Estimation of the PM2.5 Pollution Levels in Beijing Based on Nighttime Light Data from the Defense Meteorological Satellite Program-Operational Linescan System

Nighttime light data record the artificial light on the Earth’s surface and can be used to estimate the degree of pollution associated with particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) in the ground-level atmosphere. This study proposes a simple method for monitoring P...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Runya Li [verfasserIn] Xiangnan Liu [verfasserIn] Xuqing Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	PM2.5 DMSP-OLS nighttime light data BP neural-network Beijing

Übergeordnetes Werk:	In: Atmosphere - MDPI AG, 2011, 6(2015), 5, Seite 607-622
Übergeordnetes Werk:	volume:6 ; year:2015 ; number:5 ; pages:607-622

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/atmos6050607

Katalog-ID:	DOAJ011623152

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ011623152
003	DE-627
005	20230502071100.0
007	cr uuu---uuuuu
008	230225s2015 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.3390/atmos6050607 \|2 doi
035			\|a (DE-627)DOAJ011623152
035			\|a (DE-599)DOAJ6ac17c7c83b6484cafae7f87aa49a0a1
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a QC851-999
100	0		\|a Runya Li \|e verfasserin \|4 aut
245	1	0	\|a Estimation of the PM2.5 Pollution Levels in Beijing Based on Nighttime Light Data from the Defense Meteorological Satellite Program-Operational Linescan System
264		1	\|c 2015
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Nighttime light data record the artificial light on the Earth’s surface and can be used to estimate the degree of pollution associated with particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) in the ground-level atmosphere. This study proposes a simple method for monitoring PM2.5 concentrations at night by using nighttime light imagery from the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS). This research synthesizes remote sensing and geographic information system techniques and establishes a back propagation neural-network (BP network) model. The BP network model for nighttime light data performed well in estimating the PM2.5 pollution in Beijing. The correlation coefficient between the BP network model predictions and the corrected PM2.5 concentration was 0.975; the root mean square error was 26.26 μg/m3, with a corresponding average PM2.5 concentration of 155.07 μg/m3; and the average accuracy was 0.796. The accuracy of the results primarily depended on the method of selecting regions in the DMSP nighttime light data. This study provides an opportunity to measure the nighttime environment. Furthermore, these results can assist government agencies in determining particulate matter pollution control areas and developing and implementing environmental conservation planning.
650		4	\|a PM2.5
650		4	\|a DMSP-OLS
650		4	\|a nighttime light data
650		4	\|a BP neural-network
650		4	\|a Beijing
653		0	\|a Meteorology. Climatology
700	0		\|a Xiangnan Liu \|e verfasserin \|4 aut
700	0		\|a Xuqing Li \|e verfasserin \|4 aut
773	0	8	\|i In \|t Atmosphere \|d MDPI AG, 2011 \|g 6(2015), 5, Seite 607-622 \|w (DE-627)657584010 \|w (DE-600)2605928-9 \|x 20734433 \|7 nnns
773	1	8	\|g volume:6 \|g year:2015 \|g number:5 \|g pages:607-622
856	4	0	\|u https://doi.org/10.3390/atmos6050607 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/6ac17c7c83b6484cafae7f87aa49a0a1 \|z kostenfrei
856	4	0	\|u http://www.mdpi.com/2073-4433/6/5/607 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2073-4433 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a SSG-OLC-PHA
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 6 \|j 2015 \|e 5 \|h 607-622

