Knowledge based convolutional transformer for joint estimation of PM2.5 and O3 concentrations

Most of the methods for predicting air pollutant concentrations are targeting at single pollutants, which is time-consuming and laborious. To solve this problem, this study proposes a Convolutional Transformer (Convtrans) model that incorporates knowledge to make a collaborative estimation of PM 2.5...

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Published inScientific reports Vol. 15; no. 1; pp. 25340 - 16
Main Authors Ren, Ying, Wang, Siyuan, Xia, Bisheng, Xia, Biesheng
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 14.07.2025
Nature Publishing Group
Nature Portfolio
Subjects
639/705
704/106/35
Aerosols
Air pollution
Convtrans
Deep learning
Design
Environmental monitoring
Humanities and Social Sciences
Humidity
Joint Estimation
Knowledge
Machine learning
multidisciplinary
Neural networks
Particulate matter
PM2.5
Pollutants
Radiation
Remote sensing
Science
Science (multidisciplinary)
Spatial variations
Time series
VOCs
Volatile organic compounds
Beijing China
China
Convtrans
Joint Estimation
PM
Knowledge
O
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Abstract Most of the methods for predicting air pollutant concentrations are targeting at single pollutants, which is time-consuming and laborious. To solve this problem, this study proposes a Convolutional Transformer (Convtrans) model that incorporates knowledge to make a collaborative estimation of PM 2.5 and O 3 by combining ground, satellite, and reanalysis data. Knowledge is introduced into the model by the shared and specific inputs, the PM 2.5 -O 3 interaction module, and the weighted loss function designed with the correlation between PM 2.5 and O 3 concentrations. To verify the accuracy of the Convtrans model, its prediction result was compared with that of CNN-LSTM, Transformer, RF, and XGB models. Estimating the pollutant concentration in typical Chinese cities, the cross-validation results show that Convtrans has the minimum error (PM 2.5 :RMSE = 6.136 µg/m³, O 3 :RMSE = 8.250 µg/m³) and the highest prediction accuracy (PM 2.5 :R 2  = 0.923, O 3 :R 2  = 0.898). Finally, a map of pollutant concentrations was drawn according to the pollutant concentration values predicted by the model, showing the spatial variations of pollutant. This study indicates that it is feasible to integrate knowledge into a data-driven model for a joint estimation of atmospheric pollutant concentrations. In addition, the joint estimation framework for pollutants proposed in this study can be applied to multivariate retrieval or estimation in multiple fields.
AbstractList Abstract Most of the methods for predicting air pollutant concentrations are targeting at single pollutants, which is time-consuming and laborious. To solve this problem, this study proposes a Convolutional Transformer (Convtrans) model that incorporates knowledge to make a collaborative estimation of PM2.5 and O3 by combining ground, satellite, and reanalysis data. Knowledge is introduced into the model by the shared and specific inputs, the PM2.5-O3 interaction module, and the weighted loss function designed with the correlation between PM2.5 and O3 concentrations. To verify the accuracy of the Convtrans model, its prediction result was compared with that of CNN-LSTM, Transformer, RF, and XGB models. Estimating the pollutant concentration in typical Chinese cities, the cross-validation results show that Convtrans has the minimum error (PM2.5:RMSE = 6.136 µg/m³, O3:RMSE = 8.250 µg/m³) and the highest prediction accuracy (PM2.5:R2 = 0.923, O3:R2 = 0.898). Finally, a map of pollutant concentrations was drawn according to the pollutant concentration values predicted by the model, showing the spatial variations of pollutant. This study indicates that it is feasible to integrate knowledge into a data-driven model for a joint estimation of atmospheric pollutant concentrations. In addition, the joint estimation framework for pollutants proposed in this study can be applied to multivariate retrieval or estimation in multiple fields.
