|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Allowance and allocation of industrial volatile organic compounds emission in China for year 2020 and 2030显示文摘As an effective pollution control method, emission allowance and allocation just implemented in volatile organic compounds(VOCs) control strategy of China in 2016. This article presents a possible way to set the emission allowance targets and establishes an allowance allocation model for the object year, 2020 and 2030, using 2010 as the reference year. On the basis of regression and scenario analysis method, the emission allowance targets were designed,which were 17.902 Tg and 18.224 Tg for 2020 and 2030, with an increasing rate of 28.75% and31.06% compared to 2010. From the perspective of industries, processes using VOCscontaining products, like machinery and equipment manufacturing, would continue to be the most significant industrial VOCs emission sources in the future of China. Four allocation indicators were selected, which are per capita GDP of each province, per capita industrial VOCs emission of each province, the economic contribution of industrial sector to regional economy of each province, and the emission intensity per land area of each province, respectively.Based on information entropy, the weights of the indicators were calculated and an emission allocation model was established, and the results showed that provinces like Shandong,Jiangsu, Guangdong, Zhejiang and Fujian were calculated to obtain more emission allowance while burden more reduction responsibility. Meanwhile, provinces like Guizhou, Ningxia,Hainan, Qinghai and Xizang were on the contrary. This paper suggests governments to enhance or ease to industrial VOCs reduction burden of each province in order to stimulate its economy or change its way of economy development. | Jiani Zhang Jingfang Xiao Xiaofang Chen Xiaoming Liang Liya Fan Daiqi Ye | 2018 | Journal of Environmental Sciences2018，30，7： | 13 |
| 2 | Relationships of ozone formation sensitivity with precursors emissions,meteorology and land use types,in Guangdong-Hong Kong-Macao Greater Bay Area,China显示文摘Due to the influences of precursors emissions,meteorology,geography and other factors,ozone formation sensitivity(OFS)is generally spatially and temporally heterogeneous.This study characterized detailed spatial and temporal variations of OFS in Guangdong-Hong Kong-Macao Greater Bay Area(GBA)from 2012 to 2016 based on OMI satellite data,and analyzed the relationships of OFS with precursors emissions,meteorology and land use types(LUTs).From 2012 to 2016,the OFS tended to be NOx-limited in GBA,with the value of FNR(HCHO/NO 2)increasing from 2.04 to 2.22.According to the total annual emission statistics of precursors,NOx emissions decreased by 33.1%and VOCs emissions increased by 35.2%from 2012 to 2016,directly resulting in OFS tending to be NO x-limited.The Grey Relation Analysis results show that total column water(TCW),surface net solar radiation(SSR),air temperature at 2 m(T2)and surface pressure(SP)are the top four meteorological factors with the greatest influences on OFS.There are significant positive correlations between FNR and T2,SSR,TCW,and significant negative correlations between FNR and SP.In GBA,the OFS tends to be NO x-limited regime in wet season(higher T2,SSR,TCW and lower SP)and VOCs-limited regime in dry season(lower T2,SSR,TCW and higher SP).The FNR displays obvious gradient variations on different LUTs,with the highest in“Rural areas”,second in“Suburban areas”and lowest in“Urban areas”. | Yuping Chen Hui Yan Yijuan Yao Chunling Zeng Ping Gao Liyue Zhuang Liya Fan Daiqi Ye | 2020 | Journal of Environmental Sciences2020，32，8： | 5 |
| 3 | Volatile organic compounds concentration profiles and control strategy in container manufacturing industry:Case studies in China显示文摘Volatile organic compounds(VOCs),important precursors of ozone(O_(3))and fine particulate matter(PM_(2.5)),are the key to curb the momentum of O_(3)growth and further reducing PM_(2.5)in China.Container manufacturing industry is one of the major VOC emitters,and more than 96%containers of the world are produced in China,with the annual usage of coatings of over 200,000 tons in recent years.This is the first research on the emission characteristics of VOCs in Chinese container manufacturing industry,including concentration and ozone formation potential(OFP)of each species.The result shows that the largest amounts of VOCs are emitted during the pretreatment process,followed by the paint mixing process and primer painting process,and finally other sprays process.The average VOC concentrations in the workshops,the exhausts before treatment and the exhausts after treatment are ranging from 82.67–797.46,170–1,812.65,66.20–349.63 mg/m^(3),respectively.Benzenes,alcohols and ethers are main species,which contribute more than 90%OFP together.Based on the emission characteristics of VOCs and the technical feasibility,it is recommended to set the emission limit in standard of benzene to 1.0 mg/m^(3),toluene to 10 mg/m^(3),xylene to 20 mg/m^(3),benzenes to 40 mg/m^(3),alcohols and ethers to 50 mg/m^(3),and VOCs to 100 mg/m^(3).The study reports the industry emission characteristics and discusses the standard limits,which is a powerful support to promote VOCs emission reduction,and to promote the coordinated control of PM_(2.5)and O_(3)pollution. | Yunting Ke Ruiyuan Liu Xiaofang Chen Yang Feng Ping Gao Haomin Huang Liya Fan Daiqi Ye | 2021 | Journal of Environmental Sciences2021，33，6： | 2 |
