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1 Ergebnisse
1
Predicting Atmospheric Water-Soluble Organic Mass Reversibl..:
Marwa M. H. El-Sayed
;
Siddharth S. Parida
;
Prashant Shekhar
..
doi:10.1021/acs.est.3c01259.s001. , 2023
Link:
https://doi.org/10.1021/acs.est.3c01259.s001
RT Journal T1
Predicting Atmospheric Water-Soluble Organic Mass Reversibly Partitioned to Aerosol Liquid Water in the Eastern United States
UL https://suche.suub.uni-bremen.de/peid=base-ftdeakinunifig:oai:figshare.com:article_24549332&Exemplar=1&LAN=DE A1 Marwa M. H. El-Sayed A1 Siddharth S. Parida A1 Prashant Shekhar A1 Amy Sullivan A1 Christopher J. Hennigan YR 2023 K1 Biochemistry K1 Ecology K1 Space Science K1 Biological Sciences not elsewhere classified K1 Chemical Sciences not elsewhere classified K1 total atmospheric aerosol K1 soluble organic matter K1 machine learning models K1 different models corresponding K1 100 decision trees K1 predicting atmospheric water K1 aerosol liquid water K1 relative feature importance K1 machine learning model K1 machine learning approach K1 low model uncertainties K1 evaporated organic mass K1 best performance (< K1 three consecutive summers K1 results presented herein K1 atmospheric data sets K1 1 %) leading K1 sample drying using K1 predict summertime concentrations K1 2 </ sup K1 water evaporation K1 relative humidity K1 particle drying K1 novel approach K1 model validation K1 evaporated organics K1 determination (< K1 >< sup K1 x </ K1 r </ K1 particle concentrations K1 isoprene concentrations K1 thus used K1 sub >< K1 square error K1 rmse ) K1 rh levels K1 rf model K1 random forest K1 highly uncertain K1 complex behavior K1 2017 ) JF doi:10.1021/acs.est.3c01259.s001 LK http://dx.doi.org/https://doi.org/10.1021/acs.est.3c01259.s001 DO https://doi.org/10.1021/acs.est.3c01259.s001 SF ELIB - SuUB Bremen
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