Most of the methods for predicting air pollutant concentrations are targeting at single pollutants, which is time-consuming and laborious. To solve this problem, this study proposes a Convolutional Transformer (Convtrans) model that incorporates knowledge to make a collaborative estimation of PM2.5 and O3 by combining ground, satellite, and reanalysis data. Knowledge is introduced into the model by the shared and specific inputs, the PM2.5-O3 interaction module, and the weighted loss function designed with the correlation between PM2.5 and O3 concentrations. To verify the accuracy of the Convtrans model, its prediction result was compared with that of CNN-LSTM, Transformer, RF, and XGB models. Estimating the pollutant concentration in typical Chinese cities, the cross-validation results show that Convtrans has the minimum error (PM2.5:RMSE = 6.136 µg/m³, O3:RMSE = 8.250 µg/m³) and the highest prediction accuracy (PM2.5:R2 = 0.923, O3:R2 = 0.898). Finally, a map of pollutant concentrations was drawn according to the pollutant concentration values predicted by the model, showing the spatial variations of pollutant. This study indicates that it is feasible to integrate knowledge into a data-driven model for a joint estimation of atmospheric pollutant concentrations. In addition, the joint estimation framework for pollutants proposed in this study can be applied to multivariate retrieval or estimation in multiple fields.
Most of the methods for predicting air pollutant concentrations are targeting at single pollutants, which is time-consuming and laborious. To solve this problem, this study proposes a Convolutional Transformer (Convtrans) model that incorporates knowledge to make a collaborative estimation of PM2.5 and O3 by combining ground, satellite, and reanalysis data. Knowledge is introduced into the model by the shared and specific inputs, the PM2.5-O3 interaction module, and the weighted loss function designed with the correlation between PM2.5 and O3 concentrations. To verify the accuracy of the Convtrans model, its prediction result was compared with that of CNN-LSTM, Transformer, RF, and XGB models. Estimating the pollutant concentration in typical Chinese cities, the cross-validation results show that Convtrans has the minimum error (PM2.5:RMSE = 6.136 µg/m³, O3:RMSE = 8.250 µg/m³) and the highest prediction accuracy (PM2.5:R2 = 0.923, O3:R2 = 0.898). Finally, a map of pollutant concentrations was drawn according to the pollutant concentration values predicted by the model, showing the spatial variations of pollutant. This study indicates that it is feasible to integrate knowledge into a data-driven model for a joint estimation of atmospheric pollutant concentrations. In addition, the joint estimation framework for pollutants proposed in this study can be applied to multivariate retrieval or estimation in multiple fields.Most of the methods for predicting air pollutant concentrations are targeting at single pollutants, which is time-consuming and laborious. To solve this problem, this study proposes a Convolutional Transformer (Convtrans) model that incorporates knowledge to make a collaborative estimation of PM2.5 and O3 by combining ground, satellite, and reanalysis data. Knowledge is introduced into the model by the shared and specific inputs, the PM2.5-O3 interaction module, and the weighted loss function designed with the correlation between PM2.5 and O3 concentrations. To verify the accuracy of the Convtrans model, its prediction result was compared with that of CNN-LSTM, Transformer, RF, and XGB models. Estimating the pollutant concentration in typical Chinese cities, the cross-validation results show that Convtrans has the minimum error (PM2.5:RMSE = 6.136 µg/m³, O3:RMSE = 8.250 µg/m³) and the highest prediction accuracy (PM2.5:R2 = 0.923, O3:R2 = 0.898). Finally, a map of pollutant concentrations was drawn according to the pollutant concentration values predicted by the model, showing the spatial variations of pollutant. This study indicates that it is feasible to integrate knowledge into a data-driven model for a joint estimation of atmospheric pollutant concentrations. In addition, the joint estimation framework for pollutants proposed in this study can be applied to multivariate retrieval or estimation in multiple fields.
