$ PM_{2.5} $ mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models
Abstract An uncertainty in the relationship between aerosol optical depth (AOD) and fine particulate matter ($ PM_{2.5} $) comes from the uncertainty of AOD by aerosol models and the estimated surface reflectance, a mismatch in spatiotemporal resolution, integration of AOD and $ PM_{2.5} $ data, and...
Ausführliche Beschreibung
Autor*in: |
Chu, Hone-Jay [verfasserIn] |
---|
Format: |
Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2018 |
---|
Schlagwörter: |
---|
Anmerkung: |
© Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
---|
Übergeordnetes Werk: |
Enthalten in: Environmental science and pollution research - Springer Berlin Heidelberg, 1994, 26(2018), 2 vom: 20. Nov., Seite 1902-1910 |
---|---|
Übergeordnetes Werk: |
volume:26 ; year:2018 ; number:2 ; day:20 ; month:11 ; pages:1902-1910 |
Links: |
---|
DOI / URN: |
10.1007/s11356-018-3763-7 |
---|
Katalog-ID: |
OLC2040537384 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | OLC2040537384 | ||
003 | DE-627 | ||
005 | 20230606195044.0 | ||
007 | tu | ||
008 | 200820s2018 xx ||||| 00| ||eng c | ||
024 | 7 | |a 10.1007/s11356-018-3763-7 |2 doi | |
035 | |a (DE-627)OLC2040537384 | ||
035 | |a (DE-He213)s11356-018-3763-7-p | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | 4 | |a 570 |a 360 |a 333.7 |q VZ |
082 | 0 | 4 | |a 690 |a 333.7 |a 540 |q VZ |
084 | |a BIODIV |q DE-30 |2 fid | ||
100 | 1 | |a Chu, Hone-Jay |e verfasserin |4 aut | |
245 | 1 | 0 | |a $ PM_{2.5} $ mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models |
264 | 1 | |c 2018 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
338 | |a Band |b nc |2 rdacarrier | ||
500 | |a © Springer-Verlag GmbH Germany, part of Springer Nature 2018 | ||
520 | |a Abstract An uncertainty in the relationship between aerosol optical depth (AOD) and fine particulate matter ($ PM_{2.5} $) comes from the uncertainty of AOD by aerosol models and the estimated surface reflectance, a mismatch in spatiotemporal resolution, integration of AOD and $ PM_{2.5} $ data, and data modeling. In this study, an integrated geographically temporally weighted regression (GTWR) and RANdom SAmple Consensus (RANSAC) models, which provide fine goodness-of-fit between observed $ PM_{2.5} $ and AOD data, were used for mapping of $ PM_{2.5} $ over Taiwan for the year 2014. For this, dark target (DT) AOD observations at 3-km resolution ($ DT_{3K} $) only for high-quality assurance flag (QA = 3) were obtained from the scientific data set (SDS) “Optical_Depth_Land_And_Ocean”. AOD observations were also obtained from the merged DT and DB (deep blue) product ($ DTB_{3K} $) which was generated using the simplified merge scheme (SMS), i.e., using an average of the DT and DB highest quality AOD retrievals or the available one. The GTWR model integrated with RANSAC can use the effective sampling and fitting to overcome the estimation problem of AOD-$ PM_{2.5} $ with the uncertainty and outliers of observation data. Results showed that the model dealing with spatiotemporal heterogeneity and uncertainty is a powerful tool to infer patterns of $ PM_{2.5} $ from a RANSAC subset samples. Moreover, spatial variability and hotspot analysis were applied after $ PM_{2.5} $ mapping. The hotspot and spatial variability of $ PM_{2.5} $ maps can give us a summary of the spatiotemporal patterns of $ PM_{2.5} $ variations. | ||
650 | 4 | |a GTWR | |
650 | 4 | |a RANSAC | |
650 | 4 | |a PM | |
650 | 4 | |a AOD | |
650 | 4 | |a DTB | |
650 | 4 | |a Taiwan | |
700 | 1 | |a Bilal, Muhammad |0 (orcid)0000-0003-1022-3999 |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Environmental science and pollution research |d Springer Berlin Heidelberg, 1994 |g 26(2018), 2 vom: 20. Nov., Seite 1902-1910 |w (DE-627)171335805 |w (DE-600)1178791-0 |w (DE-576)038875101 |x 0944-1344 |7 nnns |
773 | 1 | 8 | |g volume:26 |g year:2018 |g number:2 |g day:20 |g month:11 |g pages:1902-1910 |
856 | 4 | 1 | |u https://doi.org/10.1007/s11356-018-3763-7 |z lizenzpflichtig |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_OLC | ||
912 | |a FID-BIODIV | ||
912 | |a SSG-OLC-UMW | ||
912 | |a SSG-OLC-ARC | ||
912 | |a SSG-OLC-TEC | ||
912 | |a SSG-OLC-CHE | ||
912 | |a SSG-OLC-FOR | ||
912 | |a SSG-OLC-DE-84 | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_252 | ||
912 | |a GBV_ILN_267 | ||