Indexfelder

author_variant	r l rl x l xl x l xl
matchkey_str	article:20734433:2015----::siainfhp2pluineesnejnbsdnihtmlgtaarmhdfneeerlgclael
hierarchy_sort_str	2015
callnumber-subject-code	QC
publishDate	2015
allfields	10.3390/atmos6050607 doi (DE-627)DOAJ011623152 (DE-599)DOAJ6ac17c7c83b6484cafae7f87aa49a0a1 DE-627 ger DE-627 rakwb eng QC851-999 Runya Li verfasserin aut Estimation of the PM2.5 Pollution Levels in Beijing Based on Nighttime Light Data from the Defense Meteorological Satellite Program-Operational Linescan System 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Nighttime light data record the artificial light on the Earth’s surface and can be used to estimate the degree of pollution associated with particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) in the ground-level atmosphere. This study proposes a simple method for monitoring PM2.5 concentrations at night by using nighttime light imagery from the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS). This research synthesizes remote sensing and geographic information system techniques and establishes a back propagation neural-network (BP network) model. The BP network model for nighttime light data performed well in estimating the PM2.5 pollution in Beijing. The correlation coefficient between the BP network model predictions and the corrected PM2.5 concentration was 0.975; the root mean square error was 26.26 μg/m3, with a corresponding average PM2.5 concentration of 155.07 μg/m3; and the average accuracy was 0.796. The accuracy of the results primarily depended on the method of selecting regions in the DMSP nighttime light data. This study provides an opportunity to measure the nighttime environment. Furthermore, these results can assist government agencies in determining particulate matter pollution control areas and developing and implementing environmental conservation planning. PM2.5 DMSP-OLS nighttime light data BP neural-network Beijing Meteorology. Climatology Xiangnan Liu verfasserin aut Xuqing Li verfasserin aut In Atmosphere MDPI AG, 2011 6(2015), 5, Seite 607-622 (DE-627)657584010 (DE-600)2605928-9 20734433 nnns volume:6 year:2015 number:5 pages:607-622 https://doi.org/10.3390/atmos6050607 kostenfrei https://doaj.org/article/6ac17c7c83b6484cafae7f87aa49a0a1 kostenfrei http://www.mdpi.com/2073-4433/6/5/607 kostenfrei https://doaj.org/toc/2073-4433 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2015 5 607-622
spelling	10.3390/atmos6050607 doi (DE-627)DOAJ011623152 (DE-599)DOAJ6ac17c7c83b6484cafae7f87aa49a0a1 DE-627 ger DE-627 rakwb eng QC851-999 Runya Li verfasserin aut Estimation of the PM2.5 Pollution Levels in Beijing Based on Nighttime Light Data from the Defense Meteorological Satellite Program-Operational Linescan System 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Nighttime light data record the artificial light on the Earth’s surface and can be used to estimate the degree of pollution associated with particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) in the ground-level atmosphere. This study proposes a simple method for monitoring PM2.5 concentrations at night by using nighttime light imagery from the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS). This research synthesizes remote sensing and geographic information system techniques and establishes a back propagation neural-network (BP network) model. The BP network model for nighttime light data performed well in estimating the PM2.5 pollution in Beijing. The correlation coefficient between the BP network model predictions and the corrected PM2.5 concentration was 0.975; the root mean square error was 26.26 μg/m3, with a corresponding average PM2.5 concentration of 155.07 μg/m3; and the average accuracy was 0.796. The accuracy of the results primarily depended on the method of selecting regions in the DMSP nighttime light data. This study provides an opportunity to measure the nighttime environment. Furthermore, these results can assist government agencies in determining particulate matter pollution control areas and developing and implementing environmental conservation planning. PM2.5 DMSP-OLS nighttime light data BP neural-network Beijing Meteorology. Climatology Xiangnan Liu verfasserin aut Xuqing Li verfasserin aut In Atmosphere MDPI AG, 2011 6(2015), 5, Seite 607-622 (DE-627)657584010 (DE-600)2605928-9 20734433 nnns volume:6 year:2015 number:5 pages:607-622 https://doi.org/10.3390/atmos6050607 kostenfrei https://doaj.org/article/6ac17c7c83b6484cafae7f87aa49a0a1 kostenfrei http://www.mdpi.com/2073-4433/6/5/607 kostenfrei https://doaj.org/toc/2073-4433 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2015 5 607-622
allfields_unstemmed	10.3390/atmos6050607 doi (DE-627)DOAJ011623152 (DE-599)DOAJ6ac17c7c83b6484cafae7f87aa49a0a1 DE-627 ger DE-627 rakwb eng QC851-999 Runya Li verfasserin aut Estimation of the PM2.5 Pollution Levels in Beijing Based on Nighttime Light Data from the Defense Meteorological Satellite Program-Operational Linescan System 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Nighttime light data record the artificial light on the Earth’s surface and can be used to estimate the degree of pollution associated with particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) in the ground-level atmosphere. This study proposes a simple method for monitoring PM2.5 concentrations at night by using nighttime light imagery from the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS). This research synthesizes remote sensing and geographic