Most of the methods for predicting air pollutant concentrations are targeting at single pollutants, which is time-consuming and laborious. To solve this problem, this study proposes a Convolutional Transformer (Convtrans) model that incorporates knowledge to make a collaborative estimation of PM 2.5 and O 3 by combining ground, satellite, and reanalysis data. Knowledge is introduced into the model by the shared and specific inputs, the PM 2.5 -O 3 interaction module, and the weighted loss function designed with the correlation between PM 2.5 and O 3 concentrations. To verify the accuracy of the Convtrans model, its prediction result was compared with that of CNN-LSTM, Transformer, RF, and XGB models. Estimating the pollutant concentration in typical Chinese cities, the cross-validation results show that Convtrans has the minimum error (PM 2.5 :RMSE = 6.136 µg/m³, O 3 :RMSE = 8.250 µg/m³) and the highest prediction accuracy (PM 2.5 :R 2  = 0.923, O 3 :R 2  = 0.898). Finally, a map of pollutant concentrations was drawn according to the pollutant concentration values predicted by the model, showing the spatial variations of pollutant. This study indicates that it is feasible to integrate knowledge into a data-driven model for a joint estimation of atmospheric pollutant concentrations. In addition, the joint estimation framework for pollutants proposed in this study can be applied to multivariate retrieval or estimation in multiple fields.
ArticleNumber 25340
Author Ren, Ying
Xia, Biesheng
Wang, Siyuan
Xia, Bisheng
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Cites_doi 10.1016/j.cja.2019.07.011
10.1016/j.envpol.2018.08.029
10.5194/gmd-2022-64
10.1007/s11063-020-10327-3
10.3390/rs14122762
10.1016/j.cosrev.2021.100379
10.1021/acs.est.3c03317
10.3390/rs12020264
10.1016/j.uclim.2021.100837
10.1016/j.isprsjprs.2020.06.019
10.1016/j.neucom.2017.11.077
10.1016/j.atmosenv.2022.119297
10.1007/s40726-020-00170-4
10.1039/c8pp90059k
10.1016/j.envres.2017.11.020
10.1080/10408340500207482
10.1016/j.scs.2023.104951
10.1038/s41597-019-0055-0
10.1016/j.atmosres.2021.105633
10.1002/int.22370
10.1016/j.apr.2023.101703
10.1016/j.rse.2014.08.008
10.1016/j.scitotenv.2020.144502
10.1016/j.jes.2022.10.033
10.1016/j.scitotenv.2021.152449
10.1016/j.jclepro.2022.130988
10.4209/aaqr.200471
10.1016/j.ijepes.2021.107744
10.1016/j.buildenv.2021.108436
10.1007/s41095-022-0274-8
10.1038/s41377-021-00680-w
10.2166/hydro.2008.015
10.1038/s41377-021-00630-6
10.1016/j.atmosres.2013.08.015
10.1016/j.energy.2023.126781
10.1016/j.ecoenv.2023.114960
10.1016/j.scitotenv.2022.156312
10.1007/s00521-021-06007-5
10.1002/2017GL075710
10.1016/j.eswa.2022.118707
10.1016/j.ins.2019.10.069
10.1016/j.scitotenv.2021.145392
10.1029/2021JD035227
10.1016/j.atmosenv.2009.06.040
10.1016/j.atmosenv.2023.119957
10.1016/j.atmosenv.2019.05.049
10.1093/nar/gkab016
10.1186/s40537-014-0007-7
10.1016/j.ymssp.2021.108201
10.1016/j.chemosphere.2023.138830
10.1016/j.apr.2023.101866
10.1016/j.envpol.2022.120392
10.1016/j.cageo.2021.104869
10.1016/S2542-5196(20)30298-9
10.1016/j.asoc.2023.110955
10.1016/j.jenvman.2021.114210
