQGLB PQQKQ PROAC Q2X RKB RNS TR2 TUS 7QH 7TG 7TN 7UA 8FD AZQEC C1K DWQXO F1W H8D H96 KL. L.G L7M PKEHL PQEST PQUKI PRINS
ID	FETCH-LOGICAL-c435t-db9da75fe420abacc7991865c4c507e9adb079a8427c52017ef8e95fe010488f3
IEDL.DBID	RKB
ISICitedReferencesCount	1
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001438020800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1867-8548 1867-1381
IngestDate	Tue Oct 14 19:05:24 EDT 2025 Fri Jul 25 21:30:31 EDT 2025 Tue Nov 11 10:51:46 EST 2025 Tue Nov 04 18:13:01 EST 2025 Thu Nov 13 16:00:09 EST 2025 Sat Nov 29 08:12:58 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	5
Language	English
License	https://creativecommons.org/licenses/by/4.0
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c435t-db9da75fe420abacc7991865c4c507e9adb079a8427c52017ef8e95fe010488f3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0003-0922-5014 0000-0003-4481-5386 0000-0002-6048-0515 0000-0003-3000-622X
OpenAccessLink	https://doaj.org/article/9dbefcd7e7a4428983eea2e516bb71fa
PQID	3174355232
PQPubID	105742
PageCount	14
ParticipantIDs	doaj_primary_oai_doaj_org_article_9dbefcd7e7a4428983eea2e516bb71fa proquest_journals_3174355232 gale_infotracmisc_A829859770 gale_infotracacademiconefile_A829859770 gale_incontextgauss_ISR_A829859770 crossref_primary_10_5194_amt_18_1149_2025
PublicationCentury	2000
PublicationDate	2025-03-06
PublicationDateYYYYMMDD	2025-03-06
PublicationDate_xml	– month: 03 year: 2025 text: 2025-03-06 day: 06
PublicationDecade	2020
PublicationPlace	Katlenburg-Lindau
PublicationPlace_xml	– name: Katlenburg-Lindau
PublicationTitle	Atmospheric measurement techniques
PublicationYear	2025
Publisher	Copernicus GmbH Copernicus Publications
Publisher_xml	– name: Copernicus GmbH – name: Copernicus Publications
References	ref13 ref12 ref15 ref14 ref11 ref10 ref17 ref16 ref19 ref18 ref51 ref50 ref46 ref45 ref48 ref47 ref42 ref41 ref44 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref2 ref1 ref39 ref38 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29
References_xml	– ident: ref2 doi: 10.1080/02786820500421521 – ident: ref38 doi: 10.1364/AO.42.003726 – ident: ref26 doi: 10.1080/02786826.2010.484450 – ident: ref51 doi: 10.1093/nsr/nwaa307 – ident: ref25 doi: 10.1016/j.jaerosci.2007.12.002 – ident: ref27 doi: 10.1080/02786826.2023.2202243 – ident: ref46 doi: 10.5194/acp-21-7863-2021 – ident: ref29 doi: 10.1029/2021JD036055 – ident: ref39 doi: 10.5194/acp-10-219-2010 – ident: ref9 doi: 10.3390/jmse9050496 – ident: ref50 doi: 10.1109/ACCESS.2019.2897754 – ident: ref42 doi: 10.5194/amt-8-1701-2015 – ident: ref41 doi: 10.1371/journal.pcbi.0030116 – ident: ref6 doi: 10.1016/j.agwat.2019.105758 – ident: ref12 doi: 10.1126/science.aaa8415 – ident: ref13 – ident: ref1 doi: 10.1109/ACCESS.2022.3165792 – ident: ref28 doi: 10.1029/2008JD010680 – ident: ref23 doi: 10.1038/s41467-018-05635-1 – ident: ref5 doi: 10.1103/RevModPhys.91.045002 – ident: ref32 doi: 10.1029/2006JD007076 – ident: ref20 doi: 10.1016/j.accre.2024.06.007 – ident: ref24 doi: 10.1080/02786820701199728 – ident: ref45 doi: 10.1029/2021GL096437 – ident: ref40 doi: 10.1016/j.frl.2018.12.032 – ident: ref8 doi: 10.1029/1998JD100069 – ident: ref3 doi: 10.1002/jgrd.50171 – ident: ref35 doi: 10.1080/02786826.2015.1074978 – ident: ref48 doi: 10.1016/j.apr.2020.02.011 – ident: ref16 doi: 10.1016/j.atmosres.2022.106238 – ident: ref15 doi: 10.5194/amt-5-1031-2012 – ident: ref4 doi: 10.1126/science.1223447 – ident: ref33 doi: 10.1029/2007JD009042 – ident: ref19 doi: 10.1038/ngeo2901 – ident: ref11 doi: 10.1038/35055518 – ident: ref22 – ident: ref49 doi: 10.5194/amt-9-1833-2016 – ident: ref37 doi: 10.1080/02786820701197078 – ident: ref31 doi: 10.1029/2005JD006046 – ident: ref47 doi: 10.1016/j.jenvman.2020.111061 – ident: ref43 doi: 10.1021/acs.estlett.7b00418 – ident: ref36 doi: 10.1029/2008JD010546 – ident: ref10 doi: 10.1080/02786820601118398 – ident: ref14 doi: 10.5194/gmd-12-1209-2019 – ident: ref17 doi: 10.3390/s18082674 – ident: ref21 – ident: ref34 doi: 10.1029/2012GL050905 – ident: ref30 doi: 10.1038/ngeo156 – ident: ref18 doi: 10.5194/acp-14-10061-2014 – ident: ref44 doi: 10.1029/2021JD034620 – ident: ref7 doi: 10.1073/pnas.1919723117
SSID	ssj0064172
Score	2.3967314
Snippet	The radiative properties of black carbon (BC) are significantly influenced by its mixing state. The single-particle soot photometer (SP2) is a widely...
SourceID	doaj proquest gale crossref
SourceType	Open Website Aggregation Database Index Database
StartPage	1149
SubjectTerms	Aerosols Algorithms Analysis Black carbon Carbon Data analysis Data mining Data processing Decision making Ensemble learning Lasers Leading edges Learning algorithms Light Machine learning Optical properties Particle size Photometers Physical properties Protective coatings Radiation Real time Sensors Signal to noise ratio
SummonAdditionalLinks	– databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1NT9wwELWqVQ-9IFpaEViQhaqiHiLWThzbR0CsoFJRxYfEzbIdZ1lpd4Oy2ao_nxkni9hDxYVjnDnEM7bnzWT8hpDvkmfeacvSAP4PUzcidVmpUunR-ZYO1oiPzSbk9bV6eNB_XrX6wpqwjh64U9yJLl2ofCmDtDlAZa2yECwPghXOSVZFaDSSeh1MdWdwkbPYtgnZ2pBlj3U_KAGt5Cd23qYMIicIDWCJYIvsVw4p8vb_73SOLme8TbZ6rEhPu2_8TD6ExReS_AaYWzcxG05_0PPZFDBnfNohv5A1I-a_qJ1Naoj7H-e0rqjDLB31tnHwZj79B-6KxptEFJ1YSXE0FlUG2neRmHwl9-OLu_PLtG-WkHpAPG1aOl1aKaqQ85F11nsJyE8VwuceIF_QtnSgJqtyLr0Ary9DpYIGeQzIlKqyb2SwqBdhl1APUFZy5wKXLBcZh3NIWi6FZ4WQLtiE_FxrzDx1nBgGYgnUrgHtGqbwrrM2qN2EnKFKX-SQzToOgI1Nb2Pzlo0TcoQGMchXscCCmIldLZfm6vbGnCquFXLojRJy3AtVddtYb_v7BTAnpLjakBxuSMKG8puv13Y3_YZemgwjNwFRO997jxntk0-onVjMVgzJoG1W4YB89H_b6bI5jGv5GW6W94c priority: 102 providerName: Directory of Open Access Journals – databaseName: Advanced Technologies & Aerospace Database dbid: P5Z