912 | |a GBV_ILN_2018 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4277 | ||
951 | |a AR | ||
952 | |d 26 |j 2018 |e 2 |b 20 |c 11 |h 1902-1910 |
author_variant |
h j c hjc m b mb |
---|---|
matchkey_str |
article:09441344:2018----::m2mpigsnitgaegorpialtmoalwihergesogwadad |
hierarchy_sort_str |
2018 |
publishDate |
2018 |
allfields |
10.1007/s11356-018-3763-7 doi (DE-627)OLC2040537384 (DE-He213)s11356-018-3763-7-p DE-627 ger DE-627 rakwb eng 570 360 333.7 VZ 690 333.7 540 VZ BIODIV DE-30 fid Chu, Hone-Jay verfasserin aut $ PM_{2.5} $ mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract An uncertainty in the relationship between aerosol optical depth (AOD) and fine particulate matter ($ PM_{2.5} $) comes from the uncertainty of AOD by aerosol models and the estimated surface reflectance, a mismatch in spatiotemporal resolution, integration of AOD and $ PM_{2.5} $ data, and data modeling. In this study, an integrated geographically temporally weighted regression (GTWR) and RANdom SAmple Consensus (RANSAC) models, which provide fine goodness-of-fit between observed $ PM_{2.5} $ and AOD data, were used for mapping of $ PM_{2.5} $ over Taiwan for the year 2014. For this, dark target (DT) AOD observations at 3-km resolution ($ DT_{3K} $) only for high-quality assurance flag (QA = 3) were obtained from the scientific data set (SDS) “Optical_Depth_Land_And_Ocean”. AOD observations were also obtained from the merged DT and DB (deep blue) product ($ DTB_{3K} $) which was generated using the simplified merge scheme (SMS), i.e., using an average of the DT and DB highest quality AOD retrievals or the available one. The GTWR model integrated with RANSAC can use the effective sampling and fitting to overcome the estimation problem of AOD-$ PM_{2.5} $ with the uncertainty and outliers of observation data. Results showed that the model dealing with spatiotemporal heterogeneity and uncertainty is a powerful tool to infer patterns of $ PM_{2.5} $ from a RANSAC subset samples. Moreover, spatial variability and hotspot analysis were applied after $ PM_{2.5} $ mapping. The hotspot and spatial variability of $ PM_{2.5} $ maps can give us a summary of the spatiotemporal patterns of $ PM_{2.5} $ variations. GTWR RANSAC PM AOD DTB Taiwan Bilal, Muhammad (orcid)0000-0003-1022-3999 aut Enthalten in Environmental science and pollution research Springer Berlin Heidelberg, 1994 26(2018), 2 vom: 20. Nov., Seite 1902-1910 (DE-627)171335805 (DE-600)1178791-0 (DE-576)038875101 0944-1344 nnns volume:26 year:2018 number:2 day:20 month:11 pages:1902-1910 https://doi.org/10.1007/s11356-018-3763-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-FOR SSG-OLC-DE-84 GBV_ILN_70 GBV_ILN_252 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4277 AR 26 2018 2 20 11 1902-1910 |
spelling |
10.1007/s11356-018-3763-7 doi (DE-627)OLC2040537384 (DE-He213)s11356-018-3763-7-p DE-627 ger DE-627 rakwb eng 570 360 333.7 VZ 690 333.7 540 VZ BIODIV DE-30 fid Chu, Hone-Jay verfasserin aut $ PM_{2.5} $ mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract An uncertainty in the relationship between aerosol optical depth (AOD) and fine particulate matter ($ PM_{2.5} $) comes from the uncertainty of AOD by aerosol models and the estimated surface reflectance, a mismatch in spatiotemporal resolution, integration of AOD and $ PM_{2.5} $ data, and data modeling. In this study, an integrated geographically temporally weighted regression (GTWR) and RANdom SAmple Consensus (RANSAC) models, which provide fine goodness-of-fit between observed $ PM_{2.5} $ and AOD data, were used for mapping of $ PM_{2.5} $ over Taiwan for the year 2014. For this, dark target (DT) AOD observations at 3-km resolution ($ DT_{3K} $) only for high-quality assurance flag (QA = 3) were obtained from the scientific data set (SDS) “Optical_Depth_Land_And_Ocean”. AOD observations were also obtained from the merged DT and DB (deep blue) product ($ DTB_{3K} $) which was generated using the simplified merge scheme (SMS), i.e., using an average of the DT and DB highest quality AOD retrievals or the available one. The GTWR model integrated with RANSAC can use the effective sampling and fitting to overcome the estimation problem of AOD-$ PM_{2.5} $ with the uncertainty and outliers of observation data. Results showed that the model dealing with spatiotemporal heterogeneity and uncertainty is a powerful tool to infer patterns of $ PM_{2.5} $ from a RANSAC