| 4 | Epidemiologic Characteristics of Cases for Influenza A(H7N9) Virus Infections in China显示文摘 | Wenyi Zhang Liya Wang Wenbiao Hu Fan Ding Hailong Sun Shenlong Li Liuyu Huang Chengyi Li | 2013 | Clinical Infectious Diseases2013，，4： | 1 |
| 5 | Adenocarcinoma transformed into squamous cell carcinoma in non-small cell lung cancer显示文摘Non-small cell lung cancer(NSCLC)is the most common form of lung cancer which remains the deadliest malignancy worldwide(Siegel et al.,2019).In general,NSCLC can be divided into several subtypes,including adenocarcinoma(ADC),squamous cell carcinoma(SCC),adeno-squamous cell carcinoma(AD-SCC)and large cell carcinoma(LCC). | Yueqiang Jiang Jun Zhang Jin Feng Yaping Lu Yuan Fan Ling Cheng Xin Liao Liya Hu Shiying Yu Tiejun Yin | 2021 | Journal of Genetics and Genomics2021，48，7： | 1 |
| 6 | Genome Analyses of Single Human Oocytes显示文摘 | Yu Hou Wei Fan Liying Yan Rong Li Ying Lian Jin Huang Jinsen Li Liya Xu Fuchou Tang X. Sunney Xie Jie Qiao | 2013 | Cell2013，，7： | 1 |
| 7 | On strongly g-preinvex functions 显示文摘 | Fan Liya Guo Yunlian | 2007 | J Math Anal Appl2007，330，2： | 1 |
| 8 | Feature extraction using fuzzy maximum margin criterion显示文摘 | Cui Yan Fan Liya | 2012 | Neurooomputing2012，86，1： | 1 |
| 9 | Epidemiologic features of Severe fever with Thrombocytopenia Syndrome in China, 2011 - 2012 显示文摘 | Fan Ding Wenyi Zhang Liya Wang | 2013 | Clinical Infectious Diseases2013，56，11： | 1 |
| 10 | Kernel-based weighted Discriminant Analysis with QR Decomposition and Its Application Face Recognition显示文摘 | GAO Jianqiang FAN Liya | 2011 | WSEAS Transactions on Mathematics2011，10，10： | 1 |
| 11 | Automatically extraction of semantic hierar- chical structures from HTML tables 显示文摘 | Fan Liya Xiao Tianyuan | 2007 | Journal of Tsinghua Universi- ty2007，47，10： | 1 |
| 12 | Generalized monotonicity convexity of non-differenfiable functions显示文摘 | FAN Liya LIU Sanyang GAO Shuping | 2003 | J Math Anal Appl2003，279，1： | 1 |
| 13 | On strongly α-preinvex functions显示文摘 | FAN Liya GUO Yunlian | 2007 | J Math Anal Appl2007，330，2： | 1 |
| 14 | Genome Analyses of Single Human Oocytes显示文摘 | Yu Hou Wei Fan Liying Yan Rong Li Ying Lian Jin Huang Jinsen Li Liya Xu Fuchou Tang X. Sunney Xie Jie Qiao | 2013 | Cell2013，，7： | 1 |
| 15 | Generalized invexity of nonsmooth functions显示文摘 | FAN Liya | 2008 | Nonlinear Analysis2008，69，11： | 1 |
| 16 | Generalized monotonicity and generalized convexity of nondifferentiable functions 显示文摘 | FAN Liya LIU Sanyang GAO Shuping | 2003 | J Math Anal Appl2003，279，1： | 1 |
| 17 | Median null(Sw)-based method for fiice feature recognition显示文摘 | Gao Jianqiang Fan Liya Xu Lizhong | 2013 | Applied Mathematics and Confutation2013，219，12： | 1 |
| 18 | On Strongly-preinvex Functions显示文摘 | Liya Fan Yunlian Guo | | 0，，： | 1 |
| 19 | Kernel-based weighteddiscriminant analysis with QR decomposition and its^plication &ce recognition显示文摘 | Gao Jianqiang Fan Liya | 2011 | WSEAS Transactions onMathematics2011，10，10： | 1 |
| 20 | Edge Detection of COVID-19 CT Image Based on GF_SSR, Improved Multiscale Morphology, and Adaptive Threshold显示文摘Edge detection is an effective method for image segmentation and feature extraction.Therefore,extracting weak edges with the inhomogeneous gray of Corona Virus Disease 2019(COVID-19)CT images is extremely important.Multiscale morphology has been widely used in the edge detection of medical images due to its excellent boundary detection accuracy.In this paper,we propose a weak edge detection method based on Gaussian filtering and singlescale Retinex(GF_SSR),and improved multiscale morphology and adaptive threshold binarization(IMSM_ATB).As all the CT images have noise,we propose to remove image noise by Gaussian filtering.The edge of CT images is enhanced using the SSR algorithm.In addition,based on the extracted edge of CT images using improved Multiscale morphology,a particle swarm optimization(PSO)algorithm is introduced to binarize the image by automatically getting the optimal threshold.To evaluate our method,we use images from three datasets,namely COVID-19,Kaggle-COVID-19,and COVID-Chestxray,respectively.The average values of results are worthy of reference,with the Shannon information entropy of 1.8539,the Precision of 0.9992,the Recall of 0.8224,the F-Score of 1.9158,running time of 11.3000.Finally,three types of lesion images in the COVID-19 dataset are selected to evaluate the visual effects of the proposed algorithm.Compared with the other four algorithms,the proposed algorithm effectively detects the weak edge of the lesion and provides help for image segmentation and feature extraction. | Shouming Hou Chaolan Jia Kai Li Liya Fan Jincheng Guo Mackenzie Brown | 2022 | Computer Modeling in Engineering & Sciences2022，，7： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员