Ayibota Tuerxunbieke
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Ayibota Tuerxunbieke has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Ayibota Tuerxunbieke
School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
1 shared publication - Ling Qi
School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
1 shared publication - Ning Qin
School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
1 shared publication - Xiangyu Xu
Center for Environmental Risk and Damage Assessment, Chinese Academy of Environmental Planning, Beijing 100041, China
1 shared publication - Xiaoli Duan
School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
1 shared publication
Latest Publications
- Open Access
Development of Phase and Seasonally Dependent Land-Use Regression Models to Predict Atmospheric PAH Levels
Polycyclic aromatic hydrocarbons (PAHs) are an important class of pollutants in China. The land use regression (LUR) model has been used to predict the selected PAH concentrations and screen the key influencing factors. However, most previous studies have focused on particle-associated PAHs, and research on gaseous PAHs was limited. This study measured representative PAHs in both gaseous phases and particle-associated during the windy, non-heating and heating seasons from 25 sampling sites in different areas of Taiyuan City. We established separate prediction models of 15 PAHs. Acenaphthene (Ace), Fluorene (Flo), and benzo [g,h,i] perylene (BghiP) were selected to analyze the relationship between PAH concentration and influencing factors. The stability and accuracy of the LUR models were quantitatively evaluated using leave-one-out cross-validation. We found that Ace and Flo models show good performance in the gaseous phase (Ace: adj. R2 = 0.14–0.82; Flo: adj. R2 = 0.21–0.85), and the model performance of BghiP is better in the particle phase (adj. R2 = 0.20–0.42). Additionally, better model performance was observed in the heating season (adj R2 = 0.68–0.83) than in the non-heating (adj R2 = 0.23–0.76) and windy seasons (adj R2 = 0.37–0.59). Those gaseous PAHs were highly affected by traffic emissions, elevation, and latitude, whereas BghiP was affected by point sources. This study reveals the strong seasonal and phase dependence of PAH concentrations. Building separate LUR models in different phases and seasons improves the prediction accuracy of PAHs.
Open AccessDevelopment of Phase and Seasonally Dependent Land-Use Regression Models to Predict Atmospheric PAH Levels
Polycyclic aromatic hydrocarbons (PAHs) are an important class of pollutants in China. The land use regression (LUR) model has been used to predict the selected PAH concentrations and screen the key influencing factors. However, most previous studies have focused on particle-associated PAHs, and research on gaseous PAHs was limited. This study measured representative PAHs in both gaseous phases and particle-associated during the windy, non-heating and heating seasons from 25 sampling sites in different areas of Taiyuan City. We established separate prediction models of 15 PAHs. Acenaphthene (Ace), Fluorene (Flo), and benzo [g,h,i] perylene (BghiP) were selected to analyze the relationship between PAH concentration and influencing factors. The stability and accuracy of the LUR models were quantitatively evaluated using leave-one-out cross-validation. We found that Ace and Flo models show good performance in the gaseous phase (Ace: adj. R2 = 0.14–0.82; Flo: adj. R2 = 0.21–0.85), and the model performance of BghiP is better in the particle phase (adj. R2 = 0.20–0.42). Additionally, better model performance was observed in the heating season (adj R2 = 0.68–0.83) than in the non-heating (adj R2 = 0.23–0.76) and windy seasons (adj R2 = 0.37–0.59). Those gaseous PAHs were highly affected by traffic emissions, elevation, and latitude, whereas BghiP was affected by point sources. This study reveals the strong seasonal and phase dependence of PAH concentrations. Building separate LUR models in different phases and seasons improves the prediction accuracy of PAHs.