subset samples. Moreover, spatial variability and hotspot analysis were applied after $ PM_{2.5} $ mapping. The hotspot and spatial variability of $ PM_{2.5} $ maps can give us a summary of the spatiotemporal patterns of $ PM_{2.5} $ variations. GTWR RANSAC PM AOD DTB Taiwan Bilal, Muhammad (orcid)0000-0003-1022-3999 aut Enthalten in Environmental science and pollution research Springer Berlin Heidelberg, 1994 26(2018), 2 vom: 20. Nov., Seite 1902-1910 (DE-627)171335805 (DE-600)1178791-0 (DE-576)038875101 0944-1344 nnns volume:26 year:2018 number:2 day:20 month:11 pages:1902-1910 https://doi.org/10.1007/s11356-018-3763-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-FOR SSG-OLC-DE-84 GBV_ILN_70 GBV_ILN_252 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4277 AR 26 2018 2 20 11 1902-1910 |
allfields_unstemmed |
10.1007/s11356-018-3763-7 doi (DE-627)OLC2040537384 (DE-He213)s11356-018-3763-7-p DE-627 ger DE-627 rakwb eng 570 360 333.7 VZ 690 333.7 540 VZ BIODIV DE-30 fid Chu, Hone-Jay verfasserin aut $ PM_{2.5} $ mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract An uncertainty in the relationship between aerosol optical depth (AOD) and fine particulate matter ($ PM_{2.5} $) comes from the uncertainty of AOD by aerosol models and the estimated surface reflectance, a mismatch in spatiotemporal resolution, integration of AOD and $ PM_{2.5} $ data, and data modeling. In this study, an integrated geographically temporally weighted regression (GTWR) and RANdom SAmple Consensus (RANSAC) models, which provide fine goodness-of-fit between observed $ PM_{2.5} $ and AOD data, were used for mapping of $ PM_{2.5} $ over Taiwan for the year 2014. For this, dark target (DT) AOD observations at 3-km resolution ($ DT_{3K} $) only for high-quality assurance flag (QA = 3) were obtained from the scientific data set (SDS) “Optical_Depth_Land_And_Ocean”. AOD observations were also obtained from the merged DT and DB (deep blue) product ($ DTB_{3K} $) which was generated using the simplified merge scheme (SMS), i.e., using an average of the DT and DB highest quality AOD retrievals or the available one. The GTWR model integrated with RANSAC can use the effective sampling and fitting to overcome the estimation problem of AOD-$ PM_{2.5} $ with the uncertainty and outliers of observation data. Results showed that the model dealing with spatiotemporal heterogeneity and uncertainty is a powerful tool to infer patterns of $ PM_{2.5} $ from a RANSAC subset samples. Moreover, spatial variability and hotspot analysis were applied after $ PM_{2.5} $ mapping. The hotspot and spatial variability of $ PM_{2.5} $ maps can give us a summary of the spatiotemporal patterns of $ PM_{2.5} $ variations. GTWR RANSAC PM AOD DTB Taiwan Bilal, Muhammad (orcid)0000-0003-1022-3999 aut Enthalten in Environmental science and pollution research Springer Berlin Heidelberg, 1994 26(2018), 2 vom: 20. Nov., Seite 1902-1910 (DE-627)171335805 (DE-600)1178791-0 (DE-576)038875101 0944-1344 nnns volume:26 year:2018 number:2 day:20 month:11 pages:1902-1910 https://doi.org/10.1007/s11356-018-3763-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-FOR SSG-OLC-DE-84 GBV_ILN_70 GBV_ILN_252 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4277 AR 26 2018 2 20 11 1902-1910 |
allfieldsGer |
10.1007/s11356-018-3763-7 doi (DE-627)OLC2040537384 (DE-He213)s11356-018-3763-7-p DE-627 ger DE-627 rakwb eng 570 360 333.7 VZ 690 333.7 540 VZ BIODIV DE-30 fid Chu, Hone-Jay verfasserin aut $ PM_{2.5} $ mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract An uncertainty in the relationship between aerosol optical depth (AOD) and fine particulate matter ($ PM_{2.5} $) comes from the uncertainty of AOD by aerosol models and the estimated surface reflectance, a mismatch in spatiotemporal resolution, integration of AOD and $ PM_{2.5} $ data, and data modeling. In this study, an integrated geographically temporally weighted regression (GTWR) and RANdom SAmple Consensus (RANSAC) models, which provide fine goodness-of-fit between observed $ PM_{2.5} $ and AOD data, were used for mapping of $ PM_{2.5} $ over Taiwan for the year 2014. For this, dark target (DT) AOD observations at 3-km resolution ($ DT_{3K} $) only for high-quality assurance flag (QA = 3) were obtained from the scientific data set (SDS) “Optical_Depth_Land_And_Ocean”. AOD observations were also obtained from the merged DT and DB (deep blue) product ($ DTB_{3K} $) which was generated using the simplified merge scheme (SMS), i.e., using an average of the DT and DB highest quality AOD retrievals or the available one. The GTWR model integrated with RANSAC can use the effective sampling and fitting to overcome the estimation problem of AOD-$ PM_{2.5} $ with the uncertainty and outliers of observation data. Results showed that the model dealing with spatiotemporal heterogeneity and uncertainty is a powerful tool to infer patterns of $ PM_{2.5} $ from a RANSAC subset samples. Moreover, spatial variability and hotspot analysis were applied after $ PM_{2.5} $ mapping. The hotspot and spatial variability of $ PM_{2.5} $ maps can give us a summary of the spatiotemporal patterns of $ PM_{2.5} $ variations. GTWR RANSAC PM AOD DTB Taiwan Bilal, Muhammad (orcid)0000-0003-1022-3999 aut Enthalten in Environmental science and pollution research Springer Berlin Heidelberg, 1994 26(2018), 2 vom: 20. Nov., Seite 1902-1910 (DE-627)171335805 (DE-600)1178791-0 (DE-576)038875101 0944-1344 nnns volume:26 year:2018 number:2 day:20 month:11 pages:1902-1910 https://doi.org/10.1007/s11356-018-3763-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-FOR SSG-OLC-DE-84 GBV_ILN_70 GBV_ILN_252 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4277 AR 26 2018 2 20 11 1902-1910 |
allfieldsSound |
10.1007/s11356-018-3763-7 doi (DE-627)OLC2040537384 (DE-He213)s11356-018-3763-7-p DE-627 ger DE-627 rakwb eng 570 360 333.7 VZ 690 333.7 540 VZ BIODIV DE-30 fid Chu, Hone-Jay verfasserin aut $ PM_{2.5} $ mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract An uncertainty in the relationship between aerosol optical depth (AOD) and fine particulate matter ($ PM_{2.5} $) comes from the uncertainty of AOD by aerosol models and the estimated surface reflectance, a mismatch in spatiotemporal resolution, integration of AOD and $ PM_{2.5} $ data, and data modeling. In this study, an integrated geographically temporally weighted regression (GTWR) and RANdom SAmple Consensus (RANSAC) models, which provide fine goodness-of-fit between observed $ PM_{2.5} $ and AOD data, were used for mapping of $ PM_{2.5} $ over Taiwan for the year 2014. For this, dark target (DT) AOD observations at 3-km resolution ($ DT_{3K} $) only for high-quality assurance flag (QA = 3) were obtained from the scientific data set (SDS) “Optical_Depth_Land_And_Ocean”. AOD observations were also obtained from the merged DT and DB (deep blue) product ($ DTB_{3K} $) which was generated using the simplified merge scheme (SMS), i.e., using an average of the DT and DB highest quality AOD retrievals or the available one. The GTWR model integrated with RANSAC can use the effective sampling and fitting to overcome the estimation problem of AOD-$ PM_{2.5} $ with the uncertainty and outliers of observation data. Results showed that the model dealing with spatiotemporal heterogeneity and uncertainty is a powerful tool to infer patterns of $ PM_{2.5} $ from a RANSAC subset samples. Moreover, spatial variability and hotspot analysis were applied after $ PM_{2.5} $ mapping. The hotspot and spatial variability of $ PM_{2.5} $ maps can give us a summary of the spatiotemporal patterns of $ PM_{2.5} $ variations. GTWR RANSAC PM AOD DTB Taiwan Bilal, Muhammad (orcid)0000-0003-1022-3999 aut Enthalten in Environmental science and pollution research Springer Berlin Heidelberg, 1994 26(2018), 2 vom: 20. Nov., Seite 1902-1910 (DE-627)171335805 (DE-600)1178791-0 (DE-576)038875101 0944-1344 nnns volume:26 year:2018 number:2 day:20 month:11 pages:1902-1910 https://doi.org/10.1007/s11356-018-3763-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-FOR SSG-OLC-DE-84 GBV_ILN_70 GBV_ILN_252 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4277 AR 26 2018 2 20 11 1902-1910 |
language |
English |
source |
Enthalten in Environmental science and pollution research 26(2018), 2 vom: 20. Nov., Seite 1902-1910 volume:26 year:2018 number:2 day:20 month:11 pages:1902-1910 |
sourceStr |
Enthalten in Environmental science and pollution research 26(2018), 2 vom: 20. Nov., Seite 1902-1910 volume:26 year:2018 number:2 day:20 month:11 pages:1902-1910 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
GTWR RANSAC PM AOD DTB Taiwan |
dewey-raw |
570 |
isfreeaccess_bool |
false |
container_title |
Environmental science and pollution research |
authorswithroles_txt_mv |
Chu, Hone-Jay @@aut@@ Bilal, Muhammad @@aut@@ |
