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Distributions and sources of n-alkanes in PM_(2.5) at urban,industrial and coastal sites in Tianjin,China

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Journal of Environmental Sciences 2010年第10期22卷 1551-1557页

作者： Weifang Li Yue Peng Zhipeng Bai State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control College of Environmental Science and Engineering Nankai University Tianjin 300071 China. Tianjin Academy of Environmental Protection Science Tianjin 300191 China Liaoning Provincial Environmental Monitoring Center Shenyang 110031 China

Aliphatic hydrocarbons （n-alkanes） associated with fine particulate matter were determined in the ambient air of urban, industrial and coastal areas in Tianjin, China, where intensive coal burning for industrial and domestic purpose takes place, n-Alkane homologues from C12 to C35 were quantifiable in all samples with C20-C31 being the most abundant species. Average concentrations of the total n-alkanes were 148.7, 250.1 and 842.0 ng/m3 in July, April and January, respectively. Seasonal variations were mainly attributed to ambient temperature changes and coal combustion for residential heating. Among the three studied areas, the highest levels of n-alkanes were observed in the industrial complex in winter and spring, but in summer the coastal alkane concentration moved up to the highest. A mono-modal distribution for n-alkanes was observed in spring and summer with odd carbon number predominance and a maximum centered at C27-C31, suggesting the release of plant wax into the atmosphere. The bimodal distribution with maxima at C22 and C26 observed in winter indicated a substantial influence of fossil fuel sources. All the CPIs （CPI1, CPI2, CPI3） values, varying between 0.64 and 1.97, indicated the influence of anthropogenic emissions on fine organic aerosols. The estimated contributions of plant wax to total n-alkanes were on average of 12.9%, 19.1% and 26.1% for winter, spring and summer, respectively.

关键词： PM2.5 n-alkanes distribution pattern CPIs plant wax

Emission factors, ozone and secondary organic aerosol formation potential of volatile organic compounds emitted from industrial biomass boilers

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Journal of Environmental Sciences 2019年第9期31卷 64-72页

作者： Chunmei Geng Wen Yang Xuesong Sun Xinhu Wang Zhipeng Bai Xia Zhang State Key Laboratory of Environmental Criteria and Risk Assessment Chinese Research Academy of Environmental Sciences Beijing 100012 China Beijing Municipal Research Institute of Environmental Protection Beijing 100037 China China National Environmental Monitoring Centre Beijing 100012 China

To evaluate the potential benefits of biomass use for air pollution control, this paper identified and quantified the emissions of major reactive organic compounds anticipated from biomass-fired industrial boilers. Wood pellets(WP) and straw pellets(SP) were burned to determine the volatile organic compound emission profiles for each biomass-boiler combination. More than 100 types of volatile organic compounds(VOCs) were measured from the two biomass boilers. The measured VOC species included alkanes, alkenes and acetylenes, aromatics, halocarbons and carbonyls. A single coal-fired boiler(CB) was also studied to provide a basis for comparison. Biomass boiler 1(BB1) emitted relatively high proportions of alkanes(28.9%–38.1% by mass) and alkenes and acetylenes(23.4%–40.8%),while biomass boiler 2(BB2) emitted relatively high proportions of aromatics(27.9%–29.2%)and oxygenated VOCs(33.0%–44.8%). The total VOC(TVOC) emission factors from BB1(128.59–146.16 mg/kg) were higher than those from BB2(41.26–85.29 mg/kg). The total ozone formation potential(OFP) ranged from 6.26 to 81.75 mg/m^3 with an average of 33.66 mg/m^3 for the two biomass boilers. The total secondary organic aerosol potential(SOAP) ranged from 61.56 to 211.67 mg/m^3 with an average of 142.27 mg/m^3 for the two biomass boilers.The emission factors(EFs) of TVOCs from biomass boilers in this study were similar to those for industrial coal-fired boilers with the same thermal power. These data can supplement existing VOC emission factors for biomass combustion and thus enrich the VOC emission inventory.

关键词： Biomass boiler Volatile organic compounds(VOCs) Emission factor Ozone formation potential(OFP) Secondary organic aerosol potential(SOAP)

Theoretical study on the reaction mechanism of ozone addition to the double bonds of keto-limonene

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Journal of Environmental Sciences 2012年第1期24卷 147-151页

作者： Lei Jiang Yisheng Xu Baohui Yin Zhipeng Bai College of Environmental Sciences and Engineering Peking UniversityBeijing 100871China State Key Laboratory of Environmental Criteria and Risk Assessment Chinese Research Academy of Environmental SciencesBeijing 100012China

The reaction mechanism of ozone （O 3） addition to the double bonds of gas phase keto-limonene was investigated using ab initio methods.Two different possibilities for O 3 addition to the double bond were considered and two corresponding van der Waals complexes （Complex 1 and Complex 2） were found for 1-endo and 2-endo.The rate constants were calculated using the transition state theory at the CCSD（T）/6-31G（d）＋ CF//B3LYP/6-31G（d,p） level.The high-pressure limit of the total rate constant at 298 K was 3.51×10-16 cm 3/（molecule·sec）,which was in a good agreement with the experimental data.