information system techniques and establishes a back propagation neural-network (BP network) model. The BP network model for nighttime light data performed well in estimating the PM2.5 pollution in Beijing. The correlation coefficient between the BP network model predictions and the corrected PM2.5 concentration was 0.975; the root mean square error was 26.26 μg/m3, with a corresponding average PM2.5 concentration of 155.07 μg/m3; and the average accuracy was 0.796. The accuracy of the results primarily depended on the method of selecting regions in the DMSP nighttime light data. This study provides an opportunity to measure the nighttime environment. Furthermore, these results can assist government agencies in determining particulate matter pollution control areas and developing and implementing environmental conservation planning. PM2.5 DMSP-OLS nighttime light data BP neural-network Beijing Meteorology. Climatology Xiangnan Liu verfasserin aut Xuqing Li verfasserin aut In Atmosphere MDPI AG, 2011 6(2015), 5, Seite 607-622 (DE-627)657584010 (DE-600)2605928-9 20734433 nnns volume:6 year:2015 number:5 pages:607-622 https://doi.org/10.3390/atmos6050607 kostenfrei https://doaj.org/article/6ac17c7c83b6484cafae7f87aa49a0a1 kostenfrei http://www.mdpi.com/2073-4433/6/5/607 kostenfrei https://doaj.org/toc/2073-4433 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2015 5 607-622
allfieldsGer	10.3390/atmos6050607 doi (DE-627)DOAJ011623152 (DE-599)DOAJ6ac17c7c83b6484cafae7f87aa49a0a1 DE-627 ger DE-627 rakwb eng QC851-999 Runya Li verfasserin aut Estimation of the PM2.5 Pollution Levels in Beijing Based on Nighttime Light Data from the Defense Meteorological Satellite Program-Operational Linescan System 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Nighttime light data record the artificial light on the Earth’s surface and can be used to estimate the degree of pollution associated with particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) in the ground-level atmosphere. This study proposes a simple method for monitoring PM2.5 concentrations at night by using nighttime light imagery from the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS). This research synthesizes remote sensing and geographic information system techniques and establishes a back propagation neural-network (BP network) model. The BP network model for nighttime light data performed well in estimating the PM2.5 pollution in Beijing. The correlation coefficient between the BP network model predictions and the corrected PM2.5 concentration was 0.975; the root mean square error was 26.26 μg/m3, with a corresponding average PM2.5 concentration of 155.07 μg/m3; and the average accuracy was 0.796. The accuracy of the results primarily depended on the method of selecting regions in the DMSP nighttime light data. This study provides an opportunity to measure the nighttime environment. Furthermore, these results can assist government agencies in determining particulate matter pollution control areas and developing and implementing environmental conservation planning. PM2.5 DMSP-OLS nighttime light data BP neural-network Beijing Meteorology. Climatology Xiangnan Liu verfasserin aut Xuqing Li verfasserin aut In Atmosphere MDPI AG, 2011 6(2015), 5, Seite 607-622 (DE-627)657584010 (DE-600)2605928-9 20734433 nnns volume:6 year:2015 number:5 pages:607-622 https://doi.org/10.3390/atmos6050607 kostenfrei https://doaj.org/article/6ac17c7c83b6484cafae7f87aa49a0a1 kostenfrei http://www.mdpi.com/2073-4433/6/5/607 kostenfrei https://doaj.org/toc/2073-4433 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2015 5 607-622
allfieldsSound	10.3390/atmos6050607 doi (DE-627)DOAJ011623152 (DE-599)DOAJ6ac17c7c83b6484cafae7f87aa49a0a1 DE-627 ger DE-627 rakwb eng QC851-999 Runya Li verfasserin aut Estimation of the PM2.5 Pollution Levels in Beijing Based on Nighttime Light Data from the Defense Meteorological Satellite Program-Operational Linescan System 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Nighttime light data record the artificial light on the Earth’s surface and can be used to estimate the degree of pollution associated with particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) in the ground-level atmosphere. This study proposes a simple method for monitoring PM2.5 concentrations at night by using nighttime light imagery from the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS). This research synthesizes remote sensing and geographic information system techniques and establishes a back propagation neural-network (BP network) model. The BP network model for nighttime light data performed well in estimating the PM2.5 pollution in Beijing. The correlation coefficient between the BP network model predictions and the corrected PM2.5 concentration was 0.975; the root mean square error was 26.26 μg/m3, with a corresponding average PM2.5 concentration of 155.07 μg/m3; and the average accuracy was 0.796. The accuracy of the results primarily depended on the method of selecting regions in the DMSP nighttime light data. This study provides an opportunity to measure the nighttime environment. Furthermore, these results can assist government agencies in determining particulate matter pollution control areas and developing and implementing environmental conservation planning. PM2.5 DMSP-OLS nighttime light data BP neural-network Beijing Meteorology. Climatology Xiangnan Liu verfasserin aut Xuqing Li verfasserin aut In Atmosphere MDPI AG, 2011 6(2015), 5, Seite 607-622 (DE-627)657584010 (DE-600)2605928-9 20734433 nnns volume:6 year:2015 number:5 pages:607-622 https://doi.org/10.3390/atmos6050607 kostenfrei https://doaj.org/article/6ac17c7c83b6484cafae7f87aa49a0a1 kostenfrei http://www.mdpi.com/2073-4433/6/5/607 kostenfrei https://doaj.org/toc/2073-4433 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2015 5 607-622