publishDateDaySort_date |
2018-11-20T00:00:00Z |
hierarchy_top_id |
171335805 |
dewey-sort |
3570 |
id |
OLC2040537384 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2040537384</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230606195044.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2018 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11356-018-3763-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2040537384</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11356-018-3763-7-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="a">360</subfield><subfield code="a">333.7</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">690</subfield><subfield code="a">333.7</subfield><subfield code="a">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BIODIV</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Chu, Hone-Jay</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">$ PM_{2.5} $ mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag GmbH Germany, part of Springer Nature 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract An uncertainty in the relationship between aerosol optical depth (AOD) and fine particulate matter ($ PM_{2.5} $) comes from the uncertainty of AOD by aerosol models and the estimated surface reflectance, a mismatch in spatiotemporal resolution, integration of AOD and $ PM_{2.5} $ data, and data modeling. In this study, an integrated geographically temporally weighted regression (GTWR) and RANdom SAmple Consensus (RANSAC) models, which provide fine goodness-of-fit between observed $ PM_{2.5} $ and AOD data, were used for mapping of $ PM_{2.5} $ over Taiwan for the year 2014. For this, dark target (DT) AOD observations at 3-km resolution ($ DT_{3K} $) only for high-quality assurance flag (QA = 3) were obtained from the scientific data set (SDS) “Optical_Depth_Land_And_Ocean”. AOD observations were also obtained from the merged DT and DB (deep blue) product ($ DTB_{3K} $) which was generated using the simplified merge scheme (SMS), i.e., using an average of the DT and DB highest quality AOD retrievals or the available one. The GTWR model integrated with RANSAC can use the effective sampling and fitting to overcome the estimation problem of AOD-$ PM_{2.5} $ with the uncertainty and outliers of observation data. Results showed that the model dealing with spatiotemporal heterogeneity and uncertainty is a powerful tool to infer patterns of $ PM_{2.5} $ from a RANSAC subset samples. Moreover, spatial variability and hotspot analysis were applied after $ PM_{2.5} $ mapping. The hotspot and spatial variability of $ PM_{2.5} $ maps can give us a summary of the spatiotemporal patterns of $ PM_{2.5} $ variations.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">GTWR</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">RANSAC</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">PM</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">AOD</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">DTB</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Taiwan</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bilal, Muhammad</subfield><subfield code="0">(orcid)0000-0003-1022-3999</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Environmental science and pollution research</subfield><subfield code="d">Springer Berlin Heidelberg, 1994</subfield><subfield code="g">26(2018), 2 vom: 20. Nov., Seite 1902-1910</subfield><subfield code="w">(DE-627)171335805</subfield><subfield code="w">(DE-600)1178791-0</subfield><subfield code="w">(DE-576)038875101</subfield><subfield code="x">0944-1344</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:26</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:2</subfield><subfield code="g">day:20</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:1902-1910</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11356-018-3763-7</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-BIODIV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-UMW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-ARC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-CHE</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-FOR</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-DE-84</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_252</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_267</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">26</subfield><subfield code="j">2018</subfield><subfield code="e">2</subfield><subfield code="b">20</subfield><subfield code="c">11</subfield><subfield code="h">1902-1910</subfield></datafield></record></collection>