10.1016/j.scitotenv.2020.144241
10.1016/j.petrol.2021.109202
10.1016/j.scitotenv.2022.153309
10.3390/rs14225841
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Issue 1
Keywords Convtrans
Joint Estimation
PM
Knowledge
O
Language English
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References H Dai (95019_CR34) 2023; 257
A Pandey (95019_CR4) 2021; 5
AF Bais (95019_CR44) 2019; 18
T Li (95019_CR54) 2020; 167
M Zhu (95019_CR55) 2023; 211
J Dong (95019_CR36) 2022; 315
Y Ren (95019_CR22) 2023; 14
Z Liu (95019_CR10) 2021; 772
Y Xiao (95019_CR59) 2021; 36
S Wang (95019_CR24) 2023; 331
MO Turkoglu (95019_CR26) 2021; 44
J Sun (95019_CR12) 2021; 762
D Cao (95019_CR63) 2022; 208
W Duan (95019_CR16) 2022; 820
R Li (95019_CR23) 2023; 269
KR Baker (95019_CR35) 2023; 57
J Shin (95019_CR60) 2023; 310
W Wang (95019_CR30) 2022; 8
A Haldorai (95019_CR47) 2021; 53
95019_CR58
J Shao (95019_CR48) 2020; 33
X Wang (95019_CR50) 2022; 813
Q Xu (95019_CR13) 2021; 765
Y Luo (95019_CR49) 2022; 838
MM Najafabadi (95019_CR56) 2015; 2
X Zhang (95019_CR1) 2022; 346
Y Xu (95019_CR7) 2018; 242
D Wang (95019_CR46) 2021; 49
K Han (95019_CR29) 2021; 34
C Zuo (95019_CR65) 2022; 11
Y Zhang (95019_CR51) 2023; 123
S Wang (95019_CR17) 2023; 14
C Wu (95019_CR67) 2022; 207
SR Shams (95019_CR32) 2021; 37
A Rahman (95019_CR3) 2019; 212
L Zhang (95019_CR33) 2021; 155
Y Li (95019_CR6) 2021; 7
L Li (95019_CR39) 2020; 12
DP Solomatine (95019_CR37) 2008; 10
M Michulec (95019_CR40) 2005; 35
J Zhang (95019_CR20) 2020; 33
J Chen (95019_CR15) 2022; 112
V Barzegar (95019_CR21) 2022; 164
Q Xu (95019_CR38) 2022; 14
J Cai (95019_CR57) 2018; 300
S Dong (95019_CR64) 2021; 40
M Todorovic (95019_CR53) 2023; 149
95019_CR9
J Li (95019_CR11) 2022; 303
CW Lu (95019_CR2) 2021; 10
RA Hamad (95019_CR45) 2021; 33
95019_CR31
L Qi (95019_CR42) 2022; 14
X Jurado (95019_CR66) 2023; 99
95019_CR62
L Huang (95019_CR41) 2021; 258
R Sivarethinamohan (95019_CR5) 2021; 37
MA Akbarzadeh (95019_CR52) 2018; 161
Y Zhang (95019_CR19) 2022; 136
MM Hassan (95019_CR25) 2020; 513
T Li (95019_CR14) 2021; 101
MA Ali (95019_CR43) 2022; 288
95019_CR28
W Song (95019_CR8) 2014; 154
F Cifuentes (95019_CR18) 2021; 21
Y Zhang (95019_CR68) 2019; 6
J Tao (95019_CR61) 2014; 135
DL Crouse (95019_CR27) 2009; 43
References_xml – volume: 112
  start-page: 102955
  year: 2022
  ident: 95019_CR15
  publication-title: Int. J. Appl. Earth Obs Geoinf.
– volume: 33
  start-page: 439
  year: 2020
  ident: 95019_CR20
  publication-title: Chin. J. Aeronaut.
  doi: 10.1016/j.cja.2019.07.011
– volume: 242
  start-page: 1417
  year: 2018
  ident: 95019_CR7
  publication-title: Environ. Pollut
  doi: 10.1016/j.envpol.2018.08.029
– ident: 95019_CR58
  doi: 10.5194/gmd-2022-64
– volume: 53
  start-page: 2385
  year: 2021
  ident: 95019_CR47
  publication-title: Neural Process. Lett.
  doi: 10.1007/s11063-020-10327-3
– volume: 14
  start-page: 2762
  year: 2022
  ident: 95019_CR42
  publication-title: Remote Sens.
  doi: 10.3390/rs14122762
– volume: 37
  start-page: 2725
  year: 2021
  ident: 95019_CR5
  publication-title: Mater. Today: Proc.