关键词： keto-limonene O3 transition state theory

A forecasting model for wave heights based on a long short-term memory neural network

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Acta Oceanologica Sinica 2021年第1期40卷 62-69页

作者： Song Gao Juan Huang Yaru Li Guiyan Liu Fan Bi Zhipeng Bai North China Sea Marine Forecasting Center of State Oceanic Administration Qingdao 266061China Shandong Provincial Key Laboratory of Marine Ecological Environment and Disaster Prevention and Mitigation Qingdao 266061China Mailbox 5111 Beijing 100094China

To explore new operational forecasting methods of waves,a forecasting model for wave heights at three stations in the Bohai Sea has been developed.This model is based on long short-term memory(LSTM)neural network with sea surface wind and wave heights as training samples.The prediction performance of the model is evaluated,and the error analysis shows that when using the same set of numerically predicted sea surface wind as input,the prediction error produced by the proposed LSTM model at Sta.N01 is 20%,18%and 23%lower than the conventional numerical wave models in terms of the total root mean square error(RMSE),scatter index(SI)and mean absolute error(MAE),respectively.Particularly,for significant wave height in the range of 3–5 m,the prediction accuracy of the LSTM model is improved the most remarkably,with RMSE,SI and MAE all decreasing by 24%.It is also evident that the numbers of hidden neurons,the numbers of buoys used and the time length of training samples all have impact on the prediction accuracy.However,the prediction does not necessary improve with the increase of number of hidden neurons or number of buoys used.The experiment trained by data with the longest time length is found to perform the best overall compared to other experiments with a shorter time length for training.Overall,long short-term memory neural network was proved to be a very promising method for future development and applications in wave forecasting.

关键词： long short-term memory marine forecast neural network significant wave height

Gravimetric analysis for PM_(2.5) mass concentration based on year-round monitoring at an urban site in Beijing

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Journal of Environmental Sciences 2016年第2期28卷 154-160页

作者： Yanli Wang Wen Yang Bin Han Wenjie Zhang Mindong Chen Zhipeng Bai State Key Laboratory of Environmental Criteria and Risk Assessment Chinese Research Academy of Environmental Sciences Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science & Technology

Daily PM_（2.5）（particulate matter with an aerodynamic diameter of below 2.5 μm） mass concentrations were measured by gravimetric analysis in Chinese Research Academy of Environmental Sciences（CRAES）, in the northern part of the Beijing urban area, from December 2013 to April 2015. Two pairs of Teflon（T1/T2） and Quartz（Q1/Q2） samples were obtained, for a total number of 1352 valid filters. Results showed elevated pollution in Beijing,with an annual mean PM_（2.5）mass concentration of 102 μg/m^3. According to the calculated PM_（2.5）mass concentration, 50% of our sampling days were acceptable（PM_（2.5）〈 75 μg/m^3）, 30% had slight/medium pollution（75–150 μg/m^3）, and 7% had severe pollution（〉 250 μg/m^3）. Sampling interruption occurred frequently for the Teflon filter group（75%） in severe pollution periods,resulting in important data being missing. Further analysis showed that high PM_（2.5）combined with high relative humidity（RH） gave rise to the interruptions. The seasonal variation of PM_（2.5）was presented, with higher monthly average mass concentrations in winter（peak value in February, 422 μg/m^3）, and lower in summer（7 μg/m^3 in June）. From May to August, the typical summer period, least severe pollution events were observed, with high precipitation levels accelerating the process of wet deposition to remove PM_（2.5）. The case of February presented the most serious pollution, with monthly averaged PM_（2.5）of 181 μg/m^3 and 32% of days with severe pollution. The abundance of PM_（2.5）in winter could be related to increased coal consumption for heating needs.

关键词： PM_（2.5） Gravimetric analysis Sampling interruption Pollution level Relative humidity（RH） Seasonal variation

Machine learning based altitude-dependent empirical LoS probability model for air-to-ground communications

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Frontiers of Information Technology & Electronic Engineering 2022年第9期23卷 1378-1389页

作者： Minghui PANG Qiuming ZHU zhipeng LIN Fei bai Yue TIAN Zhuo LI Xiaomin CHEN Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space College of Electronic and Information EngineeringNanjing University of Aeronautics and AstronauticsNanjing211106China State Key Laboratory of Integrated Services Networks Xidian UniversityXi’an710000China Key Laboratory of Radar Imaging and Microwave Photonics Ministry of EducationCollege of Electronic and Information EngineeringNanjing University of Aeronautics and AstronauticsNanjing211106China

Line-of-sight(LoS)probability prediction is critical to the performance optimization of wireless communication systems.However,it is challenging to predict the LoS probability of air-to-ground(A2G)communication scenarios,because the altitude of unmanned aerial vehicles(UAVs)or other aircraft varies from dozens of meters to several kilometers.This paper presents an altitude-dependent empirical LoS probability model for A2G scenarios.Before estimating the model parameters,we design a K-nearest neighbor(KNN)based strategy to classify LoS and non-LoS(NLoS)paths.Then,a two-layer back propagation neural network(BPNN)based parameter estimation method is developed to build the relationship between every model parameter and the UAV altitude.Simulation results show that the results obtained using our proposed model has good consistency with the ray tracing(RT)data,the measurement data,and the results obtained using the standard models.Our model can also provide wider applicable altitudes than other LoS probability models,and thus can be applied to different altitudes under various A2G scenarios.