|
author |
Chu, Hone-Jay |
spellingShingle |
Chu, Hone-Jay ddc 570 ddc 690 fid BIODIV misc GTWR misc RANSAC misc PM misc AOD misc DTB misc Taiwan $ PM_{2.5} $ mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models |
authorStr |
Chu, Hone-Jay |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)171335805 |
format |
Article |
dewey-ones |
570 - Life sciences; biology 360 - Social problems & services; associations 333 - Economics of land & energy 690 - Buildings 540 - Chemistry & allied sciences |
delete_txt_mv |
keep |
author_role |
aut aut |
collection |
OLC |
remote_str |
false |
illustrated |
Not Illustrated |
issn |
0944-1344 |
topic_title |
570 360 333.7 VZ 690 333.7 540 VZ BIODIV DE-30 fid $ PM_{2.5} $ mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models GTWR RANSAC PM AOD DTB Taiwan |
topic |
ddc 570 ddc 690 fid BIODIV misc GTWR misc RANSAC misc PM misc AOD misc DTB misc Taiwan |
topic_unstemmed |
ddc 570 ddc 690 fid BIODIV misc GTWR misc RANSAC misc PM misc AOD misc DTB misc Taiwan |
topic_browse |
ddc 570 ddc 690 fid BIODIV misc GTWR misc RANSAC misc PM misc AOD misc DTB misc Taiwan |
format_facet |
Aufsätze Gedruckte Aufsätze |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
nc |
hierarchy_parent_title |
Environmental science and pollution research |
hierarchy_parent_id |
171335805 |
dewey-tens |
570 - Life sciences; biology 360 - Social problems & social services 330 - Economics 690 - Building & construction 540 - Chemistry |
hierarchy_top_title |
Environmental science and pollution research |
isfreeaccess_txt |
false |
familylinks_str_mv |
(DE-627)171335805 (DE-600)1178791-0 (DE-576)038875101 |
title |
$ PM_{2.5} $ mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models |
ctrlnum |
(DE-627)OLC2040537384 (DE-He213)s11356-018-3763-7-p |
title_full |
$ PM_{2.5} $ mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models |
author_sort |
Chu, Hone-Jay |
journal |
Environmental science and pollution research |
journalStr |
Environmental science and pollution research |
lang_code |
eng |
isOA_bool |
false |
dewey-hundreds |
500 - Science 300 - Social sciences 600 - Technology |
recordtype |
marc |
publishDateSort |
2018 |
contenttype_str_mv |
txt |
container_start_page |
1902 |
author_browse |
Chu, Hone-Jay Bilal, Muhammad |
container_volume |
26 |
class |
570 360 333.7 VZ 690 333.7 540 VZ BIODIV DE-30 fid |
format_se |
Aufsätze |
author-letter |
Chu, Hone-Jay |
doi_str_mv |
10.1007/s11356-018-3763-7 |
normlink |
(ORCID)0000-0003-1022-3999 |
normlink_prefix_str_mv |
(orcid)0000-0003-1022-3999 |
dewey-full |
570 360 333.7 690 540 |
title_sort |
$ pm_{2.5} $ mapping using integrated geographically temporally weighted regression (gtwr) and random sample consensus (ransac) models |
title_auth |
$ PM_{2.5} $ mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models |
abstract |
Abstract An uncertainty in the relationship between aerosol optical depth (AOD) and fine particulate matter ($ PM_{2.5} $) comes from the uncertainty of AOD by aerosol models and the estimated surface reflectance, a mismatch in spatiotemporal resolution, integration of AOD and $ PM_{2.5} $ data, and data modeling. In this study, an integrated geographically temporally weighted regression (GTWR) and RANdom SAmple Consensus (RANSAC) models, which provide fine goodness-of-fit between observed $ PM_{2.5} $ and AOD data, were used for mapping of $ PM_{2.5} $ over Taiwan for the year 2014. For this, dark target (DT) AOD observations at 3-km resolution ($ DT_{3K} $) only for high-quality assurance flag (QA = 3) were obtained from the scientific data set (SDS) “Optical_Depth_Land_And_Ocean”. AOD observations were also obtained from the merged DT and DB (deep blue) product ($ DTB_{3K} $) which was generated using the simplified merge scheme (SMS), i.e., using an average of the DT and DB highest quality AOD retrievals or the available one. The GTWR model integrated with RANSAC can use the effective sampling and fitting to overcome the estimation problem of AOD-$ PM_{2.5} $ with the uncertainty and outliers of observation data. Results showed that the model dealing with spatiotemporal heterogeneity and uncertainty is a powerful tool to infer patterns of $ PM_{2.5} $ from a RANSAC subset samples. Moreover, spatial variability and hotspot analysis were applied after $ PM_{2.5} $ mapping. The hotspot and spatial variability of $ PM_{2.5} $ maps can give us a summary of the spatiotemporal patterns of $ PM_{2.5} $ variations. © Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
