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| 1 | Urban air quality, meteorology and traffic linkages: Evidence from a sixteen-day particulate matter pollution event in December 2015, Beijing显示文摘A heavy 16-day pollution episode occurred in Beijing from December 19, 2015 to January 3,2016. The mean daily AQI and PM_(2.5) were 240.44 and 203.6 μg/m^3. We analyzed the spatiotemporal characteristics of air pollutants, meteorology and road space speed during this period, then extended to reveal the combined effects of traffic restrictions and meteorology on urban air quality with observational data and a multivariate mutual information model. Results of spatiotemporal analysis showed that five pollution stages were identified with remarkable variation patterns based on evolution of PM_(2.5) concentration and weather conditions. Southern sites(DX, YDM and DS) experienced heavier pollution than northern ones(DL, CP and WL). Stage P2 exhibited combined functions of meteorology and traffic restrictions which were delayed peak-clipping effects on PM_(2.5).Mutual information values of Air quality–Traffic–Meteorology(ATM–MI) revealed that additive functions of traffic restrictions, suitable relative humidity and temperature were more effective on the removal of fine particles and CO than NO_2. | Dongmei Hu Jianping Wu Kun Tian Lyuchao Liao Ming Xu Yiman Du | 2017 | Journal of Environmental Sciences2017，29，9： | 3 |
| 2 | Predicting vehicle fuel consumption patterns using floating vehicle data显示文摘The status of energy consumption and air pollution in China is serious. It is important to analyze and predict the different fuel consumption of various types of vehicles under different influence factors. In order to fully describe the relationship between fuel consumption and the impact factors, massive amounts of floating vehicle data were used.The fuel consumption pattern and congestion pattern based on large samples of historical floating vehicle data were explored, drivers' information and vehicles' parameters from different group classification were probed, and the average velocity and average fuel consumption in the temporal dimension and spatial dimension were analyzed respectively.The fuel consumption forecasting model was established by using a Back Propagation Neural Network. Part of the sample set was used to train the forecasting model and the remaining part of the sample set was used as input to the forecasting model. | Yiman Du Jianping Wu Senyan Yang Liutong Zhou | 2017 | Journal of Environmental Sciences2017，29，9： | 1 |
| 3 | Nonlinear dynamic characteristic analysis of speech for Chinese显示文摘Nonlinear dynamic method is used in studying Chinese spoken in normal speed, and the improved correlation dimension algorithm are made for the characterization of speech signal. The reconstructed phase space and correlation dimension curves of unvoiced fricative consonants and vowels are also given. It is found that the correlation dimension algorithm can distinguish fricative from vowel because of the different mechanism between them. And the study shows that it can provide information for distinguishing four basic tones in mandarin. | HU Shuiqing ZHANG Yu HUA Yiman DU Gonghuan (Institute of Acoustics & State Key Lab of Modern Acoustics, Naning University Nanjing 210093) | 2000 | Chinese Journal of Acoustics2000，19，3： | 0 |
| 4 | Simulation study on improvement of air quality by introducing electric vehicles显示文摘Recently,Chinese megacities have suffered serious air pollution.Previous studies have pointed out that transportation systems have become one of the major sources of air pollution and on-road pollutant concentrations are significantly higher than off-road.Electric vehicle(EV)introduction is proposed as a method to alleviate the current situation.In order to better understand the benefit of the use of EVs in Beijing,a simulation platform has been developed to evaluate the improvement of air quality with the use of EVs quantitatively within the selected area.Four scenarios with different EV penetration rates are proposed and the results revealed 5%,10%,15%EV penetration rates which will bring about improvement of 0.86%,9.01%and 12.23%for PM2.5,0.92%,9.01%and 13.32%for nitrogen oxides(NO_(x)),0.95%,8.86%and 13.73%for CO,respectively.The results revealed a promising improvement of air quality with the introduction of EVs. | Yiman Du Jianping Wu Kezhen Hu Yue Guo | 2015 | International Journal of Modeling, Simulation, and Scientific Computing2015，6，4： | 0 |
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