PMID- 33548647
OWN - NLM
STAT- MEDLINE
DCOM- 20210512
LR  - 20210512
IS  - 1879-1298 (Electronic)
IS  - 0045-6535 (Linking)
VI  - 274
DP  - 2021 Jul
TI  - Bagging based ensemble learning approaches for modeling the emission of PCDD/Fs 
      from municipal solid waste incinerators.
PG  - 129802
LID - S0045-6535(21)00271-X [pii]
LID - 10.1016/j.chemosphere.2021.129802 [doi]
AB  - The conventional method for determining polychlorinated dibenzo-p-dioxins and 
      polychlorinated dibenzofurans (PCDD/Fs) emission concentrations from municipal 
      solid waste incinerators (MSWIs) is accurate but complex, costly, and 
      time-consuming. In this study, we utilized machine learning approaches to model 
      International Toxic Equivalent Quality (I-TEQ) values of PCDD/Fs through 
      chlorobenzene (CBz). Sampled data from a MSWI with a grate furnace and three 
      MSWIs with fluidized bed furnaces were randomly divided into the training and 
      test set. A two-step data preprocessing consists of Box-Cox transformation and 
      standardization was applied to improve the model performances. The models 
      including artificial neural network (ANN) and decision tree (DT). With the help 
      of bagging, ANN and DT were stacked into bagging-based ensemble neural networks 
      (BGNN) and random forest (RF) to improve the accuracy and robustness. BGNN was 
      proved to be the best model with the relatively good perfomance in the test set. 
      The Shapley additive explanation (SHAP) method was employed to improve the 
      interpretability of BGNN. The results show that 1,2,3-trichlorobenzene (TrCBz), 
      1,4-dichlorobenzene (DiCBz), 1,2,4-TrCBz, and 1,3-DiCBz make positive 
      contributions to I-TEQ values. Employing machine learning models, particularly 
      BGNN, can be an effective method to determine the emission of PCDD/Fs within a 
      wide range of I-TEQ values and in multiple MSWIs with strong generalization 
      ability. It is helpful to realize the on-line measurement and controlling of 
      PCDD/Fs emission.
CI  - Copyright (c) 2021 Elsevier Ltd. All rights reserved.
FAU - Chen, Ken
AU  - Chen K
AD  - State Key Laboratory of Clean Energy Utilization, Institute for Thermal Power 
      Engineering, Zhejiang University, Hangzhou, 310027, China.
FAU - Peng, Yaqi
AU  - Peng Y
AD  - State Key Laboratory of Clean Energy Utilization, Institute for Thermal Power 
      Engineering, Zhejiang University, Hangzhou, 310027, China.
FAU - Lu, Shengyong
AU  - Lu S
AD  - State Key Laboratory of Clean Energy Utilization, Institute for Thermal Power 
      Engineering, Zhejiang University, Hangzhou, 310027, China. Electronic address: 
      lushy@zju.edu.cn.
FAU - Lin, Binbin
AU  - Lin B
AD  - State Key Laboratory of Clean Energy Utilization, Institute for Thermal Power 
      Engineering, Zhejiang University, Hangzhou, 310027, China.
FAU - Li, Xiaodong
AU  - Li X
AD  - State Key Laboratory of Clean Energy Utilization, Institute for Thermal Power 
      Engineering, Zhejiang University, Hangzhou, 310027, China.
LA  - eng
PT  - Journal Article
DEP - 20210128
PL  - England
TA  - Chemosphere
JT  - Chemosphere
JID - 0320657
RN  - 0 (Air Pollutants)
RN  - 0 (Benzofurans)
RN  - 0 (Dibenzofurans)
RN  - 0 (Dibenzofurans, Polychlorinated)
RN  - 0 (Polychlorinated Dibenzodioxins)
RN  - 0 (Solid Waste)
SB  - IM
MH  - *Air Pollutants/analysis
MH  - *Benzofurans/analysis
MH  - Dibenzofurans
MH  - Dibenzofurans, Polychlorinated/analysis
MH  - Environmental Monitoring
MH  - Incineration
MH  - Machine Learning
MH  - *Polychlorinated Dibenzodioxins/analysis
MH  - Solid Waste/analysis
OTO - NOTNLM
OT  - Chlorobenzene (CBz)
OT  - Data preprocessing
OT  - Machine learning
OT  - Model interpretation
OT  - Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs)
COIS- Declaration of competing interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2021/02/07 06:00
MHDA- 2021/05/13 06:00
CRDT- 2021/02/06 20:11
PHST- 2020/12/15 00:00 [received]
PHST- 2021/01/21 00:00 [revised]
PHST- 2021/01/23 00:00 [accepted]
PHST- 2021/02/07 06:00 [pubmed]
PHST- 2021/05/13 06:00 [medline]
PHST- 2021/02/06 20:11 [entrez]
AID - S0045-6535(21)00271-X [pii]
AID - 10.1016/j.chemosphere.2021.129802 [doi]
PST - ppublish
SO  - Chemosphere. 2021 Jul;274:129802. doi: 10.1016/j.chemosphere.2021.129802. Epub 
      2021 Jan 28.