– volume: 40
  start-page: 100379
  year: 2021
  ident: 95019_CR64
  publication-title: Comput. Sci. Rev.
  doi: 10.1016/j.cosrev.2021.100379
– volume: 57
  start-page: 14626
  year: 2023
  ident: 95019_CR35
  publication-title: Environ. Sci. Technol.
  doi: 10.1021/acs.est.3c03317
– volume: 12
  start-page: 264
  year: 2020
  ident: 95019_CR39
  publication-title: Remote Sens.
  doi: 10.3390/rs12020264
– volume: 37
  start-page: 100837
  year: 2021
  ident: 95019_CR32
  publication-title: Urban Clim.
  doi: 10.1016/j.uclim.2021.100837
– volume: 167
  start-page: 178
  year: 2020
  ident: 95019_CR54
  publication-title: ISPRS J. Photogramm Remote Sens.
  doi: 10.1016/j.isprsjprs.2020.06.019
– volume: 300
  start-page: 70
  year: 2018
  ident: 95019_CR57
  publication-title: Neurocomputing
  doi: 10.1016/j.neucom.2017.11.077
– volume: 288
  start-page: 119297
  year: 2022
  ident: 95019_CR43
  publication-title: Atmos. Environ.
  doi: 10.1016/j.atmosenv.2022.119297
– volume: 7
  start-page: 72
  year: 2021
  ident: 95019_CR6
  publication-title: Curr. Pollut Rep.
  doi: 10.1007/s40726-020-00170-4
– volume: 18
  start-page: 602
  year: 2019
  ident: 95019_CR44
  publication-title: Photochem. Photobiol Sci.
  doi: 10.1039/c8pp90059k
– volume: 161
  start-page: 299
  year: 2018
  ident: 95019_CR52
  publication-title: Environ. Res.
  doi: 10.1016/j.envres.2017.11.020
– ident: 95019_CR31
– volume: 35
  start-page: 117
  year: 2005
  ident: 95019_CR40
  publication-title: Crit. Rev. Anal. Chem.
  doi: 10.1080/10408340500207482
– volume: 33
  start-page: 13434
  year: 2020
  ident: 95019_CR48
  publication-title: Adv. Neural. Inf. Process. Syst.
– volume: 99
  start-page: 104951
  year: 2023
  ident: 95019_CR66
  publication-title: Sustainable Cities Soc.
  doi: 10.1016/j.scs.2023.104951
– volume: 6
  start-page: 52
  year: 2019
  ident: 95019_CR68
  publication-title: Sci. Data
  doi: 10.1038/s41597-019-0055-0
– volume: 258
  start-page: 105633
  year: 2021
  ident: 95019_CR41
  publication-title: Atmos. Res.
  doi: 10.1016/j.atmosres.2021.105633
– volume: 36
  start-page: 2036
  year: 2021
  ident: 95019_CR59
  publication-title: Int. J. Intell. Syst.
  doi: 10.1002/int.22370
– volume: 14
  start-page: 101703
  year: 2023
  ident: 95019_CR22
  publication-title: Atmos. Pollut Res.
  doi: 10.1016/j.apr.2023.101703
– volume: 154
  start-page: 1
  year: 2014
  ident: 95019_CR8
  publication-title: Remote Sens. Environ.
  doi: 10.1016/j.rse.2014.08.008
– volume: 762
  start-page: 144502
  year: 2021
  ident: 95019_CR12
  publication-title: Sci. Total Environ.
  doi: 10.1016/j.scitotenv.2020.144502
– volume: 123
  start-page: 545
  year: 2023
  ident: 95019_CR51
  publication-title: J. Environ. Sci.