language	English
source	In Atmosphere 6(2015), 5, Seite 607-622 volume:6 year:2015 number:5 pages:607-622
sourceStr	In Atmosphere 6(2015), 5, Seite 607-622 volume:6 year:2015 number:5 pages:607-622
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	PM2.5 DMSP-OLS nighttime light data BP neural-network Beijing Meteorology. Climatology
isfreeaccess_bool	true
container_title	Atmosphere
authorswithroles_txt_mv	Runya Li @@aut@@ Xiangnan Liu @@aut@@ Xuqing Li @@aut@@
publishDateDaySort_date	2015-01-01T00:00:00Z
hierarchy_top_id	657584010
id	DOAJ011623152
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">DOAJ011623152</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502071100.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230225s2015 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/atmos6050607</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ011623152</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ6ac17c7c83b6484cafae7f87aa49a0a1</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="050" ind1=" " ind2="0"><subfield code="a">QC851-999</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Runya Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Estimation of the PM2.5 Pollution Levels in Beijing Based on Nighttime Light Data from the Defense Meteorological Satellite Program-Operational Linescan System</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</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">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Nighttime light data record the artificial light on the Earth’s surface and can be used to estimate the degree of pollution associated with particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) in the ground-level atmosphere. This study proposes a simple method for monitoring PM2.5 concentrations at night by using nighttime light imagery from the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS). This research synthesizes remote sensing and geographic information system techniques and establishes a back propagation neural-network (BP network) model. The BP network model for nighttime light data performed well in estimating the PM2.5 pollution in Beijing. The correlation coefficient between the BP network model predictions and the corrected PM2.5 concentration was 0.975; the root mean square error was 26.26 μg/m3, with a corresponding average PM2.5 concentration of 155.07 μg/m3; and the average accuracy was 0.796. The accuracy of the results primarily depended on the method of selecting regions in the DMSP nighttime light data. This study provides an opportunity to measure the nighttime environment. Furthermore, these results can assist government agencies in determining particulate matter pollution control areas and developing and implementing environmental conservation planning.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">PM2.5</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">DMSP-OLS</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">nighttime light data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">BP neural-network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Beijing</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Meteorology. Climatology</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xiangnan Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xuqing Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Atmosphere</subfield><subfield code="d">MDPI AG, 2011</subfield><subfield code="g">6(2015), 5, Seite 607-622</subfield><subfield code="w">(DE-627)657584010</subfield><subfield code="w">(DE-600)2605928-9</subfield><subfield code="x">20734433</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:6</subfield><subfield code="g">year:2015</subfield><subfield code="g">number:5</subfield><subfield code="g">pages:607-622</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/atmos6050607</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/6ac17c7c83b6484cafae7f87aa49a0a1</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.mdpi.com/2073-4433/6/5/607</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2073-4433</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</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_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</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_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</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_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">6</subfield><subfield code="j">2015</subfield><subfield code="e">5</subfield><subfield code="h">607-622</subfield></datafield></record></collection>
callnumber-first	Q - Science
author	Runya Li
spellingShingle	Runya Li misc QC851-999 misc PM2.5 misc DMSP-OLS misc nighttime light data misc BP neural-network misc Beijing misc Meteorology. Climatology Estimation of the PM2.5 Pollution Levels in Beijing Based on Nighttime Light Data from the Defense Meteorological Satellite Program-Operational Linescan System
authorStr	Runya Li
ppnlink_with_tag_str_mv	@@773@@(DE-627)657584010
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	QC851-999
illustrated	Not Illustrated
issn	20734433
