Detecting plumes in mobile air quality monitoring time series with density-based spatial clustering of applications with noise

Mobile monitoring is becoming an increasingly popular technique to assess air pollution on fine spatial scales, but methods to determine specific source contributions to measured pollutants are sorely needed. One approach is to isolate plumes from mobile monitoring time series and analyze them separ...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Atmospheric measurement techniques Ročník 16; číslo 14; s. 3547 - 3559
Hlavní autoři: Actkinson, Blake, Griffin, Robert J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Katlenburg-Lindau Copernicus GmbH 25.07.2023
Copernicus Publications
Témata:
Air monitoring
Air pollution
Air quality
Algorithms
Anomalies
Censuses
Clustering
Datasets
Density
Emission analysis
Environmental monitoring
Global positioning systems
GPS
Land use
Machine learning
Methods
Monitoring
Nitrogen dioxide
Noise monitoring
Outdoor air quality
Plumes
Pollutants
Spatial variations
Technology application
Time series
Unsupervised learning
Vehicle emissions
ISSN:1867-8548, 1867-1381, 1867-8548
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Abstract Mobile monitoring is becoming an increasingly popular technique to assess air pollution on fine spatial scales, but methods to determine specific source contributions to measured pollutants are sorely needed. One approach is to isolate plumes from mobile monitoring time series and analyze them separately, but methods that are suitable for large mobile monitoring time series are lacking. Here we discuss a novel method used to detect and isolate plumes from an extensive mobile monitoring data set. The new method relies on density-based spatial clustering of applications with noise (DBSCAN), an unsupervised machine learning technique. The new method systematically runs DBSCAN on mobile monitoring time series by day and identifies a subset of points as anomalies for further analysis. When applied to a mobile monitoring data set collected in Houston, Texas, analyzed anomalies reveal patterns associated with different types of vehicle emission profiles. We observe spatial differences in these patterns and reveal striking disparities by census tract. These results can be used to inform stakeholders of spatial variations in emission profiles not obvious using data from stationary monitors alone.
AbstractList Mobile monitoring is becoming an increasingly popular technique to assess air pollution on fine spatial scales, but methods to determine specific source contributions to measured pollutants are sorely needed. One approach is to isolate plumes from mobile monitoring time series and analyze them separately, but methods that are suitable for large mobile monitoring time series are lacking. Here we discuss a novel method used to detect and isolate plumes from an extensive mobile monitoring data set. The new method relies on density-based spatial clustering of applications with noise (DBSCAN), an unsupervised machine learning technique. The new method systematically runs DBSCAN on mobile monitoring time series by day and identifies a subset of points as anomalies for further analysis. When applied to a mobile monitoring data set collected in Houston, Texas, analyzed anomalies reveal patterns associated with different types of vehicle emission profiles. We observe spatial differences in these patterns and reveal striking disparities by census tract. These results can be used to inform stakeholders of spatial variations in emission profiles not obvious using data from stationary monitors alone.
Mobile monitoring is becoming an increasingly popular technique to assess air pollution on fine spatial scales, but methods to determine specific source contributions to measured pollutants are sorely needed. One approach is to isolate plumes from mobile monitoring time series and analyze them separately, but methods that are suitable for large mobile monitoring time series are lacking. Here we discuss a novel method used to detect and isolate plumes from an extensive mobile monitoring data set. The new method relies on density-based spatial clustering of applications with noise (DBSCAN), an unsupervised machine learning technique. The new method systematically runs DBSCAN on mobile monitoring time series by day and identifies a subset of points as anomalies for further analysis. When applied to a mobile monitoring data set collected in Houston, Texas, analyzed anomalies reveal patterns associated with different types of vehicle emission profiles. We observe spatial differences in these patterns and reveal striking disparities by census tract. These results can be used to inform stakeholders of spatial variations in emission profiles not obvious using data from stationary monitors alone.
Audience Academic
Author Actkinson, Blake
Griffin, Robert J.
Author_xml – sequence: 1
  givenname: Blake
  orcidid: 0000-0002-4180-0990
  surname: Actkinson
  fullname: Actkinson, Blake
– sequence: 2
  givenname: Robert J.
  surname: Griffin
  fullname: Griffin, Robert J.