关键词： Line-of-sight probability model Air-to-ground channel Machine learning Ray tracing

Exposure measurement, risk assessment and source identification for exposure of traffic assistants to particle-bound PAHs in Tianjin, China

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Journal of Environmental Sciences 2014年第2期26卷 448-457页

作者： Xiaodan Xue Yan You Jianhui Wu Bin Han Zhipeng Bai Naijun Tang Liwen Zhang College of Public Health Tianjin Medical University College of Environmental Science and Engineering Nankai University Research Center for Eco-Environmental Sciences Chinese Academy of Sciences Chinese Research Academy of Environmental Sciences

To investigate the levels of exposure to particulate-bound polycyclic aromatic hydrocarbon （PAH） and to estimate the risk these levels pose to traffic assistants （TAs） in Tianjin （a rnegacity in North China）, a measurement campaign （33 all-day exposure samples, 25 occupational-exposure samples and 10 indoor samples） was conducted to characterize the TAs＇ exposure to PAHs, assess the cancer risk and identify the potential sources of exposure. The average total exposure concentration of 14 PAHs was approximately 2871 ＋ 928 ng/rn3 （on-duty）, and 1622 ＋ 457 ng/m3 （all-day）. The indoor PAHs level was 1257 ＋ 107 ng/m3. After 8000 Monte Carlo simulations, the cancer risk resulting from exposure to PAHs was found to be approximately 1.05 x 10-4. A multivariate analysis was applied to identify the potential sources, and the results showed that, in addition to vehicle exhaust, coal combustion and cooking fumes were also another two important contributors to personal PAH exposure. The diagnostic ratios of PAH compounds agree with the source apportionment results derived from principal component analysis.

关键词： polycyclic aromatic hydrocarbons exposure risk assessment traffic assistants source identification

Asyncflow: A visual programming tool for game artificial intelligence

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Visual Informatics 2021年第4期5卷 20-25页

作者： zhipeng Hu Changjie Fan Qiwei Zheng Wei Wu bai Liu College of Computer Science and Technology Zhejiang UniversityChina NetEase Inc. HangzhouChina

Visual programming tools are widely applied in the game industry to assist game designers in developing game artificial intelligence(game AI)and gameplay.However,testing multiple game engines is a time-consuming operation,which degrades development efficiency.To provide an asynchronous platform for game designers,this paper introduces Asyncflow,an open-source visual programming solution.It consists of a flowchart maker for game logic explanation and a runtime framework integrating an asynchronous mechanism based on an event-driven architecture.Asyncflow supports multiple programming languages and can be easily embedded in various game engines to run flowcharts created by game designers.©2021 The Authors.

关键词： Visual programming Flowchart Game Artificial Intelligence

Emission factors of polycyclic aromatic hydrocarbons from domestic coal combustion in China

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Journal of Environmental Sciences 2014年第1期26卷 160-166页

作者： Chunmei Geng Jianhua Chen Xiaoyang Yang Lihong Ren Baohui Yin Xiaoyu Liu Zhipeng Bai State Key Laboratory of Environmental Criteria and Risk Assessment Chinese Research Academy of Environmental Sciences

Domestic coal stove is widely used in China, especially for countryside during heating period of winter, and polycyclic aromatic hydrocarbons （PAHs） are important in flue gas of the stove. By using dilution tunnel system, samples of both gaseous and particulate phases from domestic coal combustion were collected and 18 PAH species were analyzed by GC-MS. The average emission factors of total 18 PAH species was 171.73 mg/kg, ranging from 140.75 to 229.11 mg/kg for bituminous coals, while was 93.98 mg/kg, ranging from 58.48 to 129.47 mg/kg for anthracite coals. PAHs in gaseous phases occupied 95% of the total of PAHs emission of coal combustion. In particulate phase, 3-ring and 4- ring PAHs were the main components, accounting for 80% of the total particulate PAHs. The total toxicity potency evaluated by benzo[a]pyrene-equivalent carcinogenic power, sum of 7 carcinogenic PAH components and 2,3,7,8-tetrachlorodibenzodioxin had a similar tendency. And as a result, the toxic potential of bituminous coal was higher than that of anthracite coal. Efficient emission control should be conducted to reduce PAH emissions in order to protect ecosystem and human health.

关键词： domestic coal combustion emission factor polycyclic aromatic hydrocarbons potential toxicity risk diagnostic ratio