topic_title	QC851-999 Estimation of the PM2.5 Pollution Levels in Beijing Based on Nighttime Light Data from the Defense Meteorological Satellite Program-Operational Linescan System PM2.5 DMSP-OLS nighttime light data BP neural-network Beijing
topic	misc QC851-999 misc PM2.5 misc DMSP-OLS misc nighttime light data misc BP neural-network misc Beijing misc Meteorology. Climatology
topic_unstemmed	misc QC851-999 misc PM2.5 misc DMSP-OLS misc nighttime light data misc BP neural-network misc Beijing misc Meteorology. Climatology
topic_browse	misc QC851-999 misc PM2.5 misc DMSP-OLS misc nighttime light data misc BP neural-network misc Beijing misc Meteorology. Climatology
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Atmosphere
hierarchy_parent_id	657584010
hierarchy_top_title	Atmosphere
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)657584010 (DE-600)2605928-9
title	Estimation of the PM2.5 Pollution Levels in Beijing Based on Nighttime Light Data from the Defense Meteorological Satellite Program-Operational Linescan System
ctrlnum	(DE-627)DOAJ011623152 (DE-599)DOAJ6ac17c7c83b6484cafae7f87aa49a0a1
title_full	Estimation of the PM2.5 Pollution Levels in Beijing Based on Nighttime Light Data from the Defense Meteorological Satellite Program-Operational Linescan System
author_sort	Runya Li
journal	Atmosphere
journalStr	Atmosphere
callnumber-first-code	Q
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2015
contenttype_str_mv	txt
container_start_page	607
author_browse	Runya Li Xiangnan Liu Xuqing Li
container_volume	6
class	QC851-999
format_se	Elektronische Aufsätze
author-letter	Runya Li
doi_str_mv	10.3390/atmos6050607
author2-role	verfasserin
title_sort	estimation of the pm2.5 pollution levels in beijing based on nighttime light data from the defense meteorological satellite program-operational linescan system
callnumber	QC851-999
title_auth	Estimation of the PM2.5 Pollution Levels in Beijing Based on Nighttime Light Data from the Defense Meteorological Satellite Program-Operational Linescan System
abstract	Nighttime light data record the artificial light on the Earth’s surface and can be used to estimate the degree of pollution associated with particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) in the ground-level atmosphere. This study proposes a simple method for monitoring PM2.5 concentrations at night by using nighttime light imagery from the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS). This research synthesizes remote sensing and geographic information system techniques and establishes a back propagation neural-network (BP network) model. The BP network model for nighttime light data performed well in estimating the PM2.5 pollution in Beijing. The correlation coefficient between the BP network model predictions and the corrected PM2.5 concentration was 0.975; the root mean square error was 26.26 μg/m3, with a corresponding average PM2.5 concentration of 155.07 μg/m3; and the average accuracy was 0.796. The accuracy of the results primarily depended on the method of selecting regions in the DMSP nighttime light data. This study provides an opportunity to measure the nighttime environment. Furthermore, these results can assist government agencies in determining particulate matter pollution control areas and developing and implementing environmental conservation planning.
abstractGer	Nighttime light data record the artificial light on the Earth’s surface and can be used to estimate the degree of pollution associated with particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) in the ground-level atmosphere. This study proposes a simple method for monitoring PM2.5 concentrations at night by using nighttime light imagery from the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS). This research synthesizes remote sensing and geographic information system techniques and establishes a back propagation neural-network (BP network) model. The BP network model for nighttime light data performed well in estimating the PM2.5 pollution in Beijing. The correlation coefficient between the BP network model predictions and the corrected PM2.5 concentration was 0.975; the root mean square error was 26.26 μg/m3, with a corresponding average PM2.5 concentration of 155.07 μg/m3; and the average accuracy was 0.796. The accuracy of the results primarily depended on the method of selecting regions in the DMSP nighttime light data. This study provides an opportunity to measure the nighttime environment. Furthermore, these results can assist government agencies in determining particulate matter pollution control areas and developing and implementing environmental conservation planning.
abstract_unstemmed	Nighttime light data record the artificial light on the Earth’s surface and can be used to estimate the degree of pollution associated with particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) in the ground-level atmosphere. This study proposes a simple method for monitoring PM2.5 concentrations at night by using nighttime light imagery from the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS). This research synthesizes remote sensing and geographic information system techniques and establishes a back propagation neural-network (BP network) model. The BP network model for nighttime light data performed well in estimating the PM2.5 pollution in Beijing. The correlation coefficient between the BP network model predictions and the corrected PM2.5 concentration was 0.975; the root mean square error was 26.26 μg/m3, with a corresponding average PM2.5 concentration of 155.07 μg/m3; and the average accuracy was 0.796. The accuracy of the results primarily depended on the method of selecting regions in the DMSP nighttime light data. This study provides an opportunity to measure the nighttime environment. Furthermore, these results can assist government agencies in determining particulate matter pollution control areas and developing and implementing environmental conservation planning.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