BookMark eNptkktr3DAUhU1JoUnafZeCrrpwqrflZUhfA4FCHmshS9dTDbbkSDJtNv3t1WRC24GihcTVdw73cs9ZcxJigKZ5S_CFID3_YObSEtkywbuWYspeNKdEya5VgquTf96vmrOcdxhLTjp62vz6CAVs8WGLlmmdISMf0BwHPwEyPqGH1Uy-PNZS8CWmPVf8DChD8hX-4ct35CDkyrSDyeBQXkzxZkJ2WnOBJ0UckVmWydv6E8OzKkSf4XXzcjRThjfP93lz__nT3dXX9vrbl83V5XVrGVeslUpZp8hgOO3o0FlwXNqOghj5SLh03BEYjBV2FJINgoq-HzgGVqd0EoRj583m4Oui2ekl-dmkRx2N10-FmLbapOLtBFoBdsRJS-jAOCFc4R5zRwmonlk-qOr17uC1pPiwQi56F9cUavuaKk5oJwkmf6mtqaY-jLEkY2efrb7shOJYMdlX6uI_VD0OZm_rhse6iGPB-yNBZQr8LFuz5qw3tzfHLD6wNsWcE4x_BidY70Oja2g0kXofGr0PDfsNYua2lA
Cites_doi 10.1016/j.scitotenv.2011.12.002
10.18637/jss.v091.i01
10.32614/RJ-2018-009
10.1021/acsestengg.0c00167
10.5194/amt-2023-50
10.1021/es301936c
10.1016/j.atmosenv.2016.12.037
10.1016/j.apr.2020.10.020
10.5194/amt-14-5809-2021
10.1080/10473289.2011.595981
10.1021/acs.est.8b02977
10.1016/j.atmosenv.2017.04.032
10.5194/acp-18-16325-2018
10.1021/acs.est.0c05572
10.1021/acs.est.0c01864
10.1021/acs.est.9b05523
10.1016/j.jaerosci.2020.105704
10.1021/es402875u
10.5194/amt-5-1443-2012
10.1021/acs.est.8b03395
ContentType Journal Article
Copyright COPYRIGHT 2023 Copernicus GmbH
2023. 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 2023 Copernicus GmbH
– notice: 2023. 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-16-3547-2023
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
ProQuest Central UK/Ireland
Advanced Technologies & Computer Science Collection
ProQuest Central Essentials - QC
ProQuest Central
Continental Europe Database
Technology Collection
Natural Science Collection
Earth, Atmospheric & Aquatic Science Collection
Environmental Sciences and Pollution Management
ProQuest One Community College
ProQuest Central Korea
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 (New)
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

CrossRef
Publicly Available Content Database
Database_xml – sequence: 1
  dbid: DOA
  name: DOAJ 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 3559
ExternalDocumentID oai_doaj_org_article_8e0d1d6c12b3411480904d21e893c4b8
A758408369
10_5194_amt_16_3547_2023
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-c3483-688cd81ba4272b7ced46c72e5f4f146d4d1ebac5cf563b52599b40e3064d6e5d3
IEDL.DBID RKB
ISSN 1867-8548
1867-1381
IngestDate Tue Oct 14 18:58:33 EDT 2025
Fri Jul 25 22:54:42 EDT 2025
Tue Nov 11 10:37:49 EST 2025
Tue Nov 04 18:42:54 EST 2025
Thu Nov 13 16:43:07 EST 2025
Sat Nov 29 04:52:26 EST 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 14
Language English
License https://creativecommons.org/licenses/by/4.0
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c3483-688cd81ba4272b7ced46c72e5f4f146d4d1ebac5cf563b52599b40e3064d6e5d3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ORCID 0000-0002-4180-0990