abstractGer |
Abstract An uncertainty in the relationship between aerosol optical depth (AOD) and fine particulate matter ($ PM_{2.5} $) comes from the uncertainty of AOD by aerosol models and the estimated surface reflectance, a mismatch in spatiotemporal resolution, integration of AOD and $ PM_{2.5} $ data, and data modeling. In this study, an integrated geographically temporally weighted regression (GTWR) and RANdom SAmple Consensus (RANSAC) models, which provide fine goodness-of-fit between observed $ PM_{2.5} $ and AOD data, were used for mapping of $ PM_{2.5} $ over Taiwan for the year 2014. For this, dark target (DT) AOD observations at 3-km resolution ($ DT_{3K} $) only for high-quality assurance flag (QA = 3) were obtained from the scientific data set (SDS) “Optical_Depth_Land_And_Ocean”. AOD observations were also obtained from the merged DT and DB (deep blue) product ($ DTB_{3K} $) which was generated using the simplified merge scheme (SMS), i.e., using an average of the DT and DB highest quality AOD retrievals or the available one. The GTWR model integrated with RANSAC can use the effective sampling and fitting to overcome the estimation problem of AOD-$ PM_{2.5} $ with the uncertainty and outliers of observation data. Results showed that the model dealing with spatiotemporal heterogeneity and uncertainty is a powerful tool to infer patterns of $ PM_{2.5} $ from a RANSAC subset samples. Moreover, spatial variability and hotspot analysis were applied after $ PM_{2.5} $ mapping. The hotspot and spatial variability of $ PM_{2.5} $ maps can give us a summary of the spatiotemporal patterns of $ PM_{2.5} $ variations. © Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
abstract_unstemmed |
Abstract An uncertainty in the relationship between aerosol optical depth (AOD) and fine particulate matter ($ PM_{2.5} $) comes from the uncertainty of AOD by aerosol models and the estimated surface reflectance, a mismatch in spatiotemporal resolution, integration of AOD and $ PM_{2.5} $ data, and data modeling. In this study, an integrated geographically temporally weighted regression (GTWR) and RANdom SAmple Consensus (RANSAC) models, which provide fine goodness-of-fit between observed $ PM_{2.5} $ and AOD data, were used for mapping of $ PM_{2.5} $ over Taiwan for the year 2014. For this, dark target (DT) AOD observations at 3-km resolution ($ DT_{3K} $) only for high-quality assurance flag (QA = 3) were obtained from the scientific data set (SDS) “Optical_Depth_Land_And_Ocean”. AOD observations were also obtained from the merged DT and DB (deep blue) product ($ DTB_{3K} $) which was generated using the simplified merge scheme (SMS), i.e., using an average of the DT and DB highest quality AOD retrievals or the available one. The GTWR model integrated with RANSAC can use the effective sampling and fitting to overcome the estimation problem of AOD-$ PM_{2.5} $ with the uncertainty and outliers of observation data. Results showed that the model dealing with spatiotemporal heterogeneity and uncertainty is a powerful tool to infer patterns of $ PM_{2.5} $ from a RANSAC subset samples. Moreover, spatial variability and hotspot analysis were applied after $ PM_{2.5} $ mapping. The hotspot and spatial variability of $ PM_{2.5} $ maps can give us a summary of the spatiotemporal patterns of $ PM_{2.5} $ variations. © Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-FOR SSG-OLC-DE-84 GBV_ILN_70 GBV_ILN_252 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4277 |
container_issue |
2 |
title_short |
$ PM_{2.5} $ mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models |
url |
https://doi.org/10.1007/s11356-018-3763-7 |
remote_bool |
false |
author2 |
Bilal, Muhammad |
author2Str |
Bilal, Muhammad |
ppnlink |
171335805 |
mediatype_str_mv |
n |
isOA_txt |
false |
hochschulschrift_bool |
false |
doi_str |
10.1007/s11356-018-3763-7 |
up_date |
2024-07-04T02:33:15.540Z |
_version_ |