link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELZo6aGXlqfYPpCFEIhD1LUTx_apaisqQKKqeEgVF8uvLCt1N22SVvz8zni90D3AhWPiURJnJjPfTOxvCHkteemdtqyIEP-wdCMKVwZVSI_BNziwEZ-aTcizM3Vxoc9zwa3PyyqXPjE56tB6rJEflAidBaRN_PDqusCuUfh3NbfQWCMPkSUBWzecix9LT1xXLDVvQs425Npji9-UgFmqAzsbCgb5EyQIYCjYKPteWErs_X_z0SnwnG7_7yM_IlsZctKjhY08Jg_i_AkZfQa03HapqE7f0JPLKUDXdPSUfELyjVRGo_ZyAlccfs5o21CHxT7qbedgZDb9BVGPpg1JFGNhoHg2rc2MNDejmDwj30_ffzv5UOSeC4WHZx2K4HSwUjSx4mPrrPcSAKSqha88IMeobXBjqa2quPQCwIOMjYoa5DGvU6opn5P1eTuPLwj1gIgldy5yySpRcnBn0nIpPKuFdNGOyLvlKzdXC2oNAykJqseAegxTuGVaG1TPiByjTn7LISl2OtF2E5O_MaODi40PMkpbQValVRmj5VGw2jnJGrjhK9SoQdqLOa6rmdibvjcfv34xR4prhVR84xF5m4Waduist3mbAswJmbJWJPdWJOG79KvDS6sw2S_05o9J7Px7eJds4rzTard6j6wP3U3cJxv-dpj23ctk5nf0nwP5 priority: 102 providerName: ProQuest
Title	Inversion algorithm of black carbon mixing state based on machine learning
URI	https://www.proquest.com/docview/3174355232 https://doaj.org/article/9dbefcd7e7a4428983eea2e516bb71fa
Volume	18
WOSCitedRecordID	wos001438020800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAGF databaseName: Copernicus Publications customDbUrl: eissn: 1867-8548 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0064172 issn: 1867-8548 databaseCode: RKB dateStart: 20080101 isFulltext: true titleUrlDefault: http://publications.copernicus.org/open-access_journals/open_access_journals_a_z.html providerName: Copernicus Gesellschaft – providerCode: PRVAON databaseName: Directory of Open Access Journals customDbUrl: eissn: 1867-8548 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0064172 issn: 1867-8548 databaseCode: DOA dateStart: 20080101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1867-8548 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0064172 issn: 1867-8548 databaseCode: P5Z dateStart: 20100501 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Continental Europe Database customDbUrl: eissn: 1867-8548 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0064172 issn: 1867-8548 databaseCode: BFMQW dateStart: 20100501 isFulltext: true titleUrlDefault: https://search.proquest.com/conteurope providerName: ProQuest – providerCode: PRVPQU databaseName: Earth, Atmospheric & Aquatic Science Database customDbUrl: eissn: 1867-8548 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0064172 issn: 1867-8548 databaseCode: PCBAR dateStart: 20100501 isFulltext: true titleUrlDefault: https://search.proquest.com/eaasdb providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1867-8548 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0064172 issn: 1867-8548 databaseCode: BENPR dateStart: 20100501 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content Database customDbUrl: eissn: 1867-8548 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0064172 issn: 1867-8548 databaseCode: PIMPY dateStart: 20100501 