OpenAccessLink https://doaj.org/article/8e0d1d6c12b3411480904d21e893c4b8
PQID 2841276101
PQPubID 105742
PageCount 13
ParticipantIDs doaj_primary_oai_doaj_org_article_8e0d1d6c12b3411480904d21e893c4b8
proquest_journals_2841276101
gale_infotracmisc_A758408369
gale_infotracacademiconefile_A758408369
gale_incontextgauss_ISR_A758408369
crossref_primary_10_5194_amt_16_3547_2023
PublicationCentury 2000
PublicationDate 2023-07-25
PublicationDateYYYYMMDD 2023-07-25
PublicationDate_xml – month: 07
  year: 2023
  text: 2023-07-25
  day: 25
PublicationDecade 2020
PublicationPlace Katlenburg-Lindau
PublicationPlace_xml – name: Katlenburg-Lindau
PublicationTitle Atmospheric measurement techniques
PublicationYear 2023
Publisher Copernicus GmbH
Copernicus Publications
Publisher_xml – name: Copernicus GmbH
– name: Copernicus Publications
References ref13
ref35
ref12
ref34
ref15
ref37
ref14
ref36
ref31
ref30
ref11
ref33
ref10
ref32
ref2
ref1
ref17
ref39
ref16
ref38
ref19
ref18
ref24
ref23
ref26
ref25
ref20
ref22
ref21
ref28
ref27
ref29
ref8
ref7
ref9
ref4
ref3
ref6
ref5
References_xml – ident: ref16
  doi: 10.1016/j.scitotenv.2011.12.002
– ident: ref18
  doi: 10.18637/jss.v091.i01
– ident: ref37
– ident: ref27
  doi: 10.32614/RJ-2018-009
– ident: ref35
  doi: 10.1021/acsestengg.0c00167
– ident: ref1
– ident: ref4
  doi: 10.5194/amt-2023-50
– ident: ref3
– ident: ref9
  doi: 10.1021/es301936c
– ident: ref22
  doi: 10.1016/j.atmosenv.2016.12.037
– ident: ref7
– ident: ref23
  doi: 10.1016/j.apr.2020.10.020
– ident: ref20
– ident: ref5
  doi: 10.5194/amt-14-5809-2021
– ident: ref26
  doi: 10.1080/10473289.2011.595981
– ident: ref29
  doi: 10.1021/acs.est.8b02977
– ident: ref19
– ident: ref32
– ident: ref17
– ident: ref34
– ident: ref15
– ident: ref13
– ident: ref36
– ident: ref2
– ident: ref38
– ident: ref39
  doi: 10.1016/j.atmosenv.2017.04.032
– ident: ref6
– ident: ref33
  doi: 10.5194/acp-18-16325-2018
– ident: ref30
  doi: 10.1021/acs.est.0c05572
– ident: ref11
  doi: 10.1021/acs.est.0c01864
– ident: ref25
  doi: 10.1021/acs.est.9b05523
– ident: ref28
– ident: ref21
– ident: ref12
  doi: 10.1016/j.jaerosci.2020.105704
– ident: ref10
  doi: 10.1021/es402875u
– ident: ref14
  doi: 10.5194/amt-5-1443-2012
– ident: ref8
– ident: ref24
  doi: 10.1021/acs.est.8b03395
– ident: ref31
SSID ssj0064172
Score 2.3372638
Snippet Mobile monitoring is becoming an increasingly popular technique to assess air pollution on fine spatial scales, but methods to determine specific source...
Mobile monitoring is becoming an increasingly popular technique to assess air pollution on fine spatial scales, but methods to determine specific source...
SourceID doaj
proquest
gale
crossref
SourceType Open Website
Aggregation Database