container_issue	5
title_short	Estimation of the PM2.5 Pollution Levels in Beijing Based on Nighttime Light Data from the Defense Meteorological Satellite Program-Operational Linescan System
url	https://doi.org/10.3390/atmos6050607 https://doaj.org/article/6ac17c7c83b6484cafae7f87aa49a0a1 http://www.mdpi.com/2073-4433/6/5/607 https://doaj.org/toc/2073-4433
remote_bool	true
author2	Xiangnan Liu Xuqing Li
author2Str	Xiangnan Liu Xuqing Li
ppnlink	657584010
callnumber-subject	QC - Physics
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.3390/atmos6050607
callnumber-a	QC851-999
up_date	2024-07-03T21:19:58.103Z
_version_	1803594338782937090
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">DOAJ011623152</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502071100.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230225s2015 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/atmos6050607</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ011623152</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ6ac17c7c83b6484cafae7f87aa49a0a1</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="050" ind1=" " ind2="0"><subfield code="a">QC851-999</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Runya Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Estimation of the PM2.5 Pollution Levels in Beijing Based on Nighttime Light Data from the Defense Meteorological Satellite Program-Operational Linescan System</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</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">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Nighttime light data record the artificial light on the Earth’s surface and can be used to estimate the degree of pollution associated with particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) in the ground-level atmosphere. This study proposes a simple method for monitoring PM2.5 concentrations at night by using nighttime light imagery from the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS). This research synthesizes remote sensing and geographic information system techniques and establishes a back propagation neural-network (BP network) model. The BP network model for nighttime light data performed well in estimating the PM2.5 pollution in Beijing. The correlation coefficient between the BP network model predictions and the corrected PM2.5 concentration was 0.975; the root mean square error was 26.26 μg/m3, with a corresponding average PM2.5 concentration of 155.07 μg/m3; and the average accuracy was 0.796. The accuracy of the results primarily depended on the method of selecting regions in the DMSP nighttime light data. This study provides an opportunity to measure the nighttime environment. Furthermore, these results can assist government agencies in determining particulate matter pollution control areas and developing and implementing environmental conservation planning.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">PM2.5</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">DMSP-OLS</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">nighttime light data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">BP neural-network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Beijing</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Meteorology. Climatology</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xiangnan Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xuqing Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Atmosphere</subfield><subfield code="d">MDPI AG, 2011</subfield><subfield code="g">6(2015), 5, Seite 607-622</subfield><subfield code="w">(DE-627)657584010</subfield><subfield code="w">(DE-600)2605928-9</subfield><subfield code="x">20734433</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:6</subfield><subfield code="g">year:2015</subfield><subfield code="g">number:5</subfield><subfield code="g">pages:607-622</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/atmos6050607</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/6ac17c7c83b6484cafae7f87aa49a0a1</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.mdpi.com/2073-4433/6/5/607</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2073-4433</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</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_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</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_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</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_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">6</subfield><subfield code="j">2015</subfield><subfield code="e">5</subfield><subfield code="h">607-622</subfield></datafield></record></collection>
score	7.400728

Nicht das Richtige dabei?

Schreiben Sie uns!

Estimation of the PM2.5 Pollution Levels in Beijing Based on Nighttime Light Data from the Defense Meteorological Satellite Program-Operational Linescan System

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?