PMID- 31633339
OWN - NLM
STAT- MEDLINE
DCOM- 20191128
LR  - 20191128
IS  - 1520-5851 (Electronic)
IS  - 0013-936X (Linking)
VI  - 53
IP  - 22
DP  - 2019 Nov 19
TI  - Deep Learning Prediction of Polycyclic Aromatic Hydrocarbons in the High Arctic.
PG  - 13238-13245
LID - 10.1021/acs.est.9b05000 [doi]
AB  - Given the lack of understanding of the complex physiochemical and environmental 
      processes of persistent organic pollutants (POPs) in the Arctic and around the 
      globe, atmospheric models often yield large errors in the predicted atmospheric 
      concentrations of POPs. Here, we developed a recurrent neural network (RNN) 
      method based on nonparametric deep learning algorithms. The RNN model was 
      implemented to predict monthly air concentrations of polycyclic aromatic 
      hydrocarbons (PAHs) at the high Arctic monitoring station Alert. To train the RNN 
      system, we used MODIS satellite remotely sensed forest fire data, air emissions, 
      meteorological data, sea ice cover area, and sampled PAH concentration data from 
      1996 to 2012. The system was applied to forecast monthly PAH concentrations from 
      2012 to 2014 at the Alert station. The results were compared with monitored PAHs 
      and an atmospheric transport model (CanMETOP) for POPs. We show that the RNN 
      significantly improved PHE and BaP predictions from 2012 to 2014 by 62.5 and 
      91.1%, respectively, compared to CanMETOP predictions. The sensitivity analysis 
      using the Shapley value reveals that air emissions determined the magnitude of 
      PAH levels in the high Arctic, whereas forest fires played a significant role in 
      the changes in PAH concentrations in the high Arctic, followed by air temperature 
      and meridional wind fields.
FAU - Zhao, Yuan
AU  - Zhao Y
AD  - Laboratory for Earth Surface Processes, College of Urban and Environmental 
      Sciences , Peking University , Beijing 100871 , China.
FAU - Wang, Li
AU  - Wang L
AD  - CAS Key Laboratory of Chemistry of Northwestern Plant Resources and Key 
      Laboratory for Natural Medicine of Gansu Province , Lanzhou Institute of Chemical 
      Physics , Chinese Academy of Sciences , Lanzhou 730000 , China.
FAU - Luo, Jinmu
AU  - Luo J
AD  - Laboratory for Earth Surface Processes, College of Urban and Environmental 
      Sciences , Peking University , Beijing 100871 , China.
FAU - Huang, Tao
AU  - Huang T
AD  - Key Laboratory for Environmental Pollution Prediction and Control, Gansu 
      Province, College of Earth and Environmental Sciences , Lanzhou University , 
      Lanzhou 730000 , China.
FAU - Tao, Shu
AU  - Tao S
AUID- ORCID: 0000-0002-7374-7063
AD  - Laboratory for Earth Surface Processes, College of Urban and Environmental 
      Sciences , Peking University , Beijing 100871 , China.
FAU - Liu, Junfeng
AU  - Liu J
AUID- ORCID: 0000-0002-7199-6357
AD  - Laboratory for Earth Surface Processes, College of Urban and Environmental 
      Sciences , Peking University , Beijing 100871 , China.
FAU - Yu, Yong
AU  - Yu Y
AD  - Key Laboratory of Wetland Ecology and Environment, Northeast Institute of 
      Geography and Agroecology , Chinese Academy of Sciences , Changchun 130102 , 
      China.
FAU - Huang, Yufei
AU  - Huang Y
AD  - Laboratory for Earth Surface Processes, College of Urban and Environmental 
      Sciences , Peking University , Beijing 100871 , China.
FAU - Liu, Xinrui
AU  - Liu X
AD  - Laboratory for Earth Surface Processes, College of Urban and Environmental 
      Sciences , Peking University , Beijing 100871 , China.
FAU - Ma, Jianmin
AU  - Ma J
AUID- ORCID: 0000-0002-6593-570X
AD  - Laboratory for Earth Surface Processes, College of Urban and Environmental 
      Sciences , Peking University , Beijing 100871 , China.
LA  - eng
PT  - Journal Article
DEP - 20191030
PL  - United States
TA  - Environ Sci Technol
JT  - Environmental science & technology
JID - 0213155
RN  - 0 (Air Pollutants)
RN  - 0 (Polycyclic Aromatic Hydrocarbons)
SB  - IM
MH  - *Air Pollutants
MH  - Arctic Regions
MH  - Deep Learning
MH  - Environmental Monitoring
MH  - *Polycyclic Aromatic Hydrocarbons
EDAT- 2019/10/22 06:00
MHDA- 2019/11/30 06:00
CRDT- 2019/10/22 06:00
PHST- 2019/10/22 06:00 [pubmed]
PHST- 2019/11/30 06:00 [medline]
PHST- 2019/10/22 06:00 [entrez]
AID - 10.1021/acs.est.9b05000 [doi]
PST - ppublish
SO  - Environ Sci Technol. 2019 Nov 19;53(22):13238-13245. doi: 
      10.1021/acs.est.9b05000. Epub 2019 Oct 30.