PMID- 34769626
OWN - NLM
STAT- MEDLINE
DCOM- 20211122
LR  - 20211122
IS  - 1660-4601 (Electronic)
IS  - 1661-7827 (Print)
IS  - 1660-4601 (Linking)
VI  - 18
IP  - 21
DP  - 2021 Oct 22
TI  - Key Factors for Improving the Carcinogenic Risk Assessment of PAH Inhalation 
      Exposure by Monte Carlo Simulation.
LID - 10.3390/ijerph182111106 [doi]
LID - 11106
AB  - Monte Carlo simulation (MCS) is a computational technique widely used in exposure 
      and risk assessment. However, the result of traditional health risk assessment 
      based on the MCS method has always been questioned due to the uncertainty 
      introduced in parameter estimation and the difficulty in result validation. 
      Herein, data from a large-scale investigation of individual polycyclic aromatic 
      hydrocarbon (PAH) exposure was used to explore the key factors for improving the 
      MCS method. Research participants were selected using a statistical sampling 
      method in a typical PAH polluted city. Atmospheric PAH concentrations from 25 
      sampling sites in the area were detected by GC-MS and exposure parameters of 
      participants were collected by field measurement. The incremental lifetime cancer 
      risk (ILCR) of participants was calculated based on the measured data and 
      considered to be the actual carcinogenic risk of the population. Predicted risks 
      were evaluated by traditional assessment method based on MCS and three improved 
      models including concentration-adjusted, age-stratified, and 
      correlated-parameter-adjusted Monte Carlo methods. The goodness of fit of the 
      models was evaluated quantitatively by comparing with the actual risk. The 
      results showed that the average risk derived by traditional and age-stratified 
      Monte Carlo simulation was 2.6 times higher, and the standard deviation was 3.7 
      times higher than the actual values. In contrast, the predicted risks of 
      concentration- and correlated-parameter-adjusted models were in good agreement 
      with the actual ILCR. The results of the comparison suggested that accurate 
      simulation of exposure concentration and adjustment of correlated parameters 
      could greatly improve the MCS. The research also reveals that the social factors 
      related to exposure and potential relationship between variables are important 
      issues affecting risk assessment, which require full consideration in assessment 
      and further study in future research.
FAU - Qin, Ning
AU  - Qin N
AD  - School of Energy and Environmental Engineering, University of Science and 
      Technology Beijing, Beijing 100083, China.
FAU - Tuerxunbieke, Ayibota
AU  - Tuerxunbieke A
AD  - School of Energy and Environmental Engineering, University of Science and 
      Technology Beijing, Beijing 100083, China.
FAU - Wang, Qin
AU  - Wang Q
AD  - Chinese Center for Disease Control and Prevention, China CDC Key Laboratory of 
      Environment and Population Health, National Institute of Environmental Health, 
      Beijing 100021, China.
FAU - Chen, Xing
AU  - Chen X
AD  - School of Energy and Environmental Engineering, University of Science and 
      Technology Beijing, Beijing 100083, China.
FAU - Hou, Rong
AU  - Hou R
AD  - School of Energy and Environmental Engineering, University of Science and 
      Technology Beijing, Beijing 100083, China.
FAU - Xu, Xiangyu
AU  - Xu X
AD  - School of Energy and Environmental Engineering, University of Science and 
      Technology Beijing, Beijing 100083, China.
FAU - Liu, Yunwei
AU  - Liu Y
AD  - School of Energy and Environmental Engineering, University of Science and 
      Technology Beijing, Beijing 100083, China.
FAU - Xu, Dongqun
AU  - Xu D
AD  - Chinese Center for Disease Control and Prevention, China CDC Key Laboratory of 
      Environment and Population Health, National Institute of Environmental Health, 
      Beijing 100021, China.
FAU - Tao, Shu
AU  - Tao S
AD  - Laboratory for Earth Surface Processes, College of Urban and Environmental 
      Sciences, Peking University, Beijing 100871, China.
FAU - Duan, Xiaoli
AU  - Duan X
AUID- ORCID: 0000-0002-7424-9656
AD  - School of Energy and Environmental Engineering, University of Science and 
      Technology Beijing, Beijing 100083, China.
LA  - eng
GR  - 2021-CKL-01/China CDC Key Laboratory of Environment and Population Health, 
      National Institute of Environmental Health, Chinese Center for Disease Control 
      and Prevention/
GR  - FRF-TP-18-071A1/Fundamental Research Funds for the Central Universities/
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20211022
PL  - Switzerland
TA  - Int J Environ Res Public Health
JT  - International journal of environmental research and public health
JID - 101238455
RN  - 0 (Carcinogens)
RN  - 0 (Polycyclic Aromatic Hydrocarbons)
SB  - IM
MH  - Carcinogens/toxicity
MH  - China
MH  - Environmental Monitoring
MH  - Humans
MH  - Inhalation Exposure/analysis
MH  - Monte Carlo Method
MH  - *Polycyclic Aromatic Hydrocarbons/analysis/toxicity
MH  - Risk Assessment
PMC - PMC8583189
OTO - NOTNLM
OT  - Monte Carlo simulation
OT  - PAHs
OT  - exposure parameter
OT  - risk assessment
OT  - sensitivity analysis
COIS- The authors declare no conflict of interest.
EDAT- 2021/11/14 06:00
MHDA- 2021/11/23 06:00
PMCR- 2021/10/22
CRDT- 2021/11/13 01:07
PHST- 2021/08/12 00:00 [received]
PHST- 2021/09/30 00:00 [revised]
PHST- 2021/10/19 00:00 [accepted]
PHST- 2021/11/13 01:07 [entrez]
PHST- 2021/11/14 06:00 [pubmed]
PHST- 2021/11/23 06:00 [medline]
PHST- 2021/10/22 00:00 [pmc-release]
AID - ijerph182111106 [pii]
AID - ijerph-18-11106 [pii]
AID - 10.3390/ijerph182111106 [doi]
PST - epublish
SO  - Int J Environ Res Public Health. 2021 Oct 22;18(21):11106. doi: 
      10.3390/ijerph182111106.