1803614049317945344 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2040537384</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230606195044.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2018 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11356-018-3763-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2040537384</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11356-018-3763-7-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="a">360</subfield><subfield code="a">333.7</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">690</subfield><subfield code="a">333.7</subfield><subfield code="a">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BIODIV</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Chu, Hone-Jay</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">$ PM_{2.5} $ mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag GmbH Germany, part of Springer Nature 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract An uncertainty in the relationship between aerosol optical depth (AOD) and fine particulate matter ($ PM_{2.5} $) comes from the uncertainty of AOD by aerosol models and the estimated surface reflectance, a mismatch in spatiotemporal resolution, integration of AOD and $ PM_{2.5} $ data, and data modeling. In this study, an integrated geographically temporally weighted regression (GTWR) and RANdom SAmple Consensus (RANSAC) models, which provide fine goodness-of-fit between observed $ PM_{2.5} $ and AOD data, were used for mapping of $ PM_{2.5} $ over Taiwan for the year 2014. For this, dark target (DT) AOD observations at 3-km resolution ($ DT_{3K} $) only for high-quality assurance flag (QA = 3) were obtained from the scientific data set (SDS) “Optical_Depth_Land_And_Ocean”. AOD observations were also obtained from the merged DT and DB (deep blue) product ($ DTB_{3K} $) which was generated using the simplified merge scheme (SMS), i.e., using an average of the DT and DB highest quality AOD retrievals or the available one. The GTWR model integrated with RANSAC can use the effective sampling and fitting to overcome the estimation problem of AOD-$ PM_{2.5} $ with the uncertainty and outliers of observation data. Results showed that the model dealing with spatiotemporal heterogeneity and uncertainty is a powerful tool to infer patterns of $ PM_{2.5} $ from a RANSAC subset samples. Moreover, spatial variability and hotspot analysis were applied after $ PM_{2.5} $ mapping. The hotspot and spatial variability of $ PM_{2.5} $ maps can give us a summary of the spatiotemporal patterns of $ PM_{2.5} $ variations.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">GTWR</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">RANSAC</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">PM</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">AOD</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">DTB</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Taiwan</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bilal, Muhammad</subfield><subfield code="0">(orcid)0000-0003-1022-3999</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Environmental science and pollution research</subfield><subfield code="d">Springer Berlin Heidelberg, 1994</subfield><subfield code="g">26(2018), 2 vom: 20. Nov., Seite 1902-1910</subfield><subfield code="w">(DE-627)171335805</subfield><subfield code="w">(DE-600)1178791-0</subfield><subfield code="w">(DE-576)038875101</subfield><subfield code="x">0944-1344</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:26</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:2</subfield><subfield code="g">day:20</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:1902-1910</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11356-018-3763-7</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-BIODIV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-UMW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-ARC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-CHE</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-FOR</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-DE-84</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_252</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_267</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">26</subfield><subfield code="j">2018</subfield><subfield code="e">2</subfield><subfield code="b">20</subfield><subfield code="c">11</subfield><subfield code="h">1902-1910</subfield></datafield></record></collection>
|
score |
7.3995314 |