Index Database
StartPage 3547
SubjectTerms Air monitoring
Air pollution
Air quality
Algorithms
Anomalies
Censuses
Clustering
Datasets
Density
Emission analysis
Environmental monitoring
Global positioning systems
GPS
Land use
Machine learning
Methods
Monitoring
Nitrogen dioxide
Noise monitoring
Outdoor air quality
Plumes
Pollutants
Spatial variations
Technology application
Time series
Unsupervised learning
Vehicle emissions
SummonAdditionalLinks – databaseName: DOAJ Directory of Open Access Journals
  dbid: DOA
  link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3Li9YwEA-yePAiPrHurgQRxUPYJs2jPa6rix5cxAfsLeRVKey2S9tP2It_-86k_WS_g3jx2HQCSWYyryS_IeSVjlXjVDDMSO2Z1BDuOC8FS1qUKQltdHC52IQ5O6vPz5svt0p94Z2wBR54WbijOpWRRx248KBwwXkvm1JGwRMY2iB9fuZbmmYbTC06WEueyzYhWhui7PHlgBK8FXnkLmfGNasUQhSUotoxSBm3_2_aOZuc0wfk_uor0uNljA_JndQ_IsVncHOHMWfD6Wt6ctGBz5m_HpPf7xOeCYA1oleodCba9fRy8LDzqetGurygvIYm3MiY0aNYW56iGAIx5mRpxBvt8zVD8xbphBeuYQzhYoOICthjaOntU--lVz90U3pCfpx--H7yka0FFlioZF0xXdchgt_qpDDCm5Ci1MGIpFrZggaNMvLkXVChVbryCiKlxssyYdASdVKxekr2-qFPzwiFCNt75YwKtZTBVK4VxvmonddNbJUqyNvtKturBUfDQvyBHLHAEcu1RY5Y5EhB3iEb_tAhAnZuALmwq1zYf8lFQV4iEy1iXPR4iean20yT_fTtqz2GGEkiKndTkDcrUTvMowtufZMAc0JYrB3Kgx1K2IRh9_dWVuyqBCYLlp8LA_4pf_4_ZrRP7uHqYGJZqAOyN4-bdEjuhl9zN40vsvzfABH_CK4
  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/eLvHCXMwpV1Jb9UwELagcODCjhooyEIIxMFq7HhJTqgUKjhQVSxSxcXylipSmzySPKRe-O3MJHnAO8CFY5KJZGvG3yy2vyHkmY5F5VQwzEjtmdSQ7jgvBUta5CkJbXRwU7MJc3xcnp5WJ0vBbViOVW4wcQLq2AWske8DjHIBOXfOX62-MewahburSwuNq-QasiRg64YT9XWDxFryqXkTcrYh1x6ftykhZpH77mJkXLNCIVFBLoottzSx9_8NoyfHc3Trf4d8m9xcQk56MNvIHXIltXdJ9gGi5a6fiur0OT08byB0nZ7ukR9vEm4tgFOjK8SugTYtveg8AAh1TU_ni5iX8ArxAAuDFFvUU7RmEMbSLo14MH68ZOglIx3w3DaMIZyvkZgB_-hq-ufm-fxX2zVDuk--HL39fPiOLX0aWChkWTBdliFC-OukMMKbkKLUwYikalkDEEcZefIuqFArXXgFCVflZZ4w94k6qVg8IDtt16ZdQiFR9145o0IpZTCFq4VxPmrndRVrpTLycqMmu5rpOCykMahSCyq1XFtUqUWVZuQ16vGXHBJpTy-6_swu69KWKY886sCFB38OuWFe5TIKniCOC9KXGXmKVmCRKqPFszhnbj0M9v2nj_YAUi2J5N5VRl4sQnU39i645WoDzAnZtbYk97YkYS2H7c8bS7ILlgz2txk9_PfnR-QGzhsrz0LtkZ2xX6fH5Hr4PjZD_2RaGj8BlWEX0g
  priority: 102
  providerName: ProQuest
Title Detecting plumes in mobile air quality monitoring time series with density-based spatial clustering of applications with noise
URI https://www.proquest.com/docview/2841276101
https://doaj.org/article/8e0d1d6c12b3411480904d21e893c4b8
Volume 16