  doi: 10.1016/j.jes.2022.10.033
– volume: 813
  start-page: 152449
  year: 2022
  ident: 95019_CR50
  publication-title: Sci. Total Environ.
  doi: 10.1016/j.scitotenv.2021.152449
– volume: 346
  start-page: 130988
  year: 2022
  ident: 95019_CR1
  publication-title: J. Clean. Prod.
  doi: 10.1016/j.jclepro.2022.130988
– volume: 21
  start-page: 200471
  year: 2021
  ident: 95019_CR18
  publication-title: Aerosol Air Qual. Res.
  doi: 10.4209/aaqr.200471
– volume: 136
  start-page: 107744
  year: 2022
  ident: 95019_CR19
  publication-title: Int. J. Electr. Power Energy Syst.
  doi: 10.1016/j.ijepes.2021.107744
– volume: 44
  start-page: 4081
  year: 2021
  ident: 95019_CR26
  publication-title: IEEE Trans. Pattern Anal. Mach. Intell.
– volume: 207
  start-page: 108436
  year: 2022
  ident: 95019_CR67
  publication-title: Build. Environ.
  doi: 10.1016/j.buildenv.2021.108436
– ident: 95019_CR28
– volume: 8
  start-page: 415
  year: 2022
  ident: 95019_CR30
  publication-title: Comput. Visual Media
  doi: 10.1007/s41095-022-0274-8
– volume: 11
  start-page: 1
  year: 2022
  ident: 95019_CR65
  publication-title: Light Sci. Appl.
  doi: 10.1038/s41377-021-00680-w
– volume: 10
  start-page: 3
  year: 2008
  ident: 95019_CR37
  publication-title: J. Hydroinf.
  doi: 10.2166/hydro.2008.015
– volume: 10
  start-page: 190
  year: 2021
  ident: 95019_CR2
  publication-title: Light Sci. Appl.
  doi: 10.1038/s41377-021-00630-6
– volume: 135
  start-page: 48
  year: 2014
  ident: 95019_CR61
  publication-title: Atmos. Res.
  doi: 10.1016/j.atmosres.2013.08.015
– volume: 269
  start-page: 126781
  year: 2023
  ident: 95019_CR23
  publication-title: Energy
  doi: 10.1016/j.energy.2023.126781
– volume: 257
  start-page: 114960
  year: 2023
  ident: 95019_CR34
  publication-title: Ecotoxicol. Environ. Saf.
  doi: 10.1016/j.ecoenv.2023.114960
– volume: 838
  start-page: 156312
  year: 2022
  ident: 95019_CR49
  publication-title: Sci. Total Environ.
  doi: 10.1016/j.scitotenv.2022.156312
– volume: 33
  start-page: 13705
  year: 2021
  ident: 95019_CR45
  publication-title: Neural Comput. Appl.
  doi: 10.1007/s00521-021-06007-5
– ident: 95019_CR9
  doi: 10.1002/2017GL075710
– volume: 211
  start-page: 118707
  year: 2023
  ident: 95019_CR55
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2022.118707
– volume: 513
  start-page: 386
  year: 2020
  ident: 95019_CR25
  publication-title: Inf. Sci.
  doi: 10.1016/j.ins.2019.10.069
– volume: 772
  start-page: 145392
  year: 2021
  ident: 95019_CR10
  publication-title: Sci. Total Environ.
  doi: 10.1016/j.scitotenv.2021.145392
– volume: 34
  start-page: 15908
  year: 2021
  ident: 95019_CR29
  publication-title: Adv. Neural. Inf. Process. Syst.