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1La9wwEBYhzaGXpk_qNl1ECQk9mKxky5KOSUhIAlnMNoW0FyHJ8nYhuw5ep_Tnd0Z2SvZQekguBstjsEaP-WY8-oaQXckz77RlaQD7h6EbkbqsUqn0aHwrB3PEx2ITcjJR19e6fFDqC3PCenrgXnEHunKh9pUM0uYAlbXKQrA8CFY4J1mN0AimIS7JKdZw6_fgImexbBOytSHLHut_UAJayQ_soksZeE7gGsAUwRLZDwxS5O3_1-4cTc7p9iM-9iV5MeBMeti_8opshOVrklwCRG7aGEmne_T4Zg54Nd69IRfIuBFjZ9TezJp23v1c0KamDiN81NvWwZPF_DeYOhpPIVE0gBXF1piQGehQgWL2lnw7Pbk6PkuHQgupB7TUpZXTlZWiDjkfW2e9l4AaVSF87gEuBm0rN5baqpxLLwAxyFCroEEenTml6uwd2Vw2y_CeUA8wWHLnApcsFxmHPUxaLoVnhZAu2IR8ude2ue35NAz4ITgyBkbGMIXnpLXBkUnIEWr4rxwyYccGULkZVG7-p_KEfMbBNMh1scRkmpm9W63M-depOVRcK-TfGydkfxCqm6613g5nE6BPSI-1JrmzJgmL0a8_vp8zZtgMViZDr0-Ax88_PEWPPpLnqJ2YCFfskM2uvQufyJb_1c1X7Yg8OzqZlNNRDC7AtRQ_oK08vyy_j-Ia-QN78w_W
linkProvider	Copernicus Gesellschaft
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VLRJceCO2FLAQD3GIunEedg4IlULVpd3VCorUnoztONuVupuSpDz-FL-RGW8C7AFuPXCMPXJk-_O87JkBeCJ4ZE2mw8Ch_CPXTRKYKJeBsCR8c4MYsb7YhBiP5dFRNlmDH10sDD2r7HiiZ9R5aclHvhWR6pyg2cRfnX0OqGoU3a52JTSWsNh337-iyVa_HL7B_X3K-e7bw529oK0qEFgcoAlyk-VaJIWL-UAbba1AFUmmiY0t6kYu07kZiEzLmAuboHgUrpAuQ3qyXKQsIhz3EqzHBPYerE-Go8lxx_vTOPTloihLHGX3C5cXo6glxVt63gQhWmxokiA0qTT3H4LQ1wv4m1Twom73-v-2SDfgWqtUs-3lKbgJa25xC_ojtAfKyl8bsGds53SGyrn_ug3vKL2IdxQyfTrFGTQnc1YWzJA7k1ldGeyZz76hXGc-5IqRtM8ZtfrXp4615Tamd-DjhUztLvQW5cLdA2ZR5xfcGMdFGCcRR4YtNBeJDdNEGKf78KLbYnW2TB6i0OgiOCiEgwolBYVniuDQh9eEgV90lPbbN5TVVLVcRGW5cYXNhRMa8SczGTmnuUvC1BgRFvjDx4QgRYk9FvRyaKrP61oNP7xX25JnkpINDvrwvCUqyqbSVreBGDgnygW2Qrm5Qomcx652dyhULeer1W8Ibvy7-xFc2TscHaiD4Xj_PlylNfBv-9JN6DXVuXsAl-2XZlZXD9tDxuDTRUP2J9kuYsk
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VLUK98EYsFLAQD3GIduM87BwQ6oMVS-mqPCp6M7bjbFfqbkqS8vhr_DpmvAmwB7j1wDH2yJHtzzPz-TED8EjwyJpMh4FD-0dbN0lgolwGwpLxzQ1ixPpkE2IykUdH2cEa_OjewtC1yk4nekWdl5b2yAcRuc4J0iY-KNprEQe7oxennwPKIEUnrV06jSVE9tz3r0jf6ufjXZzrx5yPXn7YeRW0GQYCi401QW6yXIukcDEfaqOtFeguyTSxsUU_yWU6N0ORaRlzYRM0lcIV0mUoTyxGyiLCdi_AukxTOezB-vZo_-3Hzg6kcehTR1HEOIr0Fy4PSdFjigd63gQhsjekJwhTStP9h1H0uQP-ZiG82Rtd-Z8H7Cpcbp1ttrVcHddgzS2uQ38feUJZ-eME9oTtnMzQafdfN-A1hR3xG4hMn0yxB83xnJUFM7TNyayuDNbMZ9_Q3jP_FIuRF5AzKvW3Uh1r03BMb8LhuXTtFvQW5cLdBmaRCwhujOMijJOIoyIXmovEhmkijNN9eNZNtzpdBhVRSMYIGgqhoUJJj8UzRdDowzbh4ZcchQP3BWU1Va12UVluXGFz4YSOkU9mMnJOc5eEqTEiLPCHDwlNigJ-LAgJU31W12r8_p3akjyTFIRw2IenrVBRNpW2un2ggX2iGGErkpsrkqiR7Gp1h0jVasRa_YbjnX9XP4BLiFP1ZjzZuwsbNAT-yl-6Cb2mOnP34KL90szq6n673hh8Om_E_gRWlGtp
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Inversion+algorithm+of+black+carbon+mixing+state+based+on+machine+learning&rft.jtitle=Atmospheric+measurement+techniques&rft.au=Tian%2C+Zeyuan&rft.au=Wang%2C+Jiandong&rft.au=Wang%2C+Jiaping&rft.au=Liu%2C+Chao&rft.date=2025-03-06&rft.issn=1867-8548&rft.eissn=1867-8548&rft.volume=18&rft.issue=5&rft.spage=1149&rft.epage=1162&rft_id=info:doi/10.5194%2Famt-18-1149-2025&rft.externalDBID=n%2Fa&rft.externalDocID=10_5194_amt_18_1149_2025
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1867-8548&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1867-8548&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1867-8548&client=summon