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: DOAJ 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/eLvHCXMwrV1La9VAFB6kunDjW4zWyyCiuAjNTOaRLNvaYhe9hKtCdTPMKyXQJiW5V-jG3-45SSq9C3Ghm0AmZyDJN3NeM_MdQt6qkJdWep1qoVwqFIQ71gmeRsWzGLnSytux2IReLouzs7K6VeoL94RN9MDTj9srYhZYUJ5xBwoXnPeszETgLIKh9cLhMV8YhjglV1jDbdLBSrCxbBOytSHLHpsWKMFbEXv2cp0yleYSKQoynm8ZpJG3_0_aeTQ5xw__4WUfkQezn0n3py6PyZ3YPiHJKbjIXT9m0uk7enjRgL863j0lPz9GXE8AS0avUGENtGnpZedAa1Db9HQ6fXkNTagEMBtIsS49xSEMwpjPpQF3w6-vUzSNgQ64WRvewV9skI0Be3Q1vb1iPvVqu2aIz8jX46Mvh5_SuThD6nNR5KkqCh_A57WCa-60j0Eor3mUtahB-wYRWHTWS19LlTsJUVbpRBYx4AkqypA_Jztt18YXhEJ07py0WvpCCK9zW3NtXVDWqTLUUibkww1C5mri4DAQuyCaBtA0TBlE0yCaCTlAVH7LIXv22AAwmRkm8zeYEvIGB4BBfowWN-Cc280wmJPPK7MP8ZVARu8yIe9nobpb99bb-TwDfBNSam1J7m5JwgT2249vxpmZFchgwGtgXINvy17-jy96Re7j38GkNJe7ZGfdb-Jrcs__WDdDvyB3D46W1WoxJiTgWsnv0FadnFbfFuO8-gWoex-J
linkProvider Copernicus Gesellschaft
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwELbKFgkuvBELBSzEQxyiJo7jJAeESkvVVburCorUnoxfqSJ1kyXJgvbCT-I3MpMHsAe49cAxySRKnM-fZ-zxN4Q8FzZMVWRiL-ZCe1xAuKM0Z54TzHeOiVgY1RabiGez5PQ0Pd4gP4a9MJhWOXBiS9S2NDhHvg00GjCIuf3g7eKLh1WjcHV1KKHRweLQrb5ByFa_mezB_33B2P77k90Dr68q4JmQJ6EnksRYcNYUZzHTsXGWCxMzF2U8A9qw3AZOKxOZLBKhjiA8SDX3HXrqVrjIhvDcK2STI9hHZPN4Mj0-G7hf8KAtF4UqcajuF3QLo-Al8W01b7xAeGGE0gg-C9cGwrZewN9GhXao27_5vzXSLXKjd6rpTtcLbpMNV9wh4ynEA2XVLhvQl3T3IgfnvD26S77vOVw8gWGbLpCda5oXdF5qoEiq8op2W01XcAoZD6c-aZPPHcX-CsY4eU0tpv43Kw_9AEtrzEyHdzAXS5SewDvKjP6ZHtDdVZR57e6RT5fSHPfJqCgL94DQmDOtIxVHJuHcxKHKWKy0FUqL1GZRNCavB1jIRSc4IiFQQwhJgJAMhEQISYTQmLxD3PyyQ6nw9kRZncueeWTifBtYYQKmwWOB6NdPfW5Z4MBTNVwnY_IMUSdRDKTAbKNztaxrOfn4Qe5AMMlRvjwdk1e9UVY2lTKq37wB34T6YWuWW2uWwFZm_fKAXNmzZS1_w_bhvy8_JdcOTqZH8mgyO3xErmMb4Dw7i7bIqKmW7jG5ar42eV096TsmJZ8vG-Y_AZSJdlc
linkToPdf http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Jj5RAFK6MPcZ4cTe2jloxLvFAGoqigIMxs9ixM05n3OLcytqYkExDC7SmL_4wf53vsah90NscPAIPAsVX33uvlu8R8ljYMFWRib2YC-1xAemO0px5TjDfOSZiYVRbbCKez5OTk_R4i_wY9sLgssqBE1uitqXBMfIJ0GjAIOf2g0nWL4s4Ppi-XH7xsIIUzrQO5TQ6iBy69TdI3-oXswP4108Ym776sP_a6ysMeCbkSeiJJDEWAjfFWcx0bJzlwsTMRRnPgEIst4HTykQmi0SoI0gVUs19h1G7FS6yITz3AtlOhEj8Ednemx69_TT4AcGDtnQUKsah0l_QTZJCxMQnatF4gfDCCGUSfBZuOMW2dsDfPETr9qZX_-cGu0au9ME23e16x3Wy5YobZHwEeUJZtdMJ9CndP8shaG-PbpLvBw4nVcCd0yWydk3zgi5KDdRJVV7RbgvqGk4hE-KQKG3yhaPYj8EYB7WpxS0BzdrD-MDSGleswzuYsxVKUuAdZUb_XDbQ3VWUee1ukY_n0hy3yagoC3eH0JgzrSMVRybh3MShylistBVKi9RmUTQmzweIyGUnRCIhgUM4SYCTDIREOEmE05jsIYZ-2aGEeHuirE5lz0gycb4NrDAB0xDJQFbspz63LHAQwRqukzF5hAiUKBJSIHpO1aqu5ez9O7kLSSZHWfN0TJ71RlnZVMqoflMHfBPqim1Y7mxYAouZzcsDimXPorX8DeG7_778kFwCbMs3s_nhPXIZmwCH31m0Q0ZNtXL3yUXztcnr6kHfRyn5fN4o_wnW-n73
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=Detecting+plumes+in+mobile+air+quality+monitoring+time+series+with+density-based+spatial+clustering+of+applications+with+noise&rft.jtitle=Atmospheric+measurement+techniques&rft.au=Actkinson%2C+Blake&rft.au=Griffin%2C+Robert+J&rft.date=2023-07-25&rft.pub=Copernicus+GmbH&rft.issn=1867-1381&rft.volume=16&rft.issue=14&rft.spage=3547&rft_id=info:doi/10.5194%2Famt-16-3547-2023&rft.externalDBID=ISR&rft.externalDocID=A758408369
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