– ident: 95019_CR62
  doi: 10.1029/2021JD035227
– volume: 43
  start-page: 5075
  year: 2009
  ident: 95019_CR27
  publication-title: Atmos. Environ.
  doi: 10.1016/j.atmosenv.2009.06.040
– volume: 310
  start-page: 119957
  year: 2023
  ident: 95019_CR60
  publication-title: Atmos. Environ.
  doi: 10.1016/j.atmosenv.2023.119957
– volume: 212
  start-page: 290
  year: 2019
  ident: 95019_CR3
  publication-title: Atmos. Environ.
  doi: 10.1016/j.atmosenv.2019.05.049
– volume: 49
  start-page: e46
  year: 2021
  ident: 95019_CR46
  publication-title: Nucleic Acids Res.
  doi: 10.1093/nar/gkab016
– volume: 2
  start-page: 1
  year: 2015
  ident: 95019_CR56
  publication-title: J. Big Data
  doi: 10.1186/s40537-014-0007-7
– volume: 164
  start-page: 108201
  year: 2022
  ident: 95019_CR21
  publication-title: Mech. Syst. Signal. Process.
  doi: 10.1016/j.ymssp.2021.108201
– volume: 331
  start-page: 138830
  year: 2023
  ident: 95019_CR24
  publication-title: Chemosphere
  doi: 10.1016/j.chemosphere.2023.138830
– volume: 101
  start-page: 102356
  year: 2021
  ident: 95019_CR14
  publication-title: Int. J. Appl. Earth Obs Geoinf.
– volume: 14
  start-page: 101866
  year: 2023
  ident: 95019_CR17
  publication-title: Atmos. Pollut Res.
  doi: 10.1016/j.apr.2023.101866
– volume: 315
  start-page: 120392
  year: 2022
  ident: 95019_CR36
  publication-title: Environ. Pollut.
  doi: 10.1016/j.envpol.2022.120392
– volume: 155
  start-page: 104869
  year: 2021
  ident: 95019_CR33
  publication-title: Comput. Geosci.
  doi: 10.1016/j.cageo.2021.104869
– volume: 5
  start-page: e25
  year: 2021
  ident: 95019_CR4
  publication-title: Lancet Planet. Health
  doi: 10.1016/S2542-5196(20)30298-9
– volume: 149
  start-page: 110955
  year: 2023
  ident: 95019_CR53
  publication-title: Appl. Soft Comput.
  doi: 10.1016/j.asoc.2023.110955
– volume: 303
  start-page: 114210
  year: 2022
  ident: 95019_CR11
  publication-title: J. Environ. Manage.
  doi: 10.1016/j.jenvman.2021.114210
– volume: 765
  start-page: 144241
  year: 2021
  ident: 95019_CR13
  publication-title: Sci. Total Environ.
  doi: 10.1016/j.scitotenv.2020.144241
– volume: 208
  start-page: 109202
  year: 2022
  ident: 95019_CR63
  publication-title: J. Petrol. Sci. Eng.
  doi: 10.1016/j.petrol.2021.109202
– volume: 820
  start-page: 153309
  year: 2022
  ident: 95019_CR16
  publication-title: Sci. Total Environ.
  doi: 10.1016/j.scitotenv.2022.153309
– volume: 14
  start-page: 5841
  year: 2022
  ident: 95019_CR38
  publication-title: Remote Sens.
  doi: 10.3390/rs14225841
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Snippet Most of the methods for predicting air pollutant concentrations are targeting at single pollutants, which is time-consuming and laborious. To solve this...
Abstract Most of the methods for predicting air pollutant concentrations are targeting at single pollutants, which is time-consuming and laborious. To solve...
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SubjectTerms 639/705
704/106/35
Aerosols
Air pollution
Convtrans
Deep learning
Design
Environmental monitoring
Humanities and Social Sciences
Humidity
Joint Estimation
Knowledge
Machine learning
multidisciplinary
Neural networks
Particulate matter
PM2.5
Pollutants
Radiation
Remote sensing
Science
Science (multidisciplinary)
Spatial variations
Time series
VOCs
Volatile organic compounds
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Title Knowledge based convolutional transformer for joint estimation of PM2.5 and O3 concentrations
URI https://link.springer.com/article/10.1038/s41598-025-95019-5
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Volume 15
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